WorldWideScience

Sample records for satellite land-surface climatology

  1. Egypt satellite images for land surface characterization

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay

    Satellite images provide information on the land surface properties. From optical remote sensing images in the blue, green, red and near-infrared part of the electromagnetic spectrum it is possible to identify a large number of surface features. The report briefly describes different satellite...

  2. A New Global Climatology of Annual Land Surface Temperature

    Directory of Open Access Journals (Sweden)

    Benjamin Bechtel

    2015-03-01

    Full Text Available Land surface temperature (LST is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.

  3. Climatologies at high resolution for the Earth land surface areas

    CERN Document Server

    Karger, Dirk Nikolaus; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus; Linder, H Peter; Kessler, Michael

    2016-01-01

    High resolution information of climatic conditions is essential to many application in environmental sciences. Here we present the CHELSA algorithm to downscale temperature and precipitation estimates from the European Centre for Medium-Range Weather Forecast (ECMWF) climatic reanalysis interim (ERA-Interim) to a high resolution of 30 arc sec. The algorithm for temperature is based on a statistical downscaling of atmospheric temperature from the ERA-Interim climatic reanalysis. The precipitation algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, and a bias correction using Global Precipitation Climatology Center (GPCC) gridded and Global Historical Climate Network (GHCN) station data. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We present a comparison of data derived from the CHELSA algorithm with two other high resolution gridded products with overlapping temporal resolution (Tropical R...

  4. MEaSUREs Land Surface Temperature from GOES Satellites

    Science.gov (United States)

    Pinker, Rachel T.; Chen, Wen; Ma, Yingtao; Islam, Tanvir; Borbas, Eva; Hain, Chris; Hulley, Glynn; Hook, Simon

    2017-04-01

    Information on Land Surface Temperature (LST) can be generated from observations made from satellites in low Earth orbit (LEO) such as MODIS and ASTER and by sensors in geostationary Earth orbit (GEO) such as GOES. Under a project titled: "A Unified and Coherent Land Surface Temperature and Emissivity Earth System Data Record for Earth Science" led by Jet Propulsion Laboratory, an effort is underway to develop long term consistent information from both such systems. In this presentation we will describe an effort to derive LST information from GOES satellites. Results will be presented from two approaches: 1) based on regression developed from a wide range of simulations using MODTRAN, SeeBor Version 5.0 global atmospheric profiles and the CAMEL (Combined ASTER and MODIS Emissivity for Land) product based on the standard University of Wisconsin 5 km emissivity values (UWIREMIS) and the ASTER Global Emissivity Database (GED) product; 2) RTTOV radiative transfer model driven with MERRA-2 reanalysis fields. We will present results of evaluation of these two methods against various products, such as MOD11, and ground observations for the five year period of (2004-2008).

  5. International Satellite Cloud Climatology Project (ISCCP)

    Data.gov (United States)

    National Aeronautics and Space Administration — International Satellite Cloud Climatology Project (ISCCP) focuses on the distribution and variation of cloud radiative properties to improve the understanding of the...

  6. Global Land Surface Emissivity Retrieved From Satellite Ultraspectral IR Measurements

    Science.gov (United States)

    Zhou, D. K.; Larar, A. M.; Liu, Xu; Smith, W. L.; Strow, L. L.; Yang, Ping; Schlussel, P.; Calbet, X.

    2011-01-01

    Ultraspectral resolution infrared (IR) radiances obtained from nadir observations provide information about the atmosphere, surface, aerosols, and clouds. Surface spectral emissivity (SSE) and surface skin temperature from current and future operational satellites can and will reveal critical information about the Earth s ecosystem and land-surface-type properties, which might be utilized as a means of long-term monitoring of the Earth s environment and global climate change. In this study, fast radiative transfer models applied to the atmosphere under all weather conditions are used for atmospheric profile and surface or cloud parameter retrieval from ultraspectral and/or hyperspectral spaceborne IR soundings. An inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral IR sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface or cloud microphysical parameters. This inversion scheme has been applied to the Infrared Atmospheric Sounding Interferometer (IASI). Rapidly produced SSE is initially evaluated through quality control checks on the retrievals of other impacted surface and atmospheric parameters. Initial validation of retrieved emissivity spectra is conducted with Namib and Kalahari desert laboratory measurements. Seasonal products of global land SSE and surface skin temperature retrieved with IASI are presented to demonstrate seasonal variation of SSE.

  7. Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

    Science.gov (United States)

    Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

    2017-04-01

    Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is

  8. A global satellite-assisted precipitation climatology

    Science.gov (United States)

    Funk, C.; Verdin, A.; Michaelsen, J.; Peterson, P.; Pedreros, D.; Husak, G.

    2015-10-01

    Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate

  9. A global satellite assisted precipitation climatology

    Directory of Open Access Journals (Sweden)

    C. Funk

    2015-05-01

    Full Text Available Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05° global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology

  10. A global satellite assisted precipitation climatology

    Science.gov (United States)

    Funk, Christopher C.; Verdin, Andrew P.; Michaelsen, Joel C.; Pedreros, Diego; Husak, Gregory J.; Peterson, P.

    2015-01-01

    Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05°) global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate

  11. Advancing land surface model development with satellite-based Earth observations

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

    2017-04-01

    The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

  12. Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

    Science.gov (United States)

    Xian, George

    2008-01-01

    By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.

  13. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; Velde, van der R.; Vekerdy, Z.; Su, Z.

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado

  14. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; van der Velde, R.; Vekerdy, Z.; Su, Zhongbo

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park

  15. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; van der Velde, R.; Vekerdy, Z.; Su, Zhongbo

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado

  16. NORSEWInD satellite wind climatology

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay; Badger, Merete; Mouche, Alexis

    is to provide new offshore wind climatology map for the entire area of interest based on satellite remote sensing. This has been based on Synthetic Aperture Radar (SAR) from Envisat ASAR using 9000 scenes re-processed with ECMWF wind direction and CMOD-IFR. The number of overlapping samples range from 450....... QuikSCAT ocean wind vector observations have been analysed for the same four parameters and ASCAT for mean wind speed. All satellite data has been compared to in-situ observations available in the Norsewind project. SSM/I passive microwave wind speed data from 24 years observed around 6 times per day...... are used to estimate trends in offshore winds and interestingly a shift in the seasonal pattern is notice. All satellite-based wind products are valid at 10 m, thus it is desirable to lift winds to higher levels for wind energy products. A method has been suggested to lift winds from 10 m to hub...

  17. Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

    Directory of Open Access Journals (Sweden)

    N. Ghilain

    2012-08-01

    Full Text Available Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I, showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI and Fractional Vegetation Cover (FVC products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land

  18. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

    Directory of Open Access Journals (Sweden)

    Ugur Avdan

    2016-01-01

    Full Text Available Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, for the first case, the standard deviation was 2.4°C, and for the second case, it was 2.7°C. For future studies, the tool should be refined with in situ measurements of land surface temperature.

  19. The Global Land Surface Satellite (GLASS Remote Sensing Data Processing System and Products

    Directory of Open Access Journals (Sweden)

    Gongqi Zhou

    2013-05-01

    Full Text Available Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved quantitatively through models or statistical methods. However, producing global products requires a complex computing system and massive volumes of multi-sensor and multi-temporal remotely sensed data. This manuscript describes the ground Global LAnd Surface Satellite (GLASS product generation system that can be used to generate long-sequence time series of global land surface data products based on various remotely sensed data. To ensure stabilization and efficiency in running the system, we used the methods of task management, parallelization, and multi I/O channels. An array of GLASS remote sensing products related to global land surface parameters are currently being produced and distributed by the Center for Global Change Data Processing and Analysis at Beijing Normal University in Beijing, China. These products include Leaf Area Index (LAI, land surface albedo, and broadband emissivity (BBE from the years 1981 to 2010, downward shortwave radiation (DSR and photosynthetically active radiation (PAR from the years 2008 to 2010.

  20. Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

    Directory of Open Access Journals (Sweden)

    J. M. Edwards

    2009-03-01

    Full Text Available Recently there has been significant progress in the retrieval of land surface temperature from satellite observations. Satellite retrievals of surface temperature offer several advantages, including broad spatial coverage, and such data are potentially of great value in assessing general circulation models of the atmosphere. Here, retrievals of the land surface temperature over the contiguous United States are compared with simulations from two climate models. The models generally simulate the diurnal range realistically, but show significant warm biases during the summer. The models' diurnal cycle of surface temperature is related to their surface flux budgets. Differences in the diurnal cycle of the surface flux budget between the models are found to be more pronounced than those in the diurnal cycle of surface temperature.

  1. Modeling directional effects in land surface temperature derived from geostationary satellite data

    DEFF Research Database (Denmark)

    Rasmussen, Mads Olander

    This PhD-thesis investigates the directional effects in land surface temperature (LST) estimates from the SEVIRI sensor onboard the Meteosat Second Generation (MSG) satellites. The directional effects are caused by the land surface structure (i.e. tree size and shape) interacting with the changing...... sun-target-sensor geometry. The directional effects occur because the different surface components, e.g. tree canopies and bare soil surfaces, will in many cases have significantly different temperatures. Depending on the viewing angle, different fractions of each of the components will be viewed......; shaded and sunlit canopy and background, respectively. Given data on vegetation structure and density, the model estimates the fractions of the four components as well as the directional composite temperature in the view of a sensor, given the illumination and viewing geometry. The modeling results show...

  2. Multi-Spectral Satellite Imagery and Land Surface Modeling Supporting Dust Detection and Forecasting

    Science.gov (United States)

    Molthan, A.; Case, J.; Zavodsky, B.; Naeger, A. R.; LaFontaine, F.; Smith, M. R.

    2014-12-01

    Current and future multi-spectral satellite sensors provide numerous means and methods for identifying hazards associated with polluting aerosols and dust. For over a decade, the NASA Short-term Prediction Research and Transition (SPoRT) Center at Marshall Space Flight Center in Huntsville has focused on developing new applications from near real-time data sources in support of the operational weather forecasting community. The SPoRT Center achieves these goals by matching appropriate analysis tools, modeling outputs, and other products to forecast challenges, along with appropriate training and end-user feedback to ensure a successful transition. As a spinoff of these capabilities, the SPoRT Center has recently focused on developing collaborations to address challenges with the public health community, specifically focused on the identification of hazards associated with dust and pollution aerosols. Using multispectral satellite data from the SEVIRI instrument on the Meteosat series, the SPoRT team has leveraged EUMETSAT techniques for identifying dust through false color (RGB) composites, which have been used by the National Hurricane Center and other meteorological centers to identify, monitor, and predict the movement of dust aloft. Similar products have also been developed from the MODIS and VIIRS instruments onboard the Terra and Aqua, and Suomi-NPP satellites, respectively, and transitioned for operational forecasting use by offices within NOAA's National Weather Service. In addition, the SPoRT Center incorporates satellite-derived vegetation information and land surface modeling to create high-resolution analyses of soil moisture and other land surface conditions relevant to the lofting of wind-blown dust and identification of other, possible public-health vectors. Examples of land surface modeling and relevant predictions are shown in the context of operational decision making by forecast centers with potential future applications to public health arenas.

  3. Towards a protocol for validating satellite-based Land Surface Temperature: Application to AATSR data

    Science.gov (United States)

    Ghent, Darren; Schneider, Philipp; Remedios, John

    2013-04-01

    Land surface temperature (LST) retrieval accuracy can be challenging as a result of emissivity variability and atmospheric effects. Surface emissivities can be highly variable owing to the heterogeneity of the land; a problem which is amplified in regions of high topographic variance or for larger viewing angles. Atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. Combined, atmospheric effects and emissivity variability can result in retrieval errors of several degrees. If though these are appropriately handled satellite-derived LST products can be used to improve our ability to monitor and to understand land surface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. Here we present validation of an improved LST data record from the Advanced Along-Track Scanning Radiometer (AATSR) and illustrate the improvements in accuracy and precision compared with the standard ESA LST product. Validation is a critical part of developing any satellite product, although over the land heterogeneity ensures this is a challenging undertaking. A substantial amount of previous effort has gone into the area of structuring and standardizing calibration and validation approaches within the field of Earth Observation. However, no unified approach for accomplishing this for LST has yet to be practised by the LST community. Recent work has attempted to address this situation with the development of a protocol for validating LST (Schneider et al., 2012) under the auspices of ESA and the support of the wider LST community. We report here on a first application of this protocol to satellite LST data. The approach can briefly be summarised thus: in situ validation is performed where ground-based observations are available - being predominantly homogeneous sites; heterogeneous pixels are validated by way of established radiometric-based techniques (Wan and Li

  4. The long-term Global LAnd Surface Satellite (GLASS) product suite and applications

    Science.gov (United States)

    Liang, S.

    2015-12-01

    Our Earth's environment is experiencing rapid changes due to natural variability and human activities. To monitor, understand and predict environment changes to meet the economic, social and environmental needs, use of long-term high-quality satellite data products is critical. The Global LAnd Surface Satellite (GLASS) product suite, generated at Beijing Normal University, currently includes 12 products, including leaf area index (LAI), broadband shortwave albedo, broadband longwave emissivity, downwelling shortwave radiation and photosynthetically active radiation, land surface skin temperature, longwave net radiation, daytime all-wave net radiation, fraction of absorbed photosynetically active radiation absorbed by green vegetation (FAPAR), fraction of green vegetation coverage, gross primary productivity (GPP), and evapotranspiration (ET). Most products span from 1981-2014. The algorithms for producing these products have been published in the top remote sensing related journals and books. More and more applications have being reported in the scientific literature. The GLASS products are freely available at the Center for Global Change Data Processing and Analysis of Beijing Normal University (http://www.bnu-datacenter.com/), and the University of Maryland Global Land Cover Facility (http://glcf.umd.edu). After briefly introducing the basic characteristics of GLASS products, we will present some applications on the long-term environmental changes detected from GLASS products at both global and local scales. Detailed analysis of regional hotspots, such as Greenland, Tibetan plateau, and northern China, will be emphasized, where environmental changes have been mainly associated with climate warming, drought, land-atmosphere interactions, and human activities.

  5. Satellite Observations of Wind Farm Impacts on Nocturnal Land Surface Temperature in Iowa

    Directory of Open Access Journals (Sweden)

    Ronald A. Harris

    2014-12-01

    Full Text Available Wind farms (WFs are believed to have an impact on lower boundary layer meteorology. A recent study examined satellite-measured land surface temperature data (LST and found a local nighttime warming effect attributable to a group of four large WFs in Texas. This study furthers their work by investigating the impacts of five individual WFs in Iowa, where the land surface properties and climate conditions are different from those in Texas. Two methods are used to assess WF impacts: first, compare the spatial coupling between the LST changes (after turbine construction versus before and the geographic layouts of the WFs; second, quantify the LST difference between the WFs and their immediate surroundings (non-WF areas. Each WF shows an irrefutable nighttime warming signal relative to the surrounding areas after their turbines were installed, and these warming signals are generally coupled with the geographic layouts of the wind turbines, especially in summer. This study provides further observational evidence that WFs can cause surface warming at nighttime, and that such a signal can be detected by satellite-based sensors.

  6. Use of satellite land surface temperatures in the EUSTACE global surface air temperature analysis

    Science.gov (United States)

    Ghent, D.; Good, E.; Rayner, N. A.

    2015-12-01

    EUSTACE (EU Surface Temperatures for All Corners of Earth) is a Horizon2020 project that will produce a spatially complete, near-surface air temperature (NSAT) analysis for the globe for every day since 1850. The analysis will be based on both satellite and in situ surface temperature observations over land, sea, ice and lakes, which will be combined using state-of-the-art statistical methods. The use of satellite data will enable the EUSTACE analysis to offer improved estimates of NSAT in regions that are poorly observed in situ, compared with existing in-situ based analyses. This presentation illustrates how satellite land surface temperature (LST) data - sourced from the European Space Agency (ESA) Data User Element (DUE) GlobTemperature project - will be used in EUSTACE. Satellite LSTs represent the temperature of the Earth's skin, which can differ from the corresponding NSAT by several degrees or more, particularly during the hottest part of the day. Therefore the first challenge is to develop an approach to estimate global NSAT from satellite observations. Two methods will be trialled in EUSTACE, both of which are summarised here: an established empirical regression-based approach for predicting NSAT from satellite data, and a new method whereby NSAT is calculated from LST and other parameters using a physics-based model. The second challenge is in estimating the uncertainties for the satellite NSAT estimates, which will determine how these data are used in the final blended satellite-in situ analysis. This is also important as a key component of EUSTACE is in delivering accurate uncertainty information to users. An overview of the methods to estimate the satellite NSATs is also included in this presentation.

  7. The Water Cycle from Space: Use of Satellite Data in Land Surface Hydrology and Water Resource Management

    Science.gov (United States)

    Laymon, Charles; Blankenship, Clay; Khan, Maudood; Limaye, Ashutosh; Hornbuckle, Brian; Rowlandson, Tracy

    2010-01-01

    This slide presentation reviews how our understanding of the water cycle is enhanced by our use of satellite data, and how this informs land surface hydrology and water resource management. It reviews how NASA's current and future satellite missions will provide Earth system data of unprecedented breadth, accuracy and utility for hydrologic analysis.

  8. Utilization of satellite-derived estimates of meteorological and land surface characteristics in the Land Surface Model for vast agricultural region territory

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena

    2015-04-01

    The method has been elaborated to evaluate the water and heat regime characteristics of the territory on a regional scale for the vegetation season based on a physical-mathematical model of water and heat exchange between vegetation covered land surface and atmosphere (LSM, Land Surface Model) appropriate for using satellite information on land surface and meteorological conditions. The developed model is intended for calculating soil water content, evapotranspiration (evaporation from bare soil and transpiration by vegetation), vertical water and heat fluxes as well as land surface and vegetation cover temperatures and vertical distributions of temperature and moisture in the active soil layer. Parameters of the model are soil and vegetation characteristics and input variables are meteorological characteristics. Their values have been obtained from ground-based observations at agricultural meteorological stations and satellite-based measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua and SEVIRI (geostationary satellites Meteosat-9, -10). The AVHRR data have been used to build the estimates of three types of land surface temperature (LST): land skin temperature Tsg, air temperature at a level of vegetation cover Ta and efficient radiation temperature Tseff, emissivity E, normalized vegetation index NDVI, vegetation cover fraction B, leaf area index LAI, and precipitation. The set of estimates derived from MODIS data has comprised values of LST Tls, E, NDVI and LAI. The SEVIRI-based retrievals have included Tls, Ta, Е at daylight and nighttime, LAI (daily) and precipitation. The case study has been carried out for agricultural Central Black Earth region of the European Russia of 227,300 sq.km containing 7 regions of the Russian Federation for years 2009-2013 vegetation seasons. Estimates of described characteristics have been built with the help of the developed original and improved pre-existing methods and technologies of thematic processing

  9. Calibration of the Distributed Hydrological Model mHM using Satellite derived Land Surface Temperature

    Science.gov (United States)

    Zink, M.; Samaniego, L. E.; Cuntz, M.

    2012-12-01

    A combined investigation of the water and energy balance in hydrologic models can lead to a more accurate estimation of hydrological fluxes and state variables, such as evapotranspiration and soil moisture. Hydrologic models are usually calibrated against discharge measurements, and thus are only trained on information of few points within a catchment. This procedure does not take into account any spatio-temporal variability of fluxes or state variables. Satellite data are a useful source of information to account for this spatial distributions. The objective of this study is to calibrate the distributed hydrological model mHM with satellite derived Land Surface Temperature (LST) fields provided by the Land Surface Analysis - Satellite Application Facility (LSA-SAF). LST is preferred to other satellite products such as soil moisture or evapotranspiration due to its higher precision. LST is obtained by solving the energy balance by assuming that the soil heat flux and the storage term are negligible on a daily time step. The evapotranspiration is determined by closing the water balance in mHM. The net radiation is calculated by using the incoming short- and longwave radiation, albedo and emissivity data provided by LSA-SAF. The Multiscale Parameter Regionalization technique (MPR, Samaniego et al. 2010) is used to determine the aerodynamic resistance among other parameters. The optimization is performed within the time period 2008-2010 using three objective functions that consider 1) only discharge, 2) only LST, and 3) a combination of both. The proposed method is applied to seven major German river basins: Danube, Ems, Main, Mulde, Neckar, Saale, and Weser. The annual coefficient of correlation between LSA-SAF incoming shortwave radiation and 28 meteorological stations operated by the German Weather Service (DWD) is 0.94 (RMSE = 29 W m-2) in 2009. LSA-SAF incoming longwave radiation could be further evaluated at two eddy covariance stations with a very similar

  10. Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

    Science.gov (United States)

    Şekertekin, A.; Kutoglu, Ş. H.; Kaya, S.; Marangoz, A. M.

    2015-12-01

    Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

  11. ANALYSING THE EFFECTS OF DIFFERENT LAND COVER TYPES ON LAND SURFACE TEMPERATURE USING SATELLITE DATA

    Directory of Open Access Journals (Sweden)

    A. Şekertekin

    2015-12-01

    Full Text Available Monitoring Land Surface Temperature (LST via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

  12. Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS Data

    Directory of Open Access Journals (Sweden)

    A-Ra Cho

    2013-08-01

    Full Text Available We evaluated the precision of land surface temperature (LST operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS. The split-window (SW-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4. The estimation capabilities of the COMS SW (CSW LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE = 1.41 K, correlation coefficient = 0.99; however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively.

  13. Cross-validation of satellite products over France through their integration into a land surface model

    Science.gov (United States)

    Calvet, Jean-Christophe; Barbu, Alina; Carrer, Dominique; Meurey, Catherine

    2014-05-01

    Long (more than 30 years) time series of satellite-derived products over land are now available. They concern Essential Climate Variables (ECV) such as LAI, FAPAR, surface albedo, and soil moisture. The direct validation of such Climate Data Records (CDR) is not easy, as in situ observations are limited in space and time. Therefore, indirect validation has a key role. It consists in comparing the products with similar preexisting products derived from satellite observations or from land surface model (LSM) simulations. The most advanced indirect validation technique consists in integrating the products into a LSM using a data assimilation scheme. The obtained reanalysis accounts for the synergies of the various upstream products and provides statistics which can be used to monitor the quality of the assimilated observations. Meteo-France develops the ISBA-A-gs generic LSM able to represent the diurnal cycle of the surface fluxes together with the seasonal, interannual and decadal variability of the vegetation biomass. The LSM is embedded in the SURFEX modeling platform together with a simplified extended Kalman filter. These tools form a Land Data Assimilation System (LDAS). The current version of the LDAS assimilates SPOT-VGT LAI and ASCAT surface soil moisture (SSM) products over France (8km x 8km), and a passive monitoring of albedo, FAPAR and Land Surface temperature (LST) is performed (i.e., the simulated values are compared with the satellite products). The LDAS-France system is used in the European Copernicus Global Land Service (http://land.copernicus.eu/global/) to monitor the quality of upstream products. The LDAS generates statistics whose trends can be analyzed in order to detect possible drifts in the quality of the products: (1) for LAI and SSM, metrics derived from the active monitoring (i.e. assimilation) such as innovations (observations vs. model forecast), residuals (observations vs. analysis), and increments (analysis vs. model forecast) ; (2

  14. Towards a protocol for validating satellite-based Land Surface Temperature: Theoretical considerations

    Science.gov (United States)

    Schneider, Philipp; Ghent, Darren J.; Corlett, Gary C.; Prata, Fred; Remedios, John J.

    2013-04-01

    Land Surface Temperature (LST) and emissivity are important parameters for environmental monitoring and earth system modelling. LST has been observed from space for several decades using a wide variety of satellite instruments with different characteristics, including both platforms in low-earth orbit and in geostationary orbit. This includes for example the series of Advanced Very High Resolution Radiometers (AVHRR) delivering a continuous thermal infrared (TIR) data stream since the early 1980s, the series of Along-Track Scanning Radiometers (ATSR) providing TIR data since 1991, and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard NASA's Terra and Aqua platforms, providing data since the year 2000. In addition, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard of the geostationary Meteosat satellites is now providing LST at unprecedented sub-hour frequency. The data record provided by such instruments is extremely valuable for a wide variety of applications, including climate change, land/atmosphere feedbacks, fire monitoring, modelling, land cover change, geology, crop- and water management. All of these applications, however, require a rigorous validation of the data in order to assess the product quality and the associated uncertainty. Here we report on recent work towards developing a protocol for validation of satellite-based Land Surface Temperature products. Four main validation categories are distinguished within the protocol: A) Comparison with in situ observations, B) Radiance-based validation, C) Inter-comparison with similar LST products, and D) Time-series analysis. Each category is further subdivided into several quality classes, which approximately reflect the validation accuracy that can be achieved by the different approaches, as well as the complexity involved with each method. Advice on best practices is given for methodology common to all categories. For each validation category, recommendations

  15. Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

    Science.gov (United States)

    Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

    2012-01-01

    Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

  16. Can satellite land surface temperature data be used similarly to ground discharge measurements for distributed hydrological model calibration?

    NARCIS (Netherlands)

    Corbari, C.; Mancini, M.; Li, J.; Su, Zhongbo

    2015-01-01

    This study proposes a new methodology for the calibration of distributed hydrological models at basin scale by constraining an internal model variable using satellite data of land surface temperature. The model algorithm solves the system of energy and mass balances in terms of a representative equi

  17. Derivation and evaluation of land surface temperature from the geostationary operational environmental satellite series

    Science.gov (United States)

    Fang, Li

    The Geostationary Operational Environmental Satellites (GOES) have been continuously monitoring the earth surface since 1970, providing valuable and intensive data from a very broad range of wavelengths, day and night. The National Oceanic and Atmospheric Administration's (NOAA's) National Environmental Satellite, Data, and Information Service (NESDIS) is currently operating GOES-15 and GOES-13. The design of the GOES series is now heading to the 4 th generation. GOES-R, as a representative of the new generation of the GOES series, is scheduled to be launched in 2015 with higher spatial and temporal resolution images and full-time soundings. These frequent observations provided by GOES Image make them attractive for deriving information on the diurnal land surface temperature (LST) cycle and diurnal temperature range (DTR). These parameters are of great value for research on the Earth's diurnal variability and climate change. Accurate derivation of satellite-based LSTs from thermal infrared data has long been an interesting and challenging research area. To better support the research on climate change, the generation of consistent GOES LST products for both GOES-East and GOES-West from operational dataset as well as historical archive is in great demand. The derivation of GOES LST products and the evaluation of proposed retrieval methods are two major objectives of this study. Literature relevant to satellite-based LST retrieval techniques was reviewed. Specifically, the evolution of two LST algorithm families and LST retrieval methods for geostationary satellites were summarized in this dissertation. Literature relevant to the evaluation of satellite-based LSTs was also reviewed. All the existing methods are a valuable reference to develop the GOES LST product. The primary objective of this dissertation is the development of models for deriving consistent GOES LSTs with high spatial and high temporal coverage. Proper LST retrieval algorithms were studied

  18. Land surface thermal characterization of Asian-pacific region with Japanese geostationary satellite

    Science.gov (United States)

    Oyoshi, K.; Tamura, M.

    2010-12-01

    Land Surface Temperature (LST) is a significant indicator of energy balance at the Earth's surface. It is required for a wide variety of climate, hydrological, ecological, and biogeochemical studies. Although LST is highly variable both temporally and spatially, it is impossible for polar-orbiting satellite to detect hourly changes in LST, because the satellite is able to only collect data of the same area at most twice a day. On the other hand, geostationary satellite is able to collect hourly data and has a possibility to monitor hourly changes in LST, therefore hourly measurements of geostationary satellite enables us to characterize detailed thermal conditions of the Earth's surface and improve our understanding of the surface energy balance. Multi-functional Transport Satellite (MTSAT) is a Japanese geostationary satellite launched in 2005 and covers Asia-Pacific region. MTSAT provides hourly data with 5 bands including two thermal infrared (TIR) bands in the 10.5-12.5 micron region. In this research, we have developed a methodology to retrieve hourly LST from thermal infrared data of MTSAT. We applied Generalized Split-window (GSW) equation to estimate LST from TIR data. First, the brightness temperatures measured at sensor on MTSAT was simulated by radiative transfer code (MODTRAN), and the numerical coefficients of GSW equation were optimized based on the simulation results with non-linear minimization algorithm. The standard deviation of derived GSW equation was less than or equal to 1.09K in the case of viewing zenith angle lower than 40 degree and 1.73K in 60 degree. Then, spatial distributions of LST have been mapped optimized GSW equation with brightness temperatures of MTSAT IR1 and IR2 and emissivity map from MODIS product. Finally, these maps were validated with MODIS LST product (MOD11A1) over four Asian-pacific regions such as Bangkok, Tokyo, UlanBator and Jakarta , It is found that RMSE of these regions were 4.57K, 2.22K, 2.71K and 3.92K

  19. Validation of Satellite-Derived Land Surface Temperature Products - Methods and Good Practice

    Science.gov (United States)

    Guillevic, P. C.; Hulley, G. C.; Hook, S. J.; Biard, J.; Ghent, D.

    2014-12-01

    Land Surface Temperature (LST) is a key variable for surface water and energy budget calculations that can be obtained globally and operationally from satellite observations. LST is used for many applications, including weather forecasting, short-term climate prediction, extreme weather monitoring, and irrigation and water resource management. In order to maximize the usefulness of LST for research and studies it is necessary to know the uncertainty in the LST measurement. Multiple validation methods and activities are necessary to assess LST compliance with the quality specifications of operational users. This work presents four different validation methods that have been widely used to determine the uncertainties in LST products derived from satellite measurements. 1) The temperature based validation method involves comparisons with ground-based measurements of LST. The method is strongly limited by the number and quality of available field stations. 2) Scene-based comparisons involve comparing a new satellite LST product with a heritage LST product. This method is not an absolute validation and satellite LST inter-comparisons alone do not provide an independent validation measurement. 3) The radiance-based validation method does not require ground-based measurements and is usually used for large scale validation effort or for LST products with coarser spatial resolution (> 1km). 4) Time series comparisons are used to detect problems that can occur during the instrument's life, e.g. calibration drift, or unrealistic outliers due to cloud coverage. This study enumerates the sources of errors associated with each method. The four different approaches are complementary and provide different levels of information about the quality of the retrieved LST. The challenges in retrieving the LST from satellite measurements are discussed using results obtained for MODIS and VIIRS. This work contributes to the objective of the Land Product Validation (LPV) sub-group of the

  20. Estimation of Land Surface Energy Balance Using Satellite Data of Spatial Reduced Resolution

    Science.gov (United States)

    Vintila, Ruxandra; Radnea, Cristina; Savin, Elena; Poenaru, Violeta

    2010-12-01

    The paper presents preliminary results concerning the monitoring at national level of several geo-biophysical variables retrieved by remote sensing, in particular those related to drought or aridisation. The study, which is in progress, represents also an exercise for to the implementation of a Land Monitoring Core Service for Romania, according to the Kopernikus Program and in compliance with the INSPIRE Directive. The SEBS model has been used to retrieve land surface energy balance variables, such as turbulent heat fluxes, evaporative fraction and daily evaporation, based on three information types: (1) surface albedo, emissivity, temperature, fraction of vegetation cover (fCover), leaf area index (LAI) and vegetation height; (2) air pressure, temperature, humidity and wind speed at the planetary boundary layer (PBL) height; (3) downward solar radiation and downward longwave radiation. AATSR and MERIS archived reprocessed images have provided several types of information. Thus, surface albedo, emissivity, and land surface temperature have been retrieved from AATSR, while LAI and fCover have been estimated from MERIS. The vegetation height has been derived from CORINE Land Cover and PELCOM Land Use databases, while the meteorological information at the height of PBL have been estimated from the measurements provided by the national weather station network. Other sources of data used during this study have been the GETASSE30 digital elevation model with 30" spatial resolution, used for satellite image orthorectification, and the SIGSTAR-200 geographical information system of soil resources of Romania, used for water deficit characterisation. The study will continue by processing other AATSR and MERIS archived images, complemented by the validation of SEBS results with ground data collected on the most important biomes for Romania at various phenological stages, and the transformation of evaporation / evapotranspiration into a drought index using the soil texture

  1. Study of land surface temperature and spectral emissivity using multi-sensor satellite data

    Indian Academy of Sciences (India)

    P K Srivastava; T J Majumdar; Amit K Bhattacharya

    2010-02-01

    In this study, an attempt has been made to estimate land surface temperatures (LST) and spectral emissivities over a hard rock terrain using multi-sensor satellite data. The study area, of about 6000 km2, is a part of Singhbhum–Orissa craton situated in the eastern part of India. TIR data from ASTER, MODIS and Landsat ETM+ have been used in the present study. Telatemp Model AG-42D Portable Infrared Thermometer was used for ground measurements to validate the results derived from satellite (MODIS/ASTER) data. LSTs derived using Landsat ETM+ data of two different dates have been compared with the satellite data (ASTER and MODIS) of those two dates. Various techniques, viz., temperature and emissivity separation (TES) algorithm, gray body adjustment approach in TES algorithm, Split-Window algorithms and Single Channel algorithm along with NDVI based emissivity approach have been used. LSTs derived from bands 31 and 32 of MODIS data using Split-Window algorithms with higher viewing angle (50°) (LST1 and LST2) are found to have closer agreement with ground temperature measurements (ground LST) over waterbody, Dalma forest and Simlipal forest, than that derived from ASTER data (TES with AST 13). However, over agriculture land, there is some uncertainty and difference between the measured and the estimated LSTs for both validation dates for all the derived LSTs. LST obtained using Single Channel algorithm with NDVI based emissivity method in channel 13 of ASTER data has yielded closer agreement with ground measurements recorded over vegetation and mixed lands of low spectral contrast. LST results obtained with TIR band 6 of Landsat ETM+ using Single Channel algorithm show close agreement over Dalma forest, Simlipal forest and waterbody with LSTs obtained using MODIS and ASTER data for a different date. Comparison of LSTs shows good agreement with ground measurements in thermally homogeneous area. However, results in agriculture area with less homogeneity show

  2. Global Flood Response Using Satellite Rainfall Information Coupled with Land Surface and Routing Models

    Science.gov (United States)

    Adler, R. F.; Wu, H.

    2016-12-01

    The Global Flood Monitoring System (GFMS) (http://flood.umd.edu) has been developed and used in recent years to provide real-time flood detection, streamflow estimates and inundation calculations for most of the globe. The GFMS is driven by satellite-based precipitation, with the accuracy of the flood estimates being primarily dependent on the accuracy of the precipitation analyses and the land surface and routing models used. The routing calculations are done at both 12 km and 1 km resolution. Users of GFMS results include international and national flood response organizations. The devastating floods in October 2015 in South Carolina are analyzed indicating that the GFMS estimated streamflow is accurate and useful indicating significant flooding in the upstream basins. Further downstream the GFMS streamflow underestimates due to the presence of dams which are not accounted for in GFMS. Other examples are given for Yemen and Somalia and for Sri Lanka and southern India. A forecast flood event associated with a typhoon hitting Taiwan is also examined. One-kilometer resolution inundation mapping from GFMS holds the promise of highly useful information for flood disaster response. The algorithm is briefly described and examples are shown for recent cases where inundation estimates available from optical and Synthetic Aperture Radar (SAR) satellite sensors are available. For a case of significant flooding in Texas in May and June along the Brazos River the GFMS calculated streamflow compares favorably with the observed. Available Landsat-based (May 28) and MODIS-based (June 2) inundation analyses from U. of Colorado shows generally good agreement with the GFMS inundation calculation in most of the area where skies were clear and the optical techniques could be applied. The GFMS provides very useful disaster response information on a timely basis. However, there is still significant room for improvement, including improved precipitation information from NASA's Global

  3. Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

    Science.gov (United States)

    Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

    2012-01-01

    The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters

  4. Maximizing the Use of Satellite Thermal Infrared Data for Advancing Land Surface Temperature Analysis

    Science.gov (United States)

    Weng, Q.; Fu, P.; Gao, F.

    2014-12-01

    Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and LST data by considering annual temperature cycle (ATC) and urban thermal landscape heterogeneity. The technique of linear spectral mixture analysis was employed to relate the Landsat radiance with the MODIS one, so that the temporal changes in radiance can be incorporated in the fusion model. This paper details the theoretical basis and the implementation procedures of the proposed data fusion algorithm, Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). A case study was conducted that predicted LSTs of five dates in 2005 from July to October in Los Angeles County, California. The results indicate that the prediction accuracy for the whole study area ranged from 1.3 K to 2 K. Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption.

  5. Estimation of land surface evapotranspiration with A satellite remote sensing procedure

    Science.gov (United States)

    Irmak, A.; Ratcliffe, I.; Ranade, P.; Hubbard, K.G.; Singh, R.K.; Kamble, B.; Kjaersgaard, J.

    2011-01-01

    There are various methods available for estimating magnitude and trends of evapotranspiration. Bowen ratio energy balance system and eddy correlation techniques offer powerful alternatives for measuring land surface evapotranspiration. In spite of the elegance, high accuracy, and theoretical attractions of these techniques for measuring evapotranspiration, their practical use over large areas can be limited due to the number of sites needed and the related expense. Application of evapotranspiration mapping from satellite measurements can overcome the limitations. The objective of this study was to utilize the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) model in Great Plains environmental settings to understand water use in managed ecosystems on a regional scale. We investigated spatiotemporal distribution of a fraction of reference evapotranspiration (ETrF) using eight Landsat 5 images during the 2005 and 2006 growing season for path 29, row 32. The ETrF maps generated by METRICTM allowed us to follow the magnitude and trend in ETrF for major land-use classes during the growing season. The ETrF was lower early in the growing season for agricultural crops and gradually increased as the normalized difference vegetation index of crops increased, thus presenting more surface area over which water could transpire toward the midseason. Comparison of predictions with Bowen ratio energy balance system measurements at Clay Center, NE, showed that METRICTM performed well at the field scale for predicting evapotranspiration from a cornfield. If calibrated properly, the model could be a viable tool to estimate water use in managed ecosystems in subhumid climates at a large scale.

  6. Land Surface Temperature- Comparing Data from Polar Orbiting and Geostationary Satellites

    Science.gov (United States)

    Comyn-Platt, E.; Remedios, J. J.; Good, E. J.; Ghent, D.; Saunders, R.

    2012-04-01

    Land Surface Temperature (LST) is a vital parameter in Earth climate science, driving long-wave radiation exchanges that control the surface energy budget and carbon fluxes, which are important factors in Numerical Weather Prediction (NWP) and the monitoring of climate change. Satellites offer a convenient way to observe LST consistently and regularly over large areas. A comparison between LST retrieved from a Geostationary Instrument, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), and a Polar Orbiting Instrument, the Advanced Along Track Scanning Radiometer (AATSR) is presented. Both sensors offer differing benefits. AATSR offers superior precision and spatial resolution with global coverage but given its sun-synchronous platform only observes at two local times, ~10am and ~10pm. SEVIRI provides the high-temporal resolution (every 15 minutes) required for observing diurnal variability of surface temperatures but given its geostationary platform has a poorer resolution, 3km at nadir, which declines at higher latitudes. A number of retrieval methods are applied to the raw satellite data: First order coefficient based algorithms provided on an operational basis by the LandSAF (for SEVIRI) and the University of Leicester (for AATSR); Second order coefficient based algorithms put forward by the University of Valencia; and an optimal estimation method using the 1DVar software provided by the NWP SAF. Optimal estimation is an iterative technique based upon inverse theory, thus is very useful for expanding into data assimilation systems. The retrievals are assessed and compared on both a fine scale using in-situ data from recognised validation sites and on a broad scale using two 100x100 regions such that biases can be better understood. Overall, the importance of LST lies in monitoring daily temperature extremes, e.g. for estimating permafrost thawing depth or risk of crop damage due to frost, hence the ideal dataset would use a combination of observations

  7. Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

    Directory of Open Access Journals (Sweden)

    Tao Wang

    Full Text Available Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique improve the simulation accuracy of mean seasonal (October throughout May snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the

  8. Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

    Science.gov (United States)

    Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

    2015-01-01

    Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of

  9. The HOAPS-II climatology - Release II of the satellite-derived freshwater flux climatology

    Science.gov (United States)

    Fennig, K.; Klepp, C.; Bakan, S.; Schulz, J.; Graßl, H.

    2003-04-01

    HOAPS-II (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) is the improved global climatology of sea surface parameters and surface energy and freshwater fluxes derived from satellite radiances for the time period July 1987 until the recent dates. Data from polar orbiting radiometers, all available Special Sensor Microwave/Imager (SSM/I) radiometers and the Advanced Very High Resolution Radiometer (AVHRR), have been used to get global fields of surface meteorological and oceanographic parameters but also latent heat flux, evaporation, precipitation and net freshwater flux as well as the wind speed, water vapor- and total water content over ice free ocean areas for various averaging periods and grid sizes including scan orientated data in the NetCDF data format. All retrieval methods have been validated with in situ data on a global scale with a focus on precipitation validation. The new release of the data base is freely available to the community. Additionally, applications of the HOAPS-II data base will demonstrate its ability to detect ground validated High Impact Weather over global oceans that the Global Precipitation Climatology Project (GPCP) climatology and the ECMWF model is frequently missing. Nowcasting of model-unpredicted storms is a high potential application of this new data base.

  10. Integrating satellite retrieved leaf chlorophyll into land surface models for constraining simulations of water and carbon fluxes

    KAUST Repository

    Houborg, Rasmus

    2013-07-01

    In terrestrial biosphere models, key biochemical controls on carbon uptake by vegetation canopies are typically assigned fixed literature-based values for broad categories of vegetation types although in reality significant spatial and temporal variability exists. Satellite remote sensing can support modeling efforts by offering distributed information on important land surface characteristics, which would be very difficult to obtain otherwise. This study investigates the utility of satellite based retrievals of leaf chlorophyll for estimating leaf photosynthetic capacity and for constraining model simulations of water and carbon fluxes. © 2013 IEEE.

  11. Observational and Dynamical Wave Climatologies. VOS vs Satellite Data

    Science.gov (United States)

    Grigorieva, Victoria; Badulin, Sergei; Chernyshova, Anna

    2013-04-01

    The understanding physics of wind-driven waves is crucially important for fundamental science and practical applications. This is why experimental efforts are targeted at both getting reliable information on sea state and elaborating effective tools of the sea wave forecasting. The global Visual Wave Observations and satellite data from the GLOBWAVE project of the European Space Agency are analyzed in the context of these two viewpoints. Within the first "observational" aspect we re-analyze conventional climatologies of all basic wave parameters for the last decades [5]. An alternative "dynamical" climatology is introduced as a tool of prediction of dynamical features of sea waves on global scales. The features of wave dynamics are studied in terms of one-parametric dependencies of wave heights on wave periods following the theoretical concept of self-similar wind-driven seas [3, 1, 4] and recently proposed approach to analysis of Voluntary Observing Ship (VOS) data [2]. Traditional "observational" climatologies based on VOS and satellite data collections demonstrate extremely consistent pictures for significant wave heights and dominant periods. On the other hand, collocated satellite and VOS data show significant differences in wave heights, wind speeds and, especially, in wave periods. Uncertainties of visual wave observations can explain these differences only partially. We see the key reason of this inconsistency in the methods of satellite data processing which are based on formal application of data interpolation methods rather than on up-to-date physics of wind-driven waves. The problem is considered within the alternative climatology approach where dynamical criteria of wave height-to-period linkage are used for retrieving wave periods and constructing physically consistent dynamical climatology. The key dynamical parameter - exponent R of one-parametric dependence Hs ~ TR shows dramatically less pronounced latitudinal dependence as compared to observed Hs

  12. An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model

    Science.gov (United States)

    Fang, Li; Hain, Christopher R.; Zhan, Xiwu; Anderson, Martha C.

    2016-06-01

    Significant advances have been achieved in generating soil moisture (SM) products from satellite remote sensing and/or land surface modeling with reasonably good accuracy in recent years. However, the discrepancies among the different SM data products can be considerably large, which hampers their usage in various applications. The bias of one SM product from another is well recognized in the literature. Bias estimation and spatial correction methods have been documented for assimilating satellite SM product into land surface and hydrologic models. Nevertheless, understanding the characteristics of each of these SM data products is required for many applications where the most accurate data products are desirable. This study inter-compares five SM data products from three different sources with each other, and evaluates them against in situ SM measurements over 14-year period from 2000 to 2013. Specifically, three microwave (MW) satellite based data sets provided by ESA's Climate Change Initiative (CCI) (CCI-merged, -active and -passive products), one thermal infrared (TIR) satellite based product (ALEXI), and the Noah land surface model (LSM) simulations. The in-situ SM measurements are collected from the North American Soil Moisture Database (NASMD), which involves more than 600 ground sites from a variety of networks. They are used to evaluate the accuracies of these five SM data products. In general, each of the five SM products is capable of capturing the dry/wet patterns over the study period. However, the absolute SM values among the five products vary significantly. SM simulations from Noah LSM are more stable relative to the satellite-based products. All TIR and MW satellite based products are relatively noisier than the Noah LSM simulations. Even though MW satellite based SM retrievals have been predominantly used in the past years, SM retrievals of the ALEXI model based on TIR satellite observations demonstrate skills equivalent to all the MW satellite

  13. Towards a satellite driven land surface model using SURFEX modelling platform Offline Data Assimilation: an assessment of the method over Europe and the Mediterranean basin

    Science.gov (United States)

    Albergel, Clément; Munier, Simon; Leroux, Delphine; Fairbairn, David; Dorigo, Wouter; Decharme, Bertrand; Calvet, Jean-Christophe

    2017-04-01

    Modelling platforms including Land Surface Models (LSMs), forced by gridded atmospheric variables and coupled to river routing models are necessary to increase our understanding of the terrestrial water cycle. These LSMs need to simulate biogeophysical variables like Surface and Root Zone Soil Moisture (SSM, RZSM), Leaf Area Index (LAI) in a way that is fully consistent with the representation of surface/energy fluxes and river discharge simulations. Global SSM and LAI products are now operationally available from spaceborne instruments and they can be used to constrain LSMs through Data Assimilation (DA) techniques. In this study, an offline data assimilation system implemented in Météo-France's modelling platform (SURFEX) is tested over Europe and the Mediterranean basin to increase prediction accuracy for land surface variables. The resulting Land Data Assimilation System (LDAS) makes use of a simplified Extended Kalman Filter (SEKF). It is able to ingests information from satellite derived (i) SSM from the latest version of the ESA Climate Change Initiative as well as (ii) LAI from the Copernicus GLS project to constrain the multilayer, CO2-responsive version of the Interactions Between Soil, Biosphere, and Atmosphere model (ISBA) coupled with Météo-France's version of the Total Runoff Integrating Pathways continental hydrological system (ISBA-CTRIP). ERA-Interim observations based atmospheric forcing with precipitations corrected from Global Precipitation Climatology Centre observations (GPCC) is used to force ISBA-CTRIP at a resolution of 0.5 degree over 2000-2015. The model sensitivity to the assimilated observations is presented and a set of statistical diagnostics used to evaluate the impact of assimilating SSM and LAI on different model biogeophysical variables are provided. It is demonstrated that the assimilation scheme works effectively. The SEKF is able to extract useful information from the data signal at the grid scale and distribute the RZSM

  14. A Satellite-Derived Climatological Analysis of Urban Heat Island over Shanghai during 2000–2013

    Directory of Open Access Journals (Sweden)

    Weijiao Huang

    2017-06-01

    Full Text Available The urban heat island is generally conducted based on ground observations of air temperature and remotely sensing of land surface temperature (LST. Satellite remotely sensed LST has the advantages of global coverage and consistent periodicity, which overcomes the weakness of ground observations related to sparse distributions and costs. For human related studies and urban climatology, canopy layer urban heat island (CUHI based on air temperatures is extremely important. This study has employed remote sensing methodology to produce monthly CUHI climatology maps during the period 2000–2013, revealing the spatiotemporal characteristics of daytime and nighttime CUHI during this period of rapid urbanization in Shanghai. Using stepwise linear regression, daytime and nighttime air temperatures at the four overpass times of Terra/Aqua were estimated based on time series of Terra/Aqua-MODIS LST and other auxiliary variables including enhanced vegetation index, normalized difference water index, solar zenith angle and distance to coast. The validation results indicate that the models produced an accuracy of 1.6–2.6 °C RMSE for the four overpass times of Terra/Aqua. The models based on Terra LST showed higher accuracy than those based on Aqua LST, and nighttime air temperature estimation had higher accuracy than daytime. The seasonal analysis shows daytime CUHI is strongest in summer and weakest in winter, while nighttime CUHI is weakest in summer and strongest in autumn. The annual mean daytime CUHI during 2000–2013 is 1.0 and 2.2 °C for Terra and Aqua overpass, respectively. The annual mean nighttime CUHI is about 1.0 °C for both Terra and Aqua overpass. The resultant CUHI climatology maps provide a spatiotemporal quantification of CUHI with emphasis on temperature gradients. This study has provided information of relevance to urban planners and environmental managers for assessing and monitoring urban thermal environments which are constantly

  15. Modelling Angular Dependencies in Land Surface Temperatures From the SEVIRI Instrument onboard the Geostationary Meteosat Second Generation Satellites

    DEFF Research Database (Denmark)

    Rasmussen, Mads Olander; Pinheiro, AC; Proud, Simon Richard

    2010-01-01

    Satellite-based estimates of land surface temperature (LST) are widely applied as an input to models. A model output is often very sensitive to error in the input data, and high-quality inputs are therefore essential. One of the main sources of errors in LST estimates is the dependence on vegetat......Satellite-based estimates of land surface temperature (LST) are widely applied as an input to models. A model output is often very sensitive to error in the input data, and high-quality inputs are therefore essential. One of the main sources of errors in LST estimates is the dependence...... on vegetation structure and viewing and illumination geometry. Despite this, these effects are not considered in current operational LST products from neither polar-orbiting nor geostationary satellites. In this paper, we simulate the angular dependence that can be expected when estimating LST with the viewing...... by different land covers. The results show that the sun-target-sensor geometry plays a significant role in the estimated temperature, with variations strictly due to the angular configuration of more than ±3°C in some cases. On the continental scale, the average error is small except in hot-spot conditions...

  16. Linking Satellite Derived Land Surface Temperature with Cholera: A Case Study for South Sudan

    Science.gov (United States)

    Aldaach, H. S. V.; Jutla, A.; Akanda, A. S.; Colwell, R. R.

    2014-12-01

    A sudden onset of cholera in South Sudan, in April 2014 in Northern Bari in Juba town resulted in more than 400 cholera cases after four weeks of initial outbreak with a case of fatality rate of CFR 5.4%. The total number of reported cholera cases for the period of April to July, 2014 were 5,141 including 114 deaths. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera. Conditions for occurrence of cholera were detectable at least one month in advance in South Sudan and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature, and precipitation. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over South Sudan. Preliminary results that geographically location of cholera outbreak was identifiable within 1km resolution of the LST data.

  17. Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

    Science.gov (United States)

    Broich, M.; Huete, A.; Tulbure, M. G.; Ma, X.; Xin, Q.; Paget, M.; Restrepo-Coupe, N.; Davies, K.; Devadas, R.; Held, A.

    2014-05-01

    Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray-Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the

  18. Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

    Directory of Open Access Journals (Sweden)

    M. Broich

    2014-05-01

    Full Text Available Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI. We based our analysis on Enhanced Vegetation Index (EVI data from the MODerate Resolution Imaging Spectroradiometer (MODIS from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB, the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in

  19. Prognostic land surface albedo from a dynamic global vegetation model clumped canopy radiative transfer scheme and satellite-derived geographic forest heights

    Science.gov (United States)

    Kiang, N. Y.; Yang, W.; Ni-Meister, W.; Aleinov, I. D.; Jonas, J.

    2014-12-01

    Vegetation cover was introduced into general circulations models (GCMs) in the 1980's to account for the effect of land surface albedo and water vapor conductance on the Earth's climate. Schemes assigning canopy albedoes by broad biome type have been superceded in 1990's by canopy radiative transfer schemes for homogeneous canopies obeying Beer's Law extinction as a function of leaf area index (LAI). Leaf albedo and often canopy height are prescribed by plant functional type (PFT). It is recognized that this approach does not effectively describe geographic variation in the radiative transfer of vegetated cover, particularly for mixed and sparse canopies. GCM-coupled dynamic global vegetation models (DGVMs) have retained these simple canopy representations, with little further evaluation of their albedos. With the emergence lidar-derived canopy vertical structure data, DGVM modelers are now revisiting albedo simulation. We present preliminary prognostic global land surface albedo produced by the Ent Terrestrial Biosphere Model (TBM), a DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM. The Ent TBM is a next generation DGVM designed to incorporate variation in canopy heights, and mixed and sparse canopies. For such dynamically varying canopy structure, it uses the Analytical Clumped Two-Stream (ACTS) canopy radiative transfer model, which is derived from gap probability theory for canopies of tree cohorts with ellipsoidal crowns, and accounts for soil, snow, and bare stems. We have developed a first-order global vegetation structure data set (GVSD), which gives a year of satellite-derived geographic variation in canopy height, maximum canopy leaf area, and seasonal LAI. Combined with Ent allometric relations, this data set provides population density and foliage clumping within crowns. We compare the Ent prognostic albedoes to those of the previous GISS GCM scheme, and to satellite estimates. The impact of albedo differences on surface

  20. Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

    Science.gov (United States)

    Marques da Silva, J. R.; Damásio, C. V.; Sousa, A. M. O.; Bugalho, L.; Pessanha, L.; Quaresma, P.

    2015-06-01

    Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 × 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: "thermal integral over air temperature (accumulated degree-days)". The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations.

  1. Spatial Downscaling Research of Satellite Land Surface Temperature Based on Spectral Normalization Index

    Directory of Open Access Journals (Sweden)

    LI Xiaojun

    2017-03-01

    Full Text Available Aiming at the problem that the spatial and temporal resolution of land surface temperature (LST have the contradiction with each other, a new downscaling model was put forward, based on the TsHARP(an algorithm for sharpening thermal imagery downscaling method, this research makes improvements by selecting the better correlation of spectral index(normalized difference vegetation index, NDVI; normalized difference build-up index, NDBI; modified normalized difference water index, MNDWI; enhanced bare soil index, EBSI with LST, i.e., replaces the original NDVI with new spectral index according to the different surface land-cover types, to assess the accuracy of each downscaling method based on qualitative and quantitative analysis with synchronous Landsat 8 TIRS LST data. The results show that both models could effectively enhance the spatial resolution while simultaneously preserving the characteristics and spatial distribution of the original 1 km MODIS LST image, and also eliminate the “mosaic” effect in the original 1 km image, both models were proved to be effective and applicable in our study area; global scale analysis shows that the new model (RMSE:1.635℃ is better than the TsHARP method (RMSE:2.736℃ in terms of the spatial variability and accuracy of the results; the different land-cover types of downscaling statistical analysis shows that the TsHARP method has poor downscaling results in the low vegetation coverage area, especially for the bare land and building-up area(|MBE|>3℃, the new model has obvious advantages in the description of the low vegetation coverage area. Seasonal analysis shows that the downscaling results of two models in summer and autumn are superior to those in spring and winter, the new model downscaling results are better than the TsHARP method in the four seasons, in which the spring and winter downscaling improvement is better than summer and autumn.

  2. Normalization and calibration of geostationary satellite radiances for the International Satellite Cloud Climatology Project

    Science.gov (United States)

    Desormeaux, Yves; Rossow, William B.; Brest, Christopher L.; Campbell, G. G.

    1993-01-01

    Procedures are described for normalizing the radiometric calibration of image radiances obtained from geostationary weather satellites that contributed data to the International Satellite Cloud Climatology Project. The key step is comparison of coincident and collocated measurements made by each satellite and the concurrent AVHRR on the 'afternoon' NOAA polar-orbiting weather satellite at the same viewing geometry. The results of this comparison allow transfer of the AVHRR absolute calibration, which has been established over the whole series, to the radiometers on the geostationary satellites. Results are given for Meteosat-2, 3, and 4, for GOES-5, 6, and 7, for GMS-2, 3, and 4 and for Insat-1B. The relative stability of the calibrations of these radiance data is estimated to be within +/- 3 percent; the uncertainty of the absolute calibrations is estimated to be less than 10 percent. The remaining uncertainties are at least two times smaller than for the original radiance data.

  3. Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

    Science.gov (United States)

    Naha, Shaini; Thakur, Praveen K.; Aggarwal, S. P.

    2016-06-01

    The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity) is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH) have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006). Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I) and Ensemble Kalman Filter (EnKF) that uses observations of snow covered area (SCA) to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU), rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD) data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed

  4. Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

    Directory of Open Access Journals (Sweden)

    S. Naha

    2016-06-01

    Full Text Available The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006. Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I and Ensemble Kalman Filter (EnKF that uses observations of snow covered area (SCA to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU, rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated

  5. Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites.

    Science.gov (United States)

    Yan, Wai Yeung; Mahendrarajah, Prathees; Shaker, Ahmed; Faisal, Kamil; Luong, Robin; Al-Ahmad, Mohamed

    2014-12-01

    This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites' land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring.

  6. Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area

    Directory of Open Access Journals (Sweden)

    A. Tittebrand

    2009-03-01

    Full Text Available Based on satellite data in different temporal and spatial resolution, the current use of frequency distribution functions (PDF for surface parameters and energy fluxes is one of the most promising ways to describe subgrid heterogeneity of a landscape. Objective of this study is to find typical distribution patterns of parameters (albedo, NDVI for the determination of the actual latent heat flux (L.E determined from highly resolved satellite data within pixel on coarser scale.

    Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterised by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach L.E, as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrisation. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively, further the use of accurate land use classifications (CORINE and a new Landsat-classification, and a consistent parametrisation for the three sensors were realized to obtain a uniform base for investigations of the spatial variability.

    The analyses for 4 scenes in 2002 and 2003 showed that for forest clear distribution-patterns for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.

  7. Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

    Science.gov (United States)

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-01-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  8. Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

    Science.gov (United States)

    De Lannoy, Gabrielle J.M.; Reichle, Rolf H.; Houser, Paul R.; Arsenault, Kristi R.; Verhoest, Niko E.C.; Paulwels, Valentijn R.N.

    2009-01-01

    An ensemble Kalman filter (EnKF) is used in a suite of synthetic experiments to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of satellite retrievals) into fine-scale (1 km) model simulations. Coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (re-gridding) to the fine-scale model resolution prior to data assimilation. In either case observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated fine-scale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the fine-scale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

  9. Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations

    Directory of Open Access Journals (Sweden)

    Miriam Pablos

    2016-07-01

    Full Text Available Soil moisture (SM is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST and evapotranspiration (ET. Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS and NASA’s Soil Moisture Active Passive (SMAP. LST is remotely sensed using thermal infrared (TIR sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014 of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ≈ − 0.6 to −0.8, and between SMOS SM and MODIS LST Terra/Aqua day (R ≈ − 0.7. At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ≈ − 0.5 to −0.7; satellite R ≈ − 0.4 to −0.7 indicating SM–LST coupling, than in winter (in situ R ≈ +0.3; satellite R ≈ − 0.3 indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ∼0

  10. Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces

    Science.gov (United States)

    Duan, Si-Bo; Li, Zhao-Liang; Cheng, Jie; Leng, Pei

    2017-04-01

    The collection 6 (C6) MODIS land surface temperature (LST) product is publicly available for the user community. Compared to the collection 5 (C5) MODIS LST product, the C6 MODIS LST product has been refined over bare soil pixels. Assessing the accuracy of the C6 MODIS LST product will help to facilitate the use of the LST product in various applications. In this study, we present a cross-satellite comparison to evaluate the accuracy of the C6 MODIS LST product (MOD11_L2) over bare soil surfaces under various atmospheric and surface conditions using the ASTER LST product as a reference. For comparison, the C5 MODIS LST product was also used in the analysis. The absolute biases (0.2-1.5 K) of the differences between the C6 MODIS LST and ASTER LST over bare soil surfaces are approximately two times less than those (0.6-3.8 K) of the differences between the C5 MODIS LST and ASTER LST. Furthermore, the RMSEs (0.7-2.3 K) over bare soil surfaces for the C6 MODIS LST are significantly smaller than those (0.9-4.2 K) for the C5 MODIS LST. These results indicate that the accuracy of the C6 MODIS LST product is much better than that of the C5 MODIS LST product. We recommend that the user community employs the C6 MODIS LST product in their applications.

  11. Assessment of the interannual variability of agricultural yields in France using satellite data and a generic land surface model

    Science.gov (United States)

    Canal, Nicolas; Calvet, Jean-Christophe; Szczypta, Camille

    2013-04-01

    The generic ISBA-A-gs Land Surface Model (LSM) is used to simulate the interannual variability of the maximum above-ground biomass (Bagm) of cereals and grasslands in France. Agricultural statistics are used to optimize the maximal available soil water content (MaxAWC) of the model. For a number of administrative units, significant correlations between the simulated Bagm and the agricultural yield statistics are found over the 1994-2010 period. It is shown that the interannual variability of Bagm and of the simulated soil moisture correlate at given key periods. Significant correlations are found between ten-daily averaged simulated soil moisture and the simulated (observed) Bagm (yields). The corresponding plant growth stage is determined through the Leaf Area Index (LAI). Moreover, it is shown that the interannual variability of the modelled LAI and of the new satellite-derived GEOLAND2 LAI are consistent. The predictive value of both simulated and observed LAI on the agricultural yield (10 to 40 days before harvest) is investigated. The scores are used to benchmark different configurations of the model. In particular two contrasting representations of the soil moisture profile are considered: (1) one root-zone layer, (2) several soil layers with an explicit representation of diffusion processes and an exponential root density profile, with or without a deep soil layer below the root-zone.

  12. Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale

    Directory of Open Access Journals (Sweden)

    Christiane Schmullius

    2013-05-01

    Full Text Available Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. Records from Moderate Resolution Imaging Spectroradiometer (MODIS, Advanced Very High Resolution Radiometer (AVHRR and (Advanced Along Track Scanning Radiometer ((AATSR are providing long-term time series information. Assessing the quality of remote sensing-based temperature measurements provides feedback to the climate modeling community and other users by identifying agreements and discrepancies when compared to temperature records from meteorological stations. This paper presents a comparison of state-of-the-art remote sensing-based land surface temperature data with air temperature measurements from meteorological stations on a pan-arctic scale (north of 60° latitude. Within this study, we compared land surface temperature products from (AATSR, MODIS and AVHRR with an in situ air temperature (Tair database provided by the National Climate Data Center (NCDC. Despite analyzing the whole acquisition time period of each land surface temperature product, we focused on the inter-annual variability comparing land surface temperature (LST and air temperature for the overlapping time period of the remote sensing data (2000–2005. In addition, land cover information was included in the evaluation approach by using GLC2000. MODIS has been identified as having the highest agreement in comparison to air temperature records. The time series of (AATSR is highly variable, whereas inconsistencies in land surface temperature data from AVHRR have been found.

  13. Parameterizing atmosphere-land surface exchange for climate models with satellite data: A case study for the Southern Great Plains CART site

    Science.gov (United States)

    Gao, W.

    High-resolution satellite data provide detailed, quantitative descriptions of land surface characteristics over large areas so that objective scale linkage becomes feasible. With the aid of satellite data, researchers examined the linearity of processes scaled up from 30 m to 15 km. If the phenomenon is scale invariant, then the aggregated value of a function or flux is equivalent to the function computed from aggregated values of controlling variables. The linear relation may be realistic for limited land areas having no large surface contrasts to cause significant horizontal exchange. However, for areas with sharp surface contrasts, horizontal exchange and different dynamics in the atmospheric boundary may induce nonlinear interactions, such as at interfaces of land-water, forest-farm land, and irrigated crops-desert steppe. The linear approach, however, represents the simplest scenario and is useful for developing an effective scheme for incorporating subgrid land surface processes into large-scale models. Our studies focus on coupling satellite data and ground measurements with a satellite-data-driven land surface model to parameterize surface fluxes for large-scale climate models. In this case study, we used surface spectral reflectance data from satellite remote sensing to characterize spatial and temporal changes in vegetation and associated surface parameters in an area of about 350 x 400 km covering the southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site of the US Department of Energy's Atmospheric Radiation Measurement (ARM) Program.

  14. How well do we characterize the biophysical effects of vegetation cover change? Benchmarking land surface models against satellite observations.

    Science.gov (United States)

    Duveiller, Gregory; Forzieri, Giovanni; Robertson, Eddy; Georgievski, Goran; Li, Wei; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

    2017-04-01

    Changes in vegetation cover can affect the climate by altering the carbon, water and energy cycles. The main tools to characterize such land-climate interactions for both the past and future are land surface models (LSMs) that can be embedded in larger Earth System models (ESMs). While such models have long been used to characterize the biogeochemical effects of vegetation cover change, their capacity to model biophysical effects accurately across the globe remains unclear due to the complexity of the phenomena. The result of competing biophysical processes on the surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and on the background climate (e.g. presence of snow or soil moisture). Here we present a global scale benchmarking exercise of four of the most commonly used LSMs (JULES, ORCHIDEE, JSBACH and CLM) against a dedicated dataset of satellite observations. To facilitate the understanding of the causes that lead to discrepancies between simulated and observed data, we focus on pure transitions amongst major plant functional types (PFTs): from different tree types (evergreen broadleaf trees, deciduous broadleaf trees and needleleaf trees) to either grasslands or crops. From the modelling perspective, this entails generating a separate simulation for each PFT in which all 1° by 1° grid cells are uniformly covered with that PFT, and then analysing the differences amongst them in terms of resulting biophysical variables (e.g net radiation, latent and sensible heat). From the satellite perspective, the effect of pure transitions is obtained by unmixing the signal of different 0.05° spatial resolution MODIS products (albedo, latent heat, upwelling longwave radiation) over a local moving window using PFT maps derived from the ESA Climate Change Initiative land cover map. After aggregating to a common spatial support, the observation and model-driven datasets are confronted and

  15. Recent trends of the tropical hydrological cycle inferred from Global Precipitation Climatology Project and International Satellite Cloud Climatology Project data

    Science.gov (United States)

    Zhou, Y. P.; Xu, Kuan-Man; Sud, Y. C.; Betts, A. K.

    2011-05-01

    Scores of modeling studies have shown that increasing greenhouse gases in the atmosphere impact the global hydrologic cycle; however, disagreements on regional scales are large, and thus the simulated trends of such impacts, even for regions as large as the tropics, remain uncertain. The present investigation attempts to examine such trends in the observations using satellite data products comprising Global Precipitation Climatology Project precipitation and International Satellite Cloud Climatology Project cloud and radiation. Specifically, evolving trends of the tropical hydrological cycle over the last 20-30 years were identified and analyzed. The results show (1) intensification of tropical precipitation in the rising regions of the Walker and Hadley circulations and weakening over the sinking regions of the associated overturning circulation; (2) poleward shift of the subtropical dry zones (up to 2° decade-1 in June-July-August (JJA) in the Northern Hemisphere and 0.3-0.7° decade-1 in June-July-August and September-October-November in the Southern Hemisphere) consistent with an overall broadening of the Hadley circulation; and (3) significant poleward migration (0.9-1.7° decade-1) of cloud boundaries of Hadley cell and plausible narrowing of the high cloudiness in the Intertropical Convergence Zone region in some seasons. These results support findings of some of the previous studies that showed strengthening of the tropical hydrological cycle and expansion of the Hadley cell that are potentially related to the recent global warming trends.

  16. An investigation of current and future satellite and in-situ data for the remote sensing of the land surface energy balance

    Science.gov (United States)

    Diak, George R.

    1994-01-01

    This final report from the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) summarizes a research program designed to improve our knowledge of the water and energy balance of the land surface through the application of remote sensing and in-situ data sources. The remote sensing data source investigations to be detailed involve surface radiometric ('skin') temperatures and also high-spectral-resolution infrared radiance data from atmospheric sounding instruments projected to be available at the end of the decade, which have shown promising results for evaluating the land-surface water and energy budget. The in-situ data types to be discussed are measurements of the temporal changes of the height of the planetary boundary layer and measurements of air temperature within the planetary boundary layer. Physical models of the land surface, planetary boundary layer and free atmosphere have been used as important tools to interpret the in-situ and remote sensing signals of the surface energy balance. A prototype 'optimal' system for combining multiple data sources into a three-dimensional estimate of the surface energy balance was developed and first results from this system will be detailed. Potential new sources of data for this system and suggested continuation research will also be discussed.

  17. Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

    Directory of Open Access Journals (Sweden)

    K. Atlaskina

    2015-05-01

    Full Text Available Thirteen years of MODIS surface albedo data for the Northern Hemisphere during the spring months (March–May were analysed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analysed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF has a strong influence on the albedo in the study area and can explain 56% of variation of albedo in March, 76% in April and 92% in May. Therefore the effects of other parameters were investigated only for areas with 100% SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds −15 °C. At monthly mean air temperatures below this value no albedo changes are observed. Enhanced vegetation index (EVI and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100% SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in Eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

  18. Polar low climatology over the Nordic and Barents seas based on satellite passive microwave data

    OpenAIRE

    Smirnova, Julia E.; Golubkin, Pavel A.; Bobylev, Leonid P.; Zabolotskikh, Elizaveta; Chapron, Bertrand

    2015-01-01

    A new climatology of polar lows over the Nordic and Barents seas for 14 seasons (1995/1996-2008/2009) is presented. For the first time in climatological studies of polar lows an approach based on satellite passive microwave data was adopted for polar low identification. A total of 637 polar lows were found in 14 extended winter seasons by combining total atmospheric water vapor content and sea surface wind speed fields retrieved from Special Sensor Microwave/Imager data. As derived, the polar...

  19. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    to imperfect model forecasts. It remains a crucial challenge to account for system uncertainty, so as to provide model outputs accompanied by a quantified confidence interval. Properly characterizing and reducing uncertainty opens-up the opportunity for risk-based decision-making and more effective emergency...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...... temperature are explored in a multi-objective calibration experiment to optimize the parameters in a SVAT model in the Sahel. The two satellite derived variables were effective at constraining most land-surface and soil parameters. A data assimilation framework is developed and implemented with an integrated...

  20. An Emerging Global Aerosol Climatology from the MODIS Satellite Sensors

    Science.gov (United States)

    Remer, Lorraine A.; Kleidman, Richard G.; Levy, Robert C.; Kaufman, Yoram J.; Tanre, Didier; Mattoo, Shana; Martins, J. Vandelei; Ichoku, Charles; Koren, Ilan; Hongbin, Yu; hide

    2008-01-01

    The recently released Collection 5 MODIS aerosol products provide a consistent record of the Earth's aerosol system. Comparison with ground-based AERONET observations of aerosol optical depth (AOD) we find that Collection 5 MODIS aerosol products estimate AOD to within expected accuracy more than 60% of the time over ocean and more than 72% of the time over land. This is similar to previous results for ocean, and better than the previous results for land. However, the new Collection introduces a 0.01 5 offset between the Terra and Aqua global mean AOD over ocean, where none existed previously. Aqua conforms to previous values and expectations while Terra is high. The cause of the offset is unknown, but changes to calibration are a possible explanation. We focus the climatological analysis on the better understood Aqua retrievals. We find that global mean AOD at 550 nm over oceans is 0.13 and over land 0.19. AOD in situations with 80% cloud fraction are twice the global mean values, although such situations occur only 2% of the time over ocean and less than 1% of the time over land. There is no drastic change in aerosol particle size associated with these very cloudy situations. Regionally, aerosol amounts vary from polluted areas such as East Asia and India, to the cleanest regions such as Australia and the northern continents. In almost all oceans fine mode aerosol dominates over dust, except in the tropical Atlantic downwind of the Sahara and in some months the Arabian Sea.

  1. Temporal disaggregation of satellite-derived monthly precipitation estimates and the resulting propagation of error in partitioning of water at the land surface

    Directory of Open Access Journals (Sweden)

    S.A. Margulis

    2001-01-01

    Full Text Available Global estimates of precipitation can now be made using data from a combination of geosynchronous and low earth-orbit satellites. However, revisit patterns of polar-orbiting satellites and the need to sample mixed-clouds scenes from geosynchronous satellites leads to the coarsening of the temporal resolution to the monthly scale. There are prohibitive limitations to the applicability of monthly-scale aggregated precipitation estimates in many hydrological applications. The nonlinear and threshold dependencies of surface hydrological processes on precipitation may cause the hydrological response of the surface to vary considerably based on the intermittent temporal structure of the forcing. Therefore, to make the monthly satellite data useful for hydrological applications (i.e. water balance studies, rainfall-runoff modelling, etc., it is necessary to disaggregate the monthly precipitation estimates into shorter time intervals so that they may be used in surface hydrology models. In this study, two simple statistical disaggregation schemes are developed for use with monthly precipitation estimates provided by satellites. The two techniques are shown to perform relatively well in introducing a reasonable temporal structure into the disaggregated time series. An ensemble of disaggregated realisations was routed through two land surface models of varying complexity so that the error propagation that takes place over the course of the month could be characterised. Results suggest that one of the proposed disaggregation schemes can be used in hydrological applications without introducing significant error. Keywords: precipitation, temporal disaggregation, hydrological modelling, error propagation

  2. Satellite Regional Cloud Climatology over the Great Lakes

    Directory of Open Access Journals (Sweden)

    Steven A. Ackerman

    2013-11-01

    Full Text Available Thirty-one years of imager data from polar orbiting satellites are composited to produce a satellite climate data set of cloud amount for the Great Lakes region. A trend analysis indicates a slight decreasing trend in cloud cover over the region during this time period. The trend is significant and largest (~2% per decade over the water bodies. A strong seasonal cycle of cloud cover is observed over both land and water surfaces. Winter cloud amounts are greater over the water bodies than land due to heat and moisture flux into the atmosphere. Late spring through early autumn cloud amounts are lower over the water bodies than land due to stabilization of the boundary layer by relatively cooler lake waters. The influence of the lakes on cloud cover also extends beyond their shores, affecting cloud cover and properties far down wind. Cloud amount composited by wind direction demonstrate that the increasing cloud amounts downwind of the lakes is greatest during autumn and winter. Cold air flows over relatively warm lakes in autumn and winter generate wind parallel convective cloud bands. The cloud properties of these wind parallel cloud bands over the lakes during winter are presented.

  3. Analysing the Advantages of High Temporal Resolution Geostationary MSG SEVIRI Data Compared to Polar Operational Environmental Satellite Data for Land Surface Monitoring in Africa

    Science.gov (United States)

    Fensholt, R.; Anyamba, A.; Huber, S.; Proud, S. R.; Tucker, C. J.; Small, J.; Pak, E.; Rasmussen, M. O.; Sandholt, I.; Shisanya, C.

    2011-01-01

    Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 minutes which is substantially greater than any temporal resolution that can be obtained from existing polar operational environmental satellites (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (less than 5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.

  4. Distributed land surface modeling with utilization of multi-sensor satellite data: application for the vast agricultural terrain in cold region

    Science.gov (United States)

    Muzylev, E.; Uspensky, A.; Gelfan, A.; Startseva, Z.; Volkova, E.; Kukharsky, A.; Romanov, P.; Alexandrovich, M.

    2012-04-01

    A technique for satellite-data-based modeling water and heat regimes of a large scale area has been developed and applied for the 227,300 km2 agricultural region in the European Russia. The core component of the technique is the physically based distributed Remote Sensing Based Land Surface Model (RSBLSM) intended for simulating transpiration by vegetation and evaporation from bare soil, vertical transfer of water and heat within soil and vegetation covers during a vegetation season as well as hydrothermal processes in soil and snow covers during a cold season, including snow accumulation and melt, dynamics of soil moisture and temperature during soil freezing and thawing, infiltration into frozen soil. Processes in the "atmosphere-snow-frozen soil" system are critical for cold region agriculture, as they control crop development in early spring before the vegetation season beginning. For assigning the model parameters as well as for preliminary calibrating and validating the model, available multi-year data sets of soil moisture/temperature profiles, evaporation, snow and soil freezing depth measured at the meteorological stations located within the study region have been utilized. To provide an appropriate parametrization of the model for the areas where ground-based measurements are unavailable, estimates have been utilized for vegetation, meteorological and snow characteristics derived from the multispectral measurements of AVHRR/NOAA (1999-2010), MODIS/EOS Terra & Aqua (2002-2010), AMSR-E/Aqua (2003-2004; 2008-2010), and SEVIRI/Meteosat-9 (2009-2010). The technologies of thematic processing the listed satellite data have been developed and applied to estimate the land surface and snow cover characteristics for the study area. The developed technologies of AVHRR data processing have been adapted to retrieve land surface temperature (LST) and emissivity (E), surface-air temperature at a level of vegetation cover (TA), normalized vegetation index (NDVI), leaf

  5. Assessment of North Atlantic Precipitation and Freshwater Flux from the HOAPS-3 satellite climatology

    Science.gov (United States)

    Andersson, A.; Klepp, C.; Bakan, S.; Schulz, J.

    2009-04-01

    To attain a better understanding and modeling of climate processes attaining a proper knowledge of global water cycle components is essential. For the assessment of the freshwater flux at the ocean surface on global scale, exchange processes at the air-sea interface play a key-role. With the ability to derive ocean latent heat flux and precipitation from satellite data with acceptable accuracy, and frequent global coverage, a climatological assessment of the crucial processes has become possible. The HOAPS-3 climatology (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) contains fields of precipitation, surface fluxes and atmospheric parameters over the global ice-free ocean between 1987 and 2005. Except for the NOAA Pathfinder SST, all basic state variables needed for the derivation of the fluxes are calculated from SSM/I passive microwave radiometer measurements. Multi-satellite averages, inter-sensor calibration, and an efficient sea ice detection procedure make HOAPS a suitable data set for climatological applications as well as for case studies. Gridded 0.5 degree monthly, pentad and twice daily data products are freely available from www.hoaps.org. For the precipitation parameter, quasi-global coverage is achieved by complementing HOAPS-3 over land areas using the rain gauge based "Full Data Reanalysis Product Version 4", which is provided by the Global Precipitation Climatology Centre (GPCC). North Atlantic intra-decadal precipitation variability is investigated using this combined data set. The mutual response of the two independent precipitation data sources to the North Atlantic Oscillation (NAO) reveals coherent patterns and a detailed view on the structural changes in precipitation during the high and low states of the NAO. A second focus will be put on the evaluation of HOAPS-3 ocean surface freshwater fluxes and their interaction with the NAO.

  6. Comparison of Total Water Storage Anomalies from Global Hydrologic and Land Surface Models and New GRACE Satellite Solutions

    Science.gov (United States)

    Scanlon, B. R.; Zhang, Z.; Sun, A.; Save, H.; Mueller Schmied, H.; Wada, Y.; Doll, P. M.; Eisner, S.

    2016-12-01

    There is Increasing interest in global hydrology based on modeling and remote sensing, highlighting the need to compare output from modeling and remote sensing approaches. Here we evaluate simulated terrestrial Total Water Storage anomalies (TWSA) from global hydrologic models (GHMs: WGHM and PRC-GLOBWB) and global land surface models (LSMs, such as GLDAS NOAH, MOSAIC, VIC, and CLM) using newly released GRACE mascons solutions from the Univ. of Texas Center for Space Research. The comparisons are based on monthly TWS anomalies over 13 years (April 2002 - April 2015) for 176 basins globally. Performance metrics include scatter plots of simulated and GRACE observed TWSA by basin with median slopes for different models indicating bias, correlations (shape and timing of TWS time series), and variability ratio (standard deviation of model TWSA/std. dev. GRACE observed TWSA), with optimal values of 1 indicating perfect agreement. The GRACE data were also disaggregated into long-term trends and seasonal amplitudes. Modeled TWS anomalies are biased low by 20 - 30% relative to GRACE TWSA with similar bias levels for basins in different size classes but greater bias with increasing basin aridity. Discrepancies between models and GRACE TWSA are greatest for long-term trends in TWSA with 60 - 95% underestimation of GRACE TWSA by models. There is good agreement in seasonal amplitudes from models and GRACE ( 0.9 for models with little impact of basin size or climate for most models. These comparisons highlight reliable model performance in terms of seasonal amplitudes in TWSA and underestimation of long-term trends in TWSA and in arid basins.

  7. Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the Pan-Arctic scale

    NARCIS (Netherlands)

    Urban, M.; Eberle, J.; Hüttich, C.; Schmullius, C.; Herold, M.

    2013-01-01

    Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. Records from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR) and (Advanced) Along Track Scanning Radiometer ((A)ATSR) are pr

  8. Merged dust climatology in Phoenix, Arizona based on satellite and station data

    Science.gov (United States)

    Lei, Hang; Wang, Julian X. L.; Tong, Daniel Q.; Lee, Pius

    2016-11-01

    In order to construct climate quality long-term dust storm dataset, merged dust storm climatology in Phoenix is developed based on three data sources: regular meteorological records, in situ air quality measurements, and satellite remote sensing observations. The result presented in this paper takes into account the advantages of each dataset and integrates individual analyses demonstrated and presented in previous studies that laid foundation to reconstruct a consistent and continuous time series of dust frequency. A key for the merging procedure is to determine analysis criteria suitable for each individual data source. A practical application to historic records of dust storm activities over the Phoenix area is presented to illustrate detailed steps, advantages, and limitations of the newly developed process. Three datasets are meteorological records from the Sky Harbor station, satellite observed aerosol optical depth data from moderate resolution imaging spectroradiometer, and the U.S. Environmental Protection Agency Air Quality System particulate matter data of eight sites surrounding Phoenix. Our purpose is to construct dust climatology over the Phoenix region for the period 1948-2012. Data qualities of the reconstructed dust climatology are assessed based on the availability and quality of the input data. The period during 2000-2012 has the best quality since all datasets are well archived. The reconstructed climatology shows that dust storm activities over the Phoenix region have large interannual variability. However, seasonal variations show a skewed distribution with higher frequency of dust storm activities in July and August and relatively quiet during the rest of months. Combining advantages of all the available datasets, this study presents a merged product that provides a consistent and continuous time series of dust storm activities suitable for climate studies.

  9. Assessing regional crop water demand using a satellite-based combination equation with a land surface temperature componen

    DEFF Research Database (Denmark)

    Moyano, Carmen; Garcia, Monica; Tornos, Lucia

    2015-01-01

    consumption trends in the area. The results showed that the thermal-PT-JPL model is a suitable and simple tool requiring only air temperature and incoming solar radiation apart from standard satellites-products freely available. Our results show that in comparison with the hydrological model conceptual...... to estimate soil surface conductance based on an apparent thermal inertia index. A process-based model was applied to estimate surface energy fluxes including daily ET based on a modified version of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model at 1km pixel resolution during a chrono......-sequence spanning for more than a decade (2002-2013). The thermal-PT-JPL model was forced with vegetation, albedo, reflectance and temperature products from the Moderate-resolution Imaging Spectroradiometer (MODIS) from both Aqua and Terra satellites. The study region, B-XII Irrigation District of the Lower...

  10. Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C Data

    Directory of Open Access Journals (Sweden)

    Jun Xia

    2008-02-01

    Full Text Available On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m , using the Generalized Split-Window (GSWalgorithm proposed by Wan and Dozier (1996. The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC, and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm2 provided that the Land Surface Emissivities(LSEs are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006; and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.’ method (2003. The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give

  11. Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: a synthesis of AmeriFlux observations.

    Directory of Open Access Journals (Sweden)

    Wenquan Zhu

    Full Text Available Carbon Flux Phenology (CFP can affect the interannual variation in Net Ecosystem Exchange (NEE of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands, using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU by more than 70% and End of Carbon Uptake (ECU by more than 60%. The Root Mean Square Error (RMSE of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.

  12. Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: a synthesis of AmeriFlux observations.

    Science.gov (United States)

    Zhu, Wenquan; Chen, Guangsheng; Jiang, Nan; Liu, Jianhong; Mou, Minjie

    2013-01-01

    Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.

  13. Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander

    2017-04-01

    The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of

  14. Algorithm for Recovery of Integrated Water Vapor Content in the Atmosphere over Land Surfaces Based on Satellite Spectroradiometer Data

    Science.gov (United States)

    Lisenko, S. A.

    2017-05-01

    An algorithm is proposed for making charts of the distribution of water vapor in the atmosphere based on multispectral images of the earth by the Ocean and Land Color Instrument (OLCI) on board of the European research satellite Sentinel-3. The algorithm is based on multiple regression fits of the spectral brightness coefficients at the upper boundary of the atmosphere, the geometric parameters of the satellite location (solar and viewing angles), and the total water vapor content in the atmosphere. A regression equation is derived from experimental data on the variation in the optical characteristics of the atmosphere and underlying surface, together with Monte-Carlo calculations of the radiative transfer characteristics. The equation includes the brightness coefficients in the near IR channels of the OLCI for the absorption bands of water vapor and oxygen, as well as for the transparency windows of the atmosphere. Together these make it possible to eliminate the effect of the reflection spectrum of the underlying surface and air pressure on the accuracy of the measurements. The algorithm is tested using data from a prototype OLCI, the medium resolution imaging spectrometer (MERIS). A sample chart of the distribution of water vapor in the atmosphere over Eastern Europe is constructed without using subsatellite data and digital models of the surface relief. The water vapor contents in the atmosphere determined using MERIS images and data provided by earthbound measurements with the aerosol robotic network (AERONET) are compared with a mean square deviation of 1.24 kg/m2.

  15. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend analysis from 1901–present

    Directory of Open Access Journals (Sweden)

    A. Becker

    2013-02-01

    Full Text Available The availability of highly accessible and reliable monthly gridded data sets of global land-surface precipitation is a need that was already identified in the mid-1980s when there was a complete lack of globally homogeneous gauge-based precipitation analyses. Since 1989, the Global Precipitation Climatology Centre (GPCC has built up its unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over the world. The resulting database has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations worldwide. Based on this database, this paper provides the reference publication for the four globally gridded monthly precipitation products of the GPCC, covering a 111-yr analysis period from 1901–present. As required for a reference publication, the content of the product portfolio, as well as the underlying methodologies to process and interpolate are detailed. Moreover, we provide information on the systematic and statistical errors associated with the data products. Finally, sample applications provide potential users of GPCC data products with suitable advice on capabilities and constraints of the gridded data sets. In doing so, the capabilities to access El Niño–Southern Oscillation (ENSO and North Atlantic Oscillation (NAO sensitive precipitation regions and to perform trend analyses across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology (CLIM V2011, the Full Data Reanalysis (FD V6, the Monitoring Product (MP V4, and the First Guess Product (FG, are publicly available on easily accessible latitude/longitude grids encoded in zipped clear text ASCII files for subsequent visualization and download through the GPCC download gate hosted on ftp://ftp.dwd.de/pub/data/gpcc/html/download_gate.html by the Deutscher Wetterdienst (DWD, Offenbach, Germany. Depending on the product, four (0.25°, 0.5°, 1.0°, 2.5° for CLIM, three (0.5°, 1.0°, 2.5°, for FD

  16. A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

    Directory of Open Access Journals (Sweden)

    Robert M. Parinussa

    2016-10-01

    Full Text Available Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth’s surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1–2 GHz. Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E, as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM, and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm and descending (01:30 am paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for

  17. GLORI (GLObal navigation satellite system Reflectometry Instrument): A New Airborne GNSS-R receiver for land surface applications

    Science.gov (United States)

    Motte, Erwan; Zribi, Mehrez; Fanise, Pascal

    2015-04-01

    GLORI (GLObal navigation satellite system Reflectometry Instrument) is a new receiver dedicated to the airborne measurement of surface parameters such as soil moisture and biomass above ground and sea state (wave height and direction) above oceans. The instrument is based on the PARIS concept [Martin-Neira, 1993] using both the direct and surface-reflected L-band signals from the GPS constellation as a multistatic radar source. The receiver is based on one up-looking and one down-looking dual polarization hemispherical active antennas feeding a low-cost 4-channel SDR direct down-conversion receiver tuned to the GPS L1 frequency. The raw measurements are sampled at 16.368MHz and stored as 2-bit, IQ binary files. In post-processing, GPS acquisition and tracking are performed on the direct up-looking signal while the down-looking signal is processed blindly using tracking parameters from the direct signal. The obtained direct and reflected code-correlation waveforms are the basic observables for geophysical parameters inversion. The instrument was designed to be installed aboard the ATR42 experimental aircraft from the French SAFIRE fleet as a permanent payload. The long term goal of the project is to provide real-time continuous surface information for every flight performed. The aircraft records attitude information through its Inertial Measurement Unit and a commercial GPS receiver records additional information such as estimated doppler and code phase, receiver location, satellites azimuth and elevation. A series of test flights were performed over both the Toulouse and Gulf of Lion (Mediterranean Sea) regions during the period 17-21 Nov 2014 together with the KuROS radar [Hauser et al., 2014]. Using processing methods from the literature [Egido et al., 2014], preliminary results demonstrate the instrument sensitivity to both ground and ocean surface parameters estimation. A dedicated scientific flight campaign is planned at the end of second quarter 2015 with

  18. TOWARD CALIBRATED MODULAR WIRELESS SYSTEM BASED AD HOC SENSORS FOR IN SITU LAND SURFACE TEMPERATURE MEASUREMENTS AS SUPPORT TO SATELLITE REMOTE SENSING

    Directory of Open Access Journals (Sweden)

    ASAAD CHAHBOUN

    2011-06-01

    Full Text Available This paper presents a new method for in situ Land Surface Temperature (LST measurements' campaigns for satellite algorithms validations. The proposed method based on Wireless Sensor Network (WSN is constituted by modules of node arrays. Each of which is constituted by 25 smart nodes scattered throughout a target field. Every node represents a Thermal Infra Red (TIR radiation sensor and keeps a minimum size while ensuring the functions of communication, sensing, and processing. This Wireless-LST (Wi-LST system is convenient to beinstalled on a field pointing to any type of targets (e.g. bare soil, grass, water, etc.. Ad hoc topology is adopted among the TIR nodes with multi-hop mesh routing protocol for communication, acquisition data are transmitted to the client tier wirelessly. Using these emergent technologies, we propose a practical method for Wi-LSTsystem calibration. TIR sensor (i.e. OSM101 from OMEGA society measures temperature, which is conditioned and amplified by an AD595 within a precision of 0.1 °C. Assessed LST is transmitted over thedeveloped ad hoc WSN modules (i.e. MICA2DOT from CROSSBOW society, and collected at in situ base station (i.e. PANASONIC CF19 laptop using an integrated database. LST is evaluated with a polynomialalgorithm structure as part of developed software. Finally, the comparison of the mean values of LST(Wi-LST in each site with the Moderate Resolution Imaging Spectro-radiometer (MODIS sensor, obtained from the daily LST product (MOD11C1 developed by the MODIS-NASA Science Team, on board TERRA satellite during the campaign period is provided.

  19. Ground validation of oceanic snowfall detection in satellite climatologies during LOFZY

    Science.gov (United States)

    Klepp, Christian; Bumke, Karl; Bakan, Stephan; Bauer, Peter

    2010-08-01

    A thorough knowledge of global ocean precipitation is an indispensable prerequisite for the understanding of the water cycle in the global climate system. However, reliable detection of precipitation over the global oceans, especially of solid precipitation, remains a challenging task. This is true for both, passive microwave remote sensing and reanalysis based model estimates. The optical disdrometer ODM 470 is a ground validation instrument capable of measuring rain and snowfall on ships even under high wind speeds. It was used for the first time over the Nordic Seas during the LOFZY 2005 campaign. A dichotomous verification of precipitation occurrence resulted in a perfect correspondence between the disdrometer, a precipitation detector and a shipboard observer's log. The disdrometer data is further point-to-area collocated against precipitation from the satellite based Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data (HOAPS) climatology. HOAPS precipitation turns out to be overall consistent with the disdrometer data resulting in a detection accuracy of 0.96. The collocated data comprises light precipitation events below 1 mm h-1. Therefore two LOFZY case studies with high precipitation rates are presented that indicate plausible HOAPS satellite precipitation rates. Overall, this encourages longer term measurements of ship-to-satellite collocated precipitation in the near future.

  20. High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes

    Science.gov (United States)

    Manz, Bastian; Buytaert, Wouter; Zulkafli, Zed; Lavado, Waldo; Willems, Bram; Robles, Luis Alberto; Rodríguez-Sánchez, Juan-Pablo

    2016-02-01

    Satellite precipitation products are becoming increasingly useful to complement rain gauge networks in regions where these are too sparse to capture spatial precipitation patterns, such as in the Tropical Andes. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (TPR) was active for 17 years (1998-2014) and has generated one of the longest single-sensor, high-resolution, and high-accuracy rainfall records. In this study, high-resolution (5 km) gridded mean monthly climatological precipitation is derived from the raw orbital TPR data (TRMM 2A25) and merged with 723 rain gauges using multiple satellite-gauge (S-G) merging approaches. The resulting precipitation products are evaluated by cross validation and catchment water balances (runoff ratios) for 50 catchments across the Tropical Andes. Results show that the TPR captures major synoptic and seasonal precipitation patterns and also accurately defines orographic gradients but underestimates absolute monthly rainfall rates. The S-G merged products presented in this study constitute an improved source of climatological rainfall data, outperforming the gridded TPR product as well as a rain gauge-only product based on ordinary Kriging. Among the S-G merging methods, performance of inverse distance interpolation of satellite-gauge residuals was similar to that of geostatistical methods, which were more sensitive to gauge network density. High uncertainty and low performance of the merged precipitation products predominantly affected regions with low and intermittent precipitation regimes (e.g., Peruvian Pacific coast) and is likely linked to the low TPR sampling frequency. All S-G merged products presented in this study are available in the public domain.

  1. A climatology of gravity wave parameters based on satellite limb soundings

    Science.gov (United States)

    Ern, Manfred; Trinh, Quang Thai; Preusse, Peter; Riese, Martin

    2017-04-01

    Gravity waves are one of the main drivers of atmospheric dynamics. The resolution of most global circulation models (GCMs) and chemistry climate models (CCMs), however, is too coarse to properly resolve the small scales of gravity waves. Horizontal scales of gravity waves are in the range of tens to a few thousand kilometers. Gravity wave source processes involve even smaller scales. Therefore GCMs/CCMs usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified, and comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. In our study, we present a global data set of gravity wave distributions observed in the stratosphere and the mesosphere by the infrared limb sounding satellite instruments High Resolution Dynamics Limb Sounder (HIRDLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). We provide various gravity wave parameters (for example, gravity variances, potential energies and absolute momentum fluxes). This comprehensive climatological data set can serve for comparison with other instruments (ground based, airborne, or other satellite instruments), as well as for comparison with gravity wave distributions, both resolved and parametrized, in GCMs and CCMs. The purpose of providing various different parameters is to make our data set useful for a large number of potential users and to overcome limitations of other observation techniques, or of models, that may be able to provide only one of those parameters. We present a climatology of typical average global distributions and of zonal averages, as well as their natural range of variations. In addition, we discuss seasonal variations of the global distribution of gravity waves, as well as limitations of our method of deriving gravity wave parameters from satellite data.

  2. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend analysis from 1901–present

    Directory of Open Access Journals (Sweden)

    A. Becker

    2012-09-01

    Full Text Available The availability of highly accessible and reliable monthly gridded data sets of the global land-surface precipitation is a need that has already been identified in the mid-80s when there was a complete lack of a globally homogeneous gauge based precipitation analysis. Since 1989 the Global Precipitation Climatology Centre (GPCC has built up a unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over the world. The resulting data base has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations world-wide. This paper provides the reference publication for the four globally gridded monthly precipitation products of the GPCC covering a 111-yr analysis period from 1901–present, processed from this data base. As required for a reference publication, the content of the product portfolio, as well as the underlying methodologies to process and interpolate are detailed. Moreover, we provide information on the systematic and statistical errors associated with the data products. Finally, sample applications provide potential users of GPCC data products with suitable advice on capabilities and constraints of the gridded data sets. In doing so, the capabilities to access ENSO and NAO sensitive precipitation regions and to perform trend analysis across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology V2011 (CLIM, the Full Data Reanalysis (FD V6, the Monitoring Product (MP V4, and the First Guess Product (FG are public available on easy accessible latitude longitude grids encoded in zipped clear text ASCII files for subsequent visualization and download through the GPCC download gate hosted on ftp://ftp.dwd.de/pub/data/gpcc/html/download_gate.html by the Deutscher Wetterdienst (DWD, Offenbach, Germany. Depending on the product four (0.25°, 0.5°, 1.0°, 2.5° for CLIM

  3. Comparison of Satellite-Derived TOA Shortwave Clear-Sky Fluxes to Estimates from GCM Simulations Constrained by Satellite Observations of Land Surface Characteristics

    Science.gov (United States)

    Anantharaj, Valentine G.; Nair, Udaysankar S.; Lawrence, Peter; Chase, Thomas N.; Christopher, Sundar; Jones, Thomas

    2010-01-01

    Clear-sky, upwelling shortwave flux at the top of the atmosphere (S(sub TOA raised arrow)), simulated using the atmospheric and land model components of the Community Climate System Model 3 (CCSM3), is compared to corresponding observational estimates from the Clouds and Earth's Radiant Energy System (CERES) sensor. Improvements resulting from the use of land surface albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the simulations are also examined. Compared to CERES observations, CCSM3 overestimates global, annual averaged S(sub TOA raised arrow) over both land and oceans. However, regionally, CCSM3 overestimates S(sub TOA raised arrow) over some land and ocean areas while underestimating it over other sites. CCSM3 underestimates S(sub TOA raised arrow) over the Saharan and Arabian Deserts and substantial differences exist between CERES observations and CCSM3 over agricultural areas. Over selected sites, after using groundbased observations to remove systematic biases that exist in CCSM computation of S(sub TOA raised arrow), it is found that use of MODIS albedo improves the simulation of S(sub TOA raised arrow). Inability of coarse resolution CCSM3 simulation to resolve spatial heterogeneity of snowfall over high altitude sites such as the Tibetan Plateau causes overestimation of S(sub TOA raised arrow) in these areas. Discrepancies also exist in the simulation of S(sub TOA raised arrow) over ocean areas as CCSM3 does not account for the effect of wind speed on ocean surface albedo. This study shows that the radiative energy budget at the TOA is improved through the use of MODIS albedo in Global Climate Models.

  4. Lightning climatology over Jakarta, Indonesia, based on long-term surface operational, satellite, and campaign observations

    Science.gov (United States)

    Mori, Shuichi; Wu, Peiming; Yamanaka, Manabu D.; Hattori, Miki; Hamada, Jun-Ichi; Arbain, Ardhi A.; Lestari, Sopia; Sulistyowati, Reni; Syamsudin, Fadli

    2016-04-01

    Lightning frequency over Indonesian Maritime Continent (MC) is quite high (Petersen and Rutledge 2001, Christian et al. 2003, Takayabu 2006, etc). In particular, Bogor (south of Jakarta, west Jawa) had 322 days of lightning in one year (Guinness Book in 1988). Lightning causes serious damage on nature and society over the MC; forest fore, power outage, inrush/surge currents on many kinds of electronics. Lightning climatology and meso-scale characteristics of thunderstorm over the MC, in particular over Jakarta, where social damage is quite serious, were examined. We made Statistical analysis of lightning and thunderstorm based on TRMM Lightning Image Sensor (LIS) and Global Satellite Mapping of Precipitation (GSMaP) together with long-term operational surface observation data (SYNOP) in terms of diurnal, intraseasonal, monsoonal, and interannual variations. In addition, we carried out a campaign observation in February 2015 in Bogor to obtain meso-scale structure and dynamics of thunderstorm over Jakarta to focus on graupel and other ice phase particles inside by using an X-band dual-polarimetric (DP) radar. Recently, Virts et al. (2013a, b) showed comprehensive lightning climatology based on the World Wide Lightning Location Network (WWLLN). However, they also reported problems with its detection efficiency (< 10%) and small sampling frequency (< 0.1% of the time fly over tropics) by satellites. Therefore, we firstly examine in situ lightning data based on SYNOP observed by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) because lightning is quite local and sporadic phenomena. We've started to analyze lightning characteristics over Jakarta region based on SYNOP as the ground truth data and GSMaP. Variability of lightning frequency around Jakarta was affected much by local conditions, e.g., topography (elevation) and proximity to the coastline. We confirmed the lightning frequency and its diurnal variation around Jakarta were much

  5. Numerical modeling and remote sensing of global water management systems: Applications for land surface modeling, satellite missions, and sustainable water resources

    Science.gov (United States)

    Solander, Kurt C.

    The ability to accurately quantify water storages and fluxes in water management systems through observations or models is of increasing importance due to the expected impacts from climate change and population growth worldwide. Here, I describe three innovative techniques developed to better understand this problem. First, a model was created to represent reservoir storage and outflow with the objective of integration into a Land Surface Model (LSM) to simulate the impacts of reservoir management on the climate system. Given this goal, storage capacity represented the lone model input required that is not already available to an LSM user. Model parameterization was linked to air temperature to allow future simulations to adapt to a changing climate, making it the first such model to mimic the potential response of a reservoir operator to climate change. Second, spatial and temporal error properties of future NASA Surface Water and Ocean Topography (SWOT) satellite reservoir operations were quantified. This work invoked the use of the SWOTsim instrument simulator, which was run over a number of synthetic and actual reservoirs so the resulting error properties could be extrapolated to the global scale. The results provide eventual users of SWOT data with a blueprint of expected reservoir error properties so such characteristics can be determined a priori for a reservoir given knowledge about its topology and anticipated repeat orbit pass over its location. Finally, data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission was used in conjunction with in-situ water use records to evaluate sustainable water use at the two-digit HUC basin scale over the contiguous United States. Results indicate that the least sustainable water management region is centered in the southwest, where consumptive water use exceeded water availability by over 100% on average for some of these basins. This work represents the first attempt at evaluating sustainable

  6. Ungulate Reproductive Parameters Track Satellite Observations of Plant Phenology across Latitude and Climatological Regimes.

    Science.gov (United States)

    Stoner, David C; Sexton, Joseph O; Nagol, Jyoteshwar; Bernales, Heather H; Edwards, Thomas C

    2016-01-01

    The effect of climatically-driven plant phenology on mammalian reproduction is one key to predicting species-specific demographic responses to climate change. Large ungulates face their greatest energetic demands from the later stages of pregnancy through weaning, and so in seasonal environments parturition dates should match periods of high primary productivity. Interannual variation in weather influences the quality and timing of forage availability, which can influence neonatal survival. Here, we evaluated macro-scale patterns in reproductive performance of a widely distributed ungulate (mule deer, Odocoileus hemionus) across contrasting climatological regimes using satellite-derived indices of primary productivity and plant phenology over eight degrees of latitude (890 km) in the American Southwest. The dataset comprised > 180,000 animal observations taken from 54 populations over eight years (2004-2011). Regionally, both the start and peak of growing season ("Start" and "Peak", respectively) are negatively and significantly correlated with latitude, an unusual pattern stemming from a change in the dominance of spring snowmelt in the north to the influence of the North American Monsoon in the south. Corresponding to the timing and variation in both the Start and Peak, mule deer reproduction was latest, lowest, and most variable at lower latitudes where plant phenology is timed to the onset of monsoonal moisture. Parturition dates closely tracked the growing season across space, lagging behind the Start and preceding the Peak by 27 and 23 days, respectively. Mean juvenile production increased, and variation decreased, with increasing latitude. Temporally, juvenile production was best predicted by primary productivity during summer, which encompassed late pregnancy, parturition, and early lactation. Our findings offer a parsimonious explanation of two key reproductive parameters in ungulate demography, timing of parturition and mean annual production, across

  7. Towards a climatology of tropical cyclone morphometric structures using a newly standardized passive microwave satellite dataset

    Science.gov (United States)

    Cossuth, J.; Hart, R. E.

    2013-12-01

    storm's rainband and eyewall organization. Ultimately, this project develops a consistent climatology of TC structures using a new database of research-quality historical TC satellite microwave observations. Not only can such data sets more accurately study TC structural evolution, but they may facilitate automated TC intensity estimates and provide methods to enhance current operational and research products, such as at the NRL TC webpage (http://www.nrlmry.navy.mil/TC.html). The process of developing the dataset and possible objective definitions of TC structures using passive microwave imagery will be described, with preliminary results suggesting new methods to identify TC structures that may interrogate and expand upon physical and dynamical theories. Structural metrics such as threshold analysis of the outlines of the TC shape as well as methods to diagnose the inner-core size, completion, and magnitude will be introduced.

  8. Meteosat SEVIRI Fire Radiative Power (FRP products from the Land Surface Analysis Satellite Applications Facility (LSA SAF – Part 1: Algorithms, product contents and analysis

    Directory of Open Access Journals (Sweden)

    M. J. Wooster

    2015-06-01

    Full Text Available Characterising changes in landscape scale fire activity at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth observation (EO satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from these types of geostationary observations, often with the aim of supporting the generation of data related to biomass burning fuel consumption and trace gas and aerosol emission fields. The Fire Radiative Power (FRP products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF from data collected by the Meteosat Second Generation (MSG Spinning Enhanced Visible and Infrared Imager (SEVIRI are one such set of products, and are freely available in both near real-time and archived form. Every 15 min, the algorithms used to generate these products identify and map the location of new SEVIRI observations containing actively burning fires, and characterise their individual rates of radiative energy release (fire radiative power; FRP that is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the highest spatial resolution FRP dataset, delivered for all of Europe, northern and southern Africa, and part of South America at a spatial resolution of 3 km (decreasing away from the west African sub-satellite point at the full 15 min temporal resolution. The FRP-GRID product is an hourly summary of the FRP-PIXEL data, produced at a 5° grid cell size and including simple bias adjustments for meteorological cloud cover and for the regional underestimation of FRP caused, primarily, by the non-detection of low FRP fire pixels at SEVIRI's relatively coarse pixel size. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA algorithm used to detect the SEVIRI active fire pixels, and detail methods used to deliver atmospherically corrected FRP

  9. Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska

    Science.gov (United States)

    Huang, Shengli; Jin, Suming; Dahal, Devendra; Chen, Xuexia; Young, Claudia; Liu, Heping; Liu, Shuguang

    2013-05-01

    Land surface change caused by fires and succession is confounded by many site-specific factors and requires further study. The objective of this study was to reveal the spatially explicit land surface change by minimizing the confounding factors of weather variability, seasonal offset, topography, land cover, and drainage. In a pilot study of the Yukon River Basin of interior Alaska, we retrieved Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) from a postfire Landsat image acquired on August 5th, 2004. With a Landsat reference image acquired on June 26th, 1986, we reconstructed NDVI, albedo, and LST of 1987-2004 fire scars for August 5th, 2004, assuming that these fires had not occurred. The difference between actual postfire and assuming-no-fire scenarios depicted the fires and succession impact. Our results demonstrated the following: (1) NDVI showed an immediate decrease after burning but gradually recovered to prefire levels in the following years, in which burn severity might play an important role during this process; (2) Albedo showed an immediate decrease after burning but then recovered and became higher than prefire levels; and (3) Most fires caused surface warming, but cooler surfaces did exist; time-since-fire affected the prefire and postfire LST difference but no absolute trend could be found. Our approach provided spatially explicit land surface change rather than average condition, enabling a better understanding of fires and succession impact on ecological consequences at the pixel level.

  10. Alpine cloud climatology using long-term NOAA-AVHRR satellite data

    Energy Technology Data Exchange (ETDEWEB)

    Kaestner, M.; Kriebel, K.T.

    2000-07-01

    Three different climates have been identified by our evaluation of AVHRR (advanced very high resolution radiometer) data using APOLLO (AVHRR processing scheme over land, clouds and ocean) for a five-years cloud climatology of the Alpine region. The cloud cover data from four layers were spatially averaged in boxes of 15 km by 14 km. The study area only comprises 540 km by 560 km, but contains regions with moderate, Alpine and Mediterranean climate. Data from the period July 1989 until December 1996 have been considered. The temporal resolution is one scene per day, the early afternoon pass, yielding monthly means of satellite derived cloud coverages 5% to 10% above the daily mean compared to conventional surface observation. At nonvegetated sites the cloudiness is sometimes significantly overestimated. Averaging high resolution cloud data seems to be superior to low resolution measurements of cloud properties and averaging is favourable in topographical homogeneous regions only. The annual course of cloud cover reveals typical regional features as foehn or temporal singularities as the so-called Christmas thaw. The cloud cover maps in spatially high resolution show local luff/lee features which outline the orography. Less cloud cover is found over the Alps than over the forelands in winter, an accumulation of thick cirrus is found over the High Alps and an accumulation of thin cirrus north of the Alps. (orig.)

  11. A climatology of fine absorbing biomass burning, urban and industrial aerosols detected from satellites

    Science.gov (United States)

    Kalaitzi, Nikoleta; Hatzianastassiou, Nikos; Gkikas, Antonis; Papadimas, Christos D.; Torres, Omar; Mihalopoulos, Nikos

    2017-04-01

    Natural biomass burning (BB) along with anthropogenic urban and industrial aerosol particles, altogether labeled here as BU aerosols, contain black and brown carbon which both absorb strongly the solar radiation. Thus, BU aerosols warm significantly the atmosphere also causing adjustments to cloud properties, which traditionally are known as cloud indirect and semi-direct effects. Given the role of the effects of BU aerosols for contemporary and future climate change, and the uncertainty associated with BU, both ascertained by the latest IPCC reports, there is an urgent need for improving our knowledge on the spatial and temporal variability of BU aerosols all over the globe. Over the last few decades, thanks to the rapid development of satellite observational techniques and retrieval algorithms it is now possible to detect BU aerosols based on satellite measurements. However, care must be taken in order to ensure the ability to distinguish BU from other aerosol types usually co-existing in the Earth's atmosphere. In the present study, an algorithm is presented, based on a synergy of different satellite measurements, aiming to identify and quantify BU aerosols over the entire globe and during multiple years. The objective is to build a satellite-based climatology of BU aerosols intended for use for various purposes. The produced regime, namely the spatial and temporal variability of BU aerosols, emphasizes the BU frequency of occurrence and their intensity, in terms of aerosol optical depth (AOD). The algorithm is using the following aerosol optical properties describing the size and atmospheric loading of BU aerosols: (i) spectral AOD, (ii) Ångström Exponent (AE), (iii) Fine Fraction (FF) and (iv) Aerosol Index (AI). The relevant data are taken from Collection 006 MODIS-Aqua, except for AI which is taken from OMI-Aura. The identification of BU aerosols by the algorithm is based on a specific thresholding technique, with AI≥1.5, AE≥1.2 and FF≥0.6 threshold

  12. The L-band PBMR measurements of surface soil moisture in FIFE. [First International satellite land surface climatology project Field Experiment

    Science.gov (United States)

    Wang, James R.; Shiue, James C.; Schmugge, Thomas J.; Engman, Edwin T.

    1990-01-01

    The NASA Langley Research Center's L-band pushbroom microwave radiometer (PBMR) aboard the NASA C-130 aircraft was used to map surface soil moisture at and around the Konza Prairie Natural Research Area in Kansas during the four intensive field campaigns of FIFE in May-October 1987. There was a total of 11 measurements was made when soils were known to be saturated. This measurement was used for the calibration of the vegetation effect on the microwave absorption. Based on this calibration, the data from other measurements on other days were inverted to generate the soil moisture maps. Good agreement was found when the estimated soil moisture values were compared to those independently measured on the ground at a number of widely separated locations. There was a slight bias between the estimated and measured values, the estimated soil moisture on the average being lower by about 1.8 percent. This small bias, however, was accounted for by the difference in time of the radiometric measurements and the soil moisture ground sampling.

  13. Modeling Land Surface Phenology Using Earthlight

    Science.gov (United States)

    Henebry, G. M.

    2005-12-01

    Microwave radiometers have long been used in earth observation, but the coarse spatial resolution of the data has discouraged its use in investigations of the vegetated land surface. The Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite acquires multifrequency observations twice daily (1:30 and 13:30). From these brightness temperatures come two data products relevant to land surface phenology: soil moisture and vegetation water content. Although the nominal spatial resolution of these products is coarse (25 km), the fine temporal sampling allows characterization of the diel variation in surface moisture as contained in the uppermost soil layer and bound in the vegetation canopy. The ephermal dynamics of surficial soil moisture are difficult to validate due to the scale discrepancy between the 625 sq km coverage of a single pixel and the sparse network of weather stations. In contrast, canopy dynamics are more readily validated using finer spatial resolution data products and/or ecoregionalizations. For sites in the North American Great Plains and Northern Eurasia dominated by herbaceous vegetation, I will present land surface phenologies modeled using emitted earthlight and compare them with land surface phenologies modeled using reflected sunlight. I will also explore whether some key climate modes have a significant effect on the microwave-retrieved land surface phenologies.

  14. Resolution and Content Improvements to MISR Aerosol and Land Surface Products

    Science.gov (United States)

    Garay, M. J.; Bull, M. A.; Diner, D. J.; Hansen, E. G.; Kalashnikova, O. V.

    2015-12-01

    Since early 2000, the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra satellite has been providing operational Level 2 (swath-based) aerosol optical depth (AOD) and particle property retrievals at 17.6 km spatial resolution and atmospherically corrected land surface products at 1.1 km resolution. The performance of the aerosol product has been validated against ground-based Aerosol Robotic Network (AERONET) observations, model comparisons, and climatological assessments. This product has played a major role in studies of the impacts of aerosols on climate and air quality. The surface product has found a variety of uses, particularly at regional scales for assessing vegetation and land surface change. A major development effort has led to the release of an update to the operational (Version 22) MISR Level 2 aerosol and land surface retrieval products, which has been in production since December 2007. The new release is designated Version 23. The resolution of the aerosol product has been increased to 4.4 km, allowing more detailed characterization of aerosol spatial variability, especially near local sources and in urban areas. The product content has been simplified and updated to include more robust measures of retrieval uncertainty and other fields to benefit users. The land surface product has also been updated to incorporate the Version 23 aerosol product as input and to improve spatial coverage, particularly over mountainous terrain and snow/ice-covered surfaces. We will describe the major upgrades incorporated in Version 23 and present validation of the aerosol product against both the standard AERONET historical database, as well as high spatial density AERONET-DRAGON deployments. Comparisons will also be shown relative to the Version 22 aerosol and land surface products. Applications enabled by these product updates will be discussed.

  15. Land surface phenology from SPOT VEGETATION time series

    Directory of Open Access Journals (Sweden)

    A. Verger

    2016-12-01

    Full Text Available Land surface phenology from time series of satellite data are expected to contribute to improve the representation of vegetation phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from GEOCLIM-LAI, a global climatology of leaf area index (LAI derived from 1-km SPOT VEGETATION time series for 1999-2010. The calibration with ground measurements showed that the start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests in Europe, cherry in Asia and lilac shrubs in North America showed an overall root mean square error lower than 19 days for the start, end and length of season, and good agreement between the latitudinal gradients of VEGETATION LAI phenology and ground data.

  16. An Estimation of Land Surface Temperatures from Landsat ETM+ ...

    African Journals Online (AJOL)

    Dr-Adeline

    2 National Authority for Remote Sensing and Space Sciences, Cairo, Egypt. 3University of ... Keywords: Urban growth, urban heat Island, land surface temperatures, satellite remote sensing .... observed target includes green vegetation or not.

  17. Determination of Land Surface Temperature (LST) and Potential ...

    African Journals Online (AJOL)

    Determination of Land Surface Temperature (LST) and Potential Urban Heat Island Effect in Parts of Lagos State using Satellite ... Changes in temperature appear to be closely related to concentrations of atmospheric carbon dioxide.

  18. An ensemble Kalman filter dual assimilation of thermal infrared and microwave satellite observations of soil moisture into the Noah land surface model

    Science.gov (United States)

    Hain, Christopher R.; Crow, Wade T.; Anderson, Martha C.; Mecikalski, John R.

    2012-11-01

    Studies that have assimilated remotely sensed soil moisture (SM) into land surface models (LSMs) have generally focused on retrievals from microwave (MW) sensors. However, retrievals from thermal infrared (TIR) sensors have also been shown to add unique information, especially where MW sensors are not able to provide accurate retrievals (due to, e.g., dense vegetation). In this study, we examine the assimilation of a TIR product based on surface evaporative flux estimates from the Atmosphere Land Exchange Inverse (ALEXI) model and the MW-based VU Amsterdam NASA surface SM product generated with the Land Parameter Retrieval Model (LPRM). A set of data assimilation experiments using an ensemble Kalman filter are performed over the contiguous United States to assess the impact of assimilating ALEXI and LPRM SM retrievals in isolation and together in a dual-assimilation case. The relative skill of each assimilation case is assessed through a data denial approach where a LSM is forced with an inferior precipitation data set. The ability of each assimilation case to correct for precipitation errors is quantified by comparing with a simulation forced with a higher-quality precipitation data set. All three assimilation cases (ALEXI, LPRM, and Dual assimilation) show relative improvements versus the open loop (i.e., reduced RMSD) for surface and root zone SM. In the surface zone, the dual assimilation case provides the largest improvements, followed by the LPRM case. However, the ALEXI case performs best in the root zone. Results from the data denial experiment are supported by comparisons between assimilation results and ground-based SM observations from the Soil Climate Analysis Network.

  19. Subsurface Emission Effects in AMSR-E Measurements: Implications for Land Surface Microwave Emissivity Retrieval

    Science.gov (United States)

    Galantowicz, John F.; Moncet, Jean-Luc; Liang, Pan; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

    2011-01-01

    An analysis of land surface microwave emission time series shows that the characteristic diurnal signature associated with subsurface emission in sandy deserts carry over to arid and semi-arid region worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (AMSR-E) measurements. The AMSR-E orbit has equator crossing times near 01:30 and 13 :30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. AMSR-E measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from AMSR-E, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions

  20. Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar operational environmental satellite data for land surface monitoring in Africa

    DEFF Research Database (Denmark)

    Fensholt, Rasmus; Anyamba, Assaf; Huber Gharib, Silvia

    2011-01-01

    Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth’s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which oft...

  1. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    Recent advances in integrated hydrological and soil-vegetation-atmosphere transfer (SVAT) modelling have led to improved water resource management practices, greater crop production, and better flood forecasting systems. However, uncertainty is inherent in all numerical models ultimately leading...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...... hydrological and tested by assimilating synthetic hydraulic head observations in a catchment in Denmark. Assimilation led to a substantial reduction of model prediction error, and better model forecasts. Also, a new assimilation scheme is developed to downscale and bias-correct coarse satellite derived soil...

  2. The SPARC Data Initiative: comparisons of CFC-11, CFC-12, HF and SF6 climatologies from international satellite limb sounders

    Science.gov (United States)

    Tegtmeier, S.; Hegglin, M. I.; Anderson, J.; Funke, B.; Gille, J.; Jones, A.; Smith, L.; von Clarmann, T.; Walker, K. A.

    2016-02-01

    A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991-2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1σ multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a

  3. Inter-Comparison of In-Situ Sensors for Land Surface Temperature Measurements

    Science.gov (United States)

    Krishnan, P.; Kochendorfer, J.; Meyers, T. P.; Guillevic, P. C.; Hook, S. J.

    2014-12-01

    Land Surface Temperature (LST) is a key variable in the determination of land surface processes from local to global scales. It has been identified as one of the most important environmental data records and is widely used in meteorological, climatological, hydrological, ecological, biophysical, and biochemical studies. Despite its importance, accurate in-situ measurements of LST are not yet available for the whole globe and are not routinely conducted at weather stations along with standard meteorological observations, with few exceptions including NOAA's United States Climate Reference Network. Even though satellite radiometric measurements of LST are a powerful tool, there are still large uncertainties associated with the retrieval of remotely sensed LST measurements. To improve confidence in the methods, algorithms, and parameters used to derive remotely sensed LST, validation of satellite data using high-quality ground-based measurements is required. With the objective of improving the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based measurements, intensive experiments were conducted at NOAA/ATDD in Oak ridge, TN from September 2013 to 2014. During the study period, multiple measurements of land surface skin temperature were made using infra-red temperature sensors - including the JPL radiometer, two models of Apogee infrared radiometers, and thermocouples embedded in the ground surface. In addition, aspirated air temperature and four-band net radiation measurements were also made. Overall the in situ LST measurements from the different sensors were in good agreement with each other, with a correlation coefficient of ~1 and root mean square error of <1 oC.

  4. Global solar radiation: comparison of satellite-based climatology with station records

    Science.gov (United States)

    Skalak, Petr; Zahradnicek, Pavel; Stepanek, Petr; Farda, Ales

    2016-04-01

    We analyze surface incoming shortwave radiation (SIS) from the SARAH dataset prepared by the EUMETSAT Climate Monitoring Satellite Applications Facility from satellite observations of the visible channels of the MVIRI and SEVIRI instruments onboard the geostationary Meteosat satellites. The satellite SIS data are evaluated within the period 1984-2014 on various time scales: from individual months and years to long-term climate means. The validation is performed using the ground measurements of global solar radiation (GLBR) carried out on 11 meteorological stations of the Czech Hydrometeorological Institute in the Czech Republic with at least 30 years long data series. Our aim is to explore whether the SIS data could potentially serve as an alternative source of information on GLBR outside of a relatively sparse network of meteorological stations recording GLBR. Acknowledgement: Supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Program I (NPU I), grant number LO1415.

  5. Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

    Science.gov (United States)

    Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Kumar, Sujay; Ringerud, Sarah

    2014-01-01

    Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

  6. On The Reproducibility of Seasonal Land-surface Climate

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, T J

    2004-10-22

    The sensitivity of the continental seasonal climate to initial conditions is estimated from an ensemble of decadal simulations of an atmospheric general circulation model with the same specifications of radiative forcings and monthly ocean boundary conditions, but with different initial states of atmosphere and land. As measures of the ''reproducibility'' of continental climate for different initial conditions, spatio-temporal correlations are computed across paired realizations of eleven model land-surface variables in which the seasonal cycle is either included or excluded--the former case being pertinent to climate simulation, and the latter to seasonal anomaly prediction. It is found that the land-surface variables which include the seasonal cycle are impacted only marginally by changes in initial conditions; moreover, their seasonal climatologies exhibit high spatial reproducibility. In contrast, the reproducibility of a seasonal land-surface anomaly is generally low, although it is substantially higher in the Tropics; its spatial reproducibility also markedly fluctuates in tandem with warm and cold phases of the El Nino/Southern Oscillation. However, the overall degree of reproducibility depends strongly on the particular land-surface anomaly considered. It is also shown that the predictability of a land-surface anomaly implied by its reproducibility statistics is consistent with what is inferred from more conventional predictability metrics. Implications of these results for climate model intercomparison projects and for operational forecasts of seasonal continental climate also are elaborated.

  7. Sea and Land Surface Temperature Radiometer detection assembly design and performance

    Science.gov (United States)

    Coppo, Peter; Mastrandrea, Carmine; Stagi, Moreno; Calamai, Luciano; Nieke, Jens

    2014-01-01

    The Sea and Land Surface Temperature Radiometers (SLSTRs) are high-accuracy radiometers selected for the Copernicus mission Sentinel-3 space component to provide sea surface temperature (SST) data continuity with respect to previous (Advanced) Along Track Scanning Radiometers [(A)ATSRs] for climatology. Many satellites are foreseen over a 20-year period, each with a 7.5-year lifetime. Sentinel-3A will be launched in 2015 and Sentinel-3B at least six months later, implying that two identical satellites will be maintained in the same orbit with a 180-deg phase delay. Each SLSTR has an improved design with respect to AATSR affording wider near-nadir and oblique view swaths (1400 and 740 km) for SST/land surface temperature global coverage at a 1-km spatial resolution (at SSP) with a daily revisit time (with two satellites), appropriate for both climate and meteorology. Cloud screening and other products are obtained with 0.5 km spatial resolution [at sub-satellite point (SSP)] in visible and short wave infrared (SWIR) bands, while two additional channels are included to monitor high temperature events such as forest fires. The two swaths are obtained with two conical scans and telescopes combined optically at a common focus, representing the input of a cooled focal plane assembly, where nine channels are separated with dichroic and are focalized on detectors with appropriate optical relays. IR and SWIR optics/detectors are cooled to 85 K by an active mechanical cryo-cooler with vibration compensation, while the VIS ones are maintained at a stable temperature. The opto-mechanical design and the expected electro-optical performance of the focal plane assembly are described and the model predictions at system level are compared with experimental data acquired in the vacuum chamber in flight representative thermal conditions or in the laboratory.

  8. Analysis of Anomaly in Land Surface Temperature Using MODIS Products

    Science.gov (United States)

    Yorozu, K.; Kodama, T.; Kim, S.; Tachikawa, Y.; Shiiba, M.

    2011-12-01

    Atmosphere-land surface interaction plays a dominant role on the hydrologic cycle. Atmospheric phenomena cause variation of land surface state and land surface state can affect on atmosphereic conditions. Widely-known article related in atmospheric-land interaction was published by Koster et al. in 2004. The context of this article is that seasonal anomaly in soil moisture or soil surface temperature can affect summer precipitation generation and other atmospheric processes especially in middle North America, Sahel and south Asia. From not only above example but other previous research works, it is assumed that anomaly of surface state has a key factor. To investigate atmospheric-land surface interaction, it is necessary to analyze anomaly field in land surface state. In this study, soil surface temperature should be focused because it can be globally and continuously observed by satellite launched sensor. To land surface temperature product, MOD11C1 and MYD11C1 products which are kinds of MODIS products are applied. Both of them have 0.05 degree spatial resolution and daily temporal resolution. The difference of them is launched satellite, MOD11C1 is Terra and MYD11C1 is Aqua. MOD11C1 covers the latter of 2000 to present and MYD11C1 covers the early 2002 to present. There are unrealistic values on provided products even if daily product was already calibrated or corrected. For pre-analyzing, daily data is aggregated into 8-days data to remove irregular values for stable analysis. It was found that there are spatial and temporal distribution of 10-years average and standard deviation for each 8-days term. In order to point out extreme anomaly in land surface temperature, standard score for each 8-days term is applied. From the analysis of standard score, it is found there are large anomaly in land surface temperature around north China plain in early April 2005 and around Bangladesh in early May 2009.

  9. Changes in satellite-derived impervious surface area at US historical climatology network stations

    Science.gov (United States)

    Gallo, Kevin; Xian, George

    2016-10-01

    The difference between 30 m gridded impervious surface area (ISA) between 2001 and 2011 was evaluated within 100 and 1000 m radii of the locations of climate stations that comprise the US Historical Climatology Network. The amount of area associated with observed increases in ISA above specific thresholds was documented for the climate stations. Over 32% of the USHCN stations exhibited an increase in ISA of ⩾20% between 2001 and 2011 for at least 1% of the grid cells within a 100 m radius of the station. However, as the required area associated with ISA change was increased from ⩾1% to ⩾10%, the number of stations that were observed with a ⩾20% increase in ISA between 2001 and 2011 decreased to 113 (9% of stations). When the 1000 m radius associated with each station was examined, over 52% (over 600) of the stations exhibited an increase in ISA of ⩾20% within at least 1% of the grid cells within that radius. However, as the required area associated with ISA change was increased to ⩾10% the number of stations that were observed with a ⩾20% increase in ISA between 2001 and 2011 decreased to 35 (less than 3% of the stations). The gridded ISA data provides an opportunity to characterize the environment around climate stations with a consistently measured indicator of a surface feature. Periodic evaluations of changes in the ISA near the USHCN and other networks of stations are recommended to assure the local environment around the stations has not significantly changed such that observations at the stations may be impacted.

  10. A calibrated, high-resolution goes satellite solar insolation product for a climatology of Florida evapotranspiration

    Science.gov (United States)

    Paech, S.J.; Mecikalski, J.R.; Sumner, D.M.; Pathak, C.S.; Wu, Q.; Islam, S.; Sangoyomi, T.

    2009-01-01

    Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10-year period (1995-2004). These insolation estimates were developed into well-calibrated half-hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2-week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground-based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three-step process: (1) comparison with ground-based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station-averaged model error of 2.2 MJ m-2/day (13%). Calibration reduced errors to 1.7 MJ m -2/day (10%), and also removed temporal-related, seasonal-related, and satellite sensor-related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2-km resolution maps of estimated daily reference and potential evapotranspiration for water management-related activities. ?? 2009 American Water Resources Association.

  11. The HOAPS Climatology V4: updates and results from comparisons to various satellite, buoy and ship data records

    Science.gov (United States)

    Schroeder, Marc; Graw, Kathrin; Andersson, Axel; Fennig, Karsten; Bakan, Stephan; Klepp, Christian

    2017-04-01

    The global water cycle is a key component of the global climate system as it describes and links many important processes such as evaporation, convection, cloud formation and precipitation. Through latent heat release, it is also closely connected to the global energy cycle and its changes. The difference between precipitation and evaporation yields the freshwater flux, which indicates if a particular region of the earth receives more water through precipitation than it loses through evaporation or vice versa. On global scale and long time periods, however, the amounts of evaporation and precipitation are balanced. A profound understanding of the water cycle is therefore a key prerequisite for successful climate modelling. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) set is a fully satellite based climatology of precipitation, evaporation and freshwater budget as well as related turbulent heat fluxes and atmospheric state variables over the global ice free oceans. All geophysical parameters are derived from passive microwave radiometers, except for the SST, which is taken from AVHRR measurements based on thermal emission of the Earth. Starting with the release 3.1, the HOAPS climate data record is hosted by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) and the further development is shared with the University of Hamburg and the MPI-M. While the HOAPS release 3.2 in 2012 covered the entire record of the passive microwave radiometer SSM/I, the new version of the HOAPS data set, version 4, includes also the SSMIS record up to December 2014 and uncertainty estimates for parameters related to evaporation. These HOAPS data products are available as monthly averages and 6-hourly composites on a regular latitude/longitude grid with a spatial resolution of 0.5° x 0.5° from July 1987 to December 2014 (December 2008 for HOAPS3.2). Covering nearly 28 years the new HOAPS data set is highly valuable for climate

  12. Towards a surface radiation climatology: Retrieval of downward irradiances from satellites

    Science.gov (United States)

    Schmetz, Johannes

    Methods are reviewed for retrieving the downward shortwave (0.3-4 μm) and longwave (4-100 μm) irradiances at the earth's surface from satellites. Emphasis is placed on elucidating the physical aspects relevant to the satellite retrieval. For the shortwave irradiance an example of a retrieval is presented. The shortwave retrieval is facilitated by a close linear coupling between the reflected radiance field at the top of the atmosphere and the surface irradiance. A linear relationship between planetary albedo and surface irradiance does also account properly for cloud absorption, since cloud absorption and albedo are linearly related. In the longwave the retrieval is more difficult since only atmospheric window radiances at the top of the atmosphere can bear information on the near-surface radiation field. For the remainder of the longwave spectrum the radiation regimes at the top of the atmosphere and at the surface are decoupled. More than 80% of the clear-sky longwave flux reaching the surface is emitted within the lowest 500 m of the atmosphere. In cloudy conditions the radiation fields at the surface and at the top of the atmosphere are entirely decoupled. Cloud contributions to the surface irradiance are important within the atmospheric window (8-13 μm) and the relative contribution increases in drier climates. Summaries are presented of various techniques devised for both the solar and longwave surface irradiances. A compilation of reported standard errors of shortwave techniques in comparison with ground measurements yields median values of about 5% and 10% for monthly and daily mean values, respectively. Standard errors for the longwave are of the order of 10-25 W m -2. Reported biases are typically of the order of 5 W m -2. For the shortwave retrieval there are fairly good prospects to obtain monthly mean estimates with the requested accuracy of about 10 W m -2 over regional scale areas. The inherent problems of the longwave still entails improvements

  13. The retrieval of land surface albedo in rugged terrain

    NARCIS (Netherlands)

    Gao, B.; Jia, L.; Menenti, M.

    2012-01-01

    Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed s

  14. DISAGGREGATION OF GOES LAND SURFACE TEMPERATURES USING SURFACE EMISSIVITY

    Science.gov (United States)

    Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfo...

  15. The retrieval of land surface albedo in rugged terrain

    NARCIS (Netherlands)

    Gao, B.; Jia, L.; Menenti, M.

    2012-01-01

    Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed s

  16. The retrieval of land surface albedo in rugged terrain

    NARCIS (Netherlands)

    Gao, B.; Jia, L.; Menenti, M.

    2012-01-01

    Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed

  17. Modelling land surface - atmosphere interactions

    DEFF Research Database (Denmark)

    Rasmussen, Søren Højmark

    related to inaccurate land surface modelling, e.g. enhanced warm bias in warm dry summer months. Coupling the regional climate model to a hydrological model shows the potential of improving the surface flux simulations in dry periods and the 2 m air temperature in general. In the dry periods......The study is investigates modelling of land surface – atmosphere interactions in context of fully coupled climatehydrological model. With a special focus of under what condition a fully coupled model system is needed. Regional climate model inter-comparison projects as ENSEMBLES have shown bias...... representation of groundwater in the hydrological model is found to important and this imply resolving the small river valleys. Because, the important shallow groundwater is found in the river valleys. If the model does not represent the shallow groundwater then the area mean surface flux calculation...

  18. SHADOZ (Southern Hemisphere ADditional OZonesondes): An Ozonesonde Network for Satellite Validation, Climatology and Modeling

    Science.gov (United States)

    Thompson, Anne M.; Witte, Jacquelyn C.; Schmidlin, Francis J.; Oltmans, Samuel J.; McPeters, Richard D.; Einaudi, Franco (Technical Monitor)

    2000-01-01

    In the past 5 years, new tropical ozone data products have been developed from TOMS and other satellites, During this period, global chemical-transport models have been used for ozone assessment studies. However, there has been a lack of independent ozone profiles in the tropics for evaluation of the data sets and models. In 1998, NASA's Goddard Space Flight Center, Wallops Flight Facility and NOAA's CMDL (Climate Monitoring and Diagnostics Lab), began a 2-year project to collect a consistent data set by augmenting ozonesonde launches at southern hemisphere tropical sites The measurements are available to the scientific community at a single electronic location - the SHADOZ website at NASA/Goddard: http://code9l6.gsfc.nasa.gov/Data services/Shadoz/shadoz hmpg2.html. Stations in SHADOZ include four islands in the Pacific: Fiji, Tahiti, San Cristobal (Galapagos) and American Samoa. Two sites are at and in the Atlantic: Natal (Brazil) and Ascension Island. Three other sites span Africa (Nairobi and Irene, South Africa) and the Indian Ocean (Reunion Island and Watukosek in Java, Indonesia). All SHADOZ sites are using ECC-type sondes, with the conversion from JMD sondes at Java in 1999, but there are variations in sonde preparation technique and data processing. During the 1998-1999 period, more than 550 sondes were incorporated into the SHADOZ data base. Examples from these measurements illustrate the tropical wave-one pattern in total ozone which is easily detectable by satellite. They also show that the wave-one pattern appears to be in the troposphere, as assumed in creating the modified-residual tropospheric ozone data product from TOMS. SHADOZ will add data from intensive field campaigns from time to time. Recent contributions to the SHADOZ archive are from the INDOEX (Indian Ocean Experiment January-March 1999)sondes at the Maldives (5N, 73E) and 27 sondes on the US NOAA oceanographic vessel, the FIN Ronald H Brown between Virginia (US) and Mauritius via Cape

  19. The Land Surface Temperature Synergistic Processor in BEAM: A Prototype towards Sentinel-3

    OpenAIRE

    Ruescas, Ana Belen; Danne, Olaf; Fomferra, Norman; Brockmann, Carsten

    2016-01-01

    Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temp...

  20. Application of a land surface model for simulating river streamflow in high latitudes

    Science.gov (United States)

    Gusev, Yeugeniy; Nasonova, Olga; Dzhogan, Larissa

    2010-05-01

    Nowadays modelling runoff from the pan-Arctic river basins, which represents nearly 50% of water flow to the Arctic Ocean, is of great interest among hydrological modelling community because these regions are very sensitive to natural and anthropogenic impacts. This motivates the necessity of increase of the accuracy of hydrological estimations, runoff predictions, and water resources assessments in high latitudes. However, in these regions, observations required for model simulations (to specify model parameters and forcing inputs) are very scarce or even absent (especially this concerns land surface parameters). At the same time river discharge measurements are usually available that makes it possible to estimate model parameters by their calibration against measured discharge. Such a situation is typical of most of the northern basins of Russia. The major goal of the work is to reveal whether a physically-based land surface model (LSM) Soil Water - Atmosphere - Plants (SWAP) is able to reproduce snowmelt and rain driven daily streamflow in high latitudes (using poor input information) with the accuracy acceptable for hydrologic applications. Three river basins, located on the north of the European part of Russia, were chosen for investigation. They are the Mezen River basin (area: area: 78 000 km2), the Pechora River basin (area: 312 000 km2) and the Severnaya Dvina River basin (area: 348 000 km2). For modeling purposes the basins were presented, respectively, by 10, 57 and 62 one-degree computational grid boxes connected by river network. A priori estimation of the land surface parameters for each grid box was based on the global one-degree datasets prepared within the framework of the International Satellite Land-Surface Climatology Project Initiative II (ISLSCP) / the Second Global Soil Wetness Project (GSWP-2). Three versions of atmospheric forcing data prepared for the basins were based on: (1) NCEP/DOE reanalysis dataset; (2) NCEP/DOE reanalysis product

  1. Assimilation of neural network soil moisture in land surface models

    Science.gov (United States)

    Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias

    2017-04-01

    In this study a set of land surface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests

  2. Galactic cosmic ray and El Nino Southern Oscillation trends in International Satellite Cloud Climatology Project D2 low-cloud properties

    DEFF Research Database (Denmark)

    Marsh, N.; Svensmark, Henrik

    2003-01-01

    [1] The recently reported correlation between clouds and galactic cosmic rays (GCR) implies the existence of a previously unknown process linking solar variability and climate. An analysis of the interannual variability of International Satellite Cloud Climatology Project D2 (ISCCP-D2) low....... Instead, it is argued that a mechanism involving solar variability via GCR ionization of the atmosphere is consistent with these results. However, the results are marginal when including the recently extended ISCCP-D2 data covering the period until September 2001. This, we believe, is related to problems...

  3. Global observation-based diagnosis of soil moisture control on land surface flux partition

    Science.gov (United States)

    Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Veal, Karen L.; Folwell, Sonja S.

    2016-04-01

    Soil moisture plays a central role in the partition of available energy at the land surface between sensible and latent heat flux to the atmosphere. As soils dry out, evapotranspiration becomes water-limited ("stressed"), and both land surface temperature (LST) and sensible heat flux rise as a result. This change in surface behaviour during dry spells directly affects critical processes in both the land and the atmosphere. Soil water deficits are often a precursor in heat waves, and they control where feedbacks on precipitation become significant. State-of-the-art global climate model (GCM) simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) disagree on where and how strongly the surface energy budget is limited by soil moisture. Evaluation of GCM simulations at global scale is still a major challenge owing to the scarcity and uncertainty of observational datasets of land surface fluxes and soil moisture at the appropriate scale. Earth observation offers the potential to test how well GCM land schemes simulate hydrological controls on surface fluxes. In particular, satellite observations of LST provide indirect information about the surface energy partition at 1km resolution globally. Here, we present a potentially powerful methodology to evaluate soil moisture stress on surface fluxes within GCMs. Our diagnostic, Relative Warming Rate (RWR), is a measure of how rapidly the land warms relative to the overlying atmosphere during dry spells lasting at least 10 days. Under clear skies, this is a proxy for the change in sensible heat flux as soil dries out. We derived RWR from MODIS Terra and Aqua LST observations, meteorological re-analyses and satellite rainfall datasets. Globally we found that on average, the land warmed up during dry spells for 97% of the observed surface between 60S and 60N. For 73% of the area, the land warmed faster than the atmosphere (positive RWR), indicating water stressed conditions and increases in sensible heat flux

  4. Real Time Land-Surface Hydrologic Modeling Over Continental US

    Science.gov (United States)

    Houser, Paul R.

    1998-01-01

    The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

  5. High spatial resolution Land Surface Temperature estimation over urban areas with uncertainty indices

    Science.gov (United States)

    Mitraka, Zina; Lazzarini, Michele; Doxani, Georgia; Del Frate, Fabio; Ghedira, Hosni

    2014-05-01

    Land Surface Temperature (LST) is a key variable for studying land surface processes and interactions with the atmosphere and it is listed in the Earth System Data Records (ESDRs) identified by international organizations like Global Climate Observing System. It is a valuable source of information for a range of topics in earth sciences and essential for urban climatology studies. Detailed, frequent and accurate LST mapping may support various urban applications, like the monitoring of urban heat island. Currently, no spaceborne instruments provide frequent thermal imagery at high spatial resolution, thus there is a need for synergistic algorithms that combine different kinds of data for LST retrieval. Moreover, knowing the confidence level of any satellite-derived product is highly important to the users, especially when referred to the urban environment, which is extremely heterogenic. The developed method employs spatial-spectral unmixing techniques for improving the spatial resolution of thermal measurements, combines spectral library information for emissivity estimation and applies a split-window algorithm to estimate LST with an uncertainty estimation inserted in the final product. A synergistic algorithm that utilizes the spatial information provided by visible and near-infrared measurements with more frequent low resolution thermal measurements provides excellent means for high spatial resolution LST estimation. Given the low spatial resolution of thermal infrared sensors, the measured radiation is a combination of radiances of different surface types. High spatial resolution information is used to quantify the different surface types in each pixel and then the measured radiance of each pixel is decomposed. The several difficulties in retrieving LST from space measurements, mainly related to the temperature-emissivity coupling and the atmospheric contribution to the thermal measurements, and the measurements themselves, introduce uncertainties in the final

  6. Estimation of daily minimum land surface air temperature using MODIS data in southern Iran

    Science.gov (United States)

    Didari, Shohreh; Norouzi, Hamidreza; Zand-Parsa, Shahrokh; Khanbilvardi, Reza

    2016-10-01

    Land surface air temperature (LSAT) is a key variable in agricultural, climatological, hydrological, and environmental studies. Many of their processes are affected by LSAT at about 5 cm from the ground surface (LSAT5cm). Most of the previous studies tried to find statistical models to estimate LSAT at 2 m height (LSAT2m) which is considered as a standardized height, and there is not enough study for LSAT5cm estimation models. Accurate measurements of LSAT5cm are generally acquired from meteorological stations, which are sparse in remote areas. Nonetheless, remote sensing data by providing rather extensive spatial coverage can complement the spatiotemporal shortcomings of meteorological stations. The main objective of this study was to find a statistical model from the previous day to accurately estimate spatial daily minimum LSAT5cm, which is very important in agricultural frost, in Fars province in southern Iran. Land surface temperature (LST) data were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites at daytime and nighttime periods with normalized difference vegetation index (NDVI) data. These data along with geometric temperature and elevation information were used in a stepwise linear model to estimate minimum LSAT5cm during 2003-2011. The results revealed that utilization of MODIS Aqua nighttime data of previous day provides the most applicable and accurate model. According to the validation results, the accuracy of the proposed model was suitable during 2012 (root mean square difference (RMSD) = 3.07 °C, {R}_{adj}^2 = 87 %). The model underestimated (overestimated) high (low) minimum LSAT5cm. The accuracy of estimation in the winter time was found to be lower than the other seasons (RMSD = 3.55 °C), and in summer and winter, the errors were larger than in the remaining seasons.

  7. On the Potential Predictability of Seasonal Land-Surface Climate

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, T J

    2001-10-01

    The chaotic behavior of the continental climate of an atmospheric general circulation model is investigated from an ensemble of decadal simulations with common specifications of radiative forcings and monthly ocean boundary conditions, but different initial states of atmosphere and land. The variability structures of key model land-surface processes appear to agree sufficiently with observational estimates to warrant detailed examination of their predictability on seasonal time scales. This predictability is inferred from several novel measures of spatio-temporal reproducibility applied to eleven model variables. The reproducibility statistics are computed for variables in which the seasonal cycle is included or excluded, the former case being most pertinent to climate model simulations, and the latter to predictions of the seasonal anomalies. Because the reproducibility metrics in the latter case are determined in the context of a ''perfectly'' known ocean state, they are properly viewed as estimates of the potential predictability of seasonal climate. Inferences based on these reproducibility metrics are shown to be in general agreement with those derived from more conventional measures of potential predictability. It is found that the land-surface variables which include the seasonal cycle are impacted only marginally by changes in initial conditions; moreover, their seasonal climatologies exhibit high spatial reproducibility. In contrast, the reproducibility of a seasonal land-surface anomaly is generally low, although it is considerably higher in the Tropics; its spatial reproducibility also fluctuates in tandem with warm and cold phases of the El Nino/Southern Oscillation phenomenon. However, the detailed sensitivities to initial conditions depend somewhat on the land-surface process: pressure and temperature anomalies exhibit the highest temporal reproducibilities, while hydrological and turbulent flux anomalies show the highest spatial

  8. Land Surface Temperature at Night

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth...

  9. Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

    Science.gov (United States)

    Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

    2017-04-01

    Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates

  10. Derived Land Surface Emissivity From Suomi NPP CrIS

    Science.gov (United States)

    Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

    2012-01-01

    Presented here is the land surface IR spectral emissivity retrieved from the Cross-track Infrared Sounder (CrIS) measurements. The CrIS is aboard the Suomi National Polar-orbiting Partnership (NPP) satellite launched on October 28, 2011. We describe the retrieval algorithm, demonstrate the surface emissivity retrieved with CrIS measurements, and inter-comparison with the Infrared Atmospheric Sounding Interferometer (IASI) emissivity. We also demonstrate that surface emissivity from satellite measurements can be used in assistance of monitoring global surface climate change, as a long-term measurement of IASI and CrIS will be provided by the series of EUMETSAT MetOp and US Joint Polar Satellite System (JPSS) satellites. Monthly mean surface properties are produced using last 5-year IASI measurements. A temporal variation indicates seasonal diversity and El Nino/La Nina effects not only shown on the water but also on the land. Surface spectral emissivity and skin temperature from current and future operational satellites can be utilized as a means of long-term monitoring of the Earth's environment. CrIS spectral emissivity are retrieved and compared with IASI. The difference is small and could be within expected retrieval error; however it is under investigation.

  11. How can we use MODIS land surface temperature to validate long-term urban model simulations?

    Science.gov (United States)

    Hu, Leiqiu; Brunsell, Nathaniel A.; Monaghan, Andrew J.; Barlage, Michael; Wilhelmi, Olga V.

    2014-03-01

    High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.

  12. Seasonal evaluation of the land surface sheme HTESSEL against remote sensing derived energy fluxes of the Transdanubian regions in Hungary

    NARCIS (Netherlands)

    Wipfler, E.L.; Metselaar, K.; Dam, van J.C.; Feddes, R.A.; Meijgaard, van E.; Ulft, van L.H.; Hurk, van den B.; Zwart, S.J.; Bastiaanssen, W.G.M.

    2011-01-01

    The skill of the land surface model HTESSEL is assessed to reproduce evaporation in response to land surface characteristics and atmospheric forcing, both being spatially variable. Evaporation estimates for the 2005 growing season are inferred from satellite observations of the Western part of

  13. Rayleigh LIDAR and satellite (HALOE, SABER, CHAMP and COSMIC) measurements of stratosphere-mesosphere temperature over a southern sub-tropical site, Reunion (20.8° S; 55.5° E): climatology and comparison study

    CSIR Research Space (South Africa)

    Sivakumar, V

    2011-01-01

    Full Text Available For the first time, climatology of the middle atmosphere thermal structure is presented, based on 14 years of LIDAR and satellite (HALOE, SABER, CHAMP and COSMIC) temperature measurements. The data is collected over a southern sub-tropical site...

  14. Improved in-situ methods for determining land surface emissivity

    Science.gov (United States)

    Göttsche, Frank; Olesen, Folke; Hulley, Glynn

    2014-05-01

    The accurate validation of LST satellite products, such as the operational LST retrieved by the Land Surface Analysis - Satellite Application Facility (LSA-SAF), requires accurate knowledge of emissivity for the areas observed by the ground radiometers as well as for the area observed by the satellite sensor. Especially over arid regions, the relatively high uncertainty in land surface emissivity (LSE) limits the accuracy with which land surface temperature (LST) can be retrieved from thermal infrared (TIR) radiance measurements. LSE uncertainty affects LST obtained from satellite measurements and in-situ radiance measurements alike. Furthermore, direct comparisons between satellite sensors and ground based sensors are complicated by spatial scale mismatch: ground radiometers usually observe some 10 m2, whereas satellite sensors typically observe between 1 km2 and 100 km2. Therefore, validation sites have to be carefully selected and need to be characterised on the scale of the ground radiometer as well as on the scale of the satellite pixel. The permanent stations near Gobabeb (Namibia; hyper-arid desert climate) and Dahra (Senegal; hot-arid steppe-prairie climate) are two of KIT's four dedicated LST validation stations. Gobabeb station is located on vast and flat gravel plains (several 100 km2), which are mainly covered by coarse gravel, sand, and desiccated grass. The gravel plains are highly homogeneous in space and time, which makes them ideal for validating a broad range of satellite-derived products. Dahra station is located in so called 'tiger bush' and is covered by strongly seasonal grass (95%) and sparse, evergreen trees (dominantly acacia trees) with a background of reddish sand. The strong seasonality is caused by a pronounced rainy season, during which LST retrieval is highly challenging. Outside the rainy season, both sites have relatively large fractions of bare ground and desiccated vegetation: therefore, they are particularly prone to be

  15. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    Science.gov (United States)

    Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

  16. Climate Variability in Coastal Ecosystems - Use of MODIS Land Surface and Sea Surface Temperature Observations

    Science.gov (United States)

    Chintalapati, S.; Lakshmi, V.

    2007-12-01

    The intertidal zone, with its complex blend of marine and terrestrial environments, is one of the intensively studied ecosystems, in understanding the effects of climate change on species abundance and distribution. As climatic conditions change, the geographic limits of the intertidal species will likely move towards more tolerable coastal conditions. Traditionally, understanding climate change effects through species physiologic response have involved use of in situ measurements and thermal engineering models. But these approaches are constrained by their data intensive requirements and may not be suitable for predicting change patterns relevant to large scale species distributions. Satellite remote sensing provides an alternate approach, given the regular global coverage at moderate spatial resolutions. The present study uses six years of land surface temperature (LST) and sea surface temperature (SST) data from MODIS/Terra instrument along various coastlines around the globe - East and West Coast US, Southern Africa, Northern Japan and New Zealand. Apart from the dominant annual cycle in LST and SST, the other seasonal cycles vary from dominant semi-annual cycles in lower latitudes to 1.5 and 2 year cycles at higher latitudes. The monthly anomalies show strong spatial structure at lower latitudes when compared to higher latitudes, with the exception of US east coast, where the spatial structure extended almost along the whole coastline, indicating strong regulation from the Gulf Stream. The patterns along different coast lines are consistent with the atmospheric and ocean circulation patterns existing at those regions. These results suggest that the climatology at the coastal regions can be adequately represented using satellite-based temperature data, thus enabling further research in understanding the effects of climate change on species abundance and distribution at larger scales.

  17. Land Surface Modeling Applications for Famine Early Warning

    Science.gov (United States)

    McNally, A.; Verdin, J. P.; Peters-Lidard, C. D.; Arsenault, K. R.; Wang, S.; Kumar, S.; Shukla, S.; Funk, C. C.; Pervez, M. S.; Fall, G. M.; Karsten, L. R.

    2015-12-01

    AGU 2015 Fall Meeting Session ID#: 7598 Remote Sensing Applications for Water Resources Management Land Surface Modeling Applications for Famine Early Warning James Verdin, USGS EROS Christa Peters-Lidard, NASA GSFC Amy McNally, NASA GSFC, UMD/ESSIC Kristi Arsenault, NASA GSFC, SAIC Shugong Wang, NASA GSFC, SAIC Sujay Kumar, NASA GSFC, SAIC Shrad Shukla, UCSB Chris Funk, USGS EROS Greg Fall, NOAA Logan Karsten, NOAA, UCAR Famine early warning has traditionally required close monitoring of agro-climatological conditions, putting them in historical context, and projecting them forward to anticipate end-of-season outcomes. In recent years, it has become necessary to factor in the effects of a changing climate as well. There has also been a growing appreciation of the linkage between food security and water availability. In 2009, Famine Early Warning Systems Network (FEWS NET) science partners began developing land surface modeling (LSM) applications to address these needs. With support from the NASA Applied Sciences Program, an instance of the Land Information System (LIS) was developed to specifically support FEWS NET. A simple crop water balance model (GeoWRSI) traditionally used by FEWS NET took its place alongside the Noah land surface model and the latest version of the Variable Infiltration Capacity (VIC) model, and LIS data readers were developed for FEWS NET precipitation forcings (NOAA's RFE and USGS/UCSB's CHIRPS). The resulting system was successfully used to monitor and project soil moisture conditions in the Horn of Africa, foretelling poor crop outcomes in the OND 2013 and MAM 2014 seasons. In parallel, NOAA created another instance of LIS to monitor snow water resources in Afghanistan, which are an early indicator of water availability for irrigation and crop production. These successes have been followed by investment in LSM implementations to track and project water availability in Sub-Saharan Africa and Yemen, work that is now underway. Adoption of

  18. Quantifying Uncertainties in Land Surface Microwave Emissivity Retrievals

    Science.gov (United States)

    Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid-Ahmed; Furuzawa, Fumie A.; Masunaga, Hirohiko

    2012-01-01

    Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 14% (312 K) over desert and 17% (320 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.52% (26 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 1017 K under the most severe conditions.

  19. A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

    Science.gov (United States)

    Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and

  20. A Satellite-Based Surface Radiation Climatology Derived by Combining Climate Data Records and Near-Real-Time Data

    Directory of Open Access Journals (Sweden)

    Bodo Ahrens

    2013-09-01

    Full Text Available This study presents a method for adjusting long-term climate data records (CDRs for the integrated use with near-real-time data using the example of surface incoming solar irradiance (SIS. Recently, a 23-year long (1983–2005 continuous SIS CDR has been generated based on the visible channel (0.45–1 μm of the MVIRI radiometers onboard the geostationary Meteosat First Generation Platform. The CDR is available from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF. Here, it is assessed whether a homogeneous extension of the SIS CDR to the present is possible with operationally generated surface radiation data provided by CM SAF using the SEVIRI and GERB instruments onboard the Meteosat Second Generation satellites. Three extended CM SAF SIS CDR versions consisting of MVIRI-derived SIS (1983–2005 and three different SIS products derived from the SEVIRI and GERB instruments onboard the MSG satellites (2006 onwards were tested. A procedure to detect shift inhomogeneities in the extended data record (1983–present was applied that combines the Standard Normal Homogeneity Test (SNHT and a penalized maximal T-test with visual inspection. Shift detection was done by comparing the SIS time series with the ground stations mean, in accordance with statistical significance. Several stations of the Baseline Surface Radiation Network (BSRN and about 50 stations of the Global Energy Balance Archive (GEBA over Europe were used as the ground-based reference. The analysis indicates several breaks in the data record between 1987 and 1994 probably due to artefacts in the raw data and instrument failures. After 2005 the MVIRI radiometer was replaced by the narrow-band SEVIRI and the broadband GERB radiometers and a new retrieval algorithm was applied. This induces significant challenges for the homogenisation across the satellite generations. Homogenisation is performed by applying a mean-shift correction depending on the shift size of

  1. A satellite-based climatology (1989-2012) of lake surface water temperature from AVHRR 1-km for Central European water bodies

    Science.gov (United States)

    Riffler, Michael; Wunderle, Stefan

    2013-04-01

    The temperature of lakes is an important parameter for lake ecosystems influencing the speed of physio-chemical reactions, the concentration of dissolved gazes (e.g. oxygen), and vertical mixing. Even small temperature changes might have irreversible effects on the lacustrine system due to the high specific heat capacity of water. These effects could alter the quality of lake water depending on parameters like lake size and volume. Numerous studies mention lake water temperature as an indicator of climate change and in the Global Climate Observing System (GCOS) requirements it is listed as an essential climate variable. In contrast to in situ observations, satellite imagery offers the possibility to derive spatial patterns of lake surface water temperature (LSWT) and their variability. Moreover, although for some European lakes long in situ time series are available, the temperatures of many lakes are not measured or only on a non-regular basis making these observations insufficient for climate monitoring. However, only few satellite sensors offer the possibility to analyze time series which cover more than 20 years. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown on the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES) and on the Meteorological Operational Satellites (MetOp) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) as a heritage instrument for almost 35 years. It will be carried on for at least ten more years finally offering a unique opportunity for satellite-based climate studies. Herein we present the results from a study initiated by the Swiss GCOS office to generate a satellite-based LSWT climatology for the pre-alpine water bodies in Switzerland. It relies on the extensive AVHRR 1-km data record (1985-2012) of the Remote Sensing Research Group at the University of Bern (RSGB) and has been derived from the AVHRR/2

  2. Remote sensing of land surface phenology

    Science.gov (United States)

    Meier, G.A.; Brown, J.F.

    2014-01-01

    Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

  3. ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

    Directory of Open Access Journals (Sweden)

    S. Faroux

    2013-04-01

    Full Text Available The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo. Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover and GLC2000 (Global Land Cover by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography. The leaf area index (LAI from MODIS and normalized difference vegetation index (NDVI from SPOT/Vegetation (a global monitoring system of vegetation yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas and, inside the nature tile, fractions of 12 plant functional types (PFTs representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of

  4. Sensitivity of Land Surface Parameters on Thunderstorm Simulation through HRLDAS-WRF Coupling Mode

    Science.gov (United States)

    Kumar, Dinesh; Kumar, Krishan; Mohanty, U. C.; Kisore Osuri, Krishna

    2016-07-01

    Land surface characteristics play an important role in large scale, regional and mesoscale atmospheric process. Representation of land surface characteristics can be improved through coupling of mesoscale atmospheric models with land surface models. Mesoscale atmospheric models depend on Land Surface Models (LSM) to provide land surface variables such as fluxes of heat, moisture, and momentum for lower boundary layer evolution. Studies have shown that land surface properties such as soil moisture, soil temperature, soil roughness, vegetation cover, have considerable effect on lower boundary layer. Although, the necessity to initialize soil moisture accurately in NWP models is widely acknowledged, monitoring soil moisture at regional and global scale is a very tough task due to high spatial and temporal variability. As a result, the available observation network is unable to provide the required spatial and temporal data for the most part of the globe. Therefore, model for land surface initializations rely on updated land surface properties from LSM. The solution for NWP land-state initialization can be found by combining data assimilation techniques, satellite-derived soil data, and land surface models. Further, it requires an intermediate step to use observed rainfall, satellite derived surface insolation, and meteorological analyses to run an uncoupled (offline) integration of LSM, so that the evolution of modeled soil moisture can be forced by observed forcing conditions. Therefore, for accurate land-state initialization, high resolution land data assimilation system (HRLDAS) is used to provide the essential land surface parameters. Offline-coupling of HRLDAS-WRF has shown much improved results over Delhi, India for four thunder storm events. The evolution of land surface variables particularly soil moisture, soil temperature and surface fluxes have provided more realistic condition. Results have shown that most of domain part became wetter and warmer after

  5. Cloud cover climatologies in the Mediterranean obtained from satellites, surface observations, reanalyses, and CMIP5 simulations: validation and future scenarios

    Science.gov (United States)

    Enriquez-Alonso, Aaron; Sanchez-Lorenzo, Arturo; Calbó, Josep; González, Josep-Abel; Norris, Joel R.

    2016-07-01

    Clouds are an important regulator of climate due to their connection to the water balance of the atmosphere and their interaction with solar and infrared radiation. In this study, monthly total cloud cover (TCC) records from different sources have been inter-compared on annual and seasonal basis for the Mediterranean region and the period 1984-2005. Specifically, gridded databases from satellite projects (ISCCP, CLARA, PATMOS-x), from reanalysis products (ERA-Interim, MERRA), and from surface observations over land (EECRA) and ocean (ICOADS) have been examined. Then, simulations from 44 climate runs of the Coupled Model Intercomparison Project phase 5 corresponding to the historical scenario have been compared against the observations. Overall, we find good agreement between the mean values of TCC estimated from the three satellite products and from surface observations, while reanalysis products show much lower values across the region. Nevertheless, all datasets show similar behavior regarding the annual cycle of TCC. In addition, our results indicate an underestimation of TCC from climate model simulations as compared to the satellite products, especially during summertime, although the annual cycle is well simulated by most models. This result is quite general and apparently independent of the cloud parameterizations included in each particular model. Equally, similar results are obtained if the ISCCP simulator included in the Cloud Feedback Model Intercomparison Project Observation Simulator Package is considered, despite only few models provide the post-processed results. Finally, GCM projections of TCC over the Mediterranean are presented. These projections predict a reduction of TCC during the 21st century in the Mediterranean. Specifically, for an extreme emission scenario (RCP8.5) the projected relative rate of TCC decrease is larger than 10 % by the end of the century.

  6. A New Estimate of the Earth's Land Surface Temperature History

    Science.gov (United States)

    Muller, R. A.; Curry, J. A.; Groom, D.; Jacobsen, B.; Perlmutter, S.; Rohde, R. A.; Rosenfeld, A.; Wickham, C.; Wurtele, J.

    2011-12-01

    The Berkeley Earth Surface Temperature team has re-evaluated the world's atmospheric land surface temperature record using a linear least-squares method that allow the use of all the digitized records back to 1800, including short records that had been excluded by prior groups. We use the Kriging method to estimate an optimal weighting of stations to give a world average based on uniform weighting of the land surface. We have assembled a record of the available data by merging 1.6 billion temperature reports from 16 pre-existing data archives; this data base will be made available for public use. The former Global Historic Climatology Network (GHCN) monthly data base shows a sudden drop in the number of stations reporting monthly records from 1980 to the present; we avoid this drop by calculating monthly averages from the daily records. By using all the data, we reduce the effects of potential data selection bias. We make an independent estimate of the urban heat island effect by calculating the world land temperature trends based on stations chosen to be far from urban sites. We calculate the effect of poor station quality, as documented in the US by the team led by Anthony Watts by estimating the temperature trends based solely on the stations ranked good (1,2 or 1,2,3 in the NOAA ranking scheme). We avoid issues of homogenization bias by using raw data; at times when the records are discontinuous (e.g. due to station moves) we break the record into smaller segments and analyze those, rather than attempt to correct the discontinuity. We estimate the uncertainties in the final results using the jackknife procedure developed by J. Tukey. We calculate spatial uncertainties by measuring the effects of geographical exclusion on recent data that have good world coverage. The results we obtain are compared to those published by the groups at NOAA, NASA-GISS, and Hadley-CRU in the UK.

  7. Impacts on the regional climate modeling and improvements of modern land surface model over the Tibet Plateau

    Science.gov (United States)

    Gao, Yanhong; Xiao, Linhong; Chen, Fei; Chen, Deliang

    2017-04-01

    Three WRF dynamical downscaling simulations were designed and conducted with two different GCM forcings and two land-surface schemes (Noah versus Noah-MP). Two of them used the same forcing (ERA) and two of them shared the same land-surface model physics (Noah-MP). Simulated surface air temperature and precipitation were assessed in terms of seasonal and annual mean Climatology, and spatial characteristics and linear trends over the TP for the period 1980-2005. Major findings are summarized in the following: 1) A common cold and wet bias exists in all three simulations regardless the type of large-scale forcing or land-surface model being used. This is especially true in the western TP during the cold season, indicating that the WRF model is still deficient in capturing cold-season processes at high elevations. However, such biases in DDM were greatly constrained compared to these in forcing GCM. The land-surface model impacts the surface air temperature and precipitation climatology and spatial distribution significantly more than the large -scale forcing. Large-scale forcing has more influence on the trends rather than on the spatial characteristics in DDM. 2) The land-surface model affects precipitation over the TP through including the surface heating differential over the TP. The heating differential caused surface heat fluxes differences resulting in a stronger or weaker upward vertical motion and divergence (convergence) at upper (low) levels. This in turn brings different moist air from the ocean.

  8. Improvements of modern land surface model and impacts on the regional climate modeling in the Tibet Plateau

    Science.gov (United States)

    Gao, Y.; Linhong, X., Sr.; Chen, F.

    2016-12-01

    Three WRF dynamical downscaling simulations were designed and conducted with two different GCM forcings and two land-surface schemes (Noah versus Noah-MP). Two of them used the same forcing (ERA) and two of them shared the same land-surface model physics (Noah-MP). Simulated surface air temperature and precipitation were assessed in terms of seasonal and annual mean climatology, and spatial characteristics and linear trends over the TP for the period 1980-2005. Major findings are summarized in the following: 1) A common cold and wet bias exists in all three simulations regardless the type of large-scale forcing or land-surface model being used. This is especially true in the western TP during the cold season, indicating that the WRF model is still deficient in capturing cold-season processes at high elevations. However, such biases in DDM were greatly constrained compared to these in forcing GCM. The land-surface model impacts the surface air temperature and precipitation climatology and spatial distribution significantly more than the large -scale forcing. Large-scale forcing has more influence on the trends rather than on the spatial characteristics in DDM. 2) The land-surface model affects precipitation over the TP through including the surface heating differential over the TP. The heating differential caused surface heat fluxes differences resulting in a stronger or weaker upward vertical motion and divergence (convergence) at upper (low) levels. This in turn brings different moist air from the ocean.

  9. Persistent and Widespread Winter Haze & Fog over the Indo-Gangetic Plains: A climatological perspective from satellite observations

    Science.gov (United States)

    Gautam, R.

    2014-12-01

    Each year during winter season (December-January), dense fog engulfs the Indo-Gangetic Plains (IGP) in southern Asia, for more than a month, disrupting daily life of millions of people inhabiting the IGP. The widespread nature of the fog is frequently visible in satellite imagery, extending over a stretch of ~1500 km; that covers parts of Pakistan, northern India, Nepal and Bangladesh. Both, haze and fog are a tightly-coupled system over the IGP, during winter months, and have been a major environmental/climatic issue since the past several decades. Trends in poor visibility suggest a significant increase in worsening air quality and foggy days over the IGP. The persistent and widespread nature of the winter haze and fog is strongly influenced by the regional meteorology during wintertime, i.e. a stable boundary layer, low temperatures, high relative humidity and light winds. The valley-type topography of the IGP, adjacent to the towering Himalaya, and high concentrations of pollution aerosols, further favors the persistence of hazy/foggy conditions. A satellite-based observational portrayal will be presented, using various cloud, aerosol and radiation datasets, to characterize the widespread nature of winter haze and fog, based on a multi-sensor assessment from MODIS, CERES, AVHRR and CALIPSO datasets. More specifically, based on these observations, we will present results on: long-term trends/variability of winter haze and fog, vertical characterization of aerosol/fog/low-clouds, as well as assessment of the direct radiative effect of the region-wide haze/fog system. Results from this work are anticipated to shed light on the overall interactions within the highly persistent and tightly-coupled haze-fog phenomena. Additionally, against the backdrop of a changing climate scenario, possible linkages between the winter-time fog cover, regional meteorology and aerosol loading will also be discussed over the IGP.

  10. Physically plausible prescription of land surface model soil moisture

    Science.gov (United States)

    Hauser, Mathias; Orth, René; Thiery, Wim; Seneviratne, Sonia

    2016-04-01

    Land surface hydrology is an important control of surface weather and climate, especially under extreme dry or wet conditions where it can amplify heat waves or floods, respectively. Prescribing soil moisture in land surface models is a valuable technique to investigate this link between hydrology and climate. It has been used for example to assess the influence of soil moisture on temperature variability, mean and extremes (Seneviratne et al. 2006, 2013, Lorenz et al., 2015). However, perturbing the soil moisture content artificially can lead to a violation of the energy and water balances. Here we present a new method for prescribing soil moisture which ensures water and energy balance closure by using only water from runoff and a reservoir term. If water is available, the method prevents soil moisture decrease below climatological values. Results from simulations with the Community Land Model (CLM) indicate that our new method allows to avoid soil moisture deficits in many regions of the world. We show the influence of the irrigation-supported soil moisture content on mean and extreme temperatures and contrast our findings with that of earlier studies. Additionally, we will assess how long into the 21st century the new method will be able to maintain present-day climatological soil moisture levels for different regions. Lorenz, R., Argüeso, D., Donat, M.G., Pitman, A.J., den Hurk, B.V., Berg, A., Lawrence, D.M., Chéruy, F., Ducharne, A., Hagemann, S. and Meier, A., 2015. Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. Journal of Geophysical Research: Atmospheres. Seneviratne, S.I., Lüthi, D., Litschi, M. and Schär, C., 2006. Land-atmosphere coupling and climate change in Europe. Nature, 443(7108), pp.205-209. Seneviratne, S.I., Wilhelm, M., Stanelle, T., Hurk, B., Hagemann, S., Berg, A., Cheruy, F., Higgins, M.E., Meier, A., Brovkin, V. and Claussen, M., 2013. Impact of soil moisture

  11. A protocol for validating Land Surface Temperature from Sentinel-3

    Science.gov (United States)

    Ghent, D.

    2015-12-01

    One of the main objectives of the Sentinel-3 mission is to measure sea- and land-surface temperature with high-end accuracy and reliability in support of environmental and climate monitoring in an operational context. Calibration and validation are thus key criteria for operationalization within the framework of the Sentinel-3 Mission Performance Centre (S3MPC).Land surface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of land surface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties. The Sentinel-3 Cal-Val Plan for evaluating the level-2 SL_2_LST product builds on an established validation protocol for satellite-based LST. This set of guidelines provides a standardized framework for structuring LST validation activities, and is rapidly gaining international recognition. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for SLSTR which is designed around biome-based coefficients, thus emphasizing the importance of

  12. The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

    Science.gov (United States)

    Roşca, Cristina Florina; Harpa, Gabriela Victoria; Croitoru, Adina-Eliza; Herbel, Ioana; Imbroane, Alexandru Mircea; Burada, Doina Cristina

    2016-09-01

    Land surface temperature is one of the most important parameters related to global warming. It depends mainly on soil type, discontinuous vegetation cover, or lack of precipitation. The main purpose of this paper is to investigate the relationship between high LST, synoptic conditions and air masses trajectories, vegetation cover, and soil type in one of the driest region in Romania. In order to calculate the land surface temperature and normalized difference vegetation index, five satellite images of LANDSAT missions 5 and 7, covering a period of 26 years (1986-2011), were selected, all of them collected in the month of June. The areas with low vegetation density were derived from normalized difference vegetation index, while soil types have been extracted from Corine Land Cover database. HYSPLIT application was employed to identify the air masses origin based on their backward trajectories for each of the five study cases. Pearson, logarithmic, and quadratic correlations were used to detect the relationships between land surface temperature and observed ground temperatures, as well as between land surface temperature and normalized difference vegetation index. The most important findings are: strong correlation between land surface temperature derived from satellite images and maximum ground temperature recorded in a weather station located in the area, as well as between areas with land surface temperature equal to or higher than 40.0 °C and those with lack of vegetation; the sandy soils are the most prone to high land surface temperature and lack of vegetation, followed by the chernozems and brown soils; extremely severe drought events may occur in the region.

  13. Estimating land-surface temperature under clouds using MSG/SEVIRI observations

    NARCIS (Netherlands)

    Lu, L.; Venus, V.; Skidmore, A.K.; Wang, T.; Luo, G.

    2011-01-01

    The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the di

  14. Estimating land-surface temperature under clouds using MSG/SEVIRI observations

    NARCIS (Netherlands)

    Lu, L.; Venus, V.; Skidmore, A.K.; Wang, T.; Luo, G.

    2011-01-01

    The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the

  15. Land-surface modelling in hydrological perspective

    DEFF Research Database (Denmark)

    Overgaard, Jesper; Rosbjerg, Dan; Butts, M.B.

    2006-01-01

    The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches, ......, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed....

  16. Land-surface modelling in hydrological perspective

    DEFF Research Database (Denmark)

    Overgaard, Jesper; Rosbjerg, Dan; Butts, M.B.

    2006-01-01

    The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches......, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed....

  17. Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

    Science.gov (United States)

    Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid-Ahmed; Furuzawa, Fumie A.; Masunaga, Hirohiko

    2013-01-01

    Uncertainties in the retrievals of microwaveland-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors inthe retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

  18. A novel tropopause-related climatology of ozone profiles

    NARCIS (Netherlands)

    Sofieva, V.F.; Tamminen, J.; Kyrola, E.; Mielonen, T.; Veefkind, J.P.; Hassler, B.; Bodeker, G.E.

    2014-01-01

    A new ozone climatology, based on ozonesonde and satellite measurements, spanning the altitude region between the earth's surface and ~60 km is presented (TpO3 climatology). This climatology is novel in that the ozone profiles are categorized according to calendar month, latitude and local tropopaus

  19. Global water balances reconstructed by multi-model offline simulations of land surface models under GSWP3 (Invited)

    Science.gov (United States)

    Oki, T.; KIM, H.; Ferguson, C. R.; Dirmeyer, P.; Seneviratne, S. I.

    2013-12-01

    As the climate warms, the frequency and severity of flood and drought events is projected to increase. Understanding the role that the land surface will play in reinforcing or diminishing these extremes at regional scales will become critical. In fact, the current development path from atmospheric (GCM) to coupled atmosphere-ocean (AOGCM) to fully-coupled dynamic earth system models (ESMs) has brought new awareness to the climate modeling community of the abundance of uncertainty in land surface parameterizations. One way to test the representativeness of a land surface scheme is to do so in off-line (uncoupled) mode with controlled, high quality meteorological forcing. When multiple land schemes are run in-parallel (with the same forcing data), an inter-comparison of their outputs can provide the basis for model confidence estimates and future model refinements. In 2003, the Global Soil Wetness Project Phase 2 (GSWP2) provided the first global multi-model analysis of land surface state variables and fluxes. It spanned the decade of 1986-1995. While it was state-of-the art at the time, physical schemes have since been enhanced, a number of additional processes and components in the water-energy-eco-systems nexus can now be simulated, , and the availability of global, long-term observationally-based datasets that can be used for forcing and validating models has grown. Today, the data exists to support century-scale off-line experiments. The ongoing follow-on to GSWP2, named GSWP3, capitalizes on these new feasibilities and model functionalities. The project's cornerstone is its century-scale (1901-2010), 3-hourly, 0.5° meteorological forcing dataset that has been dynamically downscaled from the Twentieth Century Reanalysis and bias-corrected using monthly Climate Research Unit (CRU) temperature and Global Precipitation Climatology Centre (GPCC) precipitation data. However, GSWP3 also has an important long-term future climate component that spans the 21st century

  20. Analysis of the Interaction and Transport of Aerosols with Cloud or Fog in East Asia from AERONET and Satellite Remote Sensing: 2012 DRAGON Campaigns and Climatological Data

    Science.gov (United States)

    Eck, T. F.; Holben, B. N.; Reid, J. S.; Lynch, P.; Schafer, J.; Giles, D. M.; Kim, J.; Kim, Y. J.; Sano, I.; Arola, A. T.; Munchak, L. A.; O'Neill, N. T.; Lyapustin, A.; Sayer, A. M.; Hsu, N. Y. C.; Randles, C. A.; da Silva, A. M., Jr.; Govindaraju, R.; Hyer, E. J.; Pickering, K. E.; Crawford, J. H.; Sinyuk, A.; Smirnov, A.

    2015-12-01

    Ground-based remote sensing observations from Aerosol Robotic Network (AERONET) sun-sky radiometers have recently shown several instances where cloud-aerosol interaction had resulted in modification of aerosol properties and/or in difficulty identifying some major pollution transport events due to aerosols being imbedded in cloud systems. Major Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaigns involving multiple AERONET sites in Japan and South Korea during Spring of 2012 have yielded observations of aerosol transport associated with clouds and/or aerosol properties modification as a result of fog interaction. Analysis of data from the Korean and Japan DRAGON campaigns shows that major fine-mode aerosol transport events are sometimes associated with extensive cloud cover and that cloud-screening of observations often filter out significant pollution aerosol transport events. The Spectral De-convolution Algorithm (SDA) algorithm was utilized to isolate and analyze the fine-mode aerosol optical depth (AODf) signal from AERONET data for these cases of persistent and extensive cloud cover. Satellite retrievals of AOD from MODIS sensors (from Dark Target, Deep Blue and MAIAC algorithms) were also investigated to assess the issue of detectability of high AOD events associated with high cloud fraction. Underestimation of fine mode AOD by the Navy Aerosol Analysis and Prediction System (NAAPS) and by the NASA Modern-Era Retrospective Analysis For Research And Applications Aerosol Re-analysis (MERRAaero) models at very high AOD at sites in China and Korea was observed, especially for observations that are cloud screened by AERONET (Level 2 data). Additionally, multi-year monitoring at several AERONET sites are examined for climatological statistics of cloud screening of fine mode aerosol events. Aerosol that has been affected by clouds or the near-cloud environment may be more prevalent than AERONET data suggest due to inherent difficulty in

  1. AATSR Land Surface Temperature Product Validation Using Ground Measurements in China and Implications for SLSTR

    Science.gov (United States)

    Zhou, Ji; Zmuda, Andy; Desnos, Yves-Louis; Ma, Jin

    2016-08-01

    Land surface temperature (LST) is one of the most important parameters at the interface between the earth's surface and the atmosphere. It acts as a sensitive indicator of climate change and is an essential input parameter for land surface models. Because of the intense variability at different spatial and temporal scales, satellite remote sensing provides the sole opportunity to acquire LSTs over large regions. Validation of the LST products is an necessary step before their applications conducted by scientific community and it is essential for the developers to improve the LST products.

  2. Terrestrial Ecosystems - Land Surface Forms of the Conterminous United States

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The U.S. Geological Survey (USGS) has generated land surface form classes for the contiguous United States. These land surface form classes were created as part of...

  3. A comparison of all-weather land surface temperature products

    Science.gov (United States)

    Martins, Joao; Trigo, Isabel F.; Ghilain, Nicolas; Goettche, Frank-M.; Ermida, Sofia; Olesen, Folke-S.; Gellens-Meulenberghs, Françoise; Arboleda, Alirio

    2017-04-01

    The Satellite Application Facility on Land Surface Analysis (LSA-SAF, http://landsaf.ipma.pt) has been providing land surface temperature (LST) estimates using SEVIRI/MSG on an operational basis since 2006. The LSA-SAF service has since been extended to provide a wide range of satellite-based quantities over land surfaces, such as emissivity, albedo, radiative fluxes, vegetation state, evapotranspiration, and fire-related variables. Being based on infra-red measurements, the SEVIRI/MSG LST product is limited to clear-sky pixels only. Several all-weather LST products have been proposed by the scientific community either based on microwave observations or using Soil-Vegetation-Atmosphere Transfer models to fill the gaps caused by clouds. The goal of this work is to provide a nearly gap-free operational all-weather LST product and compare these approaches. In order to estimate evapotranspiration and turbulent energy fluxes, the LSA-SAF solves the surface energy budget for each SEVIRI pixel, taking into account the physical and physiological processes occurring in vegetation canopies. This task is accomplished with an adapted SVAT model, which adopts some formulations and parameters of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) model operated at the European Center for Medium-range Weather Forecasts (ECMWF), and using: 1) radiative inputs also derived by LSA-SAF, which includes surface albedo, down-welling fluxes and fire radiative power; 2) a land-surface characterization obtained by combining the ECOCLIMAP database with both LSA-SAF vegetation products and the H(ydrology)-SAF snow mask; 3) meteorological fields from ECMWF forecasts interpolated to SEVIRI pixels, and 4) soil moisture derived by the H-SAF and LST from LSA-SAF. A byproduct of the SVAT model is surface skin temperature, which is needed to close the surface energy balance. The model skin temperature corresponds to the radiative temperature of the interface between soil and atmosphere

  4. Evaluation and Monitoring of Jpss Land Surface Temperature Data

    Science.gov (United States)

    Yu, Y.; Yu, P.; Liu, Y.; Csiszar, I. A.

    2016-12-01

    Land Surface Temperature (LST) is one of environmental data records (EDRs) produced operationally through the U.S. Joint Polar Satellite System (JPSS) mission. LST is an important parameter for understanding climate change, modeling the hydrological and biogeochemical cycles, and is a prime candidate for Numerical Weather Prediction (NWP) assimilation models. Recently, the international LST and Emissivity Working Ggroup (ILSTE-WG) is promoting to the inclusion of the LST as essential climate variable (ECV) in the Global Climate Observation System (GCOS) of the Word Meteorological Organization (WMO). At the Center for Satellite Applications and Research (STAR) of National Atmospheric and Oceanic Administration (NOAA), we, are as a science team, are responsible to for the science of JPSS LST production. In this work, we present our activities and accomplishments on the JPSS LST evaluation and monitoring since the launch of the first JPSS satellite, i.e. S-NPP, satellite. Beta version, provisional version, and validated stage 1 version of the S-NPP LST products which were announced in May 2013, July 2014, and March 2015, respectively. Evaluation of the LST products have been performed versus ground measurements and other polar-orbiting satellite LST data (e,g. MODIS LSTs); some results will be illustrated. A daily monitoring system of the JPSS LST production has been developed, which presents daily, weekly and monthly global LST maps and inter-comparison results on the STAR JPSS program website. Further, evaluation of the enterprise LST algorithm for JPSS mission which is in development at STAR currently are presented in this work. Finally, evaluation and monitoring plan of the LST production for the JPSS-1 satellite are also presented.

  5. Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

    Science.gov (United States)

    Baatz, Roland; Hendricks Franssen, Harrie-Jan; Han, Xujun; Hoar, Tim; Bogena, Heye; Vereecken, Harry

    2017-04-01

    Land surface models describe biogeophysical and biogeochemical processes at the land surface, and represent the lower boundary condition of atmospheric circulation models. Their key role is the quantification of mass and energy fluxes between the land surface and the atmosphere. Predictions with land surface models are affected by many unknown parameters, uncertain meteorological forcings and incomplete process understanding. Measurement data (e.g., soil moisture) can help to improve land surface model predictions. However, soil moisture products obtained from satellite remote sensing are normally available at a very coarse resolution and give information on the upper few centimetres of the soil only. Therefore, the recently developed Cosmic Ray Neutron Sensors (CRNS) are of high interest for predictions by land surface models, because they measure neutron count intensity, which is related to integral soil moisture content at the field scale (about 20 hectares). High resolution land surface models are also able to provide solutions at the same scale. In this study, we investigated whether the assimilation of soil moisture retrievals from CRNS data measured by a network of nine CRNS located in the Rur catchment in Germany (2354 km2) can improve land surface model prediction. The assimilation of soil moisture retrievals from neutron count intensity data was used to update model states and parameters of the land surface model CLM 4.5 over a two year time period, followed by a one year evaluation period without updates. This updating was done with the local ensemble transform Kalman filter. The real world experiment tested the value of CRNS using jackknifing experiments and three different initial soil maps. During the assimilation period, soil moisture predictions generally improved, for a biased soil map from an RMSE of 0.11 cm3/cm3 in the open loop run to 0.03 cm3/cm3 while during the evaluation period soil moisture predictions improved from 0.12 cm3/cm3 to 0.06 cm3

  6. Progress in remote sensing of global land surface heat fluxes and evaporations with a turbulent heat exchange parameterization method

    Science.gov (United States)

    Chen, Xuelong; Su, Bob

    2017-04-01

    Remote sensing has provided us an opportunity to observe Earth land surface with a much higher resolution than any of GCM simulation. Due to scarcity of information for land surface physical parameters, up-to-date GCMs still have large uncertainties in the coupled land surface process modeling. One critical issue is a large amount of parameters used in their land surface models. Thus remote sensing of land surface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earth land surface with optical, thermal and microwave bands. Some basic Earth land surface status (land surface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earth land and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based land surface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of land surface process simulation will also be discussed.

  7. Integrative inversion of land surface component temperature

    Institute of Scientific and Technical Information of China (English)

    FAN Wenjie; XU Xiru

    2005-01-01

    In this paper, the row winter wheat was selected as the example to study the component temperature inversion method of land surface target in detail. The result showed that the structural pattern of row crop can affect the inversion precision of component temperature evidently. Choosing appropriate structural pattern of row crop can improve the inversion precision significantly. The iterative method combining inverse matrix was a stable method that was fit for inversing component temperature of land surface target. The result of simulation and field experiment showed that the integrative method could remarkably improve the inversion accuracy of the lighted soil surface temperature and the top layer canopy temperature, and enhance inversion stability of components temperature. Just two parameters were sufficient for accurate atmospheric correction of multi-angle and multi-spectral thermal infrared data: atmospheric transmittance and the atmospheric upwelling radiance. If the atmospheric parameters and component temperature can be inversed synchronously, the really and truly accurate atmospheric correction can be achieved. The validation using ATSRII data showed that the method was useful.

  8. Land surface processes and Sahel climate

    Science.gov (United States)

    Nicholson, Sharon

    2000-02-01

    This paper examines the question of land surface-atmosphere interactions in the West African Sahel and their role in the interannual variability of rainfall. In the Sahel, mean rainfall decreased by 25-40% between 1931-1960 and 1968-1997; every year in the 1950s was wet, and nearly every year since 1970 has been anomalously dry. Thus the intensity and multiyear persistence of drought conditions are unusual and perhaps unique features of Sahel climate. This article presents arguments for the role of land surface feedback in producing these features and reviews research relevant to land surface processes in the region, such as results from the 1992 Hydrologic Atmospheric Pilot Experiment (HAPEX)-Sahel experiment and recent studies on aerosols and on the issue of desertification in the region, a factor implicated by some as a cause of the changes in rainfall. Included also is a summary of evidence of feedback on meteorological processes, presented from both model results and observations. The reviewed studies demonstrate numerous ways in which the state of the land surface can influence interactions with the atmosphere. Surface hydrology essentially acts to delay and prolong the effects of meteorological drought. Each evaporative component of the surface water balance has its own timescale, with the presence of vegetation affecting the process both by delaying and prolonging the return of soil moisture to the atmosphere but at the same time accelerating the process through the evaporation of canopy-intercepted water. Hence the vegetation structure, including rooting depth, can modulate the land-atmosphere interaction. Such processes take on particular significance in the Sahel, where there is a high degree of recycling of atmospheric moisture and where the meteorological processes from the scale of boundary layer development to mesoscale disturbance generation are strongly influenced by moisture. Simple models of these feedback processes and their various timescales

  9. Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

    Science.gov (United States)

    Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

    2011-01-01

    Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

  10. Land surface Verification Toolkit (LVT – a generalized framework for land surface model evaluation

    Directory of Open Access Journals (Sweden)

    S. V. Kumar

    2012-02-01

    Full Text Available Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS, it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

  11. Land surface Verification Toolkit (LVT – a generalized framework for land surface model evaluation

    Directory of Open Access Journals (Sweden)

    S. V. Kumar

    2012-06-01

    Full Text Available Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS, it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

  12. Complex land surface phenologies of moisture status

    Science.gov (United States)

    Henebry, G. M.; Doubkova, M.

    2006-12-01

    Making cross-scale linkages from experimental plots or flux tower footprints to regional and continental extents is made difficult by disparate spatial and temporal scales between process and observation. While exchanges between the vegetated land surface and the atmospheric boundary layer are continual, sampling and observations are typically intermittent in time and limited across space. Remote sensing of reflected sunlight has proven useful to track ecological dynamics. These observations are, however, restricted to daytime and often obscured by cloud cover, necessitating production of multi-date composites. The current generation of passive microwave radiometers can observe the land surface both day and night regardless of cloudiness, albeit at a spatial resolution coarser than typically used in ecological remote sensing. Datastreams from the AMSR-E (Advanced Microwave Scanning Radiometer-EOS) onboard NASA's Aqua platform are processed daily at the National Snow and Ice Data Center (NSIDC) into various products, including global retrievals of surficial soil moisture and vegetation water content based on microwave brightness temperatures observed at multiple frequencies. Due to sensor orbit and swath width, gaps occur at the lower latitudes in daily products. We have further processed the product-streams from the descending (01:30) and ascending (13:30) orbits into separate smoothed daily composites using an 8-day retrospective moving average. Of particular interest for synoptic ecology is the diel difference in vegetation water content. When the difference between the pre-dawn and the early afternoon values is positive, it suggests that the supply of moisture from the root zone is not able to keep pace with evapotranspiration during the day, but the soil and canopy moisture equalize overnight. Time series of the diel difference show rapid changes in moisture status in response to precipitation events and dry spells. What constitutes the appropriate baseline

  13. Toward Transfer Functions for Land Surface Phenologies

    Science.gov (United States)

    Henebry, G. M.

    2010-12-01

    A key problem in projecting future landscapes is simulating the associated land surface phenologies (or LSPs). A recent study of land surface models concluded that the representations of crop phenologies among the models diverged sufficiently to impede a useful intercomparison of simulation results from their associated climate models. Grassland phenologies are far more complicated than cropland phenologies due to multiple forcing factors, photosynthetic pathways (C3 vs C4), and spatial heterogeneities in both resource availabilities and land management practices. Furthermore, many tallgrass species (such as switchgrass) are widely distributed across temperature, but not moisture, gradients, resulting in significant ecotypic variation across the species' geographic range. Thus, how feasible is "transplanting" tallgrass LSPs across isotherms—but along isohyets—to simulate a shift in cultivation from maize-soy to switchgrass? Prior work has shown a quadratic model can provide a parsimonious link between a Normalized Difference Vegetation Index (or NDVI) time series and thermal time, measured in terms of accumulated growing degree-days (or AGDD). Moreover, the thermal time to peak NDVI (or TTP) is a simple function of the parameter coefficients of fitted model. I fitted quadratic models to MODIS NDVI and weather station data at multiple sites across the Northern Great Plains over ten growing seasons, 2000-2009. There is a strong latitudinal gradient in TTP that results in part from a quasi-linear gradient in accumulated daylight hours (or ADH) between 30 and 50 degrees north. However, AGDD improves upon ADH by providing sensitivity to the variability of growing season weather. In the quadratic parameter coefficients there is a geographic pattern apparent as a function of TTP, although it is more variable at shorter TTPs. Using these patterns, an LSP transfer function was implemented along a latitudinal transect to simulate switchgrass cultivation in areas now

  14. Land surface parameter optimisation through data assimilation: the adJULES system

    Science.gov (United States)

    Raoult, Nina; Jupp, Tim; Cox, Peter

    2017-04-01

    Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. We present adJULES in a data assimilation framework and demonstrate its ability to improve the model-data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85% of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

  15. Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

    Science.gov (United States)

    Prodhomme, Chloé; Doblas-Reyes, Francisco; Bellprat, Omar; Dutra, Emanuel

    2016-08-01

    Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions.

  16. Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

    Science.gov (United States)

    Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.

    1997-01-01

    variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.

  17. Seasonal transition of precipitation characteristics associated with land surface conditions in and around Bangladesh

    Science.gov (United States)

    Ono, M.; Takahashi, H. G.

    2016-10-01

    This study examined the seasonal transition of precipitation characteristics and its association with land surface conditions in and around Bangladesh, where land surface conditions are predominantly wet. Hourly rain rate data from the Global Satellite Mapping of Precipitation Microwave-Infrared Combined Product and 10 day soil moisture data from the Advanced Microwave Scanning Radiometer Earth Observing System were used over the 7 years from 2003 to 2009. Area mean values of soil moisture, and precipitation amount, frequency, and intensity were calculated for each 10 day period. Results showed that higher precipitation amount and frequency were observed over the wet soil conditions, which indicates that soil moisture was influenced by previous precipitation events. However, the soil moisture could also control the precipitation characteristics. The seasonal and interannual variations in all regions suggested that precipitation amount and frequency increased in moist soil conditions, which is associated with an increase of water vapor supplied from the moist land surface. Over a flat plain (87°E-91°E, 23°N-25°N), a higher afternoon precipitation intensity was observed over drier land surfaces. This relationship was observed on seasonal and interannual variations. This suggests that the land surface conditions in this region can affect the afternoon precipitation intensity to some extent, although changes of atmospheric conditions can be a major factor particularly for the seasonal changes. However, this relationship was not observed in mountainous regions. This can be explained by other factors, such as thermally induced local circulations by the surrounding topography, being stronger than the impact of land surface conditions.

  18. Spatial validation of large scale land surface models against monthly land surface temperature patterns using innovative performance metrics.

    Science.gov (United States)

    Koch, Julian; Siemann, Amanda; Stisen, Simon; Sheffield, Justin

    2016-04-01

    Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. This study features two innovative spatial performance metrics, namely EOF- and connectivity-analysis, to validate predicted LST patterns by three LSMs (Mosaic, Noah, VIC) over the contiguous USA. The LST validation dataset is derived from global High-Resolution-Infrared-Radiometric-Sounder (HIRS) retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality, and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly driven by air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.

  19. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements

    KAUST Repository

    Niu, Guo-Yue

    2011-06-24

    This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP\\'s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework. Copyright © 2011 by the American Geophysical Union.

  20. Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

    OpenAIRE

    José Luis Gómez-Dans; Philip Edward Lewis; Mathias Disney

    2016-01-01

    There is an increasing need to consistently combine observations from different sensors to monitor the state of the land surface. In order to achieve this, robust methods based on the inversion of radiative transfer (RT) models can be used to interpret the satellite observations. This typically results in an inverse problem, but a major drawback of these methods is the computational complexity. We introduce the concept of Gaussian Process (GP) emulators: surrogate functions that accurately ap...

  1. Improving land surface models with FLUXNET data

    Directory of Open Access Journals (Sweden)

    Y. -P. Wang

    2009-07-01

    Full Text Available There is a growing consensus that land surface models (LSMs that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF. MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT – we would expect similar parameter

  2. Improving land surface models with FLUXNET data

    Directory of Open Access Journals (Sweden)

    M. Williams

    2009-03-01

    Full Text Available There is a growing consensus that land surface models (LSMs that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for fusing LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF. MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs from MDF can be used to interpret model process validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT – we would expect similar parameter estimates among sites

  3. Derivation of Land Surface Albedo at High Resolution by Combining HJ-1A/B Reflectance Observations with MODIS BRDF Products

    Directory of Open Access Journals (Sweden)

    Bo Gao

    2014-09-01

    Full Text Available Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio–temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio–temporal resolution (space: 30 m and time: 2–4 days. The proposed method works by combining the land surface reflectance data at 30 m spatial resolution obtained from the charge-coupled devices in the Huanjing-1A and -1B (HJ-1A/B satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS land surface bidirectional reflectance distribution function (BRDF parameters product (MCD43A1, which is at a spatial resolution of 500 m. First, the land surface BRDF parameters for HJ-1A/B land surface reflectance with a spatial–temporal resolutions of 30 m and 2–4 day are calculated on the basis of the prior knowledge from the MODIS BRDF product; then, the calculated high resolution BRDF parameters are integrated over the illuminating/viewing hemisphere to produce the white- and black-sky albedos at 30 m resolution. These results form the basis for the final land surface albedo derivation by accounting for the proportion of direct and diffuse solar radiation arriving at the ground. The albedo retrieved by this novel method is compared with MODIS land surface albedo products, as well as with ground measurements. The results show that the derived land surface albedo during the growing season of 2012 generally achieved a mean absolute accuracy of ±0.044, and a root mean square error of 0.039, confirming the effectiveness of the newly proposed method.

  4. Global distribution of clay-size minerals on land surface for biogeochemical and climatological studies.

    Science.gov (United States)

    Ito, Akihiko; Wagai, Rota

    2017-08-22

    Clay-size minerals play important roles in terrestrial biogeochemistry and atmospheric physics, but their data have been only partially compiled at global scale. We present a global dataset of clay-size minerals in the topsoil and subsoil at different spatial resolutions. The data of soil clay and its mineralogical composition were gathered through a literature survey and aggregated by soil orders of the Soil Taxonomy for each of the ten groups: gibbsite, kaolinite, illite/mica, smectite, vermiculite, chlorite, iron oxide, quartz, non-crystalline, and others. Using a global soil map, a global dataset of soil clay-size mineral distribution was developed at resolutions of 2' to 2° grid cells. The data uncertainty associated with data variability and assumption was evaluated using a Monte Carlo method, and validity of the clay-size mineral distribution obtained in this study was examined by comparing with other datasets. The global soil clay data offer spatially explicit studies on terrestrial biogeochemical cycles, dust emission to the atmosphere, and other interdisciplinary earth sciences.

  5. Quantification of the relative role of land-surface processes and large-scale forcing in dynamic downscaling over the Tibetan Plateau

    Science.gov (United States)

    Gao, Yanhong; Xiao, Linhong; Chen, Deliang; Chen, Fei; Xu, Jianwei; Xu, Yu

    2017-03-01

    Dynamical downscaling modeling (DDM) is important to understand regional climate change and develop local mitigation strategies, and the accuracy of DDM depends on the physical processes involved in the regional climate model as well as the forcing datasets derived from global models. This study investigates the relative role of the land surface schemes and forcing datasets in the DDM over the Tibet Plateau (TP), a region complex in topography and vulnerable to climate change. Three Weather Research and Forecasting model dynamical downscaling simulations configured with two land surface schemes [Noah versus Noah with multiparameterization (Noah-MP)] and two forcing datasets are performed over the period of 1980-2005. The downscaled temperature and precipitation are evaluated with observations and inter-compared regarding temporal trends, spatial distributions, and climatology. Results show that the temporal trends of the temperature and precipitation are determined by the forcing datasets, and the forcing dataset with the smallest trend bias performs the best. Relative to the forcing datasets, land surface processes play a more critical role in the DDM over the TP due to the strong heating effects on the atmospheric circulation from a vast area at exceptionally high elevations. By changing the vertical profiles of temperature in the atmosphere and the horizontal patterns of moisture advection during the monsoon seasons, the land surface schemes significantly regulate the downscaled temperature and precipitation in terms of climatology and spatial patterns. This study emphasizes the selection of land surface schemes is of crucial importance in the successful DDM over the TP.

  6. The Land Surface Temperature Synergistic Processor in BEAM: A Prototype towards Sentinel-3

    CERN Document Server

    Ruescas, Ana Belen; Fomferra, Norman; Brockmann, Carsten

    2016-01-01

    Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Alo...

  7. Remote Sensing Parameterization of Land Surface Heat Fluxes over Arid and Semi-arid Areas

    Institute of Scientific and Technical Information of China (English)

    马耀明; 王介民; 黄荣辉; 卫国安; MassimoMENENTI; 苏中波; 胡泽勇; 高峰; 文军

    2003-01-01

    Dealing with the regional land surfaces heat fluxes over inhomogeneous land surfaces in arid and semi-arid areas is an important but not an easy issue. In this study, one parameterization method based on satellite remote sensing and field observations is proposed and tested for deriving the regional land surface heat fluxes over inhomogeneous landscapes. As a case study, the method is applied to the Dunhuang experimental area and the HEIFE (Heihe River Field Experiment, 1988-1994) area. The Dunhuang area is selected as a basic experimental area for the Chinese National Key Programme for Developing Basic Sciences: Research on the Formation Mecbanism and Prediction Theory of Severe Climate Disaster in China (G1998040900, 1999-2003). The four scenes of Landsat TM data used in this study are 3 June 2000,22 August 2000, and 29 January 2001 for the Dunhuang area and 9 July 1991 for the HEIFE area. The regional distributions of land surface variables, vegetation variables, and heat fluxes over inhomogeneous landscapes in arid and semi-arid areas are obtained in this study.

  8. Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

    Science.gov (United States)

    Kumar, Sujay V.; Peters-Lidard, Christa; Reichl, Rolf; Harrison, Kenneth; Santanello, Joseph

    2010-01-01

    Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

  9. A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data

    Institute of Scientific and Technical Information of China (English)

    MAO KeBiao; SHI JianCheng; LI ZhaoLiang; QIN ZhiHao; LI ManChun; XU Bin

    2007-01-01

    AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface temperature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM, the difference of different frequencies can eliminate the influence of water in soil and atmosphere, and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately, the land surface should be at least classified into three types: water covered surface, snow covered surface, and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm, we built different equations for different ranges of temperature. The average land surface temperature error is about 2-3℃ relative to the MODIS LST product.

  10. A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface tem-perature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM,the difference of different frequen-cies can eliminate the influence of water in soil and atmosphere,and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately,the land surface should be at least classified into three types:water covered surface,snow covered surface,and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm,we built different equations for different ranges of temperature. The average land surface temperature er-ror is about 2―3℃ relative to the MODIS LST product.

  11. Classes of land-surface form in the United States

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset describes classes of land-surface form in the conterminous United States. The source of the data is the map of land-surface form in the 1970...

  12. Daily monitoring of the land surface of the Earth

    Science.gov (United States)

    Mascaro, J.

    2016-12-01

    Planet is an integrated aerospace and data analytics company that operates the largest fleet of Earth-imaging satellites. With more than 140 cube-sats successfully launched to date, Planet is now collecting approximately 10 million square kilometers of imagery per day (3-5m per pixel, in red, green, blue and near infrared spectral bands). By early 2017, Planet's constellation will image the entire land surface of the Earth on a daily basis. Due to investments in cloud storage and computing, approximately 75% of imagery collected is available to Planet's partners within 24 hours of capture through an Application Program Interface. This unique dataset has enormous applications for monitoring the status of Earth's natural ecosystems, as well as human settlements and agricultural welfare. Through our Ambassadors Program, Planet has made data available for researchers in areas as disparate as human rights monitoring in refugee camps, to assessments of the impact of hydroelectric installations, to tracking illegal gold mining in Amazon forests, to assessing the status of the cryosphere. Here, we share early results from Planet's research partner network, including enhanced spatial and temporal resolution of NDVI data for agricultural health in Saudi Arabia, computation of rates of illegal deforestation in Southern Peru, estimates of tropical forest carbon stocks based on data integration with active sensors, and estimates of glacial flow rates. We synthesize the potentially enormous research and scientific value of Planet's persistent monitoring capability, and discuss methods by which the data will be disseminated into the scientific community.

  13. Afforestation in China cools local land surface temperature.

    Science.gov (United States)

    Peng, Shu-Shi; Piao, Shilong; Zeng, Zhenzhong; Ciais, Philippe; Zhou, Liming; Li, Laurent Z X; Myneni, Ranga B; Yin, Yi; Zeng, Hui

    2014-02-25

    China has the largest afforested area in the world (∼62 million hectares in 2008), and these forests are carbon sinks. The climatic effect of these new forests depends on how radiant and turbulent energy fluxes over these plantations modify surface temperature. For instance, a lower albedo may cause warming, which negates the climatic benefits of carbon sequestration. Here, we used satellite measurements of land surface temperature (LST) from planted forests and adjacent grasslands or croplands in China to understand how afforestation affects LST. Afforestation is found to decrease daytime LST by about 1.1 ± 0.5 °C (mean ± 1 SD) and to increase nighttime LST by about 0.2 ± 0.5 °C, on average. The observed daytime cooling is a result of increased evapotranspiration. The nighttime warming is found to increase with latitude and decrease with average rainfall. Afforestation in dry regions therefore leads to net warming, as daytime cooling is offset by nighttime warming. Thus, it is necessary to carefully consider where to plant trees to realize potential climatic benefits in future afforestation projects.

  14. Revising Hydrology of a Land Surface Model

    Science.gov (United States)

    Le Vine, Nataliya; Butler, Adrian; McIntyre, Neil; Jackson, Christopher

    2015-04-01

    Land Surface Models (LSMs) are key elements in guiding adaptation to the changing water cycle and the starting points to develop a global hyper-resolution model of the terrestrial water, energy and biogeochemical cycles. However, before this potential is realised, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. An important limitation is the simplistic or non-existent representation of the deep subsurface in LSMs; and another is the lack of connection of LSM parameterisations to relevant hydrological information. In this context, the paper uses a case study of the JULES (Joint UK Land Environmental Simulator) LSM applied to the Kennet region in Southern England. The paper explores the assumptions behind JULES hydrology, adapts the model structure and optimises the coupling with the ZOOMQ3D regional groundwater model. The analysis illustrates how three types of information can be used to improve the model's hydrology: a) observations, b) regionalized information, and c) information from an independent physics-based model. It is found that: 1) coupling to the groundwater model allows realistic simulation of streamflows; 2) a simple dynamic lower boundary improves upon JULES' stationary unit gradient condition; 3) a 1D vertical flow in the unsaturated zone is sufficient; however there is benefit in introducing a simple dual soil moisture retention curve; 4) regionalized information can be used to describe soil spatial heterogeneity. It is concluded that relatively simple refinements to the hydrology of JULES and its parameterisation method can provide a substantial step forward in realising its potential as a high-resolution multi-purpose model.

  15. Mycorrhizal fungi and global land surface models?

    Science.gov (United States)

    Brzostek, E. R.; Fisher, J. B.; Shi, M.; Phillips, R.

    2013-12-01

    In the current generation of Land Surface Models (LSMs), the representation of coupled carbon (C) and nutrient cycles does not account for allocation of C by plants to mycorrhizal fungi in exchange for limiting nutrients. Given that the amount of C transferred to mycorrhizae can exceed 20% of net primary production (NPP), mycorrhizae can supply over half of the nitrogen (N) needed to support NPP, and that large majority of plants form associations with mycorrhizae; integrating these mechanisms into LSMs may significantly alter our understanding of the role of the terrestrial biosphere in mitigating climate change. Here, we present results from the integration of a mycorrhizal framework into a cutting-edge global plant nitrogen model -- Fixation & Uptake of Nitrogen (FUN; Fisher et al., 2010) -- that can be coupled into existing LSMs. In this mycorrhizal framework, the C cost of N acquisition varies as a function of mycorrhizal type with: (1) plants that support arbuscular mycorrhizae (AM) benefiting when N is plentiful and (2) plants that support ectomycorrhizae (ECM) benefiting when N is limiting. At the plot scale (15 x 15m), the My-FUN model improved predictions of retranslocation, N uptake, and the amount of C transferred into the soil relative to the base model across 45 plots that vary in mycorrhizal type in Indiana, USA. At the ecosystem scale, when we coupled this new framework into the Community Land Model (CLM-CN), the model estimated lower C uptake than the base model and more accurately predicted C uptake at the Morgan Monroe State Forest AmeriFlux site. These results suggest that the inclusion of a mycorrhizal framework into LSMs will enhance our ability to predict feedbacks between global change and the terrestrial biosphere.

  16. Enhancing the representation of subgrid land surface characteristics in land surface models

    Directory of Open Access Journals (Sweden)

    Y. Ke

    2013-03-01

    Full Text Available Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types. In this study, we developed a new subgrid classification method (SGC that accounts for the topographic variability of the vegetation cover. Each model grid cell was represented with a number of elevation classes and each elevation class was further described by a number of vegetation types. The numbers of elevation classes and vegetation types were variable and optimized for each model grid so that the spatial variability of both elevation and vegetation can be reasonably explained given a pre-determined total number of classes. The subgrid structure of the Community Land Model (CLM was used as an example to illustrate the newly developed method in this study. With similar computational burden as the current subgrid vegetation representation in CLM, the new method is able to explain at least 80% of the total subgrid Plant Functional Types (PFTs and greatly reduced the variations of elevation within each subgrid class compared to the baseline method where a single elevation class is assigned to each subgrid PFT. The new method was also evaluated against two other subgrid methods (SGC1 and SGC2 that assigned fixed numbers of elevation and vegetation classes for each model grid with different perspectives of surface cover classification. Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0° and 2.0° with three maximum-allowed total number of classes Nclass of 24, 18 and 12 representing different computational burdens over the North America (NA continent, the new method showed variable performances compared to the SGC1 and SGC2 methods. However, the advantage of the SGC method over the other two methods clearly emerged at coarser model resolutions and with moderate computational

  17. Spatial Aggregation of Land Surface Characteristics: Impact of resolution of remote sensing data on land surface modelling

    NARCIS (Netherlands)

    Pelgrum, H.

    2000-01-01

    Land surface models describe the exchange of heat, moisture and momentum between the land surface and the atmosphere. These models can be solved regionally using remote sensing measurements as input. Input variables which can be derived from remote sensing measurements are surface albedo, surface te

  18. An Integrated Snow Radiance and Snow Physics Modeling Framework for Cold Land Surface Modeling

    Science.gov (United States)

    Kim, Edward J.; Tedesco, Marco

    2006-01-01

    Recent developments in forward radiative transfer modeling and physical land surface modeling are converging to allow the assembly of an integrated snow/cold lands modeling framework for land surface modeling and data assimilation applications. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. Together these form a flexible framework for self-consistent remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. Each element of this framework is modular so the choice of element can be tailored to match the emphasis of a particular study. For example, within our framework, four choices of a FRTM are available to simulate the brightness temperature of snow: Two models are available to model the physical evolution of the snowpack and underlying soil, and two models are available to handle the water/energy balance at the land surface. Since the framework is modular, other models-physical or statistical--can be accommodated, too. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster at the NASA Goddard Space Flight Center. The advantages of such an integrated modular framework built on the LIS will be described through examples-e.g., studies to analyze snow field experiment observations, and simulations of future satellite missions for snow and cold land processes.

  19. Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

    Science.gov (United States)

    Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

    2012-01-01

    Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

  20. Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

    Directory of Open Access Journals (Sweden)

    Yuanyuan Chen

    2017-02-01

    Full Text Available Land surface temperature (LST is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs. To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W and atmospheric temperature (T0 in the bottom layer. To parameterize the constant, an exponential approximation between W and T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NEΔT = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5 satellite data. The GF-5 satellite is scheduled to be launched in 2017.

  1. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

    Science.gov (United States)

    Case. Jonathan; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    ) will be run at a comparable resolution to provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Additionally, real-time green vegetation fraction data from the Visible Infrared Imaging Radiometer Suite will be incorporated into the KMD-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service. Finally, model verification capabilities will be transitioned to KMD using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. The transition of these MET tools will enable KMD to monitor model forecast accuracy in near real time. This presentation will highlight preliminary verification results of WRF runs over east Africa using the LIS land surface initialization.

  2. Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm

    Directory of Open Access Journals (Sweden)

    Offer Rozenstein

    2014-03-01

    Full Text Available Land surface temperature (LST is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS. This paper presents an adjustment of the split window algorithm (SWA for TIRS that uses atmospheric transmittance and land surface emissivity (LSE as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.

  3. Regional Climate Variability Responses to Future Land Surface Forcing in the Brazilian Amazon

    Directory of Open Access Journals (Sweden)

    Tao Zhang

    2013-01-01

    Full Text Available Tropical deforestation could destabilize regional climate changes. This paper aimed to model the potential climatological variability caused by future forest vulnerability in the Brazilian Amazon over the 21th century. The underlying land surface changes between 2005 and 2100 are first projected based on the respectable output produced by Hurtt et al. Then the weather research and forecasting (WRF model is applied to assess the impacts of future deforestation on regional climate during 2090–2100. The study results show that the forests in the Brazilian Amazon will primarily be converted into dryland cropland and pasture in the northwest part and into cropland/woodland mosaic in the southeast part, with 5.12% and 13.11%, respectively. These land surface changes will therefore lead to the significant reduction of the sum of sensible heat flux and latent heat flux and precipitation and the increase of the surface temperature. Furthermore, the variability of surface temperature is observed with close link to the deforested areas.

  4. eMODIS Global Land Surface Temperature Version 6

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The EROS Moderate Resolution Imaging Spectroradiometer (eMODIS) Aqua Land Surface Temperature (LST) product is similar to the Land Processes Distributed Active...

  5. Effects of Crop Growth and Development on Land Surface Fluxes

    Institute of Scientific and Technical Information of China (English)

    CHEN Feng; XIE Zhenghui

    2011-01-01

    In this study, the Crop Estimation through Resource and Environment Synthesis model (CERES3.0) was coupled into the Biosphere-Atmosphere Transfer Scheme (BATS), which is called BATS_CERES, to represent interactions between the land surface and crop growth processes. The effects of crop growth and development on land surface processes were then studied based on numerical simulations using the land surface models. Six sensitivity experiments by BATS show that the land surface fluxes underwent substantial changes when the leaf area index was changed from 0 to 6 n2 m-2. Numerical experiments for Yucheng and Taoyuan stations reveal that the coupled model could capture not only the responses of crop growth and development to environmental conditions, but also the feedbacks to land surface processes.For quantitative evaluation of the effects of crop growth and development on surface fluxes in China, two numerical experiments were conducted over continental China: one by BATS_CERES and one by the original BATS. Comparison of the two runs shows decreases of leaf area index and fractional vegetation cover when incorporating dynamic crops in land surface simulation, which lead to less canopy interception, vegetation transpiration, total evapotranspiration, top soil moisture, and more soil evaporation, surface runoff, and root zone soil moisture. These changes are accompanied by decreasing latent heat flux and increasing sensible heat flux in the cropland region. In addition, the comparison between the simulations and observations proved that incorporating the crop growth and development process into the land surface model could reduce the systematic biases of the simulated leaf area index and top soil moisture, hence improve the simulation of land surface fluxes.

  6. Global Precipitation Climatology Project (GPCP) - Monthly, Version 2.2

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Precipitation Climatology Project (GPCP) comprises a total of 27 products with the two primary products being the monthly satellite-gauge and associated...

  7. Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion

    Science.gov (United States)

    Wu, Yuling; Nair, Udaysankar S.; Pielke, Roger A., Sr.; McNider, Richard T.; Christopher, Sundar A.; Anantharaj, Valentine G.

    2009-01-01

    Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities, but past studies do not consider the impact of a realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While these focussed on dispersion in the convective boundary layer, the present work also considers dispersion in the nocturnal boundary layer and above. Using a Lagrangian particle dispersion model (LPDM) coupled to the Eulerian Regional Atmospheric Modeling System (RAMS), the impact of topographic, vegetation, and soil moisture heterogeneities on daytime and nighttime atmospheric dispersion is examined. In addition, the sensitivity to the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spatial distributions of vegetation characteristics on atmospheric dispersion is also studied. The impact of vegetation and terrain heterogeneities on atmospheric dispersion is strongly modulated by soil moisture, with the nature of dispersion switching from non-Gaussian to near- Gaussian behaviour for wetter soils (fraction of saturation soil moisture content exceeding 40%). For drier soil moisture conditions, vegetation heterogeneity produces differential heating and the formation of mesoscale circulation patterns that are primarily responsible for non-Gaussian dispersion patterns. Nighttime dispersion is very sensitive to topographic, vegetation, soil moisture, and soil type heterogeneity and is distinctly non-Gaussian for heterogeneous land-surface conditions. Sensitivity studies show that soil type and vegetation heterogeneities have the most dramatic impact on atmospheric dispersion. To provide more skillful dispersion calculations, we recommend the utilisation of satellite-derived vegetation characteristics coupled with data assimilation techniques that constrain soil-vegetation-atmosphere transfer (SVAT) models to generate realistic spatial distributions of surface energy fluxes.

  8. Improving winter leaf area index estimation in coniferous forests and its significance in estimating the land surface albedo

    Science.gov (United States)

    Wang, Rong; Chen, Jing M.; Pavlic, Goran; Arain, Altaf

    2016-09-01

    Winter leaf area index (LAI) of evergreen coniferous forests exerts strong control on the interception of snow, snowmelt and energy balance. Simulation of winter LAI and associated winter processes in land surface models is challenging. Retrieving winter LAI from remote sensing data is difficult due to cloud contamination, poor illumination, lower solar elevation and higher radiation reflection by snow background. Underestimated winter LAI in evergreen coniferous forests is one of the major issues limiting the application of current remote sensing LAI products. It has not been fully addressed in past studies in the literature. In this study, we used needle lifespan to correct winter LAI in a remote sensing product developed by the University of Toronto. For the validation purpose, the corrected winter LAI was then used to calculate land surface albedo at five FLUXNET coniferous forests in Canada. The RMSE and bias values for estimated albedo were 0.05 and 0.011, respectively, for all sites. The albedo map over coniferous forests across Canada produced with corrected winter LAI showed much better agreement with the GLASS (Global LAnd Surface Satellites) albedo product than the one produced with uncorrected winter LAI. The results revealed that the corrected winter LAI yielded much greater accuracy in simulating land surface albedo, making the new LAI product an improvement over the original one. Our study will help to increase the usability of remote sensing LAI products in land surface energy budget modeling.

  9. Linkages between Land Surface Phenology Metrics and Natural and Anthropogenic Events in Drylands (Invited)

    Science.gov (United States)

    de Beurs, K.; Brown, M. E.; Ahram, A.; Walker, J.; Henebry, G. M.

    2013-12-01

    Tracking vegetation dynamics across landscapes using remote sensing, or 'land surface phenology,' is a key mechanism that allows us to understand ecosystem changes. Land surface phenology models rely on vegetation information from remote sensing, such as the datasets derived from the Advanced Very High Resolution Radiometer (AVHRR), the newer MODIS sensors on Aqua and Terra, and sometimes the higher spatial resolution Landsat data. Vegetation index data can aid in the assessment of variables such as the start of season, growing season length and overall growing season productivity. In this talk we use Landsat, MODIS and AVHRR data and derive growing season metrics based on land surface phenology models that couple vegetation indices with satellite derived accumulated growing degreeday and evapotranspiration estimates. We calculate the timing and the height of the peak of the growing season and discuss the linkage of these land surface phenology metrics with natural and anthropogenic changes on the ground in dryland ecosystems. First we will discuss how the land surface phenology metrics link with annual and interannual price fluctuations in 229 markets distributed over Africa. Our results show that there is a significant correlation between the peak height of the growing season and price increases for markets in countries such as Nigeria, Somalia and Niger. We then demonstrate how land surface phenology metrics can improve models of post-conflict resolution in global drylands. We link the Uppsala Conflict Data Program's dataset of political, economic and social factors involved in civil war termination with an NDVI derived phenology metric and the Palmer Drought Severity Index (PDSI). An analysis of 89 individual conflicts in 42 dryland countries (totaling 892 individual country-years of data between 1982 and 2005) revealed that, even accounting for economic and political factors, countries that have higher NDVI growth following conflict have a lower risk of

  10. Passive remote sensing of the atmospheric water vapour content above land surfaces

    Science.gov (United States)

    Bartsch, B.; Bakan, S.; Fischer, J.

    The global distribution of the atmospheric water vapour content plays an important role in the weather forecast and climate research. Nowadays there exist various methods dealing with remote sensing of the atmospheric water vapour content. Unfortunately, most of them are restricted to ocean areas, since, in general, the emission of land surfaces is not known well enough. Therefore, a new method is developed which allows the detection of the atmospheric total water vapour content from aircraft or satellite with the aid of backscattered solar radiation in the near infrared above land surfaces. The Matrix-Operator-Method has been used to simulate backscattered solar radiances, including various atmospheric profiles of temperature, pressure, water vapour, and aerosols of various types, several sun zenith angles, and different types of land surfaces. From these calculations it can be concluded, that the detection of water vapour content in cloudless atmospheres is possible with an error of < 10 % even for higher aerosol contents. In addition to the theoretical results first comparisons with aircraft measurements of the backscattered solar radiances are shown. These measurements have been carried out with the aid of OVID (Optical Visible and near Infrared Detector), a new multichannel array spectrometer, in 1993.

  11. Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia

    Science.gov (United States)

    Evans, Jason P.; Meng, Xianhong; McCabe, Matthew F.

    2017-01-01

    In this study, we have examined the ability of a regional climate model (RCM) to simulate the extended drought that occurred throughout the period of 2002 through 2007 in south-east Australia. In particular, the ability to reproduce the two drought peaks in 2002 and 2006 was investigated. Overall, the RCM was found to reproduce both the temporal and the spatial structure of the drought-related precipitation anomalies quite well, despite using climatological seasonal surface characteristics such as vegetation fraction and albedo. This result concurs with previous studies that found that about two-thirds of the precipitation decline can be attributed to the El Niño-Southern Oscillation (ENSO). Simulation experiments that allowed the vegetation fraction and albedo to vary as observed illustrated that the intensity of the drought was underestimated by about 10 % when using climatological surface characteristics. These results suggest that in terms of drought development, capturing the feedbacks related to vegetation and albedo changes may be as important as capturing the soil moisture-precipitation feedback. In order to improve our modelling of multi-year droughts, the challenge is to capture all these related surface changes simultaneously, and provide a comprehensive description of land surface-precipitation feedback during the droughts development.

  12. Temporal Dynamics of Soil Moisture Variability at the Landscape Scale: Implications for Land Surface Models.

    Science.gov (United States)

    Montaldo, N.; Albertson, J. D.

    2001-12-01

    Meteorological and hydrological forecasting models share soil moisture as a critical boundary condition. Partitioning of received energy at the land surface depends directly on this variable, as does the partitioning of rainfall into its possible routes over and through the soil. In Land Surface Models (LSMs) the temporal dynamic of soil moisture spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations soil moisture values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on soil moisture, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, microbial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the soil moisture. But soil moisture variances affect flux predictions, which depend nonlinearly on soil moisture, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on soil moisture. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a conservation equation for the horizontal variance of soil moisture. And, through closure arguments, we are able to describe the individual terms that produce and destroy spatial variance through time in terms of the mean soil moisture state and other observable system properties such as vegetation and soil properties variability. Finally, we calculate land surface fluxes from second order Taylor expansion, using our soil moisture variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions using the proposed soil moisture

  13. Temporal Dynamics of Soil Moisture Variability: Implications For Land Surface Models

    Science.gov (United States)

    Montaldo, N.; Albertson, J. D.

    Meteorological and hydrological forecasting models share soil moisture as a critical boundary condition. Partitioning of received energy at the land surface depends di- rectly on this variable, as does the partitioning of rainfall into its possible routes over and through the soil. In Land Surface Models (LSMs) the temporal dynamic of soil moisture spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations soil moisture values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on soil moisture, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, mi- crobial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the soil moisture. But soil moisture variances af- fect flux predictions, which depend nonlinearly on soil moisture, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on soil moisture. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a con- servation equation for the horizontal variance of soil moisture. And, through closure arguments, we are able to describe the individual terms that produce and destroy spa- tial variance through time in terms of the mean soil moisture state and other observable system properties such as vegetation and soil properties variability. Finally, we calcu- late land surface fluxes from second order Taylor expansion, using our soil moisture variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions us- ing the proposed

  14. Development of the mechanical cryocooler system for the Sea Land Surface Temperature Radiometer

    Science.gov (United States)

    Camilletti, Adam; Burgess, Christopher; Donchev, Anton; Watson, Stuart; Weatherstone Akbar, Shane; Gamo-Albero, Victoria; Romero-Largacha, Victor; Caballero-Olmo, Gema

    2014-11-01

    The Sea Land Surface Temperature Radiometer is a dual view Earth observing instrument developed as part of the European Global Monitoring for Environment and Security programme. It is scheduled for launch on two satellites, Sentinel 3A and 3B in 2014. The instrument detectors are cooled to below 85 K by two split Stirling Cryocoolers running in hot redundancy. These coolers form part of a cryocooler system that includes a support structure and drive electronics. Aspects of the system design, including control and reduction of exported vibration are discussed; and results, including thermal performance and exported vibration from the Engineering Model Cryooler System test campaign are presented.

  15. On the effective inversion by imposing a priori information for retrieval of land surface parameters

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF models can be inverted to estimate the important biological or climatological parameters of the earth surface such as leaf area index and albedo. The state-of-the-art of BRDF is the use of the linear kernel-driven models, mathematically described as the linear combination of the isotropic kernel, volume scattering kernel and geometric optics kernel. The computational stability is characterized by the algebraic operator spectrum of the kernel-matrix and the observation errors. Therefore, the retrieval of the model coefficients is of great importance for computation of the land surface albedos. We first consider the smoothing solution method of the kernel-driven BRDF models for retrieval of land surface albedos. This is known as an ill-posed inverse problem. The ill-posedness arises from that the linear kernel driven BRDF model is usually underdetermined if there are too few looks or poor directional ranges, or the observations are highly dependent. For example, a single angular observation may lead to an under-determined system whose solution is infinite (the null space of the kernel operator contains nonzero vectors) or no solution (the rank of the coefficient matrix is not equal to the augmented matrix). Therefore, some smoothing or regularization technique should be applied to suppress the ill-posedness. So far, least squares error methods with a priori knowledge, QR decomposition method for inversion of the BRDF model and regularization theories for ill-posed inversion were developed. In this paper, we emphasize on imposing a priori information in different spaces. We first propose a gen-eral a priori imposed regularization model problem, and then address two forms of regularization scheme. The first one is a regularized singular value decomposition

  16. Galactic cosmic ray and El Nino Southern Oscillation trends in International Satellite Cloud Climatology Project D2 low-cloud properties

    DEFF Research Database (Denmark)

    Marsh, N.; Svensmark, Henrik

    2003-01-01

    -cloud properties over the period July 1983 to August 1994 suggests that low clouds are statistically related to two processes, (1) GCR and (2) El Nino-Southern Oscillation (ENSO), with GCR explaining a greater percentage of the total variance. Areas where satellites have an unobstructed view of low cloud possess...

  17. Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

    Science.gov (United States)

    Raoult, Nina M.; Jupp, Tim E.; Cox, Peter M.; Luke, Catherine M.

    2016-08-01

    Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

  18. A Satellite Time Slots Climatology of the Urban Heat Island of Guadalajara Megacity in Mexico from NOAA ¡/AVHRR THERMAL Infrared Monitoring (TIR)

    Science.gov (United States)

    Galindo, I.

    2009-04-01

    The urban heat island (UHI) of the metropolitan area of the second megacity of Mexico, named Guadalajara in Mexico is studied using thermal infrared data (TIR) (10 £ l £ 12 mm) obtained from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA polar orbitters whose signals are received on real time at our ground station for the period 1996-2006. The TIR data are selected by means of a software, since they depend on natural causes (e.g., atmospheric transparency, surface temperature, spectral emissivity and topography) and observational (time and incidence angle of the satellite pass, season of the year, etc.). The above conditions have a variable contribution to the measurements which it can be so high that they simulate the temporal-space fluctuations considered as thermal anomalies. Using a Geographic Information System and spatial analysis techniques temperatures are obtained for diofferent times of the day (satellite slots) and dropped into a grid with a 2.5 km distance between points (latitude, longitude). The temperatures obtained for each satellite pass slot (four per day) are monthly averaged and the temperature anomalies are represented in thermal isolines for the study area. The temperature difference usually is larger at night than during the day, reaching a thermal gradient of 9 °C.

  19. A closer look at the climatological discontinuities present in the NCEP/NCAR reanalysis temperature due to the introduction of satellite data

    Energy Technology Data Exchange (ETDEWEB)

    Sturaro, G. [Institute of Atmospheric Sciences and Climate, CNR-ISAC, Unita Operativa Clima e Microclima, Corso Stati Uniti, 4 I-35127 Padova (Italy)

    2003-09-01

    Principal component analysis was applied to NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalyses data for monthly temperature at given pressure levels between 1948-2000. The series composed with the time coefficients of the main components were tested for possible discontinuities. The study proved useful in gaining a better understanding of the impact of satellite observations in the reanalyses. The period 1975-1979 proved to be the most affected by inhomogeneities, in particular in August-September 1976 and December 1978-January 1979. The latter time corresponds with the introduction of satellite infrared and microwave retrievals, which gave global coverage to the observing network. Inhomogeneities due to satellite data especially affect patterns in the tropics for levels between 700 and 50 hPa and over the Southern Ocean in the layer 500 to 250 hPa, i.e. the affected regions are larger than previously determined with other methods. Greatest shifts were observed in the tropics at 100 and 150 hPa, where the discontinuity is equal to 1.6-2.0 standard deviations. (orig.)

  20. Long-term MAX-DOAS network observations of NO2 in Russia and Asia (MADRAS) during the period 2007-2012: instrumentation, elucidation of climatology, and comparisons with OMI satellite observations and global model simulations

    Science.gov (United States)

    Kanaya, Y.; Irie, H.; Takashima, H.; Iwabuchi, H.; Akimoto, H.; Sudo, K.; Gu, M.; Chong, J.; Kim, Y. J.; Lee, H.; Li, A.; Si, F.; Xu, J.; Xie, P.-H.; Liu, W.-Q.; Dzhola, A.; Postylyakov, O.; Ivanov, V.; Grechko, E.; Terpugova, S.; Panchenko, M.

    2014-08-01

    We conducted long-term network observations using standardized Multi-Axis Differential optical absorption spectroscopy (MAX-DOAS) instruments in Russia and ASia (MADRAS) from 2007 onwards and made the first synthetic data analysis. At seven locations (Cape Hedo, Fukue and Yokosuka in Japan, Hefei in China, Gwangju in Korea, and Tomsk and Zvenigorod in Russia) with different levels of pollution, we obtained 80 927 retrievals of tropospheric NO2 vertical column density (TropoNO2VCD) and aerosol optical depth (AOD). In the technique, the optimal estimation of the TropoNO2VCD and its profile was performed using aerosol information derived from O4 absorbances simultaneously observed at 460-490 nm. This large data set was used to analyze NO2 climatology systematically, including temporal variations from the seasonal to the diurnal scale. The results were compared with Ozone Monitoring Instrument (OMI) satellite observations and global model simulations. Two NO2 retrievals of OMI satellite data (NASA ver. 2.1 and Dutch OMI NO2 (DOMINO) ver. 2.0) generally showed close correlations with those derived from MAX-DOAS observations, but had low biases of up to ~50%. The bias was distinct when NO2 was abundantly present near the surface and when the AOD was high, suggesting a possibility of incomplete accounting of NO2 near the surface under relatively high aerosol conditions for the satellite observations. Except for constant biases, the satellite observations showed nearly perfect seasonal agreement with MAX-DOAS observations, suggesting that the analysis of seasonal features of the satellite data were robust. Weekend reduction in the TropoNO2VCD found at Yokosuka and Gwangju was absent at Hefei, implying that the major sources had different weekly variation patterns. While the TropoNO2VCD generally decreased during the midday hours, it increased exceptionally at urban/suburban locations (Yokosuka, Gwangju, and Hefei) during winter. A global chemical transport model, MIROC

  1. Advanced microwave forward model for the land surface data assimilation

    Science.gov (United States)

    Park, Chang-Hwan; Pause, Marion; Gayler, Sebastian; Wollschlaeger, Ute; Jackson, Thomas J.; LeDrew, Ellsworth; Behrendt, Andreas; Wulfmeyer, Volker

    2015-04-01

    From local to global scales, microwave remote-sensing techniques can provide temporally and spatially highly resolved observations of land surface properties including soil moisture and temperature as well as the state of vegetation. These variables are critical for agricultural productivity and water resource management. Furthermore, having accurate information of these variables allows us to improve the performances of numerical weather forecasts and climate prediction models. However, it is challenging to translate a measured brightness temperature into the multiple land surface properties because of the inherent inversion problem. In this study, we introduce a novel forward model for microwave remote sensing to resolve this inversion problem and to close the gap between land surface modeling and observations. It is composed of the Noah-MP land surface model as well as new models for the dielectric mixing and the radiative transfer. For developing a realistic forward operator, the land surface model must simulate soil and vegetation processes properly. The Noah-MP land surface model provides an excellent starting point because it contains already a sophisticated soil texture and land cover data set. Soil moisture transport is derived using the Richards equation in combination with a set of soil hydraulic parameters. Vegetation properties are considered using several photosynthesis models with different complexity. The energy balance is closed for the top soil and the vegetation layers. The energy flux becomes more realistic due to including not only the volumetric ratio of land surface properties but also their surface fraction as sub-grid scale information (semitile approach). Dielectric constant is the fundamental link to quantify the land surface properties. Our physical based new dielectric-mixing model is superior to previous calibration and semi-empirical approaches. Furthermore, owing to the consideration of the oversaturated surface dielectric behaviour

  2. A framework for global diurnally-resolved observations of Land Surface Temperature

    Science.gov (United States)

    Ghent, Darren; Remedios, John

    2014-05-01

    Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Being a key boundary condition in land surface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within Global models, the requirement for global diurnal observations of LST is well founded. Earth Observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. Here we present a framework for the production of global, diurnally resolved, data sets for LST which is a key request from users of LST data. We will show how the sampling of both geostationary and low earth orbit data sets could conceptually be employed to build combined, multi-sensor, pole-to-pole data sets. Although global averages already exist for individual instruments and merging of geostationary based LST is already being addressed operationally (Freitas, et al., 2013), there are still a number of important challenges to overcome. In this presentation, we will consider three of the issues still open in LST remote sensing: 1) the consistency amongst retrievals; 2) the clear-sky bias and its quantification; and 3) merging methods and the propagation of uncertainties. For example, the combined use of both geostationary earth orbit (GEO) and low earth orbit (LEO) data, and both infra-red and microwave data are relatively unexplored but are necessary to make the most progress. Hence this study will suggest what is state-of-the-art and how considerable advances can be made, accounting also for recent improvements in techniques and data quality. The GlobTemperature initiative under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013

  3. Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

    Science.gov (United States)

    Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

    2017-06-01

    Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with

  4. Land use changes and its impact on land surface temperature of Yancheng City from 2000 to 2009 analysis

    Science.gov (United States)

    Wang, Xinghan

    2014-02-01

    In the paper, based on the technology of remote sensing and geographic information system, and according to the Landsat TM images obtained the land use database and land surface temperature of Yancheng city in the year of 2000 and 2009. Five land use types were identified, namely: farmland, building site, forest and grassland, water, and beach wetland. And then analysis of the urban expansion model based on the Defense Meteorological satellite data. The results show that: (1) In the five kinds of land use types, the largest rate of land use change is beach wetland, which is -8.23, followed by water as -5.17, forest and grassland is 3.27, building site is 2.24, farmland is 0.69. (2) During the 2000-2009, the towns of Yancheng city continuous outward expansion. In the old town, the expansion model is similar to the concentric circles spread to the periphery, but in the new district, which mainly concentrated in the northeast and southeast, the expansion model is re-planning, development and construction. (3) The land use structure change, especially the changes of beach wetland have a largest influence on the land surface temperature of Yancheng city. Among them, the average land surface temperature has increased over 8 degrees. However, the farmland change due to the overall land surface temperature decreased. And the increase of building site, making the urban heat island effect has been enhanced, while the town where the land surface temperature increases in value added in 0 to 5 degrees. At the same time, the water changes, this due to the land surface temperature increases and the added value in the range of 5 to 8 degrees.

  5. Historical Climatology Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Historical Climatology Series (HCS) is a set of climate-related publications published by NOAA's National Climatic Data Center beginning in 1978. HCS is...

  6. Preliminary Monthly Climatological Summaries

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary Local Climatological Data, recorded since 1970 on Weather Burean Form 1030 and then National Weather Service Form F-6. The preliminary climate data pages...

  7. Global Synoptic Climatology Network

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Global Synoptic Climatology Network is a digital data set archived at the National Climatic Data Center (NCDC). This record combines the various types of data that...

  8. Climatological Data National Summary

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The CDNS was published from 1950 - 1980. Monthly and annual editions contain summarized climatological information from the following publications: Local...

  9. Climatological Services Memorandums

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Climatological Services Memorandums were a series of memoranda issued by the Weather Bureau for the purpose of keeping all stations informed on the status and...

  10. Reference Climatological Stations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Reference Climatological Stations (RCS) network represents the first effort by NOAA to create and maintain a nationwide network of stations located only in areas...

  11. Validation of Land Surface Temperature products in arid climate regions with permanent in-situ measurements

    Science.gov (United States)

    Goettsche, F.; Olesen, F.; Trigo, I.; Hulley, G. C.

    2013-12-01

    Land Surface Temperature (LST) is operationally obtained from several space-borne sensors, e.g. from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) by the Land Surface Analysis - Satellite Application Facility (LSA-SAF) and from the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-Terra by the MODIS Land Team. The relative accuracy of LST products can be assessed by cross-validating different products. Alternatively, the so-called 'radiance based validation' can be used to compare satellite-retrieved LST with results from radiative transfer models: however, this requires precise a priori knowledge of land surface emissivity (LSE) and atmospheric conditions. Ultimately, in-situ measurements (';ground truth') are needed for validating satellite LST&E products. Therefore, the LST product derived by LSA-SAF is validated with independent in-situ measurements (';temperature based validation') at permanent validation stations located in different climate regions on the SEVIRI disk. In-situ validation is largely complicated by the spatial scale mismatch between satellite sensors and ground based sensors, i.e. areas observed by ground radiometers usually cover about 10 m2, whereas satellite measurements in the thermal infrared typically cover between 1 km2 and 100 km2. Furthermore, an accurate characterization of the surface is critical for all validation approaches, but particularly over arid regions, as shown by in-situ measurements revealing that LSE products can be wrong by more than 3% [1]. The permanent stations near Gobabeb (Namibia; hyper-arid desert climate) and Dahra (Senegal; hot-arid steppe-prairie climate) are two of KIT's four dedicated LST validation stations. Gobabeb station is located on vast and flat gravel plains (several 100 km2), which are mainly covered by coarse gravel, sand, and desiccated grass. The gravel plains are highly homogeneous in space and time, which makes them ideal for

  12. GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring.

    Science.gov (United States)

    Motte, Erwan; Zribi, Mehrez; Fanise, Pascal; Egido, Alejandro; Darrozes, José; Al-Yaari, Amen; Baghdadi, Nicolas; Baup, Frédéric; Dayau, Sylvia; Fieuzal, Remy; Frison, Pierre-Louis; Guyon, Dominique; Wigneron, Jean-Pierre

    2016-05-20

    Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than -15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than -30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

  13. Determining Land Surface Temperature Relations with Land Use-Land Cover and Air Pollution

    Science.gov (United States)

    Kahya, Ceyhan; Bektas Balcik, Filiz; Burak Oztaner, Yasar; Guney, Burcu

    2016-04-01

    Rapid population growth in conjunction with unplanned urbanization, expansion, and encroachment into the limited agricultural fields and green areas have negative impacts on vegetated areas. Land Surface Temperature (LST), Urban Heat Islands (UHI) and air pollution are the most important environmental problems that the extensive part of the world suffers from. The main objective of this research is to investigate the relationship between LST, air pollution and Land Use-Land Cover (LULC) in Istanbul, using Landsat 8 OLI satellite image. Mono-window algorithm is used to compute LST from Landsat 8 TIR data. In order to determine the air pollution, in-situ measurements of particulate matter (PM10) of the same day as the Landsat 8 OLI satellite image are obtained. The results of this data are interpolated using the Inverse Distance Weighted (IDW) method and LULC categories of Istanbul were determined by using remote sensing indices. Error matrix was created for accuracy assessment. The relationship between LST, air pollution and LULC categories are determined by using regression analysis method. Keywords: Land Surface Temperature (LST), air pollution, Land Use-Land Cover (LULC), Istanbul

  14. GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring

    Directory of Open Access Journals (Sweden)

    Erwan Motte

    2016-05-01

    Full Text Available Global Navigation Satellite System-Reflectometry (GNSS-R has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI, dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

  15. Long-term MAX-DOAS network observations of NO2 in Russia and Asia (MADRAS during 2007–2012: instrumentation, elucidation of climatology, and comparisons with OMI satellite observations and global model simulations

    Directory of Open Access Journals (Sweden)

    Y. Kanaya

    2014-01-01

    Full Text Available We conducted long-term network observations using standardized Multi-Axis Differential optical absorption spectroscopy (MAX-DOAS instruments in Russia and ASia (MADRAS from 2007 onwards. At seven locations (Cape Hedo, Fukue, and Yokosuka in Japan, Hefei in China, Gwangju in Korea, and Tomsk and Zvenigorod in Russia with different levels of pollution, we obtained 80 927 retrievals of tropospheric NO2 vertical column density (TropoNO2VCD and aerosol optical depth (AOD. In the technique, the optimal estimation of the TropoNO2VCD and its profile was performed using aerosol information derived from O4 absorbances simultaneously observed at 460–490 nm. This large data set was used to analyze NO2 climatology systematically, including temporal variations from the seasonal to the diurnal scale. The results were compared with Ozone Monitoring Instrument (OMI satellite observations and global model simulations. Two NO2 retrievals of OMI satellite data (NASA ver. 2.1 and Dutch OMI NO2 (DOMINO ver. 2.0 generally showed close correlations with those derived from MAX-DOAS observations, but had low biases of ~50%. The bias was distinct when NO2 was abundantly present near the surface and when the AOD was high, suggesting that the aerosol shielding effect could be important, especially for clean sites where the difference could not be attributed to the spatial inhomogeneity. Except for constant biases, the satellite observations showed nearly perfect seasonal agreement with MAX-DOAS observations, suggesting that the analysis of seasonal features of the satellite data were robust. The prevailing seasonal patterns with a wintertime maximum implied the dominance of anthropogenic emissions around our sites. The presence of weekend reductions at Yokosuka and Gwangju suggested the dominance of emissions from diesel vehicles, with significant weekly cycles, whereas the absence of such a reduction at Hefei suggested the importance of other sources. A global chemical

  16. Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

    Science.gov (United States)

    Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

    2011-01-01

    The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

  17. Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

    Science.gov (United States)

    Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

    2011-01-01

    The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

  18. Determining Land-Surface Parameters from the ERS Wind Scatterometer

    NARCIS (Netherlands)

    Woodhouse, I.H.; Hoekman, D.H.

    2000-01-01

    The ERS-1 wind scatterometer (WSC) has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. In order to retrieve quantitative geophysical parameters over land surfaces using this instrument, a method is presen

  19. estimation of land surface temperature of kaduna metropolis, nigeria

    African Journals Online (AJOL)

    Zaharaddeen et. al

    Understanding the spatial variation of Land Surface Temperature. (LST), will be ... positive correlation between mean of surface emissivity with date and ... deviation of 1.92 of LST and coefficient determinant R2 (0.46) show a ... (LST), as the prime and basic physical parameter of the earth's ..... thorough review of the paper.

  20. Object-based Dimensionality Reduction in Land Surface Phenology Classification

    Directory of Open Access Journals (Sweden)

    Brian E. Bunker

    2016-11-01

    Full Text Available Unsupervised classification or clustering of multi-decadal land surface phenology provides a spatio-temporal synopsis of natural and agricultural vegetation response to environmental variability and anthropogenic activities. Notwithstanding the detailed temporal information available in calibrated bi-monthly normalized difference vegetation index (NDVI and comparable time series, typical pre-classification workflows average a pixel’s bi-monthly index within the larger multi-decadal time series. While this process is one practical way to reduce the dimensionality of time series with many hundreds of image epochs, it effectively dampens temporal variation from both intra and inter-annual observations related to land surface phenology. Through a novel application of object-based segmentation aimed at spatial (not temporal dimensionality reduction, all 294 image epochs from a Moderate Resolution Imaging Spectroradiometer (MODIS bi-monthly NDVI time series covering the northern Fertile Crescent were retained (in homogenous landscape units as unsupervised classification inputs. Given the inherent challenges of in situ or manual image interpretation of land surface phenology classes, a cluster validation approach based on transformed divergence enabled comparison between traditional and novel techniques. Improved intra-annual contrast was clearly manifest in rain-fed agriculture and inter-annual trajectories showed increased cluster cohesion, reducing the overall number of classes identified in the Fertile Crescent study area from 24 to 10. Given careful segmentation parameters, this spatial dimensionality reduction technique augments the value of unsupervised learning to generate homogeneous land surface phenology units. By combining recent scalable computational approaches to image segmentation, future work can pursue new global land surface phenology products based on the high temporal resolution signatures of vegetation index time series.

  1. Status of the Sea & Land Surface Temperature Radiometer (SLSTR) for the Sentinel 3 GMES Mission

    Science.gov (United States)

    Coppo, Peter; Cosi, Massimo; Engel, Wolfgang; Nieke, Jens; Smith, Dave; Bianchi, Stephane

    2010-10-01

    The Sea & Land Surface Temperature Radiometer (SLSTR) is a high accuracy infrared radiometer selected as optical payload for the Sentinel 3 component of the GMES mission, to provide climatological data continuity respect to the previous ERS and ESA Envisat missions, that embarked respectively the ATSR, ATSR-2 and AATSR payloads. The instrument design follows the dual view concept of the ATSR series with some notable improvements. An increased swath width in both nadir and oblique views (1400 and 740 km) provides measurements at global coverage of Sea and Land Surface Temperature (SST/LST) with daily revisit times, which is useful for climate and meteorology (1 Km spatial resolution). Improved day-time cloud screening and other atmospheric products will be possible from the increased spatial resolution (0.5 Km) of the VIS and SWIR channels and additional SWIR channels at 1.375μm and 2.25μm. Two additional channels using dedicated detector and electronics elements are also included for high temperature events monitoring (1 km spatial resolution). The two Earth viewing swaths are generated using two telescopes and scan mirrors that are optically combined by means of a switching mirror at the entrance of a common Focal Plane Assembly. The eleven spectral channels (3 VIS, 3 SWIR, 2 MWIR, 3 TIR) are split within the FPA using a series of dichroics. The SWIR, MWIR and TIR optics/detectors are cooled down to 80 K with an active cryocooler, while the VIS detectors work at a stabilised uncooled temperature. The paper highlights the technical and programmatic status of the project, which is now in phase C.

  2. Land Surface Temperature retrieval from Sentinel 2 and 3 Missions: a conceptual framework

    Science.gov (United States)

    Sobrino, J. A.; Jimenez-Muñoz, J. C.; Ruescas, A.; Brockmann, C.; Heckel, A.; North, P. R. J.; Remedios, J. J.; Darren, G.; Merchant, C.; Berger, M.; Soria, G.; Danne, O.

    2012-04-01

    Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all the surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. Because of the strong heterogeneity in land surface characteristics such as vegetation, topography and soil physical properties, LST changes rapidly in space as well as in time. An adequate characterization of LST distribution and its temporal evolution, therefore, requires measurements with detailed spatial and temporal frequencies. With the advent of the ESA's Sentinel 2 and 3 series of satellites a unique opportunity exists to go beyond the current state of the art of single instrument algorithms. In this work we explore the synergistic use of future MSI instrument on board Sentinel-2 platform and OLCI/SLSTR instruments on board Sentinel-3 platform in order to improve LST products currently derived from the single AATSR instrument on board the ENVISAT satellite. For this purpose, the high spatial resolution data from Sentinel2/MSI will be used for a good characterization of the land surface sub-pixel heterogeneity, in particular for a precise parameterization of surface emissivity using a land cover map and spectral mixture techniques. On the other hand, the high spectral resolution of OLCI instrument, suitable for a better characterization of the atmosphere, along with the dual-view available in the SLTSR instrument, will allow a better atmospheric correction through improved aerosol/water vapor content retrievals and the implementation of novel cloud screening procedures. Effective emissivity and atmospheric corrections will allow accurate LST retrievals using the SLTSR thermal bands by developing a synergistic split-window/dual-angle algorithm. ENVISAT MERIS and AATSR instruments and different high spatial resolution data (Landsat/TM, Proba/CHRIS, Terra/ASTER) will be used as a benchmark for the future OLCI

  3. Missing North Atlantic cyclonic precipitation in ECMWF numerical weather prediction and ERA-40 data detected through the satellite climatology HOAPS II

    Energy Technology Data Exchange (ETDEWEB)

    Klepp, C.P.; Bakan, S.; Grassl, H. [Max-Planck Inst. fuer Meteorologie and Meteorologisches Inst., Univ. Hamburg (Germany)

    2005-12-01

    Intense precipitation associated with wintertime North Atlantic cyclones occurs not only in connection with frontal zones but also, and often mainly, embedded in strong cold air outbreaks to the west of mature cold fronts. Coherent structures of cloud clusters organized in mesoscale postfrontal low-pressure systems are frequently found in satellite data. Such postfrontal lows (PFL) can develop into severe weather events within few hours and can even reach Europe causing intense convective rainfall and gale force winds. Despite predicting the major storm systems numerical weather prediction (NWP) additionally needs to account for PFLs due to their frequent occurrence connected with high impact weather. But while the major cyclone systems are mostly well predicted, the forecast of PFLs remains poor. Using North Atlantic weather observations from the 1997 fronts and Atlantic storm track experiment (FASTEX) along with the standard voluntary observing ship (VOS) data led to a high quality validation data set for this usually data sparse region. For individual case studies of FASTEX cyclones with mesoscale PFLs investigations were carried out using the well calibrated precipitation estimates from HOAPS (Hamburg Ocean Atmosphere Parameters and fluxes from satellite data) compared to the NWP model output of the ECMWF (European Centre for medium-range weather forecasts). Preceding studies showed that the HOAPS precipitation structure and intensities are in good agreement with the VOS observations for all observed precipitation types within the cyclones, including PFLs. To assure that the results found in the 1997 data are still valid in the more recent ECMWF model system, a PFL rainfall comparison is carried out using HOAPS and ERA-40 (ECMWF Re-Analysis) data for the winter of 2001 and 2002. The results indicate that the ECMWF model is mostly well reproducing precipitation structures and intensities associated with frontal systems as observed in the VOS and HOAPS data

  4. New dynamic NNORSY ozone profile climatology

    Directory of Open Access Journals (Sweden)

    A. K. Kaifel

    2012-01-01

    Full Text Available Climatological ozone profile data are widely used as a-priori information for total ozone using DOAS type retrievals as well as for ozone profile retrieval using optimal estimation, for data assimilation or evaluation of 3-D chemistry-transport models and a lot of other applications in atmospheric sciences and remote sensing. For most applications it is important that the climatology represents not only long term mean values but also the links between ozone and dynamic input parameters. These dynamic input parameters should be easily accessible from auxiliary datasets or easily measureable, and obviously should have a high correlation with ozone. For ozone profile these parameters are mainly total ozone column and temperature profile data. This was the outcome of a user consultation carried out in the framework of developing a new, dynamic ozone profile climatology.

    The new ozone profile climatology is based on the Neural Network Ozone Retrieval System (NNORSY widely used for ozone profile retrieval from UV and IR satellite sounder data. NNORSY allows implicit modelling of any non-linear correspondence between input parameters (predictors and ozone profile target vector. This paper presents the approach, setup and validation of a new family of ozone profile climatologies with static as well as dynamic input parameters (total ozone and temperature profile. The neural network training relies on ozone profile measurement data of well known quality provided by ground based (ozonesondes and satellite based (SAGE II, HALOE, and POAM-III measurements over the years 1995–2007. In total, four different combinations (modes for input parameters (date, geolocation, total ozone column and temperature profile are available.

    The geophysical validation spans from pole to pole using independent ozonesonde, lidar and satellite data (ACE-FTS, AURA-MLS for individual and time series comparisons as well as for analysing the vertical and meridian

  5. HadISDH: an updateable land surface specific humidity product for climate monitoring

    Directory of Open Access Journals (Sweden)

    K. M. Willett

    2013-03-01

    Full Text Available HadISDH is a near-global land surface specific humidity monitoring product providing monthly means from 1973 onwards over large-scale grids. Presented herein to 2012, annual updates are anticipated. HadISDH is an update to the land component of HadCRUH, utilising the global high-resolution land surface station product HadISD as a basis. HadISD, in turn, uses an updated version of NOAA's Integrated Surface Database. Intensive automated quality control has been undertaken at the individual observation level, as part of HadISD processing. The data have been subsequently run through the pairwise homogenisation algorithm developed for NCDC's US Historical Climatology Network monthly temperature product. For the first time, uncertainty estimates are provided at the grid-box spatial scale and monthly timescale. HadISDH is in good agreement with existing land surface humidity products in periods of overlap, and with both land air and sea surface temperature estimates. Widespread moistening is shown over the 1973–2012 period. The largest moistening signals are over the tropics with drying over the subtropics, supporting other evidence of an intensified hydrological cycle over recent years. Moistening is detectable with high (95% confidence over large-scale averages for the globe, Northern Hemisphere and tropics, with trends of 0.089 (0.080 to 0.098 g kg−1 per decade, 0.086 (0.075 to 0.097 g kg−1 per decade and 0.133 (0.119 to 0.148 g kg−1 per decade, respectively. These changes are outside the uncertainty range for the large-scale average which is dominated by the spatial coverage component; station and grid-box sampling uncertainty is essentially negligible on large scales. A very small moistening (0.013 (−0.005 to 0.031 g kg−1 per decade is found in the Southern Hemisphere, but it is not significantly different from zero and uncertainty is large. When globally averaged, 1998 is the moistest year since monitoring began in 1973, closely

  6. HadISDH: an updateable land surface specific humidity product for climate monitoring

    Science.gov (United States)

    Willett, K. M.; Williams, C. N., Jr.; Dunn, R. J. H.; Thorne, P. W.; Bell, S.; de Podesta, M.; Jones, P. D.; Parker, D. E.

    2013-03-01

    HadISDH is a near-global land surface specific humidity monitoring product providing monthly means from 1973 onwards over large-scale grids. Presented herein to 2012, annual updates are anticipated. HadISDH is an update to the land component of HadCRUH, utilising the global high-resolution land surface station product HadISD as a basis. HadISD, in turn, uses an updated version of NOAA's Integrated Surface Database. Intensive automated quality control has been undertaken at the individual observation level, as part of HadISD processing. The data have been subsequently run through the pairwise homogenisation algorithm developed for NCDC's US Historical Climatology Network monthly temperature product. For the first time, uncertainty estimates are provided at the grid-box spatial scale and monthly timescale. HadISDH is in good agreement with existing land surface humidity products in periods of overlap, and with both land air and sea surface temperature estimates. Widespread moistening is shown over the 1973-2012 period. The largest moistening signals are over the tropics with drying over the subtropics, supporting other evidence of an intensified hydrological cycle over recent years. Moistening is detectable with high (95%) confidence over large-scale averages for the globe, Northern Hemisphere and tropics, with trends of 0.089 (0.080 to 0.098) g kg-1 per decade, 0.086 (0.075 to 0.097) g kg-1 per decade and 0.133 (0.119 to 0.148) g kg-1 per decade, respectively. These changes are outside the uncertainty range for the large-scale average which is dominated by the spatial coverage component; station and grid-box sampling uncertainty is essentially negligible on large scales. A very small moistening (0.013 (-0.005 to 0.031) g kg-1 per decade) is found in the Southern Hemisphere, but it is not significantly different from zero and uncertainty is large. When globally averaged, 1998 is the moistest year since monitoring began in 1973, closely followed by 2010, two strong El

  7. NUMERICAL SIMULATIONS OF EFFECTS OF LAND SURFACE PROCESSES ON CLIMATE——IMPLEMENTING OF SSiB IN IAP/LASG AGCM AND ITS PERFORMANCE

    Institute of Scientific and Technical Information of China (English)

    孙岚; 吴国雄; 孙菽芬

    2001-01-01

    This is an investigation of exchanges of energy and water between the atmosphere and the vegetated continents, and the impact of and mechanisms for land surface-atmosphere interactions on hydrological cycle and general circulation by implementing the Simplified Simple Biosphere (SSiB) model in a modified version of IAP/LASG global spectral general model (L9R15 AGCM).This study reveals that the SSiB model produces a better partitioning of the land surface heat and moisture fluxes and its diurnal variations, and also gives the transport of energy and water among atmosphere, vegetation and soil explicitly and realistically. Thus the coupled SSiB-AGCM runs lead to the more conspicuous improvement in the simulated circulation, precipitation, mean water vapor content and its transport, particularly in the Asian monsoon region in the real world than CTL-AGCM runs. It is also pointed out that both the implementation of land surface parameterizations and the variations in land surface into the GOALS model have greatly improved hydrological balance over continents and have a significant impact on the simulated climate,particularly over the massive continents.Improved precipitation recycling model was employed to verify the mechanisms for land surface hydrology parameterizations on hydrological cycle and precipitation climatology in AGCM.It can be argued that the recycling precipitation rate is significantly reduced, particularly in the arid and semi-arid region of the boreal summer hemisphere, coincident with remarkable reduction in evapotranspiration over the continental area. Therefore the coupled SSiB-AGCM runs reduce the bias of too much precipitation over land surface in most AGCMs, thereby bringing the simulated precipitation closer to observations in many continental regions of the world than CTL-AGCM runs.

  8. Improving land surface emissivty parameter for land surface models using portable FTIR and remote sensing observation in Taklimakan Desert

    Science.gov (United States)

    Liu, Yongqiang; Mamtimin, Ali; He, Qing

    2014-05-01

    Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply assumption, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, 0.96 for soil and wetland in the Global and Regional Assimilation and Prediction System (GRAPES) Common Land Model (CoLM). This is the so-called emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the emissivity induces errors in modeling the surface energy budget over Taklimakan Desert where ɛ is far smaller than original value. One feasible solution to this problem is to apply the accurate broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity required by land surface models. In order to calibrate the regression equations, using a portable Fourier Transform infrared (FTIR) spectrometer instrument, crossing Taklimakan Desert along with highway from north to south, to measure the accurate broadband emissivity. The observed emissivity data show broadband ɛ around 0.89-0.92. To examine the impact of improved ɛ to radiative energy redistribution, simulation studies were conducted using offline CoLM. The results illustrate that large impacts of surface ɛ occur over desert, with changes up in surface skin temperature, as well as evident changes in sensible heat fluxes. Keywords: Taklimakan Desert, surface broadband emissivity, Fourier Transform infrared spectrometer, MODIS, CoLM

  9. Simulation and validation of land surface temperature algorithms for MODIS and AATSR data

    Directory of Open Access Journals (Sweden)

    J. M. Galve

    2007-01-01

    Full Text Available A database of global, cloud-free, atmospheric radiosounding profiles was compiled with the aim of simulating radiometric measurements from satellite-borne sensors in the thermal infrared. The objective of the simulation was to use Terra/Moderate Resolution Imaging Spectroradiometer (MODIS and Envisat/Advanced Along Track Scanning Radiometer (AATSR data to generate split-window (SW and dual-angle (DA algorithms for the retrieval of land surface temperature (LST. The database contains 382 radiosounding profiles acquired from land surfaces, with an almost uniform distribution of precipitable water between 0 and 5.5 cm. Radiative transfer calculations were performed with the MODTRAN 4 code for six different viewing angles between 0 and 65º. The resulting radiance spectra were integrated with the response filter functions of MODIS bands 31 and 32 and AATSR channels at 11 and 12 μm. Using the simulation database, SW algorithms adapted for MODIS and AATSR data, and DA algorithms for AATSR data were developed. Both types of algorithms are quadratic in the brightness temperature difference, and depend explicitly on the land surface emissivity. These SW and DA algorithms were validated with actual ground measurements of LST collected concurrently with MODIS and AATSR observations in a large, flat and thermally homogeneous area of rice crops located close to the city of Valencia, Spain. The results were not bias and had a standard deviation of around ± 0.5 K for SW algorithms at the nadir of both sensors; the SW algorithm used in the forward view resulted in a bias of 0.5 K and a standard deviation of ± 0.8 K. The least accurate results were obtained in the DA algorithms with a bias close to -2.0 K and a standard deviation of almost ± 1.0 K.

  10. Altitude-dependent influence of snow cover on alpine land surface phenology

    Science.gov (United States)

    Xie, Jing; Kneubühler, Mathias; Garonna, Irene; Notarnicola, Claudia; De Gregorio, Ludovica; De Jong, Rogier; Chimani, Barbara; Schaepman, Michael E.

    2017-05-01

    Snow cover impacts alpine land surface phenology in various ways, but our knowledge about the effect of snow cover on alpine land surface phenology is still limited. We studied this relationship in the European Alps using satellite-derived metrics of snow cover phenology (SCP), namely, first snow fall, last snow day, and snow cover duration (SCD), in combination with land surface phenology (LSP), namely, start of season (SOS), end of season, and length of season (LOS) for the period of 2003-2014. We tested the dependency of interannual differences (Δ) of SCP and LSP metrics with altitude (up to 3000 m above sea level) for seven natural vegetation types, four main climatic subregions, and four terrain expositions. We found that 25.3% of all pixels showed significant (p < 0.05) correlation between ΔSCD and ΔSOS and 15.3% between ΔSCD and ΔLOS across the entire study area. Correlations between ΔSCD and ΔSOS as well as ΔSCD and ΔLOS are more pronounced in the northern subregions of the Alps, at high altitudes, and on north and west facing terrain—or more generally, in regions with longer SCD. We conclude that snow cover has a greater effect on alpine phenology at higher than at lower altitudes, which may be attributed to the coupled influence of snow cover with underground conditions and air temperature. Alpine ecosystems may therefore be particularly sensitive to future change of snow cover at high altitudes under climate warming scenarios.

  11. Water Balance in the Amazon Basin from a Land Surface Model Ensemble

    Science.gov (United States)

    Getirana, Augusto C. V.; Dutra, Emanuel; Guimberteau, Matthieu; Kam, Jonghun; Li, Hong-Yi; Decharme, Bertrand; Zhang, Zhengqiu; Ducharne, Agnes; Boone, Aaron; Balsamo, Gianpaolo; Rodell, Matthew; Toure, Ally M.; Xue, Yongkang; Peters-Lidard, Christa D.; Kumar, Sujay V.; Arsenault, Kristi; Drapeau, Guillaume; Leung, L. Ruby; Ronchail, Josyane; Sheffield, Justin

    2014-01-01

    Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day(exp -1) and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

  12. Evaluation of ESTARFM based algorithm for generating land surface temperature products by fusing ASTER and MODIS data during the HiWATER-MUSOEXE

    Science.gov (United States)

    Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various alg...

  13. How you cannot find rain with changes in land surface temperature

    Science.gov (United States)

    Wanders, Niko

    2017-04-01

    Estimating precipitation from space-born sensors is valuable source of observation in poorly-gauged regions. For example, hydrological modelling and monitoring greatly benefits from the increased near-real time data availability for improved accuracy in the simulations of water resources. As is true for all satellite product, precipitation estimated from space are far from perfect and scientist have used many techniques to improve their accuracy. In this study, I tried to improve the space-born precipitation estimates by using remotely sensed soil moisture to observe sudden increases in soil wetness as a result of precipitation. After a month of massaging the data and applied methodology I realized that the gain was very marginal and I was drilling a dry hole. Driven by these disappointing results I tried some random other satellite products to see if they showed correlation with the precipitation signal. There I found a causality that I had not expected at the start of this study, linking land surface temperature to precipitation. It seemed that using changes in land surface temperature strongly correlated with precipitation totals, driven by a cooling of the soil as a result of increase wetness. This link could not only be modelled, but more surprisingly it could be observed from space and used to improve the satellite precipitation estimates. The reduction in the precipitation uncertainty was far better than for any of the three soil moisture products, contrary to what one might expect. This was far from the anticipated result but it showed me that sometimes you should think out of the box and not only use observations for their intended purpose. This experience has motivated me to not only use the obvious observation or method and try techniques and methods from other disciplines to see if we can improve our understanding of the hydrological cycle.

  14. SGP Cloud and Land Surface Interaction Campaign (CLASIC): Measurement Platforms

    Energy Technology Data Exchange (ETDEWEB)

    MA Miller; R Avissar; LK Berg; SA Edgerton; ML Fischer; TJ Jackson; B. Kustas; PJ Lamb; G McFarquhar; Q Min; B Schmid; MS Torn; DD Tuner

    2007-06-01

    The Cloud and Land Surface Interaction Campaign (CLASIC) will be conducted from June 8 to June 30, 2007, at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site. Data will be collected using eight aircraft equipped with a variety of specialized sensors, four specially instrumented surface sites, and two prototype surface radar systems. The architecture of CLASIC includes a high-altitude surveillance aircraft and enhanced vertical thermodynamic and wind profile measurements that will characterize the synoptic scale structure of the clouds and the land surface within the ACRF SGP site. Mesoscale and microscale structures will be sampled with a variety of aircraft, surface, and radar observations. An overview of the measurement platforms that will be used during the CLASIC are described in this report. The coordination of measurements, especially as it relates to aircraft flight plans, will be discussed in the CLASIC Implementation Plan.

  15. Use of Landsat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin Area, Turkey.

    Science.gov (United States)

    Orhan, Osman; Ekercin, Semih; Dadaser-Celik, Filiz

    2014-01-01

    The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX) were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2) values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2°C) in the Salt Lake Basin area during the 28-year period (1984-2011). Analysis of air temperature data also showed increases at a rate of 1.5-2°C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

  16. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey

    Directory of Open Access Journals (Sweden)

    Osman Orhan

    2014-01-01

    Full Text Available The main purpose of this paper is to investigate multitemporal land surface temperature (LST changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI, vegetation condition index (VCI, and temperature vegetation index (TVX were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2 values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2∘C in the Salt Lake Basin area during the 28-year period (1984–2011. Analysis of air temperature data also showed increases at a rate of 1.5–2∘C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

  17. Evaluating the use of sharpened land surface temperature for daily evapotranspiration estimation over irrigated crops in arid lands

    KAUST Repository

    Rosas, Jorge

    2014-12-01

    Satellite remote sensing provides data on land surface characteristics, useful for mapping land surface energy fluxes and evapotranspiration (ET). Land-surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of ET and surface moisture status. However, TIR imagery usually operates at a coarser resolution than that of shortwave sensors on the same satellite platform, making it sometimes unsuitable for monitoring of field-scale crop conditions. This study applies the data mining sharpener (DMS; Gao et al., 2012) technique to data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which sharpens the 1 km thermal data down to the resolution of the optical data (250-500 m) based on functional LST and reflectance relationships established using a flexible regression tree approach. The DMS approach adopted here has been enhanced/refined for application over irrigated farming areas located in harsh desert environments in Saudi Arabia. The sharpened LST data is input to an integrated modeling system that uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI) in conjunction with model reanalysis data and remotely sensed data from polar orbiting (MODIS) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate daily estimates of evapotranspiration. Results are evaluated against available flux tower observations over irrigated maize near Riyadh in Saudi Arabia. Successful monitoring of field-scale changes in surface fluxes are of importance towards an efficient water use in areas where fresh water resources are scarce and poorly monitored. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287-3319.

  18. Relating trends in land surface-air temperature difference to soil moisture and evapotranspiration

    Science.gov (United States)

    Veal, Karen; Taylor, Chris; Gallego-Elvira, Belen; Ghent, Darren; Harris, Phil; Remedios, John

    2016-04-01

    Soil water is central to both physical and biogeochemical processes within the Earth System. Drying of soils leads to evapotranspiration (ET) becoming limited or "water-stressed" and is accompanied by rises in land surface temperature (LST), land surface-air temperature difference (delta T), and sensible heat flux. Climate models predict sizable changes to the global water cycle but there is variation between models in the time scale of ET decay during dry spells. The e-stress project is developing novel satellite-derived diagnostics to assess the ability of Earth System Models (ESMs) to capture behaviour that is due to soil moisture controls on ET. Satellite records of LST now extend 15 years or more. MODIS Terra LST is available from 2000 to the present and the Along-Track Scanning Radiometer (ATSR) LST record runs from 1995 to 2012. This paper presents results from an investigation into the variability and trends in delta T during the MODIS Terra mission. We use MODIS Terra and MODIS Aqua LST and ESA GlobTemperature ATSR LST with 2m air temperatures from reanalyses to calculate trends in delta T and "water-stressed" area. We investigate the variability of delta T in relation to soil moisture (ESA CCI Passive Daily Soil Moisture), vegetation (MODIS Monthly Normalized Difference Vegetation Index) and precipitation (TRMM Multi-satellite Monthly Precipitation) and compare the temporal and spatial variability of delta T with model evaporation data (GLEAM). Delta T anomalies show significant negative correlations with soil moisture, in different seasons, in several regions across the planet. Global mean delta T anomaly is small (magnitude mostly less than 0.2 K) between July 2002 and July 2008 and decreases to a minimum in early 2010. The reduction in delta T anomaly coincides with an increase in soil moisture anomaly and NDVI anomaly suggesting an increase in evapotranspiration and latent heat flux with reduced sensible heat flux. In conclusion there have been

  19. Land-surface studies with a directional neutron detector.

    Energy Technology Data Exchange (ETDEWEB)

    Desilets, Darin (Sandia National Laboratories, Albuquerque, NM); Brennan, James S.; Mascarenhas, Nicholas; Marleau, Peter

    2009-09-01

    Direct measurements of cosmic-ray neutron intensity were recorded with a neutron scatter camera developed at SNL. The instrument used in this work is a prototype originally designed for nuclear non-proliferation work, but in this project it was used to characterize the response of ambient neutrons in the 0.5-10 MeV range to water located on or above the land surface. Ambient neutron intensity near the land surface responds strongly to the presence of water, suggesting the possibility of an indirect method for monitoring soil water content, snow water equivalent depth, or canopy intercepted water. For environmental measurements the major advantage of measuring neutrons with the scatter camera is the limited (60{sup o}) field of view that can be obtained, which allows observations to be conducted at a previously unattainable spatial scales. This work is intended to provide new measurements of directional fluxes which can be used in the design of new instruments for passively and noninvasively observing land-surface water. Through measurements and neutron transport modeling we have demonstrated that such a technique is feasible.

  20. Photosynthesis sensitivity to climate change in land surface models

    Science.gov (United States)

    Manrique-Sunen, Andrea; Black, Emily; Verhoef, Anne; Balsamo, Gianpaolo

    2016-04-01

    Accurate representation of vegetation processes within land surface models is key to reproducing surface carbon, water and energy fluxes. Photosynthesis determines the amount of CO2 fixated by plants as well as the water lost in transpiration through the stomata. Photosynthesis is calculated in land surface models using empirical equations based on plant physiological research. It is assumed that CO2 assimilation is either CO2 -limited, radiation -limited ; and in some models export-limited (the speed at which the products of photosynthesis are used by the plant) . Increased levels of atmospheric CO2 concentration tend to enhance photosynthetic activity, but the effectiveness of this fertilization effect is regulated by environmental conditions and the limiting factor in the photosynthesis reaction. The photosynthesis schemes at the 'leaf level' used by land surface models JULES and CTESSEL have been evaluated against field photosynthesis observations. Also, the response of photosynthesis to radiation, atmospheric CO2 and temperature has been analysed for each model, as this is key to understanding the vegetation response that climate models using these schemes are able to reproduce. Particular emphasis is put on the limiting factor as conditions vary. It is found that while at present day CO2 concentrations export-limitation is only relevant at low temperatures, as CO2 levels rise it becomes an increasingly important restriction on photosynthesis.

  1. Local Climatological Data ACSII Format

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Comma-delimited text files used to create the Local Climatological Data PDF files found in the Local Climatological Data library. Period of record begins in 1998,...

  2. Changes in Land Surface Water Dynamics since the 1990s and Relation to Population Pressure

    Science.gov (United States)

    Prigent, C.; Papa, F.; Aires, F.; Jimenez, C.; Rossow, W. B.; Matthews, E.

    2012-01-01

    We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Citation: Prigent, C., F. Papa, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews (2012), Changes in land surface water dynamics since the 1990s and relation to population pressure, in section 4, insisting on the potential applications of the wetland dataset.

  3. The Influence of Land Surface Changes on Regional Climate in Northwest China

    Institute of Scientific and Technical Information of China (English)

    XU Xingkui; ZHANG Feng; Jason K.LEVY

    2007-01-01

    Land surface changes effect the regional climate due to the complex coupling of land-atmosphere interactions. From 1995 to 2000, a decrease in the vegetation density and an increase in ground-level thermodynamic activity has been documented by multiple data sources in Northwest China, including meteorological, reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF), National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and TIROS Operational Vertical Sounder (TOVS) satellite remote sensing data. As the ground-level thermodynamic activity increases, humid air from the surrounding regions converge toward desert (and semi-desert) regions, causing areas with high vegetation cover to become gradually more arid. Furthermore, land surface changes in Northwest China are responsible for a decrease in total cloud cover, a decline in the fraction of low and middle clouds, an increase in high cloud cover (due to thermodynamic activity) and other regional climatic adaptations. It is proposed that, beginning in 1995, these cloud cover changes contributed to a "greenhouse" effect, leading to the rapid air temperature increases and other regional climate impacts that have been observed over Northwest China.

  4. The new single-channel approaches for retrieving land surface temperature and the preliminary results

    Science.gov (United States)

    Chen, Feng; Yang, Song; Liu, Lin; Zhao, Xiaofeng

    2014-11-01

    Two satellites named HJ-1A and HJ-1B were launched on 6 September 2008, which are intended for environment and disaster monitoring and forecasting. The infrared scanner (IRS) onboard HJ-1B has one thermal infrared band. Currently, for sensors with one thermal band (e.g. Landsat TM/ETM+ and HJ-1B), several empirical algorithms have been developed to estimate land surface temperature (LST). However, surface emissivity and atmospheric parameters which are not readily accessible to general users are required for these empirical methods. To resolve this problem, particularly for HJ-1B, new retrieval methodology is desired. According to proper assumptions, two approaches were proposed, which included the single-channel method based on temporal and spatial information (MTSC) and the image based single-channel method (IBSC). The newly developed methods are mainly for estimating LST accurately from one thermal band, even without any accurate information related to the atmospheric parameters and land surface emissivity. In this paper, we introduce and give preliminary assessments on the new approaches. Assessments generally show good agreement between the HJ-1B retrieved results and the MODIS references. Especially, over sea and water areas the biases were less than 1K while the root mean square errors were about 1K for both MTSC and IBSC methods. As expected, the MTSC method did superiorly to the IBSC method, owning to spatiotemporal information is incorporated into the MTSC method, although more experiments and comparisons should be conducted further.

  5. A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms

    Directory of Open Access Journals (Sweden)

    João P. A. Martins

    2016-09-01

    Full Text Available Land surface temperature (LST is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This study analyzes calibration strategies while considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere, indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis—Satellite Application Facility to calibrate its LST algorithms, applied both for current and forthcoming sensors.

  6. Developing a synergy algorithm for land surface temperature: the SEN4LST project

    Science.gov (United States)

    Sobrino, Jose A.; Jimenez, Juan C.; Ghent, Darren J.

    2013-04-01

    Land surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. An adequate characterization of LST distribution and its temporal evolution requires measurements with detailed spatial and temporal frequencies. With the advent of the Sentinel 2 (S2) and 3 (S3) series of satellites a unique opportunity exists to go beyond the current state of the art of single instrument algorithms. The Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project aims at developing techniques to fully utilize synergy between S2 and S3 instruments in order to improve LST retrievals. In the framework of the SEN4LST project, three LST retrieval algorithms were proposed using the thermal infrared bands of the Sea and Land Surface Temperature Retrieval (SLSTR) instrument on board the S3 platform: split-window (SW), dual-angle (DA) and a combined algorithm using both split-window and dual-angle techniques (SW-DA). One of the objectives of the project is to select the best algorithm to generate LST products from the synergy between S2/S3 instruments. In this sense, validation is a critical step in the selection process for the best performing candidate algorithm. A unique match-up database constructed at University of Leicester (UoL) of in situ observations from over twenty ground stations and corresponding brightness temperature (BT) and LST match-ups from multi-sensor overpasses is utilised for validating the candidate algorithms. Furthermore, their performance is also evaluated against the standard ESA LST product and the enhanced offline UoL LST product. In addition, a simulation dataset is constructed using 17 synthetic images of LST and the radiative transfer model MODTRAN carried under 66 different atmospheric conditions. Each candidate LST

  7. Subsurface temperature estimation from climatology and satellite SST for the sea around Korean Peninsula 1Bong-Guk, Kim, 1Yang-Ki, Cho, 1Bong-Gwan, Kim, 1Young-Gi, Kim, 1Ji-Hoon, Jung 1School of Earth and Environmental Sciences, Seoul National University

    Science.gov (United States)

    Kim, Bong-Guk; Cho, Yang-Ki; Kim, Bong-Gwan; Kim, Young-Gi; Jung, Ji-Hoon

    2015-04-01

    Subsurface temperature plays an important role in determining heat contents in the upper ocean which are crucial in long-term and short-term weather systems. Furthermore, subsurface temperature affects significantly ocean ecology. In this study, a simple and practical algorithm has proposed. If we assume that subsurface temperature changes are proportional to surface heating or cooling, subsurface temperature at each depth (Sub_temp) can be estimated as follows PIC whereiis depth index, Clm_temp is temperature from climatology, dif0 is temperature difference between satellite and climatology in the surface, and ratio is ratio of temperature variability in each depth to surface temperature variability. Subsurface temperatures using this algorithm from climatology (WOA2013) and satellite SST (OSTIA) where calculated in the sea around Korean peninsula. Validation result with in-situ observation data show good agreement in the upper 50 m layer with RMSE (root mean square error) less than 2 K. The RMSE is smallest with less than 1 K in winter when surface mixed layer is thick, and largest with about 2~3 K in summer when surface mixed layer is shallow. The strong thermocline and large variability of the mixed layer depth might result in large RMSE in summer. Applying of mixed layer depth information for the algorithm may improve subsurface temperature estimation in summer. Spatial-temporal details on the improvement and its causes will be discussed.

  8. Visualization and Analysis of Multi-scale Land Surface Products via Giovanni Portals

    Science.gov (United States)

    Shen, Suhung; Kempler, Steven J.; Gerasimov, Irina V.

    2013-01-01

    Large volumes of MODIS land data products at multiple spatial resolutions have been integrated into the Giovanni online analysis system to support studies on land cover and land use changes,focused on the Northern Eurasia and Monsoon Asia regions through the LCLUC program. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data.Customized Giovanni Web portals (Giovanni-NEESPI andGiovanni-MAIRS) have been created to integrate land, atmospheric,cryospheric, and societal products, enabling researchers to do quick exploration and basic analyses of land surface changes, and their relationships to climate, at global and regional scales. This presentation shows a sample Giovanni portal page, lists selected data products in the system, and illustrates potential analyses with imagesand time-series at global and regional scales, focusing on climatology and anomaly analysis. More information is available at the GES DISCMAIRS data support project portal: http:disc.sci.gsfc.nasa.govmairs.

  9. HadISDH land surface multi-variable humidity and temperature record for climate monitoring

    Directory of Open Access Journals (Sweden)

    K. M. Willett

    2014-06-01

    Full Text Available HadISDH.2.0.0 is the first gridded, multi-variable humidity and temperature climate-data product that is homogenised and annually updated. It provides physically consistent estimates for specific humidity, vapour pressure, relative humidity, dew point temperature, wet bulb temperature, dew point depression and temperature. It is a monthly-mean gridded (5° by 5° product with uncertainty estimates that account for spatio-temporal sampling, climatology calculation, homogenisation and irreducible random measurement effects. It provides a unique tool for the monitoring of a variety of humidity-related variables which have different impacts and implications for society. HadISDH.2.0.0 is shown to be in good agreement both with other estimates where they are available, and with theoretical understanding. The dataset is available from 1973 to the present. The theme common to all variables is of a warming world with more water vapour present in the atmosphere. The largest increases in water vapour are found over the tropics and Mediterranean. Over the tropics and high northern latitudes the surface air over land is becoming more saturated. However, despite increasing water vapour over the mid-latitudes and Mediterranean, the surface air over land is becoming less saturated. These observed features may be due to atmospheric circulation changes, land–sea warming disparities and reduced water availability or changed land surface properties.

  10. Comparison of MODIS Land Surface Temperature and Air Temperature over the Continental USA Meteorological Stations

    Science.gov (United States)

    Zhang, Ping; Bounoua, Lahouari; Imhoff, Marc L.; Wolfe, Robert E.; Thome, Kurtis

    2014-01-01

    The National Land Cover Database (NLCD) Impervious Surface Area (ISA) and MODIS Land Surface Temperature (LST) are used in a spatial analysis to assess the surface-temperature-based urban heat island's (UHIS) signature on LST amplitude over the continental USA and to make comparisons to local air temperatures. Air-temperature-based UHIs (UHIA), calculated using the Global Historical Climatology Network (GHCN) daily air temperatures, are compared with UHIS for urban areas in different biomes during different seasons. NLCD ISA is used to define urban and rural temperatures and to stratify the sampling for LST and air temperatures. We find that the MODIS LST agrees well with observed air temperature during the nighttime, but tends to overestimate it during the daytime, especially during summer and in nonforested areas. The minimum air temperature analyses show that UHIs in forests have an average UHIA of 1 C during the summer. The UHIS, calculated from nighttime LST, has similar magnitude of 1-2 C. By contrast, the LSTs show a midday summer UHIS of 3-4 C for cities in forests, whereas the average summer UHIA calculated from maximum air temperature is close to 0 C. In addition, the LSTs and air temperatures difference between 2006 and 2011 are in agreement, albeit with different magnitude.

  11. Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6

    Science.gov (United States)

    Benavides Pinjosovsky, Hector Simon; Thiria, Sylvie; Ottlé, Catherine; Brajard, Julien; Badran, Fouad; Maugis, Pascal

    2017-01-01

    The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons). In addition, a distributed version is available in the case for which only the land surface temperature is observed.

  12. Improved meteorology and ozone air quality simulations using MODIS land surface parameters in the Yangtze River Delta urban cluster, China

    Science.gov (United States)

    Li, Mengmeng; Wang, Tijian; Xie, Min; Zhuang, Bingliang; Li, Shu; Han, Yong; Song, Yu; Cheng, Nianliang

    2017-03-01

    Land surface parameters play an important role in the land-atmosphere coupling and thus are critical to the weather and dispersion of pollutants in the atmosphere. This work aims at improving the meteorology and air quality simulations for a high-ozone (O3) event in the Yangtze River Delta urban cluster of China, through incorporation of satellite-derived land surface parameters. Using Moderate Resolution Imaging Spectroradiometer (MODIS) input to specify the land cover type, green vegetation fraction, leaf area index, albedo, emissivity, and deep soil temperature provides a more realistic representation of surface characteristics. Preliminary evaluations reveal clearly improved meteorological simulation with MODIS input compared with that using default parameters, particularly for temperature (from -2.5 to -1.7°C for mean bias) and humidity (from 9.7% to 4.3% for mean bias). The improved meteorology propagates through the air quality system, which results in better estimates for surface NO2 (from 11.5 to 8.0 ppb for mean bias) and nocturnal O3 low-end concentration values (from -18.8 to -13.6 ppb for mean bias). Modifications of the urban land surface parameters are the main reason for model improvement. The deeper urban boundary layer and intense updraft induced by the urban heat island are favorable for pollutant dilution, thus contributing to lower NO2 and elevated nocturnal O3. Furthermore, the intensified sea-land breeze circulation may exacerbate O3 pollution at coastal cities through pollutant recirculation. Improvement of mesoscale meteorology and air quality simulations with satellite-derived land surface parameters will be useful for air pollution monitoring and forecasting in urban areas.

  13. Developing first time-series of land surface temperature from AATSR with uncertainty estimates

    Science.gov (United States)

    Ghent, Darren; Remedios, John

    2013-04-01

    Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Earth Observation satellites provide the opportunity to obtain global coverage of LST approximately every 3 days or less. One such source of satellite retrieved LST has been the Advanced Along-Track Scanning Radiometer (AATSR); with LST retrieval being implemented in the AATSR Instrument Processing Facility in March 2004. Here we present first regional and global time-series of LST data from AATSR with estimates of uncertainty. Mean changes in temperature over the last decade will be discussed along with regional patterns. Although time-series across all three ATSR missions have previously been constructed (Kogler et al., 2012), the use of low resolution auxiliary data in the retrieval algorithm and non-optimal cloud masking resulted in time-series artefacts. As such, considerable ESA supported development has been carried out on the AATSR data to address these concerns. This includes the integration of high resolution auxiliary data into the retrieval algorithm and subsequent generation of coefficients and tuning parameters, plus the development of an improved cloud mask based on the simulation of clear sky conditions from radiance transfer modelling (Ghent et al., in prep.). Any inference on this LST record is though of limited value without the accompaniment of an uncertainty estimate; wherein the Joint Committee for Guides in Metrology quote an uncertainty as "a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand that is the value of the particular quantity to be measured". Furthermore, pixel level uncertainty fields are a mandatory requirement in the on-going preparation of the LST product for the upcoming Sea and Land Surface Temperature (SLSTR) instrument on-board Sentinel-3

  14. Land Surface Albedos Computed from BRF Measurements with a Study of Conversion Formulae

    Directory of Open Access Journals (Sweden)

    Aku Riihelä

    2010-08-01

    Full Text Available Land surface hemispherical albedos of several targets have been resolved using the bidirectional reflectance factor (BRF library of the Finnish Geodetic Institute (FGI. The library contains BRF data measured by FGI during the years 2003–2009. Surface albedos are calculated using selected BRF datasets from the library. Polynomial interpolation and extrapolation have been used in computations. Several broadband conversion formulae generally used for satellite based surface albedo retrieval have been tested. The albedos were typically found to monotonically increase with increasing zenith angle of the Sun. The surface albedo variance was significant even within each target category / surface type. In general, the albedo estimates derived using diverse broadband conversion formulas and estimates obtained by direct integration of the measured spectra were in line.

  15. Geothermal Heat Flux Assessment Using Remote Sensing Land Surface Temperature and Simulated Data. Case Studies at the Kenyan Rift and Yellowstone Geothermal Areas

    Science.gov (United States)

    Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C.; van der Meer, F. D.

    2015-12-01

    In this work we propose an innovative approach to assess the geothermal heat flux anomalies in the regions of the Kenyan Rift and the Yellowstone geothermal areas. The method is based on the land surface temperature (LST) differences obtained between remote sensing data and land surface model simulations. The hypothesis is that the model simulations do not account for the subsurface geothermal heat source in the formulation. Remote sensing of surface emitted radiances is able to detect at least the radiative portion of the geothermal signal that is not in the models. Two methods were proposed to assess the geothermal component of LST (LSTgt) based on the aforementioned hypothesis: a physical model and a data mining approach. The LST datasets were taken from the Land Surface Analysis Satellite Application Facilities products over Africa and the Copernicus Programme for North America, at a spatial resolution of 3-5 km. These correspond to Meteosat Second Generation and Geostationary Operational Environmental Satellite system satellites data respectively. The Weather Research and Forecasting model was used to simulate LST based on atmospheric and surface characteristics using the Noah land surface model. The analysis was carried out for a period of two months by using nighttime acquisitions. Higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer data were also used on the Kenyan area to produce similar outputs employing existing methods. The comparison of the results from both methods and areas illustrated the potential of the data and methodologies for geothermal applications.

  16. Vegetation, land surface brightness, and temperature dynamics after aspen forest die-off

    Science.gov (United States)

    Huang, Cho-ying; Anderegg, William R. L.

    2014-07-01

    Forest dynamics following drought-induced tree mortality can affect regional climate through biophysical surface properties. These dynamics have not been well quantified, particularly at the regional scale, and are a large uncertainty in ecosystem-climate feedback. We investigated regional biophysical characteristics through time (1995-2011) in drought-impacted (2001-2003), trembling aspen (Populus tremuloides Michx.) forests by utilizing Landsat time series green and brown vegetation cover, surface brightness (total shortwave albedo), and daytime land surface temperature. We quantified the temporal dynamics and postdrought recovery of these characteristics for aspen forests experiencing severe drought-induced mortality in the San Juan National Forest in southwestern Colorado, USA. We partitioned forests into three categories from healthy to severe mortality (Healthy, Intermediate, and Die-off) by referring to field observations of aspen canopy mortality and live aboveground biomass losses. The vegetation cover of die-off areas in 2011 (26.9% of the aspen forest) was significantly different compared to predrought conditions (decrease of 7.4% of the green vegetation cover and increase of 12.1% of the brown vegetation cover compared to 1999). The surface brightness of the study region 9 years after drought however was comparable to predrought estimates (12.7-13.7%). Postdrought brightness was potentially influenced by understory shrubs, since they became the top layer green canopies in disturbed sites from a satellite's point of view. Satellite evidence also showed that the differences of land surface temperature among the three groups increased substantially (≥45%) after drought, possibly due to the reduction of plant evapotranspiration in the Intermediate and Die-off sites. Our results suggest that the mortality-affected systems have not recovered in terms of the surface biophysical properties. We also find that the temporal dynamics of vegetation cover holds

  17. Geostatistical Solutions for Downscaling Remotely Sensed Land Surface Temperature

    Science.gov (United States)

    Wang, Q.; Rodriguez-Galiano, V.; Atkinson, P. M.

    2017-09-01

    Remotely sensed land surface temperature (LST) downscaling is an important issue in remote sensing. Geostatistical methods have shown their applicability in downscaling multi/hyperspectral images. In this paper, four geostatistical solutions, including regression kriging (RK), downscaling cokriging (DSCK), kriging with external drift (KED) and area-to-point regression kriging (ATPRK), are applied for downscaling remotely sensed LST. Their differences are analyzed theoretically and the performances are compared experimentally using a Landsat 7 ETM+ dataset. They are also compared to the classical TsHARP method.

  18. Potential association of dengue hemorrhagic fever incidence and remote senses land surface temperature, Thailand, 1998.

    Science.gov (United States)

    Nitatpattana, Narong; Singhasivanon, Pratap; Kiyoshi, Honda; Andrianasolo, Haja; Yoksan, Sutee; Gonzalez, Jean-Paul; Barbazan, Philippe

    2007-05-01

    A pilot study was designed to analyze a potential association between dengue hemorrhagic fever (DHF) incidence and, temperature computed by satellite. DHF is a mosquito transmitted disease, and water vapor and humidity are known to have a positive effect on mosquito life by increasing survival time and shortening the development cycle. Among other available satellite data, Land Surface Temperature (LST) was chosen as an indicator that combined radiated earth temperature and atmospheric water vapor concentration. Monthly DHF incidence was recorded by province during the 1998 epidemic and obtained as a weekly combined report available from the National Ministry of Public Health. Conversely, LST was calculated using remotely sensed data obtained from thermal infrared sensors of NOAA satellites and computed on a provincial scale. Out of nine selected study provinces, five (58.3%) exhibited an LST with a significant positive correlation with rainfall (p < 0.05). In four out of nineteen surveyed provinces (21.3%), LST showed a significant positive correlation with DHF incidence (p < 0.05). Positive association between LST and DHF incidence was significantly correlated in 75% of the cases during non-epidemic months, while no correlation was found during epidemic months. Non-climatic factors are supposed to be at the origin of this discrepancy between seasonality in climate (LST) and DHF incidence during epidemics.

  19. Declassified intelligence satellite photographs

    Science.gov (United States)

    ,

    1998-01-01

    Recently declassified photographs from spy satellites are an important addition to the record of the Earth?s land surface held by the U.S. Geological Survey (USGS). More than 800,000 high-resolution photos taken between 1959 through 1972 were made available by Executive Order of the President. The collection is held at the USGS EROS Data Center, near Sioux Falls, S. Dak., and are offered for public sale. For some purposes in earth science studies, these photos extend the record of changes in the land surface another decade back in time from the advent of the Landsat earth-observing satellite program.

  20. Land surface changes enhanced drought over the Loess Plateau

    Science.gov (United States)

    Xu, Xianli; Liu, Meixian

    2017-04-01

    In order to prevent the severe soil-water erosion over the Loess Plateau (LP), the Chinese Government initiated large scale ecological restoration (ER) in the past half century. The ER had successfully reduced soil erosion however also changed the land surface and altered the regional water-energy balance and consequently the dryness/wetness conditions, which in turn affects the vegetation. Knowledge of the impacts of the ER on dryness/wetness conditions is essential for developing future effective ER measures. For this purpose, a new drought index, the standardized wetness index (SWI), was proposed. The SWI can represent the dryness/wetness brought by solely climate change (denoted as SWIf in this case), and the dryness/wetness brought by the joint effects of climate change and land surface change (SWI_m). A total of 13 main catchments were selected to investigate the effects of ER on dryness/wetness conditions during 1961-2009 over LP. Results showed that the overall increasing parameter n (a parameter of the Budyko formulae) could be well explained by the ER measures (R^2=1) in these catchments. The SWIf and SWIm had similar fluctuating features and exhibited downward trends. However, the SWIm had larger negative trends than the SWI_f, implying that ER actions enhanced the drought conditions over the drying LP in the past decades. Therefore, we suggest that the government should manage and maintain the existing achievements but not further expand revegetation because of unintended consequences on drought vulnerability.

  1. A New Approach for Parameter Optimization in Land Surface Model

    Institute of Scientific and Technical Information of China (English)

    LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin

    2011-01-01

    In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

  2. The international surface temperature initiative's global land surface databank

    Science.gov (United States)

    Lawrimore, J. H.; Rennie, J.; Gambi de Almeida, W.; Christy, J.; Flannery, M.; Gleason, B.; Klein-Tank, A.; Mhanda, A.; Ishihara, K.; Lister, D.; Menne, M. J.; Razuvaev, V.; Renom, M.; Rusticucci, M.; Tandy, J.; Thorne, P. W.; Worley, S.

    2013-09-01

    The International Surface Temperature Initiative (ISTI) consists of an end-to-end process for land surface air temperature analyses. The foundation is the establishment of a global land surface Databank. This builds upon the groundbreaking efforts of scientists in the 1980s and 1990s. While using many of their principles, a primary aim is to improve aspects including data provenance, version control, openness and transparency, temporal and spatial coverage, and improved methods for merging disparate sources. The initial focus is on daily and monthly timescales. A Databank Working Group is focused on establishing Stage-0 (original observation forms) through Stage-3 data (merged dataset without quality control). More than 35 sources of data have already been added and efforts have now turned to development of the initial version of the merged dataset. Methods have been established for ensuring to the extent possible the provenance of all data from the point of observation through all intermediate steps to final archive and access. Databank submission procedures were designed to make the process of contributing data as easy as possible. All data are provided openly and without charge. We encourage the use of these data and feedback from interested users.

  3. On the effective inversion by imposing a priori infor-mation for retrieval of land surface parameters

    Institute of Scientific and Technical Information of China (English)

    WANG YanFei; MA ShiQian; YANG Hua; WANG JinDi; LI XiaoWen

    2009-01-01

    The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF models can be inverted to estimate the important biological or climatological parameters of the earth surface such as leaf area index and albedo. The state-of-the-art of BRDF is the use of the linear kernel-driven models, mathematically described as the linear combination of the isotropic kernel, volume scattering kernel and geometric optics kernel. The computational stability is characterized by the algebraic operator spectrum of the kernel-matrix and the observation errors. Therefore, the retrieval of the model coefficients is of great importance for computation of the land surface albedos. We first consider the smoothing solution method of the kernel-driven BRDF models for retrieval of land surface albedos. This is known as an ill-posed inverse problem. The ill-posedness arises from that the linear kernel driven BRDF model is usually underdetermined if there are too few looks or poor directional ranges, or the observations are highly dependent. For example, a single angular observation 05 lead to an under-determined system whose solution is infinite (the null space of the kernel operator contains nonzero vectors) or no solution (the rank of the coefficient matrix is not equal to the augmented matrix). Therefore, some smoothing or regularization technique should be applied to suppress the ill-posedness. So far, least squares error methods with a priori knowledge, QR decomposition method for inversion of the BRDF model and regularization theories for ill-posed inversion were developed. In this paper, we emphasize on imposing a priori information in different spaces. We first propose a gen-eral a priori imposed regularization model problem, and then address two forms of regularization scheme. The first one is a regularized singular value decomposition

  4. An Open and Transparent Databank of Global Land Surface Temperature

    Science.gov (United States)

    Rennie, J.; Thorne, P.; Lawrimore, J. H.; Gleason, B.; Menne, M. J.; Williams, C.

    2013-12-01

    The International Surface Temperature Initiative (ISTI) consists of an effort to create an end-to-end process for land surface air temperature analyses. The foundation of this process is the establishment of a global land surface databank. The databank builds upon the groundbreaking efforts of scientists who led efforts to construct global land surface datasets in the 1980's and 1990's. A primary aim of the databank is to improve aspects including data provenance, version control, temporal and spatial coverage, and improved methods for bringing dozens of source data together into an integrated dataset. The databank consists of multiple stages, with each successive stage providing a higher level of processing, quality and integration. Currently more than 50 sources of data have been added to the databank. An automated algorithm has been developed that merges these sources into one complete dataset by removing duplicate station records, identifying two or more station records that can be merged into a single record, and incorporating new and unique stations. The program runs iteratively through all the sources which are ordered based upon criteria established by the ISTI. The highest preferred source, known as the target, runs through all the candidate sources, calculating station comparisons that are acceptable for merging. The process is probabilistic in approach, and the final fate of a candidate station is based upon metadata matching and data equivalence criteria. If there is not enough information, the station is withheld for further investigation. The algorithm has been validated using a pseudo-source of stations with a known time of observation bias, and correct matches have been made nearly 95% of the time. The final product, endorsed and recommended by ISTI, contains over 30,000 stations, however slight changes in the algorithm can perturb results. Subjective decisions, such as the ordering of the sources, or changing metadata and data matching thresholds

  5. Towards an improved land surface scheme for prairie landscapes

    Science.gov (United States)

    Mekonnen, M. A.; Wheater, H. S.; Ireson, A. M.; Spence, C.; Davison, B.; Pietroniro, A.

    2014-04-01

    The prairie region of Canada and the United States is characterized by millions of small depressions of glacial origin called prairie potholes. The transfer of surface runoff in this landscape is mainly through a “fill and spill” mechanism among neighboring potholes. While non-contributing areas, that is small internally drained basins, are common on this landscape, during wet periods these areas can become hydrologically connected to larger regional drainage systems. Accurate prediction of prairie surface runoff generation and streamflow thus requires realistic representation of the dynamic threshold-mediated nature of these contributing areas. This paper presents a new prairie surface runoff generation algorithm for land surface schemes and large scale hydrological models that conceptualizes a hydrologic unit as a combination of variable and interacting storage elements. The proposed surface runoff generation algorithm uses a probability density function to represent the spatial variation of pothole storages and assumes a unique relationship between storage and the fractional contributing area for runoff (and hence amount of direct runoff generated) within a grid cell. In this paper the parameters that define this relationship are obtained by calibration against streamflow. The model was compared to an existing hydrology-land surface scheme (HLSS) applied to a typical Canadian prairie catchment, the Assiniboine River. The existing configuration is based on the Canadian Land Surface Scheme (CLASS) and WATROF (a physically-based overland and interflow scheme). The new configuration consists of CLASS coupled with the new PDMROF model. Results showed that the proposed surface runoff generation algorithm performed better at simulating streamflow, and appears to capture the dynamic nature of contributing areas in an effective and parsimonious manner. A pilot evaluation based on 1 m LiDAR data from a small (10 km2) experimental area suggests that the shape of the

  6. Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data.

    Science.gov (United States)

    Chakraborty, Surya Deb; Kant, Yogesh; Mitra, Debashis

    2015-01-15

    Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000-2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of ±2 °C than MODIS with an error of ±3 °C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 °C & for MODIS data is 3.7 °C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m(2) and 62 W/m(2) respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect.

  7. Innovative approach to retrieve land surface emissivity and land surface temperature in areas of highly dynamic emissivity changes by using thermal infrared data

    Science.gov (United States)

    Heinemann, Sascha; Muro, Javier; Burkart, Andreas; Schultz, Johannes; Thonfeld, Frank; Menz, Gunter

    2016-04-01

    The land surface temperature (LST) is an extremely significant parameter in order to understand the processes of energetic interactions between the Earth's surface and the atmosphere. This knowledge is significant for various environmental research questions, particularly with regard to climate change. The current challenge is to reduce the higher deviations during daytime especially for bare areas with a maximum of 5.7 Kelvin. These temperature differences are time and vegetation cover dependent. This study shows an innovative approach to retrieve land surface emissivity (LSE) and LST by using thermal infrared (TIR) data from satellite sensors, such as SEVIRI and AATSR. So far there are no methods to derive LSE/LST particularly in areas of highly dynamic emissivity changes. Therefore especially for regions with large surface temperature amplitude in the diurnal cycle such as bare and uneven soil surfaces but also for regions with seasonal changes in vegetation cover including various surface areas such as grassland, mixed forests or agricultural land different methods were investigated to identify the most appropriate one. The LSE is retrieved by using the day/night Temperature-Independent Spectral Indices (TISI) method, while the Generalised Split-Window (GSW) method is used to retrieve the LST. Nevertheless different GSW algorithms show that equal LSEs lead to large LST differences. For bare surfaces during daytime the difference is about 6 Kelvin. Additionally LSE is also measured using a NDVI-based threshold method (NDVITHM) to distinguish between soil, dense vegetation cover and pixel composed of soil and vegetation. The data used for this analysis were derived from MODIS TIR. The analysis is implemented with IDL and an intercomparison is performed to determine the most effective methods. To compensate temperature differences between derived and ground truth data appropriate correction terms, by comparing derived LSE/LST data with ground-based measurements

  8. Improving the spatial estimation of evapotranspiration by assimilating land surface temperature data

    Science.gov (United States)

    Zink, Matthias; Samaniego, Luis; Cuntz, Matthias

    2013-04-01

    A combined investigation of the water and energy balance in hydrologic models might lead to a more accurate estimation of hydrological fluxes and state variables, such as evapotranspiration ET and soil moisture. Hydrologic models are usually calibrated against discharge measurements, and thus are only trained on the integrated signal at few points within a catchment. This procedure does not take into account any spatial variability of fluxes or state variables. Satellite data are a useful source of information to incorporate spatial information into hydrologic models. The objective of this study is to improve the estimation of evapotranspiration in the spatial domain by using satellite derived land surface temperature Ts for the calibration of the distributed hydrological model mHM. The satellite products are based on data of Meteosat Second Generation (MSG) and are provided by the Land Surface Analysis - Satellite Application Facility (LSA-SAF). mHM simulations of Ts are obtained by solving the energy balance wherein evapotranspiration is determined by closing the water balance. Net radiation is calculated by using incoming short- and longwave radiation, albedo and emissivity data provided by LSA-SAF. The Multiscale Parameter Regionalization technique (MPR, Samaniego et al. 2010) is applied to determine the aerodynamic resistance among other parameters. The optimization is performed for the year 2009 using three objective functions that consider (1) only discharge, (2) only Ts, and (3) both discharge and Ts. For the spatial comparison of satellite derived and estimated Ts fields, a new measure accounting for local spatial variabilities is introduced. The proposed method is applied to seven major German river basins, i.e. Danube, Ems, Main, Mulde, Neckar, Saale, and Weser. The results of the Ts simulations show a bias of 4.1 K compared to the satellite data. We hypothesize that this bias is inherent to the satellite data rather than to the model simulations. This

  9. Spatial-temporal variation of the land surface temperature field and present-day tectonic activity

    Directory of Open Access Journals (Sweden)

    Jin Ma

    2010-10-01

    Full Text Available This study attempts to acquire information on tectonic activity in western China from land surface temperature (LST field data. On the basis of the established relationship between heat and strain, we analyzed the LST distribution in western China using the satellite data product MODIS/Terra. Our results show that: 1. There are departures from annual changes of LST in some areas, and that these changes are associated with the activity of some active tectonic zones. 2. When annual-change background values caused by climate factors are removed, the long-period component (LSTLOW of temperature residual (ΔT of the LST is able to serve as an indicator for tectonic activity. We have found that a major earthquake can produce different effects on the LST fields of surrounding areas. These effects are characterized by both rises and drops in temperature. For example, there was a noteworthy temperature decline associated with the Sumatran M9 earthquake of 2004 in the Bayan Har-Songpan block of central Tibetan Plateau. 3. On the other hand, the LST field of a single area may respond differently to major shocks occurring in different areas in the regions surrounding China. For instance, the Kunlun M 8.1 event made the LST on the Longmen Mountains fault zone increase, whereas the Zaisan Lake M 7.9 quake of 2003, and the Sumatran M 9 event of 2004, caused decreases in the same area’s LST. 4. The variations of land surface temperature (LST over time are different in different tectonic areas. These phenomena may provide clues for the study of tectonic deformation processes. On the basis of these phenomena, we use a combination of temperature data obtained at varied depths, regional seismicity and strain results obtained with GPS measurements, to test the information related to tectonic activity derived from variations of the LST field, and discuss its implications to the creation of models of regional tectonic deformation.

  10. A novel interpolation method for MODIS land surface temperature data on the Tibetan Plateau

    Science.gov (United States)

    Yu, Wenjun; Wu, Tonghua; Nan, Zhuotong; Zhao, Lin; Wang, Zhiwei

    2014-11-01

    MODIS satellites provide continuous global observations on land surface temperature. It is more important in data-sparse area, such as on the Tibetan Plateau (TP) with very few meteorological stations. Images with severe data missing or poor quality pixels were often found in MODIS LST products, which mostly were caused by the influences of clouds. The traditional geo-statistic methods, including ordinary Kriging and inverse distance weighted (IDW) methods, cannot well interpolate missing-data pixels for a large area. Assuming that the changes of LST at one location would be similar with that at the locations with similar features, a novel method was proposed to interpolate the missing-data pixels by making use of other pixels with the most similar features. MODIS/Terra LST covering TP in 2005 were used as experimental data, and pixels with cloud coverage, average emissivity error greater than 0.04, and average LST error greater than 2K were identified as missing-data pixels. The images with less than 10% missing-data pixels were selected as reference images, in which the missing-data pixels were interpolated with IDW. Distances for different land surface features in images, such as DEM, slope, NDVI and LST, from the interpolating pixel to the other pixels with known LST were calculated. Similar pixels are identified as the distances less than a given threshold. Relationship of LST for those similar pixels was regressed, and was applied to estimate LSTs for the missing pixels. Compared with IDW and Kriging, the proposed method could interpolate the MODIS LST much better on the Tibetan Plateau.

  11. Physics-based approach for the monitoring of Land Surface Phenology

    Science.gov (United States)

    de Beurs, K. M.; Henebry, G. M.

    2008-12-01

    The onset of greening, the start of senescence, the timing of the maximum of the growing season, and the growing season length are frequently calculated metrics of phenology based on satellite imagery. Data collected by the MODIS sensors as opposed to AVHRR sensors give us a wider range of available reflectance bands to monitor vegetation development. Nevertheless, statistical time series analysis is still typically applied to the Normalized Difference Vegetation Index (NDVI) based on only the red and near infrared bands. The Enhanced Vegetation Index (EVI) might be the only frequently used vegetation index utilizing the enhanced spectral capabilities of MODIS. While short wave infrared (SWIR) bands have proven to be extremely useful in vegetation monitoring with Landsat data, they are still rarely used in MODIS vegetation analysis. Furthermore, even though skin temperature and NDVI have been combined in energy balance and land cover studies, the combination is rarely used to investigate land surface phenology. Here we use a set of reflectance and emittance bands to provide a characterization of the growing season in North America. Using NDVI, the Normalized Difference Infrared Index (NDII), MODIS Tasseled Cap indices, and the MODIS land surface temperature, we create a broader range of phenological metrics for the characterization of the growing season vegetation activity. We demonstrate the methods using MODIS/Terra+Aqua Nadir BRDF-Adjusted Reflectance 16-Day L3 Global 0.05Deg CMG V005 (MCD43C4) data from 2001 to 2007 for North America. To illustrate the temporal stability of the selected indices, we calculate the temporal coefficient of variation and temporal interquartile range. Results show that both regions with typically very high NDVI values and regions with snow cover are better characterized using the combined approach.

  12. Calibration plan for the sea and land surface temperature radiometer

    Science.gov (United States)

    Smith, David L.; Nightingale, Tim J.; Mortimer, Hugh; Middleton, Kevin; Edeson, Ruben; Cox, Caroline V.; Mutlow, Chris T.; Maddison, Brian J.

    2013-10-01

    The Sea and Land Surface Temperature Radiometer (SLSTR) to be flown on ESA's Sentinel-3 mission is a multichannel scanning radiometer that will continue the 21-year datasets of the Along Track Scanning Radiometer (ATSR) series. As its name implies, measurements from SLSTR will be used to retrieve global sea surface temperatures to an uncertainty of SLSTR instrument, infrared calibration sources and alignment equipment. The calibration rig has been commissioned and results of these tests will be presented. Finally the authors will present the planning for the on-orbit monitoring and calibration activities to ensure that calibration is maintained. These activities include vicarious calibration techniques that have been developed through previous missions, and the deployment of ship-borne radiometers.

  13. A map of radon flux at the Australian land surface

    Directory of Open Access Journals (Sweden)

    A. D. Griffiths

    2010-06-01

    Full Text Available A time-dependent map of radon-222 flux density at the Australian land surface has been constructed with a spatial resolution of 0.05° and temporal resolution of one month. Radon flux density was calculated from a simple model utilising data from national gamma-ray aerial surveys, modelled soil moisture, and maps of soil properties. The model was calibrated against a large data set of accumulation-chamber measurements, thereby constraining it with experimental data. A notable application of the map is in atmospheric mixing and transport studies which use radon as a tracer, where it is a clear improvement on the common assumption of uniform radon flux density.

  14. A map of radon flux at the Australian land surface

    Directory of Open Access Journals (Sweden)

    A. D. Griffiths

    2010-09-01

    Full Text Available A time-dependent map of radon-222 flux density at the Australian land surface has been constructed with a spatial resolution of 0.05° and temporal resolution of one month. Radon flux density was calculated from a simple model utilising data from national gamma-ray aerial surveys; modelled soil moisture, available from 1900 in near real-time; and maps of soil properties. The model was calibrated against a data set of accumulation chamber measurements, thereby constraining it with experimental data. A notable application of the map is in atmospheric mixing and transport studies which use radon as a tracer, where it is a clear improvement on the common assumption of uniform radon flux density.

  15. The Land Surface Temperature Impact to Land Cover Types

    Science.gov (United States)

    Ibrahim, I.; Abu Samah, A.; Fauzi, R.; Noor, N. M.

    2016-06-01

    Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R2 = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

  16. Hydrologic Remote Sensing and Land Surface Data Assimilation.

    Science.gov (United States)

    Moradkhani, Hamid

    2008-05-06

    Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear

  17. Hydrologic Remote Sensing and Land Surface Data Assimilation

    Directory of Open Access Journals (Sweden)

    Hamid Moradkhani

    2008-05-01

    Full Text Available Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF and Particle filter (PF, for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law and could be a strong alternative to EnKF which is subject to some

  18. The climate responses of tropical and boreal ecosystems with an improved land surface model (JULES)

    Science.gov (United States)

    Harper, Anna; Friedlingstein, Pierre; Cox, Peter; Wiltshire, Andy; Jones, Chris

    2016-04-01

    The Joint UK Land Environment Simulator (JULES) is the land surface of the next generation UK Earth System Model (UKESM1). Recently, JULES was updated with new plant functional types and physiology based on a global plant trait database. These developments improved the simulation of terrestrial gross and net primary productivity on local and global scales, and enabled a more realistic representation of the global distribution of vegetation. In this study, we explore the present-day distribution of ecosystems and their vulnerability to climate change in JULES with these improvements, focusing on tropical and boreal ecosystems. Changes to these ecosystems will have implications for biogeophysical and biogeochemical feedbacks to climate change and need to be understood. First, we examine the simulated and observed rainforest-savannah boundary, which is strongly related to annual precipitation and the maximum climatological water deficit. Second, we assess the length of growing season and biomass stored in boreal ecosystems, where 20th century warming has likely extended the growing season. In each case, we first evaluate the ability of JULES to capture observed climate-vegetation relationships and trends. Finally, we run JULES to 2100 using climate data from 3 models and 2 RCP scenarios, and examine potential 21st century changes to these ecosystems. For example, do the tropical forests shrink in response to changes in tropical rainfall seasonality? And, how does the composition of boreal ecosystems change in response to climate warming? Given the potential for climate feedbacks and the inherent value in these ecosystems, it is essential to assess their responses to a range of climate change scenarios.

  19. Comparison of Sensible Heat Flux from Eddy Covariance and Scintillometer over different land surface conditions

    Science.gov (United States)

    Zeweldi, D. A.; Gebremichael, M.; Summis, T.; Wang, J.; Miller, D.

    2008-12-01

    The large source of uncertainty in satellite-based evapotranspiration algorithm results from the estimation of sensible heat flux H. Traditionally eddy covariance sensors, and recently large-aperture scintillometers, have been used as ground truth to evaluate satellite-based H estimates. The two methods rely on different physical measurement principles, and represent different foot print sizes. In New Mexico, we conducted a field campaign during summer 2008 to compare H estimates obtained from the eddy covariance and scintillometer methods. During this field campaign, we installed sonic anemometers; one propeller eddy covariance (OPEC) equipped with net radiometer and soil heat flux sensors; large aperture scintillometer (LAS); and weather station consisting of wind speed, direction and radiation sensors over three different experimental areas consisting of different roughness conditions (desert, irrigated area and lake). Our results show the similarities and differences in H estimates obtained from these various methods over the different land surface conditions. Further, our results show that the H estimates obtained from the LAS agree with those obtained from the eddy covariance method when high frequency thermocouple temperature, instead of the typical weather station temperature measurements, is used in the LAS analysis.

  20. Land Surface Albedo From EPS/AVHRR : Method For Retrieval and Validation

    Science.gov (United States)

    Jacob, G.

    2015-12-01

    The scope of Land Surface Analysis Satellite Applications Facility (LSA-SAF) is to increase benefit from EUMETSAT Satellites (MSG and EPS) data by providing added value products for the meteorological and environmental science communities with main applications in the fields of climate modelling, environmental management, natural hazards management, and climate change detection. The MSG/SEVIRI daily albedo product is disseminated operationally by the LSA-SAF processing centre based in Portugal since 2009. This product so-called MDAL covers Europe and Africa includes in the visible, near infrared and shortwave bands at a resolution of 3km at the equator. Recently, an albedo product at 1km so-called ETAL has been built from EPS/AVHRR observations in order to primarily MDAL product outside the MSG disk, while ensuring a global coverage. The methodology is common to MSG and EPS data and relies on the inversion of the BRDF (Bidirectional Reflectance Distribution Function) model of Roujean et al. On a given target, ETAL products exploits the variability of viewing angles whereas MDAL looks at the variations of solar illumination. The comparison of ETAL albedo product against MODIS and MSG/SEVIRI products over the year 2015 is instructive in many ways and shows in general a good agreement between them. The dispersion may be accounted by different factors that will be explained The additional information provided by EPS appears to be particularly beneficial for high latitudes during winter and for snow albedo.

  1. Effect of land use/cover change on land surface temperatures - The Nile Delta, Egypt

    Science.gov (United States)

    Hereher, Mohamed E.

    2017-02-01

    In this study remote sensing techniques were employed to investigate the impact of land use/cover change on land surface temperatures (LST) for a highly dynamic landscape, i.e. the Nile Delta. Land use change was determined from analyzing a 15 years of bi-monthly normalized difference vegetation index (NDVI) dataset acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite along with a synchronized 13 years of bi-monthly LST dataset retrieved from MODIS Aqua satellite. Time series analysis for NDVI and LST data was carried out at selected locations experiencing land use change. Mean LST change was determined for each location before and after the land use change. Results indicate that NDVI composite data for 15 years proved sufficient for delineating land use change. Significant spatial changes include the transformation from agriculture to urban land, which increased the LST by 1.7 °C during the 13 years and the transformation of bare land to agriculture, which decreased the LST by 0.52 °C for the same period. Due to the explosive population growth in the Nile Delta, urban encroachment upon agricultural land could, hence, promote a prolonged regional warming by modifying the micro-climate and other climate-related phenomena.

  2. Investigating Satellite Microwave observations of Precipitation in Different Climate Regimes

    Science.gov (United States)

    Wang, N.; Ferraro, R. R.

    2013-12-01

    Microwave satellite remote sensing of precipitation over land is a challenging problem due to the highly variable land surface emissivity, which, if not properly accounted for, can be much greater than the precipitation signal itself, especially in light rain/snow conditions. Additionally, surfaces such as arid land, deserts and snow cover have brightness temperature characteristics similar to precipitation Ongoing work by GPM microwave radiometer team is constructing databases through a variety of means, however, there is much uncertainty as to what is the optimal information needed for the wide array of sensors in the GPM constellation, including examination of regional conditions. The original data sets will focus on stratification by emissivity class, surface temperature and total perceptible water. We'll perform sensitivity studies to determine the potential role of ancillary data (e.g., land surface temperature, snow cover/water equivalent, etc.) to improve precipitation estimation over land in different climate regimes, including rain and snow. In other words, what information outside of the radiances can help describe the background and subsequent departures from it that are active precipitating regions? It is likely that this information will be a function of the various precipitation regimes. Statistical methods such as Principal Component Analysis (PCA) will be utilized in this task. Databases from a variety of sources are being constructed. They include existing satellite microwave measurements of precipitating and non-precipitating conditions, ground radar precipitation rate estimates, surface emissivity climatology from satellites, surface temperature and TPW from NWP reanalysis. Results from the analysis of these databases with respect to the microwave precipitation sensitivity to the variety of environmental conditions in different climate regimes will be discussed.

  3. Eight Year Climatologies from Observational (AIRS) and Model (MERRA) Data

    Science.gov (United States)

    Hearty, Thomas; Savtchenko, Andrey; Won, Young-In; Theobalk, Mike; Vollmer, Bruce; Manning, Evan; Smith, Peter; Ostrenga, Dana; Leptoukh, Greg

    2010-01-01

    We examine climatologies derived from eight years of temperature, water vapor, cloud, and trace gas observations made by the Atmospheric Infrared Sounder (AIRS) instrument flying on the Aqua satellite and compare them to similar climatologies constructed with data from a global assimilation model, the Modern Era Retrospective-Analysis for Research and Applications (MERRA). We use the AIRS climatologies to examine anomalies and trends in the AIRS data record. Since sampling can be an issue for infrared satellites in low earth orbit, we also use the MERRA data to examine the AIRS sampling biases. By sampling the MERRA data at the AIRS space-time locations both with and without the AIRS quality control we estimate the sampling bias of the AIRS climatology and the atmospheric conditions where AIRS has a lower sampling rate. While the AIRS temperature and water vapor sampling biases are small at low latitudes, they can be more than a few degrees in temperature or 10 percent in water vapor at higher latitudes. The largest sampling biases are over desert. The AIRS and MERRA data are available from the Goddard Earth Sciences Data and Information Services Center (GES DISC). The AIRS climatologies we used are available for analysis with the GIOVANNI data exploration tool. (see, http://disc.gsfc.nasa.gov).

  4. Estonian total ozone climatology

    Directory of Open Access Journals (Sweden)

    K. Eerme

    Full Text Available The climatological characteristics of total ozone over Estonia based on the Total Ozone Mapping Spectrometer (TOMS data are discussed. The mean annual cycle during 1979–2000 for the site at 58.3° N and 26.5° E is compiled. The available ground-level data interpolated before TOMS, have been used for trend detection. During the last two decades, the quasi-biennial oscillation (QBO corrected systematic decrease of total ozone from February–April was 3 ± 2.6% per decade. Before 1980, a spring decrease was not detectable. No decreasing trend was found in either the late autumn ozone minimum or in the summer total ozone. The QBO related signal in the spring total ozone has an amplitude of ± 20 DU and phase lag of 20 months. Between 1987–1992, the lagged covariance between the Singapore wind and the studied total ozone was weak. The spring (April–May and summer (June–August total ozone have the best correlation (coefficient 0.7 in the yearly cycle. The correlation between the May and August total ozone is higher than the one between the other summer months. Seasonal power spectra of the total ozone variance show preferred periods with an over 95% significance level. Since 1986, during the winter/spring, the contribution period of 32 days prevails instead of the earlier dominating 26 days. The spectral densities of the periods from 4 days to 2 weeks exhibit high interannual variability.

    Key words. Atmospheric composition and structure (middle atmosphere – composition and chemistry; volcanic effects – Meteorology and atmospheric dynamics (climatology

  5. Information-Theoretic Benchmarking of Land Surface Models

    Science.gov (United States)

    Nearing, Grey; Mocko, David; Kumar, Sujay; Peters-Lidard, Christa; Xia, Youlong

    2016-04-01

    Benchmarking is a type of model evaluation that compares model performance against a baseline metric that is derived, typically, from a different existing model. Statistical benchmarking was used to qualitatively show that land surface models do not fully utilize information in boundary conditions [1] several years before Gong et al [2] discovered the particular type of benchmark that makes it possible to *quantify* the amount of information lost by an incorrect or imperfect model structure. This theoretical development laid the foundation for a formal theory of model benchmarking [3]. We here extend that theory to separate uncertainty contributions from the three major components of dynamical systems models [4]: model structures, model parameters, and boundary conditions describe time-dependent details of each prediction scenario. The key to this new development is the use of large-sample [5] data sets that span multiple soil types, climates, and biomes, which allows us to segregate uncertainty due to parameters from the two other sources. The benefit of this approach for uncertainty quantification and segregation is that it does not rely on Bayesian priors (although it is strictly coherent with Bayes' theorem and with probability theory), and therefore the partitioning of uncertainty into different components is *not* dependent on any a priori assumptions. We apply this methodology to assess the information use efficiency of the four land surface models that comprise the North American Land Data Assimilation System (Noah, Mosaic, SAC-SMA, and VIC). Specifically, we looked at the ability of these models to estimate soil moisture and latent heat fluxes. We found that in the case of soil moisture, about 25% of net information loss was from boundary conditions, around 45% was from model parameters, and 30-40% was from the model structures. In the case of latent heat flux, boundary conditions contributed about 50% of net uncertainty, and model structures contributed

  6. Retrieval of aerosol optical depth over land surfaces from AVHRR data

    Directory of Open Access Journals (Sweden)

    L. Mei

    2013-02-01

    Full Text Available The Advanced Very High Resolution Radiometer (AVHRR radiance data provide a global, long-term, consistent time series having high spectral and spatial resolution and thus being valuable for the retrieval of surface spectral reflectance, albedo and surface temperature. Long term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. These data have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land and land surface albedo are challenging because of the information content of the measurement is limited and the inversion of these data products being ill defined. Solving the radiative transfer equations requires additional information and knowledge to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in Moderate Resolution Imaging Spectroradiometer (MODIS data. Next, following the MODIS dark target approach, the surface reflectance at 0.64 μm was obtained. The comparison of the estimated surface reflectance at 0.64 μm with MODIS reflectance products (MOD09 shows a strong correlation (R = 0.7835. Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to Advanced Very High Resolution Radiometer (AVHRR data. A simplified Look-Up Table (LUT method, adopted from Bremen AErosol Retrieval (BAER algorithm, was used in the retrieval. The Aerosol Optical Depth (AOD values retrieved from AVHRR with this method compare favourably with ground-based measurements, with a correlation coefficient R = 0.861 and Root Mean Square Error (RMSE = 0.17. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to

  7. Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

    DEFF Research Database (Denmark)

    Guzinski, R.; Nieto, H.; Stisen, S.

    2015-01-01

    Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distribu...... derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.......-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case...

  8. On evaluation of ShARP passive rainfall retrievals over snow-covered land surfaces and coastal zones

    CERN Document Server

    Ebtehaj, Ardeshir M; Foufoula-Georgiou, Efi

    2015-01-01

    For precipitation retrievals over land, using satellite measurements in microwave bands, it is important to properly discriminate the weak rainfall signals from strong and highly variable background surface emission. Traditionally, land rainfall retrieval methods often rely on a weak signal of rainfall scattering on high-frequency channels (85 GHz) and make use of empirical thresholding and regression-based techniques. Due to the increased ground surface signal interference, precipitation retrieval over radiometrically complex land surfaces, especially over snow-covered lands, deserts and coastal areas, is of particular challenge for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken locally linear embedding Algorithm for Retrieval of Precipitation (ShARP), over a radiometrically complex terrain and coastal areas using the data provided by the Tropical Rainfall Measuring Mission (TRMM) satellite. To this end, the ShARP retrieval experiments are performed ove...

  9. Retrieving Land Surface Temperature and Emissivity from Multispectral and Hyperspectral Thermal Infrared Instruments

    Science.gov (United States)

    Hook, Simon; Hulley, Glynn; Nicholson, Kerry

    2017-04-01

    Land Surface Temperature and Emissivity (LST&E) data are critical variables for studying a variety of Earth surface processes and surface-atmosphere interactions such as evapotranspiration, surface energy balance and water vapor retrievals. LST&E have been identified as an important Earth System Data Record (ESDR) by NASA and many other international organizations Accurate knowledge of the LST&E is a key requirement for many energy balance models to estimate important surface biophysical variables such as evapotranspiration and plant-available soil moisture. LST&E products are currently generated from sensors in low earth orbit (LEO) such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites as well as from sensors in geostationary Earth orbit (GEO) such as the Geostationary Operational Environmental Satellites (GOES) and airborne sensors such as the Hyperspectral Thermal Emission Spectrometer (HyTES). LST&E products are generated with varying accuracies depending on the input data, including ancillary data such as atmospheric water vapor, as well as algorithmic approaches. NASA has identified the need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. We will discuss the different approaches that can be used to retrieve surface temperature and emissivity from multispectral and hyperspectral thermal infrared sensors using examples from a variety of different sensors such as those mentioned, and planned new sensors like the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Hyperspectral Infrared Imager (HyspIRI). We will also discuss a project underway at NASA to develop a single unified product from some the individual sensor products and assess the errors associated with the product.

  10. The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

    Science.gov (United States)

    Huffman, George J.; Adler, Robert F.; Arkin, Philip; Chang, Alfred; Ferraro, Ralph; Gruber, Arnold; Janowiak, John; McNab, Alan; Rudolf, Bruno; Schneider, Udo

    1997-01-01

    The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit -satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 deg x 2.5 deg latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

  11. A coupled atmosphere and multi-layer land surface model for improving heavy rainfall simulation

    Directory of Open Access Journals (Sweden)

    M. Haggag

    2008-04-01

    Full Text Available A multi-layer land surface model (SOLVEG is dynamically coupled to the non-hydrostatic atmospheric model (MM5 in order to represent better spatial variations and changes in land surface characteristics compared with the land surface parameterization schemes included in the MM5. In this coupling, calculations of the atmosphere and land surface models are carried out as independent tasks of different processors; a model coupler controls these calculations and data exchanges among models using Message Passing Interface (MPI. This coupled model is applied to the record-breaking heavy rain events occurred in Kyushu Island, the southernmost of Japan's main islands, from 20 July to 25 July in 2006. The test computations are conducted by using both the developed coupled model and the original land surface parameterization of MM5. The result of these computations shows that SOLVEG reproduce higher ground temperature than land surface parameterization schemes in the MM5. This result indicates the feedback of land surface processes between MM5 and SOLVEG plays an important role in the computation. The most pronounced difference is in the rainfall simulation that shows the importance of coupling SOLVEG and MM5. The coupled model accurately reproduces the heavy rainfall events observed in Kyushu Island compared to the original MM5 from both the spatial and temporal point of view. This paper clearly shows that realistic simulation of rainfall event strongly depends on land-surface processes interacting with cloud development that depends on surface heat and moisture fluxes, which in turn are mainly determined by land surface vegetation and soil moisture storage. Soil temperature/moisture changes significantly affect the localized precipitation and modest improvement in the land surface representation can enhance the heavy rain simulation. MM5-SOLVEG coupling shows a clear image of land surface-atmosphere interactions and the dynamic feedback on

  12. Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

    Science.gov (United States)

    Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.

    2013-12-01

    This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heat-related mortality data. The current HWWS do not take into account intra-urban spatial variations in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with land surface temperature (LST) estimates derived from thermal remote sensing data. In order to further improve the assessment of intra-urban variations in risk from extreme heat, we developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. We will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

  13. Forest biomass allometry in global land surface models

    Science.gov (United States)

    Wolf, Adam; Ciais, Philippe; Bellassen, Valentin; Delbart, Nicolas; Field, Christopher B.; Berry, Joseph A.

    2011-09-01

    A number of global land surface models simulate photosynthesis, respiration, and disturbance, important flows in the carbon cycle that are widely tested against flux towers and CO2 concentration gradients. The resulting forest biomass is examined in this paper for its resemblance to realistic stands, which are characterized using allometric theory. The simulated biomass pools largely do not conform to widely observed allometry, particularly for young stands. The best performing models had an explicit treatment of stand-thinning processes, which brought the slope of the allometry of these models closer to observations. Additionally, models that had relatively shorter wood turnover times performed were generally closer to observed allometries. The discrepancy between the pool distribution between models and data suggests estimates of NEE have biases when integrated over the long term, as compared to observed biomass data, and could therefore compromise long-term predictions of land carbon sources and sinks. We think that this presents a practical obstacle for improving models by informing them better with data. The approach taken in this paper, examining biomass pools allometrically, offers a simple approach to improving the characteristic behaviors of global models with the relatively sparse data that is available globally by forest inventory.

  14. Land surface temperature shaped by urban fractions in megacity region

    Science.gov (United States)

    Zhang, Xiaoxuan; Hu, Yonghong; Jia, Gensuo; Hou, Meiting; Fan, Yanguo; Sun, Zhongchang; Zhu, Yuxiang

    2017-02-01

    Large areas of cropland and natural vegetation have been replaced by impervious surfaces during the recent rapid urbanization in China, which has resulted in intensified urban heat island effects and modified local or regional warming trends. However, it is unclear how urban expansion contributes to local temperature change. In this study, we investigated the relationship between land surface temperature (LST) change and the increase of urban land signals. The megacity of Tianjin was chosen for the case study because it is representative of the urbanization process in northern China. A combined analysis of LST and urban land information was conducted based on an urban-rural transect derived from Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectrometer (MODIS), and QuickBird images. The results indicated that the density of urban land signals has intensified within a 1-km2 grid in the urban center with an impervious land fraction >60 %. However, the construction on urban land is quite different with low-/mid-rise buildings outnumbering high-rise buildings in the urban-rural transect. Based on a statistical moving window analysis, positive correlation ( R 2 > 0.9) is found between LST and urban land signals. Surface temperature change (ΔLST) increases by 0.062 °C, which was probably caused by the 1 % increase of urbanized land (ΔIF) in this case region.

  15. Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

    Science.gov (United States)

    Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel

    2013-01-01

    This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

  16. Advances in tourism climatology

    Energy Technology Data Exchange (ETDEWEB)

    Matzarakis, A.; Freitas, C.R. de; Scott, D. (eds.)

    2004-11-01

    This publication grew out of the Second International Workshop of the International Society of Biometeorology, Commission on Climate Tourism and Recreation (ISB-CCTR) that took place at the Orthodox Academy of Crete in Kolimbari, Greece, 8-11 June 2004. The aim of the meeting was to (a) bring together a selection of researchers and tourism experts to review the current state of knowledge of tourism and recreation climatology and (b) explore possibilities for future research and the role of the ISB-CCTR in this. A total of 40 delegates attended the June 2004 ISB-CCTR Workshop. Their fields of expertise included biometeorology, bioclimatology, thermal comfort and heat balance modelling, tourism marketing and planning, urban and landscape planning, architecture, climate change, emission reduction and climate change impact assessment. Participants came from universities and research institutions in Australia, Austria, Canada, Croatia, France, Germany, Greece, Hungary, Italy, the Netherlands, New Zealand, Portugal, Slovenia, United Kingdom and United States of America. Business conducted at the Workshop was divided between five sessions: assessment of climatic resources; climate change; health; weather, sports and risk forecasts; and behaviour and perception. However, the content of this publication is organised so that it reflects the new perspectives and methods that have evolved since the ISB-CCTR was established. (orig.)

  17. Evaluation of a photosyntheses-based canopy resistance formulation in the Noah Land-surface model

    Science.gov (United States)

    Accurately representing complex land-surface processes balancing complexity and realism remains one challenge that the weather modelling community is facing nowadays. In this study, a photosynthesis-based Gas-exchange Evapotranspiration Model (GEM) is integrated into the Noah land-surface model repl...

  18. The sand extraction potential of embedded land surface lowering in the Netherlands

    NARCIS (Netherlands)

    Meulen, M.J. van der; Kleine, M.P.E. de; Veldkamp, J.G.; Dubelaar, C.W.; Pietersen, H.S.

    2004-01-01

    In the Netherlands, mineral extraction by means of dredging or quarrying meets with considerable societal resistance. Land surface lowering prior to large land reconstruction projects may raise fewer objections. We have calculated the potential yields of sand and gravel from land surface lowering

  19. The sand extraction potential of embedded land surface lowering in the Netherlands

    NARCIS (Netherlands)

    Van der Meulen, M.J.; De Kleine, M.P.E.; Veldkamp, J.G.; Dubbelaar, C.W.; Pietersen, H.S.

    2004-01-01

    In the Netherlands, mineral extraction by means of dredging or quarrying meets with considerable societal resistance. Land surface lowering prior to large land reconstruction projects may raise fewer objections. We have calculated the potential yields of sand and gravel form land surface lowering

  20. Relative Humidity as an Indicator for Cloud Formation over Heterogeneous land surfaces

    NARCIS (Netherlands)

    Heerwaarden, van C.C.; Vilà-Guerau de Arellano, J.

    2008-01-01

    The influence of land surface heterogeneity on potential cloud formation is investigated using relative humidity as an indicator. This is done by performing numerical experiments using a large-eddy simulation model (LES). The land surface in the model was divided into two patches that had the same s

  1. Biomes computed from simulated climatologies

    National Research Council Canada - National Science Library

    Claussen, M; Esch, M

    1992-01-01

    The biome model of Prentice is used to predict global patterns of potential natural plant formations, or biomes, from climatologies simulated by ECHAM, a model used for climate simulations at the Max...

  2. Local Climatological Data (LCD) Publication

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Local Climatological Data (LCD) contains summaries from major airport weather stations that include a daily account of temperature extremes, degree days,...

  3. Incorporation of water vapor transfer in the JULES land surface model: Implications for key soil variables and land surface fluxes

    Science.gov (United States)

    Garcia Gonzalez, Raquel; Verhoef, Anne; Luigi Vidale, Pier; Braud, Isabelle

    2012-05-01

    This study focuses on the mechanisms underlying water and heat transfer in upper soil layers, and their effects on soil physical prognostic variables and the individual components of the energy balance. The skill of the JULES (Joint UK Environment Simulator) land surface model (LSM) to simulate key soil variables, such as soil moisture content and surface temperature, and fluxes such as evaporation, is investigated. The Richards equation for soil water transfer, as used in most LSMs, was updated by incorporating isothermal and thermal water vapor transfer. The model was tested for three sites representative of semiarid and temperate arid climates: the Jornada site (New Mexico, USA), Griffith site (Australia), and Audubon site (Arizona, USA). Water vapor flux was found to contribute significantly to the water and heat transfer in the upper soil layers. This was mainly due to isothermal vapor diffusion; thermal vapor flux also played a role at the Jornada site just after rainfall events. Inclusion of water vapor flux had an effect on the diurnal evolution of evaporation, soil moisture content, and surface temperature. The incorporation of additional processes, such as water vapor flux among others, into LSMs may improve the coupling between the upper soil layers and the atmosphere, which in turn could increase the reliability of weather and climate predictions.

  4. The role of GMES / Sentinels in Land-Surface Earth System Science

    Science.gov (United States)

    Moreno, J.

    2009-04-01

    A general trend in the current status of representation of Land Surface schemes into Earth System models is driven by the parameterisation of "cycles" instead of individual processes. Particular emphasis is made to account for couplings among the individual cycles, as between the carbon and water cycles. Moreover, the current tendency is to use the measured data -time series in most cases- together with models, in a data assimilation scenario where inputs from multiple sources are integrated. Such approach is more and more necessary as land models tend to be more complex, and particularly due to the fact that land surface variability is not just driven by physical and chemical processes, but intricate biological processes also altered by anthropogenic influences. Human influences in the land system (land use changes, urban development, etc.) and the impacts of natural disasters are becoming also part of land models, but critical data in high spatial and temporal resolutions are needed to properly model such processes. Until now, problems with data availability, data inconsistency and lack of adequate temporal sampling have limited the potential usefulness of such observations in modelling land surface processes. The availability of the GMES / Sentinel series of satellites represents a quite unique opportunity for consolidation of current tendencies and development of new science based on the new type of data that soon will become available. The usefulness of the different Sentinel missions for Land science has been recognised. Although the Sentinel satellite series were primarily designed to provide observations for operational services and routine applications, there is a growing interest in the scientific community towards the usage of Sentinel data for more advanced and innovative science. Moreover, the availability of consistent time series covering a period of over 20 years opens possibilities never explored before, such as systematic data assimilation

  5. IDENTIFYING THE LOCAL SURFACE URBAN HEAT ISLAND THROUGH THE MORPHOLOGY OF THE LAND SURFACE TEMPERATURE

    Directory of Open Access Journals (Sweden)

    J. Wang

    2016-06-01

    Full Text Available Current characterization of the Land Surface Temperature (LST at city scale insufficiently supports efficient mitigations and adaptations of the Surface Urban Heat Island (SUHI at local scale. This research intends to delineate the LST variation at local scale where mitigations and adaptations are more feasible. At the local scale, the research helps to identify the local SUHI (LSUHI at different levels. The concept complies with the planning and design conventions that urban problems are treated with respect to hierarchies or priorities. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. The continuous and smooth latent LST is first recovered from the raw images. The Multi-Scale Shape Index (MSSI is then applied to the latent LST to extract morphological indicators. The local scale variation of the LST is quantified by the indicators such that the LSUHI can be identified morphologically. The results are promising. It can potentially be extended to investigate the temporal dynamics of the LST and LSUHI. This research serves to the application of remote sensing, pattern analysis, urban microclimate study, and urban planning at least at 2 levels: (1 it extends the understanding of the SUHI to the local scale, and (2 the characterization at local scale facilitates problem identification and support mitigations and adaptations more efficiently.

  6. Evaluation of BAER surface model for aerosol optical thickness retrieval over land surface

    Directory of Open Access Journals (Sweden)

    Y. S. Chiang

    2012-04-01

    Full Text Available Estimation of surface reflectance is essential for an accurate retrieval of aerosol optical thickness (AOT by satellite remote sensing approach. Due to the variability of surface reflectance over land surfaces, a surface model is required to take into account the crucial factor controlling this variability. In the present study, we attempted to simulate surface reflectance in the short-wave channels with two methods, namely the land cover type dependent method and a two-source linear model. In the two-source linear model, we assumed that the spectral property can be described by a mixture of vegetated and non-vegetated area, and both the normalized difference vegetation index (NDVI, and the vegetation continuous field (VCF was applied to summarize this surface characteristic. By comparing our estimation with surface reflectance data derived from Moderate Resolution Imaging Spectroradiometer (MODIS, it indicated that the land cover type approach did not provide a better estimation because of inhomogeneous land cover pattern and the mixing pixel properties. For the two-source linear method, the study suggested that the use of NDVI as parameterization for vegetation fraction can reflect the spectral behavior of shortwave surface reflectance, despite of some deviation due to the averaging characteristics in our linear combination process. A channel-dependent offset and scalar factor could enhance reflectance estimation and further improve AOT retrieval by the current Bremen AErosol Retrieval (BAER approach.

  7. Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

    Directory of Open Access Journals (Sweden)

    Bayes Ahmed

    2013-11-01

    Full Text Available Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth on LST and consequently to devise appropriate policy measures.

  8. An enhanced single-channel algorithm for retrieving land surface temperature from Landsat series data

    Science.gov (United States)

    Wang, Mengmeng; Zhang, Zhaoming; He, Guojin; Wang, Guizhou; Long, Tengfei; Peng, Yan

    2016-10-01

    Land surface temperature (LST) is a critical parameter in the physics of Earth surface processes and is required for many applications related to ecology and environment. Landsat series satellites have provided more than 30 years of thermal information at medium spatial resolution. This paper proposes an enhanced single-channel algorithm (SCen) for retrieving LST from Landsat series data (Landsat 4 to Landsat 8). The SCen algorithm includes three atmospheric functions (AFs), and the latitude and acquisition month of Landsat image were added to the AF models to improve LST retrieval. Performance of the SCen algorithm was assessed with both simulated and in situ data, and accuracy of three single-channel algorithms (including the monowindow algorithm developed by Qin et al., SCQin, and the generalized single-channel algorithm developed by Jiménez-Muñoz and Sobrino, SCJ&S) were compared. The accuracy assessments with simulated data had root-mean-square deviations (RMSDs) for the SCen, SCJ&S, and SCQin algorithms of 1.363 K, 1.858 K, and 2.509 K, respectively. Validation with in situ data showed RMSDs for the SCen and SCJ&S algorithms of 1.04 K and 1.49 K, respectively. It was concluded that the SCen algorithm is very operational, has good precision, and can be used to develop an LST product for Landsat series data.

  9. Trends in ERS and Envisat (A)ATSR Global Land Surface Temperature Data Since 1991

    Science.gov (United States)

    Kogler, Christian; Pinnock, Simon; Arino, Olivier; Casadio, Stefano; Corlett, Gary; Prata, Fred; Bras, Teresa

    2010-12-01

    Land surface temperature (LST) is a key parameter in the physical study of atmosphere-land interactions as well as for global warming and climate change monitoring on a longer timescale. The main tool to obtain LST as such a key parameter at global scale with different spatial and temporal resolutions is remote sensing. To retrieve highly accurate LSTs, measured radiances at the sensor have to be corrected for emissivity, atmospheric effects and contaminating clouds. This study is based on LST data provided by the Along-Track Scanning Radiometer (ATSR) and the Advanced ATSR (AATSR) on board the three ESA satellites ERS-1, ERS-2 and ENVISAT. The analysis covers data from August 1991 up to December 2009 and contains detailed investigations on global as well as on regional scale with a temporal resolution of one month, outlining problems and restrictions within the time series due to cloud contamination and failing cloud detection tests. It is demonstrated that trends, for cooling as well as for warming, rather show trends in cloud contamination, than real trends in LST.

  10. Reconnoitering the effect of shallow groundwater on land surface temperature and surface energy balance using MODIS and SEBS

    Directory of Open Access Journals (Sweden)

    F. Alkhaier

    2012-07-01

    Full Text Available The possibility of observing shallow groundwater depth and areal extent using satellite measurements can support groundwater models and vast irrigation systems management. Moreover, these measurements can help to include the effect of shallow groundwater on surface energy balance within land surface models and climate studies, which broadens the methods that yield more reliable and informative results. To examine the capacity of MODIS in detecting the effect of shallow groundwater on land surface temperature and the surface energy balance in an area within Al-Balikh River basin in northern Syria, we studied the interrelationship between in-situ measured water table depths and land surface temperatures measured by MODIS. We, also, used the Surface Energy Balance System (SEBS to calculate surface energy fluxes, evaporative fraction and daily evaporation, and inspected their relationships with water table depths. We found out that the daytime temperature increased while the nighttime temperature decreased when the depth of the water table increased. And, when the water table depth increased, net radiation, latent and ground heat fluxes, evaporative fraction and daily evaporation decreased, while sensible heat flux increased. This concords with the findings of a companion paper (Alkhaier et al., 2012. The observed clear relationships were the result of meeting both conditions that were concluded in the companion paper, i.e. high potential evaporation and big contrast in day-night temperature. Moreover, the prevailing conditions in this study area helped SEBS to yield accurate estimates. Under bare soil conditions and under the prevailing weather conditions, we conclude that MODIS is suitable for detecting the effect of shallow groundwater because it has proper imaging times and adequate sensor accuracy; nevertheless, its coarse spatial resolution is disadvantageous.

  11. The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas

    Directory of Open Access Journals (Sweden)

    Jiong Wang

    2015-12-01

    Full Text Available Current characterization of the Urban Heat Island (UHI remains insufficient to support the effective mitigation and adaptation of increasing temperatures in urban areas. Planning and design strategies are restricted to the investigation of temperature anomalies at a city scale. By focusing on Land Surface Temperature of Wuhan, China, this research examines the temperature variations locally where mitigation and adaptation would be more feasible. It shows how local temperature anomalies can be identified morphologically. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. They are first considered as noisy observations of the latent temperature patterns. The continuous latent patterns of the temperature are then recovered from these discrete observations by using the non-parametric Multi-Task Gaussian Process Modeling. The Multi-Scale Shape Index is then applied in the area of focus to extract the local morphological features. A triplet of shape, curvedness and temperature is formed as the criteria to extract local heat islands. The behavior of the local heat islands can thus be quantified morphologically. The places with critical deformations are identified as hotpots. The hotspots with certain yearly behavior are further associated with land surface composition to determine effective mitigation and adaptation strategies. This research can assist in the temperature and planning field on two levels: (1 the local land surface temperature patterns are characterized by decomposing the variations into fundamental deformation modes to allow a process-based understanding of the dynamics; and (2 the characterization at local scale conforms to planning and design conventions where mitigation and adaptation strategies are supposed to be more practical. The weaknesses and limitations of the study are addressed in the closing section.

  12. Reconstruction of MODIS daily land surface temperature under clouds

    Science.gov (United States)

    Sun, L.; Gao, F.; Chen, Z.; Song, L.; Xie, D.

    2015-12-01

    Land surface temperature (LST), generally defined as the skin temperature of the Earth's surface, controls the process of evapotranspiration, surface energy balance, soil moisture change and climate change. Moderate Resolution Imaging Spectrometer (MODIS) is equipped with 1km resolution thermal sensor andcapable of observing the earth surface at least once per day.Thermal infrared bands cannot penetrate cloud, which means we cannot get consistency drought monitoring condition at one area. However, the cloudy-sky conditions represent more than half of the actual day-to-day weather around the global. In this study, we developed an LST filled model based on the assumption that under good weather condition, LST difference between two nearby pixels are similar among the closest 8 days. We used all the valid pixels covered by a 9*9 window to reconstruct the gap LST. Each valid pixel is assigned a weight which is determined by the spatial distance and the spectral similarity. This model is applied in the Middle-East of China including Gansu, Ningxia, Shaanxi province. The terrain is complicated in this area including plain and hill. The MODIS daily LST product (MOD11A3) from 2000 to 2004 is tested. Almost all the gap pixels are filled, and the terrain information is reconstructed well and smoothly. We masked two areas in order to validate the model, one located in the plain, another located in the hill. The correlation coefficient is greater than 0.8, even up to 0.92 in a few days. We also used ground measured day maximum and mean surface temperature to valid our model. Although both the temporal and spatial scale are different between ground measured temperature and MODIS LST, they agreed well in all the stations. This LST filled model is operational because it only needs LST and reflectance, and does not need other auxiliary information such as climate factors. We will apply this model to more regions in the future.

  13. Estimation of Land Surface Temperature from 1-km AVHRR data

    Science.gov (United States)

    Frey, Corinne

    2016-04-01

    In order to re-process DLRs 1km AVHRR data archive to different geophysical and descriptive parameters of the land surface and the atmosphere, a series of scientific data processors are being developed in the framework of the TIMELINE project. The archive of DLR ranges back to the 80ies. One of the data processors is SurfTemp, which processes L2 LST and emissivity datasets from AVHRR L1b data. The development of the data processor included the selection of statistical procedures suitable for time series processing, including four mono-window and six split window algorithms. For almost all of these algorithms, new constants were generated, which better account for different atmospheric and geometric acquisition situations. The selection of optimal algorithms for SurfTemp is based on a round robin approach, in which the selected mono-window and split window algorithms are tested on the basis of a large number of TOA radiance/LST pairs, which were generated using a radiative transfer model and the SeeBorV5 profile database. The original LSTs are thereby compared to the LSTs derived from the TOA radiances using the mono- and split window algorithms. The algorithm comparison includes measures of precision, as well as the sensitivity of a method to the accuracy of its input data. The results of the round robin are presented, as well as the implementation of selected algorithms into SurfTemp. Further, first cross-validation results between the AVHRR LST and MODIS LST are shown.

  14. On the sensitivity of Land Surface Temperature estimates in arid irrigated lands using MODTRAN

    KAUST Repository

    Rosas, Jorge

    2015-11-29

    Land surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of evapotranspiration (ET) and surface moisture status. However, in order to retrieve the ET with an accuracy approaching 10%, LST should be retrieved to within 1 ◦C or better, disregarding other elements of uncertainty. The removal of atmospheric effects is key towards achieving a precise estimation of LST and it requires detailed information on water vapor. The Thermal Infrared Sensor (TIRS) onboard Landsat 8 captures data in two long wave thermal bands with 100-meter resolution. However, the US Geological Survey has reported a calibration problem of TIRS bands caused by stray light, resulting in a higher bias in one of its two bands (4% in band 11, 2% in band 10). Therefore, split-window algorithms for the estimation of LST might not be reliable. Our work will focus on the impact of using different atmospheric profiles (e.g. weather prediction models, satellite) for the estimation of LST derived from MODTRAN by using one of the TIRS bands onboard Landsat 8 (band 10). Sites with in-situ measurements of LST are used as evaluation sources. Comparisons between the measured LST and LST derived based on different atmospheric profile inputs to MODTRAN are carried out from 2 Landsat-overpass days (DOY 153 and 160 2015). Preliminary results show a mean absolute error of around 3 ◦C between in-situ and estimated LST over two different crops (alfalfa and carrot) and bare soil.

  15. Using Microwave Observations to Estimate Land Surface Temperature during Cloudy Conditions

    Science.gov (United States)

    Holmes, T. R.; Crow, W. T.; Hain, C.; Anderson, M. C.

    2014-12-01

    Land surface temperature (LST), a key ingredient for physically-based retrieval algorithms of hydrological states and fluxes, remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and passive microwave observations (MW). TIR is the most commonly used approach and the method of choice to provide standard LST products for various satellite missions. MW-based LST retrievals on the other hand are not as widely adopted for land applications; currently their principle use is in soil moisture retrieval algorithms. MW and TIR technologies present two highly complementary and independent means of measuring LST. MW observations have a high tolerance to clouds but a low spatial resolution, and TIR has a high spatial resolution with temporal sampling restricted to clear skies. The nature of the temperature at the very surface layer of the land makes it difficult to combine temperature estimates between different methods. The skin temperature is characterized by a strong diurnal cycle that is dependant in timing and amplitude on the exact sensing depth and thermal properties of the vegetation. This paper builds on recent progress in characterizing the main structural components of the DTC that explain differences in TIR and MW estimates of LST. Spatial patterns in DTC timing (phase lag with solar noon) and DTC amplitude have been calculated for TIR, MW and compared to weather prediction estimates. Based on these comparisons MW LST can be matched to the TIR record. This paper will compare in situ measurements of LST with satellite estimates from (downscaled) TIR and (reconciled) MW products. By contrasting the validation results of clear sky days with those of cloudy days the expected tolerance to clouds of the MW observations will be tested. The goal of this study is to determine the weather conditions in which MW can supplement the TIR LST record.

  16. Cloud tolerance of remote-sensing technologies to measure land surface temperature

    Science.gov (United States)

    Holmes, Thomas R. H.; Hain, Christopher R.; Anderson, Martha C.; Crow, Wade T.

    2016-08-01

    Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive-microwave (MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25° resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product.This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR-based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

  17. [A Novel Method of Soil Moisture Content Monitoring by Land Surface Temperature and LAI].

    Science.gov (United States)

    Gao, Zhong-ling; Zheng, Xiao-po; Sun, Yue-jun; Wang, Jian-hua

    2015-11-01

    Land surface temperature (Ts) is influenced by soil background and vegetation growing conditions, and the combination of Ts and vegetation indices (Vis) can indicate the status of surface soil moisture content (SMC). In this study, Advanced Temperature Vegetation Dryness Index (ATVDI) used for monitoring SMC was proposed on the basis of the simulation results with agricultural climate model CUPID. Previous studies have concluded that Normalized Difference Vegetation Index (NDVI) easily reaches the saturation point, andLeaf Area Index (LAI) was then used instead of NDVI to estimate soil moisture content in the paper. With LAI-Ts scatter diagram established by the simulation results of CUPID model; how Ts varied with LAI and SMC was found. In the case of the identical soil background, the logarithmic relations between Ts and LAI were more accurate than the linear relations included in Temperature Vegetation Dryness Index (TVDI), based on which ATVDI was then developed. LAI-Ts scatter diagram with satellite imagery were necessary for determining the expression of the upper and lower logarithmic curves while ATVDI was used for monitoring SMC. Ts derived from satellite imagery were then transformed to the Ts-value which has the same SMC and the minimum LAI in study area with look-up table. The measured SMC from the field sites in Weihe Plain, Shanxi Province, China, and the products of LAI and Ts (MOD15A2 and MOD11A2, respectively) produced by the image derived from Moderate Resolution Imaging Spectrometer (MODIS) were collected to validate the new method proposed in this study. The validation results shown that ATVDI (R² = 0.62) was accurate enough to monitor SMC, and it achieved better result than TVDI. Moreover, ATVDI-derived result were Ts values with some physical meanings, which made it comparative in different periods. Therefore, ATVDI is a promising method for monitoring SMC in different time-spatial scales in agricultural fields.

  18. Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

    Science.gov (United States)

    Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

    2010-12-01

    High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

  19. Disaggregation of remotely sensed land surface temperature: A new dynamic methodology

    Science.gov (United States)

    Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Zhu, Xiaolin; Gao, Lun; Zhou, Ji; Ju, Weimin

    2016-09-01

    The trade-off between the spatial and temporal resolutions of satellite-derived land surface temperature (LST) gives birth to disaggregation of LST (DLST). However, the concurrent enhancement of the spatiotemporal resolutions of LST remains difficult, and many studies disregard the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs. Here we propose a new dynamic methodology to enhance concurrently the spatiotemporal resolutions of satellite-derived LSTs. This methodology conducts DLST by the controlling parameters of the temperature cycle models, i.e., the diurnal temperature cycle (DTC) model and annual temperature cycle (ATC) model, rather than directly by the LST. To achieve the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs, herein we incorporate a modulation procedure that adds temporal thermal details to coarse resolution LSTs rather than straightforwardly transforms fine-resolution scaling factors into LSTs. Indirect validations at the same resolution show that the mean absolute error (MAE) between the predicted and reference LSTs is around 1.0 K during a DTC; the associated MAE is around 2.0 K during an ATC, but this relatively lower accuracy is due more to the uncertainty of the ATC model. The upscaling validations indicate that the MAE is around 1.0 K and the normalized mean absolute error is around 0.3. Comparisons between the DTC- and ATC-based DLST illustrate that the former retains a higher accuracy, but the latter holds a higher flexibility on days when background low-resolution LSTs are unavailable. This methodology alters the static DLST into a dynamic way, and it is able to provide temporally continuous fine-resolution LSTs; it will also promote the design of DLST methods for the generation of high-quality LSTs.

  20. Developing a confidence metric for the Landsat land surface temperature product

    Science.gov (United States)

    Laraby, Kelly G.; Schott, John R.; Raqueno, Nina

    2016-05-01

    Land Surface Temperature (LST) is an important Earth system data record that is useful to fields such as change detection, climate research, environmental monitoring, and smaller scale applications such as agriculture. Certain Earth-observing satellites can be used to derive this metric, and it would be extremely useful if such imagery could be used to develop a global product. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a LST product for the Landsat series of satellites has been developed. Currently, it has been validated for scenes in North America, with plans to expand to a trusted global product. For ideal atmospheric conditions (e.g. stable atmosphere with no clouds nearby), the LST product underestimates the surface temperature by an average of 0.26 K. When clouds are directly above or near the pixel of interest, however, errors can extend to several Kelvin. As the product approaches public release, our major goal is to develop a quality metric that will provide the user with a per-pixel map of estimated LST errors. There are several sources of error that are involved in the LST calculation process, but performing standard error propagation is a difficult task due to the complexity of the atmospheric propagation component. To circumvent this difficulty, we propose to utilize the relationship between cloud proximity and the error seen in the LST process to help develop a quality metric. This method involves calculating the distance to the nearest cloud from a pixel of interest in a scene, and recording the LST error at that location. Performing this calculation for hundreds of scenes allows us to observe the average LST error for different ranges of distances to the nearest cloud. This paper describes this process in full, and presents results for a large set of Landsat scenes.

  1. Seasonal evaluation of the land surface scheme HTESSEL against remote sensing derived energy fluxes of the Transdanubian region in Hungary

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    E. L. Wipfler

    2011-04-01

    Full Text Available The skill of the land surface model HTESSEL is assessed to reproduce evaporation in response to land surface characteristics and atmospheric forcing, both being spatially variable. Evaporation estimates for the 2005 growing season are inferred from satellite observations of the Western part of Hungary and compared to model outcomes. Atmospheric forcings are obtained from a hindcast run with the Regional Climate Model RACMO2. Although HTESSEL slightly underpredicts the seasonal evaporative fraction as compared to satellite estimates, the mean, 10th and 90th percentile of this variable are of the same magnitude as the satellite observations. The initial water as stored in the soil and snow layer does not have a significant effect on the statistical properties of the evaporative fraction. However, the spatial distribution of the initial soil and snow water significantly affects the spatial distribution of the calculated evaporative fraction and the models ability to reproduce evaporation correctly in low precipitation areas in the considered region. HTESSEL performs weaker in dryer areas. In Western Hungary these areas are situated in the Danube valley, which is partly covered by irrigated cropland and which also may be affected by shallow groundwater. Incorporating (lateral groundwater flow and irrigation, processes that are not included now, may improve HTESSELs ability to predict evaporation correctly. Evaluation of the model skills using other test areas and larger evaluation periods is needed to confirm the results.

    Based on earlier sensitivity analysis, the effect of a number of modifications to HTESSEL has been assessed. A more physically based reduction function for dry soils has been introduced, the soil depth is made variable and the effect of swallow groundwater included. However, the combined modification does not lead to a significantly improved performance of HTESSEL.

  2. Variational assimilation of land surface temperature observations for enhanced river flow predictions

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    Ercolani, Giulia; Castelli, Fabio

    2016-04-01

    Data assimilation (DA) has the potential of improving hydrologic forecasts. However, many issues arise in case it is employed for spatially distributed hydrologic models that describes processes in various compartments: large dimensionality of the inverse problem, layers governed by different equations, non-linear and discontinuous model structure, complex topology of domains such as surface drainage and river network.On the other hand, integrated models offer the possibility of improving prediction of specific states by exploiting observations of quantities belonging to other compartments. In terms of forecasting river discharges, and hence for their enhancement, soil moisture is a key variable, since it determines the partitioning of rainfall into infiltration and surface runoff. However, soil moisture measurements are affected by issues that could prevent a successful DA and an actual improvement of discharge predictions.In-situ measurements suffer a dramatic spatial scarcity, while observations from satellite are barely accurate and provide spatial information only at a very coarse scale (around 40 km).Hydrologic models that explicitly represent land surface processes of coupled water and energy balance provide a valid alternative to direct DA of soil moisture.They gives the possibility of inferring soil moisture states through DA of remotely sensed Land Surface Temperature (LST), whose measurements are more accurate and with a higher spatial resolution in respect to those of soil moisture. In this work we present the assimilation of LST data in a hydrologic model (Mobidic) that is part of the operational forecasting chain for the Arno river, central Italy, with the aim of improving flood predictions. Mobidic is a raster based, continuous in time and distributed in space hydrologic model, with coupled mass and energy balance at the surface and coupled groundwater and surface hydrology. The variational approach is adopted for DA, since it requires less

  3. Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

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    Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

    2011-01-01

    A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by

  4. Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

    Science.gov (United States)

    Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

    2011-01-01

    A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by

  5. Land surface phenological responses to land use and climate variation in a changing Central Asia

    Science.gov (United States)

    Kariyeva, Jahan

    During the last few decades Central Asia has experienced widespread changes in land cover and land use following the socio-economic and institutional transformations of the region catalyzed by the USSR collapse in 1991. The decade-long drought events and steadily increasing temperature regimes in the region came on top of these institutional transformations, affecting the long term and landscape scale vegetation responses. This research is based on the need to better understand the potential ecological and policy implications of climate variation and land use practices in the contexts of landscape-scale changes dynamics and variability patterns of land surface phenology responses in Central Asia. The land surface phenology responses -- the spatio-temporal dynamics of terrestrial vegetation derived from the remotely sensed data -- provide measurements linked to the timing of vegetation growth cycles (e.g., start of growing season) and total vegetation productivity over the growing season, which are used as a proxy for the assessment of effects of variations in environmental settings. Local and regional scale assessment of the before and after the USSR collapse vegetation response patterns in the natural and agricultural systems of the Central Asian drylands was conducted to characterize newly emerging links (since 1991) between coupled human and natural systems, e.g., socio-economic and policy drivers of altered land and water use and distribution patterns. Spatio-temporal patterns of bioclimatic responses were examined to determine how phenology is associated with temperature and precipitation in different land use types, including rainfed and irrigated agricultural types. Phenological models were developed to examine relationship between environmental drivers and effect of their altitudinal and latitudinal gradients on the broad-scale vegetation response patterns in non-cropland ecosystems of the desert, steppe, and mountainous regional landscapes of Central Asia

  6. Air Temperature estimation from Land Surface temperature and solar Radiation parameters

    Science.gov (United States)

    Lazzarini, Michele; Eissa, Yehia; Marpu, Prashanth; Ghedira, Hosni

    2013-04-01

    Air Temperature (AirT) is a fundamental parameter in a wide range of applications such as climate change studies, weather forecast, energy balance modeling, efficiency of Photovoltaic (PV) solar cells, etc. Air temperature data are generally obtained through regular measurements from meteorological stations. The distribution of these stations is normally sparse, so the spatial pattern of this parameter cannot be accurately estimated by interpolation methods. This work investigated the relationship between Air Temperature measured at meteorological stations and spatially contiguous measurements derived from Remote Sensing techniques, such as Land Surface Temperature (LST) maps, emissivity maps and shortwave radiation maps with the aim of creating a continuous map of AirT. For LST and emissivity, MSG-SEVIRI LST product from Land Surface Analysis Satellite Applications Facility (LSA-SAF) has been used. For shortwave radiation maps, an Artificial Neural Networks ensemble model has been developed and previously tested to create continuous maps from Global Horizontal Irradiance (GHI) point measurements, utilizing six thermal channels of MSG-SEVIRI. The testing sites corresponded to three meteorological stations located in the United Arab Emirates (UAE), where in situ measurements of Air Temperature were available. From the starting parameters, energy fluxes and net radiation have been calculated, in order to have information on the incoming and outgoing long-wave radiation and the incoming short-wave radiation. The preliminary analysis (day and Night measurements, cloud free) showed a strong negative correlation (0.92) between Outgoing long-wave radiation - GHI and LST- AirT, with a RMSE of 1.84 K in the AirT estimation from the initial parameters. Regression coefficients have been determined and tested on all the ground stations. The analysis also demonstrated the predominant impact of the incoming short-wave radiation in the AirT hourly variation, while the incoming

  7. Assessment of the consistency among global microwave land surface emissivity products

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    H. Norouzi

    2014-09-01

    Full Text Available The goal of this work is to inter-compare a number of global land surface emissivity products over various land-cover conditions to assess their consistency. Ultimately, the discrepancies between the studied emissivity products will help interpreting the divergences among numerical weather prediction models in which land emissivity is a key surface boundary parameter. The intercompared retrieved land emissivity products were generated over five-year period (2003–2007 using observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E, Special Sensor Microwave Imager (SSM/I, The Tropical Rainfall Measuring Mission (TRMM Microwave Imager (TMI and Windsat. First, all products were reprocessed in the same projection and spatial resolution as they were generated from sensors with various configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for each product to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe. The emissivity values from four products were also compared to soil moisture estimates and satellite-based vegetation index to assess their sensitivities to the changes in land surface conditions. Results show that systematic differences among products exist and variation of emissivities at each product has similar frequency dependency at any land cover type. Monthly means of emissivity values from AMSR-E in the vertical and horizontal polarizations seem to be systematically lower across various land cover condition which may be attributed to the 1.30 a.m./p.m. overpass time of the sensor and possibly a residual skin temperature effect in the product. The standard deviation of the analysed products was the lowest (less than 0.01 in rain forest regions for all products and the highest in northern latitudes, above 0.04 for AMSR-E and SSM/I and around 0.03 for WindSat. Despite differences in absolute

  8. Observed and simulated effect of plant physiology and structure on land surface energy fluxes and soil conditions

    Science.gov (United States)

    Lu, Yen-Sen; Rihani, Jehan; Langensiepen, Matthias; Simmer, Clemens

    2016-04-01

    The parameterization of stomatal conductance and leaf area index (LAI) in land surface models largely influence simulated terrestrial system states. While stomatal conductance mainly controls transpiration, latent heat flux, and root-water-uptake, LAI impacts additionally the radiative energy exchange. Thus both affect canopy evaporation and transpiration and land surface energy and water fluxes as a whole. Common parameterizations of stomatal conductance follow either semi-mechanistic forms based on photosynthesis (Ball-Berry Type (BB)) or forms which consider environmental factors such as impact of light, temperature, humidity and soil moisture (Jarvis-Stewart Type (JS)). Both approaches differ also in the interpretation of humidity effects and light-use efficiency. While soil moisture plays an important role for root-water-uptake there is no clear conclusion yet about how soil moisture interacts with stomata activity. Values for LAI can be obtained from field measurements, satellite estimates or modelling and are used as an essential model input. While field measurements are very time consuming and only represent single points, satellite estimates may have biases caused by variable albedo and sensor limitations. Representing LAI within land surface models requires complex schemes in order to represent all processes contributing to plant growth. We use the Terrestrial System Modelling Platform (TerrSysMP) over the Rur watershed in Germany for studying the influence of plant physiology and structure on the state of the terrestrial system. The Transregional Collaborative Research Center 32 (TR32) extensively monitors this catchment for almost a decade. The land surface (CLM3.5) and the subsurface (ParFlow) modules of TerrSysMP are conditioned based on satellite-retrieved land cover and the soil map from FAO and forced with a high-resolution reanalysis by DWD. For studying the effect of plant physiology, the Ball-Berry-Leuning, and Jarvis-Stewart stomatal

  9. Detection of open water dynamics with ENVISAT ASAR in support of land surface modelling at high latitudes

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    A. Bartsch

    2012-02-01

    Full Text Available Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4. The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km. The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected.

    C-band data from ENVISAT ASAR (Advanced SAR operating in wide swath mode (150 m resolution were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian

  10. A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature

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    Yongmin Yang

    2017-01-01

    Full Text Available The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H to analytically estimate the radiometric-convective resistance (rae via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD of H and latent heat flux (LE from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System. Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation

  11. Fuel moisture content estimation: a land-surface modelling approach applied to African savannas

    Science.gov (United States)

    Ghent, D.; Spessa, A.; Kaduk, J.; Balzter, H.

    2009-04-01

    Despite the importance of fire to the global climate system, in terms of emissions from biomass burning, ecosystem structure and function, and changes to surface albedo, current land-surface models do not adequately estimate key variables affecting fire ignition and propagation. Fuel moisture content (FMC) is considered one of the most important of these variables (Chuvieco et al., 2004). Biophysical models, with appropriate plant functional type parameterisations, are the most viable option to adequately predict FMC over continental scales at high temporal resolution. However, the complexity of plant-water interactions, and the variability associated with short-term climate changes, means it is one of the most difficult fire variables to quantify and predict. Our work attempts to resolve this issue using a combination of satellite data and biophysical modelling applied to Africa. The approach we take is to represent live FMC as a surface dryness index; expressed as the ratio between the Normalised Difference Vegetation Index (NDVI) and land-surface temperature (LST). It has been argued in previous studies (Sandholt et al., 2002; Snyder et al., 2006), that this ratio displays a statistically stronger correlation to FMC than either of the variables, considered separately. In this study, simulated FMC is constrained through the assimilation of remotely sensed LST and NDVI data into the land-surface model JULES (Joint-UK Land Environment Simulator). Previous modelling studies of fire activity in Africa savannas, such as Lehsten et al. (2008), have reported significant levels of uncertainty associated with the simulations. This uncertainty is important because African savannas are among some of the most frequently burnt ecosystems and are a major source of greenhouse trace gases and aerosol emissions (Scholes et al., 1996). Furthermore, regional climate model studies indicate that many parts of the African savannas will experience drier and warmer conditions in future

  12. SYNOPTIC GLOBAL REMOTE SENSING OF LAND SURFACE VEGETATION: OVERVIEW OF DAILY DATA QUALITY, CHALLENGES, AND OPPORTUNITIES

    Science.gov (United States)

    Barreto-Munoz, A.; Didan, K.

    2009-12-01

    Continuous acquisition of global satellite imagery over the years has contributed to the creation of a long data record from AVHRR, MODIS, TM, SPOT VGT, and other sensors. These records account now for 30+ years, and as the archive grows, it becomes an invaluable source of data for many environmental related studies dealing with trends and changes from local to global scale. Synoptic global remote sensing provides a multitude of land surface state variables and serves as a major foundation for global change research. However, these records are inhibited with problems that need to be accounted for in order to understand the limits and improve the science results derived from these records. The presence of clouds, aerosols, spatial gaps, variable viewing geometry, inconsistent atmosphere corrections, multiple reprocessing, and different sensors characteristics, makes it difficult to obtain frequently high quality data everywhere and every time. Moreover, these issues are location and season dependent making it even more difficult to construct the consistent time series required to study change over time. To evaluate these records, we analyzed 30+ years (1981 to 1999 and 2000 to 2009) of daily global land surface measurements (CMG resolution) from AVHRR (N07, N09, N11 and N14) and MODIS (AQUA and TERRA, Collection 5, C5). We stratified the data based on land cover, latitudinal zone, and season and we examined the daily data quality, including cloud persistence, aerosol loads, data gaps, and an index of reliability that measures how likely an observation is acceptable for research. The aim was to generate aggregate maps of cloud distribution, aerosol levels distribution, and data reliability distribution in both time and space. This information was then converted into an uncertainty measure at the pixel level that indicates how suspect or significant a result could potentially be, depending on its location and season and consequently what geographic locations and times

  13. A climatology of visible surface reflectance spectra

    Science.gov (United States)

    Zoogman, Peter; Liu, Xiong; Chance, Kelly; Sun, Qingsong; Schaaf, Crystal; Mahr, Tobias; Wagner, Thomas

    2016-09-01

    We present a high spectral resolution climatology of visible surface reflectance as a function of wavelength for use in satellite measurements of ozone and other atmospheric species. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is planned to measure backscattered solar radiation in the 290-740 nm range, including the ultraviolet and visible Chappuis ozone bands. Observation in the weak Chappuis band takes advantage of the relative transparency of the atmosphere in the visible to achieve sensitivity to near-surface ozone. However, due to the weakness of the ozone absorption features this measurement is more sensitive to errors in visible surface reflectance, which is highly variable. We utilize reflectance measurements of individual plant, man-made, and other surface types to calculate the primary modes of variability of visible surface reflectance at a high spectral resolution, comparable to that of TEMPO (0.6 nm). Using the Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirection Reflectance Distribution Function (BRDF)/albedo product and our derived primary modes we construct a high spatial resolution climatology of wavelength-dependent surface reflectance over all viewing scenes and geometries. The Global Ozone Monitoring Experiment-2 (GOME-2) Lambertian Equivalent Reflectance (LER) product provides complementary information over water and snow scenes. Preliminary results using this approach in multispectral ultraviolet+visible ozone retrievals from the GOME-2 instrument show significant improvement to the fitting residuals over vegetated scenes.

  14. The effects of land surface process perturbations in a global ensemble forecast system

    Science.gov (United States)

    Deng, Guo; Zhu, Yuejian; Gong, Jiandong; Chen, Dehui; Wobus, Richard; Zhang, Zhe

    2016-10-01

    Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

  15. Examining the Impacts of High-Resolution Land Surface Initialization on Model Predictions of Convection in the Southeastern U.S.

    Science.gov (United States)

    Case, Jonathan L.; Kumar, Sujay V.; Santos, Pablo; Medlin, Jeffrey M.; Jedlovec, Gary J.

    2009-01-01

    simulations over the Mobile, AL and Miami, FL NWS county warning areas. Future efforts may involve examining the impacts of assimilating remotely-sensed soil moisture data, and/or introducing weekly greenness vegetation fraction composites (as opposed to monthly climatologies) into ol'fline NASA LIS runs. Based on positive impacts, the offline LIS runs could be transitioned into an operational mode, providing land surface initialization data to NWS forecast offices in real time.

  16. The Climatology of Australian Aerosol

    Science.gov (United States)

    Mitchell, Ross M.; Forgan, Bruce W.; Campbell, Susan K.

    2017-04-01

    Airborne particles or aerosols have long been recognised for their major contribution to uncertainty in climate change. In addition, aerosol amounts must be known for accurate atmospheric correction of remotely sensed images, and are required to accurately gauge the available solar resource. However, despite great advances in surface networks and satellite retrievals over recent years, long-term continental-scale aerosol data sets are lacking. Here we present an aerosol assessment over Australia based on combined sun photometer measurements from the Bureau of Meteorology Radiation Network and CSIRO/AeroSpan. The measurements are continental in coverage, comprising 22 stations, and generally decadal in timescale, totalling 207 station-years. Monthly climatologies are given at all stations. Spectral decomposition shows that the time series can be represented as a weighted sum of sinusoids with periods of 12, 6 and 4 months, corresponding to the annual cycle and its second and third harmonics. Their relative amplitudes and phase relationships lead to sawtooth-like waveforms sharply rising to an austral spring peak, with a slower decline often including a secondary peak during the summer. The amplitude and phase of these periodic components show significant regional change across the continent. Fits based on this harmonic analysis are used to separate the periodic and episodic components of the aerosol time series. An exploratory classification of the aerosol types is undertaken based on (a) the relative periodic amplitudes of the Ångström exponent and aerosol optical depth, (b) the relative amplitudes of the 6- and 4-month harmonic components of the aerosol optical depth, and (c) the ratio of episodic to periodic variation in aerosol optical depth. It is shown that Australian aerosol can be broadly grouped into three classes: tropical, arid and temperate. Statistically significant decadal trends are found at 4 of the 22 stations. Despite the apparently small

  17. Towards a public, standardized, diagnostic benchmarking system for land surface models

    Directory of Open Access Journals (Sweden)

    G. Abramowitz

    2012-02-01

    Full Text Available We examine different conceptions of land surface model benchmarking and illustrate the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. We additionally show how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and give an example of how these expectations might be quantified. Finally, we introduce the Protocol for the Analysis of Land Surface models (PALS, a free, online land surface model benchmarking application, and show how it is structured to meet both of these goals.

  18. Towards a public, standardized, diagnostic benchmarking system for land surface models

    Directory of Open Access Journals (Sweden)

    G. Abramowitz

    2012-06-01

    Full Text Available This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.

  19. The sea and land surface temperature radiometer (SLSTR) detection assembly design and performance

    Science.gov (United States)

    Coppo, Peter; Mastrandrea, Carmine; Stagi, Moreno; Calamai, Luciano; Barilli, Marco; Nieke, Jens

    2013-10-01

    The SLSTRs are high accuracy radiometers selected for the GMES mission Sentinel-3 space component to provide SST data continuity respect to previous (A)ATSRs for climatology. Many satellites, each with 7.5-year lifetime, over a 20- year period are foreseen. Sentinel-3A will be launched in 2014 and Sentinel-3B at least 6 months later implying that two identical satellites will be maintained in the same orbit with 180° phase delay. Each SLSTR has an improved design respect to AATSR affording large near nadir and oblique view swaths (1400 and 740 km) for SST/LST global coverage at 1 km spatial resolution with a daily revisit time (with two satellites), appropriate for climate and meteorology. Clouds screening and other products are obtained with 0.5 Km spatial resolution in visible and SWIR bands while two additional channels are included to monitor high temperature events, such as forest fires. The two swaths are obtained with two conical scans and telescopes combined optically at a common focus, representing the input of a cooled Focal Plane Assembly, where nine channels are separated with dichroic and focalized on detectors with appropriate optical relays. IR and SWIR optics/detectors are cooled to 85 K by an active mechanical cryo-cooler with vibration compensation, while the VIS ones are maintained at a stable temperature. The opto-mechanical design and the expected electro-optical performance of the Focal Plane Assembly are described and the models predictions at system level are compared with experimental data acquired in the vacuum chamber in flight representative thermal conditions or in laboratory.

  20. Role of climate forecasts and initial land-surface conditions in developing operational streamflow and soil moisture forecasts in a rainfall-runoff regime: skill assessment

    Directory of Open Access Journals (Sweden)

    T. Sinha

    2012-04-01

    Full Text Available Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall-runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC play a critical role. The goal of this study is to quantify the role of monthly updated precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow for a rainfall-runoff mechanism dominated basin – Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC land surface model is implemented with two forcings: (a monthly updated precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM forced with sea surface temperature forecasts and (b daily climatological ensemble. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR model. Mean square error (MSE in predicting monthly streamflow using the above VIC model are compared with the MSE of streamflow climatology under ENSO conditions as well as under normal years. Results indicate that VIC forecasts, at 1–2 month lead time, obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensemble over all the seasons except forecasts issued in fall and the PCR models perform better during the fall months. Over longer lead times (3–6 months, VIC forecasts derived using ECHAM4.5 forcings alone performed better

  1. Evaluations of SST Climatologies in the Tropical Pacific Ocean

    Science.gov (United States)

    2009-02-27

    boundaries Casey and Cornillon, 1999]. An observation-based clima - r 1 -ru 1 r.u- • . .r e...along with SSTs from AVHRR satellite retrievals. The NOAA SST product was built from two intermediate climatologies: a 2° SST clima - tology developed...2D-Var data sets, depending on the period considered. Thus, ECMWF clima - tologies examined here are the analyses of SST observations prescribed as

  2. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    Science.gov (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  3. Evaluation of MODIS Land Surface Temperature with In Situ Snow Surface Temperature from CREST-SAFE

    Science.gov (United States)

    Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Munoz, J.; Khanbilvardi, R.; Yu, Y.

    2016-12-01

    This paper presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration (NASA) Terra and Aqua Earth Observing System satellites using in situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center - Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January-April) and 2014 (February-April). A total of 314 day and night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. Additionally, this investigation incorporated supplementary analyses using meteorological CREST-SAFE in situ variables (i.e. wind speed, cloud cover, incoming solar radiation) to study their effects on in situ snow surface temperature (T-skin) and T-air. Furthermore, a single pixel (1km2) and several spatially averaged pixels were used for satellite LST validation by increasing the MODIS window size to 5x5, 9x9, and 25x25 windows for comparison. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and nighttime values. Results indicate that, although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C), both suggesting that MODIS LST retrievals are reliable for similar land cover classes and atmospheric conditions. Results from the CREST-SAFE in situ variables' analyses indicate that T-air is commonly higher than T-skin, and that a lack of cloud cover results in: lower T-skin and higher T-air minus T-skin difference (T-diff). Additionally, the study revealed that T-diff is inversely proportional to cloud cover, wind speed, and incoming solar radiation. Increasing the MODIS window size

  4. High-resolution climate and land surface interactions modeling over Belgium: current state and decennial scale projections

    Science.gov (United States)

    Jacquemin, Ingrid; Henrot, Alexandra-Jane; Beckers, Veronique; Berckmans, Julie; Debusscher, Bos; Dury, Marie; Minet, Julien; Hamdi, Rafiq; Dendoncker, Nicolas; Tychon, Bernard; Hambuckers, Alain; François, Louis

    2016-04-01

    The interactions between land surface and climate are complex. Climate changes can affect ecosystem structure and functions, by altering photosynthesis and productivity or inducing thermal and hydric stresses on plant species. These changes then impact socio-economic systems, through e.g., lower farming or forestry incomes. Ultimately, it can lead to permanent changes in land use structure, especially when associated with other non-climatic factors, such as urbanization pressure. These interactions and changes have feedbacks on the climate systems, in terms of changing: (1) surface properties (albedo, roughness, evapotranspiration, etc.) and (2) greenhouse gas emissions (mainly CO2, CH4, N2O). In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), we aim at improving regional climate model projections at the decennial scale over Belgium and Western Europe by combining high-resolution models of climate, land surface dynamics and socio-economic processes. The land surface dynamics (LSD) module is composed of a dynamic vegetation model (CARAIB) calculating the productivity and growth of natural and managed vegetation, and an agent-based model (CRAFTY), determining the shifts in land use and land cover. This up-scaled LSD module is made consistent with the surface scheme of the regional climate model (RCM: ALARO) to allow simulations of the RCM with a fully dynamic land surface for the recent past and the period 2000-2030. In this contribution, we analyze the results of the first simulations performed with the CARAIB dynamic vegetation model over Belgium at a resolution of 1km. This analysis is performed at the species level, using a set of 17 species for natural vegetation (trees and grasses) and 10 crops, especially designed to represent the Belgian vegetation. The CARAIB model is forced with surface atmospheric variables derived from the monthly global CRU climatology or ALARO outputs

  5. The Utility of Remotely-Sensed Land Surface Temperature from Multiple Platforms For Testing Distributed Hydrologic Models over Complex Terrain

    Science.gov (United States)

    Xiang, T.; Vivoni, E. R.; Gochis, D. J.

    2011-12-01

    Land surface temperature (LST) is a key parameter in watershed energy and water budgets that is relatively unexplored as a validation metric for distributed hydrologic models. Ground-based or remotely-sensed LST datasets can provide insights into a model's ability in reproducing water and energy fluxes across a large range of terrain, vegetation, soil and meteorological conditions. As a result, spatiotemporal LST observations can serve as a strong constraint for distributed simulations and can augment other available in-situ data. LST fields are particular useful in mountainous areas where temperature varies with terrain properties and time-variable surface conditions. In this study, we collect and process remotely-sensed fields from several satellite platforms - Landsat 5/7, MODIS and ASTER - to capture spatiotemporal LST dynamics at multiple resolutions and with frequent repeat visits. We focus our analysis of these fields over the Sierra Los Locos basin (~100 km2) in Sonora, Mexico, for a period encompassing the Soil Moisture Experiment in 2004 and the North American Monsoon Experiment (SMEX04-NAME). Satellite observations are verified using a limited set of ground data from manual sampling at 30 locations and continuous measurements at 2 sites. First, we utilize the remotely-sensed fields to understand the summer seasonal evolution of LST in the basin in response to the arrival of summer storms and the vigorous ecosystem greening organized along elevation bands. Then, we utilize the ground and remote-sensing datasets to test the distributed predictions of the TIN-based Real-time Integrated Basin Simulator (tRIBS) under conditions accounting static and dynamic vegetation patterns. Basin-averaged and distributed comparisons are carried out for two different terrain products (INEGI aerial photogrammetry and ASTER stereo processing) used to derive the distributed model domain. Results from the comparisons are discussed in light of the utility of remotely-sensed LST

  6. Retrieval of Land Surface Temperature over the Heihe River Basin Using HJ-1B Thermal Infrared Data

    Directory of Open Access Journals (Sweden)

    Xiaoying Ouyang

    2014-12-01

    Full Text Available The reliable estimation of spatially distributed Land Surface Temperature (LST is useful for monitoring regional land surface heat fluxes. A single-channel method is developed to derive the LST over the Heihe River Basin in China using data from the infrared sensor (IRS onboard the Chinese “Environmental and Disaster Monitoring and Forecasting with a Small Satellite Constellation” (HJ-1B for short for one of the satellites, with ancillary water vapor information from Moderate Resolution Imaging Spectroradiometer (MODIS products (MOD05 and in situ automatic sun tracking photometer CE318 data for the first time. In situ LST data for the period from mid-June to mid-September 2012 were acquired from automatic meteorological stations (AMS that are part of Heihe Watershed Allied Telemetry Experimental Research (HiWATER project. MOD05-based LST and CE318-based LST are compared with in situ measurements at 16 AMS sites with land cover types of vegetable, maize and orchards. The results show that the use of the MOD05 product could achieve a comparable accuracy in LST retrieval with that achieved using the CE318 data. The largest difference between the MOD05-based LST and CE318-based LST is 0.84 K throughout the study period over the Heihe River Basin. The standard deviation (STD, root mean square error (RMSE, and correlation coefficient (R of HJ-1B/IRS vs. the in situ measurements are 2.45 K, 2.78 K, and 0.67, respectively, whereas those for the MODIS 1 km LST product vs. the in situ measurements are 4.07 K, 2.98 K, and 0.79, respectively. The spatial pattern of the HJ-1B/LST over the study area in the Heihe River Basin generally agreed well with the MODIS 1 km LST product and contained more detailed spatial textures.

  7. Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

    Science.gov (United States)

    Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

    2010-01-01

    Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

  8. Geostatistical improvements of evapotranspiration spatial information using satellite land surface and weather stations data

    Science.gov (United States)

    de Carvalho Alves, Marcelo; de Carvalho, Luiz Gonsaga; Vianello, Rubens Leite; Sediyama, Gilberto C.; de Oliveira, Marcelo Silva; de Sá Junior, Arionaldo

    2013-07-01

    The objective of the present study was to use the simple cokriging methodology to characterize the spatial variability of Penman-Monteith reference evapotranspiration and Thornthwaite potential evapotranspiration methods based on Moderate Resolution Imaging Spetroradiometer (MODIS) global evapotranspiration products and high-resolution surfaces of WordClim temperature and precipitation data. The climatic element data referred to 39 National Institute of Meteorology climatic stations located in Minas Gerais state, Brazil and surrounding states. The use of geostatistics and simple cokriging technique enabled the characterization of the spatial variability of the evapotranspiration providing uncertainty information on the spatial prediction pattern. Evapotranspiration and precipitation surfaces were implemented for the climatic classification in Minas Gerais. Multivariate geostatistical determined improvements of evapotranspiration spatial information. The regions in the south of Minas Gerais derived from the moisture index estimated with the MODIS evapotranspiration (2000-2010), presented divergence of humid conditions when compared to the moisture index derived from the simple kriged and cokriged evapotranspiration (1961-1990), indicating climate change in this region. There was stronger pattern of crossed covariance between evapotranspiration and precipitation rather than temperature, indicating that trends in precipitation could be one of the main external drivers of the evapotranspiration in Minas Gerais state, Brazil.

  9. Modeling directional effects in land surface temperature derived from geostationary satellite data

    DEFF Research Database (Denmark)

    Rasmussen, Mads Olander

    varying magnitude and sign on both diurnal and seasonal scales, which will have implications if using LST products in downstream applications like hydrological or soil vegetation atmosphere transfer (SVAT) models. The directional effects will cause uncertainties in LST estimates that are different...... in terms of timing than the uncertainties in data from polar orbiting sensors, which will cause discrepancies between measurements from the two types of sensors. An assessment of the performance of current LST algorithms from MSG SEVIRI for semi-arid West Africa was carried out, using data from two field...... the illumination geometry changes both over the course of the day and with the seasons. In the present study, the directional effects are assessed at different scales using a modeling approach. The model applied, the Modified Geometry Projection (MGP) model, represents the surface as a composite of four components...

  10. International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 2 Monthly

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

  11. International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 2 Daily

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

  12. International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 3 Monthly

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Land Surface Temperature Databank contains monthly timescale mean, maximum, and minimum temperature for approximately 40,000 stations globally. It was...

  13. International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 1 Daily

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

  14. International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 1 Monthly

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

  15. Modeling the impact of land surface feedbacks on post landfall tropical cyclones

    Science.gov (United States)

    Subramanian, Subashini

    The land surface is an important component of numerical models. The land surface models are modules that control energy partitioning, compute surface exchange coefficients and form the only physical boundary in a regional scale numerical model. Thus, an accurate representation of land surface is critical to compute surface fluxes, represent the boundary layer evolution and affect changes in weather systems. Land surface can affect landfalling tropical cyclones in two ways: (i) when the cyclone is offshore and land can influence cyclones by introducing dry (or moist) air that can weaken (or strengthen) the organized convective structure of cyclones, and (ii) land can affect the evolution of cyclones post landfall by modifying the surface heat fluxes and introducing additional surface drag. In this dissertation, the hypothesis that improved representation of land surface conditions will improve the prediction of landfalling tropical cyclones is tested. To that effect, a comprehensive review of land surface effects on tropical cyclones was undertaken and an idealized study was conducted to study the impact of antecedent soil temperature on the sustenance/reintensification of tropical cyclones over land. Rainfall verification for cyclone events over the Atlantic Ocean was conducted and a comparison study between land models--GFDL Slab and Noah, also considers the sensitivity of tropical cyclone models to land surface parameterizations. The recent adoption of Noah land model with hydrology products in HWRF offers a unique opportunity to couple a river routing model to HWRF to provide streamflow estimations from the HWRF model and this dissertation has outlined techniques to real time predict streamflow for United States with HWRF forcing. Results from this dissertation research indicate antecedent land surface conditions can affect tropical cyclone evolution post landfall and high soil temperature and thermally diffusive soil texture of land surface are critical factors

  16. Remote Sensing and Synchronous Land Surface Measurements of Soil Moisture and Soil Temperature in the Field

    Science.gov (United States)

    Kolev, N. V.; Penev, K. P.; Kirkova, Y. M.; Krustanov, B. S.; Nazarsky, T. G.; Dimitrov, G. K.; Levchev, C. P.; Prodanov, H. I.; Kraleva, L. H.

    1998-01-01

    The paper presents the results of remote sensing and synchronous land surface measurements for estimation of soil (surface and profile) water content and soil temperature for different soil types in Bulgaria. The relationship between radiometric temperature and soil surface water content is shown. The research is illustrated by some results from aircraft and land surface measurements carried out over three test areas near Pleven, Sofia and Plovdiv, respectively, during the period 1988-1990.

  17. Simulation Experiments of Land Surface Physical Processes and Ecological Effect over Different Underlying Surface

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    Based on the existing Land Surface Physical Process Models(Deardorff, Dickinson, LIU, Noilhan, Seller, ZHAO), a Comprehensive Land Surface Physical Process Model (CLSPPM) is developed by considering the different physical processes of the earth's surface-vegetation-atmosphere system more completely. Compared with SiB and BATS, which are famous for their detailed parameterizations of physical variables, this simplified model is more convenient and saves much more computation time. Though simple, the feas...

  18. On the balance of precipitation and evaporation over global oceans in satellite based and reanalysis data sets

    Science.gov (United States)

    Bakan, S.; Andersson, A.; Fennig, K.; Klepp, C.; Klocke, D.; Schulz, J.

    2009-04-01

    Over the global oceans, precipitation should be smaller than evaporation and the balance should be compensated by the global runoff from land surfaces. But to which extent do satellite climatologies and reanalysis products reproduce this basic feature of the global water cycle? The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data set, HOAPS-3 (www.hoaps.org), contains fields of precipitation and evaporation over the global ocean and all basic state variables needed for the derivation of the fluxes. Except for the NOAA Pathfinder SST data set, all variables are derived from SSM/I satellite data over the ice free global ocean between 1987 and 2005. Special emphasis has been put into quality control and inter-satellite calibration in order to derive the data fields as homogeneous as possible. One of the major design goals of HOAPS was to provide a data set that is based exclusively on retrieval procedures which avoid any additional model or reanalysis input. On a global scale, the average evaporation since 1987 exceeds precipitation rate over the oceans in HOAPS-3 systematically, with almost negligible yearly cycle and small monthly variations. While the globally averaged precipitation time series does not exhibit any significant trend over the study period, evaporation shows a continuous increase during this time. Regionally, this increase concentrates in the subtropics and is, together with some reduction in precipitation, consistent with a strengthening of the Hadley circulation during the observation period. These results are compared with similar data fields of the same period from various satellite climatologies to insure the consistency of our results and to the NCEP and ERA40 as well as ERAInterim reanalysis products. Remarkable similarities and differences between the different information sources have been found and will be discussed in the presentation.

  19. Human-induced greening of the northern extratropical land surface

    Science.gov (United States)

    Mao, Jiafu; Ribes, Aurélien; Yan, Binyan; Shi, Xiaoying; Thornton, Peter E.; Séférian, Roland; Ciais, Philippe; Myneni, Ranga B.; Douville, Hervé; Piao, Shilong; Zhu, Zaichun; Dickinson, Robert E.; Dai, Yongjiu; Ricciuto, Daniel M.; Jin, Mingzhou; Hoffman, Forrest M.; Wang, Bin; Huang, Mengtian; Lian, Xu

    2016-10-01

    Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.

  20. Attenuating the surface Urban Heat Island within the Local Thermal Zones through land surface modification.

    Science.gov (United States)

    Wang, Jiong; Ouyang, Wanlu

    2017-02-01

    Inefficient mitigation of excessive heat is attributed to the discrepancy between the scope of climate research and conventional planning practice. This study approaches this problem at both domains. Generally, the study, on one hand, claims that the climate research of the temperature phenomenon should be at local scale, where implementation of planning and design strategies can be more feasible. On the other hand, the study suggests that the land surface factors should be organized into zones or patches, which conforms to the urban planning and design manner. Thus in each zone, the land surface composition of those excessively hot places can be compared to the zonal standard. The comparison gives guidance to the modification of the land surface factors at the target places. Specifically, this study concerns the Land Surface Temperature (LST) in Wuhan, China. The land surface is classified into Local Thermal Zones (LTZ). The specifications of temperature sensitive land surface factors are relative homogeneous in each zone and so is the variation of the LST. By extending the city scale analysis of Urban Heat Island into local scale, the Local Surface Urban Heat Islands (LSUHIs) are extracted. Those places in each zone that constantly maintain as LSUHI and exceed the homogenous LST variation are considered as target places or hotspots with higher mitigation or adaptation priority. The operation is equivalent to attenuate the abnormal LST variation in each zone. The framework is practical in the form of prioritization and zoning, and mitigation strategies are essentially operated locally.

  1. Characterizing land surface phenology and responses to rainfall in the Sahara desert

    Science.gov (United States)

    Yan, Dong; Zhang, Xiaoyang; Yu, Yunyue; Guo, Wei; Hanan, Niall P.

    2016-08-01

    Land surface phenology (LSP) in the Sahara desert is poorly understood due to the difficulty in detecting subtle variations in vegetation greenness. This study examined the spatial and temporal patterns of LSP and its responses to rainfall seasonality in the Sahara desert. We first generated daily two-band enhanced vegetation index (EVI2) from half-hourly observations acquired by the Spinning Enhanced Visible and Infrared Imager on board the Meteosat Second Generation series of geostationary satellites from 2006 to 2012. The EVI2 time series was used to retrieve LSP based on the Hybrid Piecewise Logistic Model. We further investigated the associations of spatial and temporal patterns in LSP with those in rainfall seasonality derived from the daily rainfall time series of the Tropical Rainfall Measurement Mission. Results show that the spatial shifts in the start of the vegetation growing season generally follow the rainy season onset that is controlled by the summer rainfall regime in the southern Sahara desert. In contrast, the end of the growing season significantly lags the end of the rainy season without any significant dependence. Vegetation growing season can unfold during the dry seasons after onset is triggered during rainy seasons. Vegetation growing season can be as long as 300 days or more in some areas and years. However, the EVI2 amplitude and accumulation across the Sahara region was very low indicating sparse vegetation as expected in desert regions. EVI2 amplitude and accumulated EVI2 strongly depended on rainfall received during the growing season and the preceding dormancy period.

  2. Multi-site evaluation of the JULES land surface model using global and local data

    Directory of Open Access Journals (Sweden)

    D. Slevin

    2015-02-01

    Full Text Available This study evaluates the ability of the JULES land surface model (LSM to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM climate model. Firstly, gross primary productivity (GPP estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET. Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI. Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.

  3. Patterns in the Land Surface Phenology of North American Mountain Systems from 2000 to 2011

    Science.gov (United States)

    Hudson Dunn, A.; de Beurs, K. M.; Prisley, S. P.

    2011-12-01

    Mountain and alpine ecosystems cover more than twenty percent of the Earth's land surface spanning an area from the equator to just near the poles. In addition to the commonly known characteristics of a marked topographic variation resulting in steep slopes and varied aspects, mountains are highly diverse systems in flora, fauna, and human ethnicity, and are found, at varying altitudes, on every continent. These regions experience unique climate patterns aiding in the creation of niche vegetation zones; the development of alpine and tundra environments; as well as glaciers; and are expected to experience growing impacts due to shifts in climate patterns currently being seen in all ecosystems worldwide. In order to understand future natural and anthropogenic impacts on these high elevation areas it is essential that we first capture the spatial and temporal patterns and processes that are occurring there. One vital step in this process is the understanding of vegetation phenology throughout. Here, we use the MODIS/Terra satellite 16-day Nadir BRDF Adjusted Reflectance product, to assess the annual seasonality of a diverse variety of North American mountain environments from Alaska to the Appalachian Mountains and down to Sierra Madres in Mexico for the years of 2000 to 2011. Independent data for elevation, slope, aspect, solar radiation, temperature, and precipitation as well as longitude and latitude were related to the seasonal outputs for start of season (SOS), end of season (EOS), maximum photosynthetic activity (MPA), and growing season length (GSL). Preliminary results of these analyses show that the seasonal vegetation pattern within these zones is primarily controlled by elevation, aspect, latitude, and temperature.

  4. Statistical analysis of land surface temperature-vegetation indexes relationship through thermal remote sensing.

    Science.gov (United States)

    Kumar, Deepak; Shekhar, Sulochana

    2015-11-01

    Vegetation coverage has a significant influence on the land surface temperature (LST) distribution. In the field of urban heat islands (UHIs) based on remote sensing, vegetation indexes are widely used to estimate the LST-vegetation relationship. This paper devises two objectives. The first analyzes the correlation between vegetation parameters/indicators and LST. The subsequent computes the occurrence of vegetation parameter, which defines the distribution of LST (for quantitative analysis of urban heat island) in Kalaburagi (formerly Gulbarga) City. However, estimation work has been done on the valuation of the relationship between different vegetation indexes and LST. In addition to the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is attempted to explore the impacts of the green land to the build-up land on the urban heat island by calculating the evaluation index of sub-urban areas. The results indicated that the effect of urban heat island in Kalaburagi city is mainly located in the sub-urban areas or Rurban area especially in the South-Eastern and North-Western part of the city. The correlation between LST and NDVI, indicates the negative correlation. The NDVI suggests that the green land can weaken the effect on urban heat island, while we perceived the positive correlation between LST and NDBI, which infers that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM thermal bands data) has been applied to test the distribution of urban heat islands, but the method still needs to be refined with in situ measurements of LST in future studies.

  5. Studying the Effect of Runoff Parameterization and Interaction between Atmosphere and Land Surface in Land Surface Schemes Used in NWP Models

    Science.gov (United States)

    Khodamorad Poor, M.; Irannejad, P.

    2009-04-01

    Land Surface Schemes that is one of the most important components in climate and numerical weather prediction models (NWP) has concentrated on surface energy and water budgets. Water budget is the hydrologic core of the land surface schemes and it is presented as the precipitation which is divided into evapotranspiration, runoff and changing in soil moisture. It is also introduced by different parameterizations among land surface schemes. Since Runoff is the major component of the water budget, unrealistic simulation of it can have some effects on the other components used in water budget and hence on the laten heat flux between atmosphere and land surface. Different representations of runoff in NWP models are relatively simple because runoff is conceptually difficult to be parameterized. Regarding that topography has a major control on the distribution of soil moisture and runoff, the main objective in this study is to find the parameterization runoff which is better to be introduced in NWP models. The algorithm used in Simple TOP Model (SIMTOP) for runoff parameterization is put in NOAH LSM utilized in Weather Research and Forecasting model (WRF). In SIMTOP, surface and subsurface runoff are considered as exponential functions of water table depth, but in NOAH LSM runoff is produced by extra maximum soil infiltration. The SIMTOP is like TOPMODEL that implemented topographic information (expressed by topographic index) and the nature of soil (indicated by reducing hydraulic conductivity with soil depth). The SIMTOP is simpler than TOPMODEL because of reducing in parameters that are needed to be calibrated. The surface runoff is the sum of two components, the first generated by infiltration excess (Horton mechanism) and the second, referring to variable contributed area, by saturation excess (Dunn mechanism). The subsurface runoff is represented by topographic control, bottom drainage and saturation excess. Although the river routing is very important for

  6. U.S. Local Climatological Data (LCD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Local Climatological Data (LCD) are summaries of climatological conditions from airport and other prominent weather stations managed by NWS, FAA, and DOD. The...

  7. Automation of SimSphere Land Surface Model Use as a Standalone Application and Integration With EO Data for Deriving Key Land Surface Parameters

    Science.gov (United States)

    Petropoulos, George P.; Konstas, Ioannis; Carlson, Toby N.

    2013-04-01

    Use of simulation process models has played a key role in extending our abilities to study Earth system processes and enhancing our understanding on how different components of it interplay. Use of such models combined with Earth Observation (EO) data provides a promising direction towards deriving accurately spatiotemporal estimates of key parameters characterising land surface interactions, by combining the horizontal coverage and spectral resolution of remote sensing data with the vertical coverage and fine temporal continuity of those models. SimSphere is such a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth's land surface. Its use is at present continually expanding worldwide both as an educational and as a research tool for scientific investigations. It is being used either as a stand-alone application or synergistically with EO data. Herein we present recent advancements introduced to SimSphere in different aspects of the model aiming to make its use more robust when used both as a standalone application and synergistically with EO data. We have extensively tested and updated the model code, as well as enhanced it with new functionalities. These included for example taking into account the thermal inertia variation in soil moisture, simulating additional parameters characterising land surface interactions, automating the model use when integrating it with EO data via the "triangle" method and developing batch processing operations. Use of these recently introduced to the model functionalities are illustrated herein using a variety of examples. Our work is significant to the users' community of the model and very timely, given the potential use of SimSphere in an EO-based method being under development for deriving operationally regional estimates of energy fluxes and soil moisture from EO data provided by non-commercial vendors. KEYWORDS: land surface interactions, land surface process

  8. On the fall 2010 Enhancements of the Global Precipitation Climatology Centre's Data Sets

    Science.gov (United States)

    Becker, A. W.; Schneider, U.; Meyer-Christoffer, A.; Ziese, M.; Finger, P.; Rudolf, B.

    2010-12-01

    Precipitation is meanwhile a top listed parameter on the WMO GCOS list of 44 essential climate variables (ECV). This is easily justified by its crucial role to sustain any form of life on earth as major source of fresh water, its major impact on weather, climate, climate change and related issues of society’s adaption to the latter. Finally its occurrence is highly variable in space and time thus bearing the potential to trigger major flood and draught related disasters. Since its start in 1989 the Global precipitation Climatology Centre (GPCC) performs global analyses of monthly precipitation for the earth’s land-surface on the basis of in-situ measurements. The effort was inaugurated as part of the Global Precipitation Climatology Project of the WMO World Climate Research Program (WCRP). Meanwhile, the data set has continuously grown both in temporal coverage (original start of the evaluation period was 1986), as well as extent and quality of the underlying data base. The number of stations involved in the related data base has approximately doubled in the past 8 years by trespassing the 40, 60 and 80k thresholds in 2002, 2006 and 2010. Core data source of the GPCC analyses are the data from station networks operated by the National Meteorological Services worldwide; data deliveries have been received from ca. 190 countries. The GPCC integrates also other global precipitation data collections (i.e. FAO, CRU and GHCN), as well as regional data sets. Currently the Africa data set from S. Nicholson (Univ. Tallahassee) is integrated. As a result of these efforts the GPCC holds the worldwide largest and most comprehensive collection of precipitation data, which is continuously updated and extended. Due to the high spatial-temporal variability of precipitation, even its global analysis requires this high number of stations to provide for a sufficient density of measurement data on almost any place on the globe. The acquired data sets are pre-checked, reformatted

  9. Status of Land Surface Temperature Product Development at NOAA/NESDIS/STAR for JPSS and GOES-R Missions

    Science.gov (United States)

    Yu, Yunyue; Liu, yuling; yu, peng; Casiszar, Ivan; Zhou, Lihang

    2016-04-01

    Land surface temperature (LST) is of fundamental importance to many aspects of the geosciences, e.g., net radiation budget at the Earth surface, monitoring state of crops and vegetation, as well as an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Satellite LST measurements provide unique data sources for regional and global coverage in fairly good temporal, spatial resolution and time span. Therefore, LST is one of baseline products in both JPSS and GOES-R satellite missions. The Center for SaTellite Applications and Research (STAR) of NOAA/NESDIS is responsible for developing high quality LST products for a variety of satellite missions including JPSS and GOES-R. The JPSS LST data, which is produced for each swath of observations, has reached its beta, provisional and validated stage 1 status in October 2013, May 2014, and December 2015, respectively. A routine validation and monitoring toolkit has been developed and its results are available through a public web site. Our validation results against the U.S. SURFRAD ground stations show that uncertainty of the VIIRS LST is less than 2.35K (vs. the JPSS mission requirement of 2.5K). Improvement of the JPSS LST product is on-going, which counts surface emissive variation explicitly in retrieval algorithm. Further, a gridded daily global LST product will be available by end of 2016. In terms of the GOES-R LST product, we have evaluated the retrieval algorithm using SEVIRI and AHI data as proxies. The evaluation results show that the accuracy of GOES-R LST is expected to be less than 2.30K (GOES-R mission requirement). The validation toolkit developed for JPSS mission will be extended and applicable for the GOES-R mission as well. A detailed Readiness, Implementation and Management Plan (RIMP) of GOES-R LST beta and provisional validation has been developed for the GOES-R launch that is scheduled in October 2016.

  10. On the dynamics of the July 2010 Pakistan precipitation events and the central role of land surface - atmosphere interactions

    Science.gov (United States)

    Martius, O.; Sodemann, H.; Joos, H.; Pfahl, S.; Winnschall, A.; Croci-Maspoli, M.; Graf, M.; Madonna, E.; Mueller, B.; Schemm, S.; Sedlacek, J.; Sprenger, M.; Wernli, H.

    2012-04-01

    In July and early August 2010 Russia was afflicted by an unprecedented heat wave and by drought conditions that lead to the death of several thousand people, forest fires, and significant losses in crop harvest. Concomitantly several severe precipitation events and ensuing floods affected Pakistan. At times more than 20% of the country was submerged, causing a humanitarian catastrophe and resulting in significant agricultural and economical losses. The two events did not occur independently and were dynamically closely linked. At upper-levels the flow over western Russia was dominated by the recurrent formation of long-lived anticyclones, so-called blocks during July and early August. Along the downstream flank of these blocks several upper-level troughs formed. These troughs were positive upper-level potential vorticity (PV) anomalies that significantly influenced the low-level wind field over Pakistan. The upper-level anomalies enforced the surface flow component perpendicular to the Himalaya mountains. The ensuing forced lifting of very moist air masses resulted in significant precipitation amounts. Compared to climatology the frequency of upper-level troughs was highly unusual. Besides the upper-level forcing, evapotranspiration and large-scale advection over land was of fundamental importance for the precipitation events. Detailed analyses of the moisture pathways from the Indian Ocean to northeastern Pakistan using trajectory analyses revealed that a substantial fraction of the moisture that was rained-out over northeastern Pakistan stemmed from the land surface. This is confirmed by model experiments where the moisture flux from the land surface into the atmosphere over Pakistan was turned off 48 hours prior to the onset of a major precipitation event. For this event the area mean precipitation over northeastern Pakistan was reduced by 60% in the experiment compared to a control simulation (this corresponds to a reduction of the area mean 48-hour

  11. Declassified Intelligence Satellite Photographs

    Science.gov (United States)

    ,

    2008-01-01

    Declassified photographs from U.S. intelligence satellites provide an important worldwide addition to the public record of the Earth's land surface. This imagery was released to the National Archives and Records Administration (NARA) and the U.S. Geological Survey (USGS) in accordance with Executive Order 12951 on February 23, 1995. The NARA has the original declassified film and a viewing copy. The USGS has another copy of the film to complement the Landsat archive. The declassified collection involves more than 990,000 photographs taken from 1959 through 1980 and was released on two separate occasions: February 1995 (Declass 1) and September 2002 (Declass 2). The USGS copy is maintained by the Earth Resources Observation and Science (EROS) Center, near Sioux Falls, South Dakota. Both the NARA and EROS provide public access to this unique collection that extends the record of land-surface change back another decade from the advent of the Landsat program that began satellite operations in 1972.

  12. Sea Temperature Fiducial Reference Measurements for the Validation and Data Gap Bridging of Satellite SST Data Products

    Science.gov (United States)

    Wimmer, Werenfrid

    2016-08-01

    The Infrared Sea surface temperature Autonomous Radiometer (ISAR) was developed to provide reference data for the validation of satellite Sea Surface Temperature at the Skin interface (SSTskin) temperature data products, particularly the Advanced Along Track Scanning Radiometer (AATSR). Since March 2004 ISAR instruments have been deployed nearly continuously on ferries crossing the English Channel and the Bay of Biscay, between Portsmouth (UK) and Bilbao/Santander (Spain). The resulting twelve years of ISAR data, including an individual uncertainty estimate for each SST record, are calibrated with traceability to national standards (National Institute of Standards and Technology, USA (NIST) and National Physical Laboratory, Teddigton, UK (NPL), Fiducial Reference Measurements for satellite derived surface temperature product validation (FRM4STS)). They provide a unique independent in situ reference dataset against which to validate satellite derived products. We present results of the AATSR validation, and show the use of ISAR fiducial reference measurements as a common traceable validation data source for both AATSR and Sea and Land Surface Temperature Radiometer (SLSTR). ISAR data were also used to review performance of the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature (SST) analysis before and after the demise of ESA Environmental Satellite (Envisat) when AATSR inputs ceased This demonstrates use of the ISAR reference data set for validating the SST climatologies that will bridge the data gap between AATSR and SLSTR.

  13. Sensitivity of a general circulation model to land surface parameters in African tropical deforestation experiments

    Energy Technology Data Exchange (ETDEWEB)

    Maynard, K.; Royer, J.F. [Meteo-France CNRM, 42 Avenue G. Coriolis, 31057, Toulouse Cedex 1 (France)

    2004-06-01

    During the last two decades, several land surface schemes for use in climate, regional and/or mesoscale, hydrological and ecological models have been designed. Incorrect parametrization of land-surface processes and prescription of the surface parameters in atmospheric modeling, can result in artificial changes of the horizontal gradient of the sensible heat flux. Thus, an error in horizontal temperature gradient within the lower atmosphere may be introduced. The reliability of the model depends on the quality of boundary layer scheme implemented and its sensitivity to the bare soil and vegetation parameters. In this study, a series of sensitivity experiments has been conducted over broad time scales, using a version of the ARPEGE Climate Model coupled to the ISBA land surface scheme in order to investigate model sensitivity to separate changes in land surface parameters over Africa. Effects of perturbing vegetation cover, distribution of soil depth, albedo of vegetation, roughness length, leaf area index and minimum stomatal resistance were explored by using a simple statistical analysis. Identifying which parameters are important in controlling turbulent energy fluxes, temperature and soil moisture is dependent on which variables are used to determine sensibility, which type of vegetation and climate regime is being simulated and the magnitude and sign of the parameter change. This study does not argue that a particular parameter is important in ISBA, rather it shows that no general ranking of parameters is possible. So, it is essential to specify all land surface parameters with greater precision when attempting to determine the climate response to modification of the land surface. The implication of ISBA being sensitive to parameters that cannot be validated suggests that there will always be considerable doubt over the predictive quality of land-surface schemes. (orig.)

  14. Simulating Land Surface Hydrology at a 30-meter Spatial Resolution over the Contiguous United States

    Science.gov (United States)

    Wood, E. F.; Pan, M.; Cai, X.; Chaney, N.

    2016-12-01

    Big data, high performance computing, and recent advances in hydrologic similarity present a unique opportunity for macroscale hydrology: the land surface hydrology can be modeled at field scales over continental extents while ensuring computational efficiency to enable robust ensemble frameworks. In this presentation we will illustrate this potential breakthrough in macroscale hydrology by discussing results from a 30-meter simulation over the contiguous United States using the HydroBlocks land surface model. HydroBlocks is a novel land surface model that represents field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs) [Chaney et al., 2016]. The model is a coupling between the Noah-MP land surface model and the Dynamic TOPMODEL hydrologic model. The HRUs are defined by clustering proxies of the drivers of spatial heterogeneity using hyperresolution land data. For the simulations over CONUS, HydroBlocks is run at every HUC10 catchment using 100 HRUs per catchment between 2004 and 2014. The simulations are forced with the 4 km Stage IV radar rainfall product and a spatially downscaled version of NLDAS-2. We will show how this approach to macroscale hydrology ensures computational efficiency while providing field-scale hydrologic information over continental extents. We will illustrate how this approach provides a novel approach in both the application and validation of macroscale land surface and hydrologic models. Finally, using these results, we will discuss the important role that big data and high performance computing can play in providing solutions to longstanding challenges to not only flood and drought monitoring systems but also to numerical weather prediction, seasonal forecasting, and climate prediction. References Chaney, N., P. Metcalfe, and E. F. Wood (2016), HydroBlocks: A Field-scale Resolving Land Surface Model for Application Over Continental Extents, Hydrological Processes, (in press.)

  15. Comprehensive Assessment of Land Surface, Snow, and Soil Moisture-Climate Feedbacks by Multi-model Experiments of Land Surface Models under LS3MIP

    Science.gov (United States)

    Oki, T.; Kim, H.; Hurk, B. V. D.; Krinner, G.; Derksen, C.; Seneviratne, S. I.

    2015-12-01

    The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and its predictability, including effects on the energy and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. The Land surface, snow and soil moisture model inter-comparison project (LS3MIP) experiments address together the following objectives: an evaluation of the current state of land processes including surface fluxes, snow cover and soil moisture representation in CMIP6 DECK runs (LMIP-protoDECK) a multi-model estimation of the long-term terrestrial energy/water/carbon cycles, using the surface modules of CMIP6 models under observation constrained historical (land reanalysis) and projected future (impact assessment) conditions considering land use/land cover changes. (LMIP) an assessment of the role of snow and soil moisture feedbacks in the regional response to altered climate forcings, focusing on controls of climate extremes, water availability and high-latitude climate in historical and future scenario runs (LFMIP) an assessment of the contribution of land surface processes to the current and future predictability of regional temperature/precipitation patterns. (LFMIP) These LS3MIP outcomes will contribute to the improvement of climate change projections by reducing the systematic biases from the land surface component of climate models, and a better representation of feedback mechanisms related to snow and soil moisture in climate models. Further, LS3MIP will enable the assessment of probable historical changes in energy, water, and carbon cycles over land surfaces extending more than 100 years, including spatial variability and trends in global runoff, snow cover, and soil moisture that are hard to detect purely based on observations. LS3MIP will also enable the impact assessments of climate changes on hydrological regimes and available

  16. The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

    Science.gov (United States)

    Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

    2006-01-01

    Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

  17. Assessment of post-fire changes in land surface temperature and surface albedo, and their relation with fire-burn severity using multitemporal MODIS imagery

    OpenAIRE

    Veraverbeke, Sander; Verstraeten, Willem W.; Lhermitte, Stefaan; Van De Kerchove, Ruben; Goossens, Rudi

    2012-01-01

    This study evaluates the effects of the large 2007 Peloponnese (Greece) wildfires on changes in broadband surface albedo (a), daytime land surface temperature (LSTd) and night-time LST (LSTn) using a 2-year post-fire time series of Moderate Resolution Imaging Spectroradiometer satellite data. In addition, it assesses the potential of remotely sensed a and LST as indicators for fire-burn severity. Immediately after the fire event, mean a dropped up to 0.039 (standard deviation = 0.012) (P < 0....

  18. Land Surface Temperature in Łódź Obtained from Landsat 5TM

    Science.gov (United States)

    Jędruszkiewicz, Joanna; Zieliński, Mariusz

    2012-01-01

    The main aim of this paper is to present the spatial differentiation of Land Surface Temperature LST in Łódź based on Landsat 5 Thematic Mapper (L5TM) images. Analysis was performed for all L5TM images from 2011, with clear sky over Łódź. Land surface temperature (LST) play an important role in determination of weather conditions in boundary layer of atmosphere, especially connected with convection. Environmental satellites from Landsat series delivers the high resolution images of Earth's surface and according to the estimations made on the ground of it are precise. LST depends widely on surface emissivity. In this paper the emissivity was estimated from MODIS sensor as well as NDVI index, then both method were compared. The processed images allowed to determine the warmest and the coldest areas in the administrative boundaries of Łódź. The highest LST values has been found in industrial areas and the in the heart of the city. However, there are some places lying in city outskirts, where the LST values are as high, for instance Lodz Airport. On the contrary the lowest LST values occur mostly in terrains covered with vegetation i.e. forests or city parks. Głównym celem tego opracowania było oszacowanie temperatury powierzchni Ziemi w Łodzi, na podstawie obrazów satelitarnych pochodzących z satelity Landsat 5 Thematic Mapper (L5TM). Analizę wykonane dla obrazów wszystkich dostępnych obrazów z 2011 roku, na których zachmurzenie nie wystąpiło nad obszarem Łodzi. Temperatura powierzchni Ziemi odgrywa istotną rolę w kształtowaniu warunków pogodowych w warstwie granicznej, szczególnie związanych z konwekcją. Satelity środowiskowe z serii Landsat dostarczają obrazów w dużej rozdzielczości, dzięki czemu pozwalają na stosunkowo dokładne oszacowanie tego parametru. Wielkość temperatury w dużym stopniu zależy od emisyjności danej powierzchni. W niniejszym opracowaniu porównano temperaturę powierzchniową obliczoną dla emisyjno

  19. Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures

    Science.gov (United States)

    Ackerley, Duncan; Dommenget, Dietmar

    2016-06-01

    General circulation models (GCMs) are valuable tools for understanding how the global ocean-atmosphere-land surface system interacts and are routinely evaluated relative to observational data sets. Conversely, observational data sets can also be used to constrain GCMs in order to identify systematic errors in their simulated climates. One such example is to prescribe sea surface temperatures (SSTs) such that 70 % of the Earth's surface temperature field is observationally constrained (known as an Atmospheric Model Intercomparison Project, AMIP, simulation). Nevertheless, in such simulations, land surface temperatures are typically allowed to vary freely, and therefore any errors that develop over the land may affect the global circulation. In this study therefore, a method for prescribing the land surface temperatures within a GCM (the Australian Community Climate and Earth System Simulator, ACCESS) is presented. Simulations with this prescribed land surface temperature model produce a mean climate state that is comparable to a simulation with freely varying land temperatures; for example, the diurnal cycle of tropical convection is maintained. The model is then developed further to incorporate a selection of "proof of concept" sensitivity experiments where the land surface temperatures are changed globally and regionally. The resulting changes to the global circulation in these sensitivity experiments are found to be consistent with other idealized model experiments described in the wider scientific literature. Finally, a list of other potential applications is described at the end of the study to highlight the usefulness of such a model to the scientific community.

  20. Estimation of four land surface essential climate variables (albedo, LAI/FAPAR, and Fcover) from VIIRS data

    Science.gov (United States)

    Liang, Shunlin

    2016-07-01

    As the successor of MODIS, the Visible Infrared Imaging Radiometer Suite (VIIRS) from the Suomi National Polar-orbiting Partnership (S-NPP) and future Joint Polar Satellite System (JPSS) brings us into a new era of global daily Earth observations. VIIRS was designed to improve upon the capabilities of the operational AVHRR and provide observation continuity with MODIS. This presentation will describe the progress in estimating four Essential Climate Variables (ECV): shortwave albedo (Wang, et al., 2013; Zhou, et al., 2016), leaf area index (LAI) (Xiao et al., 2016), fraction of absorbed photosynthetically active radiation (FAPAR) (Xiao et al., 2016), and fractional vegetation coverage (Fcover) (Li, et al., 2016) from VIIRS data. The algorithms have been peer reviewed, and shortwave albedo has been operationally produced by NOAA and accessible to the scientific community. Li, Y., K. Jia, S. Liang, Z. Xiao, X. Wang, L. Yang, (2016), An operational algorithm for estimating fractional vegetation cover from VIIRS reflectance data based on general regression neural networks, Remote Sensing, revised Xiao, Z., S. Liang, T. Wang, and B. Jiang, (2016), Retrieval of Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation from VIIRS Time Series Data, Remote Sensing, revised. Wang, D., S. Liang, T. He, and Y. Yu, (2013), Direct Estimation of Land Surface Albedo from VIIRS Data: Algorithm Improvement and Preliminary Validation, Journal of Geophysical Research, 118(22):12,577-12,586 Zhou, Y., D. Wang, S. Liang, Y. Yu, and T. He, (2016), Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps, Remote Sensing, in press.

  1. Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

    Directory of Open Access Journals (Sweden)

    F. Zabel

    2010-10-01

    Full Text Available Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

  2. Analyzing land surface temperature variations during Fogo Island (Cape Verde) 2014-2015 eruption with Landsat 8 images

    Science.gov (United States)

    Vieira, D.; Teodoro, A.; Gomes, A.

    2016-10-01

    Land Surface Temperature (LST) is an important parameter related to land surface processes that changes continuously through time. Assessing its dynamics during a volcanic eruption has both environmental and socio-economical interest. Lava flows and other volcanic materials produced and deposited throughout an eruption transform the landscape, contributing to its heterogeneity and altering LST measurements. This paper aims to assess variations of satellite-derived LST and to detect patterns during the latest Fogo Island (Cape Verde) eruption, extending from November 2014 through February 2015. LST data was obtained through four processed Landsat 8 images, focused on the caldera where Pico do Fogo volcano sits. QGIS' plugin Semi-Automatic Classification was used in order to apply atmospheric corrections and radiometric calibrations. The algorithm used to retrieve LST values is a single-channel method, in which emissivity values are known. The absence of in situ measurements is compensated by the use of MODIS sensor-derived LST data, used to compare with Landsat retrieved measurements. LST data analysis shows as expected that the highest LST values are located inside the caldera. High temperature values were also founded on the south-facing flank of the caldera. Although spatial patterns observed on the retrieved data remained roughly the same during the time period considered, temperature values changed throughout the area and over time, as it was also expected. LST values followed the eruption dynamic experiencing a growth followed by a decline. Moreover, it seems possible to recognize areas affected by lava flows of previous eruptions, due to well-defined LST spatial patterns.

  3. Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

    Directory of Open Access Journals (Sweden)

    Bo-Hui Tang

    2015-03-01

    Full Text Available This work estimated and validated the land surface temperature (LST from thermal-infrared Channels 4 (10.8 µm and 5 (12.0 µm of the Visible and Infrared Radiometer (VIRR onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs were proportional to the viewing zenith angles (VZAs and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm2, provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.

  4. Clear sky atmosphere at cm-wavelengths from climatology data

    CERN Document Server

    Lew, Bartosz

    2015-01-01

    We utilise ground-based, balloon-born and satellite climatology data to reconstruct site and season-dependent vertical profiles of precipitable water vapour (PWV). We use these profiles to numerically solve radiative transfer through the atmosphere, and derive atmospheric brightness temperature ($T_{\\rm atm}$) and optical depth ($\\tau$) at the centimetre wavelengths. We validate the reconstruction by comparing the model column PWV, with photometric measurements of PWV, performed in the clear sky conditions towards the Sun. Based on the measurements, we devise a selection criteria to filter the climatology data to match the PWV levels to the expectations of the clear sky conditions. We apply the reconstruction to the location of the Polish 32-metre radio telescope, and characterise $T_{\\rm atm}$ and $\\tau$ year-round, at selected frequencies. We also derive the zenith distance dependence for these parameters, and discuss shortcomings of using planar, single-layer, and optically thin atmospheric model approxima...

  5. Using SMOS brightness temperature and derived surface-soil moisture to characterize surface conditions and validate land surface models.

    Science.gov (United States)

    Polcher, Jan; Barella-Ortiz, Anaïs; Piles, Maria; Gelati, Emiliano; de Rosnay, Patricia

    2017-04-01

    The SMOS satellite, operated by ESA, observes the surface in the L-band. On continental surface these observations are sensitive to moisture and in particular surface-soil moisture (SSM). In this presentation we will explore how the observations of this satellite can be exploited over the Iberian Peninsula by comparing its results with two land surface models : ORCHIDEE and HTESSEL. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies. When comparing the surface-soil moisture of the models with the product derived operationally by ESA from SMOS observations similar results are found. The spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates is poor (ρ 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products. Other reasons have to

  6. Detection of land surface memory by correlations between thickness of colluvial deposits and morphometric variables

    Science.gov (United States)

    Mitusov, A. V.; Dreibrodt, S.; Mitusova, O. E.; Khamnueva, S. V.; Bork, H.-R.

    2013-06-01

    Some morphometric variables store information about past land surfaces longer than others. This property of morphometric variables is recognised as land surface memory. Slope deposits, soils, and vegetation also have this memory. In this study, a memory effect was quantitatively detected by Spearman correlations between thickness of colluvium and morphometric variables of the modern land surface. During long-term sedimentation, the sign of horizontal curvature (kh) may be inverted from minus to plus, suggesting that locations with positive kh values are not accumulation zones. However, the thickness of colluvial deposits at such locations in our study area indicates sediment accumulation. The sign of minimal curvature (kmin) tends to be more stable and remains negative. This difference provides the stronger correlation of colluvial layer thickness with kmin than with kh. The strongest correlation was found for total thickness of the colluvial deposits of the Neolithic and Iron Age with kmin (- 0.84); the correlation with kh was weaker (- 0.71).

  7. An Assessment of Land Surface and Lightning Characteristics Associated with Lightning-Initiated Wildfires

    Science.gov (United States)

    Coy, James; Schultz, Christopher J.; Case, Jonathan L.

    2017-01-01

    Can we use modeled information of the land surface and characteristics of lightning beyond flash occurrence to increase the identification and prediction of wildfires? Combine observed cloud-to-ground (CG) flashes with real-time land surface model output, and Compare data with areas where lightning did not start a wildfire to determine what land surface conditions and lightning characteristics were responsible for causing wildfires. Statistical differences between suspected fire-starters and non-fire-starters were peak-current dependent 0-10 cm Volumetric and Relative Soil Moisture comparisons were statistically dependent to at least the p = 0.05 independence level for both polarity flash types Suspected fire-starters typically occurred in areas of lower soil moisture than non-fire-starters. GVF value comparisons were only found to be statistically dependent for -CG flashes. However, random sampling of the -CG non-fire starter dataset revealed that this relationship may not always hold.

  8. Modeling the land surface reflectance for optical remote sensing data in rugged terrain

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.

  9. Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

    Science.gov (United States)

    Famiglietti, J. S.; Wood, Eric F.

    1993-01-01

    A land surface hydrology parameterization for use in atmospheric GCM's is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well as the dynamics of land surface-atmosphere interactions.

  10. Modeling the land surface reflectance for optical remote sensing data in rugged terrain

    Institute of Scientific and Technical Information of China (English)

    WEN JianGuang; LIU QinHuo; XIAO Qing; LIU Qiang; LI XiaoWen

    2008-01-01

    A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented. Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively, the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model. A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper. The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.

  11. Letter to the EditorRetrieval of land surface temperature from combined AVHRR data

    Directory of Open Access Journals (Sweden)

    H. Fischer

    Full Text Available Accurate retrievals of land surface temperature (LST from space are of high interest for studies of land surface processes. Here, an operationally applicable method to retrieve LST from NOAA/AVHRR data is proposed, which combines a split-window technique (SWT for atmospheric correction with a Normalised Difference Vegetation Index threshold method for the retrieval of land surface emissivity. Preliminary results of LST retrievals with this "combined method" are in good agreement with ground truth measurements for bare soil and wheat crops. The results are also compared with results from the same SWT but using emissivities from laboratory measurements.Key words. Meteorology and atmospheric dynamics (radiation processes; instruments and techniques – Radio science (remote sensing

  12. Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

    Science.gov (United States)

    Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

    2015-01-01

    Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments we