Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

Directory of Open Access Journals (Sweden)

Morteza Sadeghi

2016-12-01

Full Text Available P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical. In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

Assimilation of SMOS (and SMAP) Retrieved Soil Moisture into the Land Information System

Science.gov (United States)

Blankenship, Clay; Zavodsky, Bradley; Case, Jonathan; Stano, Geoffrey

2016-01-01

Goal: Accurate, high-resolution (approx.3 km) soil moisture in near-real time. Situational awareness (drought assessment, flood and fire threat). Local modeling applications (to improve sfc-PBL exchanges) Method: Assimilate satellite soil moisture retrievals into a land surface model. Combines high-resolution geophysical model data with latest satellite observations.

Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

Directory of Open Access Journals (Sweden)

Zhiqu Liu

2017-11-01

Full Text Available The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM for the soil layer and the Michigan microwave canopy scattering (MIMICS model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD of 0.053 m3/m3, a mean average deviation (MAD of 0.034 m3/m3, and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF, and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

Inter-Comparison of Retrieved and Modelled Soil Moisture and Coherency of Remotely Sensed Hydrology Data

Science.gov (United States)

Kolassa, Jana; Aires, Filipe

2013-04-01

A neural network algorithm has been developed for the retrieval of Soil Moisture (SM) from global satellite observations. The algorithm estimates soil moisture from a synergy of passive and active microwave, infrared and visible satellite observations in order to capture the different SM variabilities that the individual sensors are sensitive to. The advantages and drawbacks of each satellite observation have been analysed and the information type and content carried by each observation have been determined. A global data set of monthly mean soil moisture for the 1993-2000 period has been computed with the neural network algorithm (Kolassa et al., in press, 2012). The resulting soil moisture retrieval product has then been used in an inter-comparison study including soil moisture from (1) the HTESSEL model (Balsamo et al., 2009), (2) the WACMOS satellite product (Liu et al., 2011), and (3) in situ measurements from the International Soil Moisture Network (Dorigo et al., 2011). The analysis showed that the satellite remote sensing products are well-suited to capture the spatial variability of the in situ data and even show the potential to improve the modelled soil moisture. Both satellite retrievals also display a good agreement with the temporal structures of the in situ data, however, HTESSEL appears to be more suitable for capturing the temporal variability (Kolassa et al., in press, 2012). The use of this type of neural network approach is currently being investigated as a retrieval option for the SMOS mission. Our soil moisture retrieval product has also been used in a coherence study with precipitation data from GPCP (Adler et al., 2003) and inundation estimates from GIEMS (Prigent et al., 2007). It was investigated on a global scale whether the three observation-based datasets are coherent with each other and show the expected behaviour. For most regions of the Earth, the datasets were consistent and the behaviour observed could be explained with the known

Space-based passive microwave soil moisture retrievals and the correction for a dynamic open water fraction

Directory of Open Access Journals (Sweden)

B. T. Gouweleeuw

2012-06-01

Full Text Available The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies (if not accounted for cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies on near-surface soil moisture products derived from actual (non-synthetic data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E for three areas in Oklahoma, USA. Differences between on-ground observations, model estimates and AMSR-E retrievals were related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data, a second derived from the Moderate Resolution Imaging Spectroradiometer (MODIS and a third through inversion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of relatively small areas (<0.05 of open water in or near the sensor footprint, possibly in combination with increased, below-critical vegetation density conditions (optical density <0.8, which contribute to seasonally varying biases in excess of 0.2 (m³ m⁻³ soil water content. These errors need to be addressed, either through elimination or accurate characterisation, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

Science.gov (United States)

Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

2011-01-01

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Influence of Surface Roughness Spatial Variability and Temporal Dynamics on the Retrieval of Soil Moisture from SAR Observations

Directory of Open Access Journals (Sweden)

Jesús Álvarez-Mozos

2009-01-01

Full Text Available Radar-based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on currently available spaceborne sensors. The main difficulty experienced so far results from the strong influence of other surface characteristics, mainly roughness, on the backscattering coefficient, which hinders the soil moisture inversion. This is especially true for single configuration observations where the solution to the surface backscattering problem is ill-posed. Over agricultural areas cultivated with winter cereal crops, roughness can be assumed to remain constant along the growing cycle allowing the use of simplified approaches that facilitate the estimation of the moisture content of soils. However, the field scale spatial variability and temporal variations of roughness can introduce errors in the estimation of soil moisture that are difficult to evaluate. The objective of this study is to assess the impact of roughness spatial variability and roughness temporal variations on the retrieval of soil moisture from radar observations. A series of laser profilometer measurements were performed over several fields in an experimental watershed from September 2004 to March 2005. The influence of the observed roughness variability and its temporal variations on the retrieval of soil moisture is studied using simulations performed with the Integral Equation Model, considering different sensor configurations. Results show that both field scale roughness spatial variability and its temporal variations are aspects that need to be taken into account, since they can introduce large errors on the retrieved soil moisture values.

Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

Science.gov (United States)

Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt

2010-01-01

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

High-resolution humidity profiles retrieved from wind profiler radar measurements

Science.gov (United States)

Saïd, Frédérique; Campistron, Bernard; Di Girolamo, Paolo

2018-03-01

The retrieval of humidity profiles from wind profiler radars has already been documented in the past 30 years and is known to be neither as straightforward and nor as robust as the retrieval of the wind velocity. The main constraint to retrieve the humidity profile is the necessity to combine measurements from the wind profiler and additional measurements (such as observations from radiosoundings at a coarser time resolution). Furthermore, the method relies on some assumptions and simplifications that restrict the scope of its application. The first objective of this paper is to identify the obstacles and limitations and solve them, or at least define the field of applicability. To improve the method, we propose using the radar capacity to detect transition levels, such as the top level of the boundary layer, marked by a maximum in the radar reflectivity. This forces the humidity profile from the free troposphere and from the boundary layer to coincide at this level, after an optimization of the calibration coefficients, and reduces the error. The resulting mean bias affecting the specific humidity profile never exceeds 0.25 g kg-1. The second objective is to explore the capability of the algorithm to retrieve the humidity vertical profiles for an operational purpose by comparing the results with observations from a Raman lidar.

Vertical grid of retrieved atmospheric profiles

International Nuclear Information System (INIS)

Ceccherini, Simone; Carli, Bruno; Raspollini, Piera

2016-01-01

The choice of the vertical grid of atmospheric profiles retrieved from remote sensing observations is discussed considering the two cases of profiles used to represent the results of individual measurements and of profiles used for subsequent data fusion applications. An ozone measurement of the MIPAS instrument is used to assess, for different vertical grids, the quality of the retrieved profiles in terms of profile values, retrieval errors, vertical resolutions and number of degrees of freedom. In the case of individual retrievals no evident advantage is obtained with the use of a grid finer than the one with a reduced number of grid points, which are optimized according to the information content of the observations. Nevertheless, this instrument dependent vertical grid, which seems to extract all the available information, provides very poor results when used for data fusion applications. A loss of about a quarter of the degrees of freedom is observed when the data fusion is made using the instrument dependent vertical grid relative to the data fusion made using a vertical grid optimized for the data fusion product. This result is explained by the analysis of the eigenvalues of the Fisher information matrix and leads to the conclusion that different vertical grids must be adopted when data fusion is the expected application. - Highlights: • Data fusion application is taken into account for the choice of the vertical grid. • The study is performed using ozone profiles retrieved from MIPAS measurements. • A very fine vertical grid is not needed for the analysis of a single instrument. • The instrument dependent vertical grid is not the best choice for data fusion. • A data fusion dependent vertical grid must be used for profiles that will be fused.

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals

Science.gov (United States)

Koster, Randal D.; Brocca, Luca; Crow, Wade T.; Burgin, Mariko S.; De Lannoy, Gabrielle J. M.

2016-01-01

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS data sets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to approximately100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

Thermodynamic and cloud parameter retrieval using infrared spectral data

Science.gov (United States)

Zhou, Daniel K.; Smith, William L., Sr.; Liu, Xu; Larar, Allen M.; Huang, Hung-Lung A.; Li, Jun; McGill, Matthew J.; Mango, Stephen A.

2005-01-01

High-resolution infrared radiance spectra obtained from near nadir observations provide atmospheric, surface, and cloud property information. A fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. The retrieval algorithm is presented along with its application to recent field experiment data from the NPOESS Airborne Sounding Testbed - Interferometer (NAST-I). The retrieval accuracy dependence on cloud properties is discussed. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with an accuracy of approximately 1.0 km. Preliminary NAST-I retrieval results from the recent Atlantic-THORPEX Regional Campaign (ATReC) are presented and compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL).

Assimilation of SMOS Soil Moisture Retrievals in the Land Information System

Science.gov (United States)

Blakenship, Clay; Zavodsky, Bradley; Cae, Jonathan

2014-01-01

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.

A simple algorithm to retrieve soil moisture and vegetation biomass using passive microwave measurements over crop fields

International Nuclear Information System (INIS)

Wigneron, J.P.; Chanzy, A.; Calvet, J.C.; Bruguier, N.

1995-01-01

A simple algorithm to retrieve sail moisture and vegetation water content from passive microwave measurements is analyzed in this study. The approach is based on a zeroth-order solution of the radiative transfer equations in a vegetation layer. In this study, the single scattering albedo accounts for scattering effects and two parameters account for the dependence of the optical thickness on polarization, incidence angle, and frequency. The algorithm requires only ancillary information about crop type and surface temperature. Retrievals of the surface parameters from two radiometric data sets acquired over a soybean and a wheat crop have been attempted. The model parameters have been fitted in order to achieve best match between measured and retrieved surface data. The results of the inversion are analyzed for different configurations of the radiometric observations: one or several look angles, L-band, C-band or (L-band and C-band). Sensitivity of the retrievals to the best fit values of the model parameters has also been investigated. The best configurations, requiring simultaneous measurements at L- and C-band, produce retrievals of soil moisture and biomass with a 15% estimated precision (about 0.06 m 3 /m 3 for soil moisture and 0.3 kg/m 2 for biomass) and exhibit a limited sensitivity to the best fit parameters. (author)

Retrieving soil moisture for non-forested areas using PALS radiometer measurements in SMAPVEX12 field campaign

Science.gov (United States)

In this paper we investigate retrieval of soil moisture based on L-band brightness temperature under diverse conditions and land cover types. We apply the PALS (Passive Active L-band System) radiometer data collected in the SMAPVEX12 (Soil Moisture Active Passive Validation Experiment 2012) field ex...

Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop

Directory of Open Access Journals (Sweden)

S. Zhang

2017-09-01

Full Text Available This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS Signal to Noise Ratio (SNR data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR. Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March. The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R2  =  0.74, RMSE  =  0.009 m3 m−3 when the wheat is smaller than one wavelength (∼ 19 cm. The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R2  =  0.98, RMSE  =  6

Intercomparison of AMSR2 and AMSR-E Soil Moisture Retrievals with MERRA-L data set over Australia

Science.gov (United States)

Cho, E.; Choi, M.; Su, C. H.; Ryu, D.; Kim, H.; Jacobs, J. M.

2015-12-01

Soil moisture is an important variable in the hydrological cycle on the land surface and plays an essential role in hydrological and meteorological processes. The Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor on board the Aqua satellite offered valuable soil moisture data set from June 2002 and October 2011 and has been used in a wide range of applications. However, the AMSR-E sensor stopped operation from 4 October 2011 due to a problem with its antenna. AMSR-E was replaced by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on the Global Climate Change Observation Mission 1 - Water (GCOM-W1) satellite in May 2012. Assessment of AMSR2 soil moisture retrievals as compared to AMSR-E has not yet been extensively evaluated. This task is critical if AMSR2 soil moisture products are used as a continuous dataset continuing the legacy of AMSR-E. The purpose of this study is to inter-compare AMSR2 and AMSR-E microwave based soil moisture over Australia, mediated by using model-based soil moisture data set to determine statistically similar inter-comparison periods from time periods of the individual sensors. This work use NASA-VUA AMSR2 and AMSR-E based soil moisture products derived by the Land Parameter Retrieval Model (LPRM) and the modelled soil moisture from NASA's MERRA-L (Modern Era Retrospective-analysis for Research and Applications-Land) re-analysis. The satellite soil moisture products are compared against the MERRA-L using traditional metrics, and the random errors in individual products are estimated using lagged instrumental variable regression analysis. Generally, the results demonstrate that the two satellite-based soil moisture retrievals have reasonable agreement with MERRA-L soil moisture data set. The error differences are notable, with the zonal error statistics are higher for AMSR2 in all climate zones, though the error maps of AMSR2 and AMSR-E are spatially similar over the Australia regions. This study leads

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

Science.gov (United States)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data

A retrieval algorithm of hydrometer profile for submillimeter-wave radiometer

Science.gov (United States)

Liu, Yuli; Buehler, Stefan; Liu, Heguang

2017-04-01

Vertical profiles of particle microphysics perform vital functions for the estimation of climatic feedback. This paper proposes a new algorithm to retrieve the profile of the parameters of the hydrometeor(i.e., ice, snow, rain, liquid cloud, graupel) based on passive submillimeter-wave measurements. These parameters include water content and particle size. The first part of the algorithm builds the database and retrieves the integrated quantities. Database is built up by Atmospheric Radiative Transfer Simulator(ARTS), which uses atmosphere data to simulate the corresponding brightness temperature. Neural network, trained by the precalculated database, is developed to retrieve the water path for each type of particles. The second part of the algorithm analyses the statistical relationship between water path and vertical parameters profiles. Based on the strong dependence existing between vertical layers in the profiles, Principal Component Analysis(PCA) technique is applied. The third part of the algorithm uses the forward model explicitly to retrieve the hydrometeor profiles. Cost function is calculated in each iteration, and Differential Evolution(DE) algorithm is used to adjust the parameter values during the evolutionary process. The performance of this algorithm is planning to be verified for both simulation database and measurement data, by retrieving profiles in comparison with the initial one. Results show that this algorithm has the ability to retrieve the hydrometeor profiles efficiently. The combination of ARTS and optimization algorithm can get much better results than the commonly used database approach. Meanwhile, the concept that ARTS can be used explicitly in the retrieval process shows great potential in providing solution to other retrieval problems.

Microphysical retrievals from simultaneous polarimetric and profiling radar observations

Directory of Open Access Journals (Sweden)

M. P. Morris

2009-12-01

Full Text Available The character of precipitation detected at the surface is the final product of many microphysical interactions in the cloud above, the combined effects of which may be characterized by the observed drop size distribution (DSD. This necessitates accurate retrieval of the DSD from remote sensing data, especially radar as it offers large areal coverage, high spatial resolution, and rigorous quality control and testing. Combined instrument observations with a UHF wind profiler, an S-band polarimetric weather radar, and a video disdrometer are analyzed for two squall line events occuring during the calendar year 2007. UHF profiler Doppler velocity spectra are used to estimate the DSD aloft, and are complemented by DSDs retrieved from an exponential model applied to polarimetric data. Ground truth is provided by the disdrometer. A complicating factor in the retrieval from UHF profiler spectra is the presence of ambient air motion, which can be corrected using the method proposed by Teshiba et al. (2009, in which a comparison between idealized Doppler spectra calculated from the DSDs retrieved from KOUN and those retrieved from contaminated wind profiler spectra is performed. It is found that DSDs measured using the distrometer at the surface and estimated using the wind profiler and polarimetric weather radar generally showed good agreement. The DSD retrievals using the wind profiler were improved when the estimates of the vertical wind were included into the analysis, thus supporting the method of Teshiba et al. (2009. Furthermore, the the study presents a method of investigating the time and height structure of DSDs.

Retrieval of canopy moisture content for dynamic fire risk assessment using simulated MODIS bands

Science.gov (United States)

Maffei, Carmine; Leone, Antonio P.; Meoli, Giuseppe; Calabrò, Gaetano; Menenti, Massimo

2007-10-01

Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation of intervention resources. Various factors contribute to the quantification of fire hazard, and among them vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements might be problematic, since it is calculated from two biophysical properties that independently affect vegetation reflectance spectrum. The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer (MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC. However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared (SWIR) channels to improve the retrieval accuracy of FMC (r2 = 0.73). The effects of canopy structural properties on MODIS

Evaluating soil moisture retrievals from ESA’s SMOS and NASA’s SMAP brightness temperature datasets

Science.gov (United States)

Al-Yaari, A.; Wigneron, J.-P.; Kerr, Y.; Rodriguez-Fernandez, N.; O’Neill, P. E.; Jackson, T. J.; De Lannoy, G.J.M.; Al Bitar, A; Mialon, A.; Richaume, P.; Walker, JP; Mahmoodi, A.; Yueh, S.

2018-01-01

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d’études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 – 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in

Parameterization of L-, C- and X-band Radiometer-based Soil Moisture Retrieval Algorithm Using In-situ Validation Sites

Science.gov (United States)

Gao, Y.; Colliander, A.; Burgin, M. S.; Walker, J. P.; Chae, C. S.; Dinnat, E.; Cosh, M. H.; Caldwell, T. G.

2017-12-01

Passive microwave remote sensing has become an important technique for global soil moisture estimation over the past three decades. A number of missions carrying sensors at different frequencies that are capable for soil moisture retrieval have been launched. Among them, there are Japan Aerospace Exploration Agency's (JAXA's) Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched in May 2002 on the National Aeronautics and Space Administration (NASA) Aqua satellite (ceased operation in October 2011), European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission launched in November 2009, JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite launched in May 2012, and NASA's Soil Moisture Active Passive (SMAP) mission launched in January 2015. Therefore, there is an opportunity to develop a consistent inter-calibrated long-term soil moisture data record based on the availability of these four missions. This study focuses on the parametrization of the tau-omega model at L-, C- and X-band using the brightness temperature (TB) observations from the four missions and the in-situ soil moisture and soil temperature data from core validation sites across various landcover types. The same ancillary data sets as the SMAP baseline algorithm are applied for retrieval at different frequencies. Preliminary comparison of SMAP and AMSR2 TB observations against forward-simulated TB at the Yanco site in Australia showed a generally good agreement with each other and higher correlation for the vertical polarization (R=0.96 for L-band and 0.93 for C- and X-band). Simultaneous calibrations of the vegetation parameter b and roughness parameter h at both horizontal and vertical polarizations are also performed. Finally, a set of model parameters for successfully retrieving soil moisture at different validation sites at L-, C- and X-band respectively are presented. The research described in this paper is supported by the Jet Propulsion

Profiles and Context for Structured Text Retrieval

DEFF Research Database (Denmark)

Koolen, Marijn; Bogers, Toine

2017-01-01

The combination of structured information retrieval with user profile information represents the scenario where systems search with an explicit statement of the information need—a search query—as well as a profile of a user, which can contain information about previous interactions, search histor...

The NAFE'05/CoSMOS Data Set: Toward SMOS Soil Moisture Retrieval, Downscaling, and Assimilation

DEFF Research Database (Denmark)

Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse D.

2008-01-01

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (COSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign......-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and COSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x...

Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission

Directory of Open Access Journals (Sweden)

Dugwon Seo

2010-05-01

Full Text Available Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS and measured field soil moisture from Southern Great Plains experiment (SGP99. The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP mission.

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR Soil Moisture Retrieval Errors

Directory of Open Access Journals (Sweden)

Ju Hyoung Lee

2016-04-01

Full Text Available Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE, while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

Retrieving topsoil moisture using RADARSAT-2 data, a novel approach applied at the east of the Netherlands

Science.gov (United States)

Eweys, Omar Ali; Elwan, Abeer A.; Borham, Taha I.

2017-12-01

This manuscript proposes an approach for estimating soil moisture content over corn fields using C-band SAR data acquired by RADARSAT-2 satellite. An image based approach is employed to remove the vegetation contribution to the satellite signals. In particular, the absolute difference between like and cross polarized signals (ADLC) is employed for segmenting the canopy growth cycle into tiny stages. Each stage is represented by a Cumulative Distribution Function (CDF) of the like polarized signals. For periods of bare soils and vegetation cover, CDFs are compared and the vegetation contribution is quantified. The portion which represent the soil contributions (σHHsoil°) to the satellite signals; are employed for inversely running Oh model and the water cloud model for estimating soil moisture, canopy water content and canopy height respectively. The proposed approach shows satisfactory performance where high correlation of determination (R2) is detected between the field observations and the corresponding retrieved soil moisture, canopy water content and canopy height (R2 = 0.64, 0.97 and 0.98 respectively). Soil moisture retrieval is associated with root mean square error (RMSE) of 0.03 m3 m-3 while estimating canopy water content and canopy height have RMSE of 0.38 kg m-2 and 0.166 m respectively.

Retrieval of vertical wind profiles during monsoon from satellite ...

Indian Academy of Sciences (India)

Complex EOF analysis; cloud motion vector winds; wind profiles; retrieval; monsoon. Proc. Indian Acad. Sci. .... The data gaps are removed using simple linear interpolation .... retrieved via standard linear regression using the two independent ...

Dead fuel moisture estimation with MSG-SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content

DEFF Research Database (Denmark)

Nieto Solana, Hector; Sandholt, Inge; Aguado, Inmaculada

2010-01-01

In this study we propose to use remote sensing data to estimate hourly meteorological data and then assess the moisture content of dead fuels. Three different models to estimate the equilibrium moisture content (EMC) were applied together with remotely sensed retrieved air temperature and relative...... humidity. The input data were acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, on board the Meteosat Second Generation (MSG) satellite, from which air temperature and relative humidity were estimated every 15 min. Air temperature estimations are based on the Temperature-Vegetation...... Index (TVX) algorithm. This algorithm exploits the inverse linear relationship between the land surface temperature and the vegetation fractional cover. This relationship was evaluated in a spatial window where the meteorological forcing is assumed to be constant. To estimate the vapour pressure...

Developing Soil Moisture Profiles Utilizing Remotely Sensed MW and TIR Based SM Estimates Through Principle of Maximum Entropy

Science.gov (United States)

Mishra, V.; Cruise, J. F.; Mecikalski, J. R.

2015-12-01

Developing accurate vertical soil moisture profiles with minimum input requirements is important to agricultural as well as land surface modeling. Earlier studies show that the principle of maximum entropy (POME) can be utilized to develop vertical soil moisture profiles with accuracy (MAE of about 1% for a monotonically dry profile; nearly 2% for monotonically wet profiles and 3.8% for mixed profiles) with minimum constraints (surface, mean and bottom soil moisture contents). In this study, the constraints for the vertical soil moisture profiles were obtained from remotely sensed data. Low resolution (25 km) MW soil moisture estimates (AMSR-E) were downscaled to 4 km using a soil evaporation efficiency index based disaggregation approach. The downscaled MW soil moisture estimates served as a surface boundary condition, while 4 km resolution TIR based Atmospheric Land Exchange Inverse (ALEXI) estimates provided the required mean root-zone soil moisture content. Bottom soil moisture content is assumed to be a soil dependent constant. Mulit-year (2002-2011) gridded profiles were developed for the southeastern United States using the POME method. The soil moisture profiles were compared to those generated in land surface models (Land Information System (LIS) and an agricultural model DSSAT) along with available NRCS SCAN sites in the study region. The end product, spatial soil moisture profiles, can be assimilated into agricultural and hydrologic models in lieu of precipitation for data scarce regions.Developing accurate vertical soil moisture profiles with minimum input requirements is important to agricultural as well as land surface modeling. Previous studies have shown that the principle of maximum entropy (POME) can be utilized with minimal constraints to develop vertical soil moisture profiles with accuracy (MAE = 1% for monotonically dry profiles; MAE = 2% for monotonically wet profiles and MAE = 3.8% for mixed profiles) when compared to laboratory and field

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

Directory of Open Access Journals (Sweden)

Mutian Han

2018-02-01

Full Text Available The Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR technique on soil moisture remote sensing was studied. A semi-empirical Signal-to-Noise Ratio (SNR model was proposed as a curve-fitting model for SNR data routinely collected by a GNSS receiver. This model aims at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This is achieved empirically through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al., T. Yang et al., this model can improve the Quality of Fit (QoF with little prior knowledge needed and can allow soil permittivity to be estimated from the reconstructed signals. In developing this model, we showed how noise affects the receiver SNR estimation and thus the model performance through simulations under the bare soil assumption. Results showed that the reconstructed signals with a grazing angle of 5°–15° were better for soil moisture retrieval. The QoF was improved by around 45%, which resulted in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected. Experimental data collected at Lamasquère, France, were also used to validate the proposed model. The results were compared with the simulation and previous works. It was found that the model could ensure good fitting quality even in the case of irregular SNR variation. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements. A deeper insight into the Larson model and the proposed model was given at this stage, which formed a possible explanation of this fact. Furthermore, frequency and phase information

Surface soil moisture retrievals from remote sensing: Current status, products & future trends

Science.gov (United States)

Petropoulos, George P.; Ireland, Gareth; Barrett, Brian

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth's land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today's world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Directory of Open Access Journals (Sweden)

Franziska Koch

2016-07-01

Full Text Available Soil moisture (SM is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

Evaluation of the tau-omega model for passive microwave soil moisture retrieval using SMAPEx data sets

Science.gov (United States)

The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau-omega model from 1) SMAP ATBD for ...

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

Science.gov (United States)

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2015-01-01

The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and

Retrieval of ozone profiles from OMPS limb scattering observations

Science.gov (United States)

Arosio, Carlo; Rozanov, Alexei; Malinina, Elizaveta; Eichmann, Kai-Uwe; von Clarmann, Thomas; Burrows, John P.

2018-04-01

This study describes a retrieval algorithm developed at the University of Bremen to obtain vertical profiles of ozone from limb observations performed by the Ozone Mapper and Profiler Suite (OMPS). This algorithm is based on the technique originally developed for use with data from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument. As both instruments make limb measurements of the scattered solar radiation in the ultraviolet (UV) and visible (Vis) spectral ranges, an underlying objective of the study is to obtain consolidated and consistent ozone profiles from the two satellites and to produce a combined data set. The retrieval algorithm uses radiances in the UV and Vis wavelength ranges normalized to the radiance at an upper tangent height to obtain ozone concentrations in the altitude range of 12-60 km. Measurements at altitudes contaminated by clouds in the instrument field of view are identified and filtered out. An independent aerosol retrieval is performed beforehand and its results are used to account for the stratospheric aerosol load in the ozone inversion. The typical vertical resolution of the retrieved profiles varies from ˜ 2.5 km at lower altitudes ( passive satellite observations or measured in situ by balloon-borne sondes. Between 20 and 60 km, OMPS ozone profiles typically agree with data from the Microwave Limb Sounder (MLS) v4.2 within 5-10 %, whereas in the lower altitude range the bias becomes larger, especially in the tropics. The comparison of OMPS profiles with ozonesonde measurements shows differences within ±5 % between 13 and 30 km at northern middle and high latitudes. At southern middle and high latitudes, an agreement within 5-7 % is also achieved in the same altitude range. An unexpected bias of approximately 10-20 % is detected in the lower tropical stratosphere. The processing of the 2013 data set using the same retrieval settings and its validation against ozonesondes reveals a much

Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

Science.gov (United States)

Ebrahimi-Khusfi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-05-01

This study was carried out to evaluate possible improvements of the soil moisture (SM) retrievals from the SMAP observations, based on the synergy between SMAP and SMOS. We assessed the impacts of the vegetation and soil roughness parameters on SM retrievals from SMAP observations. To do so, the effects of three key input parameters including the vegetation optical depth (VOD), effective scattering albedo (ω) and soil roughness (HR) parameters were assessed with the emphasis on the synergy with the VOD product derived from SMOS-IC, a new and simpler version of the SMOS algorithm, over two years of data (April 2015 to April 2017). First, a comprehensive comparison of seven SM retrieval algorithms was made to find the best one for SM retrievals from the SMAP observations. All results were evaluated against in situ measurements over 548 stations from the International Soil Moisture Network (ISMN) in terms of four statistical metrics: correlation coefficient (R), root mean square error (RMSE), bias and unbiased RMSE (UbRMSE). The comparison of seven SM retrieval algorithms showed that the dual channel algorithm based on the additional use of the SMOS-IC VOD product (selected algorithm) led to the best results of SM retrievals over 378, 399, 330 and 271 stations (out of a total of 548 stations) in terms of R, RMSE, UbRMSE and both R & UbRMSE, respectively. Moreover, comparing the measured and retrieved SM values showed that this synergy approach led to an increase in median R value from 0.6 to 0.65 and a decrease in median UbRMSE from 0.09 m3/m3 to 0.06 m3/m3. Second, using the algorithm selected in a first step and defined above, the ω and HR parameters were calibrated over 218 rather homogenous ISMN stations. 72 combinations of various values of ω and HR were used for the calibration over different land cover classes. In this calibration process, the optimal values of ω and HR were found for the different land cover classes. The obtained results indicated that the

Retrieval of vertical concentration profiles from OSIRIS UV-visible limb spectra

International Nuclear Information System (INIS)

Strong, K.; Joseph, B.M.; Dosanjh, R.; McDade, I.C.; McLinden, C.A.; McConnell, J.C.; Stegman, J.; Murtagh, D.P.; Llewellyn, E.J.

2002-01-01

The OSIRIS instrument, launched on the Odin satellite in February 2001, includes an optical spectrograph that will record UV-visible spectra of sunlight scattered from the limb over a range of tangent heights. These spectra will be used to retrieve vertical profiles of ozone, NO 2 , OC1O, BrO, NO 3 , O 2 , and aerosols, for the investigation of both stratospheric and mesospheric processes, particularly those related to ozone chemistry. In this work, the retrieval of vertical profiles of trace-gas concentrations from OSIRIS limb-radiance spectra is described. A forward model has been developed to simulate these spectra, and it consists of a single-scattering radiative-transfer model with partial spherical geometry, trace-gas absorption, Mic scattering by stratospheric aerosols, a Lambertian surface contribution, and OSIRIS instrument response and noise. Number-density profiles have been retrieved by using optimal estimation (OE) to combine an a priori profile with the information from sets of synthetic 'measurements'. For ozone, OE has been applied both to limb radiances at one or more discrete wavelengths and to effective-column abundances retrieved over a broad spectral range using differential optical absorption spectroscopy (DOAS). The results suggest that, between 15 and 35 km, ozone number densities can be retrieved to 10% accuracy or better on 1 and 2 km grids and to 5% on a 5 km grid. The combined DOAS-OE approach has also been used to retrieve NO 2 number densities, yielding 13% accuracy or better for altitudes from 18 to 36 km (in a 2 km grid. Differential optical absorption spectroscopy - optimal estimation retrievals of BrO and OC1O reproduce the true profiles above 15 km in the noise-free case, but the quality of the retrievals is highly sensitive to noise on the simulated OSIRIS spectra because of the weak absorption of these two gases. The development of inversion methods for the retrieval of trace-gas concentrations from OSIRIS spectra is continuing

The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

Directory of Open Access Journals (Sweden)

Robin van der Schalie

2018-01-01

Full Text Available Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003–2013 from two different satellite sensors, i.e., SMOS (2010–2013 and AMSR-E (2003–2011, and are based on a neural network (NN, regressions (REG, and the Land Parameter Retrieval Model (LPRM. The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the Rvalue, and secondly the triple collocation analysis (TCA. ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land–atmosphere coupling, the Sahel (SA and the central Great Plains (CGP of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The Rvalue and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The

Retrieval of ozone profiles from OMPS limb scattering observations

Directory of Open Access Journals (Sweden)

C. Arosio

2018-04-01

Full Text Available This study describes a retrieval algorithm developed at the University of Bremen to obtain vertical profiles of ozone from limb observations performed by the Ozone Mapper and Profiler Suite (OMPS. This algorithm is based on the technique originally developed for use with data from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY instrument. As both instruments make limb measurements of the scattered solar radiation in the ultraviolet (UV and visible (Vis spectral ranges, an underlying objective of the study is to obtain consolidated and consistent ozone profiles from the two satellites and to produce a combined data set. The retrieval algorithm uses radiances in the UV and Vis wavelength ranges normalized to the radiance at an upper tangent height to obtain ozone concentrations in the altitude range of 12–60 km. Measurements at altitudes contaminated by clouds in the instrument field of view are identified and filtered out. An independent aerosol retrieval is performed beforehand and its results are used to account for the stratospheric aerosol load in the ozone inversion. The typical vertical resolution of the retrieved profiles varies from  ∼  2.5 km at lower altitudes ( < 30 km to  ∼  1.5 km (about 45 km and becomes coarser at upper altitudes. The retrieval errors resulting from the measurement noise are estimated to be 1–4 % above 25 km, increasing to 10–30 % in the upper troposphere. OMPS data are processed for the whole of 2016. The results are compared with the NASA product and validated against profiles derived from passive satellite observations or measured in situ by balloon-borne sondes. Between 20 and 60 km, OMPS ozone profiles typically agree with data from the Microwave Limb Sounder (MLS v4.2 within 5–10 %, whereas in the lower altitude range the bias becomes larger, especially in the tropics. The comparison of OMPS profiles with ozonesonde

An Intercomparison of Vegetation Products from Satellite-based Observations used for Soil Moisture Retrievals

Science.gov (United States)

Vreugdenhil, Mariette; de Jeu, Richard; Wagner, Wolfgang; Dorigo, Wouter; Hahn, Sebastian; Bloeschl, Guenter

2013-04-01

Vegetation and its water content affect active and passive microwave soil moisture retrievals and need to be taken into account in such retrieval methodologies. This study compares the vegetation parameterisation that is used in the TU-Wien soil moisture retrieval algorithm to other vegetation products, such as the Vegetation Optical Depth (VOD), Net Primary Production (NPP) and Leaf Area Index (LAI). When only considering the retrieval algorithm for active microwaves, which was developed by the TU-Wien, the effect of vegetation on the backscattering coefficient is described by the so-called slope [1]. The slope is the first derivative of the backscattering coefficient in relation to the incidence angle. Soil surface backscatter normally decreases quite rapidly with the incidence angle over bare or sparsely vegetated soils, whereas the contribution of dense vegetation is fairly uniform over a large range of incidence angles. Consequently, the slope becomes less steep with increasing vegetation. Because the slope is a derivate of noisy backscatter measurements, it is characterised by an even higher level of noise. Therefore, it is averaged over several years assuming that the state of the vegetation doesn't change inter-annually. The slope is compared to three dynamic vegetation products over Australia, the VOD, NPP and LAI. The VOD was retrieved from AMSR-E passive microwave data using the VUA-NASA retrieval algorithm and provides information on vegetation with a global coverage of approximately every two days [2]. LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. In this study LAI is used from the Geoland2 products derived from SPOT Vegetation*. The NPP is the net rate at which plants build up carbon through photosynthesis and is a model-based estimate from the BiosEquil model [3, 4]. Results show that VOD and slope correspond reasonably well over vegetated areas, whereas in arid

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

Science.gov (United States)

Miernecki, Maciej; Wigneron, Jean-Pierre; Lopez-Baeza, Ernesto; Kerr, Yann; DeJeu, Richard; DeLannoy, Gabielle J. M.; Jackson, Tom J.; O'Neill, Peggy E.; Shwank, Mike; Moran, Roberto Fernandez;

Soil Temperature and Moisture Profile (STAMP) System Handbook

Energy Technology Data Exchange (ETDEWEB)

Cook, David R. [Argonne National Lab. (ANL), Argonne, IL (United States)

2016-11-01

The soil temperature and moisture profile system (STAMP) provides vertical profiles of soil temperature, soil water content (soil-type specific and loam type), plant water availability, soil conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of soil energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are also useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil. The STAMP system replaced the SWATS system in early 2016.

Characterizing vertical heterogeneity of permafrost soils in support of ABoVE radar retrievals

Science.gov (United States)

Tabatabaeenejad, A.; Chen, R. H.; Silva, A.; Schaefer, K. M.; Moghaddam, M.

2017-12-01

Permafrost-affected soils, including the top active layer and underlying permafrost, have unique seasonal variations in terms of soil temperature, soil moisture, and freeze/thaw-state profiles. The presence of a perennially frozen and impermeable substrate maintains the required temperature gradient for the descending thawing front, and causes meltwater to accumulate and form the saturated zone in the active layer. Radar backscattering measurements are sensitive to dielectric properties of subsurface soils, which are strongly correlated with unfrozen water content and soil texture/composition. To enable accurate radar retrievals, we need to properly characterize soil profile heterogeneity, which can be modeled with layered soil or depth-dependent functions. To this end, we first cross compare the measured radar backscatter and model-predicted radar backscatter using in-situ dielectric profile measurements as well as mathematical or hydrologic-based profile functions. Since radar signal's backscatter has limited penetration, to fully capture the true heterogeneity profile, we determine the optimal profile function by minimizing the error between predicted and measured radar backscatter signals as well as between in-situ and fitted profiles. The in-situ soil profile data (temperature, dielectric constant, unfrozen water content, organic/mineral soils) are collected from the Soil Moisture Sensing Controller And oPtimal Estimator (SoilSCAPE) sensor networks and from the Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign in August 2017 (concurrent with the ABoVE August flights over Alaska North Slope) while the radar data are acquired by NASA's P-band AirMOSS and L-band UAVSAR as part of the ABoVE airborne campaign. The retrieval results using our new heterogeneity model will be compared with the results from retrievals that model soil as a layered medium. This analysis can advance the accuracy of retrieval of active layer properties using low-frequency SAR

Microwave remote sensing of soil moisture for estimation of profile soil property

International Nuclear Information System (INIS)

Mattikalli, N.M.; Engman, E.T.; Ahuja, L.R.; Jackson, T.J.

1998-01-01

Multi-temporal microwave remotely-sensed soil moisture has been utilized for the estimation of profile soil property, viz. the soil hydraulic conductivity. Passive microwave remote sensing was employed to collect daily soil moisture data across the Little Washita watershed, Oklahoma, during 10-18 June 1992. The ESTAR (Electronically Steered Thin Array Radiometer) instrument operating at L -band was flown on a NASA C-130 aircraft. Brightness temperature (TB) data collected at a ground resolution of 200m were employed to derive spatial distribution of surface soil moisture. Analysis of spatial and temporal soil moisture information in conjunction with soils data revealed a direct relation between changes in soil moisture and soil texture. A geographical information system (GIS) based analysis suggested that 2-days initial drainage of soil, measured from remote sensing, was related to an important soil hydraulic property viz. the saturated hydraulic conductivity (Ksat). A hydrologic modelling methodology was developed for estimation of Ksat of surface and sub-surface soil layers. Specifically, soil hydraulic parameters were optimized to obtain a good match between model estimated and field measured soil moisture profiles. Relations between 2-days soil moisture change and Ksat of 0-5 cm, 0-30 cm and 0-60cm depths yielded correla tions of 0.78, 0.82 and 0.71, respectively. These results are comparable to the findings of previous studies involving laboratory-controlled experiments and numerical simulations, and support their extension to the field conditions of the Little Washita watershed. These findings have potential applications of microwave remote sensing to obtain 2-days of soil moisture and then to quickly estimate the spatial distribution of Ksat over large areas. (author)

Physically-Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements

Science.gov (United States)

Zhou, Daniel K.; Smith, William L., Sr.; Liu, Xu; Larar, Allen M.; Mango, Stephen A.; Huang, Hung-Lung

2007-01-01

A physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multi-variable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with relatively high accuracy (i.e., error < 1 km). NPOESS Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the Atlantic-THORPEX Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the Hyperspectral Environmental Suite (HES). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on Polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project and the following NPOESS series of satellites.

Sensitivity of the OMI ozone profile retrieval (OMO3PR) to a priori assumptions

NARCIS (Netherlands)

Mielonen, T.; De Haan, J.F.; Veefkind, J.P.

2014-01-01

We have assessed the sensitivity of the operational OMI ozone profile retrieval (OMO3PR) algorithm to a number of a priori assumptions. We studied the effect of stray light correction, surface albedo assumptions and a priori ozone profiles on the retrieved ozone profile. Then, we studied how to

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

Science.gov (United States)

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

The OMPS Limb Profiler Instrument: Two-Dimensional Retrieval Algorithm

Science.gov (United States)

Rault, Didier F.

2010-01-01

The upcoming Ozone Mapper and Profiler Suite (OMPS), which will be launched on the NPOESS Preparatory Project (NPP) platform in early 2011, will continue monitoring the global distribution of the Earth's middle atmosphere ozone and aerosol. OMPS is composed of three instruments, namely the Total Column Mapper (heritage: TOMS, OMI), the Nadir Profiler (heritage: SBUV) and the Limb Profiler (heritage: SOLSE/LORE, OSIRIS, SCIAMACHY, SAGE III). The ultimate goal of the mission is to better understand and quantify the rate of stratospheric ozone recovery. The focus of the paper will be on the Limb Profiler (LP) instrument. The LP instrument will measure the Earth's limb radiance (which is due to the scattering of solar photons by air molecules, aerosol and Earth surface) in the ultra-violet (UV), visible and near infrared, from 285 to 1000 nm. The LP simultaneously images the whole vertical extent of the Earth's limb through three vertical slits, each covering a vertical tangent height range of 100 km and each horizontally spaced by 250 km in the cross-track direction. Measurements are made every 19 seconds along the orbit track, which corresponds to a distance of about 150km. Several data analysis tools are presently being constructed and tested to retrieve ozone and aerosol vertical distribution from limb radiance measurements. The primary NASA algorithm is based on earlier algorithms developed for the SOLSE/LORE and SAGE III limb scatter missions. All the existing retrieval algorithms rely on a spherical symmetry assumption for the atmosphere structure. While this assumption is reasonable in most of the stratosphere, it is no longer valid in regions of prime scientific interest, such as polar vortex and UTLS regions. The paper will describe a two-dimensional retrieval algorithm whereby the ozone distribution is simultaneously retrieved vertically and horizontally for a whole orbit. The retrieval code relies on (1) a forward 2D Radiative Transfer code (to model limb

SMAP Multi-Temporal Soil Moisture and Vegetation Optical Depth Retrievals in Vegetated Regions Including Higher-Order Soil-Canopy Interactions

Science.gov (United States)

Feldman, A.; Akbar, R.; Konings, A. G.; Piles, M.; Entekhabi, D.

2017-12-01

The Soil Moisture Active Passive (SMAP) mission utilizes a zeroth order radiative transfer model, known as the tau-omega model, to retrieve soil moisture from microwave brightness temperature observations. This model neglects first order scattering which is significant at L-Band in vegetated regions, or 30% of land cover. Previous higher order algorithms require extensive in-situ measurements and characterization of canopy layer physical properties. We propose a first order retrieval algorithm that approximately characterizes the eight first order emission pathways using rough surface reflectivity, vegetation optical depth (VOD), and scattering albedo terms. The recently developed Multi-Temporal Dual Channel Algorithm (MT-DCA) then retrieves these three parameters in a forward model without ancillary information under the assumption of temporally static albedo and constant vegetation water content between three day SMAP revisits. The approximated scattering terms are determined to be conservative estimates of analytically derived first order scattering terms. In addition, we find the first order algorithm to be more sensitive to surface emission than the tau-omega model. The simultaneously retrieved VOD, previously demonstrated to be proportional to vegetation water content, can provide insight into vegetation dynamics in regions with significant phenology. Specifically, dry tropical forests exhibit an increase in VOD during the dry season in alignment with prior studies that suggest that certain vegetative species green up during the dry season despite limited water availability. VOD retrieved using the first order algorithm and MT-DCA framework can therefore contribute to understanding of tropical forests' role in the carbon, energy, and water cycles, which has yet to be fully explained.

Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

Science.gov (United States)

De Lannoy, Gabrielle; Reichle, Rolf; Gruber, Alexander; Bechtold, Michel; Quets, Jan; Vrugt, Jasper; Wigneron, Jean-Pierre

2018-01-01

The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers

Science.gov (United States)

Liu, Cheng; Qian, Hongzhou; Cao, Weixing; Ni, Jun

2018-01-01

To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0–100 cm. According to the calibration results, the degree of fitting (R2) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0–1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R2 of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R2 between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R2 between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for

Monitoring soil moisture dynamics via ground-penetrating radar survey of agriculture fields after irrigation

Science.gov (United States)

Muro, G.

2015-12-01

It is possible to examine the quality of ground-penetrating radar (GPR) as a measure of soil moisture content in the shallow vadose zone, where roots are most abundant and water conservation best management practices are critical in active agricultural fields. By analyzing temporal samplings of 100 Mhz reflection profiles and common-midpoint (CMP) soundings over a full growing season, the variability of vertical soil moisture distribution directly after irrigation events are characterized throughout the lifecycle of a production crop. Reflection profiles produce high-resolution travel time data and summed results of CMP sounding data provide sampling depth estimates for the weak, but coherent reflections amid strong point scatterers. The high ratio of clay in the soil limits the resolution of downward propagation of infiltrating moisture after irrigation; synthetic data analysis compared against soil moisture lysimeter logs throughout the profile allow identification of the discrete soil moisture content variation in the measured GPR data. The nature of short duration irrigation events, evapotranspiration, and drainage behavior in relation to root depths observed in the GPR temporal data allow further examination and comparison with the variable saturation model HYDRUS-1D. After retrieving soil hydraulic properties derived from laboratory measured soil samples and simplified assumptions about boundary conditions, the project aims to achieve good agreement between simulated and measured soil moisture profiles without the need for excessive model calibration for GPR-derived soil moisture estimates in an agricultural setting.

Web User Profile Using XUL and Information Retrieval Techniques

Directory of Open Access Journals (Sweden)

Dan MUNTEANU

2008-12-01

Full Text Available This paper presents the importance of user profile in information retrieval, information filtering and recommender systems using explicit and implicit feedback. A Firefox extension (based on XUL used for gathering data needed to infer a web user profile and an example file with collected data are presented. Also an algorithm for creating and updating the user profile and keeping track of a fixed number k of subjects of interest is presented.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

Directory of Open Access Journals (Sweden)

Ahmad Tamimi

Full Text Available Profile Hidden Markov Model (Profile-HMM is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Importance of A Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

Science.gov (United States)

Johnson, M. S.; Sullivan, J. T.; Liu, X.; Zoogman, P.; Newchurch, M.; Kuang, S.; McGee, T. J.; Leblanc, T.

2017-12-01

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's operational GEOS-5 FP model and reanalysis data from MERRA2) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 km) and tropospheric (0-10 km) TOLNet observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from

A Comparison of the Red Green Blue Air Mass Imagery and Hyperspectral Infrared Retrieved Profiles

Science.gov (United States)

Berndt, E. B.; Folmer, Michael; Dunion, Jason

2014-01-01

The Red Green Blue (RGB) Air Mass imagery is derived from multiple channels or paired channel differences. Multiple channel products typically provide additional information than a single channel can provide alone. The RGB Air Mass imagery simplifies the interpretation of temperature and moisture characteristics of air masses surrounding synoptic and mesoscale features. Despite the ease of interpretation of multiple channel products, the combination of channels and channel differences means the resulting product does not represent a quantity or physical parameter such as brightness temperature in conventional single channel satellite imagery. Without a specific quantity to reference, forecasters are often confused as to what RGB products represent. Hyperspectral infrared retrieved profiles of temperature, moisture, and ozone can provide insight about the air mass represented on the RGB Air Mass product and provide confidence in the product and representation of air masses despite the lack of a quantity to reference for interpretation. This study focuses on RGB Air Mass analysis of Hurricane Sandy as it moved north along the U.S. East Coast, while transitioning to a hybrid extratropical storm. Soundings and total column ozone retrievals were analyzed using data from the Cross-track Infrared and Advanced Technology Microwave Sounder Suite (CrIMSS) on the Suomi National Polar Orbiting Partnership satellite and the Atmospheric Infrared Sounder (AIRS) on the National Aeronautics and Space Administration Aqua satellite along with dropsondes that were collected from National Oceanic and Atmospheric Administration and Air Force research aircraft. By comparing these datasets to the RGB Air Mass, it is possible to capture quantitative information that could help in analyzing the synoptic environment enough to diagnose the onset of extratropical transition. This was done by identifying any stratospheric air intrusions (SAIs) that existed in the vicinity of Sandy as the wind

Retrieval of aerosol profiles combining sunphotometer and ceilometer measurements in GRASP code

Science.gov (United States)

Román, R.; Benavent-Oltra, J. A.; Casquero-Vera, J. A.; Lopatin, A.; Cazorla, A.; Lyamani, H.; Denjean, C.; Fuertes, D.; Pérez-Ramírez, D.; Torres, B.; Toledano, C.; Dubovik, O.; Cachorro, V. E.; de Frutos, A. M.; Olmo, F. J.; Alados-Arboledas, L.

2018-05-01

In this paper we present an approach for the profiling of aerosol microphysical and optical properties combining ceilometer and sun/sky photometer measurements in the GRASP code (General Retrieval of Aerosol and Surface Properties). For this objective, GRASP is used with sun/sky photometer measurements of aerosol optical depth (AOD) and sky radiances, both at four wavelengths and obtained from AErosol RObotic NETwork (AERONET), and ceilometer measurements of range corrected signal (RCS) at 1064 nm. A sensitivity study with synthetic data evidences the capability of the method to retrieve aerosol properties such as size distribution and profiles of volume concentration (VC), especially for coarse particles. Aerosol properties obtained by the mentioned method are compared with airborne in-situ measurements acquired during two flights over Granada (Spain) within the framework of ChArMEx/ADRIMED (Chemistry-Aerosol Mediterranean Experiment/Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) 2013 campaign. The retrieved aerosol VC profiles agree well with the airborne measurements, showing a mean bias error (MBE) and a mean absolute bias error (MABE) of 0.3 μm3/cm3 (12%) and 5.8 μm3/cm3 (25%), respectively. The differences between retrieved VC and airborne in-situ measurements are within the uncertainty of GRASP retrievals. In addition, the retrieved VC at 2500 m a.s.l. is shown and compared with in-situ measurements obtained during summer 2016 at a high-atitude mountain station in the framework of the SLOPE I campaign (Sierra Nevada Lidar AerOsol Profiling Experiment). VC from GRASP presents high correlation (r = 0.91) with the in-situ measurements, but overestimates them, MBE and MABE being equal to 23% and 43%.

Impact of NO2 Profile Shape in OMI Tropospheric NO2 Retrievals

Science.gov (United States)

Lamsal, Lok; Krotkov, Nickolay A.; Pickering, K.; Schwartz, W. H.; Celarier, E. A.; Bucsela, E. J.; Gleason, J. F.; Philip, S.; Nowlan, C.; Martin, R. V.;

Assimilation of Remotely Sensed Soil Moisture Profiles into a Crop Modeling Framework for Reliable Yield Estimations

Science.gov (United States)

Mishra, V.; Cruise, J.; Mecikalski, J. R.

2017-12-01

Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the

Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions

Science.gov (United States)

Ebrahimi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-02-01

The objective of this study was to exploit the synergy between SMOS and SMAP based on vegetation optical depth (VOD) to improve brightness temperature (TB) simulations and land surface soil moisture (SM) retrievals in arid regions of the world. In the current operational algorithm of SMAP (level 2), vegetation water content (VWC) is considered as a proxy to compute VOD which is calculated by an empirical conversion function of NDVI. Avoiding the empirical estimation of VOD, the SMOS algorithm is used to retrieve simultaneously SM and VOD from TB observations. The present study attempted to improve SMAP TB simulations and SM retrievals by benefiting from the advantages of the SMOS (L-MEB) algorithm. This was achieved by using a synergy method based on replacing the default value of SMAP VOD with the retrieved value of VOD from the SMOS multi angular and bi-polarization observations of TB. The insitu SM measurements, used as reference SM in this study, were obtained from the International Soil Moisture Network (ISMN) over 180 stations located in arid regions of the world. Furthermore, four stations were randomly selected to analyze the temporal variations in VOD and SM. Results of the synergy method showed that the accuracy of the TB simulations and SM retrievals was respectively improved at 144 and 124 stations (out of a total of 180 stations) in terms of coefficient of determination (R2) and unbiased root mean squared error (UbRMSE). Analyzing the temporal variations in VOD showed that the SMOS VOD, conversely to the SMAP VOD, can better illustrate the presence of herbaceous plants and may be a better indicator of the seasonal changes in the vegetation density and biomass over the year.

Effect of grain moisture content during milling on pasting profile and functional properties of amaranth fractions.

Science.gov (United States)

Kumar, K Vishnuswamy Preetham; Dharmaraj, Usha; Sakhare, Suresh D; Inamdar, Aashitosh A

2016-05-01

Evaluation of functional properties of milled fractions of grain amaranth may be useful to decide the end uses of the grain. Hence, pasting profiles of amaranth fractions obtained by milling the grains at different moisture contents were studied in relation with their starch profile and also with their swelling power and solubility indices. It was observed that, for flour fraction, the viscosity parameters were lowest at 14-16 % moisture content. Swelling power and solubility indices of the samples varied as a function of grain moisture content. The middling fraction also showed similar pasting pattern with the variation of grain moisture content. The seed coat fractions showed higher gelatinization temperature compared to that of fine flour and middling fractions. However, starch content of the fine seed coat fraction was comparable with that of the flour and middling fractions. The coarse seed coat fraction showed lower viscosity parameters than the other samples. Viscosity parameters correlated well among themselves while, they did not show significant correlation with the starch content. However, the viscosity parameters showed negative correlation with the soluble amylose content. The study revealed that, the fractions obtained by milling the grains at different moisture content show differential pasting profiles and functional properties.

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

Science.gov (United States)

McJannet, David; Hawdon, Aaron; Baker, Brett; Renzullo, Luigi; Searle, Ross

2017-12-01

Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Evaluation of ozone profile and tropospheric ozone retrievals from GEMS and OMI spectra

Directory of Open Access Journals (Sweden)

J. Bak

2013-02-01

Full Text Available South Korea is planning to launch the GEMS (Geostationary Environment Monitoring Spectrometer instrument into the GeoKOMPSAT (Geostationary Korea Multi-Purpose SATellite platform in 2018 to monitor tropospheric air pollutants on an hourly basis over East Asia. GEMS will measure backscattered UV radiances covering the 300–500 nm wavelength range with a spectral resolution of 0.6 nm. The main objective of this study is to evaluate ozone profiles and stratospheric column ozone amounts retrieved from simulated GEMS measurements. Ozone Monitoring Instrument (OMI Level 1B radiances, which have the spectral range 270–500 nm at spectral resolution of 0.42–0.63 nm, are used to simulate the GEMS radiances. An optimal estimation-based ozone profile algorithm is used to retrieve ozone profiles from simulated GEMS radiances. Firstly, we compare the retrieval characteristics (including averaging kernels, degrees of freedom for signal, and retrieval error derived from the 270–330 nm (OMI and 300–330 nm (GEMS wavelength ranges. This comparison shows that the effect of not using measurements below 300 nm on retrieval characteristics in the troposphere is insignificant. However, the stratospheric ozone information in terms of DFS decreases greatly from OMI to GEMS, by a factor of ∼2. The number of the independent pieces of information available from GEMS measurements is estimated to 3 on average in the stratosphere, with associated retrieval errors of ~1% in stratospheric column ozone. The difference between OMI and GEMS retrieval characteristics is apparent for retrieving ozone layers above ~20 km, with a reduction in the sensitivity and an increase in the retrieval errors for GEMS. We further investigate whether GEMS can resolve the stratospheric ozone variation observed from high vertical resolution Earth Observing System (EOS Microwave Limb Sounder (MLS. The differences in stratospheric ozone profiles between GEMS and MLS are comparable to those

The retrieval of profile and chemical information from ground-based UV-visible spectroscopic measurements

International Nuclear Information System (INIS)

Schofield, R.; Connor, B.J.; Kreher, K.; Johnston, P.V.; Rodgers, C.D.

2004-01-01

An algorithm has been developed to retrieve altitude information at different diurnal stages for trace gas species by combining direct-sun and zenith-sky UV-visible differential slant column density (DSCD) measurements. DSCDs are derived here using differential optical absorption spectroscopy. Combining the complementary zenith-sky measurements (sensitive to the stratosphere) with direct-sun measurements (sensitive to the troposphere) allows this vertical distinction. Trace gas species such as BrO and NO 2 have vertical profiles with strong diurnal dependence. Information about the diurnal variation is simultaneously retrieved with the altitude distribution of the trace gas. The retrieval is a formal optimal estimation profile retrieval, allowing a complete assessment of information content and errors

An extended Kalman-Bucy filter for atmospheric temperature profile retrieval with a passive microwave sounder

Science.gov (United States)

Ledsham, W. H.; Staelin, D. H.

1978-01-01

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.

Retrieval of MetOp-A/IASI CO profiles and validation with MOZAIC data

Directory of Open Access Journals (Sweden)

E. De Wachter

2012-11-01

Full Text Available The IASI (Infrared Atmospheric Sounding Interferometer nadir-looking thermal infrared sounder onboard MetOp-A enables the monitoring of atmospheric constituents on a global scale. This paper presents a quality assessment of IASI CO profiles retrieved by the two different retrieval algorithms SOFRID and FORLI, by an intercomparison with airborne in-situ CO profiles from the MOZAIC program for the 2008–2009 period. Lower (surface–480 hPa and upper tropospheric partial column (480–225 hPa comparisons as well as profile comparisons are made. The retrieval errors of the IASI products are less than 21% in the lower troposphere and less than 10% in the upper troposphere. A statistical analysis shows similar correlation coefficients for the two retrieval algorithms and smoothed MOZAIC of r ~ 0.8 and r ~ 0.7 in the lower and upper troposphere respectively. Comparison with smoothed MOZAIC data of the temporal variation of the CO profiles at the airports of Frankfurt and Windhoek demonstrates that the IASI products are able to capture the seasonal variability at these sites. At Frankfurt SOFRID (respectively FORLI is positively biased by 10.5% (13.0% compared to smoothed MOZAIC in the upper (lower troposphere, and the limited sensitivity of the IASI instrument to the boundary layer when thermal contrast is low is identified. At Windhoek, the impact of the vegetation fires in Southern Africa from July to November is captured by both SOFRID and FORLI, with an overestimation of the CO background values (fire maxima by SOFRID (FORLI by 12.8% (10%. Profile comparisons at Frankfurt and Windhoek show that the largest discrepancies are found between the two IASI products and MOZAIC for the nighttime retrievals.

Australian Soil Moisture Field Experiments in Support of Soil Moisture Satellite Observations

Science.gov (United States)

Kim, Edward; Walker, Jeff; Rudiger, Christopher; Panciera, Rocco

2010-01-01

Large-scale field campaigns provide the critical fink between our understanding retrieval algorithms developed at the point scale, and algorithms suitable for satellite applications at vastly larger pixel scales. Retrievals of land parameters must deal with the substantial sub-pixel heterogeneity that is present in most regions. This is particularly the case for soil moisture remote sensing, because of the long microwave wavelengths (L-band) that are optimal. Yet, airborne L-band imagers have generally been large, heavy, and required heavy-lift aircraft resources that are expensive and difficult to schedule. Indeed, US soil moisture campaigns, have been constrained by these factors, and European campaigns have used non-imagers due to instrument and aircraft size constraints. Despite these factors, these campaigns established that large-scale soil moisture remote sensing was possible, laying the groundwork for satellite missions. Starting in 2005, a series of airborne field campaigns have been conducted in Australia: to improve our understanding of soil moisture remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for soil moisture satellite missions, and most recently with the launch of the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission, to provide validation measurements over a multi-pixel area. The campaigns to date have included a preparatory campaign in 2005, two National Airborne Field Experiments (NAFE), (2005 and 2006), two campaigns to the Simpson Desert (2008 and 2009), and one Australian Airborne Cal/val Experiment for SMOS (AACES), just concluded in the austral spring of 2010. The primary airborne sensor for each campaign has been the Polarimetric L-band Microwave Radiometer (PLMR), a 6-beam pushbroom imager that is small enough to be compatible with light aircraft, greatly facilitating the execution of the series of campaigns, and a key to their success. An

SMOS validation of soil moisture and ocen salinity (SMOS) soil moisture over watershed networks in the U.S.

Science.gov (United States)

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must b...

Remote Sensing of Surface Soil Moisture using Semi-Concurrent Radar and Radiometer Observations

Science.gov (United States)

Li, L.; Ouellette, J. D.; Colliander, A.; Cosh, M. H.; Caldwell, T. G.; Walker, J. P.

2017-12-01

Radar backscatter and radiometer brightness temperature both have well-documented sensitivity to surface soil moisture, particularly in the microwave regime. While radiometer-derived soil moisture retrievals have been shown to be stable and accurate, they are only available at coarse spatial resolutions on the order of tens of kilometers. Backscatter from Synthetic Aperture Radar (SAR) is similarly sensitive to soil moisture but can yield higher spatial resolutions, with pixel sizes about an order of magnitude smaller. Soil moisture retrieval from radar backscatter is more difficult, however, due to the combined sensitivity of radar scattering to surface roughness, vegetation structure, and soil moisture. The algorithm uses a time-series of SAR data to retrieval soil moisture information, constraining the SAR-derived soil moisture estimates with radiometer observations. This effectively combines the high spatial resolution offered by SAR with the precision offered by passive radiometry. The algorithm is a change detection approach which maps changes in the radar backscatter to changes in surface soil moisture. This new algorithm differs from existing retrieval techniques in that it does not require ancillary vegetation information, but assumes vegetation and surface roughness are stable between pairs of consecutive radar overpasses. Furthermore, this method does not require a radar scattering model for the vegetation canopy, nor the use of a training data set. The algorithm works over a long time series, and is constrained by hard bounds which are defined using a coarse-resolution radiometer soil moisture product. The presentation will include soil moisture retrievals from Soil Moisture Active/Passive (SMAP) SAR data. Two sets of optimization bounds will constrain the radar change detection algorithm: one defined by SMAP radiometer retrievals and one defined by WindSat radiometer retrievals. Retrieved soil moisture values will be presented on a world map and will

First retrievals of MLT sodium profiles based on satellite sodium nightglow observations

Science.gov (United States)

Von Savigny, Christian; Zilker, Bianca; Langowski, Martin

2016-07-01

The Na D lines are a well known feature of the terrestrial airglow and have been identified for the first time in 1929. During the daytime the Na airglow emission is caused by resonance fluorescence, while during the night the excitation occurs by chemiluminescent reactions. Knowledge of Na in the mesopause region is of interest, because the Na layer is thought to be maintained by meteoric ablation and Na measurements allow constraining the meteoric mass influx into the Earth system. In this contribution we employ SCIAMACHY/Envisat nighttime limb measurements of the Na D-line airglow from fall 2002 to spring 2012 - in combination with photochemical models - in order to retrieve Na concentration profiles in the 75 - 100 km altitude range. The Na profiles show realistic peak altitudes, number densities and seasonal variations. The retrieval scheme, sample results and comparisons to ground-based LIDAR measurements of Na as well as SCIAMACHY daytime retrievals will be presented. Moreover, uncertainties in the assumed photochemical scheme and their impact on the Na retrievals will be discussed.

Greenhouse gas profiling by infrared-laser and microwave occultation: retrieval algorithm and demonstration results from end-to-end simulations

Directory of Open Access Journals (Sweden)

V. Proschek

2011-10-01

Full Text Available Measuring greenhouse gas (GHG profiles with global coverage and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS is key for improved monitoring of GHG concentrations in the free atmosphere. In this respect a new satellite mission concept adding an infrared-laser part to the already well studied microwave occultation technique exploits the joint propagation of infrared-laser and microwave signals between Low Earth Orbit (LEO satellites. This synergetic combination, referred to as LEO-LEO microwave and infrared-laser occultation (LMIO method, enables to retrieve thermodynamic profiles (pressure, temperature, humidity and accurate altitude levels from the microwave signals and GHG profiles from the simultaneously measured infrared-laser signals. However, due to the novelty of the LMIO method, a retrieval algorithm for GHG profiling is not yet available. Here we introduce such an algorithm for retrieving GHGs from LEO-LEO infrared-laser occultation (LIO data, applied as a second step after retrieving thermodynamic profiles from LEO-LEO microwave occultation (LMO data. We thoroughly describe the LIO retrieval algorithm and unveil the synergy with the LMO-retrieved pressure, temperature, and altitude information. We furthermore demonstrate the effective independence of the GHG retrieval results from background (a priori information in discussing demonstration results from LMIO end-to-end simulations for a representative set of GHG profiles, including carbon dioxide (CO₂, water vapor (H₂O, methane (CH₄, and ozone (O₃. The GHGs except for ozone are well retrieved throughout the UTLS, while ozone is well retrieved from about 10 km to 15 km upwards, since the ozone layer resides in the lower stratosphere. The GHG retrieval errors are generally smaller than 1% to 3% r.m.s., at a vertical resolution of about 1 km. The retrieved profiles also appear unbiased, which points

NOAA Soil Moisture Products System (SMOPS) Daily Blended Products

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Soil Moisture Operational Products System (SMOPS) combines soil moisture retrievals from multiple satellite sensors to provide a global soil moisture map with...

The impact of using different ozone cross sections on ozone profile retrievals from OMI UV measurements

International Nuclear Information System (INIS)

Liu, Cheng; Liu, Xiong; Chance, Kelly

2013-01-01

We compare three datasets of high-resolution O 3 cross sections and evaluate the effects of using these cross sections on O 3 profile retrievals from OMI UV (270–330 nm) measurements. These O 3 cross sections include Brion–Daumont–Malicet (BDM), Bass–Paur (BP) and a new dataset measured by Serdyuchenko et al. (SGWCB), which is made from measurements at more temperatures and in a wider temperature range than BDM and BP, 193–293 K. Relative to the BDM dataset, the SGWCB data have systematic biases of −2 to +4% for 260–340 nm, and the BP data have smaller biases of 1–2% below 315 nm but larger spiky biases of up to ±6% at longer wavelengths. These datasets show distinctly different temperature dependences. Using different cross sections can significantly affect atmospheric retrievals. Using SGWCB data leads to retrieval failure for almost half of the OMI spatial pixels, producing large negative ozone values that cannot be handled by radiative transfer models and using BP data leads to large fitting residuals over 310–330 nm. Relative to the BDM retrievals, total ozone retrieved using original SGWCB data (with linear temperature interpolation/extrapolation) typically shows negative biases of 5–10 DU; retrieved tropospheric ozone column generally shows negative biases of 5–10 DU and 5–20 DU for parameterized and original SGWCB data, respectively. Compared to BDM retrievals, ozone profiles retrieved with BP/SGWCB data on average show large altitude-dependent oscillating differences of up to ±20–40% biases below ∼20 km with almost opposite bias patterns. Validation with ozonesonde observations demonstrates that the BDM retrievals agree well with ozonesondes, to typically within 10%, while both BP and SGWCB retrievals consistently show large altitude-dependent biases of up to ±20–70% below 20 km. Therefore, we recommend using the BDM dataset for ozone profile retrievals from UV measurements. Its improved performance is likely due to its

A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals

Directory of Open Access Journals (Sweden)

M. Ridolfi

2009-03-01

Full Text Available MIPAS is a Fourier transform spectrometer, operating onboard of the ENVISAT satellite since July 2002. The online retrieval algorithm produces geolocated profiles of temperature and of volume mixing ratios of six key atmospheric constituents: H₂O, O₃, HNO₃, CH₄, N₂O and NO₂. In the validation phase, oscillations beyond the error bars were observed in several profiles, particularly in CH₄ and N₂O.

To tackle this problem, a Tikhonov regularization scheme has been implemented in the retrieval algorithm. The applied regularization is however rather weak in order to preserve the vertical resolution of the profiles.

In this paper we present a self-adapting and altitude-dependent regularization approach that detects whether the analyzed observations contain information about small-scale profile features, and determines the strength of the regularization accordingly. The objective of the method is to smooth out artificial oscillations as much as possible, while preserving the fine detail features of the profile when related information is detected in the observations.

The proposed method is checked for self consistency, its performance is tested on MIPAS observations and compared with that of some other regularization schemes available in the literature. In all the considered cases the proposed scheme achieves a good performance, thanks to its altitude dependence and to the constraints employed, which are specific of the inversion problem under consideration. The proposed method is generally applicable to iterative Gauss-Newton algorithms for the retrieval of vertical distribution profiles from atmospheric remote sounding measurements.

Utility of CrIS/ATMS profiles to diagnose extratropical transition

Science.gov (United States)

Berndt, Emily; Folmer, Michael

2018-03-01

Anticipating changes in hurricane intensity can be challenging in data sparse regions of the North Atlantic Ocean. Hyperspectral infrared retrieved profiles have the potential to provide a wealth of information about the vertical structure of thermodynamic characteristics of the atmosphere such as temperature and moisture which can impact hurricane intensity. Increased forecaster situational awareness and identification of moist or dry layers in the near-storm environment can indicate impending changes in storm intensity. This investigation demonstrates the utility and value of hyperspectral infrared retrieved profiles to diagnose thermodynamic characteristics of the near-storm environment to anticipate changes in hurricane intensity.

High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals

Science.gov (United States)

Laloy, Eric; Huisman, Johan Alexander; Jacques, Diederik

2014-11-01

This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1 cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04 cm3 cm-3. This RMSE value reduces to less than 0.02 cm3 cm-3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors.

Towards soil property retrieval from space: Proof of concept using in situ observations

Science.gov (United States)

Bandara, Ranmalee; Walker, Jeffrey P.; Rüdiger, Christoph

2014-05-01

Soil moisture is a key variable that controls the exchange of water and energy fluxes between the land surface and the atmosphere. However, the temporal evolution of soil moisture is neither easy to measure nor monitor at large scales because of its high spatial variability. This is mainly a result of the local variation in soil properties and vegetation cover. Thus, land surface models are normally used to predict the evolution of soil moisture and yet, despite their importance, these models are based on low-resolution soil property information or typical values. Therefore, the availability of more accurate and detailed soil parameter data than are currently available is vital, if regional or global soil moisture predictions are to be made with the accuracy required for environmental applications. The proposed solution is to estimate the soil hydraulic properties via model calibration to remotely sensed soil moisture observation, with in situ observations used as a proxy in this proof of concept study. Consequently, the feasibility is assessed, and the level of accuracy that can be expected determined, for soil hydraulic property estimation of duplex soil profiles in a semi-arid environment using near-surface soil moisture observations under naturally occurring conditions. The retrieved soil hydraulic parameters were then assessed by their reliability to predict the root zone soil moisture using the Joint UK Land Environment Simulator model. When using parameters that were retrieved using soil moisture observations, the root zone soil moisture was predicted to within an accuracy of 0.04 m3/m3, which is an improvement of ∼0.025 m3/m3 on predictions that used published values or pedo-transfer functions.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

Science.gov (United States)

Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta

2017-01-01

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

Two-dimensional characterization of atmospheric profile retrievals from limb sounding observations

International Nuclear Information System (INIS)

Worden, J.R.; Bowman, K.W.; Jones, D.B.

2004-01-01

Limb sounders measure atmospheric radiation that is dependent on atmospheric temperature and constituents that have a radial and angular distribution in Earth-centered coordinates. In order to evaluate the sensitivity of a limb retrieval to radial and angular distributions of trace gas concentrations, we perform and characterize one-dimensional (vertical) and two-dimensional (radial and angular) atmospheric profile retrievals. Our simulated atmosphere for these retrievals is a distribution of carbon monoxide (CO), which represents a plume off the coast of south-east Asia. Both the one-dimensional (1D) and two-dimensional (2D) limb retrievals are characterized by evaluating their averaging kernels and error covariances on a radial and angular grid that spans the plume. We apply this 2D characterization of a limb retrieval to a comparison of the 2D retrieval with the 1D (vertical) retrieval. By characterizing a limb retrieval in two dimensions the location of the air mass where the retrievals are most sensitive can be determined. For this test case the retrievals are most sensitive to the CO concentrations about 2 deg.latitude in front of the tangent point locations. We find the information content for the 2D retrieval is an order of magnitude larger and the degrees of freedom is about a factor of two larger than that of the 1D retrieval primarily because the 2D retrieval can estimate angular distributions of CO concentrations. This 2D characterization allows the radial and angular resolution as well as the degrees of freedom and information content to be computed for these limb retrievals. We also use the 2D averaging kernel to develop a strategy for validation of a limb retrieval with an in situ measurement

A new retrieval algorithm for tropospheric temperature, humidity and pressure profiling based on GNSS radio occultation data

Science.gov (United States)

Kirchengast, Gottfried; Li, Ying; Scherllin-Pirscher, Barbara; Schwärz, Marc; Schwarz, Jakob; Nielsen, Johannes K.

2017-04-01

The GNSS radio occultation (RO) technique is an important remote sensing technique for obtaining thermodynamic profiles of temperature, humidity, and pressure in the Earth's troposphere. However, due to refraction effects of both dry ambient air and water vapor in the troposphere, retrieval of accurate thermodynamic profiles at these lower altitudes is challenging and requires suitable background information in addition to the RO refractivity information. Here we introduce a new moist air retrieval algorithm aiming to improve the quality and robustness of retrieving temperature, humidity and pressure profiles in moist air tropospheric conditions. The new algorithm consists of four steps: (1) use of prescribed specific humidity and its uncertainty to retrieve temperature and its associated uncertainty; (2) use of prescribed temperature and its uncertainty to retrieve specific humidity and its associated uncertainty; (3) use of the previous results to estimate final temperature and specific humidity profiles through optimal estimation; (4) determination of air pressure and density profiles from the results obtained before. The new algorithm does not require elaborated matrix inversions which are otherwise widely used in 1D-Var retrieval algorithms, and it allows a transparent uncertainty propagation, whereby the uncertainties of prescribed variables are dynamically estimated accounting for their spatial and temporal variations. Estimated random uncertainties are calculated by constructing error covariance matrices from co-located ECMWF short-range forecast and corresponding analysis profiles. Systematic uncertainties are estimated by empirical modeling. The influence of regarding or disregarding vertical error correlations is quantified. The new scheme is implemented with static input uncertainty profiles in WEGC's current OPSv5.6 processing system and with full scope in WEGC's next-generation system, the Reference Occultation Processing System (rOPS). Results from

Assimilation of NUCAPS Retrieved Profiles in GSI for Unique Forecasting Applications

Science.gov (United States)

Berndt, Emily Beth; Zavodsky, Bradley; Srikishen, Jayanthi; Blankenship, Clay

2015-01-01

Hyperspectral IR profiles can be assimilated in GSI as a separate observation other than radiosondes with only changes to tables in the fix directory. Assimilation of profiles does produce changes to analysis fields and evidenced by: Innovations larger than +/-2.0 K are present and represent where individual profiles impact the final temperature analysis.The updated temperature analysis is colder behind the cold front and warmer in the warm sector. The updated moisture analysis is modified more in the low levels and tends to be drier than the original model background Analysis of model output shows: Differences relative to 13-km RAP analyses are smaller when profiles are assimilated with NUCAPS errors. CAPE is under-forecasted when assimilating NUCAPS profiles, which could be problematic for severe weather forecasting Refining the assimilation technique to incorporate an error covariance matrix and creating a separate GSI module to assimilate satellite profiles may improve results.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

Science.gov (United States)

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Directory of Open Access Journals (Sweden)

Jana Kolassa

2017-11-01

Full Text Available This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP observations. Neural network (NN and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE by 0.005 m 3 m − 3 and 0.001 m 3 m − 3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m − 3 , but increased the root zone bias by 0.014 m 3 m − 3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a

Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil

KAUST Repository

Amazirh, Abdelhakim; Merlin, Olivier; Er-Raki, Salah; Gao, Qi; Rivalland, Vincent; Malbeteau, Yoann; Khabba, Said; Escorihuela, Maria José

2018-01-01

Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially

Utility of CrIS/ATMS profiles to diagnose extratropical transition

Directory of Open Access Journals (Sweden)

Emily Berndt

2018-03-01

Full Text Available Anticipating changes in hurricane intensity can be challenging in data sparse regions of the North Atlantic Ocean. Hyperspectral infrared retrieved profiles have the potential to provide a wealth of information about the vertical structure of thermodynamic characteristics of the atmosphere such as temperature and moisture which can impact hurricane intensity. Increased forecaster situational awareness and identification of moist or dry layers in the near-storm environment can indicate impending changes in storm intensity. This investigation demonstrates the utility and value of hyperspectral infrared retrieved profiles to diagnose thermodynamic characteristics of the near-storm environment to anticipate changes in hurricane intensity. Keywords: Hurricane, Sounding, Satellite

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

Science.gov (United States)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

KAUST Repository

Altaf, M. U.

2016-09-01

Soil moisture is a crucial component of the hydrologic cycle, significantly influencing runoff, infiltration, recharge, evaporation and transpiration processes. Models characterizing these processes require soil moisture as an input, either directly or indirectly. Better characterization of the spatial variability of soil moisture leads to better predictions from hydrologic/climate models. In-situ measurements have fine resolution, but become impractical in terms of coverage over large extents. Remotely sensed data have excellent spatial coverage extents, but suffer from poorer spatial and temporal resolution. We present here an innovative approach to downscaling coarse resolution soil moisture data by combining data assimilation and physically based modeling. In this approach, we exploit the features of Continuous Data Assimilation (CDA). A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (e.g., HYDRUS) are subjected to data assimilation conditioned upon the coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. The large scale features of the model output are constrained to the observations, and as a consequence, the misfit at the fine scale is reduced. The advantage of this approach is that fine resolution soil moisture maps can be generated across large spatial extents, given the coarse resolution data. The data assimilation approach also enables multi-scale data generation which is helpful to match the soil moisture input data to the corresponding modeling scale. Application of this approach is likely in generating fine and intermediate resolution soil

Assimilation of Atmospheric InfraRed Sounder (AIRS) Profiles using WRF-Var

Science.gov (United States)

Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

2008-01-01

The Weather Research and Forecasting (WRF) model contains a three-dimensional variational (3DVAR) assimilation system (WRF-Var), which allows a user to join data from multiple sources into one coherent analysis. WRF-Var combines observations with a background field traditionally generated using a previous model forecast through minimization of a cost function. In data sparse regions, remotely-sensed observations may be able to improve analyses and produce improved forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The combined AIRS/AMSU system provides radiance measurements used as input to a sophisticated retrieval scheme which has been shown to produce temperature profiles with an accuracy of 1 K over 1 km layers and humidity profiles with accuracy of 15% in 2 km layers in both clear and partly cloudy conditions. The retrieval algorithm also provides estimates of the accuracy of the retrieved values at each pressure level, allowing the user to select profiles based on the required error tolerances of the application. The purpose of this paper is to describe a procedure to optimally assimilate high-resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type using gen_be and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics in the WRF-Var. The AIRS thermodynamic profiles are obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators are used to select the highest quality temperature and moisture

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

Science.gov (United States)

Blakenship, Clay; Zavodsky, Bradley; Blackwell, William

2014-01-01

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. Forecasts are against ERA reanalyses.

Evaluation of SCIAMACHY Level-1 data versions using nadir ozone profile retrievals in the period 2003-2011

Science.gov (United States)

Shah, Sweta; Tuinder, Olaf N. E.; van Peet, Jacob C. A.; de Laat, Adrianus T. J.; Stammes, Piet

2018-04-01

Ozone profile retrieval from nadir-viewing satellite instruments operating in the ultraviolet-visible range requires accurate calibration of Level-1 (L1) radiance data. Here we study the effects of calibration on the derived Level-2 (L2) ozone profiles for three versions of SCanning Imaging Absorption spectroMeter for Atmospheric ChartograpHY (SCIAMACHY) L1 data: version 7 (v7), version 7 with m-factors (v7mfac) and version 8 (v8). We retrieve nadir ozone profiles from the SCIAMACHY instrument that flew on board Envisat using the Ozone ProfilE Retrieval Algorithm (OPERA) developed at KNMI with a focus on stratospheric ozone. We study and assess the quality of these profiles and compare retrieved L2 products from L1 SCIAMACHY data versions from the years 2003 to 2011 without further radiometric correction. From validation of the profiles against ozone sonde measurements, we find that the v8 performs better than v7 and v7mfac due to correction for the scan-angle dependency of the instrument's optical degradation. Validation for the years 2003 and 2009 with ozone sondes shows deviations of SCIAMACHY ozone profiles of 0.8-15 % in the stratosphere (corresponding to pressure range ˜ 100-10 hPa) and 2.5-100 % in the troposphere (corresponding to pressure range ˜ 1000-100 hPa), depending on the latitude and the L1 version used. Using L1 v8 for the years 2003-2011 leads to deviations of ˜ 1-11 % in stratospheric ozone and ˜ 1-45 % in tropospheric ozone. The SCIAMACHY L1 v8 data can still be improved upon in the 265-330 nm range used for ozone profile retrieval. The slit function can be improved with a spectral shift and squeeze, which leads to a few percent residue reduction compared to reference solar irradiance spectra. Furthermore, studies of the ratio of measured to simulated reflectance spectra show that a bias correction in the reflectance for wavelengths below 300 nm appears to be necessary.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

KAUST Repository

Altaf, M. U.; Jana, Raghavendra Belur; Hoteit, Ibrahim; McCabe, Matthew

2016-01-01

Soil moisture is a crucial component of the hydrologic cycle, significantly influencing runoff, infiltration, recharge, evaporation and transpiration processes. Models characterizing these processes require soil moisture as an input, either directly

Temperature profile retrievals with extended Kalman-Bucy filters

Science.gov (United States)

Ledsham, W. H.; Staelin, D. H.

1979-01-01

The Extended Kalman-Bucy Filter is a powerful technique for estimating non-stationary random parameters in situations where the received signal is a noisy non-linear function of those parameters. A practical causal filter for retrieving atmospheric temperature profiles from radiances observed at a single scan angle by the Scanning Microwave Spectrometer (SCAMS) carried on the Nimbus 6 satellite typically shows approximately a 10-30% reduction in rms error about the mean at almost all levels below 70 mb when compared with a regression inversion.

A new software suite for NO2 vertical profile retrieval from ground-based zenith-sky spectrometers

International Nuclear Information System (INIS)

Denis, L.; Roscoe, H.K.; Chipperfield, M.P.; Roozendael, M. van; Goutail, F.

2005-01-01

Here we present an operational method to improve accuracy and information content of ground-based measurements of stratospheric NO 2 . The motive is to improve the investigation of trends in NO 2 , and is important because the current trend in NO 2 appears to contradict the trend in its source, suggesting that the stratospheric circulation has changed. To do so, a new software package for retrieving NO 2 vertical profiles from slant columns measured by zenith-sky spectrometers has been created. It uses a Rodgers optimal linear inverse method coupled with a radiative transfer model for calculations of transfer functions between profiles and columns, and a chemical box model for taking into account the NO 2 variations during twilight and during the day. Each model has parameters that vary according to season and location. Forerunners of each model have been previously validated. The scheme maps random errors in the measurements and systematic errors in the models and their parameters on to the retrieved profiles. Initialisation for models is derived from well-established climatologies. The software has been tested by comparing retrieved profiles to simultaneous balloon-borne profiles at mid-latitudes in spring

Technical Note: Regularization performances with the error consistency method in the case of retrieved atmospheric profiles

Directory of Open Access Journals (Sweden)

S. Ceccherini

2007-01-01

Full Text Available The retrieval of concentration vertical profiles of atmospheric constituents from spectroscopic measurements is often an ill-conditioned problem and regularization methods are frequently used to improve its stability. Recently a new method, that provides a good compromise between precision and vertical resolution, was proposed to determine analytically the value of the regularization parameter. This method is applied for the first time to real measurements with its implementation in the operational retrieval code of the satellite limb-emission measurements of the MIPAS instrument and its performances are quantitatively analyzed. The adopted regularization improves the stability of the retrieval providing smooth profiles without major degradation of the vertical resolution. In the analyzed measurements the retrieval procedure provides a vertical resolution that, in the troposphere and low stratosphere, is smaller than the vertical field of view of the instrument.

Dynamic analysis of temporal moisture profiles in heatset printing studied with near-infrared spectroscopy

International Nuclear Information System (INIS)

Tåg, C-M; Toiviainen, M; Juuti, M; Gane, P A C

2010-01-01

Dynamic analysis of the water transfer onto coated paper, and its permeation and absorption into the porous structure were studied online in a full-scale heatset web offset printing environment. The moisture content of the paper was investigated at five different positions during the printing process. Changes in the moisture content of the paper were studied as a function of the web temperature, printing speed and silicone application in the folding unit positioned after the hot air drying oven. Additionally, the influence of fountain solution composition on the pick-up by the paper was investigated. The water content of the fountain solution transferred to the paper from the printing units was observed as changes in near-infrared absorbance. A calibration data set enabled the subsequent quantification of the dynamic moisture content of the paper at the studied locations. An increase in the printing speed reduced the water transfer to the paper and an increase in web temperature resulted in a reduction in the moisture content. An increase in the dosage level of the water–silicone mixture was observed as a re-moistening effect of the paper. Differences in the drying strategy resulted in different moisture profiles depending on the type of fountain solution used. As a conclusion, the near-infrared signal provides an effective way to characterize the moisture dynamics online at different press units

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

KAUST Repository

Altaf, Muhammad

2016-01-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model\\'s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

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 were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Retrieval of Tropospheric Profiles from IR Emission Spectra: Field Experiment and Sensitivity Study

National Research Council Canada - National Science Library

Theriault, J

1993-01-01

.... The goal of this project was the retrieval of atmospheric temperature and water vapor profiles and possibly over relevant information on clouds and aerosol properties from high resolution IR emission...

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

Directory of Open Access Journals (Sweden)

J. Schwarz

2018-05-01

Full Text Available Global Navigation Satellite System (GNSS radio occultation (RO observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere – such as pressure, temperature, and tropospheric water vapor profiles (involving background information – can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP; Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC; and Meteorological Operational Satellite A (MetOp. The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs

MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

Directory of Open Access Journals (Sweden)

T. Vlemmix

2015-02-01

Full Text Available A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012 is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides. We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%. With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method

Cloud sensitivity studies for stratospheric and lower mesospheric ozone profile retrievals from measurements of limb-scattered solar radiation

Directory of Open Access Journals (Sweden)

T. Sonkaew

2009-11-01

Full Text Available Clouds in the atmosphere play an important role in reflection, absorption and transmission of solar radiation and thus affect trace gas retrievals. The main goal of this paper is to examine the sensitivity of stratospheric and lower mesospheric ozone retrievals from limb-scattered radiance measurements to clouds using the SCIATRAN radiative transfer model and retrieval package. The retrieval approach employed is optimal estimation, and the considered clouds are vertically and horizontally homogeneous. Assuming an aerosol-free atmosphere and Mie phase functions for cloud particles, we compute the relative error of ozone profile retrievals in a cloudy atmosphere if clouds are neglected in the retrieval. To access altitudes from the lower stratosphere up to the lower mesosphere, we combine the retrievals in the Chappuis and Hartley ozone absorption bands. We find significant cloud sensitivity of the limb ozone retrievals in the Chappuis bands at lower stratospheric altitudes. The relative error in the retrieved ozone concentrations gradually decreases with increasing altitude and becomes negligible above approximately 40 km. The parameters with the largest impact on the ozone retrievals are cloud optical thickness, ground albedo and solar zenith angle. Clouds with different geometrical thicknesses or different cloud altitudes have a similar impact on the ozone retrievals for a given cloud optical thickness value, if the clouds are outside the field of view of the instrument. The effective radius of water droplets has a small influence on the error, i.e., less than 0.5% at altitudes above the cloud top height. Furthermore, the impact of clouds on the ozone profile retrievals was found to have a rather small dependence on the solar azimuth angle (less than 1% for all possible azimuth angles. For the most frequent cloud types, the total error is below 6% above 15 km altitude, if clouds are completely neglected in the retrieval. Neglecting clouds in

Statistical techniques to extract information during SMAP soil moisture assimilation

Science.gov (United States)

Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.

2017-12-01

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

Science.gov (United States)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

Multiangular L-band Datasets for Soil Moisture and Sea Surface Salinity Retrieval Measured by Airborne HUT-2D Synthetic Aperture Radiometer

Science.gov (United States)

Kainulainen, J.; Rautiainen, K.; Seppänen, J.; Hallikainen, M.

2009-04-01

SMOS is the European Space Agency's next Earth Explorer satellite due for launch in 2009. It aims for global monitoring of soil moisture and ocean salinity utilizing a new technology concept for remote sensing: two-dimensional aperture synthesis radiometry. The payload of SMOS is Microwave Imaging Radiometer by Aperture Synthesis, or MIRAS. It is a passive instrument that uses 72 individual L-band receivers for measuring the brightness temperature of the Earth. From each acquisition, i.e. integration time or snapshot, MIRAS provides two-dimensional brightness temperature of the scene in the instrument's field of view. Thus, consecutive snapshots provide multiangular measurements of the target once the instrument passes over it. Depending on the position of the target in instrument's swath, the brightness temperature of the target at incidence angles from zero up to 50 degrees can be measured with one overpass. To support the development MIRAS instrument, its calibration, and soil moisture and sea surface salinity retrieval algorithm development, Helsinki University of Technology (TKK) has designed, manufactured and tested a radiometer which operates at L-band and utilizes the same two-dimensional methodology of interferometery and aperture synthesis as MIRAS does. This airborne instrument, called HUT-2D, was designed to be used on board the University's research aircraft. It provides multiangular measurements of the target in its field of view, which spans up to 30 degrees off the boresight of the instrument, which is pointed to the nadir. The number of independent measurements of each target point depends on the flight speed and altitude. In addition to the Spanish Airborne MIRAS demonstrator (AMIRAS), HUT-2D is the only European airborne synthetic aperture radiometer. This paper presents the datasets and measurement campaigns, which have been carried out using the HUT-2D radiometer and are available for the scientific community. In April 2007 HUT-2D participated

A new software suite for NO{sub 2} vertical profile retrieval from ground-based zenith-sky spectrometers

Energy Technology Data Exchange (ETDEWEB)

Denis, L. [British Antarctic Survey/NERC, Madingley Road, Cambridge CB3 0ET (United Kingdom); Roscoe, H.K. [British Antarctic Survey/NERC, Madingley Road, Cambridge CB3 0ET (United Kingdom)]. E-mail: h.roscoe@bas.ac.uk; Chipperfield, M.P. [Environment Centre, University of Leeds, Leeds LS2 9JT (United Kingdom); Roozendael, M. van [Belgian Institute for Space Aeronomy (BIRA/IASB), 1180 Brussels (Belgium); Goutail, F. [Service d' Aeronomie du CNRS, BP3, 91271 Verrieres le Buisson (France)

2005-05-15

Here we present an operational method to improve accuracy and information content of ground-based measurements of stratospheric NO{sub 2}. The motive is to improve the investigation of trends in NO{sub 2}, and is important because the current trend in NO{sub 2} appears to contradict the trend in its source, suggesting that the stratospheric circulation has changed. To do so, a new software package for retrieving NO{sub 2} vertical profiles from slant columns measured by zenith-sky spectrometers has been created. It uses a Rodgers optimal linear inverse method coupled with a radiative transfer model for calculations of transfer functions between profiles and columns, and a chemical box model for taking into account the NO{sub 2} variations during twilight and during the day. Each model has parameters that vary according to season and location. Forerunners of each model have been previously validated. The scheme maps random errors in the measurements and systematic errors in the models and their parameters on to the retrieved profiles. Initialisation for models is derived from well-established climatologies. The software has been tested by comparing retrieved profiles to simultaneous balloon-borne profiles at mid-latitudes in spring.

Compact polarimetric synthetic aperture radar for monitoring soil moisture condition

Science.gov (United States)

Merzouki, A.; McNairn, H.; Powers, J.; Friesen, M.

2017-12-01

Coarse resolution soil moisture maps are currently operationally delivered by ESA's SMOS and NASA's SMAP passive microwaves sensors. Despite this evolution, operational soil moisture monitoring at the field scale remains challenging. A number of factors contribute to this challenge including the complexity of the retrieval that requires advanced SAR systems with enhanced temporal revisit capabilities. Since the launch of RADARSAT-2 in 2007, Agriculture and Agri-Food Canada (AAFC) has been evaluating the accuracy of these data for estimating surface soil moisture. Thus, a hybrid (multi-angle/multi-polarization) retrieval approach was found well suited for the planned RADARSAT Constellation Mission (RCM) considering the more frequent relook expected with the three satellite configuration. The purpose of this study is to evaluate the capability of C-band CP data to estimate soil moisture over agricultural fields, in anticipation of the launch of RCM. In this research we introduce a new CP approach based on the IEM and simulated RCM CP mode intensities from RADARSAT-2 images acquired at different dates. The accuracy of soil moisture retrieval from the proposed multi-polarization and hybrid methods will be contrasted with that from a more conventional quad-pol approach, and validated against in situ measurements by pooling data collected over AAFC test sites in Ontario, Manitoba and Saskatchewan, Canada.

Neural network retrieval of soil moisture: application to SMOS

Science.gov (United States)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Kolasssa, Jana; Jimenez, Carlos; Cabot, Francois; Mahmoodi, Ali

2014-05-01

We present an efficient statistical soil moisture (SM) retrieval method using SMOS brightness temperatures (BTs) complemented with MODIS NDVI and ASCAT backscattering data. The method is based on a feed-forward neural network (hereafter NN) trained with SM from ECMWF model predictions or from the SMOS operational algorithm. The best compromise to retrieve SM with NNs from SMOS brightness temperatures in a large fraction of the swath (~ 670 km) is to use incidence angles from 25 to 60 degrees (in 7 bins of 5 deg width) for both H and V polarizations. The correlation coefficient (R) of the SM retrieved by the NN and the reference SM dataset (ECMWF or SMOS L3) is 0.8. The correlation coefficient increases to 0.91 when adding as input MODIS NDVI, ECOCLIMAP sand and clay fractions and one of the following data: (i) active microwaves observations (ASCAT backscattering coefficient at 40 deg incidence angle), (ii) ECMWF soil temperature. Finally, the correlation coefficient increases to R=0.94 when using a normalization index computed locally for each latitude-longitude point with the maximum and minimum BTs and the associated SM values from the local time series. Global maps of SM obtained with NNs reproduce well the spatial structures present in the reference SM datasets, implying that the NN works well for a wide range of ecosystems and physical conditions. In addition, the results of the NNs have been evaluated at selected locations for which in situ measurements are available such as the USDA-ARS watersheds (USA), the OzNet network (AUS) and USDA-NRCS SCAN network (USA). The time series of SM obtained with NNs reproduce the temporal behavior measured with in situ sensors. For well known sites where the in situ measurement is representative of a 40 km scale like the Little Washita watershed, the NN models show a very high correlation of (R = 0.8-0.9) and a low standard deviation of 0.02-0.04 m3/m3 with respect to the in situ measurements. When comparing with all the in

Microwave radiometric measurements of soil moisture in Italy

Directory of Open Access Journals (Sweden)

G. Macelloni

2003-01-01

Full Text Available Within the framework of the MAP and RAPHAEL projects, airborne experimental campaigns were carried out by the IFAC group in 1999 and 2000, using a multifrequency microwave radiometer at L, C and X bands (1.4, 6.8 and 10 GHz. The aim of the experiments was to collect soil moisture and vegetation biomass information on agricultural areas to give reliable inputs to the hydrological models. It is well known that microwave emission from soil, mainly at L-band (1.4 GHz, is very well correlated to its moisture content. Two experimental areas in Italy were selected for this project: one was the Toce Valley, Domodossola, in 1999, and the other, the agricultural area of Cerbaia, close to Florence, where flights were performed in 2000. Measurements were carried out on bare soils, corn and wheat fields in different growth stages and on meadows. Ground data of soil moisture (SMC were collected by other research teams involved in the experiments. From the analysis of the data sets, it has been confirmed that L-band is well related to the SMC of a rather deep soil layer, whereas C-band is sensitive to the surface SMC and is more affected by the presence of surface roughness and vegetation, especially at high incidence angles. An algorithm for the retrieval of soil moisture, based on the sensitivity to moisture of the brightness temperature at C-band, has been tested using the collected data set. The results of the algorithm, which is able to correct for the effect of vegetation by means of the polarisation index at X-band, have been compared with soil moisture data measured on the ground. Finally, the sensitivity of emission at different frequencies to the soil moisture profile was investigated. Experimental data sets were interpreted by using the Integral Equation Model (IEM and the outputs of the model were used to train an artificial neural network to reproduce the soil moisture content at different depths. Keywords: microwave radiometry, soil moisture

MUSICA MetOp/IASI {H2O,δD} pair retrieval simulations for validating tropospheric moisture pathways in atmospheric models

Science.gov (United States)

Schneider, Matthias; Borger, Christian; Wiegele, Andreas; Hase, Frank; García, Omaira E.; Sepúlveda, Eliezer; Werner, Martin

2017-02-01

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.

Evaluation of a Soil Moisture Data Assimilation System Over West Africa

Science.gov (United States)

Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

2009-05-01

A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model

Development and evaluation of the MTVDI for soil moisture monitoring

Science.gov (United States)

Zhu, Wenbin; Lv, Aifeng; Jia, Shaofeng; Sun, Liang

2017-06-01

Several parameterization schemes have been developed to retrieve the soil moisture information involved in the remotely sensed surface temperature-vegetation index (Ts - VI) space. However, most of them are performed with the constraint of the dry edge of the Ts - VI space to define the maximum water stressed conditions. In view of the subjectivity and uncertainty involved in the determination of the dry edge, a new index termed as the Modified Temperature-Vegetation Dryness Index (MTVDI) was developed in this paper to reduce the reliance of the parameterization scheme on the dry edge. In the parameterization scheme of MTVDI, isopleth lines of soil moisture involved in the feature space were retrieved by the temperature-vegetation index method, and only the maximum surface temperature of bare soil (Tsmax) was indispensable in the definition of maximum water stressed conditions. For evaluation purpose, the MTVDI was demonstrated in the Southern Great Plains region of the U.S. and was compared with two other traditional soil moisture indexes developed under the constraint of dry edge. The comparison confirmed the effectivity of the MTVDI in monitoring the spatial pattern and seasonal variation of soil moisture. Our analyses also suggest that Tsmax, the only parameter needed in the definition of maximum water stressed conditions, can be retrieved directly from the parameterization scheme itself. Therefore, the retrieval of MTVDI can be performed independent of the dry edge, which is a significant improvement to the traditional parameterization schemes of soil moisture from the Ts - VI feature space.

Model-based surface soil moisture (SSM) retrieval algorithm using multi-temporal RISAT-1 C-band SAR data

Science.gov (United States)

Pandey, Dharmendra K.; Maity, Saroj; Bhattacharya, Bimal; Misra, Arundhati

2016-05-01

Accurate measurement of surface soil moisture of bare and vegetation covered soil over agricultural field and monitoring the changes in surface soil moisture is vital for estimation for managing and mitigating risk to agricultural crop, which requires information and knowledge to assess risk potential and implement risk reduction strategies and deliver essential responses. The empirical and semi-empirical model-based soil moisture inversion approach developed in the past are either sensor or region specific, vegetation type specific or have limited validity range, and have limited scope to explain physical scattering processes. Hence, there is need for more robust, physical polarimetric radar backscatter model-based retrieval methods, which are sensor and location independent and have wide range of validity over soil properties. In the present study, Integral Equation Model (IEM) and Vector Radiative Transfer (VRT) model were used to simulate averaged backscatter coefficients in various soil moisture (dry, moist and wet soil), soil roughness (smooth to very rough) and crop conditions (low to high vegetation water contents) over selected regions of Gujarat state of India and the results were compared with multi-temporal Radar Imaging Satellite-1 (RISAT-1) C-band Synthetic Aperture Radar (SAR) data in σ°HH and σ°HV polarizations, in sync with on field measured soil and crop conditions. High correlations were observed between RISAT-1 HH and HV with model simulated σ°HH & σ°HV based on field measured soil with the coefficient of determination R2 varying from 0.84 to 0.77 and RMSE varying from 0.94 dB to 2.1 dB for bare soil. Whereas in case of winter wheat crop, coefficient of determination R2 varying from 0.84 to 0.79 and RMSE varying from 0.87 dB to 1.34 dB, corresponding to with vegetation water content values up to 3.4 kg/m2. Artificial Neural Network (ANN) methods were adopted for model-based soil moisture inversion. The training datasets for the NNs were

Real-time measurements of temperature, pressure and moisture profiles in High-Performance Concrete exposed to high temperatures during neutron radiography imaging

Energy Technology Data Exchange (ETDEWEB)

Toropovs, N., E-mail: nikolajs.toropovs@rtu.lv [Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf (Switzerland); Riga Technical University, Institute of Materials and Structures, Riga (Latvia); Lo Monte, F. [Politecnico di Milano, Department of Civil and Environmental Engineering, Milan (Italy); Wyrzykowski, M. [Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf (Switzerland); Lodz University of Technology, Department of Building Physics and Building Materials, Lodz (Poland); Weber, B. [Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf (Switzerland); Sahmenko, G. [Riga Technical University, Institute of Materials and Structures, Riga (Latvia); Vontobel, P. [Paul Scherrer Institute, Laboratory for Neutron Scattering and Imaging, Villigen (Switzerland); Felicetti, R. [Politecnico di Milano, Department of Civil and Environmental Engineering, Milan (Italy); Lura, P. [Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf (Switzerland); ETH Zürich, Institute for Building Materials (IfB), Zürich (Switzerland)

2015-02-15

High-Performance Concrete (HPC) is particularly prone to explosive spalling when exposed to high temperature. Although the exact causes that lead to spalling are still being debated, moisture transport during heating plays an important role in all proposed mechanisms. In this study, slabs made of high-performance, low water-to-binder ratio mortars with addition of superabsorbent polymers (SAP) and polypropylene fibers (PP) were heated from one side on a temperature-controlled plate up to 550 °C. A combination of measurements was performed simultaneously on the same sample: moisture profiles via neutron radiography, temperature profiles with embedded thermocouples and pore pressure evolution with embedded pressure sensors. Spalling occurred in the sample with SAP, where sharp profiles of moisture and temperature were observed. No spalling occurred when PP-fibers were introduced in addition to SAP. The experimental procedure described here is essential for developing and verifying numerical models and studying measures against fire spalling risk in HPC.

Retrieval of nitrogen dioxide stratospheric profiles from ground-based zenith-sky UV-visible observations: validation of the technique through correlative comparisons

Directory of Open Access Journals (Sweden)

F. Hendrick

2004-01-01

Full Text Available A retrieval algorithm based on the Optimal Estimation Method (OEM has been developed in order to provide vertical distributions of NO2 in the stratosphere from ground-based (GB zenith-sky UV-visible observations. It has been applied to observational data sets from the NDSC (Network for Detection of Stratospheric Change stations of Harestua (60° N, 10° E and Andøya (69° N, 16° E in Norway. The information content and retrieval errors have been analyzed following a formalism used for characterizing ozone profiles retrieved from solar infrared absorption spectra. In order to validate the technique, the retrieved NO2 vertical profiles and columns have been compared to correlative balloon and satellite observations. Such extensive validation of the profile and column retrievals was not reported in previously published work on the profiling from GB UV-visible measurements. A good agreement - generally better than 25% - has been found with the SAOZ (Système d'Analyse par Observations Zénithales and DOAS (Differential Optical Absorption Spectroscopy balloons. A similar agreement has been reached with correlative satellite data from the HALogen Occultation Experiment (HALOE and Polar Ozone and Aerosol Measurement (POAM III instruments above 25km of altitude. Below 25km, a systematic underestimation - by up to 40% in some cases - of both HALOE and POAM III profiles by our GB profile retrievals has been observed, pointing out more likely a limitation of both satellite instruments at these altitudes. We have concluded that our study strengthens our confidence in the reliability of the retrieval of vertical distribution information from GB UV-visible observations and offers new perspectives in the use of GB UV-visible network data for validation purposes.

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Science.gov (United States)

Johnson, Matthew S.; Sullivan, John T.; Liu, Xiong; Newchurch, Mike; Kuang, Shi; McGee, Thomas J.; Langford, Andrew O'Neil; Senff, Christoph J.; Leblanc, Thierry; Berkoff, Timothy;

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Science.gov (United States)

Johnson, M. S.; Sullivan, J. T.; Liu, X.; Newchurch, M.; Kuang, S.; McGee, T. J.; Langford, A. O.; Senff, C. J.; Leblanc, T.; Berkoff, T.; Gronoff, G.; Chen, G.; Strawbridge, K. B.

2016-12-01

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

Potential of bias correction for downscaling passive microwave and soil moisture data

Science.gov (United States)

Passive microwave satellites such as SMOS (Soil Moisture and Ocean Salinity) or SMAP (Soil Moisture Active Passive) observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...

Passive microwave remote sensing of soil moisture

International Nuclear Information System (INIS)

Jackson, T.J.; Schmugge, T.J.

1986-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive microwave soil moisture sensors currently considered for space operation are in the range 10–20 km. The most useful frequency range for soil moisture sensing is 1–5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations

Technical Note: Variance-covariance matrix and averaging kernels for the Levenberg-Marquardt solution of the retrieval of atmospheric vertical profiles

Directory of Open Access Journals (Sweden)

S. Ceccherini

2010-03-01

Full Text Available The variance-covariance matrix (VCM and the averaging kernel matrix (AKM are widely used tools to characterize atmospheric vertical profiles retrieved from remote sensing measurements. Accurate estimation of these quantities is essential for both the evaluation of the quality of the retrieved profiles and for the correct use of the profiles themselves in subsequent applications such as data comparison, data assimilation and data fusion. We propose a new method to estimate the VCM and AKM of vertical profiles retrieved using the Levenberg-Marquardt iterative technique. We apply the new method to the inversion of simulated limb emission measurements. Then we compare the obtained VCM and AKM with those resulting from other methods already published in the literature and with accurate estimates derived using statistical and numerical estimators. The proposed method accounts for all the iterations done in the inversion and provides the most accurate VCM and AKM. Furthermore, it correctly estimates the VCM and the AKM also if the retrieval iterations are stopped when a physically meaningful convergence criterion is fulfilled, i.e. before achievement of the numerical convergence at machine precision. The method can be easily implemented in any Levenberg-Marquardt iterative retrieval scheme, either constrained or unconstrained, without significant computational overhead.

The feasibility of retrieving vertical temperature profiles from satellite nadir UV observations: A sensitivity analysis and an inversion experiment with neural network algorithms

International Nuclear Information System (INIS)

Sellitto, P.; Del Frate, F.

2014-01-01

Atmospheric temperature profiles are inferred from passive satellite instruments, using thermal infrared or microwave observations. Here we investigate on the feasibility of the retrieval of height resolved temperature information in the ultraviolet spectral region. The temperature dependence of the absorption cross sections of ozone in the Huggins band, in particular in the interval 320–325 nm, is exploited. We carried out a sensitivity analysis and demonstrated that a non-negligible information on the temperature profile can be extracted from this small band. Starting from these results, we developed a neural network inversion algorithm, trained and tested with simulated nadir EnviSat-SCIAMACHY ultraviolet observations. The algorithm is able to retrieve the temperature profile with root mean square errors and biases comparable to existing retrieval schemes that use thermal infrared or microwave observations. This demonstrates, for the first time, the feasibility of temperature profiles retrieval from space-borne instruments operating in the ultraviolet. - Highlights: • A sensitivity analysis and an inversion scheme to retrieve temperature profiles from satellite UV observations (320–325 nm). • The exploitation of the temperature dependence of the absorption cross section of ozone in the Huggins band is proposed. • First demonstration of the feasibility of temperature profiles retrieval from satellite UV observations. • RMSEs and biases comparable with more established techniques involving TIR and MW observations

Analysis of the Tikhonov regularization to retrieve thermal conductivity depth-profiles from infrared thermography data

Science.gov (United States)

Apiñaniz, Estibaliz; Mendioroz, Arantza; Salazar, Agustín; Celorrio, Ricardo

2010-09-01

We analyze the ability of the Tikhonov regularization to retrieve different shapes of in-depth thermal conductivity profiles, usually encountered in hardened materials, from surface temperature data. Exponential, oscillating, and sigmoidal profiles are studied. By performing theoretical experiments with added white noises, the influence of the order of the Tikhonov functional and of the parameters that need to be tuned to carry out the inversion are investigated. The analysis shows that the Tikhonov regularization is very well suited to reconstruct smooth profiles but fails when the conductivity exhibits steep slopes. We check a natural alternative regularization, the total variation functional, which gives much better results for sigmoidal profiles. Accordingly, a strategy to deal with real data is proposed in which we introduce this total variation regularization. This regularization is applied to the inversion of real data corresponding to a case hardened AISI1018 steel plate, giving much better anticorrelation of the retrieved conductivity with microindentation test data than the Tikhonov regularization. The results suggest that this is a promising way to improve the reliability of local inversion methods.

Nuclear techniques for measuring moisture content in soil profiles

International Nuclear Information System (INIS)

Barrada, Y.

1983-01-01

The prevailing severe shortage of animal feed in most of the developing countries could, to a considerable extent, be overcome through improved range management, which includes introduction of high yielding drought-resistant forage crops, development of adequate water conservation measures, and as far as possible growing annual forage crops on part of the vast areas of arable land currently left fallow each year. Year round measurements are essential for a good understanding of soil water and nutrients dynamics, which allow for adequate evaluation of pasture management alternatives. The methods most commonly used for moisture measurements in soil profiles are discussed because such measurements are likely to form an essential part of any investigation aimed at increasing animal feed production through the development of adequate pasture management practices. (author)

Comparison of the inversion algorithms applied to the ozone vertical profile retrieval from SCIAMACHY limb measurements

Directory of Open Access Journals (Sweden)

A. Rozanov

2007-09-01

Full Text Available This paper is devoted to an intercomparison of ozone vertical profiles retrieved from the measurements of scattered solar radiation performed by the SCIAMACHY instrument in the limb viewing geometry. Three different inversion algorithms including the prototype of the operational Level 1 to 2 processor to be operated by the European Space Agency are considered. Unlike usual validation studies, this comparison removes the uncertainties arising when comparing measurements made by different instruments probing slightly different air masses and focuses on the uncertainties specific to the modeling-retrieval problem only. The intercomparison was performed for 5 selected orbits of SCIAMACHY showing a good overall agreement of the results in the middle stratosphere, whereas considerable discrepancies were identified in the lower stratosphere and upper troposphere altitude region. Additionally, comparisons with ground-based lidar measurements are shown for selected profiles demonstrating an overall correctness of the retrievals.

Evaluation of AMSR-E derived soil moisture over Australia, /Remote Sensing of Environment

NARCIS (Netherlands)

Draper, C.S.; Walker, J.P.; Steinle, P.J.; De Jeu, R.A.M.; Holmes, T.R.H.

2009-01-01

This paper assesses remotely sensed near-surface soil moisture over Australia, derived from the passive microwave Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument. Soil moisture fields generated by the AMSR-E soil moisture retrieval algorithm developed at the Vrije

Comparison of AOD, AAOD and column single scattering albedo from AERONET retrievals and in situ profiling measurements

Science.gov (United States)

Andrews, Elisabeth; Ogren, John A.; Kinne, Stefan; Samset, Bjorn

2017-05-01

Here we present new results comparing aerosol optical depth (AOD), aerosol absorption optical depth (AAOD) and column single scattering albedo (SSA) obtained from in situ vertical profile measurements with AERONET ground-based remote sensing from two rural, continental sites in the US. The profiles are closely matched in time (within ±3 h) and space (within 15 km) with the AERONET retrievals. We have used Level 1.5 inversion retrievals when there was a valid Level 2 almucantar retrieval in order to be able to compare AAOD and column SSA below AERONET's recommended loading constraint (AOD > 0.4 at 440 nm). While there is reasonable agreement for the AOD comparisons, the direct comparisons of in situ-derived to AERONET-retrieved AAOD (or SSA) reveal that AERONET retrievals yield higher aerosol absorption than obtained from the in situ profiles for the low aerosol optical depth conditions prevalent at the two study sites. However, it should be noted that the majority of SSA comparisons for AOD440 > 0.2 are, nonetheless, within the reported SSA uncertainty bounds. The observation that, relative to in situ measurements, AERONET inversions exhibit increased absorption potential at low AOD values is generally consistent with other published AERONET-in situ comparisons across a range of locations, atmospheric conditions and AOD values. This systematic difference in the comparisons suggests a bias in one or both of the methods, but we cannot assess whether the AERONET retrievals are biased towards high absorption or the in situ measurements are biased low. Based on the discrepancy between the AERONET and in situ values, we conclude that scaling modeled black carbon concentrations upwards to match AERONET retrievals of AAOD should be approached with caution as it may lead to aerosol absorption overestimates in regions of low AOD. Both AERONET retrievals and in situ measurements suggest there is a systematic relationship between SSA and aerosol amount (AOD or aerosol light

Utility of CrIS/ATMS profiles to diagnose extratropical transition

OpenAIRE

Emily Berndt; Michael Folmer

2018-01-01

Anticipating changes in hurricane intensity can be challenging in data sparse regions of the North Atlantic Ocean. Hyperspectral infrared retrieved profiles have the potential to provide a wealth of information about the vertical structure of thermodynamic characteristics of the atmosphere such as temperature and moisture which can impact hurricane intensity. Increased forecaster situational awareness and identification of moist or dry layers in the near-storm environment can indicate impendi...

On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture

Science.gov (United States)

Santi, E.; Paloscia, S.; Pettinato, S.; Brocca, L.; Ciabatta, L.; Entekhabi, D.

2018-03-01

An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improved accuracy with respect to the original SMAP radiometric SMC product. A disaggregation technique based on the Smoothing filter based intensity modulation (SFIM) allows combining the radiometric and SAR data. Disaggregated microwave data are used as inputs of an Artificial Neural Networks (ANN) based algorithm, which is able to exploit the synergy between active and passive acquisitions. The algorithm is defined, trained and tested using the SMEX02 experimental dataset and data simulated by forward electromagnetic models based on the Radiative Transfer Theory. Then the algorithm is adapted to satellite data and tested using one year of SMAP, AMSR2 and Sentinel-1 co-located data on a flat agricultural area located in the Po Valley, in northern Italy. Spatially distributed SMC values at 0.1° × 0.1° resolution generated by the Soil Water Balance Model (SWBM) are considered as reference for this purpose. The synergy of SMAP, Sentinel-1 and AMSR2 allowed increasing the correlation between estimated and reference SMC from R ≅ 0.68 of the SMAP based retrieval up to R ≅ 0.86 of the combination SMAP + Sentinel-1 + AMSR2. The corresponding Root Mean Square Error (RMSE) decreased from RMSE ≅ 0.04 m3/m3 to RMSE ≅ 0.024 m3/m3.

Information content of ozone retrieval algorithms

Science.gov (United States)

Rodgers, C.; Bhartia, P. K.; Chu, W. P.; Curran, R.; Deluisi, J.; Gille, J. C.; Hudson, R.; Mateer, C.; Rusch, D.; Thomas, R. J.

1989-01-01

The algorithms are characterized that were used for production processing by the major suppliers of ozone data to show quantitatively: how the retrieved profile is related to the actual profile (This characterizes the altitude range and vertical resolution of the data); the nature of systematic errors in the retrieved profiles, including their vertical structure and relation to uncertain instrumental parameters; how trends in the real ozone are reflected in trends in the retrieved ozone profile; and how trends in other quantities (both instrumental and atmospheric) might appear as trends in the ozone profile. No serious deficiencies were found in the algorithms used in generating the major available ozone data sets. As the measurements are all indirect in someway, and the retrieved profiles have different characteristics, data from different instruments are not directly comparable.

Reanalysis of the Indian summer monsoon: four dimensional data assimilation of AIRS retrievals in a regional data assimilation and modeling framework

Science.gov (United States)

Attada, Raju; Parekh, Anant; Chowdary, J. S.; Gnanaseelan, C.

2018-04-01

This work is the first attempt to produce a multi-year downscaled regional reanalysis of the Indian summer monsoon (ISM) using the National Centers for Environmental Prediction (NCEP) operational analyses and Atmospheric Infrared Sounder (AIRS) version 5 temperature and moisture retrievals in a regional model. Reanalysis of nine monsoon seasons (2003-2011) are produced in two parallel setups. The first set of experiments simply downscale the original NCEP operational analyses, whilst the second one assimilates the AIRS temperature and moisture profiles. The results show better representation of the key monsoon features such as low level jet, tropical easterly jet, subtropical westerly jet, monsoon trough and the spatial pattern of precipitation when AIRS profiles are assimilated (compared to those without AIRS data assimilation). The distribution of temperature, moisture and meridional gradients of dynamical and thermodynamical fields over the monsoon region are better represented in the reanalysis that assimilates AIRS profiles. The change induced by AIRS data on the moist and thermodynamic conditions results in more realistic rendering of the vertical shear associated with the monsoon, which in turn leads to a proper moisture transport and the moist convective feedback. This feedback benefits the representation of the regional monsoon characteristics, the monsoon dynamics and the moist convective processes on the seasonal time scale. This study emphasizes the use of AIRS soundings for downscaling of ISM representation in a regional reanalysis.

Reanalysis of the Indian summer monsoon: four dimensional data assimilation of AIRS retrievals in a regional data assimilation and modeling framework

KAUST Repository

Attada, Raju

2017-07-04

This work is the first attempt to produce a multi-year downscaled regional reanalysis of the Indian summer monsoon (ISM) using the National Centers for Environmental Prediction (NCEP) operational analyses and Atmospheric Infrared Sounder (AIRS) version 5 temperature and moisture retrievals in a regional model. Reanalysis of nine monsoon seasons (2003–2011) are produced in two parallel setups. The first set of experiments simply downscale the original NCEP operational analyses, whilst the second one assimilates the AIRS temperature and moisture profiles. The results show better representation of the key monsoon features such as low level jet, tropical easterly jet, subtropical westerly jet, monsoon trough and the spatial pattern of precipitation when AIRS profiles are assimilated (compared to those without AIRS data assimilation). The distribution of temperature, moisture and meridional gradients of dynamical and thermodynamical fields over the monsoon region are better represented in the reanalysis that assimilates AIRS profiles. The change induced by AIRS data on the moist and thermodynamic conditions results in more realistic rendering of the vertical shear associated with the monsoon, which in turn leads to a proper moisture transport and the moist convective feedback. This feedback benefits the representation of the regional monsoon characteristics, the monsoon dynamics and the moist convective processes on the seasonal time scale. This study emphasizes the use of AIRS soundings for downscaling of ISM representation in a regional reanalysis.

Validation of ozone profile retrievals derived from the OMPS LP version 2.5 algorithm against correlative satellite measurements

Science.gov (United States)

Kramarova, Natalya A.; Bhartia, Pawan K.; Jaross, Glen; Moy, Leslie; Xu, Philippe; Chen, Zhong; DeLand, Matthew; Froidevaux, Lucien; Livesey, Nathaniel; Degenstein, Douglas; Bourassa, Adam; Walker, Kaley A.; Sheese, Patrick

2018-05-01

The Limb Profiler (LP) is a part of the Ozone Mapping and Profiler Suite launched on board of the Suomi NPP satellite in October 2011. The LP measures solar radiation scattered from the atmospheric limb in ultraviolet and visible spectral ranges between the surface and 80 km. These measurements of scattered solar radiances allow for the retrieval of ozone profiles from cloud tops up to 55 km. The LP started operational observations in April 2012. In this study we evaluate more than 5.5 years of ozone profile measurements from the OMPS LP processed with the new NASA GSFC version 2.5 retrieval algorithm. We provide a brief description of the key changes that had been implemented in this new algorithm, including a pointing correction, new cloud height detection, explicit aerosol correction and a reduction of the number of wavelengths used in the retrievals. The OMPS LP ozone retrievals have been compared with independent satellite profile measurements obtained from the Aura Microwave Limb Sounder (MLS), Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) and Odin Optical Spectrograph and InfraRed Imaging System (OSIRIS). We document observed biases and seasonal differences and evaluate the stability of the version 2.5 ozone record over 5.5 years. Our analysis indicates that the mean differences between LP and correlative measurements are well within required ±10 % between 18 and 42 km. In the upper stratosphere and lower mesosphere (> 43 km) LP tends to have a negative bias. We find larger biases in the lower stratosphere and upper troposphere, but LP ozone retrievals have significantly improved in version 2.5 compared to version 2 due to the implemented aerosol correction. In the northern high latitudes we observe larger biases between 20 and 32 km due to the remaining thermal sensitivity issue. Our analysis shows that LP ozone retrievals agree well with the correlative satellite observations in characterizing vertical, spatial and temporal

Influence of controlled encoding and retrieval facilitation on memory performance in patients with different profiles of mild cognitive impairment.

Science.gov (United States)

Perri, Roberta; Monaco, Marco; Fadda, Lucia; Serra, Laura; Marra, Camillo; Caltagirone, Carlo; Bruni, Amalia C; Curcio, Sabrina; Bozzali, M; Carlesimo, Giovanni A

2015-01-01

Memory tests able to differentiate encoding and retrieval processes from the memoranda storing ones should be used to differentiate patients in a very early phase of AD. In fact, individuals with mild cognitive impairment (MCI) can be characterized by two different memory profiles: a pure amnestic one (with poor learning and retrieval and poor improvement when encoding is assisted and retrieval is facilitated) and a dysexecutive one (with inefficient encoding and/or poor retrieval strategies and improvement with assisted encoding and retrieval). The amnestic profile characterizes subjects affected by medio-temporal atrophy typical of AD. In this study, a Grober-Buschke memory procedure was used to evaluate normal controls and MCI patients with different cognitive profiles: pure amnestic (aMCIsd), amnestic plus other cognitive impairments (aMCImd) and non-amnestic (naMCI). An index of sensitivity of cueing (ISC) measured the advantage passing from free to cued recall. Results showed that both strategic and consolidation abilities were impaired in the aMCIsd and aMCImd groups and were preserved in the naMCI group. aMCImd, however, compensated the memory deficit with assisted encoding and retrieval, but aMCIsd performed very poorly. When MCI subjects were defined according to the ISC value, subjects with poor ISC were primarily in the aMCIsd group and, to a lesser extent, in the aMCImd group and the naMCI group. Finally, patients with a poor ISC showed cerebral atrophy documented in the precocious phase of AD and the retrosplenial cerebral areas seemed to be the most useful areas for identifying patients in the early phase of AD.

A simple model for retrieving bare soil moisture from radar-scattering coefficients

International Nuclear Information System (INIS)

Chen, K.S.; Yen, S.K.; Huang, W.P.

1995-01-01

A simple algorithm based on a rough surface scattering model was developed to invert the bare soil moisture content from active microwave remote sensing data. In the algorithm development, a frequency mixing model was used to relate soil moisture to the dielectric constant. In particular, the Integral Equation Model (IEM) was used over a wide range of surface roughness and radar frequencies. To derive the algorithm, a sensitivity analysis was performed using a Monte Carlo simulation to study the effects of surface parameters, including height variance, correlation length, and dielectric constant. Because radar return is inherently dependent on both moisture content and surface roughness, the purpose of the sensitivity testing was to select the proper radar parameters so as to optimally decouple these two factors, in an attempt to minimize the effects of one while the other was observed. As a result, the optimal radar parameter ranges can be chosen for the purpose of soil moisture content inversion. One thousand samples were then generated with the IEM model followed by multivariate linear regression analysis to obtain an empirical soil moisture model. Numerical comparisons were made to illustrate the inversion performance using experimental measurements. Results indicate that the present algorithm is simple and accurate, and can be a useful tool for the remote sensing of bare soil surfaces. (author)

Using Airborne High Spectral Resolution Lidar Data to Evaluate Combined Active Plus Passive Retrievals of Aerosol Extinction Profiles

Science.gov (United States)

Burton, S. P.; Ferrare, R. A.; Hostetler, C. A.; Hair, J. W.; Kittaka, C.; Vaughn, M. A.; Remer, L. A.

2010-01-01

We derive aerosol extinction profiles from airborne and space-based lidar backscatter signals by constraining the retrieval with column aerosol optical thickness (AOT), with no need to rely on assumptions about aerosol type or lidar ratio. The backscatter data were acquired by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) and by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The HSRL also simultaneously measures aerosol extinction coefficients independently using the high spectral resolution lidar technique, thereby providing an ideal data set for evaluating the retrieval. We retrieve aerosol extinction profiles from both HSRL and CALIOP attenuated backscatter data constrained with HSRL, Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multiangle Imaging Spectroradiometer column AOT. The resulting profiles are compared with the aerosol extinction measured by HSRL. Retrievals are limited to cases where the column aerosol thickness is greater than 0.2 over land and 0.15 over water. In the case of large AOT, the results using the Aqua MODIS constraint over water are poorer than Aqua MODIS over land or Terra MODIS. The poorer results relate to an apparent bias in Aqua MODIS AOT over water observed in August 2007. This apparent bias is still under investigation. Finally, aerosol extinction coefficients are derived from CALIPSO backscatter data using AOT from Aqua MODIS for 28 profiles over land and 9 over water. They agree with coincident measurements by the airborne HSRL to within +/-0.016/km +/- 20% for at least two-thirds of land points and within +/-0.028/km +/- 20% for at least two-thirds of ocean points.

Retrieval of Electron Density Profile for KOMPSAT-5 GPS Radio Occultation

Directory of Open Access Journals (Sweden)

Woo-Kyoung Lee

2007-12-01

Full Text Available The AOPOD (Atmosphere Occultation and Precision Orbit Determination system, the secondary payload of KOMPSAT (KOrea Multi-Purpose SATellite-5 scheduled to be launched in 2010, shall provide GPS radio occultation data. In this paper, we simulated the GPS radio occultation characteristic of KOMPSAT-5 and retrieved electron density profiles using KROPS (KASI Radio Occultation Processing Software. The electron density retrieved from CHAMP (CHAllenging Minisatellite Payload GPS radio occultation data on June 20, 2004 was compared with IRI (International Reference Ionosphere - 2001, PLP (Planar Langmuir Probe, and ionosonde measurements. When the result was compared with ionosonde measurements, the discrepancies were 5 km on the F_2 peak height (hmF_2 and 3×10^{10} el/m^3 on the electron density of the F_2 peak height (NmF_2. By comparing with the Langmuir Probe measurements of CHAMP satellite (PLP, both agrees with 1.6×10^{11} el/m^3 at the height of 365.6 km.

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

Science.gov (United States)

Hun, Eunjin; Crow, Wade T.; Holmes, Thomas; Bolten, John

2014-01-01

Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

One-dimensional scanning of moisture in heated porous building materials with NMR.

Science.gov (United States)

van der Heijden, G H A; Huinink, H P; Pel, L; Kopinga, K

2011-02-01

In this paper we present a new dedicated NMR setup which is capable of measuring one-dimensional moisture profiles in heated porous materials. The setup, which is placed in the bore of a 1.5 T whole-body scanner, is capable of reaching temperatures up to 500 °C. Moisture and temperature profiles can be measured quasi simultaneously with a typical time resolution of 2-5 min. A methodology is introduced for correcting temperature effects on NMR measurements at these elevated temperatures. The corrections are based on the Curie law for paramagnetism and the observed temperature dependence of the relaxation mechanisms occurring in porous materials. Both these corrections are used to obtain a moisture content profile from the raw NMR signal profile. To illustrate the methodology, a one-sided heating experiment of concrete with a moisture content in equilibrium with 97% RH is presented. This kind of heating experiment is of particular interest in the research on fire spalling of concrete, since it directly reveals the moisture and heat transport occurring inside the concrete. The obtained moisture profiles reveal a moisture peak building up behind the boiling front, resulting in a saturated layer. To our knowledge the direct proof of the formation of a moisture peak and subsequent moisture clogging has not been reported before. Copyright © 2010 Elsevier Inc. All rights reserved.

Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images

Directory of Open Access Journals (Sweden)

L. Pasolli

2011-01-01

Full Text Available Soil moisture retrieval is one of the most challenging problems in the context of biophysical parameter estimation from remotely sensed data. Typically, microwave signals are used thanks to their sensitivity to variations in the water content of soil. However, especially in the Alps, the presence of vegetation and the heterogeneity of topography may significantly affect the microwave signal, thus increasing the complexity of the retrieval. In this paper, the effectiveness of RADARSAT2 SAR images for the estimation of soil moisture in an alpine catchment is investigated. We first carry out a sensitivity analysis of the SAR signal to the moisture content of soil and other target properties (e.g., topography and vegetation. Then we propose a technique for estimating soil moisture based on the Support Vector Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy over point measurements and effectiveness in handling spatially distributed data.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

Science.gov (United States)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

KAUST Repository

Altaf, Muhammad; Jana, Raghavendra Belur; Hoteit, Ibrahim; McCabe, Matthew

2016-01-01

on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Drought monitoring with soil moisture active passive (SMAP) measurements

Science.gov (United States)

Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

2017-09-01

Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an

Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method

Science.gov (United States)

Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. Error characteristics can provide insights for retrieval development and validation, and inform suitable strategies for data fus...

Validation of ozone profile retrievals derived from the OMPS LP version 2.5 algorithm against correlative satellite measurements

Directory of Open Access Journals (Sweden)

N. A. Kramarova

2018-05-01

Full Text Available The Limb Profiler (LP is a part of the Ozone Mapping and Profiler Suite launched on board of the Suomi NPP satellite in October 2011. The LP measures solar radiation scattered from the atmospheric limb in ultraviolet and visible spectral ranges between the surface and 80 km. These measurements of scattered solar radiances allow for the retrieval of ozone profiles from cloud tops up to 55 km. The LP started operational observations in April 2012. In this study we evaluate more than 5.5 years of ozone profile measurements from the OMPS LP processed with the new NASA GSFC version 2.5 retrieval algorithm. We provide a brief description of the key changes that had been implemented in this new algorithm, including a pointing correction, new cloud height detection, explicit aerosol correction and a reduction of the number of wavelengths used in the retrievals. The OMPS LP ozone retrievals have been compared with independent satellite profile measurements obtained from the Aura Microwave Limb Sounder (MLS, Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS and Odin Optical Spectrograph and InfraRed Imaging System (OSIRIS. We document observed biases and seasonal differences and evaluate the stability of the version 2.5 ozone record over 5.5 years. Our analysis indicates that the mean differences between LP and correlative measurements are well within required ±10 % between 18 and 42 km. In the upper stratosphere and lower mesosphere (> 43 km LP tends to have a negative bias. We find larger biases in the lower stratosphere and upper troposphere, but LP ozone retrievals have significantly improved in version 2.5 compared to version 2 due to the implemented aerosol correction. In the northern high latitudes we observe larger biases between 20 and 32 km due to the remaining thermal sensitivity issue. Our analysis shows that LP ozone retrievals agree well with the correlative satellite observations in characterizing

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian Peninsula

NARCIS (Netherlands)

Parinussa, R.M.; Yilmaz, M.T.; Anderson, M.; Hain, C.; de Jeu, R.A.M.

2013-01-01

Soil moisture (SM) can be retrieved from active microwave (AM), passive microwave (PM) and thermal infrared (TIR) observations, each having unique spatial and temporal coverages. A limitation of TIR-based retrievals is a dependence on cloud-free conditions, whereas microwave retrievals are almost

Improving streamflow simulations and forecasting performance of SWAT model by assimilating remotely sensed soil moisture observations

Science.gov (United States)

Patil, Amol; Ramsankaran, RAAJ

2017-12-01

This article presents a study carried out using EnKF based assimilation of coarser-scale SMOS soil moisture retrievals to improve the streamflow simulations and forecasting performance of SWAT model in a large catchment. This study has been carried out in Munneru river catchment, India, which is about 10,156 km2. In this study, an EnkF based new approach is proposed for improving the inherent vertical coupling of soil layers of SWAT hydrological model during soil moisture data assimilation. Evaluation of the vertical error correlation obtained between surface and subsurface layers indicates that the vertical coupling can be improved significantly using ensemble of soil storages compared to the traditional static soil storages based EnKF approach. However, the improvements in the simulated streamflow are moderate, which is due to the limitations in SWAT model in reflecting the profile soil moisture updates in surface runoff computations. Further, it is observed that the durability of streamflow improvements is longer when the assimilation system effectively updates the subsurface flow component. Overall, the results of the present study indicate that the passive microwave-based coarser-scale soil moisture products like SMOS hold significant potential to improve the streamflow estimates when assimilating into large-scale distributed hydrological models operating at a daily time step.

Retrieval of daytime [O3] altitude profile from measurements of 1.27 μm O2 emission in the mesosphere: a comparison of methods

Science.gov (United States)

Yankovsky, Valentine A.; Manuilova, Rada O.

2017-11-01

The altitude profiles of ozone concentration are retrieved from measurements of the volume emission rate in the 1.27 μm oxygen band in the TIMED-SABER experiment. In this study we compare the methods of retrieval of daytime [O3] altitude profile in the framework of two models: electronic-vibrational kinetics and a purely electronic kinetics of excited products of ozone and oxygen photolysis. In order to retrieve the [O3] altitude profile from the measurements of the intensity of the O2 band in the region of 1.27 μm correctly, it is necessary to use the photochemical model of the electronic-vibrational kinetics of excited products of ozone and oxygen photolysis in the mesosphere and lower thermosphere.

Assimilation of SMOS Retrievals in the Land Information System

Science.gov (United States)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.; Crosson, William L.

2016-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve.

Moisture variation inferred from a nebkha profile correlates with vegetation changes in the southwestern Mu Us Desert of China over one century.

Science.gov (United States)

Li, Jinchang; Zhao, Yanfang; Han, Liuyan; Zhang, Guoming; Liu, Rentao

2017-11-15

We inferred moisture variations from the early 1930s to the early 2010s in the southwestern Mu Us Desert of China using Rb/Sr ratio, chemical index of alteration (CIA), and organic matter (OM) content in a nebkha profile. Our results showed that the variations in moisture may have been the main factor that controlled vegetation recovery or degradation, and we believe that gradual vegetation recovery was notable throughout the study area during the past 80years, despite two notable degradation stages during the mid-1950s and the mid-1980s. The Rb/Sr ratio, CIA, and OM content revealed that moisture levels increased during the study period, though with large interannual variations. During the early stage of nebkha formation, the moisture variations were controlled by unusually low precipitation. Thereafter, the precipitation, pan evaporation and temperature determined together moisture variations, but the key factor determining moisture variations was different during different periods. The moisture variations trend revealed in this study may not be restricted to this region as it was similar with the adjacent Mongolian Plateau. Copyright © 2017 Elsevier B.V. All rights reserved.

Upper-soil moisture inter-comparison from SMOS's products and land surface models over the Iberian Peninsula

Science.gov (United States)

Polcher, Jan; Barella-Ortiz, Anaïs; Aires, Filipe; Balsamo, Gianpaolo; Gelati, Emiliano; Rodríguez-Fernández, Nemesio

2015-04-01

Soil moisture is a key state variable of the hydrological cycle. It conditions runoff, infiltration and evaporation over continental surfaces, and is key for forecasting droughts and floods. It plays thus an important role in surface-atmosphere interactions. Surface Soil Moisture (SSM) can be measured by in situ measurements, by satellite observations or modelled using land surface models. As a complementary tool, data assimilation can be used to combine both modelling and satellite observations. The work presented here is an inter-comparison of retrieved and modelled SSM data, for the 2010 - 2012 period, over the Iberian Peninsula. The region has been chosen because its vegetation cover is not very dense and includes strong contrasts in the rainfall regimes and thus a diversity of behaviours for SSM. Furthermore this semi-arid region is strongly dependent on a good management of its water resources. Satellite observations correspond to the Soil Moisture and Ocean Salinity (SMOS) retrievals: the L2 product from an optimal interpolation retrieval, and 3 other products using Neural Network retrievals with different input information: SMOS time indexes, purely SMOS data, or addition of the European Advanced Scaterometer (ASCAT) backscattering, and the Moderate-Resolution Imaging Spectrometer (MODIS) surface temperature information. The modelled soil moistures have been taken from the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEms) and the HTESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land) land surface models. Both models are forced with the same atmospheric conditions (as part of the Earth2Observe FP7 project) over the period but they represent the surface soil moisture with very different degrees of complexity. ORCHIDEE has 5 levels in the top 5 centimetres of soil while in HTESSEL this variable is part of the top soil moisture level. The two types of SMOS retrievals are compared to the model outputs in their spatial and temporal

Retrieval of NO2 stratospheric profiles from ground-based zenith-sky uv-visible measurements at 60°N

Science.gov (United States)

Hendrick, F.; van Roozendael, M.; Lambert, J.-C.; Fayt, C.; Hermans, C.; de Mazière, M.

2003-04-01

Nitrogen dioxide (NO_2) plays an important role in controlling ozone abundances in the stratosphere, either directly through the NOx (NO+NO_2) catalytic cycle, either indirectly by reaction with the radical ClO to form the reservoir species ClONO_2. In this presentation, NO_2 stratospheric profiles are retrieved from ground-based UV-visible NO_2 slant column abundances measured since 1998 at the complementary NDSC station of Harestua (Norway, 60^oN). The retrieval algorithm is based on the Rodgers optimal estimation inversion method and a forward model consisting in the IASB-BIRA stacked box photochemical model PSCBOX coupled to the radiative transfer package UVspec/DISORT. This algorithm has been applied to a set of about 50 sunrises and sunsets for which spatially and temporally coincident NO_2 measurements made by the HALOE (Halogen Occultation Experiment) instrument on board the Upper Atmosphere Research Satellite (UARS) are available. The consistency between retrieved and HALOE profiles is discussed in term of the different seasonal conditions investigated which are spring with and without chlorine activation, summer, and fall.

An improvement of the retrieval of temperature and relative humidity profiles from a combination of active and passive remote sensing

Science.gov (United States)

Che, Yunfei; Ma, Shuqing; Xing, Fenghua; Li, Siteng; Dai, Yaru

2018-03-01

This paper focuses on an improvement of the retrieval of atmospheric temperature and relative humidity profiles through combining active and passive remote sensing. Ground-based microwave radiometer and millimeter-wavelength cloud radar were used to acquire the observations. Cloud base height and cloud thickness determinations from cloud radar were added into the atmospheric profile retrieval process, and a back-propagation neural network method was used as the retrieval tool. Because a substantial amount of data are required to train a neural network, and as microwave radiometer data are insufficient for this purpose, 8 years of radiosonde data from Beijing were used as the database. The monochromatic radiative transfer model was used to calculate the brightness temperatures in the same channels as the microwave radiometer. Parts of the cloud base heights and cloud thicknesses in the training data set were also estimated using the radiosonde data. The accuracy of the results was analyzed through a comparison with L-band sounding radar data and quantified using the mean bias, root-mean-square error (RMSE), and correlation coefficient. The statistical results showed that an inversion with cloud information was the optimal method. Compared with the inversion profiles without cloud information, the RMSE values after adding cloud information reduced to varying degrees for the vast majority of height layers. These reductions were particularly clear in layers with clouds. The maximum reduction in the RMSE for the temperature profile was 2.2 K, while that for the humidity profile was 16%.

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

Science.gov (United States)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

2011-01-01

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS

Soil moisture inversion from aircraft passive microwave observations during SMEX04 using a single-frequency algorithm

International Nuclear Information System (INIS)

Zeng, J Y; Li, Z; Chen, Q; Bi, H Y

2014-01-01

Soil moisture plays a key role in global water cycles. In the study, soil moisture retrievals from airborne microwave radiometer observations using a single-frequency algorithm were presented. The algorithm is based on a simplified radiative transfer (tau-omega) model and the influence of both the roughness and vegetation is combined into a single parameter in the algorithm. The microwave polarization difference index (MPDI) is used to eliminate the effects of temperature. Then soil moisture is obtained through a nonlinear iterative procedure by making the absolute value of the differences between the simulated and observed MPDI minimum. The algorithm was validated with aircraft passive microwave data from the Polarimetric Scanning Radiometer (PSR) at the Arizona during the Soil Moisture Experiment 2004 (SMEX04). The results show that the soil moisture retrieved by the algorithm is in good agreement with ground measurements with a small bias and an overall accuracy of 0.037m 3 m −3

Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

Science.gov (United States)

Berndt, E. B.; Zavodsky, B. T.; Folmer, M. J.; Jedlovec, G. J.

2014-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), 32-km North American Regional Reanalysis (NARR) interpolated to a 12-km grid, and 13-km Rapid Refresh analyses.

The impact of cloud vertical profile on liquid water path retrieval based on the bispectral method: A theoretical study based on large-eddy simulations of shallow marine boundary layer clouds.

Science.gov (United States)

Miller, Daniel J; Zhang, Zhibo; Ackerman, Andrew S; Platnick, Steven; Baum, Bryan A

2016-04-27

Passive optical retrievals of cloud liquid water path (LWP), like those implemented for Moderate Resolution Imaging Spectroradiometer (MODIS), rely on cloud vertical profile assumptions to relate optical thickness ( τ ) and effective radius ( r e ) retrievals to LWP. These techniques typically assume that shallow clouds are vertically homogeneous; however, an adiabatic cloud model is plausibly more realistic for shallow marine boundary layer cloud regimes. In this study a satellite retrieval simulator is used to perform MODIS-like satellite retrievals, which in turn are compared directly to the large-eddy simulation (LES) output. This satellite simulator creates a framework for rigorous quantification of the impact that vertical profile features have on LWP retrievals, and it accomplishes this while also avoiding sources of bias present in previous observational studies. The cloud vertical profiles from the LES are often more complex than either of the two standard assumptions, and the favored assumption was found to be sensitive to cloud regime (cumuliform/stratiform). Confirming previous studies, drizzle and cloud top entrainment of dry air are identified as physical features that bias LWP retrievals away from adiabatic and toward homogeneous assumptions. The mean bias induced by drizzle-influenced profiles was shown to be on the order of 5-10 g/m 2 . In contrast, the influence of cloud top entrainment was found to be smaller by about a factor of 2. A theoretical framework is developed to explain variability in LWP retrievals by introducing modifications to the adiabatic r e profile. In addition to analyzing bispectral retrievals, we also compare results with the vertical profile sensitivity of passive polarimetric retrieval techniques.

The impact of cloud vertical profile on liquid water path retrieval based on the bispectral method: A theoretical study based on large-eddy simulations of shallow marine boundary layer clouds

Science.gov (United States)

Miller, Daniel J.; Zhang, Zhibo; Ackerman, Andrew S.; Platnick, Steven; Baum, Bryan A.

2018-01-01

Passive optical retrievals of cloud liquid water path (LWP), like those implemented for Moderate Resolution Imaging Spectroradiometer (MODIS), rely on cloud vertical profile assumptions to relate optical thickness (τ) and effective radius (re) retrievals to LWP. These techniques typically assume that shallow clouds are vertically homogeneous; however, an adiabatic cloud model is plausibly more realistic for shallow marine boundary layer cloud regimes. In this study a satellite retrieval simulator is used to perform MODIS-like satellite retrievals, which in turn are compared directly to the large-eddy simulation (LES) output. This satellite simulator creates a framework for rigorous quantification of the impact that vertical profile features have on LWP retrievals, and it accomplishes this while also avoiding sources of bias present in previous observational studies. The cloud vertical profiles from the LES are often more complex than either of the two standard assumptions, and the favored assumption was found to be sensitive to cloud regime (cumuliform/stratiform). Confirming previous studies, drizzle and cloud top entrainment of dry air are identified as physical features that bias LWP retrievals away from adiabatic and toward homogeneous assumptions. The mean bias induced by drizzle-influenced profiles was shown to be on the order of 5–10 g/m2. In contrast, the influence of cloud top entrainment was found to be smaller by about a factor of 2. A theoretical framework is developed to explain variability in LWP retrievals by introducing modifications to the adiabatic re profile. In addition to analyzing bispectral retrievals, we also compare results with the vertical profile sensitivity of passive polarimetric retrieval techniques. PMID:29637042

Spectropolarimetric Measurements of Scattered Sunlight in the Huggins Bands: Retrieval of Tropospheric Ozone Profiles

Science.gov (United States)

Fu, D.; Sander, S. P.; Stutz, J.; Pongetti, T. J.; Yung, Y. L.; Wong, M.; Natraj, V.; Li, K.; Shia, R.

2009-12-01

Ozone concentrations in the troposphere have increased over the past century as a result of anthropogenic emissions of NOx and volatile organic compounds. In addition to being harmful to human health and plant life, ozone is an important greenhouse gas, especially in the middle and upper troposphere. Therefore, accurate monitoring of tropospheric ozone vertical distributions is crucial for a better understanding of air quality and climate change. Simulations of vector radiative transfer in the near ultraviolet region have shown that tropospheric ozone profiles can be retrieved using polarization measurements. However, to date there has been no experimental test of this method. A new compact, portable spectropolarimeter has been built for atmospheric remote sensing. The first comprehensive description of the configuration and performance of this instrument for ground-based operation is provided and sample atmospheric scattered sunlight spectra are shown. Using optimal estimation retrieval theory we study the information content of polarization spectra in the Huggins band and uncertainties in the retrieval associated with the measurement parameters, such as aerosol scattering.

Stratospheric NO2 vertical profile retrieved from ground-based Zenith-Sky DOAS observations at Kiruna, Sweden

Science.gov (United States)

Gu, Myojeong; Enell, Carl-Fredrik; Hendrick, François; Pukite, Janis; Van Roozendael, Michel; Platt, Ulrich; Raffalski, Uwe; Wagner, Thomas

2014-05-01

Stratospheric NO2 destroys ozone and acts as a buffer against halogen-catalyzed ozone loss through the formation of reservoir species (ClONO2, BrONO2). Since the importance of both mechanisms depends on the altitude, the investigation of stratospheric NO2 vertical distribution can provide more insight into the role of nitrogen compounds in the destruction of ozone. Here we present stratospheric NO2 vertical profiles retrieved from twilight ground-based zenith-sky DOAS observations at Kiruna, Sweden (68.84°N, 20.41°E) covering 1997 - 2013 periods. This instrument observes zenith scattered sunlight. The sensitivity for stratospheric trace gases is highest during twilight due to the maximum altitude of the scattering profile and the light path through the stratosphere, which vary with the solar zenith angle. The profiling algorithm, based on the Optimal Estimation Method, has been developed by IASB-BIRA and successfully applied at other stations (Hendrick et al., 2004). The basic principle behind this profiling approach is that during twilight, the mean Rayleigh scattering altitude scans the stratosphere rapidly, providing height-resolved information on the absorption by stratospheric NO2. In this study, the long-term evolution of the stratospheric NO2 profile at polar latitude will be investigated. Hendrick, F., B. Barret, M. Van Roozendael, H. Boesch, A. Butz, M. De Mazière, F. Goutail, C. Hermans, J.-C. Lambert, K. Pfeilsticker, and J.-P. Pommereau, Retrieval of nitrogen dioxide stratospheric profiles from ground-based zenith-sky UV-visible observations: Validation of the technique through correlative comparisons, Atmospheric Chemistry and Physics, 4, 2091-2106, 2004

Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery

Directory of Open Access Journals (Sweden)

Xun Chai

2015-01-01

Full Text Available Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We eliminated the effect of vegetation cover by using the water cloud model and minimized the effect of soil surface roughness by solving the Dubois equations. Two experimental campaigns were conducted in the Qinghai Lake watershed, northeastern Tibetan Plateau in September 2012 and May 2013, respectively, with simultaneous satellite overpass. Compared with the developed Chen model, the predicted soil moisture given by the developed Dubois model agreed better with field measurements in terms of accuracy and stability. The RMSE, R2, and RPD value of the developed Dubois model were (5.4, 0.8, 1.6 and (3.05, 0.78, 1.74 for the two experiments, respectively. Validation results indicated that the developed Dubois model, needing a minimum of prior information, satisfied the requirement for soil moisture inversion in the study region.

SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V001

Data.gov (United States)

National Aeronautics and Space Administration — This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during...

SOIL moisture data intercomparison

Science.gov (United States)

Kerr, Yann; Rodriguez-Frenandez, Nemesio; Al-Yaari, Amen; Parens, Marie; Molero, Beatriz; Mahmoodi, Ali; Mialon, Arnaud; Richaume, Philippe; Bindlish, Rajat; Mecklenburg, Susanne; Wigneron, Jean-Pierre

2016-04-01

The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. Subsequently, the satellite has been in operation for over 6 years while the retrieval algorithms from Level 1 to Level 2 underwent significant evolutions as knowledge improved. Other approaches for retrieval at Level 2 over land were also investigated while Level 3 and 4 were initiated. In this présentation these improvements are assessed by inter-comparisons of the current Level 2 (V620) against the previous version (V551) and new products either using neural networks or Level 3. In addition a global evaluation of different SMOS soil moisture (SM) products is performed comparing products with those of model simulations and other satellites (AMSR E/ AMSR2 and ASCAT). Finally, all products were evaluated against in situ measurements of soil moisture (SM). The study demonstrated that the V620 shows a significant improvement (including those at level1 improving level2)) with respect to the earlier version V551. Results also show that neural network based approaches can yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved while RFI sources in several areas of the world are significantly reduced. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

Science.gov (United States)

Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

2012-01-01

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

Soil hydraulic parameters and surface soil moisture of a tilled bare soil plot inversely derived from l-band brightness temperatures

KAUST Repository

Dimitrov, Marin

2014-01-01

We coupled a radiative transfer model and a soil hydrologic model (HYDRUS 1D) with an optimization routine to derive soil hydraulic parameters, surface roughness, and soil moisture of a tilled bare soil plot using measured brightness temperatures at 1.4 GHz (L-band), rainfall, and potential soil evaporation. The robustness of the approach was evaluated using five 28-d data sets representing different meteorological conditions. We considered two soil hydraulic property models: the unimodal Mualem-van Genuchten and the bimodal model of Durner. Microwave radiative transfer was modeled by three different approaches: the Fresnel equation with depth-averaged dielectric permittivity of either 2-or 5-cm-thick surface layers and a coherent radiative transfer model (CRTM) that accounts for vertical gradients in dielectric permittivity. Brightness temperatures simulated by the CRTM and the 2-cm-layer Fresnel model fitted well to the measured ones. L-band brightness temperatures are therefore related to the dielectric permittivity and soil moisture in a 2-cm-thick surface layer. The surface roughness parameter that was derived from brightness temperatures using inverse modeling was similar to direct estimates from laser profiler measurements. The laboratory-derived water retention curve was bimodal and could be retrieved consistently for the different periods from brightness temperatures using inverse modeling. A unimodal soil hydraulic property function underestimated the hydraulic conductivity near saturation. Surface soil moisture contents simulated using retrieved soil hydraulic parameters were compared with in situ measurements. Depth-specific calibration relations were essential to derive soil moisture from near-surface installed sensors. © Soil Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA.

Retrieval method of aerosol extinction coefficient profile by an integral lidar system and case study

Science.gov (United States)

Shan, Huihui; Zhang, Hui; Liu, Junjian; Wang, Shenhao; Ma, Xiaomin; Zhang, Lianqing; Liu, Dong; Xie, Chenbo; Tao, Zongming

2018-02-01

Aerosol extinction coefficient profile is an essential parameter for atmospheric radiation model. But it is difficult to get the full aerosol extinction profile from the ground to the tropopause especially in near ground precisely using backscattering lidar. A combined measurement of side-scattering, backscattering and Raman-scattering lidar is proposed to retrieve the aerosol extinction coefficient profile from the surface to the tropopause which covered a dynamic range of 5 orders. The side-scattering technique solves the dead zone and the overlap problem caused by the traditional lidar in the near range. Using the Raman-scattering the aerosol lidar ratio (extinction to backscatter ratio) can be obtained. The cases studies in this paper show the proposed method is reasonable and feasible.

Quality Improvement of the Satellite Soil Moisture Products by Fusing In Situ and GNSS-R Observation

Science.gov (United States)

Yuan, Q.; Xu, H.; Li, T.; Shen, H.; Zhang, L.

2017-12-01

Soil moisture plays a fundamental role in the hydrological cycle as well as in the energy partitioning. On this basis, it is of great concern to derive a long-term soil moisture time series on a global scale and monitor its temporal and spatial variations for practical applications. Although passive and active microwave satellites have been shown to provide useful retrievals of near-surface soil moisture at regional and global scales, the limitations in retrieval accuracy prevent them from high-quality applications in specific areas. On the other hand, measuring soil moisture straightly through in situdevices, such as soil moisture probes, is high accuracy, but is not able to derive global soil moisture maps. Recently, the ground-based GNSS-R method is emerging in monitoring near-surface soil moisture variations but still over limited spatial scales. In this paper, a multi-source data fusion method was applied to synthesize regional high-quality soil moisture products from 2015 to 2017 in western parts of the continental United States. Firstly, we put all the three soil moisture datasets into the generalized regression neural network (GRNN) model. The input signals of the model are SMOS and SMAP satellite-derived passive level 3 soil moisture daily products combined with date and latitude and longitude information, while the in situ measured and GNSS-R retrieved soil moisture are used as target. Finally, we apply the model to all the soil moisture time series in the experiment area and obtain two high-quality regional soil moisture products for SMOS and SMAP, respectively. The results before fusion show that the correlation coefficients between site-specific soil moisture and satellite-derived soil moisture are 0.39 for SMOS and 0.27 for SMAP and that unbiased root-mean-square errors (ubRMSE) are 0.113 for SMOS and 0.128 for SMAP, respectively. After applying the GRNN-R, the model fitted correlation coefficients have reached 0.72 for SMOS and 0.75 for SMAP and the

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

Science.gov (United States)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

The Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

Science.gov (United States)

Berndt, Emily; Zavodsky, Bradley; Jedlovec, Gary; Elmer, Nicholas

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. Part 1; Overview

Science.gov (United States)

Crosson, William L.; Laymon, Charles A.; Inguva, Ramarao; Schamschula, Marius; Caulfield, John

1998-01-01

' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

A simplified model of saltcake moisture distribution. Letter report

International Nuclear Information System (INIS)

Simmons, C.S.

1995-09-01

This letter report describes the formulation of a simplified model for finding the moisture distribution in a saltcake waste profile that has been stabilized by pumping out the drainable interstitial liquid. The model is based on assuming that capillarity mainly governs the distribution of moisture in the porous saltcake waste. A stead upward flow of moisture driven by evaporation from the waste surface is conceptualized to occur for isothermal conditions. To obtain hydraulic parameters for unsaturated conditions, the model is calibrated or matched to the relative saturation distribution as measured by neutron probe scans. The model is demonstrated on Tanks 104-BY and 105-TX as examples. A value of the model is that it identifies the key physical parameters that control the surface moisture content in a waste profile. Moreover, the model can be used to estimate the brine application rate at the waste surface that would raise the moisture content there to a safe level. Thus, the model can be applied to help design a strategy for correcting the moisture conditions in a saltcake waste tank

Comparison of stratospheric NO2 profiles above Kiruna, Sweden retrieved from ground-based zenith sky DOAS measurements, SAOZ balloon measurements and SCIAMACHY limb observations

Science.gov (United States)

Gu, Myojeong; Enell, Carl-Fredrik; Hendrick, François; Pukite, Janis; Van Roozendael, Michel; Platt, Ulrich; Raffalski, Uwe; Wagner, Thomas

2015-04-01

Stratospheric NO2 not only destroys ozone but acts as a buffer against halogen catalyzed ozone loss by converting halogen species into stable nitrates. These two roles of stratospheric NO2 depend on the altitude. Hence, the objective of this study is to investigate the vertical distribution of stratospheric NO2. We compare the NO2 profiles derived from the zenith sky DOAS with those obtained from, SAOZ balloon measurements and satellite limb observations. Vertical profiles of stratospheric NO2 are retrieved from ground-based zenith sky DOAS observations operated at Kiruna, Sweden (68.84°N, 20.41°E) since 1996. To determine the profile of stratospheric NO2 measured from ground-based zenith sky DOAS, we apply the Optimal Estimation Method (OEM) to retrieval of vertical profiles of stratospheric NO2 which has been developed by IASB-BIRA. The basic principle behind this profiling approach is the dependence of the mean scattering height on solar zenith angle (SZA). We compare the retrieved profiles to two additional datasets of stratospheric NO2 profile. The first one is derived from satellite limb observations by SCIAMACHY (Scanning Imaging Absorption spectrometer for Atmospheric CHartographY) on EnviSAT. The second is derived from the SAOZ balloon measurements (using a UV/Visible spectrometer) performed at Kiruna in Sweden.

The global SMOS Level 3 daily soil moisture and brightness temperature maps

Directory of Open Access Journals (Sweden)

A. Al Bitar

2017-06-01

Full Text Available The objective of this paper is to present the multi-orbit (MO surface soil moisture (SM and angle-binned brightness temperature (TB products for the SMOS (Soil Moisture and Ocean Salinity mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive TB. The Level 3 SM V300 product is compared to the single-orbit (SO retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an

A Proposed Extension to the Soil Moisture and Ocean Salinity Level 2 Algorithm for Mixed Forest and Moderate Vegetation Pixels

Science.gov (United States)

Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse; Kim, Edward

2011-01-01

The Soil Moisture and Ocean Salinity (SMOS)mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km.Uncertainty in the retrieval of soilmoisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soilmoisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops),with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE,-3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, -0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the

[Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance].

Science.gov (United States)

Zheng, Xiao-po; Sun, Yue-jun; Qin, Qi-ming; Ren, Hua-zhong; Gao, Zhong-ling; Wu, Ling; Meng, Qing-ye; Wang, Jin-liang; Wang, Jian-hua

2015-08-01

Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.

Using JPSS Retrievals to Implement a Multisensor, Synoptic, Layered Water Vapor Product for Forecasters

Science.gov (United States)

Forsythe, J. M.; Jones, A. S.; Kidder, S. Q.; Fuell, K.; LeRoy, A.; Bikos, D.; Szoke, E.

2015-12-01

Forecasters have been using the NOAA operational blended total precipitable water (TPW) product, developed by the Cooperative Institute for Research in the Atmosphere (CIRA), since 2009. Blended TPW has a wide variety of uses related to heavy precipitation and flooding, such as measuring the amount of moisture in an atmospheric river originating in the tropics. But blended TPW conveys no information on the vertical distribution of moisture, which is relevant to a variety of forecast concerns. Vertical profile information is particularly lacking over the oceans for landfalling storms. A blended six-satellite, four-layer, layered water vapor product demonstrated by CIRA and the NASA Short-term Prediction Research and Transition Center (SPoRT) in allows forecasters to see the vertical distribution of water vapor in near real-time. National Weather Service (NWS) forecaster feedback indicated that this new, vertically-resolved view of water vapor has a substantial impact on forecasts. This product uses NOAA investments in polar orbiting satellite sounding retrievals from passive microwave radiances, in particular, the Microwave Integrated Retrieval System (MIRS). The product currently utilizes data from the NOAA-18 and -19 spacecraft, Metop-A and -B, and the Defense Meteorological Program (DMSP) F18 spacecraft. The sounding instruments onboard the Suomi-NPP and JPSS spacecraft will be cornerstone instruments in the future evolution of this product. Applications of the product to heavy rain cases will be presented and compared to commonly used data such as radiosondes and Geostationary Operational Environmental Satellite (GOES) water vapor channel imagery. Research is currently beginning to implement advective blending, where model winds are used to move the water vapor profiles to a common time. Interactions with the NOAA Satellite Analysis Branch (SAB), National Center for Environmental Prediction (NCEP) centers including the Ocean Prediction Center (OPC) and Weather

Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

Science.gov (United States)

Zhang, Sibo; Calvet, Jean-Christophe; Darrozes, José; Roussel, Nicolas; Frappart, Frédéric; Bouhours, Gilles

2018-03-01

This work assesses the estimation of surface volumetric soil moisture (VSM) using the global navigation satellite system interferometric reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 and 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show good agreement (R2 = 0.86 and RMSE = 0.04 m3 m-3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 and 5 cm, especially during light rainfall events.

Unsaturated soil moisture drying and wetting diffusion coefficient measurements in the laboratory.

Science.gov (United States)

2009-09-01

ABSTRACTTransient moisture flow in an unsaturated soil in response to suction changes is controlled by the unsaturated moisture diffusion coefficient. The moisture diffusion coefficient can be determined by measuring suction profiles over time. The l...

Empirical Soil Moisture Estimation with Spaceborne L-band Polarimetric Radars: Aquarius, SMAP, and PALSAR-2

Science.gov (United States)

Burgin, M. S.; van Zyl, J. J.

2017-12-01

Traditionally, substantial ancillary data is needed to parametrize complex electromagnetic models to estimate soil moisture from polarimetric radar data. The Soil Moisture Active Passive (SMAP) baseline radar soil moisture retrieval algorithm uses a data cube approach, where a cube of radar backscatter values is calculated using sophisticated models. In this work, we utilize the empirical approach by Kim and van Zyl (2009) which is an optional SMAP radar soil moisture retrieval algorithm; it expresses radar backscatter of a vegetated scene as a linear function of soil moisture, hence eliminating the need for ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine two coefficients of a linear model function on a global scale. These coefficients are used to estimate soil moisture with 2.5 months of L-band SMAP and L-band PALSAR-2 data. The estimated soil moisture is compared with the SMAP Level 2 radiometer-only soil moisture product; the global unbiased RMSE of the SMAP derived soil moisture corresponds to 0.06-0.07 cm3/cm3. In this study, we leverage the three diverse L-band radar data sets to investigate the impact of pixel size and pixel heterogeneity on soil moisture estimation performance. Pixel sizes range from 100 km for Aquarius, over 3, 9, 36 km for SMAP, to 10m for PALSAR-2. Furthermore, we observe seasonal variation in the radar sensitivity to soil moisture which allows the identification and quantification of seasonally changing vegetation. Utilizing this information, we further improve the estimation performance. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2017. All rights reserved.

Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER

Directory of Open Access Journals (Sweden)

S. G. Wang

2011-05-01

Full Text Available Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ²/cl and the difference in backscattering coefficient (Δσ from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

Moisture Distribution in Broccoli: Measurements by MRI Hot Air Drying Experiments

NARCIS (Netherlands)

Jin, X.; Sman, van der R.G.M.; Gerkema, E.; Vergeldt, F.J.; As, van H.; Boxtel, van A.J.B.

2011-01-01

ABSTRACT The internal moisture distribution that arise in food products during drying, is a key factor for the retention of quality attributes. To reveal the course of moisture content in a product, internal moisture profiles in broccoli florets are measured by MRI imaging during drying experiments

Moisture distribution in broccoli: measurements by MRI hot air drying experiments

NARCIS (Netherlands)

Jin, X.; Sman, van der R.G.M.; Gerkema, E.; Vergeldt, F.J.; As, van H.; Boxtel, van A.J.B.

2011-01-01

The internal moisture distribution that arise in food products during drying, is a key factor for the retention of quality attributes. To reveal the course of moisture content in a product, internal moisture profiles in broccoli florets are measured by MRI imaging during drying experiments with

Validation and Scaling of Soil Moisture in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)

Science.gov (United States)

Colliander, Andreas; Cosh, Michael H.; Misra, Sidharth; Jackson, Thomas J.; Crow, Wade T.; Chan, Steven; Bindlish, Rajat; Chae, Chun; Holifield Collins, Chandra; Yueh, Simon H.

2017-01-01

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The

Error Characterisation and Merging of Active and Passive Microwave Soil Moisture Data Sets

Science.gov (United States)

Wagner, Wolfgang; Gruber, Alexander; de Jeu, Richard; Parinussa, Robert; Chung, Daniel; Dorigo, Wouter; Reimer, Christoph; Kidd, Richard

2015-04-01

As part of the Climate Change Initiative (CCI) programme of the European Space Agency (ESA) a data fusion system has been developed which is capable of ingesting surface soil moisture data derived from active and passive microwave sensors (ASCAT, AMSR-E, etc.) flown on different satellite platforms and merging them to create long and consistent time series of soil moisture suitable for use in climate change studies. The so-created soil moisture data records (latest version: ESA CCI SM v02.1 released on 5/12/2014) are freely available and can be obtained from http://www.esa-soilmoisture-cci.org/. As described by Wagner et al. (2012) the principle steps of the data fusion process are: 1) error characterisation, 2) matching to account for data set specific biases, and 3) merging. In this presentation we present the current data fusion process and discuss how new error characterisation methods, such as the increasingly popular triple collocation method as discussed for example by Zwieback et al. (2012) may be used to improve it. The main benefit of an improved error characterisation would be a more reliable identification of the best performing microwave soil moisture retrieval(s) for each grid point and each point in time. In case that two or more satellite data sets provides useful information, the estimated errors can be used to define the weights with which each satellite data set are merged, i.e. the lower its error the higher its weight. This is expected to bring a significant improvement over the current data fusion scheme which is not yet based on quantitative estimates of the retrieval errors but on a proxy measure, namely the vegetation optical depth (Dorigo et al., 2015): over areas with low vegetation passive soil moisture retrievals are used, while over areas with moderate vegetation density active retrievals are used. In transition areas, where both products correlate well, both products are being used in a synergistic way: on time steps where only one of

Borehole-calibration methods used in cased and uncased test holes to determine moisture profiles in the unsaturated zone, Yucca Mountain, Nevada

International Nuclear Information System (INIS)

Hammermeister, D.P.; Kneiblher, C.R.; Klenke, J.

1985-01-01

The use of drilling and coring methods that minimize the disturbance of formation rock and core has permitted field calibration of neutron-moisture tools in relatively large diameter cased and uncased boreholes at Yucca Mountain, Nevada. For 5.5-inch diameter cased holes, there was reasonable agreement between a field calibration in alluvium-colluvium and a laboratory calibration in a chamber containing silica sand. There was little difference between moisture-content profiles obtained in a neutron-access hole with a hand-held neutron-moisture meter and an automated borehole-logging tool using laboratory-generated calibration curves. Field calibrations utilizing linear regression analyses and as many as 119 data pairs show a good correlation between neutron-moisture counts and volumetric water content for sections of uncased 6-inch diameter boreholes in nonwelded and bedded tuff. Regression coefficients ranged from 0.80 to 0.94. There were only small differences between calibration curves in 4.25- and 6-inch uncased sections of boreholes. Results of analyzing field calibration data to determine the effects of formation density on calibration curves were inconclusive. Further experimental and theoretical work is outlined

Estimating soil moisture using the Danish polarimetric SAR

DEFF Research Database (Denmark)

Jiankang, Ji; Thomsen, A.; Skriver, Henning

1995-01-01

The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness a...... of surface parameters with the bilinear model, the correlation coefficient between the estimated and measured soil moisture, as well as rms height, is about 0.77. To improve the result, the local incidence angles need to be taken into account......The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness...... autocorrelation functions (Gaussian and Exponential) were not able to fit natural surfaces well. A Gauss-Exp hybrid model which agreed better with the measured data has been proposed. Theoretical surface scattering models (POM, IEM), as well as an empirical model for retrieval of soil moisture and surface rms...

Retrieval of stratospheric and tropospheric BrO profiles and columns using ground-based zenith-sky DOAS observations at Harestua, 60° N

Directory of Open Access Journals (Sweden)

J. A. Pyle

2007-09-01

Full Text Available A profiling algorithm based on the optimal estimation method is applied to ground-based zenith-sky UV-visible measurements from Harestua, Southern Norway (60° N, 11° E in order to retrieve BrO vertical profiles. The sensitivity of the zenith-sky observations to the tropospheric BrO detection is increased by using for the spectral analysis a fixed reference spectrum corresponding to clear-sky noon summer conditions. The information content and retrieval errors are characterized and it is shown that the retrieved stratospheric profiles and total columns are consistent with correlative balloon and satellite observations, respectively. Tropospheric BrO columns are derived from profiles retrieved at 80° solar zenith angle during sunrise and sunset for the 2000–2006 period. They show a marked seasonality with mean column value ranging from 1.52±0.62×1013 molec/cm² in late winter/early spring to 0.92±0.38×1013 molec/cm² in summer, which corresponds to 1.0±0.4 and 0.6±0.2 pptv, respectively, if we assume that BrO is uniformly mixed in the troposphere. These column values are also consistent with previous estimates made from balloon, satellite, and other ground-based observations. Daytime (10:30 LT tropospheric BrO columns are compared to the p-TOMCAT 3-D tropospheric chemical transport model (CTM for the 2002–2003 period. p-TOMCAT shows a good agreement with the retrieved columns except in late winter/early spring where an underestimation by the model is obtained. This finding could be explained by the non-inclusion of sea-ice bromine sources in the current version of p-TOMCAT. Therefore the model cannot reproduce the possible transport of air-masses with enhanced BrO concentration due to bromine explosion events from the polar region to Harestua. The daytime stratospheric BrO columns are compared to the SLIMCAT stratospheric 3-D-CTM. The model run used in this study, which assumes 21.2 pptv for the Bry loading (15 pptv for long

Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

Directory of Open Access Journals (Sweden)

S. Zhang

2018-03-01

Full Text Available This work assesses the estimation of surface volumetric soil moisture (VSM using the global navigation satellite system interferometric reflectometry (GNSS-IR technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 and 29.4 m. The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show good agreement (R2 =  0.86 and RMSE  =  0.04 m3 m−3. It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 and 5 cm, especially during light rainfall events.

Water vapor profiling using microwave radiometry

Science.gov (United States)

Wang, J. R.; Wilheit, T. T.

1988-01-01

Water vapor is one of the most important constituents in the Earth's atmosphere. Its spatial and temporal variations affect a wide spectrum of meteorological phenomena ranging from the formation of clouds to the development of severe storms. The passive microwave technique offers an excellent means for water vapor measurements. It can provide both day and night coverage under most cloud conditions. Two water vapor absorption features, at 22 and 183 GHz, were explored in the past years. The line strengths of these features differ by nearly two orders of magnitude. As a consequence, the techniques and the final products of water vapor measurements are also quite different. The research effort in the past few years was to improve and extend the retrieval algorithm to the measurements of water vapor profiles under cloudy conditions. In addition, the retrieval of total precipitable water using 183 GHz measurements, but in a manner analogous to the use of 22 GHz measurements, to increase measurement sensitivity for atmospheres of very low moisture content was also explored.

Semi-empirical model for retrieval of soil moisture using RISAT-1 C ...

Indian Academy of Sciences (India)

Kishan Singh Rawat

2018-03-02

Mar 2, 2018 ... We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient. (σo ..... samples, 14 were used for model generation and 8 .... mechanisms; this needs to take into account the dynamic ...

Moisture Vertical Structure, Deep Convective Organization, and Convective Transition in the Amazon

Science.gov (United States)

Schiro, K. A.; Neelin, J. D.

2017-12-01

Constraining precipitation processes in climate models with observations is crucial to accurately simulating current climate and reducing uncertainties in future projections. Results from the Green Ocean Amazon (GOAmazon) field campaign (2014-2015) provide evidence that deep convection is strongly controlled by the availability of moisture in the free troposphere over the Amazon, much like over tropical oceans. Entraining plume buoyancy calculations confirm that CWV is a good proxy for the conditional instability of the environment, yet differences in convective onset as a function of CWV exist over land and ocean, as well as seasonally and diurnally over land. This is largely due to variability in the contribution of lower tropospheric humidity to the total column moisture. Boundary layer moisture shows a strong relationship to the onset during the day, which largely disappears during nighttime. Using S-Band radar, these transition statistics are examined separately for unorganized and mesoscale-organized convection, which exhibit sharp increases in probability of occurrence with increasing moisture throughout the column, particularly in the lower free troposphere. Retrievals of vertical velocity from a radar wind profiler indicate updraft velocity and mass flux increasing with height through the lower troposphere. A deep-inflow mixing scheme motivated by this — corresponding to deep inflow of environmental air into a plume that grows with height — provides a weighting of boundary layer and free tropospheric air that yields buoyancies consistent with the observed onset of deep convection across seasons and times of day, across land and ocean sites, and for all convection types. This provides a substantial improvement relative to more traditional constant mixing assumptions, and a dramatic improvement relative to no mixing. Furthermore, it provides relationships that are as strong or stronger for mesoscale-organized convection as for unorganized convection.

Influence of cracking clays on satellite estimated and model simulated soil moisture

Directory of Open Access Journals (Sweden)

Y. Y. Liu

2010-06-01

Full Text Available Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m³ m⁻³ retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA algorithm with simulations from the Community Land Model (CLM over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005, both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events

MODIS-based spatiotemporal patterns of soil moisture and evapotranspiration interactions in Tampa Bay urban watershed

Science.gov (United States)

Chang, Ni-Bin; Xuan, Zhemin; Wimberly, Brent

2011-09-01

Soil moisture and evapotranspiration (ET) is affected by both water and energy balances in the soilvegetation- atmosphere system, it involves many complex processes in the nexus of water and thermal cycles at the surface of the Earth. These impacts may affect the recharge of the upper Floridian aquifer. The advent of urban hydrology and remote sensing technologies opens new and innovative means to undertake eventbased assessment of ecohydrological effects in urban regions. For assessing these landfalls, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing images can be used for the estimation of such soil moisture change in connection with two other MODIS products - Enhanced Vegetation Index (EVI), Land Surface Temperature (LST). Supervised classification for soil moisture retrieval was performed for Tampa Bay area on the 2 kmx2km grid with MODIS images. Machine learning with genetic programming model for soil moisture estimation shows advances in image processing, feature extraction, and change detection of soil moisture. ET data that were derived by Geostationary Operational Environmental Satellite (GOES) data and hydrologic models can be retrieved from the USGS web site directly. Overall, the derived soil moisture in comparison with ET time series changes on a seasonal basis shows that spatial and temporal variations of soil moisture and ET that are confined within a defined region for each type of surfaces, showing clustered patterns and featuring space scatter plot in association with the land use and cover map. These concomitant soil moisture patterns and ET fluctuations vary among patches, plant species, and, especially, location on the urban gradient. Time series plots of LST in association with ET, soil moisture and EVI reveals unique ecohydrological trends. Such ecohydrological assessment can be applied for supporting the urban landscape management in hurricane-stricken regions.

Assessment of Mars Atmospheric Temperature Retrievals from the Thermal Emission Spectrometer Radiances

Science.gov (United States)

Hoffman, Matthew J.; Eluszkiewicz, Janusz; Weisenstein, Deborah; Uymin, Gennady; Moncet, Jean-Luc

2012-01-01

Motivated by the needs of Mars data assimilation. particularly quantification of measurement errors and generation of averaging kernels. we have evaluated atmospheric temperature retrievals from Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) radiances. Multiple sets of retrievals have been considered in this study; (1) retrievals available from the Planetary Data System (PDS), (2) retrievals based on variants of the retrieval algorithm used to generate the PDS retrievals, and (3) retrievals produced using the Mars 1-Dimensional Retrieval (M1R) algorithm based on the Optimal Spectral Sampling (OSS ) forward model. The retrieved temperature profiles are compared to the MGS Radio Science (RS) temperature profiles. For the samples tested, the M1R temperature profiles can be made to agree within 2 K with the RS temperature profiles, but only after tuning the prior and error statistics. Use of a global prior that does not take into account the seasonal dependence leads errors of up 6 K. In polar samples. errors relative to the RS temperature profiles are even larger. In these samples, the PDS temperature profiles also exhibit a poor fit with RS temperatures. This fit is worse than reported in previous studies, indicating that the lack of fit is due to a bias correction to TES radiances implemented after 2004. To explain the differences between the PDS and Ml R temperatures, the algorithms are compared directly, with the OSS forward model inserted into the PDS algorithm. Factors such as the filtering parameter, the use of linear versus nonlinear constrained inversion, and the choice of the forward model, are found to contribute heavily to the differences in the temperature profiles retrieved in the polar regions, resulting in uncertainties of up to 6 K. Even outside the poles, changes in the a priori statistics result in different profile shapes which all fit the radiances within the specified error. The importance of the a priori statistics prevents

Iterative approach to self-adapting and altitude-dependent regularization for atmospheric profile retrievals.

Science.gov (United States)

Ridolfi, Marco; Sgheri, Luca

2011-12-19

In this paper we present the IVS (Iterative Variable Strength) method, an altitude-dependent, self-adapting Tikhonov regularization scheme for atmospheric profile retrievals. The method is based on a similar scheme we proposed in 2009. The new method does not need any specifically tuned minimization routine, hence it is more robust and faster. We test the self-consistency of the method using simulated observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). We then compare the new method with both our previous scheme and the scalar method currently implemented in the MIPAS on-line processor, using both synthetic and real atmospheric limb measurements. The IVS method shows very good performances.

Examining the relationship between intermediate scale soil moisture and terrestrial evaporation within a semi-arid grassland

KAUST Repository

Jana, Raghavendra Belur

2016-05-17

Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale-mismatch when compared to coarser-resolution satellite-based soil moisture or evaporation estimates. The Cosmic Ray Soil Moisture Observing System (COSMOS) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here we present an examination of the links observed between COSMOS soil moisture retrievals and evaporation estimates over a pasture in the semi-arid central-west region of New South Wales, Australia. The COSMOS soil moisture product was compared to evaporation derived from three distinct approaches, including the Priestley-Taylor (PT-JPL), Penman-Monteith (PM-Mu) and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson’s Correlations, Quantile-Quantile (Q-Q) plots, and Analysis of Variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly two years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q-Q plots and ANOVA illustrate that the root-zone soil moisture represented by the COSMOS measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold

The Effect of Temperature on Moisture Transport in Concrete.

Science.gov (United States)

Wang, Yao; Xi, Yunping

2017-08-09

Most concrete structures and buildings are under temperature and moisture variations simultaneously. Thus, the moisture transport in concrete is driven by the moisture gradient as well as the temperature gradient. This paper presents an experimental approach for determining the effect of different temperature gradients on moisture distribution profiles in concrete. The effect of elevated temperatures under isothermal conditions on the moisture transport was also evaluated, and found not to be significant. The non-isothermal tests show that the temperature gradient accelerates the moisture transport in concrete. The part of increased moisture transfer due to the temperature gradient can be quantified by a coupling parameter D HT , which can be determined by the present test data. The test results indicated that D HT is not a constant but increases linearly with the temperature variation. A material model was developed for D HT based on the experimental results obtained in this study.

A New Approach in Downscaling Microwave Soil Moisture Product using Machine Learning

Science.gov (United States)

Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid

2016-04-01

Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

Science.gov (United States)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests

Science.gov (United States)

Liu, Jing; Skidmore, Andrew K.; Heurich, Marco; Wang, Tiejun

2017-10-01

As an important metric for describing vertical forest structure, the plant area index (PAI) profile is used for many applications including biomass estimation and wildlife habitat assessment. PAI profiles can be estimated with the vertically resolved gap fraction from airborne LiDAR data. Most research utilizes a height normalization algorithm to retrieve local or relative height by assuming the terrain to be flat. However, for many forests this assumption is not valid. In this research, the effect of topographic normalization of airborne LiDAR data on the retrieval of PAI profile was studied in a mountainous forest area in Germany. Results show that, although individual tree height may be retained after topographic normalization, the spatial arrangement of trees is changed. Specifically, topographic normalization vertically condenses and distorts the PAI profile, which consequently alters the distribution pattern of plant area density in space. This effect becomes more evident as the slope increases. Furthermore, topographic normalization may also undermine the complexity (i.e., canopy layer number and entropy) of the PAI profile. The decrease in PAI profile complexity is not solely determined by local topography, but is determined by the interaction between local topography and the spatial distribution of each tree. This research demonstrates that when calculating the PAI profile from airborne LiDAR data, local topography needs to be taken into account. We therefore suggest that for ecological applications, such as vertical forest structure analysis and modeling of biodiversity, topographic normalization should not be applied in non-flat areas when using LiDAR data.

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

Science.gov (United States)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation

A new Dobson Umkehr ozone profile retrieval method optimising information content and resolution

Science.gov (United States)

Stone, K.; Tully, M. B.; Rhodes, S. K.; Schofield, R.

2015-03-01

The standard Dobson Umkehr methodology to retrieve coarse-resolution ozone profiles used by the National Oceanographic and Atmospheric Administration uses designated solar zenith angles (SZAs). However, some information may be lost if measurements lie outside the designated SZA range (between 60° and 90°), or do not conform to the fitting technique. Also, while Umkehr measurements can be taken using multiple wavelength pairs (A, C and D), past retrieval methods have focused on a single pair (C). Here we present an Umkehr inversion method that uses measurements at all SZAs (termed operational) and all wavelength pairs. (Although, we caution direct comparison to other algorithms.) Information content for a Melbourne, Australia (38° S, 145° E) Umkehr measurement case study from 28 January 1994, with SZA range similar to that designated in previous algorithms is shown. When comparing the typical single wavelength pair with designated SZAs to the operational measurements, the total degrees of freedom (independent pieces of information) increases from 3.1 to 3.4, with the majority of the information gain originating from Umkehr layers 2 + 3 and 4 (10-20 km and 25-30 km respectively). In addition to this, using all available wavelength pairs increases the total degrees of freedom to 5.2, with the most significant increases in Umkehr layers 2 + 3 to 7 and 9+ (10-40 and 45-80 km). Investigating a case from 13 April 1970 where the measurements extend beyond the 90° SZA range gives further information gain, with total degrees of freedom extending to 6.5. Similar increases are seen in the information content. Comparing the retrieved Melbourne Umkehr time series with ozonesondes shows excellent agreement in layers 2 + 3 and 4 (10-20 and 25-30 km) for both C and A + C + D-pairs. Retrievals in layers 5 and 6 (25-30 and 30-35 km) consistently show lower ozone partial column compared to ozonesondes. This is likely due to stray light effects that are not accounted for in the

The Raam regional soil moisture monitoring network in the Netherlands

Directory of Open Access Journals (Sweden)

H.-J. F. Benninga

2018-01-01

Full Text Available We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km2, and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km2. Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m3 m−3. The first set of measurements has been retrieved for the period 5 April 2016–4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data and information (elevation, soil physical characteristics, land cover and a geohydrological model available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56.

The Raam regional soil moisture monitoring network in the Netherlands

Science.gov (United States)

Benninga, Harm-Jan F.; Carranza, Coleen D. U.; Pezij, Michiel; van Santen, Pim; van der Ploeg, Martine J.; Augustijn, Denie C. M.; van der Velde, Rogier

2018-01-01

We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km2), and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km2). Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m3 m-3. The first set of measurements has been retrieved for the period 5 April 2016-4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil physical characteristics, land cover and a geohydrological model) available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56.

An integrated GIS application system for soil moisture data assimilation

Science.gov (United States)

Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

2014-11-01

The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

On the role of visible radiation in ozone profile retrieval from nadir UV/VIS satellite measurements: An experiment with neural network algorithms inverting SCIAMACHY data

International Nuclear Information System (INIS)

Sellitto, P.; Di Noia, A.; Del Frate, F.; Burini, A.; Casadio, S.; Solimini, D.

2012-01-01

Theoretical evidence has been given on the role of visible (VIS) radiation in enhancing the accuracy of ozone retrievals from satellite data, especially in the troposphere. However, at present, VIS is not being systematically used together with ultraviolet (UV) measurements, even when possible with one single instrument, e.g., the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY). Reasons mainly reside in the defective performance of optimal estimation and regularization algorithms caused by inaccurate modeling of VIS interaction with aerosols or clouds, as well as in inconsistent intercalibration between UV and VIS measurements. Here we intend to discuss the role of VIS radiation when it feeds a retrieval algorithm based on Neural Networks (NNs) that does not need a forward radiative transfer model and is robust with respect to calibration errors. The NN we designed was trained with a set of ozonesondes (OSs) data and tested over an independent set of OS measurements. We compared the ozone concentration profiles retrieved from UV-only with those retrieved from UV plus VIS nadir data taken by SCIAMACHY. We found that VIS radiation was able to yield more than 10% increase of accuracy and to substantially reduce biases of retrieved profiles at tropospheric levels.

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

Science.gov (United States)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

A soil moisture network for SMOS validation in Western Denmark

DEFF Research Database (Denmark)

Bircher, Simone; Skou, N.; Jensen, Karsten Høgh

2012-01-01

network was established in the Skjern River Catchment, Denmark. The objectives of this article are to describe a method to implement a network suited for SMOS validation, and to present sample data collected by the network to verify the approach. The design phase included (1) selection of a single SMOS...... between the north-east and south-west were found to be small. A first comparison between the 0–5 cm network averages and the SMOS soil moisture (level 2) product is in range with worldwide validation results, showing comparable trends for SMOS retrieved soil moisture (R2 of 0.49) as well as initial soil......). Based on these findings, the network performs according to expectations and proves to be well-suited for its purpose. The discrepancies between network and SMOS soil moisture will be subject of subsequent studies...

A COMPARISON BETWEEN TWO ALGORITHMS FOR THE RETRIEVAL OF SOIL MOISTURE USING AMSR-E DATA

Directory of Open Access Journals (Sweden)

Simonetta ePaloscia

2015-04-01

Full Text Available A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR (HydroAlgo and the second one is the Single Channel Algorithm (SCA developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service. Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA, within the framework of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E and Global Change Observation Mission–Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE ≤0.06 m3/m3 and Bias <0.02 m3/m3. These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.

Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

Science.gov (United States)

Case, Jonathan L.; Blakenship, Clay B.; Zavodsky, Bradley T.

2014-01-01

As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes application-driven research to provide a fundamental understanding of how SMAP data products will be used to improve decision-making at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a real-time regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warm-season months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive L-band radiometer that is used to retrieve surface soil moisture at 35-km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive L-band instrument in conjunction with a 3-km resolution active radar component of slightly degraded accuracy. A combined radar-radiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model

Water table and the neutron moisture meter

Energy Technology Data Exchange (ETDEWEB)

Visvalingam, M [Hull Univ. (UK). Geography Dept.

1975-12-01

Measurements with a neutron moisture meter at Westlands, near Hull, showed count rates at capillary saturation to be within the error limits of count rates at full saturation. However, the saturation profiles in themselves were interesting as they indicated not only the zonation of the soil but also differences in drainable porosity when compared to count-rate profiles at the end of November.

From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

Directory of Open Access Journals (Sweden)

C. Albergel

2008-12-01

Full Text Available A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content, the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

Relationship between topography, land use and soil moisture in loess hillslopes

NARCIS (Netherlands)

Sheikh, V.; Loon, van E.E.; Stroosnijder, L.

2013-01-01

The relationship between topography, land use, and topsoil moisture storage is investigated for a small catchment with undulating deep loess hilslopes in the south of the Netherlands. For a period of 10 months, soil moisture profiles have been measured weekly at 15 locations throughout the

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

Directory of Open Access Journals (Sweden)

Rosa Oltra-Carrió

2015-03-01

Full Text Available The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed and SWIR (ShortWave InfraRed regions (from 0.4 to 2.5 µm when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON. These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R2 between 0.72 and 0.92. A clay content (CC dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC. The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m3∙m−3 in all cases and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

Moisture buffer capacity of cement-lime plasters with enhanced thermal storage capacity

Science.gov (United States)

Fořt, Jan; Pavlíková, Milena; Pavlík, Zbyšek

2017-07-01

Indoor air temperature and relative humidity represent important parameters for health and working efficiency of buildings occupants. Beside the moderation of temperature, investigation of hygric properties of building materials with connection to indoor relative humidity variation became recognized as a relevant factor for energy efficient building maintenance. The moisture buffer value introduced in the Nordtest protocol can be used for estimation of moisture buffer capacity of building materials or their multi-layered systems. In this paper, both the ideal and real moisture buffer values are examined on the basis of simulation of diurnal relative humidity fluctuations in plasters with incorporated PCM admixture. Retrieved data points to a complex effect of the tested plasters on possible moderation of buildings interior climate.

AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

Science.gov (United States)

Lakshmi, V.; Mladenova, I. E.; Narayan, U.

2009-12-01

Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

NASA Giovanni: A Tool for Visualizing, Analyzing, and Inter-comparing Soil Moisture Data

Science.gov (United States)

Teng, William; Rui, Hualan; Vollmer, Bruce; deJeu, Richard; Fang, Fan; Lei, Guang-Dih; Parinussa, Robert

2014-01-01

There are many existing satellite soil moisture algorithms and their derived data products, but there is no simple way for a user to inter-compare the products or analyze them together with other related data. An environment that facilitates such inter-comparison and analysis would be useful for validation of satellite soil moisture retrievals against in situ data and for determining the relationships between different soil moisture products. As part of the NASA Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) family of portals, which has provided users worldwide with a simple but powerful way to explore NASA data, a beta prototype Giovanni Inter-comparison of Soil Moisture Products portal has been developed. A number of soil moisture data products are currently included in the prototype portal. More will be added, based on user requirements and feedback and as resources become available. Two application examples for the portal are provided. The NASA Giovanni Soil Moisture portal is versatile and extensible, with many possible uses, for research and applications, as well as for the education community.

Patterns of Precipitation and Streamflow Responses to Moisture Fluxes during Atmospheric Rivers

Science.gov (United States)

Henn, B. M.; Wilson, A. M.; Asgari Lamjiri, M.; Ralph, M.

2017-12-01

Precipitation from landfalling atmospheric rivers (ARs) have been shown to dominate the hydroclimate of many parts of the world. ARs are associated with saturated, neutrally-stable profiles in the lower atmosphere, in which forced ascent by topography induces precipitation. Understanding the spatial and temporal variability of precipitation over complex terrain during AR-driven precipitation is critical for accurate forcing of distributed hydrologic models and streamflow forecasts. Past studies using radar wind profilers and radiosondes have demonstrated predictability of precipitation rates based on upslope water vapor flux over coastal terrain, with certain levels of moisture flux exhibiting the greatest influence on precipitation. Additionally, these relationships have been extended to show that streamflow in turn responds predictably to upslope vapor flux. However, past studies have focused on individual pairs of profilers and precipitation gauges; the question of how orographic precipitation in ARs is distributed spatially over complex terrain, at different topographic scales, is less well known. Here, we examine profiles of atmospheric moisture transport from radiosondes and wind profilers, against a relatively dense network of precipitation gauges, as well as stream gauges, to assess relationships between upslope moisture flux and the spatial response of precipitation and streamflow. We focus on California's Russian River watershed in the 2016-2017 cool season, when regular radiosonde launches were made at two locations during an active sequence of landfalling ARs. We examine how atmospheric water vapor flux results in precipitation patterns across gauges with different topographic relationships to the prevailing moisture-bearing winds, and conduct a similar comparison of runoff volume response from several unimpaired watersheds in the upper Russian watershed, taking into account antecedent soil moisture conditions that influence runoff generation. Finally

Retrieval of humidity and temperature profiles over the oceans from ...

Indian Academy of Sciences (India)

Indian Institute of Technology Madras, Chennai 600 036, India. ... inspired many researchers and the power of invok- ing prior knowledge in .... to accelerate the process of retrieval if direct ANN ... to drive a more robust retrieval algorithm. Fur-.

Smap Soil Moisture Data Assimilation for the Continental United States and Eastern Africa

Science.gov (United States)

Blankenship, C. B.; Case, J.; Zavodsky, B.; Crosson, W. L.

2016-12-01

The NASA Short-Term Prediction Research and Transition (SPoRT) Center at Marshall Space Flight Center manages near-real-time runs of the Noah Land Surface Model within the NASA Land Information System (LIS) over Continental U.S. (CONUS) and Eastern Africa domains. Soil moisture products from the CONUS model run are used by several NOAA/National Weather Service Weather Forecast Offices for flood and drought situational awareness. The baseline LIS configuration is the Noah model driven by atmospheric and combined radar/gauge precipitation analyses, and input satellite-derived real-time green vegetation fraction on a 3-km grid for the CONUS. This configuration is being enhanced by adding the assimilation of Level 2 Soil Moisture Active/Passive (SMAP) soil moisture retrievals in a parallel run beginning on 1 April 2015. Our implementation of SMAP assimilation includes a cumulative distribution function (CDF) matching approach that aggregates points with similar soil types. This method allows creation of robust CDFs with a short data record, and also permits the correction of local anomalies that may arise from poor forcing data (e.g., quality-control problems with rain gauges). Validation results using in situ soil monitoring networks in the CONUS are shown, with comparisons to the baseline SPoRT-LIS run. Initial results are also presented from a modeling run in eastern Africa, forced by Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data. Strategies for spatial downscaling and for dealing with effective depth of the retrieval product are also discussed.

Synergistic soil moisture observation - an interdisciplinary multi-sensor approach to yield improved estimates across scales

Science.gov (United States)

Schrön, M.; Fersch, B.; Jagdhuber, T.

2017-12-01

The representative determination of soil moisture across different spatial ranges and scales is still an important challenge in hydrology. While in situ measurements are trusted methods at the profile- or point-scale, cosmic-ray neutron sensors (CRNS) are renowned for providing volume averages for several hectares and tens of decimeters depth. On the other hand, airborne remote-sensing enables the coverage of regional scales, however limited to the top few centimeters of the soil.Common to all of these methods is a challenging data processing part, often requiring calibration with independent data. We investigated the performance and potential of three complementary observational methods for the determination of soil moisture below grassland in an alpine front-range river catchment (Rott, 55 km2) of southern Germany.We employ the TERENO preAlpine soil moisture monitoring network, along with additional soil samples taken throughout the catchment. Spatial soil moisture products have been generated using surveys of a car-mounted mobile CRNS (rover), and an aerial acquisition of the polarimetric synthetic aperture radar (F-SAR) of DLR.The study assesses (1) the viability of the different methods to estimate soil moisture for their respective scales and extents, and (2) how either method could support an improvement of the others. We found that in situ data can provide valuable information to calibrate the CRNS rover and to train the vegetation removal part of the polarimetric SAR (PolSAR) retrieval algorithm. Vegetation correction is mandatory to obtain the sub-canopy soil moisture patterns. While CRNS rover surveys can be used to evaluate the F-SAR product across scales, vegetation-related PolSAR products in turn can support the spatial correction of CRNS products for biomass water. Despite the different physical principles, the synthesis of the methods can provide reasonable soil moisture information by integrating from the plot to the landscape scale. The

Exploring Database Improvements for GPM Constellation Precipitation Retrievals

Science.gov (United States)

Ringerud, S.; Kidd, C.; Skofronick Jackson, G.

2017-12-01

The Global Precipitation Measurement Mission (GPM) offers an unprecedented opportunity for understanding and mapping of liquid and frozen precipitation on a global scale. GPM mission development of physically based retrieval algorithms, for application consistently across the constellation radiometers, relies on combined active-passive retrievals from the GPM core satellite as a transfer standard. Radiative transfer modeling is then utilized to compute a priori databases at the frequency and footprint geometry of each individual radiometer. The Goddard Profiling Algorithm (GPROF) performs constellation retrievals across the GPM databases in a Bayesian framework, constraining searches using model data on a pixel-by-pixel basis. This work explores how the retrieval might be enhanced with additional information available within the brightness temperature observations themselves. In order to better exploit available information content, model water vapor is replaced with retrieved water vapor. Rather than treating each footprint as a 1D profile alone in space, information regarding Tb variability in the horizontal is added as well as variability in the time dimension. This additional information is tested and evaluated for retrieval improvement in the context of the Bayesian retrieval scheme. Retrieval differences are presented as a function of precipitation and surface type for evaluation of where the added information proves most effective.

Application of Electromagnetic Induction to Monitor Changes in Soil Electrical Conductivity Profiles in Arid Agriculture

KAUST Repository

Jadoon, K.Z.

2015-09-06

In this research, multi-configuration electromagnetic induction (EMI) measurements were conducted in a corn field to estimate variation in soil electrical conductivity profiles in the roots zone. Electromagnetic forward model based on the full solution of Maxwell\\'s equation was used to simulate the apparent electrical conductivity measured with EMI system (the CMD mini-Explorer). Joint inversion of multi-configuration EMI measurements were performed to estimate the vertical soil electrical conductivity profiles. The inversion minimizes the misfit between the measured and modeled soil apparent electrical conductivity by DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, which is based on Bayesain approach. Results indicate that soil electrical conductivity profiles have low values close to the corn plants, which indicates loss of soil moisture due to the root water uptake. These results offer valuable insights into future potential and emerging challenges in the development of joint analysis of multi-configuration EMI measurements to retrieve effective soil electrical conductivity profiles.

Improvement of OMI Ozone Profile Retrievals in the Troposphere and Lower Troposphere by the Use of the Tropopause-Based Ozone Profile Climatology

Science.gov (United States)

Bak, Juseon; Liu, X.; Wei, J.; Kim, J. H.; Chance, K.; Barnet, C.

2011-01-01

An advance algorithm based on the optimal estimation technique has beeen developed to derive ozone profile from GOME UV radiances and have adapted it to OMI UV radiances. OMI vertical resolution : 7-11 km in the troposphere and 10-14 km in the stratosphere. Satellite ultraviolet measurements (GOME, OMI) contain little vertical information for the small scale of ozone, especially in the upper troposphere (UT) and lower stratosphere (LS) where the sharp O3 gradient across the tropopause and large ozone variability are observed. Therefore, retrievals depend greatly on the a-priori knowledge in the UTLS

Profiling the SO2 Plume from Volcan Turrialba: Ticosonde Balloon Measurements Compared with OMI and OMPS Retrievals

Science.gov (United States)

Selkirk, H. B.; Krotkov, N. A.; Li, C.; Morris, G.; Diaz, J. A.; Carn, S. A.; Voemel, H.; Nord, P. M.; Larson, K.

2014-12-01

The summit of Volcan Turrialba (elev. 3340 m) lies less than 50 km upstream in the prevailing easterlies from the Ticosonde balloon launch site at San Jose, Costa Rica, where ECC ozone sondes have been launched regularly since 2005. In 2006 we began to see telltale notches in the ozone profiles in the altitude range between 2 and 6 km. Given the proximity of Turrialba, it seemed likely that SO2 in the volcano's plume was interfering in the chemical reaction in the ECC ozone sonde used to detect ozone. In early 2010, fumarolic activity in the Turrialba crater increased strongly, and the profile notches in our soundings increased in frequency as well, consistent with this hypothesis. In February 2012 we tested a dual ECC sonde system, where an additional sonde is flown on the same payload using a selective SO2 filter. The difference of the measurements in the dual sonde is a direct measure of the amount of SO2 encountered. This first dual sonde passed through the plume, and the data indicated a tropospheric SO2 column of 1.4 DU, comparing favorably with a total column of 1.7 DU in the OMI 3-km linear fit (LF) product at the sonde profile location and at nearly the same time. We are now launching dual sondes on a regular basis with 18 launches in the first 12 months through July 2014; 11 of these have detectable SO2 signals. These soundings have great potential for validation of the Aura OMI and the Suomi-NPP OMPS retrievals of SO2. Here we present the sonde measurements and compare them with two satellite datasets: the Aura OMI Linear Fit (LF) product and the Suomi-NPP OMPS Principal Components Analysis (PCA) boundary layer product. The PCA algorithm reduces retrieval noise and artifacts by more accurately accounting for various interferences in SO2 retrievals such as O3 absorption and rotational Raman scattering. The comparisons with the in situ observations indicate a significant improvement of the PCA algorithm in capturing relatively weak volcanic SO2 signals.

Profiling the SO2 Plume from Volcan Turrialba: Ticosonde Balloon Measurements Compared with OMI and OMPS Retrievals

Science.gov (United States)

Selkirk, Henry; Krotkov, Nickolay; Li, Can; Morris, Gary (Inventor); Diaz, Jorge Andres; Carn, Simon; Vomel, Holger; Corrales, Ernesto; Nord, Paul; Larson, Kelsey

2014-01-01

The summit of Volcan Turrialba (elev. 3340 m) lies less than 50 km upstream in the prevailing easterlies from the Ticosonde balloon launch site at San Jose, Costa Rica, where ECC ozone sondes have been launched regularly since 2005. In 2006 we began to see telltale notches in the ozone profiles in the altitude range between 2 and 6 km. Given the proximity of Turrialba, it seemed likely that SO2 in the volcano's plume was interfering in the chemical reaction in the ECC ozone sonde used to detect ozone. In early 2010, fumarolic activity in the Turrialba crater increased strongly, and the profile notches in our soundings increased in frequency as well, consistent with this hypothesis. In February 2012 we tested a dual ECC sonde system, where an additional sonde is flown on the same payload using a selective SO2 filter. The difference of the measurements in the dual sonde is a direct measure of the amount of SO2 encountered. This first dual sonde passed through the plume, and the data indicated a tropospheric SO2 column of 1.4 DU, comparing favorably with a total column of 1.7 DU in the OMI 3-km linear fit (LF) product at the sonde profile location and at nearly the same time. We are now launching dual sondes on a regular basis with 18 launches in the first 12 months through July 2014; 11 of these have detectable SO2 signals. These soundings have great potential for validation of the Aura OMI and the Suomi-NPP OMPS retrievals of SO2. Here we present the sonde measurements and compare them with two satellite datasets: the Aura OMI Linear Fit (LF) product and the Suomi-NPP OMPS Principal Components Analysis (PCA) boundary layer product. The PCA algorithm reduces retrieval noise and artifacts by more accurately accounting for various interferences in SO2 retrievals such as O3 absorption and rotational Raman scattering. The comparisons with the in situ observations indicate a significant improvement of the PCA algorithm in capturing relatively weak volcanic SO2 signals.

Assessment of the SMAP Passive Soil Moisture Product

Science.gov (United States)

Chan, Steven K.; Bindlish, Rajat; O'Neill, Peggy E.; Njoku, Eni; Jackson, Tom; Colliander, Andreas; Chen, Fan; Burgin, Mariko; Dunbar, Scott; Piepmeier, Jeffrey;

Soil Moisture Estimate under Forest using a Semi-empirical Model at P-Band

Science.gov (United States)

Truong-Loi, M.; Saatchi, S.; Jaruwatanadilok, S.

2013-12-01

In this paper we show the potential of a semi-empirical algorithm to retrieve soil moisture under forests using P-band polarimetric SAR data. In past decades, several remote sensing techniques have been developed to estimate the surface soil moisture. In most studies associated with radar sensing of soil moisture, the proposed algorithms are focused on bare or sparsely vegetated surfaces where the effect of vegetation can be ignored. At long wavelengths such as L-band, empirical or physical models such as the Small Perturbation Model (SPM) provide reasonable estimates of surface soil moisture at depths of 0-5cm. However for densely covered vegetated surfaces such as forests, the problem becomes more challenging because the vegetation canopy is a complex scattering environment. For this reason there have been only few studies focusing on retrieving soil moisture under vegetation canopy in the literature. Moghaddam et al. developed an algorithm to estimate soil moisture under a boreal forest using L- and P-band SAR data. For their studied area, double-bounce between trunks and ground appear to be the most important scattering mechanism. Thereby, they implemented parametric models of radar backscatter for double-bounce using simulations of a numerical forest scattering model. Hajnsek et al. showed the potential of estimating the soil moisture under agricultural vegetation using L-band polarimetric SAR data and using polarimetric-decomposition techniques to remove the vegetation layer. Here we use an approach based on physical formulation of dominant scattering mechanisms and three parameters that integrates the vegetation and soil effects at long wavelengths. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the Distorted Born Approximation (DBA). The simplified model has three equations and three unknowns, preserving the three dominant scattering mechanisms of volume, double-bounce and surface for three polarized backscattering

SBUV version 8.6 Retrieval Algorithm: Error Analysis and Validation Technique

Science.gov (United States)

Kramarova, N. A.; Bhartia, P. K.; Frith, P. K.; McPeters, S. M.; Labow, R. D.; Taylor, G.; Fisher, S.; DeLand, M.

2012-01-01

SBUV version 8.6 algorithm was used to reprocess data from the Back Scattered Ultra Violet (BUV), the Solar Back Scattered Ultra Violet (SBUV) and a number of SBUV/2 instruments, which 'span a 41-year period from 1970 to 2011 (except a 5-year gap in the 1970s)[see Bhartia et al, 2012]. In the new version Daumont et al. [1992] ozone cross section were used, and new ozone [McPeters et ai, 2007] and cloud climatologies Doiner and Bhartia, 1995] were implemented. The algorithm uses the Optimum Estimation technique [Rodgers, 2000] to retrieve ozone profiles as ozone layer (partial column, DU) on 21 pressure layers. The corresponding total ozone values are calculated by summing ozone columns at individual layers. The algorithm is optimized to accurately retrieve monthly zonal mean (mzm) profiles rather than an individual profile, since it uses monthly zonal mean ozone climatology as the A Priori. Thus, the SBUV version 8.6 ozone dataset is better suited for long-term trend analysis and monitoring ozone changes rather than for studying short-term ozone variability. Here we discuss some characteristics of the SBUV algorithm and sources of error in the SBUV profile and total ozone retrievals. For the first time the Averaging Kernels, smoothing errors and weighting functions (or Jacobians) are included in the SBUV metadata. The Averaging Kernels (AK) represent the sensitivity of the retrieved profile to the true state and contain valuable information about the retrieval algorithm, such as Vertical Resolution, Degrees of Freedom for Signals (DFS) and Retrieval Efficiency [Rodgers, 2000]. Analysis of AK for mzm ozone profiles shows that the total number of DFS for ozone profiles varies from 4.4 to 5.5 out of 6-9 wavelengths used for retrieval. The number of wavelengths in turn depends on solar zenith angles. Between 25 and 0.5 hPa, where SBUV vertical resolution is the highest, DFS for individual layers are about 0.5.

Moisture movement in cement-based repair systems monitored by X-ray absorption

NARCIS (Netherlands)

Lukovic, M.; Ye, G.; Schlangen, H.E.J.G.; van Breugel, K.

2017-01-01

In concrete repair systems, material properties in the repair material and interface are greatly influenced by the initial moisture content of the concrete (or mortar) substrate. In order to quantify moisture profiles inside the repair system, X-ray absorption was used. Preliminary studies are

PRELIMINARY RESULTS OF ESTIMATING SOIL MOISTURE OVER BARE SOIL USING FULL-POLARIMETRIC ALOS-2 DATA

Directory of Open Access Journals (Sweden)

A. Sekertekin

2016-10-01

Full Text Available Synthetic Aperture Radar (SAR imaging system is one of the most effective way for Earth observation. The aim of this study is to present the preliminary results about estimating soil moisture using L-band Synthetic Aperture Radar (SAR data. Full-polarimetric (HH, HV, VV, VH ALOS-2 data, acquired on 22.04.2016 with the incidence angle of 30.4o, were used in the study. Simultaneously with the SAR acquisition, in-situ soil moisture samples over bare agricultural lands were collected and evaluated using gravimetric method. Backscattering coefficients for all polarizations were obtained and linear regression analysis was carried out with in situ moisture measurements. The best correlation coefficient was observed with VV polarization. Cross-polarized backscattering coefficients were not so sensitive to soil moisture content. In the study, it was observed that soil moisture maps can be retrieved with the accuracy about 14% (RMSE.

Retrieval method of aerosol extinction coefficient profile based on backscattering, side-scattering and Raman-scattering lidar

Science.gov (United States)

Shan, Huihui; Zhang, Hui; Liu, Junjian; Tao, Zongming; Wang, Shenhao; Ma, Xiaomin; Zhou, Pucheng; Yao, Ling; Liu, Dong; Xie, Chenbo; Wang, Yingjian

2018-03-01

Aerosol extinction coefficient profile is an essential parameter for atmospheric radiation model. It is difficult to get higher signal to noise ratio (SNR) of backscattering lidar from the ground to the tropopause especially in near range. Higher SNR problem can be solved by combining side-scattering and backscattering lidar. Using Raman-scattering lidar, aerosol extinction to backscatter ratio (lidar ratio) can be got. Based on side-scattering, backscattering and Raman-scattering lidar system, aerosol extinction coefficient is retrieved precisely from the earth's surface to the tropopause. Case studies show this method is reasonable and feasible.

Synopsis of moisture monitoring by neutron probe in the unsaturated zone at Area G

International Nuclear Information System (INIS)

Vold, E.

1997-01-01

Moisture profiles from neutron probe data provide valuable information in site characterization and to supplement ground water monitoring efforts. The neutron probe precision error (reproducibility) is found to be about 0.2 vol% under in situ field conditions where the slope in moisture content with depth is varying slowly. This error is about 2 times larger near moisture spikes (e.g., at the vapor phase notch), due to the sensitivity of the probe response to vertical position errors on the order of 0.5 inches. Calibrations were performed to correct the downhole probe response to the volumetric moisture content determined on core samples. Calibration is sensitive to borehole diameter and casing type, requiring 3 separate calibration relations for the boreholes surveyed here. Power law fits were used for calibration in this study to assure moisture content results greater than zero. Findings in the boreholes reported here confirm the broad features seen previously in moisture profiles at Area G, a near-surface region with large moisture variability, a very dry region at greater depths, and a moisture spike at the vapor phase notch (VPN). This feature is located near the interface between the vitrified and vitrified stratigraphic units and near the base of the mesa. This report describes the in-field calibration methods used for the neutron moisture probe measurements and summarizes preliminary results of the monitoring program in the in-situ monitoring network at Area G. Reported results include three main areas: calibration studies, profiles from each of the vertical boreholes at Area G, and time-dependent variations in a select subset of boreholes. Results are reported here for the vertical borehole network. Results from the horizontal borehole network will be described when available

If Frisch is true - impacts of varying beam width, resolution, frequency combinations and beam overlap when retrieving liquid water content profiles

Science.gov (United States)

Küchler, N.; Kneifel, S.; Kollias, P.; Loehnert, U.

2017-12-01

Cumulus and stratocumulus clouds strongly affect the Earth's radiation budget and are a major uncertainty source in weather and climate prediction models. To improve and evaluate models, a comprehensive understanding of cloud processes is necessary and references are needed. Therefore active and passive microwave remote sensing of clouds can be used to derive cloud properties such as liquid water path and liquid water content (LWC), which can serve as a reference for model evaluation. However, both the measurements and the assumptions when retrieving physical quantities from the measurements involve uncertainty sources. Frisch et al. (1998) combined radar and radiometer observations to derive LWC profiles. Assuming their assumptions are correct, there will be still uncertainties regarding the measurement setup. We investigate how varying beam width, temporal and vertical resolutions, frequency combinations, and beam overlap of and between the two instruments influence the retrieval of LWC profiles. Especially, we discuss the benefit of combining vertically, high resolved radar and radiometer measurements using the same antenna, i.e. having ideal beam overlap. Frisch, A. S., G. Feingold, C. W. Fairall, T. Uttal, and J. B. Snider, 1998: On cloud radar and microwave radiometer measurements of stratus cloud liquid water profiles. J. Geophys. Res.: Atmos., 103 (18), 23 195-23 197, doi:0148-0227/98/98JD-01827509.00.

An Overview of Production and Validation of the SMAP Passive Soil Moisture Product

Science.gov (United States)

Chan, S.; O'Neill, P.; Njoku, E.; Jackson, T.; Bindlish, R.

2015-01-01

The Soil Moisture Active Passive (SMAP) mission is an L-band mission scheduled for launch in Jan. 2015. The SMAP instruments consist of a radar and a radiometer to obtain complementary information from space for soil moisture and freeze/thaw state research and applications. By utilizing novel designs in antenna construction, retrieval algorithms, and acquisition hardware, SMAP provides a capability for global mapping of soil moisture and freeze/thaw state with unprecedented accuracy, resolution, and coverage. This improvement in hydrosphere state measurement is expected to advance our understanding of the processes that link the terrestrial water, energy and carbon cycles, improve our capability in flood prediction and drought monitoring, and enhance our skills in weather and climate forecast. For swath-based soil moisture measurement, SMAP generates three operational geophysical data products: (1) the radiometer-only soil moisture product (L2_SM_P) posted at 36-kilometer resolution, (2) the radar-only soil moisture product (L2_SM_A) posted at 3-kilometers resolution, and (3) the radar-radiometer combined soil moisture product (L2_SM_AP) posted at 9-kilometers resolution. Each product draws on the strengths of the underlying sensor(s) and plays a unique role in hydroclimatological and hydrometeorological applications. A full suite of SMAP data products is given in Table 1.

Towards the retrieval of tropospheric ozone with the ozone monitoring instrument (OMI)

NARCIS (Netherlands)

Mielonen, T.; De Haan, J.F.; Van Peet, J.C.A.; Eremenko, M.; Veefkind, J.P.

2015-01-01

We have assessed the sensitivity of the operational Ozone Monitoring Instrument (OMI) ozone profile retrieval algorithm to a number of a priori and radiative transfer assumptions. We studied the effect of stray light correction, surface albedo assumptions and a priori ozone profiles on the retrieved

Calibration of quantitative neutron radiography method for moisture measurement

International Nuclear Information System (INIS)

Nemec, T.; Jeraj, R.

1999-01-01

Quantitative measurements of moisture and hydrogenous matter in building materials by neutron radiography (NR) are regularly performed at TRIGA Mark II research of 'Jozef Stefan' Institute in Ljubljana. Calibration of quantitative method is performed using standard brick samples with known moisture content and also with a secondary standard, plexiglas step wedge. In general, the contribution of scattered neutrons to the neutron image is not determined explicitly what introduces an error to the measured signal. Influence of scattered neutrons is significant in regions with high gradients of moisture concentrations, where the build up of scattered neutrons causes distortion of the moisture concentration profile. In this paper detailed analysis of validity of our calibration method for different geometrical parameters is presented. The error in the measured hydrogen concentration is evaluated by an experiment and compared with results obtained by Monte Carlo calculation with computer code MCNP 4B. Optimal conditions are determined for quantitative moisture measurements in order to minimize the error due to scattered neutrons. The method is tested on concrete samples with high moisture content.(author)

Evaluation of moisture content distribution in wood by soft X-ray imaging

International Nuclear Information System (INIS)

Tanaka, T.; Avramidis, S.; Shida, S.

2009-01-01

A technique for nondestructive evaluation of moisture content distribution of Japanese cedar (sugi) during drying using a newly developed soft X-ray digital microscope was investigated. Radial, tangential, and cross-sectional samples measuring 100 x 100 x 10 mm were cut from green sugi wood. Each sample was dried in several steps in an oven and upon completion of each step, the mass was recorded and a soft X-ray image was taken. The relationship between moisture content and the average grayscale value of the soft X-ray image at each step was linear. In addition, the linear regressions overlapped each other regardless of the sample sections. These results showed that soft X-ray images could accurately estimate the moisture content. Applying this relationship to a small section of each sample, the moisture content distribution was estimated from the image differential between the soft X-ray pictures obtained from the sample in question and the same sample in the oven-dried condition. Moisture content profiles for 10-mm-wide parts at the centers of the samples were also obtained. The shapes of the profiles supported the evaluation method used in this study

Low-Cost Soil Moisture Profile Probe Using Thin-Film Capacitors and a Capacitive Touch Sensor

Directory of Open Access Journals (Sweden)

Yuki Kojima

2016-08-01

Full Text Available Soil moisture is an important property for agriculture, but currently commercialized soil moisture sensors are too expensive for many farmers. The objective of this study is to develop a low-cost soil moisture sensor using capacitors on a film substrate and a capacitive touch integrated circuit. The performance of the sensor was evaluated in two field experiments: a grape field and a mizuna greenhouse field. The developed sensor captured dynamic changes in soil moisture at 10, 20, and 30 cm depth, with a period of 10–14 days required after sensor installation for the contact between capacitors and soil to settle down. The measured soil moisture showed the influence of individual sensor differences, and the influence masked minor differences of less than 0.05 m3·m−3 in the soil moisture at different locations. However, the developed sensor could detect large differences of more than 0.05 m3·m−3, as well as the different magnitude of changes, in soil moisture. The price of the developed sensor was reduced to 300 U.S. dollars and can be reduced even more by further improvements suggested in this study and by mass production. Therefore, the developed sensor will be made more affordable to farmers as it requires low financial investment, and it can be utilized for decision-making in irrigation.

Soil-moisture transport in arid site vadose zones

International Nuclear Information System (INIS)

Isaacson, R.E.; Brownell, L.E.; Nelson, R.W.; Roetman, E.L.

1974-01-01

Soil-moisture transport processes in the arid soils of the United States Atomic Energy Commission's Hanford site are being evaluated. The depth of penetration of meteoric precipitation has been determined by profiling fall-out tritium at two locations where the water table is about 90 m below ground surface. In situ temperatures and water potentials were measured with temperature transducers and thermocouple psychrometers at the same location to obtain thermodynamic data for identifying the factors influencing soil-moisture transport. Neutron probes are being used to monitor soil-moisture changes in two lysimeters, three metres in diameter by 20 metres deep. The lysimeters are also equipped to measure pressure, temperature and relative humidity as a function of depth and time. Theoretical models based on conservation of momentum expressions are being developed to analyse non-isothermal soil-moisture transport processes. Future work will be concerned with combining the theoretical and experimental work and determining the amount of rainfall required to cause migration of soil-moisture to the water table. (author)

Seedling establishment and physiological responses to temporal and spatial soil moisture changes

Science.gov (United States)

Jeremy Pinto; John D. Marshall; Kas Dumroese; Anthony S. Davis; Douglas R. Cobos

2016-01-01

In many forests of the world, the summer season (temporal element) brings drought conditions causing low soil moisture in the upper soil profile (spatial element) - a potentially large barrier to seedling establishment. We evaluated the relationship between initial seedling root depth, temporal and spatial changes in soil moisture during drought after...

Assessment of Multi-frequency Electromagnetic Induction for Determining Soil Moisture Patterns at the Hillslope Scale

Science.gov (United States)

Tromp-van Meerveld, I.; McDonnell, J.

2009-05-01

We present an assessment of electromagnetic induction (EM) as a potential rapid and non-invasive method to map soil moisture patterns at the Panola (GA, USA) hillslope. We address the following questions regarding the applicability of EM measurements for hillslope hydrological investigations: (1) Can EM be used for soil moisture measurements in areas with shallow soils?; (2) Can EM represent the temporal and spatial patterns of soil moisture throughout the year?; and (3) can multiple frequencies be used to extract additional information content from the EM approach and explain the depth profile of soil moisture? We found that the apparent conductivity measured with the multi-frequency GEM-300 was linearly related to soil moisture measured with an Aqua-pro capacitance sensor below a threshold conductivity and represented the temporal patterns in soil moisture well. During spring rainfall events that wetted only the surface soil layers the apparent conductivity measurements explained the soil moisture dynamics at depth better than the surface soil moisture dynamics. All four EM frequencies (7290, 9090, 11250, and 14010 Hz) were highly correlated and linearly related to each other and could be used to predict soil moisture. This limited our ability to use the four different EM frequencies to obtain a soil moisture profile with depth. The apparent conductivity patterns represented the observed spatial soil moisture patterns well when the individually fitted relationships between measured soil moisture and apparent conductivity were used for each measurement point. However, when the same (master) relationship was used for all measurement locations, the soil moisture patterns were smoothed and did not resemble the observed soil moisture patterns very well. In addition, the range in calculated soil moisture values was reduced compared to observed soil moisture. Part of the smoothing was likely due to the much larger measurement area of the GEM-300 compared to the Aqua

Retrieval of convective boundary layer wind field statistics from radar profiler measurements in conjunction with large eddy simulation

Directory of Open Access Journals (Sweden)

Danny Scipión

2009-05-01

Full Text Available The daytime convective boundary layer (CBL is characterized by strong turbulence that is primarily forced by buoyancy transport from the heated underlying surface. The present study focuses on an example of flow structure of the CBL as observed in the U.S. Great Plains on June 8, 2007. The considered CBL flow has been reproduced using a numerical large eddy simulation (LES, sampled with an LES-based virtual boundary layer radar (BLR, and probed with an actual operational radar profiler. The LES-generated CBL flow data are then ingested by the virtual BLR and treated as a proxy for prevailing atmospheric conditions. The mean flow and turbulence parameters retrieved via each technique (actual radar profiler, virtual BLR, and LES have been cross-analyzed and reasonable agreement was found between the CBL wind parameters obtained from the LES and those measured by the actual radar. Averaged vertical velocity variance estimates from the virtual and actual BLRs were compared with estimates calculated from the LES for different periods of time. There is good agreement in the estimates from all three sources. Also, values of the vertical velocity skewness retrieved by all three techniques have been inter-compared as a function of height for different stages of the CBL evolution, showing fair agreement with each other. All three retrievals contain positively skewed vertical velocity structure throughout the main portion of the CBL. Radar estimates of the turbulence kinetic energy (eddy dissipation rate (ε have been obtained based on the Doppler spectral width of the returned signal for the vertical radar beam. The radar estimates were averaged over time in the same fashion as the LES output data. The agreement between estimates was generally good, especially within the mixing layer. Discrepancies observed above the inversion layer may be explained by a weak turbulence signal in particular flow configurations. The virtual BLR produces voltage

In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR

Directory of Open Access Journals (Sweden)

Christian N. Koyama

2017-06-01

Full Text Available At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I2EM based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm.

Effect of soil moisture on the temperature sensitivity of Northern soils

Science.gov (United States)

Minions, C.; Natali, S.; Ludwig, S.; Risk, D.; Macintyre, C. M.

2017-12-01

Arctic and boreal ecosystems are vast reservoirs of carbon and are particularly sensitive to climate warming. Changes in the temperature and precipitation regimes of these regions could significantly alter soil respiration rates, impacting atmospheric concentrations and affecting climate change feedbacks. Many incubation studies have shown that both temperature and soil moisture are important environmental drivers of soil respiration; this relationship, however, has rarely been demonstrated with in situ data. Here we present the results of a study at six field sites in Alaska from 2016 to 2017. Low-power automated soil gas systems were used to measure soil surface CO2 flux from three forced diffusion chambers and soil profile concentrations from three soil depth chambers at hourly intervals at each site. HOBO Onset dataloggers were used to monitor soil moisture and temperature profiles. Temperature sensitivity (Q10) was determined at each site using inversion analysis applied over different time periods. With highly resolved data sets, we were able to observe the changes in soil respiration in response to changes in temperature and soil moisture. Through regression analysis we confirmed that temperature is the primary driver in soil respiration, but soil moisture becomes dominant beyond a certain threshold, suppressing CO2 flux in soils with high moisture content. This field study supports the conclusions made from previous soil incubation studies and provides valuable insights into the impact of both temperature and soil moisture changes on soil respiration.

Extension of the Hapke bidirectional reflectance model to retrieve soil water content

Directory of Open Access Journals (Sweden)

G.-J. Yang

2011-07-01

Full Text Available Soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes. An accurate assessment of the spatial and temporal variation of soil moisture is important to the study of surface biogeophysical processes. Although remote sensing has proven to be one of the most powerful tools for obtaining land surface parameters, no effective methodology yet exists for in situ soil moisture measurement based on a Bidirectional Reflectance Distribution Function (BRDF model, such as the Hapke model. To retrieve and analyze soil moisture, this study applied the soil water parametric (SWAP-Hapke model, which introduced the equivalent water thickness of soil, to ground multi-angular and hyperspectral observations coupled with, Powell-Ant Colony Algorithm methods. The inverted soil moisture data resulting from our method coincided with in situ measurements (R² = 0.867, RMSE = 0.813 based on three selected bands (672 nm, 866 nm, 2209 nm. It proved that the extended Hapke model can be used to estimate soil moisture with high accuracy based on the field multi-angle and multispectral remote sensing data.

EXPLORING BIASES OF ATMOSPHERIC RETRIEVALS IN SIMULATED JWST TRANSMISSION SPECTRA OF HOT JUPITERS

International Nuclear Information System (INIS)

Rocchetto, M.; Waldmann, I. P.; Tinetti, G.; Venot, O.; Lagage, P.-O.

2016-01-01

With a scheduled launch in 2018 October, the James Webb Space Telescope ( JWST ) is expected to revolutionize the field of atmospheric characterization of exoplanets. The broad wavelength coverage and high sensitivity of its instruments will allow us to extract far more information from exoplanet spectra than what has been possible with current observations. In this paper, we investigate whether current retrieval methods will still be valid in the era of JWST , exploring common approximations used when retrieving transmission spectra of hot Jupiters. To assess biases, we use 1D photochemical models to simulate typical hot Jupiter cloud-free atmospheres and generate synthetic observations for a range of carbon-to-oxygen ratios. Then, we retrieve these spectra using TauREx, a Bayesian retrieval tool, using two methodologies: one assuming an isothermal atmosphere, and one assuming a parameterized temperature profile. Both methods assume constant-with-altitude abundances. We found that the isothermal approximation biases the retrieved parameters considerably, overestimating the abundances by about one order of magnitude. The retrieved abundances using the parameterized profile are usually within 1 σ of the true state, and we found the retrieved uncertainties to be generally larger compared to the isothermal approximation. Interestingly, we found that by using the parameterized temperature profile we could place tight constraints on the temperature structure. This opens the possibility of characterizing the temperature profile of the terminator region of hot Jupiters. Lastly, we found that assuming a constant-with-altitude mixing ratio profile is a good approximation for most of the atmospheres under study.

EXPLORING BIASES OF ATMOSPHERIC RETRIEVALS IN SIMULATED JWST TRANSMISSION SPECTRA OF HOT JUPITERS

Energy Technology Data Exchange (ETDEWEB)

Rocchetto, M.; Waldmann, I. P.; Tinetti, G. [Department of Physics and Astronomy, University College London, Gower Street, WC1E6BT London (United Kingdom); Venot, O. [Instituut voor Sterrenkunde, Katholieke Universiteit Leuven, Celestijnenlaan 200D, B-3001 Leuven (Belgium); Lagage, P.-O., E-mail: m.rocchetto@ucl.ac.uk [Irfu, CEA, Université Paris-Saclay, F-9119 Gif-sur Yvette (France)

2016-12-10

With a scheduled launch in 2018 October, the James Webb Space Telescope ( JWST ) is expected to revolutionize the field of atmospheric characterization of exoplanets. The broad wavelength coverage and high sensitivity of its instruments will allow us to extract far more information from exoplanet spectra than what has been possible with current observations. In this paper, we investigate whether current retrieval methods will still be valid in the era of JWST , exploring common approximations used when retrieving transmission spectra of hot Jupiters. To assess biases, we use 1D photochemical models to simulate typical hot Jupiter cloud-free atmospheres and generate synthetic observations for a range of carbon-to-oxygen ratios. Then, we retrieve these spectra using TauREx, a Bayesian retrieval tool, using two methodologies: one assuming an isothermal atmosphere, and one assuming a parameterized temperature profile. Both methods assume constant-with-altitude abundances. We found that the isothermal approximation biases the retrieved parameters considerably, overestimating the abundances by about one order of magnitude. The retrieved abundances using the parameterized profile are usually within 1 σ of the true state, and we found the retrieved uncertainties to be generally larger compared to the isothermal approximation. Interestingly, we found that by using the parameterized temperature profile we could place tight constraints on the temperature structure. This opens the possibility of characterizing the temperature profile of the terminator region of hot Jupiters. Lastly, we found that assuming a constant-with-altitude mixing ratio profile is a good approximation for most of the atmospheres under study.

Developing an A Priori Database for Passive Microwave Snow Water Retrievals Over Ocean

Science.gov (United States)

Yin, Mengtao; Liu, Guosheng

2017-12-01

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single-scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one-dimensional variational (1D-Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D-Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D-Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D-Var optimization is beneficial for snow water retrievals.

A new model for predicting moisture uptake by packaged solid pharmaceuticals.

Science.gov (United States)

Chen, Y; Li, Y

2003-04-14

A novel mathematical model has been developed for predicting moisture uptake by packaged solid pharmaceutical products during storage. High density polyethylene (HDPE) bottles containing the tablet products of two new chemical entities and desiccants are investigated. Permeability of the bottles is determined at different temperatures using steady-state data. Moisture sorption isotherms of the two model drug products and desiccants at the same temperatures are determined and expressed in polynomial equations. The isotherms are used for modeling the time-humidity profile in the container, which enables the prediction of the moisture content of individual component during storage. Predicted moisture contents agree well with real time stability data. The current model could serve as a guide during packaging selection for moisture protection, so as to reduce the cost and cycle time of screening study.

1D-VAR Retrieval Using Superchannels

Science.gov (United States)

Liu, Xu; Zhou, Daniel; Larar, Allen; Smith, William L.; Schluessel, Peter; Mango, Stephen; SaintGermain, Karen

2008-01-01

Since modern ultra-spectral remote sensors have thousands of channels, it is difficult to include all of them in a 1D-var retrieval system. We will describe a physical inversion algorithm, which includes all available channels for the atmospheric temperature, moisture, cloud, and surface parameter retrievals. Both the forward model and the inversion algorithm compress the channel radiances into super channels. These super channels are obtained by projecting the radiance spectra onto a set of pre-calculated eigenvectors. The forward model provides both super channel properties and jacobian in EOF space directly. For ultra-spectral sensors such as Infrared Atmospheric Sounding Interferometer (IASI) and the NPOESS Airborne Sounder Testbed Interferometer (NAST), a compression ratio of more than 80 can be achieved, leading to a significant reduction in computations involved in an inversion process. Results will be shown applying the algorithm to real IASI and NAST data.

Effects of near surface soil moisture profiles during evaporation on far-field ground-penetrating radar data: A numerical study

KAUST Repository

Moghadas, Davood

2013-01-01

We theoretically investigated the effect of vapor flow on the drying front that develops in soils when water evaporates from the soil surface and on GPR data. The results suggest the integration of the full-wave GPR model with a coupled water, vapor, and heat flow model to accurately estimate the soil hydraulic properties. We investigated the Effects of a drying front that emerges below an evaporating soil surface on the far-field ground-penetrating radar (GPR) data. First, we performed an analysis of the width of the drying front in soils with 12 different textures by using an analytical model. Then, we numerically simulated vertical soil moisture profiles that develop during evaporation for the soil textures. We performed the simulations using a Richards flow model that considers only liquid water flow and a model that considers coupled water, vapor, and heat flows. The GPR signals were then generated from the simulated soil water content profiles taking into account the frequency dependency of apparent electrical conductivity and dielectric permittivity. The analytical approach indicated that the width of the drying front at the end of Stage I of the evaporation was larger in silty soils than in other soil textures and smaller in sandy soils. We also demonstrated that the analytical estimate of the width of the drying front can be considered as a proxy for the impact that a drying front could have on far-field GPR data. The numerical simulations led to the conclusion that vapor transport in soil resulted in S-shaped soil moisture profiles, which clearly influenced the GPR data. As a result, vapor flow needs to be considered when GPR data are interpreted in a coupled inversion approach. Moreover, the impact of vapor flow on the GPR data was larger for silty than for sandy soils. These Effects on the GPR data provide promising perspectives regarding the use of radars for evaporation monitoring. © Soil Science Society of America 5585 Guilford Rd., Madison, WI

The Soil Moisture Dependence of TRMM Microwave Imager Rainfall Estimates

Science.gov (United States)

Seyyedi, H.; Anagnostou, E. N.

2011-12-01

This study presents an in-depth analysis of the dependence of overland rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on the soil moisture conditions at the land surface. TMI retrievals are verified against rainfall fields derived from a high resolution rain-gauge network (MESONET) covering Oklahoma. Soil moisture (SOM) patterns are extracted based on recorded data from 2000-2007 with 30 minutes temporal resolution. The area is divided into wet and dry regions based on normalized SOM (Nsom) values. Statistical comparison between two groups is conducted based on recorded ground station measurements and the corresponding passive microwave retrievals from TMI overpasses at the respective MESONET station location and time. The zero order error statistics show that the Probability of Detection (POD) for the wet regions (higher Nsom values) is higher than the dry regions. The Falls Alarm Ratio (FAR) and volumetric FAR is lower for the wet regions. The volumetric missed rain for the wet region is lower than dry region. Analysis of the MESONET-to-TMI ratio values shows that TMI tends to overestimate for surface rainfall intensities less than 12 (mm/h), however the magnitude of the overestimation over the wet regions is lower than the dry regions.

Retrieval of tropospheric carbon monoxide for the MOPITT experiment

Science.gov (United States)

Pan, Liwen; Gille, John C.; Edwards, David P.; Bailey, Paul L.; Rodgers, Clive D.

1998-12-01

A retrieval method for deriving the tropospheric carbon monoxide (CO) profile and column amount under clear sky conditions has been developed for the Measurements of Pollution In The Troposphere (MOPITT) instrument, scheduled for launch in 1998 onboard the EOS-AM1 satellite. This paper presents a description of the method along with analyses of retrieval information content. These analyses characterize the forward measurement sensitivity, the contribution of a priori information, and the retrieval vertical resolution. Ensembles of tropospheric CO profiles were compiled both from aircraft in situ measurements and from chemical model results and were used in retrieval experiments to characterize the method and to study the sensitivity to different parameters. Linear error analyses were carried out in parallel with the ensemble experiments. Results of these experiments and analyses indicate that MOPITT CO column measurements will have better than 10% precision, and CO profile measurement will have approximately three pieces of independent information that will resolve 3-5 tropospheric layers to approximately 10% precision. These analyses are important for understanding MOPITT data, both for application of data in tropospheric chemistry studies and for comparison with in situ measurements.

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

Science.gov (United States)

Girotto, Manuela

2018-01-01

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

Major Upgrades to the AIRS Version-6 Ozone Profile Methodology

Science.gov (United States)

Susskind, Joel; Blaisdell, John; Iredell, Lena

2015-01-01

This research is a continuation of part of what was shown at the last AIRS Science Team Meeting in the talk Improved Water Vapor and Ozone Profiles in SRT AIRS Version-6.X and the AIRS February 11, 2015 NetMeeting Further improvements in water vapor and ozone profiles compared to Version-6.AIRS Version-6 was finalized in late 2012 and is now operational. Version-6 contained many significant improvements in retrieval methodology compared to Version-5. However, Version-6 retrieval methodology used for the water vapor profile q(p) and ozone profile O3(p) retrievals is basically unchanged from Version-5, or even from Version-4. Subsequent research has made significant improvements in both water vapor and O3 profiles compared to Version-6. This talk will concentrate on O3 profile retrievals. Improvements in water vapor profile retrievals are given in a separate presentation.

From ASCAT to Sentinel-1: Soil Moisture Monitoring using European C-Band Radars

Science.gov (United States)

Wagner, Wolfgang; Bauer-Marschallinger, Bernhard; Hochstöger, Simon

2016-04-01

backscatter observation, the same physical processes and geophysical variables (e.g. vegetation optical depth, surface roughness, soil volume scattering, etc.) need to be considered. The difference lies mainly in the scaling, i.e. how prominently the different variables influence the C-band data at the different spatial (25 km versus 20 m) and temporal (daily versus 3-30 days repeat coverage) scales. Therefore, while the general properties of soil moisture retrievals schemes used for ASCAT and Sentinel-1 can be the same, the details of the algorithm and parameterization will be different. This presentation will review similarities and differences of soil moisture retrieval approaches used for ASCAT and Sentinel-1, with a focus on the change detection method developed by TU Wien. Some first comparisons of ASCAT and Sentinel-1 soil moisture data over Europe will also be shown. REFERENCES Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellog, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Piepmeier, J.R., Koster, R., Martin, N., McDonald, K.C., Moghaddam, M., Moran, S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W., Tsang, L., & Van Zyl, J. (2010). The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98, 704-716 Hornacek, M., Wagner, W., Sabel, D., Truong, H.L., Snoeij, P., Hahmann, T., Diedrich, E., & Doubkova, M. (2012). Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 1303-1311 Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa-Saldana, J., De Rosnay, P., Jann, A., Schneider, S., Komma, J., Kubu, G., Brugger, K., Aubrecht, C., Züger, C., Gangkofer, U., Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., Steinnocher, K., Zeil, P., & Rubel, F. (2013). The ASCAT soil moisture product: A review of its

Sensitivity Analysis for Atmospheric Infrared Sounder (AIRS) CO2 Retrieval

Science.gov (United States)

Gat, Ilana

2012-01-01

The Atmospheric Infrared Sounder (AIRS) is a thermal infrared sensor able to retrieve the daily atmospheric state globally for clear as well as partially cloudy field-of-views. The AIRS spectrometer has 2378 channels sensing from 15.4 micrometers to 3.7 micrometers, of which a small subset in the 15 micrometers region has been selected, to date, for CO2 retrieval. To improve upon the current retrieval method, we extended the retrieval calculations to include a prior estimate component and developed a channel ranking system to optimize the channels and number of channels used. The channel ranking system uses a mathematical formalism to rapidly process and assess the retrieval potential of large numbers of channels. Implementing this system, we identifed a larger optimized subset of AIRS channels that can decrease retrieval errors and minimize the overall sensitivity to other iridescent contributors, such as water vapor, ozone, and atmospheric temperature. This methodology selects channels globally by accounting for the latitudinal, longitudinal, and seasonal dependencies of the subset. The new methodology increases accuracy in AIRS CO2 as well as other retrievals and enables the extension of retrieved CO2 vertical profiles to altitudes ranging from the lower troposphere to upper stratosphere. The extended retrieval method for CO2 vertical profile estimation using a maximum-likelihood estimation method. We use model data to demonstrate the beneficial impact of the extended retrieval method using the new channel ranking system on CO2 retrieval.

Soil Moisture Estimations Based on Airborne CAROLS L-Band Microwave Data

Directory of Open Access Journals (Sweden)

Arnaud Mialon

2011-12-01

Full Text Available The SMOS satellite mission, launched in 2009, allows global soil moisture estimations to be made using the L-band Microwave Emission of the Biosphere (L-MEB model, which simulates the L-band microwave emissions produced by the soil–vegetation layer. This model was calibrated using various sources of in situ and airborne data. In the present study, we propose to evaluate the L-MEB model on the basis of a large set of airborne data, recorded by the CAROLS radiometer during the course of 20 flights made over South West France (the SMOSMANIA site, and supported by simultaneous soil moisture measurements, made in 2009 and 2010. In terms of volumetric soil moisture, the retrieval accuracy achieved with the L-MEB model, with two default roughness parameters, ranges between 8% and 13%. Local calibrations of the roughness parameter, using data from the 2009 flights for different areas of the site, allowed an accuracy of approximately 5.3% to be achieved with the 2010 CAROLS data. Simultaneously we estimated the vegetation optical thickness (t and we showed that, when roughness is locally adjusted, MODIS NDVI values are correlated (R2 = 0.36 to t. Finally, as a consequence of the significant influence of the roughness parameter on the estimated absolute values of soil moisture, we propose to evaluate the relative variability of the soil moisture, using a default soil roughness parameter. The soil moisture variations are estimated with an uncertainty of approximately 6%.

Moisture profile measurements of concrete samples in vertical flow by gamma ray attenuation method. Medidas do perfil de umidade de amostras de concreto em infiltracao vertical, atraves da atenuacao de raios gama

Energy Technology Data Exchange (ETDEWEB)

Appoloni, C R; Nardocci, A C; Obuti, M M [Universidade Estadual de Londrina, PR (Brazil). Dept. de Fisica

1988-04-01

This work deals with the study of the water diffusion in concrete by the gamma ray attenuation method. The moisture profiles, [theta] (z,t), of the vertical water flow were determined in concrete samples of different trace and porosity. The data were taken with a vertical and horizontal measurement table, a [sup 60] Co gamma ray source, a NaI (T) scintillation detector and the standard gamma ray spectrometry electronic. The [theta] (z,t) data analysis is presented using a phenomenological model of the moisture profile temporal evolution in heterogeneous materials. Two other models, Cell and Sandwich, were also applied to determine the attenuation coefficient of a non-homogeneous media from the attenuation coefficients of the components, taking into account particles-size effects. (author).

An improved triple collocation algorithm for decomposing autocorrelated and white soil moisture retrieval errors

Science.gov (United States)

If not properly account for, auto-correlated errors in observations can lead to inaccurate results in soil moisture data analysis and reanalysis. Here, we propose a more generalized form of the triple collocation algorithm (GTC) capable of decomposing the total error variance of remotely-sensed surf...

L-band HIgh Spatial Resolution Soil Moisture Mapping using SMALL UnManned Aerial Systems

Science.gov (United States)

Dai, E.; Venkitasubramony, A.; Gasiewski, A. J.; Stachura, M.; Elston, J. S.; Walter, B.; Lankford, D.; Corey, C.

2017-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 provided new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of 36 km. However, there exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters. Compared with other methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site and Yuma Colorado Irrigation Research Foundation (IRF) site in 2015 and 2016, respectively, using LDCR Revision A and Tempest sUAS. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. LDCR Revision B has been built and integrated into SuperSwift sUAS and additional field experiments will be performed at IRF in 2017. In Revision B the IF signal is sampled at 80 MS/s to enable digital correlation and RFI mitigation capabilities, in addition to analog correlation. [1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C

Retrievability as proposed in the US repository concept

International Nuclear Information System (INIS)

Harrington, P.G.

2000-01-01

The Nuclear Waste Policy Act states that any repository shall be designed and constructed to permit retrieval. Reasons for retrieval include public health and safety, environmental concerns, and recovery of economically valuable contents of spent nuclear fuel. The Nuclear Regulatory Commission requires that waste must be retrievable at any time up to 50 years after start of emplacement. The US Department of Energy intends to maintain a retrieval capability throughout the preclosure period. Possible preclosure periods range from a minimum of 50 years to as much as 300 years. Repository closure includes sealing all accessible portions of the repository, including ventilation shafts, access ramps and boreholes. Drip shields will be installed over the waste packages. Access to the repository after closure is not intended. The proposed repository includes horizontal emplacement drifts located in the unsaturated zone. The emplacement drift centerline spacing is 81 meters to provide a subboiling region between drifts for water drainage. A drip shield covers the waste packages. All emplacement drifts remain open until closure of the repository, providing performance benefits such as removing heat and moisture during the preclosure period and lowering postclosure temperatures. This does not impede retrieval, permitting a reversal of the emplacement process to accomplish retrieval under normal conditions. The preclosure period is therefore not to enhance retrievability, but does improve performance, and the resultant extension of the retrievability capability is a secondary effect. Information must be provided from the performance confirmation program to support a regulatory decision to close. Closure would isolate the repository from the accessible environment, preclude preferential flowpaths for water into the mountain, and minimize the possibility of inadvertent intrusion. (author)

Soil moisture determination with Tesla NZK 203 neutron gage

International Nuclear Information System (INIS)

Hally, J.

1977-01-01

Soil moisture was measured using the NZK 203 neutron probe manufactured by Tesla Premysleni. The individual measuring sites were spaced at a distance of 100 m. The NZK 203 set consists of a NPK 202 moisture gage and a NSK 301 scintillation detector and features the following specifications: moisture density measuring range 20 to 500 kg/m 3 , 241 Am-Be fast neutron source having a neutron flux of 7.5x10 4 n.sec -1 +-10%, operating temperature -10 to +45 degC. The measured counting rate was primarily affected by the statistical fluctuation of ionizing radiation and by instrument instability. In order that these effects should be limited each measurement was repeated 10 times with the optimum measurement time at an interval of 20 to 100 sec. The NZK 203 Tesla set was proven to be suitable for rapid and reproducible determination of moisture profiles. (J.P.)

Significance of heat-moisture treatment conditions on the pasting and gelling behaviour of various starch-rich cereal and pseudocereal flours.

Science.gov (United States)

Collar, Concha

2017-10-01

The impact of heat-moisture treatment processing conditions (15%, 25%, and 35% moisture content; 1, 3, and 5 h heating time at 120 ℃) on the viscosity pasting and gelling profiles of different grain flours matrices (barley, buckwheat, sorghum, high β-glucan barley, and wheat) was investigated by applying successive cooking and cooling cycles to rapid visco analyser canisters with highly hydrated samples (3.5:25, w:w). At a milder heat-moisture treatment conditions (15% moisture content, 1 h heating time), except for sorghum, heat-moisture treatment flours reached much higher viscosity values during earlier pasting and subsequent gelling than the corresponding native counterparts. Besides heat-moisture treatment wheat flour, the described behaviour found also for non-wheat-treated flours has not been previously reported in the literature. An increased hydrophobicity of prolamins and glutelins in low moisture-short heating time heat-moisture treatment of non-wheat flours with high protein content (12.92%-19.95%) could explain the enhanced viscosity profile observed.

Faraday Rotation for SMOS Retrievals of Ocean Salinity and Soil Moisture

Science.gov (United States)

El-Nimri, Salem; Le Vine, David M.

2016-01-01

Faraday rotation is a change in polarization as radiation propagates from the surface through the ionosphere to the sensor. At L-band (1.4 GHz) this change can be significant and can be important for the remote sensing of soil moisture and ocean salinity from space. Consequently, modern L-band radiometers (SMOS, Aquarius and SMOS) are polarimetric to measure Faraday rotation in situ so that a correction can be made. This is done using the ratio of the third and second Stokes parameters. In the case of SMOS this procedure has produced very noisy estimates. An alternate procedure is reported here in which the total electron content is estimated and averaged to reduce noise.

Deriving the slit functions from OMI solar observations and its implications for ozone-profile retrieval

Directory of Open Access Journals (Sweden)

K. Sun

2017-10-01

Full Text Available The Ozone Monitoring Instrument (OMI has been successfully measuring the Earth's atmospheric composition since 2004, but the on-orbit behavior of its slit functions has not been thoroughly characterized. Preflight measurements of slit functions have been used as a static input in many OMI retrieval algorithms. This study derives on-orbit slit functions from the OMI irradiance spectra assuming various function forms, including standard and super-Gaussian functions and a stretch to the preflight slit functions. The on-orbit slit functions in the UV bands show U-shaped cross-track dependences that cannot be fully represented by the preflight ones. The full widths at half maximum (FWHM of the stretched preflight slit functions for detector pixels at large viewing angles are up to 30 % larger than the nadir pixels for the UV1 band, 5 % larger for the UV2 band, and practically flat in the VIS band. Nonetheless, the on-orbit changes of OMI slit functions are found to be insignificant over time after accounting for the solar activity, despite of the decaying of detectors and the occurrence of OMI row anomaly. Applying the derived on-orbit slit functions to ozone-profile retrieval shows substantial improvements over the preflight slit functions based on comparisons with ozonesonde validations.

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

Science.gov (United States)

DeSouza-Machado, Sergio; Larrabee Strow, L.; Tangborn, Andrew; Huang, Xianglei; Chen, Xiuhong; Liu, Xu; Wu, Wan; Yang, Qiguang

2018-01-01

One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2-4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90 %, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

Directory of Open Access Journals (Sweden)

S. DeSouza-Machado

2018-01-01

Full Text Available One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR satellite sounders use cloud-cleared radiances (CCRs as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2–4 degrees of freedom (DOFs of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA. The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds. From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90 %, while the degrees of freedom correlate with the observed window channel brightness temperature (BT which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS and NWP

Effects of varying soil moisture contents and vegetation canopies on microwave emissions

Science.gov (United States)

Burke, H.-H. K.; Schmugge, T. J.

1982-01-01

Results of NASA airborne passive microwave scans of bare and vegetated fields for comparison with ground truth tests are discussed and a model for atmospheric scattering of radiation by vegetation is detailed. On-board radiometers obtained data at 21, 2.8, and 1.67 cm during three passes over each of 46 fields, 28 of which were bare and the others having wheat or alfalfa. Ground-based sampling included moisture in five layers down to 15 cm in addition to soil temperature. The relationships among the brightness temperature and soil moisture, as well as the surface roughness and the vegetation canopy were examined. A model was developed for the dielectric coefficient and volume scattering for a vegetation medium. L- to C-band data were found useful for retrieving soil information directly. A surface moisture content of 5-35% yielded an emissivity of 0.9-0.7. The data agreed well with a combined multilayer radiative transfer model with simple roughness correction.

Large Scale Gaussian Processes for Atmospheric Parameter Retrieval and Cloud Screening

Science.gov (United States)

Camps-Valls, G.; Gomez-Chova, L.; Mateo, G.; Laparra, V.; Perez-Suay, A.; Munoz-Mari, J.

2017-12-01

Current Earth-observation (EO) applications for image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. Spatio-temporally explicit classification methods are a requirement in a variety of Earth system data processing applications. Upcoming missions such as the super-spectral Copernicus Sentinels EnMAP and FLEX will soon provide unprecedented data streams. Very high resolution (VHR) sensors like Worldview-3 also pose big challenges to data processing. The challenge is not only attached to optical sensors but also to infrared sounders and radar images which increased in spectral, spatial and temporal resolution. Besides, we should not forget the availability of the extremely large remote sensing data archives already collected by several past missions, such ENVISAT, Cosmo-SkyMED, Landsat, SPOT, or Seviri/MSG. These large-scale data problems require enhanced processing techniques that should be accurate, robust and fast. Standard parameter retrieval and classification algorithms cannot cope with this new scenario efficiently. In this work, we review the field of large scale kernel methods for both atmospheric parameter retrieval and cloud detection using infrared sounding IASI data and optical Seviri/MSG imagery. We propose novel Gaussian Processes (GPs) to train problems with millions of instances and high number of input features. Algorithms can cope with non-linearities efficiently, accommodate multi-output problems, and provide confidence intervals for the predictions. Several strategies to speed up algorithms are devised: random Fourier features and variational approaches for cloud classification using IASI data and Seviri/MSG, and engineered randomized kernel functions and emulation in temperature, moisture and ozone atmospheric profile retrieval from IASI as a proxy to the upcoming MTG-IRS sensor. Excellent compromise between accuracy and scalability are obtained in all applications.

Uniform Atmospheric Retrievals of Ultracool Late-T and Early-Y dwarfs

Science.gov (United States)

Garland, Ryan; Irwin, Patrick

2018-01-01

A significant number of ultracool (types of objects with a uniform retrieval method, we hope to elucidate any trends and (dis)similarities found in atmospheric parameters, such as chemical abundances, temperature-pressure profile, and cloud structure, for a sample of 7 ultracool brown dwarfs as we transition from hotter (~700K) to colder objects (~450K).We perform atmospheric retrievals on two late-T and five early-Y dwarfs. We use the NEMESIS atmospheric retrieval code coupled to a Nested Sampling algorithm, along with a standard uniform model for all of our retrievals. The uniform model assumes the atmosphere is described by a gray radiative-convective temperature profile, (optionally) a self-consistent Mie scattering cloud, and a number of relevant gases. We first verify our methods by comparing it to a benchmark retrieval for Gliese 570D, which is found to be consistent. Furthermore, we present the retrieved gaseous composition, temperature structure, spectroscopic mass and radius, cloud structure and the trends associated with decreasing temperature found in this small sample of objects.

Review of crop growth and soil moisture monitoring from a ground-based instrument implementing the Interference Pattern GNSS-R Technique

Science.gov (United States)

Rodriguez-Alvarez, N.; Bosch-Lluis, X.; Camps, A.; Aguasca, A.; Vall-Llossera, M.; Valencia, E.; Ramos-Perez, I.; Park, H.

2011-12-01

Reflectometry using Global Navigation Satellite Systems signals (GNSSR) has been the focus of many studies during the past few years for a number of applications over different scenarios as land, ocean or snow and ice surfaces. In the past decade, its potential has increased yearly, with improved receivers and signal processors, from generic GNSS receivers whose signals were recorded in magnetic tapes to instruments that measure full Delay Doppler Maps (the power distribution of the reflected GNSS signal over the 2-D space of delay offsets and Doppler shifts) in real time. At present, these techniques are considered to be promising tools to retrieve geophysical parameters such as soil moisture, vegetation height, topography, altimetry, sea state and ice and snow thickness, among others. This paper focuses on the land geophysical retrievals (topography, vegetation height and soil moisture) performed from a ground-based instrument using the Interference Pattern Technique (IPT). This technique consists of the measurement of the power fluctuations of the interference signal resulting from the simultaneous reception of the direct and the reflected GNSS signals. The latest experiment performed using this technique over a maize field is shown in this paper. After a review of the previous results, this paper presents the latest experiment performed using this technique over a maize field. This new study provides a deeper analysis on the soil moisture retrieval by observing three irrigation-drying cycles and comparing them to different depths soil moisture probes. Furthermore, the height of the maize, almost 300 cm, has allowed testing the capabilities of the technique over dense and packed vegetation layers, with high vegetation water content.

How to average logarithmic retrievals?

Directory of Open Access Journals (Sweden)

B. Funke

2012-04-01

Full Text Available Calculation of mean trace gas contributions from profiles obtained by retrievals of the logarithm of the abundance rather than retrievals of the abundance itself are prone to biases. By means of a system simulator, biases of linear versus logarithmic averaging were evaluated for both maximum likelihood and maximum a priori retrievals, for various signal to noise ratios and atmospheric variabilities. These biases can easily reach ten percent or more. As a rule of thumb we found for maximum likelihood retrievals that linear averaging better represents the true mean value in cases of large local natural variability and high signal to noise ratios, while for small local natural variability logarithmic averaging often is superior. In the case of maximum a posteriori retrievals, the mean is dominated by the a priori information used in the retrievals and the method of averaging is of minor concern. For larger natural variabilities, the appropriateness of the one or the other method of averaging depends on the particular case because the various biasing mechanisms partly compensate in an unpredictable manner. This complication arises mainly because of the fact that in logarithmic retrievals the weight of the prior information depends on abundance of the gas itself. No simple rule was found on which kind of averaging is superior, and instead of suggesting simple recipes we cannot do much more than to create awareness of the traps related with averaging of mixing ratios obtained from logarithmic retrievals.

Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

Science.gov (United States)

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and comb...

Reference Evapotranspiration Retrievals from a Mesoscale Model Based Weather Variables for Soil Moisture Deficit Estimation

Directory of Open Access Journals (Sweden)

Prashant K. Srivastava

2017-10-01

Full Text Available Reference Evapotranspiration (ETo and soil moisture deficit (SMD are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF. In order to understand the performance, the Hamon’s method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE indicates that the exponential model (RMSE = 0.226; %Bias = −0.077; NSE = 0.616 is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019–0.667; %Bias range = 2.821–6.894; NSE = 0.013–0.419 used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448 as compared to the non-linear models (RMSE range = 0.022–0.707; %Bias range = −0.207–−6.088; NSE range = 0.013–0.149. Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024–0.025; %Bias range = −4.982–−3.431; r = 0.245–0.281 than the non−growing season (RMSE range = 0.011–0.12; %Bias range = 33.073–32.701; r = 0.161–0.244 for SMD estimation.

System to control contamination during retrieval of buried TRU waste

Science.gov (United States)

Menkhaus, Daniel E.; Loomis, Guy G.; Mullen, Carlan K.; Scott, Donald W.; Feldman, Edgar M.; Meyer, Leroy C.

1993-01-01

A system to control contamination during the retrieval of hazardous waste comprising an outer containment building, an inner containment building, within the outer containment building, an electrostatic radioactive particle recovery unit connected to and in communication with the inner and outer containment buildings, and a contaminate suppression system including a moisture control subsystem, and a rapid monitoring system having the ability to monitor conditions in the inner and outer containment buildings.

Moisture Transfer in Concrete: Numerical Determination of the Capillary Conductivity Coefficient

Directory of Open Access Journals (Sweden)

Simo Elie

2017-03-01

Full Text Available We numerically investigated moisture transfer in buildings made of concrete. We considered three types of concrete: normal concrete, pumice concrete and cellular concrete. We present the results of a 1-D liquid water flow in such materials. We evaluated the moisture distribution in building materials using the Runge-Kutta fourth-and-fifth-order method. The DOPRI5 code was used as an integrator. The model calculated the resulting moisture content and other moisture-dependent physical parameters. The moisture curves were plotted. The dampness data obtained was utilized for the numerical computation of the coefficient of the capillary conductivity of moisture. Different profiles of this coefficient are represented. Calculations were performed for four different values of the outdoor temperature: -5°C, 0°C, 5°C and 10°C. We determined that the curves corresponding to small time intervals of wetting are associated with great amplitudes of the capillary conductivity . The amplitudes of the coefficient of the capillary conductivity decrease as the time interval increases. High outdoor temperatures induce high amplitudes of the coefficient of the capillary conductivity.

Soil parameter retrieval under vegetation cover using SAR polarimetery

Energy Technology Data Exchange (ETDEWEB)

Jagdhuber, Thomas

2012-07-01

Soil conditions under vegetation cover and their spatial and temporal variations from point to catchment scale are crucial for understanding hydrological processes within the vadose zone, for managing irrigation and consequently maximizing yield by precision farming. Soil moisture and soil roughness are the key parameters that characterize the soil status. In order to monitor their spatial and temporal variability on large scales, remote sensing techniques are required. Therefore the determination of soil parameters under vegetation cover was approached in this thesis by means of (multi-angular) polarimetric SAR acquisitions at a longer wavelength (L-band, {lambda}{sub c}=23cm). In this thesis, the penetration capabilities of L-band are combined with newly developed (multi-angular) polarimetric decomposition techniques to separate the different scattering contributions, which are occurring in vegetation and on ground. Subsequently the ground components are inverted to estimate the soil characteristics. The novel (multi-angular) polarimetric decomposition techniques for soil parameter retrieval are physically-based, computationally inexpensive and can be solved analytically without any a priori knowledge. Therefore they can be applied without test site calibration directly to agricultural areas. The developed algorithms are validated with fully polarimetric SAR data acquired by the airborne E-SAR sensor of the German Aerospace Center (DLR) for three different study areas in Germany. The achieved results reveal inversion rates up to 99% for the soil moisture and soil roughness retrieval in agricultural areas. However, in forested areas the inversion rate drops significantly for most of the algorithms, because the inversion in forests is invalid for the applied scattering models at L-band. The validation against simultaneously acquired field measurements indicates an estimation accuracy (root mean square error) of 5-10vol.% for the soil moisture (range of in situ

Comparison of Gaussian and non-Gaussian Atmospheric Profile Retrievals from Satellite Microwave Data

Science.gov (United States)

Kliewer, A.; Forsythe, J. M.; Fletcher, S. J.; Jones, A. S.

2017-12-01

The Cooperative Institute for Research in the Atmosphere at Colorado State University has recently developed two different versions of a mixed-distribution (lognormal combined with a Gaussian) based microwave temperature and mixing ratio retrieval system as well as the original Gaussian-based approach. These retrieval systems are based upon 1DVAR theory but have been adapted to use different descriptive statistics of the lognormal distribution to minimize the background errors. The input radiance data is from the AMSU-A and MHS instruments on the NOAA series of spacecraft. To help illustrate how the three retrievals are affected by the change in the distribution we are in the process of creating a new website to show the output from the different retrievals. Here we present initial results from different dynamical situations to show how the tool could be used by forecasters as well as for educators. However, as the new retrieved values are from a non-Gaussian based 1DVAR then they will display non-Gaussian behaviors that need to pass a quality control measure that is consistent with this distribution, and these new measures are presented here along with initial results for checking the retrievals.

Numerical Investigations of Moisture Distribution in a Selected Anisotropic Soil Medium

Science.gov (United States)

Iwanek, M.

2018-01-01

The moisture of soil profile changes both in time and space and depends on many factors. Changes of the quantity of water in soil can be determined on the basis of in situ measurements, but numerical methods are increasingly used for this purpose. The quality of the results obtained using pertinent software packages depends on appropriate description and parameterization of soil medium. Thus, the issue of providing for the soil anisotropy phenomenon gains a big importance. Although anisotropy can be taken into account in many numerical models, isotopic soil is often assumed in the research process. However, this assumption can be a reason for incorrect results in the simulations of water changes in soil medium. In this article, results of numerical simulations of moisture distribution in the selected soil profile were presented. The calculations were conducted assuming isotropic and anisotropic conditions. Empirical verification of the results obtained in the numerical investigations indicated statistical essential discrepancies for the both analyzed conditions. However, better fitting measured and calculated moisture values was obtained for the case of providing for anisotropy in the simulation model.

The use of soil moisture - remote sensing products for large-scale groundwater modeling and assessment

NARCIS (Netherlands)

Sutanudjaja, E.H.

2012-01-01

In this thesis, the possibilities of using spaceborne remote sensing for large-scale groundwater modeling are explored. We focus on a soil moisture product called European Remote Sensing Soil Water Index (ERS SWI, Wagner et al., 1999) - representing the upper profile soil moisture. As a test-bed, we

The Implications of 3D Thermal Structure on 1D Atmospheric Retrieval

Energy Technology Data Exchange (ETDEWEB)

Blecic, Jasmina; Dobbs-Dixon, Ian [NYU Abu Dhabi, Abu Dhabi (United Arab Emirates); Greene, Thomas, E-mail: jasmina@nyu.edu [NASA Ames Research Center, Space Sciece and Astrobiology Division, M.S. 245-6, Moffett Field, CA 94035 (United States)

2017-10-20

Using the atmospheric structure from a 3D global radiation-hydrodynamic simulation of HD 189733b and the open-source Bayesian Atmospheric Radiative Transfer (BART) code, we investigate the difference between the secondary-eclipse temperature structure produced with a 3D simulation and the best-fit 1D retrieved model. Synthetic data are generated by integrating the 3D models over the Spitzer , the Hubble Space Telescope ( HST ), and the James Web Space Telescope ( JWST ) bandpasses, covering the wavelength range between 1 and 11 μ m where most spectroscopically active species have pronounced features. Using the data from different observing instruments, we present detailed comparisons between the temperature–pressure profiles recovered by BART and those from the 3D simulations. We calculate several averages of the 3D thermal structure and explore which particular thermal profile matches the retrieved temperature structure. We implement two temperature parameterizations that are commonly used in retrieval to investigate different thermal profile shapes. To assess which part of the thermal structure is best constrained by the data, we generate contribution functions for our theoretical model and each of our retrieved models. Our conclusions are strongly affected by the spectral resolution of the instruments included, their wavelength coverage, and the number of data points combined. We also see some limitations in each of the temperature parametrizations, as they are not able to fully match the complex curvatures that are usually produced in hydrodynamic simulations. The results show that our 1D retrieval is recovering a temperature and pressure profile that most closely matches the arithmetic average of the 3D thermal structure. When we use a higher resolution, more data points, and a parametrized temperature profile that allows more flexibility in the middle part of the atmosphere, we find a better match between the retrieved temperature and pressure profile and

The Implications of 3D Thermal Structure on 1D Atmospheric Retrieval

Science.gov (United States)

Blecic, Jasmina; Dobbs-Dixon, Ian; Greene, Thomas

2017-10-01

Using the atmospheric structure from a 3D global radiation-hydrodynamic simulation of HD 189733b and the open-source Bayesian Atmospheric Radiative Transfer (BART) code, we investigate the difference between the secondary-eclipse temperature structure produced with a 3D simulation and the best-fit 1D retrieved model. Synthetic data are generated by integrating the 3D models over the Spitzer, the Hubble Space Telescope (HST), and the James Web Space Telescope (JWST) bandpasses, covering the wavelength range between 1 and 11 μm where most spectroscopically active species have pronounced features. Using the data from different observing instruments, we present detailed comparisons between the temperature-pressure profiles recovered by BART and those from the 3D simulations. We calculate several averages of the 3D thermal structure and explore which particular thermal profile matches the retrieved temperature structure. We implement two temperature parameterizations that are commonly used in retrieval to investigate different thermal profile shapes. To assess which part of the thermal structure is best constrained by the data, we generate contribution functions for our theoretical model and each of our retrieved models. Our conclusions are strongly affected by the spectral resolution of the instruments included, their wavelength coverage, and the number of data points combined. We also see some limitations in each of the temperature parametrizations, as they are not able to fully match the complex curvatures that are usually produced in hydrodynamic simulations. The results show that our 1D retrieval is recovering a temperature and pressure profile that most closely matches the arithmetic average of the 3D thermal structure. When we use a higher resolution, more data points, and a parametrized temperature profile that allows more flexibility in the middle part of the atmosphere, we find a better match between the retrieved temperature and pressure profile and the

Use of non-contacting electromagnetic inductive method for estimating soil moisture across a landscape

International Nuclear Information System (INIS)

Khakural, B.R.; Robert, P.C.; Hugins, D.R.

1998-01-01

There is a growing interest in real-time estimation of soil moisture for site-specific crop management. Non-contacting electromagnetic inductive (EMI) methods have potentials to provide real-time estimate of soil profile water contents. Soil profile water contents were monitored with a neutron probe at selected sites. A Geonics LTD EM-38 terrain meter was used to record bulk soil electrical conductivity (EC(A)) readings across a soil-landscape in West central Minnesota with variable moisture regimes. The relationships among EC(A), selected soil and landscape properties were examined. Bulk soil electrical conductivity (0-1.0 and 0-0.5 m) was negatively correlated with relative elevation. It was positively correlated with soil profile (1.0 m) clay content and negatively correlated with soil profile coarse fragments (2 mm) and sand content. There was significant linear relationship between ECA (0-1.0 and 0-0.5) and soil profile water storage. Soil water storage estimated from ECA reflected changes in landscape and soil characteristics

The operational methane retrieval algorithm for TROPOMI

Directory of Open Access Journals (Sweden)

H. Hu

2016-11-01

Full Text Available This work presents the operational methane retrieval algorithm for the Sentinel 5 Precursor (S5P satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the column-averaged dry air volume mixing ratio of methane (XCH4, which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOspheric Monitoring Instrument (TROPOMI spectrometer aboard S5P.The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. In addition, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup.We show that the required accuracy and precision of  < 1 % for the XCH4 product are met for clear-sky measurements over land surfaces and after appropriate filtering of difficult scenes. The algorithm is very stable, having a convergence rate of 99 %. The forward model error is less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water do not influence the retrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure and temperature remain below 0.3 % and 2 K, respectively. We further find that, of all instrument calibration errors investigated here, our retrievals are the most sensitive to an error in the instrument spectral response function of the shortwave infrared channel.

Satellite Sounder Observations of Contrasting Tropospheric Moisture Transport Regimes: Saharan Air Layers, Hadley Cells, and Atmospheric Rivers

Energy Technology Data Exchange (ETDEWEB)

Nalli, Nicholas R.; Barnet, Christopher D.; Reale, Tony; Liu, Quanhua; Morris, Vernon R.; Spackman, J. Ryan; Joseph, Everette; Tan, Changyi; Sun, Bomin; Tilley, Frank; Leung, L. Ruby; Wolfe, Daniel

2016-12-01

This paper examines the performance of satellite sounder atmospheric vertical moisture proles (AVMP) under tropospheric conditions encompassing moisture contrasts driven by convection and advection transport mechanisms, specifically Atlantic Ocean Saharan air layers (SALs) and Pacific Ocean moisture conveyer belts (MCBs) commonly referred to as atmospheric rivers (ARs), both of these being mesoscale to synoptic meteorological phenomena within the vicinity of subtropical Hadley subsidence zones. Operational AVMP environmental data records retrieved from the Suomi National Polar-orbiting Partnership (SNPP) NOAA-Unique Combined Atmospheric Processing System (NUCAPS) are collocated with dedicated radiosonde observations (RAOBs) obtained from ocean-based intensive field campaigns; these RAOBs provide uniquely independent correlative truth data not assimilated into numerical weather prediction models for satellite sounder validation over open ocean. Using these marine-based data, we empirically assess the performance of the operational NUCAPS AVMP product for detecting and resolving these tropospheric moisture features over otherwise RAOB-sparse regions.

A comparison of soil moisture relations between standing and ...

African Journals Online (AJOL)

Drainage rates through the profile were established using time domain reflectometry probes while water drainage volumes were assessed using shallow plate lysimeters. Despite slow growth in the unfelled crop during the monitoring period (attributed to a pest infestation), soil moisture depletion remained rapid and ...

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

Science.gov (United States)

A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground condit...

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Directory of Open Access Journals (Sweden)

Yayong Sun

2017-03-01

Full Text Available The Soil Moisture Active Passive (SMAP satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE, unbiased root mean square error (ubRMSE, and the correlation coefficient (R, were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm3·cm−3 and 0.039 cm3·cm−3 based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm.

The relationship between brightness temperature and soil moisture. Selection of frequency range for microwave remote sensing

International Nuclear Information System (INIS)

Rao, K.S.; Chandra, G.; Rao, P.V.N.

1987-01-01

The analysis of brightness temperature data acquired from field and aircraft experiments demonstrates a linear relationship between soil moisture and brightness temperature. However, the analysis of brightness temperature data acquired by the Skylab radiometer demonstrates a non-linear relationship between soil moisture and brightness temperature. In view of the above and also because of recent theoretical developments for the calculation of the dielectric constant and brightness temperature under varying soil moisture profile conditions, an attempt is made to study the theoretical relationship between brightness temperature and soil moisture as a function of frequency. Through the above analysis, the appropriate microwave frequency range for soil moisture studies is recommended

A stochastic cloud model for cloud and ozone retrievals from UV measurements

International Nuclear Information System (INIS)

Efremenko, Dmitry S.; Schüssler, Olena; Doicu, Adrian; Loyola, Diego

2016-01-01

The new generation of satellite instruments provides measurements in and around the Oxygen A-band on a global basis and with a relatively high spatial resolution. These data are commonly used for the determination of cloud properties. A stochastic model and radiative transfer model, previously developed by the authors, is used as the forward model component in retrievals of cloud parameters and ozone total and partial columns. The cloud retrieval algorithm combines local and global optimization routines, and yields a retrieval accuracy of about 1% and a fast computational time. Retrieved parameters are the cloud optical thickness and the cloud-top height. It was found that the use of the independent pixel approximation instead of the stochastic cloud model leads to large errors in the retrieved cloud parameters, as well as, in the retrieved ozone height resolved partial columns. The latter can be reduced by using the stochastic cloud model to compute the optimal value of the regularization parameter in the framework of Tikhonov regularization. - Highlights: • A stochastic radiative transfer model for retrieving clouds/ozone is designed. • Errors of independent pixel approximation (IPA) for O3 total column are small. • The error of IPA for ozone profile retrieval may become large. • The use of stochastic model reduces the error of ozone profile retrieval.

Enhanced Soil Moisture Initialization Using Blended Soil Moisture Product and Regional Optimization of LSM-RTM Coupled Land Data Assimilation System.

Science.gov (United States)

Nair, A. S.; Indu, J.

2017-12-01

Prediction of soil moisture dynamics is high priority research challenge because of the complex land-atmosphere interaction processes. Soil moisture (SM) plays a decisive role in governing water and energy balance of the terrestrial system. An accurate SM estimate is imperative for hydrological and weather prediction models. Though SM estimates are available from microwave remote sensing and land surface model (LSM) simulations, it is affected by uncertainties from several sources during estimation. Past studies have generally focused on land data assimilation (DA) for improving LSM predictions by assimilating soil moisture from single satellite sensor. This approach is limited by the large time gap between two consequent soil moisture observations due to satellite repeat cycle of more than three days at the equator. To overcome this, in the present study, we have performed DA using ensemble products from the soil moisture operational product system (SMOPS) blended soil moisture retrievals from different satellite sensors into Noah LSM. Before the assimilation period, the Noah LSM is initialized by cycling through seven multiple loops from 2008 to 2010 forcing with Global data assimilation system (GDAS) data over the Indian subcontinent. We assimilated SMOPS into Noah LSM for a period of two years from 2010 to 2011 using Ensemble Kalman Filter within NASA's land information system (LIS) framework. Results show that DA has improved Noah LSM prediction with a high correlation of 0.96 and low root mean square difference of 0.0303 m3/m3 (figure 1a). Further, this study has also investigated the notion of assimilating microwave brightness temperature (Tb) as a proxy for SM estimates owing to the close proximity of Tb and SM. Preliminary sensitivity analysis show a strong need for regional parameterization of radiative transfer models (RTMs) to improve Tb simulation. Towards this goal, we have optimized the forward RTM using swarm optimization technique for direct Tb

Multi-target retrieval (MTR): the simultaneous retrieval of pressure, temperature and volume mixing ratio profiles from limb-scanning atmospheric measurements

International Nuclear Information System (INIS)

Dinelli, B.M.; Alpaslan, D.; Carlotti, M.; Magnani, L.; Ridolfi, M.

2004-01-01

In this paper we describe a retrieval approach for the simultaneous determination of the altitude distributions of p, T and VMR of atmospheric constituents from limb-scanning measurements of the atmosphere. This analysis method, named multi-target retrieval (MTR), has been designed and implemented in a computer code aimed at the analysis of MIPAS-ENVISAT observations; however, the concepts implemented in MTR have a general validity and can be extended to the analysis of all type of limb-scanning observations. In order to assess performance and advantages of the proposed approach, MTR has been compared with the sequential analysis system implemented by ESA as the level-2 processor for MIPAS measurements. The comparison has been performed on a common set of target species and spectral intervals. The performed tests have shown that MTR produces results of better quality than a sequential retrieval. However, the simultaneous retrieval of p, T and water VMR has not lead to satisfactory results below the tropopause, because of the high correlation occurring between p and water VMR in the troposphere. We have shown that this problem can be fixed extending the MTR analysis to at least one further target whose spectral features decouple the retrieval of pressure and water VMR. Ozone was found to be a suitable target for this purpose. The advantages of the MTR analysis system in terms of systematic errors have also been discussed

Evaluating humidity recovery efficiency of currently available heat and moisture exchangers: a respiratory system model study

Directory of Open Access Journals (Sweden)

Jeanette Janaina Jaber Lucato

2009-06-01

Full Text Available OBJECTIVES: To evaluate and compare the efficiency of humidification in available heat and moisture exchanger models under conditions of varying tidal volume, respiratory rate, and flow rate. INTRODUCTION: Inspired gases are routinely preconditioned by heat and moisture exchangers to provide a heat and water content similar to that provided normally by the nose and upper airways. The absolute humidity of air retrieved from and returned to the ventilated patient is an important measurable outcome of the heat and moisture exchangers' humidifying performance. METHODS: Eight different heat and moisture exchangers were studied using a respiratory system analog. The system included a heated chamber (acrylic glass, maintained at 37°C, a preserved swine lung, a hygrometer, circuitry and a ventilator. Humidity and temperature levels were measured using eight distinct interposed heat and moisture exchangers given different tidal volumes, respiratory frequencies and flow-rate conditions. Recovery of absolute humidity (%RAH was calculated for each setting. RESULTS: Increasing tidal volumes led to a reduction in %RAH for all heat and moisture exchangers while no significant effect was demonstrated in the context of varying respiratory rate or inspiratory flow. CONCLUSIONS: Our data indicate that heat and moisture exchangers are more efficient when used with low tidal volume ventilation. The roles of flow and respiratory rate were of lesser importance, suggesting that their adjustment has a less significant effect on the performance of heat and moisture exchangers.

New dynamic NNORSY ozone profile climatology

Science.gov (United States)

Kaifel, A. K.; Felder, M.; Declercq, C.; Lambert, J.-C.

2012-01-01

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 structure of different modes of

Study on the behavior of moisture droplets in low pressure steam turbines

International Nuclear Information System (INIS)

Kimura, Y.; Kuramoto, Y.; Yoshida, K.; Etsu, M.

1978-01-01

Low pressure stages of fossil turbines and almost all stages of nuclear and geothermal turbines operate on wet steam. Turbine operating on wet steam have the following two disadvantages: decrease of efficiency and erosion of blades. Decrease of efficiency results from an increase in profile loss caused by water films on the blade surface; loss of steam energy in breaking up the films and accelerating moisture droplets; undercooling and condensation shocks associated with it; velocity difference between water and steam phases and consequent decelerating action of moisture droplets in the rotating blades, etc. Impingement of moisture droplets on the rotating blades also causes quick erosion of the blades. In this paper, the behavior of moisture droplets in wet steam flow is described and the correlation between their behavior and the abovementioned two disadvantages of turbines operating on wet steam is clarified. (author)

Large-area Soil Moisture Surveys Using a Cosmic-ray Rover: Approaches and Results from Australia

Science.gov (United States)

Hawdon, A. A.; McJannet, D. L.; Renzullo, L. J.; Baker, B.; Searle, R.

2017-12-01

Recent improvements in satellite instrumentation has increased the resolution and frequency of soil moisture observations, and this in turn has supported the development of higher resolution land surface process models. Calibration and validation of these products is restricted by the mismatch of scales between remotely sensed and contemporary ground based observations. Although the cosmic ray neutron soil moisture probe can provide estimates soil moisture at a scale useful for the calibration and validation purposes, it is spatially limited to a single, fixed location. This scaling issue has been addressed with the development of mobile soil moisture monitoring systems that utilizes the cosmic ray neutron method, typically referred to as a `rover'. This manuscript describes a project designed to develop approaches for undertaking rover surveys to produce soil moisture estimates at scales comparable to satellite observations and land surface process models. A custom designed, trailer-mounted rover was used to conduct repeat surveys at two scales in the Mallee region of Victoria, Australia. A broad scale survey was conducted at 36 x 36 km covering an area of a standard SMAP pixel and an intensive scale survey was conducted over a 10 x 10 km portion of the broad scale survey, which is at a scale equivalent to that used for national water balance modelling. We will describe the design of the rover, the methods used for converting neutron counts into soil moisture and discuss factors controlling soil moisture variability. We found that the intensive scale rover surveys produced reliable soil moisture estimates at 1 km resolution and the broad scale at 9 km resolution. We conclude that these products are well suited for future analysis of satellite soil moisture retrievals and finer scale soil moisture models.

Software Helps Retrieve Information Relevant to the User

Science.gov (United States)

Mathe, Natalie; Chen, James

2003-01-01

The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.

Bias determination and precision validation of ozone profiles from MIPAS-Envisat retrieved with the IMK-IAA processor

Directory of Open Access Journals (Sweden)

T. Steck

2007-07-01

Full Text Available This paper characterizes vertical ozone profiles retrieved with the IMK-IAA (Institute for Meteorology and Climate Research, Karlsruhe – Instituto de Astrofisica de Andalucia science-oriented processor from high spectral resolution data (until March 2004 measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS aboard the environmental satellite Envisat. Bias determination and precision validation is performed on the basis of correlative measurements by ground-based lidars, Fourier transform infrared spectrometers, and microwave radiometers as well as balloon-borne ozonesondes, the balloon-borne version of MIPAS, and two satellite instruments (Halogen Occultation Experiment and Polar Ozone and Aerosol Measurement III. Percentage mean differences between MIPAS and the comparison instruments for stratospheric ozone are generally within ±10%. The precision in this altitude region is estimated at values between 5 and 10% which gives an accuracy of 15 to 20%. Below 18 km, the spread of the percentage mean differences is larger and the precision degrades to values of more than 20% depending on altitude and latitude. The main reason for the degraded precision at low altitudes is attributed to undetected thin clouds which affect MIPAS retrievals, and to the influence of uncertainties in the water vapor concentration.

Relationship between Depth of Soil Moisture Assessment and Turgidity of Coffee Plant in Selected Agroclimates

Directory of Open Access Journals (Sweden)

Rudy Erwiyono

2008-05-01

Full Text Available Observation on the relationship between the depth of soil moisture assessment and turgidity of coffee plant has been carried out at 3 different agroclimates by survey method, i.e. Andungsari experimental station (Andosol soil type, >1.000 m asl. high, and rainfall type of C, Sumberasin experimental station (yellowish-red Mediterranean soil type, 450-500 m asl. high, and rainfall type of C, and Kaliwining experimental station (low humic glei soil type, 45 m asl. high, and rainfall type of D in order to assess the depth of soil moisture through soil profile influencing turgidity of coffee plants at three different agroclimates. The method of assessment is by fitting the relationship between the depth of soil moisture assessment and turgidity of coffee plant and their determination coefficients through the period of dry season up to early rainy season. Plant turgidity is evaluated from its relative water contents of the leaves sampled periodically at the same time as observation of soil moisture content. Plant turgidity is affected by soil moisture condition up to a certain depth which looks to be typical of the agroclimates. At Andungsari experimental station (high land it is necessary to assess soil moisture through the soil profile up to 100 cm deep in order to evaluate water stress of the plants; inversely, at Kaliwining experimental station in order to evaluate water stress of the plants it is just justified from the soil moisture condition of the soil surface layers (0-25 cm. Whereas at Sumberasin experimental station water stress of the plants could be predicted from soil moisture assessment of the surface layer depth or through the deeper layers of the soil profile either. Andungsari-1 and Lini S-795 clones are more resistant to drought than Kartika-2 clone at Andisol soil type with C rainfall type and elevation > 1000 m asl. BP-308 clone showed its response as relatively resistant to drought at yellowish red Mediterranean soil type with C

Eight-component retrievals from ground-based MAX-DOAS observations

Directory of Open Access Journals (Sweden)

H. Irie

2011-06-01

Full Text Available We attempt for the first time to retrieve lower-tropospheric vertical profile information for 8 quantities from ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS observations. The components retrieved are the aerosol extinction coefficients at two wavelengths, 357 and 476 nm, and NO₂, HCHO, CHOCHO, H₂O, SO₂, and O₃ volume mixing ratios. A Japanese MAX-DOAS profile retrieval algorithm, version 1 (JM1, is applied to observations performed at Cabauw, the Netherlands (51.97° N, 4.93° E, in June–July 2009 during the Cabauw Intercomparison campaign of Nitrogen Dioxide measuring Instruments (CINDI. Of the retrieved profiles, we focus here on the lowest-layer data (mean values at altitudes 0–1 km, where the sensitivity is usually highest owing to the longest light path. In support of the capability of the multi-component retrievals, we find reasonable overall agreement with independent data sets, including a regional chemical transport model (CHIMERE and in situ observations performed near the surface (2–3 m and at the 200-m height level of the tall tower in Cabauw. Plumes of enhanced HCHO and SO₂ were likely affected by biogenic and ship emissions, respectively, and an improvement in their emission strengths is suggested for better agreement between CHIMERE simulations and MAX-DOAS observations. Analysis of air mass factors indicates that the horizontal spatial representativeness of MAX-DOAS observations is about 3–15 km (depending mainly on aerosol extinction, comparable to or better than the spatial resolution of current UV-visible satellite observations and model calculations. These demonstrate that MAX-DOAS provides multi-component data useful for the evaluation of satellite observations and model calculations and can play an important role in bridging different data sets having different spatial resolutions.

Two-dimensional radiative transfer for the retrieval of limb emission measurements in the martian atmosphere

Science.gov (United States)

Kleinböhl, Armin; Friedson, A. James; Schofield, John T.

2017-01-01

The remote sounding of infrared emission from planetary atmospheres using limb-viewing geometry is a powerful technique for deriving vertical profiles of structure and composition on a global scale. Compared with nadir viewing, limb geometry provides enhanced vertical resolution and greater sensitivity to atmospheric constituents. However, standard limb profile retrieval techniques assume spherical symmetry and are vulnerable to biases produced by horizontal gradients in atmospheric parameters. We present a scheme for the correction of horizontal gradients in profile retrievals from limb observations of the martian atmosphere. It characterizes horizontal gradients in temperature, pressure, and aerosol extinction along the line-of-sight of a limb view through neighboring measurements, and represents these gradients by means of two-dimensional radiative transfer in the forward model of the retrieval. The scheme is applied to limb emission measurements from the Mars Climate Sounder instrument on Mars Reconnaissance Orbiter. Retrieval simulations using data from numerical models indicate that biases of up to 10 K in the winter polar region, obtained with standard retrievals using spherical symmetry, are reduced to about 2 K in most locations by the retrieval with two-dimensional radiative transfer. Retrievals from Mars atmospheric measurements suggest that the two-dimensional radiative transfer greatly reduces biases in temperature and aerosol opacity caused by observational geometry, predominantly in the polar winter regions.

The Next-generation Berkeley High Resolution NO2 (BEHR NO2) Retrieval: Design and Preliminary Emissions Constraints

Science.gov (United States)

Laughner, J.; Cohen, R. C.

2017-12-01

Recent work has identified a number of assumptions made in NO2 retrievals that lead to biases in the retrieved NO2 column density. These include the treatment of the surface as an isotropic reflector, the absence of lightning NO2 in high resolution a priori profiles, and the use of monthly averaged a priori profiles. We present a new release of the Berkeley High Resolution (BEHR) OMI NO2 retrieval based on the new NASA Standard Product (version 3) that addresses these assumptions by: accounting for surface anisotropy by using a BRDF albedo product, using an updated method of regridding NO2 data, and revised NO2 a priori profiles that better account for lightning NO2 and daily variation in the profile shape. We quantify the effect these changes have on the retrieved NO2 column densities and the resultant impact these updates have on constraints of urban NOx emissions for select cities throughout the United States.

Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture

Science.gov (United States)

Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.

2016-06-01

Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data

Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation

Directory of Open Access Journals (Sweden)

M. Zribi

2011-01-01

Full Text Available The present paper proposes a method for the evaluation of soil evaporation, using soil moisture estimations based on radar satellite measurements. We present firstly an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images, over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events, over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This analysis is based on a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness, during the 2008–2009 vegetation cycle. Finally, a semi-empirical approach is proposed in order to relate surface moisture to the difference between soil evaporation and the climate demand, as defined by the potential evaporation. Mapping of the soil evaporation is proposed.

HCl and ClO in activated Arctic air; first retrieved vertical profiles from TELIS submillimetre limb spectra

Directory of Open Access Journals (Sweden)

A. de Lange

2012-02-01

Full Text Available The first profile retrieval results of the Terahertz and submillimeter Limb Sounder (TELIS balloon instrument are presented. The spectra are recorded during a 13-h balloon flight on 24 January 2010 from Kiruna, Sweden. The TELIS instrument was mounted on the MIPAS-B2 gondola and shared this platform with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS and the mini-Differential Optical Absorption Spectroscopy (mini-DOAS instruments. The flight took place within the Arctic vortex at an altitude of ≈34 km in chlorine activated air, and both active (ClO and inactive chlorine (HCl were measured over an altitude range of respectively ≈16–32 km and ≈10–32 km. In this altitude range, the increase of ClO concentration levels during sunrise has been recorded with a temporal resolution of one minute. During the daytime equilibrium, a maximum ClO level of 2.1 ± 0.3 ppbv has been observed at an altitude of 23.5 km. This equilibrium profile is validated against the ClO profile by the satellite instrument Microwave Limb Sounder (MLS aboard EOS Aura. HCl profiles have been determined from two different isotopes – H³⁵Cl and H³⁷Cl – and are also validated against MLS. The precision of all profiles is well below 0.01 ppbv and the overall accuracy is therefore governed by systematic effects. The total uncertainty of these effects is estimated to be maximal 0.3 ppbv for ClO around its peak value at 23.5 km during the daytime equilibrium, and for HCl it ranges from 0.05 to 0.4 ppbv, depending on altitude. In both cases the main uncertainty stems from a largely unknown non-linear response in the detector.

Final report for the cryogenic retrieval demonstration

International Nuclear Information System (INIS)

Valentich, D.J.; Yokuda, E.L.

1992-09-01

This report documents a demonstration of a proposed buried transuranic waste retrieval concept that uses cryogenic ground freezing and remote excavation. At the Idaho National Engineering Laboratory (INEL), there are over 8 million ft 3 of intermingled soil and transuranic (TRU) wastes in shallow land burial, and retrieval of the material is one of the options being considered by the Buried Waste Integrated Demonstration for the Environmental Restoration program. Cryogenically freezing contaminated soil and buried waste has been proposed as a way to greatly reduce or eliminate the climate the threat of contamination spread during retrieval activities. In support of this idea, a demonstration of an innovative ground freezing and retrieval technology was performed at the INEL. This initial demonstration was held near the Radioactive Waste Management Complex at a ''cold test pit'' that was built in 1988 as a test bed for the demonstration of retrieval contamination control technologies. This pit is not contaminated with any radioactive or hazardous wastes. Barrels and boxes filled with metals, plastics, tools, paper, cloth, etc. configured in the same manner as expected in contaminated pits and trenches are buried at the cold test pit. After design, fabrication, and shop testing, Sonsub mobilized to the field in early July 1992 to perform the field demonstration. It was planned to freeze and extract four pits, each 9 x 9 x 10 ft. Each pit represented a different configuration of buried waste (stacked boxes, stacked barrels, random dumped barrels and boxes, and random dumped barrels). Sonsub's proposed technology consisted of driving a series of freeze pipes into the soil and waste, using liquid nitrogen to freeze the mass, and extracting the soil and debris using a series of remote operated, bridge crane mounted tools. In conjunction with the freezing and removal activities, temperature and moisture measurements, and air monitoring were performed

Final report for the cryogenic retrieval demonstration

Energy Technology Data Exchange (ETDEWEB)

Valentich, D.J.; Yokuda, E.L.

1992-09-01

This report documents a demonstration of a proposed buried transuranic waste retrieval concept that uses cryogenic ground freezing and remote excavation. At the Idaho National Engineering Laboratory (INEL), there are over 8 million ft{sup 3} of intermingled soil and transuranic (TRU) wastes in shallow land burial, and retrieval of the material is one of the options being considered by the Buried Waste Integrated Demonstration for the Environmental Restoration program. Cryogenically freezing contaminated soil and buried waste has been proposed as a way to greatly reduce or eliminate the climate the threat of contamination spread during retrieval activities. In support of this idea, a demonstration of an innovative ground freezing and retrieval technology was performed at the INEL. This initial demonstration was held near the Radioactive Waste Management Complex at a ``cold test pit`` that was built in 1988 as a test bed for the demonstration of retrieval contamination control technologies. This pit is not contaminated with any radioactive or hazardous wastes. Barrels and boxes filled with metals, plastics, tools, paper, cloth, etc. configured in the same manner as expected in contaminated pits and trenches are buried at the cold test pit. After design, fabrication, and shop testing, Sonsub mobilized to the field in early July 1992 to perform the field demonstration. It was planned to freeze and extract four pits, each 9 {times} 9 {times} 10 ft. Each pit represented a different configuration of buried waste (stacked boxes, stacked barrels, random dumped barrels and boxes, and random dumped barrels). Sonsub`s proposed technology consisted of driving a series of freeze pipes into the soil and waste, using liquid nitrogen to freeze the mass, and extracting the soil and debris using a series of remote operated, bridge crane mounted tools. In conjunction with the freezing and removal activities, temperature and moisture measurements, and air monitoring were performed.

Final report for the cryogenic retrieval demonstration

Energy Technology Data Exchange (ETDEWEB)

Valentich, D.J.; Yokuda, E.L.

1992-09-01

This report documents a demonstration of a proposed buried transuranic waste retrieval concept that uses cryogenic ground freezing and remote excavation. At the Idaho National Engineering Laboratory (INEL), there are over 8 million ft[sup 3] of intermingled soil and transuranic (TRU) wastes in shallow land burial, and retrieval of the material is one of the options being considered by the Buried Waste Integrated Demonstration for the Environmental Restoration program. Cryogenically freezing contaminated soil and buried waste has been proposed as a way to greatly reduce or eliminate the climate the threat of contamination spread during retrieval activities. In support of this idea, a demonstration of an innovative ground freezing and retrieval technology was performed at the INEL. This initial demonstration was held near the Radioactive Waste Management Complex at a cold test pit'' that was built in 1988 as a test bed for the demonstration of retrieval contamination control technologies. This pit is not contaminated with any radioactive or hazardous wastes. Barrels and boxes filled with metals, plastics, tools, paper, cloth, etc. configured in the same manner as expected in contaminated pits and trenches are buried at the cold test pit. After design, fabrication, and shop testing, Sonsub mobilized to the field in early July 1992 to perform the field demonstration. It was planned to freeze and extract four pits, each 9 [times] 9 [times] 10 ft. Each pit represented a different configuration of buried waste (stacked boxes, stacked barrels, random dumped barrels and boxes, and random dumped barrels). Sonsub's proposed technology consisted of driving a series of freeze pipes into the soil and waste, using liquid nitrogen to freeze the mass, and extracting the soil and debris using a series of remote operated, bridge crane mounted tools. In conjunction with the freezing and removal activities, temperature and moisture measurements, and air monitoring were

(abstract) Using an Inversion Algorithm to Retrieve Parameters and Monitor Changes over Forested Areas from SAR Data

Science.gov (United States)

Moghaddam, Mahta

1995-01-01

In this work, the application of an inversion algorithm based on a nonlinear opimization technique to retrieve forest parameters from multifrequency polarimetric SAR data is discussed. The approach discussed here allows for retrieving and monitoring changes in forest parameters in a quantative and systematic fashion using SAR data. The parameters to be inverted directly from the data are the electromagnetic scattering properties of the forest components such as their dielectric constants and size characteristics. Once these are known, attributes such as canopy moisture content can be obtained, which are useful in the ecosystem models.

Moisture conditions in buildings

DEFF Research Database (Denmark)

Rode, Carsten

2012-01-01

Growth of mould requires the presence of moisture at a certain high level. In a heated indoor environment such moisture levels occur only if there is a reason for the moisture supply. Such moisture can come from the use of the building, because of malfunctioning constructions, or it can be the re......Growth of mould requires the presence of moisture at a certain high level. In a heated indoor environment such moisture levels occur only if there is a reason for the moisture supply. Such moisture can come from the use of the building, because of malfunctioning constructions, or it can...

The effect of cloud liquid water on tropospheric temperature retrievals from microwave measurements

Directory of Open Access Journals (Sweden)

L. Bernet

2017-11-01

Full Text Available Microwave radiometry is a suitable technique to measure atmospheric temperature profiles with high temporal resolution during clear sky and cloudy conditions. In this study, we included cloud models in the inversion algorithm of the microwave radiometer TEMPERA (TEMPErature RAdiometer to determine the effect of cloud liquid water on the temperature retrievals. The cloud models were built based on measurements of cloud base altitude and integrated liquid water (ILW, all performed at the aerological station (MeteoSwiss in Payerne (Switzerland. Cloud base altitudes were detected using ceilometer measurements while the ILW was measured by a HATPRO (Humidity And Temperature PROfiler radiometer. To assess the quality of the TEMPERA retrieval when clouds were considered, the resulting temperature profiles were compared to 2 years of radiosonde measurements. The TEMPERA instrument measures radiation at 12 channels in the frequency range from 51 to 57 GHz, corresponding to the left wing of the oxygen emission line complex. When the full spectral information with all the 12 frequency channels was used, we found a marked improvement in the temperature retrievals after including a cloud model. The chosen cloud model influenced the resulting temperature profile, especially for high clouds and clouds with a large amount of liquid water. Using all 12 channels, however, presented large deviations between different cases, suggesting that additional uncertainties exist in the lower, more transparent channels. Using less spectral information with the higher, more opaque channels only also improved the temperature profiles when clouds where included, but the influence of the chosen cloud model was less important. We conclude that tropospheric temperature profiles can be optimized by considering clouds in the microwave retrieval, and that the choice of the cloud model has a direct impact on the resulting temperature profile.

Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

Directory of Open Access Journals (Sweden)

S. Juglea

2010-08-01

Full Text Available In the framework of Soil Moisture and Ocean Salinity (SMOS Calibration/Validation (Cal/Val activities, this study addresses the use of the PERSIANN-CCS¹database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km². The study focuses on the Valencia Anchor Station (VAS experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe.

A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km² requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66% but the precipitation occurrences are in general retrieved (about 67%.

To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT model – ISBA (Interactions between Soil Biosphere Atmosphere is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced

A statistical inference approach for the retrieval of the atmospheric ozone profile from simulated satellite measurements of solar backscattered ultraviolet radiation

Science.gov (United States)

Bonavito, N. L.; Gordon, C. L.; Inguva, R.; Serafino, G. N.; Barnes, R. A.

1994-01-01

NASA's Mission to Planet Earth (MTPE) will address important interdisciplinary and environmental issues such as global warming, ozone depletion, deforestation, acid rain, and the like with its long term satellite observations of the Earth and with its comprehensive Data and Information System. Extensive sets of satellite observations supporting MTPE will be provided by the Earth Observing System (EOS), while more specific process related observations will be provided by smaller Earth Probes. MTPE will use data from ground and airborne scientific investigations to supplement and validate the global observations obtained from satellite imagery, while the EOS satellites will support interdisciplinary research and model development. This is important for understanding the processes that control the global environment and for improving the prediction of events. In this paper we illustrate the potential for powerful artificial intelligence (AI) techniques when used in the analysis of the formidable problems that exist in the NASA Earth Science programs and of those to be encountered in the future MTPE and EOS programs. These techniques, based on the logical and probabilistic reasoning aspects of plausible inference, strongly emphasize the synergetic relation between data and information. As such, they are ideally suited for the analysis of the massive data streams to be provided by both MTPE and EOS. To demonstrate this, we address both the satellite imagery and model enhancement issues for the problem of ozone profile retrieval through a method based on plausible scientific inferencing. Since in the retrieval problem, the atmospheric ozone profile that is consistent with a given set of measured radiances may not be unique, an optimum statistical method is used to estimate a 'best' profile solution from the radiances and from additional a priori information.

Retrieval of subvisual cirrus cloud optical thickness from limb-scatter measurements

Directory of Open Access Journals (Sweden)

J. T. Wiensz

2013-01-01

Full Text Available We present a technique for estimating the optical thickness of subvisual cirrus clouds detected by OSIRIS (Optical Spectrograph and Infrared Imaging System, a limb-viewing satellite instrument that measures scattered radiances from the UV to the near-IR. The measurement set is composed of a ratio of limb radiance profiles at two wavelengths that indicates the presence of cloud-scattering regions. Cross-sections and phase functions from an in situ database are used to simulate scattering by cloud-particles. With appropriate configurations discussed in this paper, the SASKTRAN successive-orders of scatter radiative transfer model is able to simulate accurately the in-cloud radiances from OSIRIS. Configured in this way, the model is used with a multiplicative algebraic reconstruction technique (MART to retrieve the cloud extinction profile for an assumed effective cloud particle size. The sensitivity of these retrievals to key auxiliary model parameters is shown, and it is shown that the retrieved extinction profile, for an assumed effective cloud particle size, models well the measured in-cloud radiances from OSIRIS. The greatest sensitivity of the retrieved optical thickness is to the effective cloud particle size. Since OSIRIS has an 11-yr record of subvisual cirrus cloud detections, the work described in this manuscript provides a very useful method for providing a long-term global record of the properties of these clouds.

Towards Improving Satellite Tropospheric NO2 Retrieval Products: Impacts of the spatial resolution and lighting NOx production from the a priori chemical transport model

Science.gov (United States)

Smeltzer, C. D.; Wang, Y.; Zhao, C.; Boersma, F.

2009-12-01

Polar orbiting satellite retrievals of tropospheric nitrogen dioxide (NO2) columns are important to a variety of scientific applications. These NO2 retrievals rely on a priori profiles from chemical transport models and radiative transfer models to derive the vertical columns (VCs) from slant columns measurements. In this work, we compare the retrieval results using a priori profiles from a global model (TM4) and a higher resolution regional model (REAM) at the OMI overpass hour of 1330 local time, implementing the Dutch OMI NO2 (DOMINO) retrieval. We also compare the retrieval results using a priori profiles from REAM model simulations with and without lightning NOx (NO + NO2) production. A priori model resolution and lightning NOx production are both found to have large impact on satellite retrievals by altering the satellite sensitivity to a particular observation by shifting the NO2 vertical distribution interpreted by the radiation model. The retrieved tropospheric NO2 VCs may increase by 25-100% in urban regions and be reduced by 50% in rural regions if the a priori profiles from REAM simulations are used during the retrievals instead of the profiles from TM4 simulations. The a priori profiles with lightning NOx may result in a 25-50% reduction of the retrieved tropospheric NO2 VCs compared to the a priori profiles without lightning. As first priority, a priori vertical NO2 profiles from a chemical transport model with a high resolution, which can better simulate urban-rural NO2 gradients in the boundary layer and make use of observation-based parameterizations of lightning NOx production, should be first implemented to obtain more accurate NO2 retrievals over the United States, where NOx source regions are spatially separated and lightning NOx production is significant. Then as consequence of a priori NO2 profile variabilities resulting from lightning and model resolution dynamics, geostationary satellite, daylight observations would further promote the next

The Influence of Aerosol Hygroscopicity on Retrieving the Aerosol Extincting Coefficient from MPL Data

Science.gov (United States)

Zhao, G.; Zhao, C.

2016-12-01

Micro-pulse Lidar (MPL) measurements have been widely used to profile the ambient aerosol extincting coefficient(). Lidar Ratio (LR) ,which highly depends on the particle number size distribution (PNSD) and aerosol hygroscopicity, is the most important factor to retrieve the profile. A constant AOD constrained LR is usually used in current algorithms, which would lead to large bias when the relative humidity (RH) in the mixed layer is high. In this research, the influences of PNSD, aerosol hygroscopicity and RH profiles on the vertical variation of LR were investigated based on the datasets from field measurements in the North China Plain (NCP). Results show that LR can have an enhancement factor of more than 120% when the RH reaches to 92%. A new algorithm of retrieving the profile is proposed based on the variation of LR due to aerosol hygroscopicity. The magnitude and vertical structures of retrieved using this method can be significantly different to that of the fiexed LR method. The relative difference can reach up to 40% when the RH in the mixed layer is higher than 90% . Sensitivity studies show that RH profile and PNSD affect most on the retrieved by fiexed LR method. In view of this, a scheme of LR enhancement factor by RH is proposed in the NCP. The relative differnce of the calculated between using this scheme and the new algorithm with the variable LR can be less than 10%.

Constraining the Structure of Hot Jupiter Atmospheres Using a Hybrid Version of the NEMESIS Retrieval Algorithm

Science.gov (United States)

Badhan, Mahmuda A.; Mandell, Avi M.; Hesman, Brigette; Nixon, Conor; Deming, Drake; Irwin, Patrick; Barstow, Joanna; Garland, Ryan

2015-11-01

Understanding the formation environments and evolution scenarios of planets in nearby planetary systems requires robust measures for constraining their atmospheric physical properties. Here we have utilized a combination of two different parameter retrieval approaches, Optimal Estimation and Markov Chain Monte Carlo, as part of the well-validated NEMESIS atmospheric retrieval code, to infer a range of temperature profiles and molecular abundances of H2O, CO2, CH4 and CO from available dayside thermal emission observations of several hot-Jupiter candidates. In order to keep the number of parameters low and henceforth retrieve more plausible profile shapes, we have used a parametrized form of the temperature profile based upon an analytic radiative equilibrium derivation in Guillot et al. 2010 (Line et al. 2012, 2014). We show retrieval results on published spectroscopic and photometric data from both the Hubble Space Telescope and Spitzer missions, and compare them with simulations from the upcoming JWST mission. In addition, since NEMESIS utilizes correlated distribution of absorption coefficients (k-distribution) amongst atmospheric layers to compute these models, updates to spectroscopic databases can impact retrievals quite significantly for such high-temperature atmospheres. As high-temperature line databases are continually being improved, we also compare retrievals between old and newer databases.

Understanding natural moisturizing mechanisms: implications for moisturizer technology.

Science.gov (United States)

Chandar, Prem; Nole, Greg; Johnson, Anthony W

2009-07-01

Dry skin and moisturization are important topics because they impact the lives of many individuals. For most individuals, dry skin is not a notable concern and can be adequately managed with current moisturizing products. However, dry skin can affect the quality of life of some individuals because of the challenges of either harsh environmental conditions or impaired stratum corneum (SC) dry skin protection processes resulting from various common skin diseases. Dry skin protection processes of the SC, such as the development of natural moisturizing factor (NMF), are complex, carefully balanced, and easily perturbed. We discuss the importance of the filaggrin-NMF system and the composition of NMF in both healthy and dry skin, and also reveal new insights that suggest the properties required for a new generation of moisturizing technologies.

Evaluation of Assimilated SMOS Soil Moisture Data for US Cropland Soil Moisture Monitoring

Science.gov (United States)

Yang, Zhengwei; Sherstha, Ranjay; Crow, Wade; Bolten, John; Mladenova, Iva; Yu, Genong; Di, Liping

2016-01-01

Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS)Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA)National Agricultural Statistics Service (NASS) survey based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASSs survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

Potential for wind extraction from 4D-Var assimilation of aerosols and moisture

Science.gov (United States)

Zaplotnik, Žiga; Žagar, Nedjeljka

2017-04-01

We discuss the potential of the four-dimensional variational data assimilation (4D-Var) to retrieve the unobserved wind field from observations of atmospheric tracers and the mass field through internal model dynamics and the multivariate relationships in the background-error term for 4D-Var. The presence of non-linear moist dynamics makes the wind retrieval from tracers very difficult. On the other hand, it has been shown that moisture observations strongly influence both tropical and mid-latitude wind field in 4D-Var. We present an intermediate complexity model that describes nonlinear interactions between the wind, temperature, aerosols and moisture including their sinks and sources in the framework of the so-called first baroclinic mode atmosphere envisaged by A. Gill. Aerosol physical processes, which are included in the model, are the non-linear advection, diffusion and sources and sinks that exist as dry and wet deposition and diffusion. Precipitation is parametrized according to the Betts-Miller scheme. The control vector for 4D-Var includes aerosols, moisture and the three dynamical variables. The former is analysed univariately whereas wind field and mass field are analysed in a multivariate fashion taking into account quasi-geostrophic and unbalanced dynamics. The OSSE type of studies are performed for the tropical region to assess the ability of 4D-Var to extract wind-field information from the time series of observations of tracers as a function of the flow nonlinearity, the observations density and the length of the assimilation window (12 hours and 24 hours), in dry and moist environment. Results show that the 4D-Var assimilation of aerosols and temperature data is beneficial for the wind analysis with analysis errors strongly dependent on the moist processes and reliable background-error covariances.

A Comparison of the Red Green Blue (RGB) Air Mass Imagery and Hyperspectral Infrared Retrieved Profiles and NOAA G-IV Dropsondes

Science.gov (United States)

Berndt, Emily; Folmer, Michael; Dunion, Jason

2014-01-01

RGB air mass imagery is derived from multiple channels or paired channel differences. The combination of channels and channel differences means the resulting imagery does not represent a quantity or physical parameter such as brightness temperature in conventional single channel imagery. Without a specific quantity to reference, forecasters are often confused as to what RGB products represent. Hyperspectral infrared retrieved profiles and NOAA G-IV dropsondes provide insight about the vertical structure of the air mass represented on the RGB air mass imagery and are a first step to validating the imagery.

Comparison of inverse Laplace and numerical inversion methods for obtaining z-depth profiles of diffraction data

International Nuclear Information System (INIS)

Xiaojing Zhu; Predecki, P.; Ballard, B.

1995-01-01

Two different inversion methods, the inverse Laplace method and the linear constrained numerical method, for retrieving the z-profiles of diffraction data from experimentally obtained i-profiles were compared using tests with a known function as the original z-profile. Two different real data situations were simulated to determine the effects of specimen thickness and missing τ-profile data at small τ-values on the retrieved z-profiles. The results indicate that although both methods are able to retrieve the z-profiles in the bulk specimens satisfactorily, the numerical method can be used for thin film samples as well. Missing τ-profile data at small τ values causes error in the retrieved z-profiles with both methods, particularly when the trend of the τ-profile at small τ is significantly changed because of the missing data. 6 refs., 3 figs

On the assessment of expertise profiles

NARCIS (Netherlands)

Berendsen, R.; Rijke, M. de; Balog, K.; Bogers, T.; Bosch, A.P.J. van den

2013-01-01

Expertise retrieval has attracted significant interest in the field of information retrieval. Expert finding has been studied extensively, with less attention going to the complementary task of expert profiling, that is, automatically identifying topics about which a person is knowledgeable. We

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

Directory of Open Access Journals (Sweden)

Sat Kumar Tomer

2016-12-01

Full Text Available Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR and passive microwave is presented. The MAPSM algorithm—Merge Active and Passive microwave Soil Moisture—uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS satellite (3 days temporal resolution and 40 km nominal spatial resolution. Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution. The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m3/m3 and 0.069 m3/m3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

Automated information retrieval using CLIPS

Science.gov (United States)

Raines, Rodney Doyle, III; Beug, James Lewis

1991-01-01

Expert systems have considerable potential to assist computer users in managing the large volume of information available to them. One possible use of an expert system is to model the information retrieval interests of a human user and then make recommendations to the user as to articles of interest. At Cal Poly, a prototype expert system written in the C Language Integrated Production System (CLIPS) serves as an Automated Information Retrieval System (AIRS). AIRS monitors a user's reading preferences, develops a profile of the user, and then evaluates items returned from the information base. When prompted by the user, AIRS returns a list of items of interest to the user. In order to minimize the impact on system resources, AIRS is designed to run in the background during periods of light system use.

Major Upgrades to the AIRS Version-6 Water Vapor Profile Methodology

Science.gov (United States)

Susskind, Joel; Blaisdell, John; Iredell, Lena

2015-01-01

This research is a continuation of part of what was shown at the last AIRS Science Team Meeting and the AIRS 2015 NetMeeting. AIRS Version 6 was finalized in late 2012 and is now operational. Version 6 contained many significant improvements in retrieval methodology compared to Version 5. Version 6 retrieval methodology used for the water vapor profile q(p) and ozone profile O3(p) retrievals is basically unchanged from Version 5, or even from Version 4. Subsequent research has made significant improvements in both water vapor and O3 profiles compared to Version 6.

Further Studies of Forest Structure Parameter Retrievals Using the Echidna® Ground-Based Lidar

Science.gov (United States)

Strahler, A. H.; Yao, T.; Zhao, F.; Yang, X.; Schaaf, C.; Wang, Z.; Li, Z.; Woodcock, C. E.; Culvenor, D.; Jupp, D.; Newnham, G.; Lovell, J.

2012-12-01

Ongoing work with the Echidna® Validation Instrument (EVI), a full-waveform, ground-based scanning lidar (1064 nm) developed by Australia's CSIRO and deployed by Boston University in California conifers (2008) and New England hardwood and softwood (conifer) stands (2007, 2009, 2010), confirms the importance of slope correction in forest structural parameter retrieval; detects growth and disturbance over periods of 2-3 years; provides a new way to measure the between-crown clumping factor in leaf area index retrieval using lidar range; and retrieves foliage profiles with more lower-canopy detail than a large-footprint aircraft scanner (LVIS), while simulating LVIS foliage profiles accurately from a nadir viewpoint using a 3-D point cloud. Slope correction is important for accurate retrieval of forest canopy structural parameters, such as mean diameter at breast height (DBH), stem count density, basal area, and above-ground biomass. Topographic slope can induce errors in parameter retrievals because the horizontal plane of the instrument scan, which is used to identify, measure, and count tree trunks, will intersect trunks below breast height in the uphill direction and above breast height in the downhill direction. A test of three methods at southern Sierra Nevada conifer sites improved the range of correlations of these EVI-retrieved parameters with field measurements from 0.53-0.68 to 0.85-0.93 for the best method. EVI scans can detect change, including both growth and disturbance, in periods of two to three years. We revisited three New England forest sites scanned in 2007-2009 or 2007-2010. A shelterwood stand at the Howland Experimental Forest, Howland, Maine, showed increased mean DBH, above-ground biomass and leaf area index between 2007 and 2009. Two stands at the Harvard Forest, Petersham, Massachusetts, suffered reduced leaf area index and reduced stem count density as the result of an ice storm that damaged the stands. At one stand, broken tops were

Validation of soil moisture ocean salinity (SMOS) satellite soil moisture products

Science.gov (United States)

The surface soil moisture state controls the partitioning of precipitation into infiltration and runoff. High-resolution observations of soil moisture will lead to improved flood forecasts, especially for intermediate to large watersheds where most flood damage occurs. Soil moisture is also key in d...

[The stratification of moisture content and its dynamics in co-composting of sewage sludge and pig manure].

Science.gov (United States)

Luo, Wei; Chen, Tong-bin; Gao, Ding; Zheng, Yu-qi; Zheng, Guo-di

2004-03-01

The experiment of co-composting of sewage sludge and pig manure was studied. The moisture contents were 50.82%-60.87% at the stage of temperature rising and 38.7%-52.17% at the stage of thermophilic fermentation, and the stratification of moisture content were not obvious for both stages because the door, the internal wall and the depth of the composting bay had little effect on the stratification. At the stage of cooling, the moisture content was 24.54%-49.39%, and the stratification of moisture content was remarkable as the door, the internal wall and the depth of the composting bay had great influence on it. At the stage of maturity, the moisture content was 19.18%-49.34%, and the stratification of moisture weakened, for which the door and the internal wall were mainly responsible. At the different composting stage, the degree of difference of moisture content on the profiles of the pile was of the order: maturity stage > cooling stage > thermophilic stage = temperature rising stage, and the moisture content in the pile was as follows: the lower > the middle > the upper. The relation between moisture content and composting time meeted with two-order kinetics equation.

Evolutionary Computing Methods for Spectral Retrieval

Science.gov (United States)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

MoisturEC: a new R program for moisture content estimation from electrical conductivity data

Science.gov (United States)

Terry, Neil; Day-Lewis, Frederick D.; Werkema, Dale D.; Lane, John W.

2018-01-01

Noninvasive geophysical estimation of soil moisture has potential to improve understanding of flow in the unsaturated zone for problems involving agricultural management, aquifer recharge, and optimization of landfill design and operations. In principle, several geophysical techniques (e.g., electrical resistivity, electromagnetic induction, and nuclear magnetic resonance) offer insight into soil moisture, but data‐analysis tools are needed to “translate” geophysical results into estimates of soil moisture, consistent with (1) the uncertainty of this translation and (2) direct measurements of moisture. Although geostatistical frameworks exist for this purpose, straightforward and user‐friendly tools are required to fully capitalize on the potential of geophysical information for soil‐moisture estimation. Here, we present MoisturEC, a simple R program with a graphical user interface to convert measurements or images of electrical conductivity (EC) to soil moisture. Input includes EC values, point moisture estimates, and definition of either Archie parameters (based on experimental or literature values) or empirical data of moisture vs. EC. The program produces two‐ and three‐dimensional images of moisture based on available EC and direct measurements of moisture, interpolating between measurement locations using a Tikhonov regularization approach.

Encoding, training and retrieval in ferroelectric tunnel junctions

Science.gov (United States)

Xu, Hanni; Xia, Yidong; Xu, Bo; Yin, Jiang; Yuan, Guoliang; Liu, Zhiguo

2016-05-01

Ferroelectric tunnel junctions (FTJs) are quantum nanostructures that have great potential in the hardware basis for future neuromorphic applications. Among recently proposed possibilities, the artificial cognition has high hopes, where encoding, training, memory solidification and retrieval constitute a whole chain that is inseparable. However, it is yet envisioned but experimentally unconfirmed. The poor retention or short-term store of tunneling electroresistance, in particular the intermediate states, is still a key challenge in FTJs. Here we report the encoding, training and retrieval in BaTiO3 FTJs, emulating the key features of information processing in terms of cognitive neuroscience. This is implemented and exemplified through processing characters. Using training inputs that are validated by the evolution of both barrier profile and domain configuration, accurate recalling of encoded characters in the retrieval stage is demonstrated.

Evaluation of a Soil Moisture Data Assimilation System Over the Conterminous United States

Science.gov (United States)

Bolten, J. D.; Crow, W. T.; Zhan, X.; Reynolds, C. A.; Jackson, T. J.

2008-12-01

A data assimilation system has been designed to integrate surface soil moisture estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) with an online soil moisture model used by the USDA Foreign Agriculture Service for global crop estimation. USDA's International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) ingests global soil moisture within a Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) to provide nowcasts of crop conditions and agricultural-drought. This information is primarily used to derive mid-season crop yield estimates for the improvement of foreign market access for U.S. agricultural products. The CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The integration of AMSR-E observations into the two-layer soil moisture model employed by IPAD can potentially enhance the reliability of the CADRE soil moisture estimates due to AMSR-E's improved repeat time and greater spatial coverage. Assimilation of the AMSR-E soil moisture estimates is accomplished using a 1-D Ensemble Kalman filter (EnKF) at daily time steps. A diagnostic calibration of the filter is performed using innovation statistics by accurately weighting the filter observation and modeling errors for three ranges of vegetation biomass density estimated using historical data from the Advanced Very High Resolution Radiometer (AVHRR). Assessment of the AMSR-E assimilation has been completed for a five year duration over the conterminous United States. To evaluate the ability of the filter to compensate for incorrect precipitation forcing into the model, a data denial approach is employed by comparing soil moisture results obtained from separate model simulations forced with precipitation products of varying uncertainty. An analysis of surface and root-zone anomalies is presented for each

Ground-penetrating radar study of the Cena Bog, Latvia: linkage of reflections with peat moisture content

Directory of Open Access Journals (Sweden)

Karušs, J.

2015-12-01

Full Text Available Present work illustrates results of the ground-penetrating radar (GPR study of the Cena Bog, Latvia. Six sub-horizontal reflections that most probably correspond to boundaries between sediments with different electromagnetic properties were identified. One of the reflections corresponds to bog peat mineral bottom interface but the rest are linked to boundaries within the peat body. The radar profiles are incorporated with sediment cores and studies of peat moisture and ash content, and degree of decomposition. Most of the electromagnetic wave reflections are related to changes in peat moisture content. The obtained data show that peat moisture content changes of at least 3 % are required to cause GPR signal reflection. However, there exist reflections that do not correlate with peat moisture content. As a result, authors disagree with a dominant opinion that all reflections in bogs are solely due to changes in volumetric peat moisture content.

On-line moisture analysis

International Nuclear Information System (INIS)

Cutmore, N.G.; Mijak, D.G

2002-01-01

Measurement of the moisture content of iron ore has become a key issue for controlling moisture additions for dust suppression. In most cases moisture content is still determined by manual or automatic sampling of the ore stream, followed by conventional laboratory analysis by oven drying. Although this procedure enables the moisture content to be routinely monitored, it is too slow for control purposes. This has generated renewed interest in on-line techniques for the accurate and rapid measurement of moisture in iron ore on conveyors. Microwave transmission techniques have emerged over the past 40 years as the dominant technology for on-line measurement of moisture in bulk materials, including iron ores. Alternative technologies have their limitations. Infra-red analysers are used in a variety of process industries, but rely on the measurement of absorption by moisture in a very thin surface layer. Consequently such probes may be compromised by particle size effects and biased presentation of the bulk material. Nuclear-based analysers measure the total hydrogen content in the sample and do not differentiate between free and combined moisture. Such analysers may also be sensitive to material presentation and elemental composition. Very low frequency electromagnetic probes, such as capacitance or conductance probes, operate in the frequency region where the DC conductivity dominates much of the response, which is a function not only of moisture content but also of ionic composition and chemistry. These problems are overcome using microwave transmission techniques, which also have the following advantages, as a true bulk moisture analysis is obtained, because a high percentage of the bulk material is analysed; the moisture estimate is mostly insensitive to any biased presentation of moisture, for example due to stratification of bulk material with different moisture content and because no physical contact is made between the sensor and the bulk material. This is

Examining the relationship between intermediate-scale soil moisture and terrestrial evaporation within a semi-arid grassland

KAUST Repository

Jana, Raghavendra B.

2016-09-30

Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale mismatch when compared to coarser-resolution satellite based soil moisture or evaporation estimates. The Cosmic Ray Neutron Probe (CRNP) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here, we present a study to assess the utility of CRNP soil moisture observations in validating model evaporation estimates. The CRNP soil moisture product from a pasture in the semi-arid central west region of New South Wales, Australia, was compared to evaporation derived from three distinct approaches, including the Priestley–Taylor (PT-JPL), Penman–Monteith (PM-Mu), and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson’s correlations, quantile–quantile (Q–Q) plots, and analysis of variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly 2 years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q–Q plots and ANOVA illustrate that the root-zone soil moisture represented by the CRNP measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold temperatures were

Validation of the CrIS fast physical NH3 retrieval with ground-based FTIR

Directory of Open Access Journals (Sweden)

E. Dammers

2017-07-01

Full Text Available Presented here is the validation of the CrIS (Cross-track Infrared Sounder fast physical NH3 retrieval (CFPR column and profile measurements using ground-based Fourier transform infrared (FTIR observations. We use the total columns and profiles from seven FTIR sites in the Network for the Detection of Atmospheric Composition Change (NDACC to validate the satellite data products. The overall FTIR and CrIS total columns have a positive correlation of r  =  0.77 (N  =  218 with very little bias (a slope of 1.02. Binning the comparisons by total column amounts, for concentrations larger than 1.0  ×  1016 molecules cm−2, i.e. ranging from moderate to polluted conditions, the relative difference is on average ∼ 0–5 % with a standard deviation of 25–50 %, which is comparable to the estimated retrieval uncertainties in both CrIS and the FTIR. For the smallest total column range (< 1.0  × 1016 molecules cm−2 where there are a large number of observations at or near the CrIS noise level (detection limit the absolute differences between CrIS and the FTIR total columns show a slight positive column bias. The CrIS and FTIR profile comparison differences are mostly within the range of the single-level retrieved profile values from estimated retrieval uncertainties, showing average differences in the range of  ∼ 20 to 40 %. The CrIS retrievals typically show good vertical sensitivity down into the boundary layer which typically peaks at  ∼ 850 hPa (∼ 1.5 km. At this level the median absolute difference is 0.87 (std  =  ±0.08 ppb, corresponding to a median relative difference of 39 % (std  =  ±2 %. Most of the absolute and relative profile comparison differences are in the range of the estimated retrieval uncertainties. At the surface, where CrIS typically has lower sensitivity, it tends to overestimate in low-concentration conditions and underestimate

The MIGHTI Wind Retrieval Algorithm: Description and Verification

Science.gov (United States)

Harding, Brian J.; Makela, Jonathan J.; Englert, Christoph R.; Marr, Kenneth D.; Harlander, John M.; England, Scott L.; Immel, Thomas J.

2017-10-01

We present an algorithm to retrieve thermospheric wind profiles from measurements by the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument on NASA's Ionospheric Connection Explorer (ICON) mission. MIGHTI measures interferometric limb images of the green and red atomic oxygen emissions at 557.7 nm and 630.0 nm, spanning 90-300 km. The Doppler shift of these emissions represents a remote measurement of the wind at the tangent point of the line of sight. Here we describe the algorithm which uses these images to retrieve altitude profiles of the line-of-sight wind. By combining the measurements from two MIGHTI sensors with perpendicular lines of sight, both components of the vector horizontal wind are retrieved. A comprehensive truth model simulation that is based on TIME-GCM winds and various airglow models is used to determine the accuracy and precision of the MIGHTI data product. Accuracy is limited primarily by spherical asymmetry of the atmosphere over the spatial scale of the limb observation, a fundamental limitation of space-based wind measurements. For 80% of the retrieved wind samples, the accuracy is found to be better than 5.8 m/s (green) and 3.5 m/s (red). As expected, significant errors are found near the day/night boundary and occasionally near the equatorial ionization anomaly, due to significant variations of wind and emission rate along the line of sight. The precision calculation includes pointing uncertainty and shot, read, and dark noise. For average solar minimum conditions, the expected precision meets requirements, ranging from 1.2 to 4.7 m/s.

SMOS near-real-time soil moisture product: processor overview and first validation results

Science.gov (United States)

Rodríguez-Fernández, Nemesio J.; Muñoz Sabater, Joaquin; Richaume, Philippe; de Rosnay, Patricia; Kerr, Yann H.; Albergel, Clement; Drusch, Matthias; Mecklenburg, Susanne

2017-10-01

Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (˜ 915 km) than that of the L2 SM dataset (˜ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

MoisturEC: A New R Program for Moisture Content Estimation from Electrical Conductivity Data.

Science.gov (United States)

Terry, Neil; Day-Lewis, Frederick D; Werkema, Dale; Lane, John W

2018-03-06

Noninvasive geophysical estimation of soil moisture has potential to improve understanding of flow in the unsaturated zone for problems involving agricultural management, aquifer recharge, and optimization of landfill design and operations. In principle, several geophysical techniques (e.g., electrical resistivity, electromagnetic induction, and nuclear magnetic resonance) offer insight into soil moisture, but data-analysis tools are needed to "translate" geophysical results into estimates of soil moisture, consistent with (1) the uncertainty of this translation and (2) direct measurements of moisture. Although geostatistical frameworks exist for this purpose, straightforward and user-friendly tools are required to fully capitalize on the potential of geophysical information for soil-moisture estimation. Here, we present MoisturEC, a simple R program with a graphical user interface to convert measurements or images of electrical conductivity (EC) to soil moisture. Input includes EC values, point moisture estimates, and definition of either Archie parameters (based on experimental or literature values) or empirical data of moisture vs. EC. The program produces two- and three-dimensional images of moisture based on available EC and direct measurements of moisture, interpolating between measurement locations using a Tikhonov regularization approach. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

4.4 Development of a 30-Year Soil Moisture Climatology for Situational Awareness and Public Health Applications

Science.gov (United States)

Case, Jonathan L.; Zavodsky, Bradley T.; White, Kristopher D.; Bell, Jesse E.

2015-01-01

This paper provided a brief background on the work being done at NASA SPoRT and the CDC to create a soil moisture climatology over the CONUS at high spatial resolution, and to provide a valuable source of soil moisture information to the CDC for monitoring conditions that could favor the development of Valley Fever. The soil moisture climatology has multi-faceted applications for both the NOAA/NWS situational awareness in the areas of drought and flooding, and for the Public Health community. SPoRT plans to increase its interaction with the drought monitoring and Public Health communities by enhancing this testbed soil moisture anomaly product. This soil moisture climatology run will also serve as a foundation for upgrading the real-time (currently southeastern CONUS) SPoRT-LIS to a full CONUS domain based on LIS version 7 and incorporating real-time GVF data from the Suomi-NPP Visible Infrared Imaging Radiometer Suite (Vargas et al. 2013) into LIS-Noah. The upgraded SPoRT-LIS run will serve as a testbed proof-of-concept of a higher-resolution NLDAS-2 modeling member. The climatology run will be extended to near real-time using the NLDAS-2 meteorological forcing from 2011 to present. The fixed 1981-2010 climatology shall provide the soil moisture "normals" for the production of real-time soil moisture anomalies. SPoRT also envisions a web-mapping type of service in which an end-user could put in a request for either an historical or real-time soil moisture anomaly graph for a specified county (as exemplified by Figure 2) and/or for local and regional maps of soil moisture proxy percentiles. Finally, SPoRT seeks to assimilate satellite soil moisture data from the current Soil Moisture Ocean Salinity (SMOS; Blankenship et al. 2014) and the recently-launched NASA Soil Moisture Active Passive (SMAP; Entekhabi et al. 2010) missions, using the EnKF capability within LIS. The 9-km combined active radar and passive microwave retrieval product from SMAP (Das et al. 2011

MOPITT Gridded Monthly CO Retrievals (Thermal Infrared Radiances) V006

Data.gov (United States)

National Aeronautics and Space Administration — The MOPITT L3 files contain daily and monthly mean gridded versions of the daily L2 CO profile and total column retrievals. The averaging kernels associated with...

MOPITT Gridded Monthly CO Retrievals (Near Infrared Radiances) V006

Data.gov (United States)

National Aeronautics and Space Administration — The MOPITT L3 files contain daily and monthly mean gridded versions of the daily L2 CO profile and total column retrievals. The averaging kernels associated with...

MOPITT Gridded Daily CO Retrievals (Thermal Infrared Radiances) V006

Data.gov (United States)

National Aeronautics and Space Administration — The MOPITT L3 files contain daily and monthly mean gridded versions of the daily L2 CO profile and total column retrievals. The averaging kernels associated with...

On-line moisture analysis

CERN Document Server

Cutmore, N G

2002-01-01

Measurement of the moisture content of iron ore has become a key issue for controlling moisture additions for dust suppression. In most cases moisture content is still determined by manual or automatic sampling of the ore stream, followed by conventional laboratory analysis by oven drying. Although this procedure enables the moisture content to be routinely monitored, it is too slow for control purposes. This has generated renewed interest in on-line techniques for the accurate and rapid measurement of moisture in iron ore on conveyors. Microwave transmission techniques have emerged over the past 40 years as the dominant technology for on-line measurement of moisture in bulk materials, including iron ores. Alternative technologies have their limitations. Infra-red analysers are used in a variety of process industries, but rely on the measurement of absorption by moisture in a very thin surface layer. Consequently such probes may be compromised by particle size effects and biased presentation of the bulk mater...

Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs

Energy Technology Data Exchange (ETDEWEB)

Woods, J.; Winkler, J.; Christensen, D.; Hancock, E.

2014-08-01

Building energy simulations can be used to predict a building's interior conditions, along with the energy use associated with keeping these conditions comfortable. These models simulate the loads on the building (e.g., internal gains, envelope heat transfer), determine the operation of the space conditioning equipment, and then calculate the building's temperature and humidity throughout the year. The indoor temperature and humidity are affected not only by the loads and the space conditioning equipment, but also by the capacitance of the building materials, which buffer changes in temperature and humidity. This research developed an empirical method to extract whole-house model inputs for use with a more accurate moisture capacitance model (the effective moisture penetration depth model). The experimental approach was to subject the materials in the house to a square-wave relative humidity profile, measure all of the moisture transfer terms (e.g., infiltration, air conditioner condensate) and calculate the only unmeasured term: the moisture absorption into the materials. After validating the method with laboratory measurements, we performed the tests in a field house. A least-squares fit of an analytical solution to the measured moisture absorption curves was used to determine the three independent model parameters representing the moisture buffering potential of this house and its furnishings. Follow on tests with realistic latent and sensible loads showed good agreement with the derived parameters, especially compared to the commonly-used effective capacitance approach. These results show that the EMPD model, once the inputs are known, is an accurate moisture buffering model.

Rapid prototyping of soil moisture estimates using the NASA Land Information System

Science.gov (United States)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy.

Science.gov (United States)

Cai, Yi; Li, Qing; Xie, Haoran; Min, Huaqin

2014-10-01

With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation.

Science.gov (United States)

Zhang, Chao; Liu, Jiangui; Shang, Jiali; Cai, Huanjie

2018-08-01

Winter wheat (Triticum aestivum L.) is a major crop in the Guanzhong Plain, China. Understanding its water status is important for irrigation planning. A few crop water indicators, such as the leaf equivalent water thickness (EWT: g cm -2 ), leaf water content (LWC: %) and canopy water content (CWC: kg m -2 ), have been estimated using remote sensing techniques for a wide range of crops, yet their suitability and utility for revealing winter wheat growth and soil moisture status have not been well studied. To bridge this knowledge gap, field-scale irrigation experiments were conducted over two consecutive years (2014 and 2015) to investigate relationships of crop water content with soil moisture and grain yield, and to assess the performance of four spectral process methods for retrieving these three crop water indicators. The result revealed that the water indicators were more sensitive to soil moisture variation before the jointing stage. All three water indicators were significantly correlated with soil moisture during the reviving stage, and the correlations were stronger for leaf water indicators than that of the canopy water indicator at the jointing stage. No correlation was observed after the heading stage. All three water indicators showed good capabilities of revealing grain yield variability in jointing stage, with R 2 up to 0.89. CWC had a consistent relationship with grain yield over different growing seasons, but the performances of EWT and LWC were growing-season specific. The partial least squares regression was the most accurate method for estimating LWC (R 2 =0.72; RMSE=3.6%) and comparable capability for EWT and CWC. Finally, the work highlights the usefulness of crop water indicators to assess crop growth, productivity, and soil water status and demonstrates the potential of various spectral processing methods for retrieving crop water contents from canopy reflectance spectrums. Copyright © 2018 Elsevier B.V. All rights reserved.

1D-Var temperature retrievals from microwave radiometer and convective scale model

Directory of Open Access Journals (Sweden)

Pauline Martinet

2015-12-01

Full Text Available This paper studies the potential of ground-based microwave radiometers (MWR for providing accurate temperature retrievals by combining convective scale numerical models and brightness temperatures (BTs. A one-dimensional variational (1D-Var retrieval technique has been tested to optimally combine MWR and 3-h forecasts from the French convective scale model AROME. A microwave profiler HATPRO (Humidity and Temperature PROfiler was operated during 6 months at the meteorological station of Bordeaux (Météo France. MWR BTs were monitored against simulations from the Atmospheric Radiative Transfer Simulator 2 radiative transfer model. An overall good agreement was found between observations and simulations for opaque V-band channels but large errors were observed for channels the most affected by liquid water and water vapour emissions (51.26 and 52.28 GHz. 1D-Var temperature retrievals are performed in clear-sky and cloudy conditions using a screening procedure based on cloud base height retrieval from ceilometer observations, infrared radiometer temperature and liquid water path derived from the MWR observations. The 1D-Var retrievals were found to improve the AROME forecasts up to 2 km with a maximum gain of approximately 50 % in root-mean-square-errors (RMSE below 500 m. They were also found to outperform neural network retrievals. A static bias correction was proposed to account for systematic instrumental errors. This correction was found to have a negligible impact on the 1D-Var retrievals. The use of low elevation angles improves the retrievals up to 12 % in RMSE in cloudy-sky in the first layers. The present implementation achieved a RMSE with respect to radiosondes within 1 K in clear-sky and 1.3 K in cloudy-sky conditions for temperature.

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

Science.gov (United States)

Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

2018-03-01

The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

Precipitable water: Its linear retrieval using leaps and bounds procedure and its global distribution from SEASAT SMMR data

Science.gov (United States)

Pandey, P. C.

1982-01-01

Eight subsets using two to five frequencies of the SEASAT scanning multichannel microwave radiometer are examined to determine their potential in the retrieval of atmospheric water vapor content. Analysis indicates that the information concerning the 18 and 21 GHz channels are optimum for water vapor retrieval. A comparison with radiosonde observations gave an rms accuracy of approximately 0.40 g sq cm. The rms accuracy of precipitable water using different subsets was within 10 percent. Global maps of precipitable water over oceans using two and five channel retrieval (average of two and five channel retrieval) are given. Study of these maps reveals the possibility of global moisture distribution associated with oceanic currents and large scale general circulation in the atmosphere. A stable feature of the large scale circulation is noticed. The precipitable water is maximum over the Bay of Bengal and in the North Pacific over the Kuroshio current and shows a general latitudinal pattern.

Geospatial metadata retrieval from web services

Directory of Open Access Journals (Sweden)

Ivanildo Barbosa

Full Text Available Nowadays, producers of geospatial data in either raster or vector formats are able to make them available on the World Wide Web by deploying web services that enable users to access and query on those contents even without specific software for geoprocessing. Several providers around the world have deployed instances of WMS (Web Map Service, WFS (Web Feature Service and WCS (Web Coverage Service, all of them specified by the Open Geospatial Consortium (OGC. In consequence, metadata about the available contents can be retrieved to be compared with similar offline datasets from other sources. This paper presents a brief summary and describes the matching process between the specifications for OGC web services (WMS, WFS and WCS and the specifications for metadata required by the ISO 19115 - adopted as reference for several national metadata profiles, including the Brazilian one. This process focuses on retrieving metadata about the identification and data quality packages as well as indicates the directions to retrieve metadata related to other packages. Therefore, users are able to assess whether the provided contents fit to their purposes.

Cloud and Thermodynamic Parameters Retrieved from Satellite Ultraspectral Infrared Measurements

Science.gov (United States)

Zhou, Daniel K.; Smith, William L.; Larar, Allen M.; Liu, Xu; Taylor, Jonathan P.; Schluessel, Peter; Strow, L. Larrabee; Mango, Stephen A.

2008-01-01

Atmospheric-thermodynamic parameters and surface properties are basic meteorological parameters for weather forecasting. A physical geophysical parameter retrieval scheme dealing with cloudy and cloud-free radiance observed with satellite ultraspectral infrared sounders has been developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric InfraRed Sounder (AIRS). The retrieved parameters presented herein are from radiance data gathered during the Joint Airborne IASI Validation Experiment (JAIVEx). JAIVEx provided intensive aircraft observations obtained from airborne Fourier Transform Spectrometer (FTS) systems, in-situ measurements, and dedicated dropsonde and radiosonde measurements for the validation of the IASI products. Here, IASI atmospheric profile retrievals are compared with those obtained from dedicated dropsondes, radiosondes, and the airborne FTS system. The IASI examples presented here demonstrate the ability to retrieve fine-scale horizontal features with high vertical resolution from satellite ultraspectral sounder radiance spectra.

Indoor microbiota in severely moisture damaged homes and the impact of interventions.

Science.gov (United States)

Jayaprakash, Balamuralikrishna; Adams, Rachel I; Kirjavainen, Pirkka; Karvonen, Anne; Vepsäläinen, Asko; Valkonen, Maria; Järvi, Kati; Sulyok, Michael; Pekkanen, Juha; Hyvärinen, Anne; Täubel, Martin

2017-10-13

The limited understanding of microbial characteristics in moisture-damaged buildings impedes efforts to clarify which adverse health effects in the occupants are associated with the damage and to develop effective building intervention strategies. The objectives of this current study were (i) to characterize fungal and bacterial microbiota in house dust of severely moisture-damaged residences, (ii) to identify microbial taxa associated with moisture damage renovations, and (iii) to test whether the associations between the identified taxa and moisture damage are replicable in another cohort of homes. We applied bacterial 16S rRNA gene and fungal ITS amplicon sequencing complemented with quantitative PCR and chemical-analytical approaches to samples of house dust, and also performed traditional cultivation of bacteria and fungi from building material samples. Active microbial growth on building materials had significant though small influence on the house dust bacterial and fungal communities. Moisture damage interventions-including actual renovation of damaged homes and cases where families moved to another home-had only a subtle effect on bacterial community structure, seen as shifts in abundance weighted bacterial profiles after intervention. While bacterial and fungal species richness were reduced in homes that were renovated, they were not reduced for families that moved houses. Using different discriminant analysis tools, we were able identify taxa that were significantly reduced in relative abundance during renovation of moisture damage. For bacteria, the majority of candidates belonged to different families within the Actinomycetales order. Results for fungi were overall less consistent. A replication study in approximately 400 homes highlighted some of the identified taxa, confirming associations with observations of moisture damage and mold. The present study is one of the first studies to analyze changes in microbiota due to moisture damage interventions

Errors induced by different approximations in handling horizontal atmospheric inhomogeneities in MIPAS/ENVISAT retrievals

Directory of Open Access Journals (Sweden)

E. Castelli

2016-11-01

Full Text Available MIPAS (Michelson Interferometer for Passive Atmospheric Sounding is a mid-infrared limb emission sounder that operated on board the polar satellite ENVISAT from 2002 to 2012. The retrieval algorithm used by the European Space Agency to process MIPAS measurements exploits the assumption that the atmosphere is horizontally homogeneous. However, previous studies highlighted how this assumption causes significant errors on the retrieved profiles of some MIPAS target species.In this paper we quantify the errors induced by this assumption and evaluate the performances of three different algorithms that can be used to mitigate the problem. We generate synthetic observations with a high spatial resolution atmospheric model and carry out the retrievals with four alternative methods. The first assumes horizontal homogeneity (1-D retrieval, the second includes a model of the horizontal gradient of atmospheric temperature (1-D plus temperature gradient retrieval, the third accounts for an horizontal gradient of temperature and composition (1-D plus temperature and composition gradient retrieval, while the fourth is the full two-dimensional (2-D inversion approach.Our results highlight that the 1-D retrieval implies errors that are significant for averages of profiles. Furthermore, for some targets (e.g. T, CH4 and N2O below 10 hPa the error induced by the 1-D approximation also becomes visible in the individual retrieved profiles. The inclusion of any kind of horizontal variability model improves all the targets with respect to the horizontal homogeneity assumption. For temperature, HNO3 and CFC-11, the inclusion of an horizontal temperature gradient leads to a significant reduction of the error. For other targets, such as H2O, O3, N2O, CH4, the improvements due to the inclusion of an horizontal temperature gradient are minor. In these cases, the inclusion of a gradient in the target volume mixing ratio leads to significant improvements. Among all the

Evaluation of Global Ozone Monitoring Experiment (GOME) ozone profiles from nine different algorithms

NARCIS (Netherlands)

Meijer, Y.J.; Swart, D.P.J.; Baier, F.; Bhartia, P.K.; Bodeker, G.E.; Casadio, S.; Chance, K.; Frate, Del F.; Erbertseder, T.; Felder, M.D.; Flynn, L.E.; Godin-Beekmann, S.; Hansen, G.; Hasekamp, O.P.; Kaifel, A.; Kelder, H.M.; Kerridge, B.J.; Lambert, J.-C.; Landgraf, J.; Latter, B.G.; Liu, X.; McDermid, I.S.; Pachepsky, Y.; Rozanov, V.; Siddans, R.; Tellmann, S.; A, van der R.J.; Oss, van R.F.; Weber, M.; Zehner, C.

2006-01-01

An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently, four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information

The Hydrosphere State (Hydros) Satellite Mission: An Earth System Pathfinder for Global Mapping of Soil Moisture and Land Freeze/Thaw

Science.gov (United States)

Entekhabi, D.; Njoku, E. G.; Spencer, M.; Kim, Y.; Smith, J.; McDonald, K. C.; vanZyl, J.; Houser, P.; Dorion, T.; Koster, R.;

Measurement of moisture motion under a temperature gradient in a concrete for SNR-300 using thermal neutrons

International Nuclear Information System (INIS)

Zelinger, A.

1975-01-01

For describing the behavior of the moisture in the concrete of the containment of SNR-300 in a hypothetical accident parameters were determined experimentally. The method is based on transmission of thermal neutrons through a plate of concrete. When a temperature of 170 deg C was applied at one end of the plate migration of moisture and evaporation took place. This could be observed by neutron radiography giving a gross picture of moisture migration. Furthermore the intensity of the transmitted neutron beam was measured with a neutron counter. From these values profiles of the change of moisture concentration could be obtained with a spatial resolution of few millimeters. The method used is entirely different from the conventional moisture meters which use fast neutrons. From the experimental data the mass transfer coefficient of vapour, the diffusion coefficient of vapour in concrete and the porosity of the concrete could be determined

Exploring the Validity Range of the Polarimetric Two-Scale Two-Component Model for Soil Moisture Retrieval by Using AGRISAR Data

Science.gov (United States)

Di Martino, Gerardo; Iodice, Antonio; Natale, Antonio; Riccio, Daniele; Ruello, Giuseppe

2015-04-01

The recently proposed polarimetric two-scale two- component model (PTSTCM) in principle allows us obtaining a reasonable estimation of the soil moisture even in moderately vegetated areas, where the volumetric scattering contribution is non-negligible, provided that the surface component is dominant and the double-bounce component is negligible. Here we test the PTSTCM validity range by applying it to polarimetric SAR data acquired on areas for which, at the same times of SAR acquisitions, ground measurements of soil moisture were performed. In particular, we employ the AGRISAR'06 database, which includes data from several fields covering a period that spans all the phases of vegetation growth.

HIRS-AMTS satellite sounding system test - Theoretical and empirical vertical resolving power. [High resolution Infrared Radiation Sounder - Advanced Moisture and Temperature Sounder

Science.gov (United States)

Thompson, O. E.

1982-01-01

The present investigation is concerned with the vertical resolving power of satellite-borne temperature sounding instruments. Information is presented on the capabilities of the High Resolution Infrared Radiation Sounder (HIRS) and a proposed sounding instrument called the Advanced Moisture and Temperature Sounder (AMTS). Two quite different methods for assessing the vertical resolving power of satellite sounders are discussed. The first is the theoretical method of Conrath (1972) which was patterned after the work of Backus and Gilbert (1968) The Backus-Gilbert-Conrath (BGC) approach includes a formalism for deriving a retrieval algorithm for optimizing the vertical resolving power. However, a retrieval algorithm constructed in the BGC optimal fashion is not necessarily optimal as far as actual temperature retrievals are concerned. Thus, an independent criterion for vertical resolving power is discussed. The criterion is based on actual retrievals of signal structure in the temperature field.

Investigating the effect of moisture protection on solid-state stability and dissolution of fenofibrate and ketoconazole solid dispersions using PXRD, HSDSC and Raman microscopy.

Science.gov (United States)

Kanaujia, Parijat; Lau, Grace; Ng, Wai Kiong; Widjaja, Effendi; Schreyer, Martin; Hanefeld, Andrea; Fischbach, Matthias; Saal, Christoph; Maio, Mario; Tan, Reginald B H

2011-09-01

Enhanced dissolution of poorly soluble active pharmaceutical ingredients (APIs) in amorphous solid dispersions often diminishes during storage due to moisture-induced re-crystallization. This study aims to investigate the influence of moisture protection on solid-state stability and dissolution profiles of melt-extruded fenofibrate (FF) and ketoconazole (KC) solid dispersions. Samples were kept in open, closed and Activ-vials(®) to control the moisture uptake under accelerated conditions. During 13-week storage, changes in API crystallinity were quantified using powder X-ray diffraction (PXRD) (Rietveld analysis) and high sensitivity differential scanning calorimetry (HSDSC) and compared with any change in dissolution profiles. Trace crystallinity was observed by Raman microscopy, which otherwise was undetected by PXRD and HSDSC. Results showed that while moisture protection was ineffective in preventing the re-crystallization of amorphous FF, KC remained X-ray amorphous despite 5% moisture uptake. Regardless of the degree of crystallinity increase in FF, the enhanced dissolution properties were similarly diminished. Moisture uptake above 10% in KC samples also led to re-crystallization and significant decrease in dissolution rates. In conclusion, eliminating moisture sorption may not be sufficient in ensuring the stability of solid dispersions. Analytical quantification of API crystallinity is crucial in detecting subtle increase in crystallinity that can diminish the enhanced dissolution properties of solid dispersions.

Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

Directory of Open Access Journals (Sweden)

Akhilesh S. Nair

2016-11-01

Full Text Available In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM and the Soil Moisture Operational Products System (SMOPS observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS using the Land Parameter Retrieval Model (LPRM AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb at an incidence angle of 55° using the Community Microwave Emission Model (CMEM Radiative transfer Model (RTM. This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

Thermal structure of the Martian atmosphere retrieved from the IR- spectrometry in the 15 mkm CO2 band

Science.gov (United States)

Zasova, L.; Formisano, V.; Grassi, D.; Igantiev, N.; Moroz, V.

Thermal IR spectrometry is one of the methods of the Martian atmosphere investigation below 55 km. The temperature profiles retrieved from the 15 μm CO2 band may be used for MIRA database. This approach gives the vertical resolution of several kilometers and accuracy of several Kelvins. An aerosol abundance, which influences the temperature profiles, is obtained from the continuum of the same spectrum. It is taken into account in the temperature retrieval procedure in a self- consistent way. Although this method has limited vertical resolution it possesses some advantages. For example, the radio occultation method gives the temperature profiles with higher spectral resolution, but the radio observations are sparse in space and local time. Direct measurements, which give the most accurate results, enable to obtain the temperature profiles only for some chosen points (landing places). Actually, the thermal IR-spectrometry is the only method, which allows to monitor the temperature profiles with good coverage both in space and local time. The first measurements of this kind were fulfilled by IRIS, installed on board of Mariner 9. This spectrometer was characterized by rather high spectral resolution (2.4 cm-1). The temperature profiles vs. local time dependencies for different latitudes and seasons were retrieved, including dust storm conditions, North polar night, Tharsis volcanoes. The obtained temperature profiles have been compared with the temperature profiles for the same conditions, taken from Climate Data Base (European GCM). The Planetary Fourier Spectrometer onboard Mars Express (which is planned to be launched in 2003) has the spectral range 1.2-45 μm and spectral resolution of 1.5 cm- 1. Temperature retrieval is one of the main scientific goals of the experiment. It opens a possibility to get a series of temperature profiles taken for different conditions, which can later be used in MIRA producing.

Evaluating lysimeter drainage against soil deep percolation modeled with profile soil moisture, field tracer propagation, and lab measured soil hydraulic properties

DEFF Research Database (Denmark)

Vasquez, Vicente; Thomsen, Anton Gårde; Iversen, Bo Vangsø

them have been reported. To compare among methods, one year of four large-scale lysimeters drainage (D) was evaluated against modeled soil deep percolation using either profile soil moisture, bromide breakthrough curves from suction cups, or measured soil hydraulic properties in the laboratory....... Measured volumetric soil water content (q) was 3-4% higher inside lysimeters than in the field probably due to a zero tension lower boundary condition inside lysimeters. D from soil hydraulic properties measured in the laboratory resulted in a 15% higher evapotranspiration and 12% lower drainage...... predictions than the model calibrated with field measured q. Bromide (Br) breakthrough curves indicated high variability between lysimeters and field suction cups with mean Br velocities at first arrival time of 110 and 33 mm/d, respectively. D was 520 mm/yr with lysimeters, 613 mm/yr with the calibrated...

Ground Albedo Neutron Sensing (GANS) method for measurements of soil moisture in cropped fields

Science.gov (United States)

Andres Rivera Villarreyes, Carlos; Baroni, Gabriele; Oswald, Sascha E.

2013-04-01

Measurement of soil moisture at the plot or hill-slope scale is an important link between local vadose zone hydrology and catchment hydrology. However, so far only few methods are on the way to close this gap between point measurements and remote sensing. This study evaluates the applicability of the Ground Albedo Neutron Sensing (GANS) for integral quantification of seasonal soil moisture in the root zone at the scale of a field or small watershed, making use of the crucial role of hydrogen as neutron moderator relative to other landscape materials. GANS measurements were performed at two locations in Germany under different vegetative situations and seasonal conditions. Ground albedo neutrons were measured at (i) a lowland Bornim farmland (Brandenburg) cropped with sunflower in 2011 and winter rye in 2012, and (ii) a mountainous farmland catchment (Schaefertal, Harz Mountains) since middle 2011. At both sites depth profiles of soil moisture were measured at several locations in parallel by frequency domain reflectometry (FDR) for comparison and calibration. Initially, calibration parameters derived from a previous study with corn cover were tested under sunflower and winter rye periods at the same farmland. GANS soil moisture based on these parameters showed a large discrepancy compared to classical soil moisture measurements. Therefore, two new calibration approaches and four different ways of integration the soil moisture profile to an integral value for GANS were evaluated in this study. This included different sets of calibration parameters based on different growing periods of sunflower. New calibration parameters showed a good agreement with FDR network during sunflower period (RMSE = 0.023 m3 m-3), but they underestimated soil moisture in the winter rye period. The GANS approach resulted to be highly affected by temporal changes of biomass and crop types which suggest the need of neutron corrections for long-term observations with crop rotation. Finally

Parameter estimation of a two-horizon soil profile by combining crop canopy and surface soil moisture observations using GLUE

Science.gov (United States)

Sreelash, K.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Guérif, M.; Buis, S.; Durand, P.; Gascuel-Odoux, C.

2012-08-01

SummaryEstimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to

Analysis and optimal design of moisture sensor for rice grain moisture measurement