Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China

OpenAIRE

Ma, Yuanyuan; Yang, Yi; Mai, Xiaoping; Qiu, Chongjian; Long, Xiao; Wang, Chenghai

2016-01-01

To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and s...

Dynamical Downscaling of Typhoon Vera (1959) and related Storm Surge based on JRA-55 Reanalysis

Science.gov (United States)

Ninomiya, J.; Takemi, T.; Mori, N.; Shibutani, Y.; Kim, S.

2015-12-01

Typhoon Vera in 1959 is historical extreme typhoon that caused severest typhoon damage mainly due to the storm surge up to 389 cm in Japan. Vera developed 895 hPa on offshore and landed with 929.2 hPa. There are many studies of the dynamical downscaling of Vera but it is difficult to simulate accurately because of the lack of the accuracy of global reanalysis data. This study carried out dynamical downscaling experiment of Vera using WRF downscaling forced by JRA-55 that are latest atmospheric model and reanalysis data. In this study, the reproducibility of five global reanalysis data for Typhoon Vera were compered. Comparison shows that reanalysis data doesn't have strong typhoon information except for JRA-55, so that downscaling with conventional reanalysis data goes wrong. The dynamical downscaling method for storm surge is studied very much (e.g. choice of physical model, nudging, 4D-VAR, bogus and so on). In this study, domain size and resolution of the coarse domain were considered. The coarse domain size influences the typhoon route and central pressure, and larger domain restrains the typhoon strength. The results of simulations with different domain size show that the threshold of developing restrain is whether the coarse domain fully includes the area of wind speed more than 15 m/s around the typhoon. The results of simulations with different resolution show that the resolution doesn't affect the typhoon route, and higher resolution gives stronger typhoon simulation.

Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China

Directory of Open Access Journals (Sweden)

Yuanyuan Ma

2016-01-01

Full Text Available To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and spectral nudging are debated. Moreover, dynamical downscaling is now performed at the convection-permitting scale to reduce the parameterization uncertainty and obtain the finer resolution. To compare the performances of the two nudging techniques in this study, three sensitivity experiments (with no nudging, analysis nudging, and spectral nudging covering a period of two months with a grid spacing of 6 km over continental China are conducted to downscale the 1-degree National Centers for Environmental Prediction (NCEP dataset with the Weather Research and Forecasting (WRF model. Compared with observations, the results show that both of the nudging experiments decrease the bias of conventional meteorological elements near the surface and at different heights during the process of dynamical downscaling. However, spectral nudging outperforms analysis nudging for predicting precipitation, and analysis nudging outperforms spectral nudging for the simulation of air humidity and wind speed.

What model resolution is required in climatological downscaling over complex terrain?

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El-Samra, Renalda; Bou-Zeid, Elie; El-Fadel, Mutasem

2018-05-01

This study presents results from the Weather Research and Forecasting (WRF) model applied for climatological downscaling simulations over highly complex terrain along the Eastern Mediterranean. We sequentially downscale general circulation model results, for a mild and wet year (2003) and a hot and dry year (2010), to three local horizontal resolutions of 9, 3 and 1 km. Simulated near-surface hydrometeorological variables are compared at different time scales against data from an observational network over the study area comprising rain gauges, anemometers, and thermometers. The overall performance of WRF at 1 and 3 km horizontal resolution was satisfactory, with significant improvement over the 9 km downscaling simulation. The total yearly precipitation from WRF's 1 km and 3 km domains exhibited quantitative measure of the potential errors for various hydrometeorological variables.

Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1)

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Quiquet, Aurélien; Roche, Didier M.; Dumas, Christophe; Paillard, Didier

2018-02-01

This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km × 40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

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Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

A New High Resolution Climate Dataset for Climate Change Impacts Assessments in New England

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Komurcu, M.; Huber, M.

2016-12-01

Assessing regional impacts of climate change (such as changes in extreme events, land surface hydrology, water resources, energy, ecosystems and economy) requires much higher resolution climate variables than those available from global model projections. While it is possible to run global models in higher resolution, the high computational cost associated with these simulations prevent their use in such manner. To alleviate this problem, dynamical downscaling offers a method to deliver higher resolution climate variables. As part of an NSF EPSCoR funded interdisciplinary effort to assess climate change impacts on New Hampshire ecosystems, hydrology and economy (the New Hampshire Ecosystems and Society project), we create a unique high-resolution climate dataset for New England. We dynamically downscale global model projections under a high impact emissions scenario using the Weather Research and Forecasting model (WRF) with three nested grids of 27, 9 and 3 km horizontal resolution with the highest resolution innermost grid focusing over New England. We prefer dynamical downscaling over other methods such as statistical downscaling because it employs physical equations to progressively simulate climate variables as atmospheric processes interact with surface processes, emissions, radiation, clouds, precipitation and other model components, hence eliminates fix relationships between variables. In addition to simulating mean changes in regional climate, dynamical downscaling also allows for the simulation of climate extremes that significantly alter climate change impacts. We simulate three time slices: 2006-2015, 2040-2060 and 2080-2100. This new high-resolution climate dataset (with more than 200 variables saved in hourly (six hourly) intervals for the highest resolution domain (outer two domains)) along with model input and restart files used in our WRF simulations will be publicly available for use to the broader scientific community to support in-depth climate

Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1

Directory of Open Access Journals (Sweden)

A. Quiquet

2018-02-01

Full Text Available This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

High-resolution surface analysis for extended-range downscaling with limited-area atmospheric models

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Separovic, Leo; Husain, Syed Zahid; Yu, Wei; Fernig, David

2014-12-01

High-resolution limited-area model (LAM) simulations are frequently employed to downscale coarse-resolution objective analyses over a specified area of the globe using high-resolution computational grids. When LAMs are integrated over extended time frames, from months to years, they are prone to deviations in land surface variables that can be harmful to the quality of the simulated near-surface fields. Nudging of the prognostic surface fields toward a reference-gridded data set is therefore devised in order to prevent the atmospheric model from diverging from the expected values. This paper presents a method to generate high-resolution analyses of land-surface variables, such as surface canopy temperature, soil moisture, and snow conditions, to be used for the relaxation of lower boundary conditions in extended-range LAM simulations. The proposed method is based on performing offline simulations with an external surface model, forced with the near-surface meteorological fields derived from short-range forecast, operational analyses, and observed temperatures and humidity. Results show that the outputs of the surface model obtained in the present study have potential to improve the near-surface atmospheric fields in extended-range LAM integrations.

Evaluation for Moroccan dynamically downscaled precipitation from GCM CHAM5 and its regional hydrologic response

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Tsou Jaw

2015-03-01

Full Text Available Study region: Morocco (excluding Western Sahara. Study focus: This study evaluated Moroccan precipitation, dynamically downscaled (0.18-degree from three runs of the studied GCM ECHAM5/MPI-OM, under the present-day (1971–2000/20C3M and future (2036–2065/A1B climate scenarios. The spatial and quantitative properties of the downscaled precipitation were evaluated by a verified, fine-resolution reference. The effectiveness of the hydrologic responses, driven by the downscaled precipitation, was further evaluated for the study region over the upstream watershed of Oum er Rbia River located in Central Morocco. New hydrological insights for the region: The raw downscaling runs reasonably featured the spatial properties but quantitatively misrepresented the mean and extreme intensities of present-day precipitation. Two proposed bias correction approaches, namely stationary Quantile-Mapping (QM and non-stationary Equidistant CDF Matching model (EDCDFm, successfully reduced the system biases existing in the raw downscaling runs. However, both raw and corrected runs projected great diversity in terms of the quantity of future precipitation. Hydrologic simulations performed by a well-calibrated Variable Infiltration Capacity model successfully reproduced the present-day streamflow. The driven flows were identified highly correlated with the effectiveness of the downscaled precipitation. The future flows were projected to be markedly diverse, mainly due to the varied precipitation projections. Two of the three flow simulation runs projected slight to severe drying scenarios, while another projected an opposite trend for the evaluated future period. Keywords: Dynamical downscaling, Moroccan precipitation, Regional hydrology

Century long observation constrained global dynamic downscaling and hydrologic implication

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Kim, H.; Yoshimura, K.; Chang, E.; Famiglietti, J. S.; Oki, T.

2012-12-01

It has been suggested that greenhouse gas induced warming climate causes the acceleration of large scale hydrologic cycles, and, indeed, many regions on the Earth have been suffered by hydrologic extremes getting more frequent. However, historical observations are not able to provide enough information in comprehensive manner to understand their long-term variability and/or global distributions. In this study, a century long high resolution global climate data is developed in order to break through existing limitations. 20th Century Reanalysis (20CR) which has relatively low spatial resolution (~2.0°) and longer term availability (140 years) is dynamically downscaled into global T248 (~0.5°) resolution using Experimental Climate Prediction Center (ECPC) Global Spectral Model (GSM) by spectral nudging data assimilation technique. Also, Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU) observational data are adopted to reduce model dependent uncertainty. Downscaled product successfully represents realistic geographical detail keeping low frequency signal in mean state and spatiotemporal variability, while previous bias correction method fails to reproduce high frequency variability. Newly developed data is used to investigate how long-term large scale terrestrial hydrologic cycles have been changed globally and how they have been interacted with various climate modes, such as El-Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO). As a further application, it will be used to provide atmospheric boundary condition of multiple land surface models in the Global Soil Wetness Project Phase 3 (GSWP3).

A statistical-dynamical downscaling procedure for global climate simulations

International Nuclear Information System (INIS)

Frey-Buness, A.; Heimann, D.; Sausen, R.; Schumann, U.

1994-01-01

A statistical-dynamical downscaling procedure for global climate simulations is described. The procedure is based on the assumption that any regional climate is associated with a specific frequency distribution of classified large-scale weather situations. The frequency distributions are derived from multi-year episodes of low resolution global climate simulations. Highly resolved regional distributions of wind and temperature are calculated with a regional model for each class of large-scale weather situation. They are statistically evaluated by weighting them with the according climate-specific frequency. The procedure is exemplarily applied to the Alpine region for a global climate simulation of the present climate. (orig.)

Precipitation Dynamical Downscaling Over the Great Plains

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Hu, Xiao-Ming; Xue, Ming; McPherson, Renee A.; Martin, Elinor; Rosendahl, Derek H.; Qiao, Lei

2018-02-01

Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm-season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic-scale circulations are faithfully reproduced while still allowing WRF to develop small-scale dynamics, thus effectively suppressing the large-scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved.

Downscaled projections of Caribbean coral bleaching that can inform conservation planning.

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van Hooidonk, Ruben; Maynard, Jeffrey Allen; Liu, Yanyun; Lee, Sang-Ki

2015-09-01

Projections of climate change impacts on coral reefs produced at the coarse resolution (~1°) of Global Climate Models (GCMs) have informed debate but have not helped target local management actions. Here, projections of the onset of annual coral bleaching conditions in the Caribbean under Representative Concentration Pathway (RCP) 8.5 are produced using an ensemble of 33 Coupled Model Intercomparison Project phase-5 models and via dynamical and statistical downscaling. A high-resolution (~11 km) regional ocean model (MOM4.1) is used for the dynamical downscaling. For statistical downscaling, sea surface temperature (SST) means and annual cycles in all the GCMs are replaced with observed data from the ~4-km NOAA Pathfinder SST dataset. Spatial patterns in all three projections are broadly similar; the average year for the onset of annual severe bleaching is 2040-2043 for all projections. However, downscaled projections show many locations where the onset of annual severe bleaching (ASB) varies 10 or more years within a single GCM grid cell. Managers in locations where this applies (e.g., Florida, Turks and Caicos, Puerto Rico, and the Dominican Republic, among others) can identify locations that represent relative albeit temporary refugia. Both downscaled projections are different for the Bahamas compared to the GCM projections. The dynamically downscaled projections suggest an earlier onset of ASB linked to projected changes in regional currents, a feature not resolved in GCMs. This result demonstrates the value of dynamical downscaling for this application and means statistically downscaled projections have to be interpreted with caution. However, aside from west of Andros Island, the projections for the two types of downscaling are mostly aligned; projected onset of ASB is within ±10 years for 72% of the reef locations. © 2015 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

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Michelle E. Miro

2018-01-01

Full Text Available NASA’s Gravity Recovery and Climate Experiment (GRACE has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km2. Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km2. Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km2 dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM-derived slope, and Natural Resources Conservation Service (NRCS soil type. The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively. Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region.

A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

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B. Liu

2017-09-01

Full Text Available The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these

High-resolution dynamical downscaling of re-analysis data over the Kerguelen Islands using the WRF model

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Fonseca, Ricardo; Martín-Torres, Javier

2018-03-01

We have used the Weather Research and Forecasting (WRF) model to simulate the climate of the Kerguelen Islands (49° S, 69° E) and investigate its inter-annual variability. Here, we have dynamically downscaled 30 years of the Climate Forecast System Reanalysis (CFSR) over these islands at 3-km horizontal resolution. The model output is found to agree well with the station and radiosonde data at the Port-aux-Français station, the only location in the islands for which observational data is available. An analysis of the seasonal mean WRF data showed a general increase in precipitation and decrease in temperature with elevation. The largest seasonal rainfall amounts occur at the highest elevations of the Cook Ice Cap in winter where the summer mean temperature is around 0 °C. Five modes of variability are considered: conventional and Modoki El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Subtropical IOD (SIOD) and Southern Annular Mode (SAM). It is concluded that a key mechanism by which these modes impact the local climate is through interaction with the diurnal cycle in particular in the summer season when it has a larger magnitude. One of the most affected regions is the area just to the east of the Cook Ice Cap extending into the lower elevations between the Gallieni and Courbet Peninsulas. The WRF simulation shows that despite the small annual variability, the atmospheric flow in the Kerguelen Islands is rather complex which may also be the case for the other islands located in the Southern Hemisphere at similar latitudes.

Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections.

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Ishida, K; Gorguner, M; Ercan, A; Trinh, T; Kavvas, M L

2017-08-15

The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region. Copyright © 2017 Elsevier B.V. All rights reserved.

Using sea surface temperatures to improve performance of single dynamical downscaling model in flood simulation under climate change

Science.gov (United States)

Chao, Y.; Cheng, C. T.; Hsiao, Y. H.; Hsu, C. T.; Yeh, K. C.; Liu, P. L.

2017-12-01

There are 5.3 typhoons hit Taiwan per year on average in last decade. Typhoon Morakot in 2009, the most severe typhoon, causes huge damage in Taiwan, including 677 casualties and roughly NT 110 billion (3.3 billion USD) in economic loss. Some researches documented that typhoon frequency will decrease but increase in intensity in western North Pacific region. It is usually preferred to use high resolution dynamical model to get better projection of extreme events; because coarse resolution models cannot simulate intense extreme events. Under that consideration, dynamical downscaling climate data was chosen to describe typhoon satisfactorily, this research used the simulation data from AGCM of Meteorological Research Institute (MRI-AGCM). Considering dynamical downscaling methods consume massive computing power, and typhoon number is very limited in a single model simulation, using dynamical downscaling data could cause uncertainty in disaster risk assessment. In order to improve the problem, this research used four sea surfaces temperatures (SSTs) to increase the climate change scenarios under RCP 8.5. In this way, MRI-AGCMs project 191 extreme typhoons in Taiwan (when typhoon center touches 300 km sea area of Taiwan) in late 21th century. SOBEK, a two dimensions flood simulation model, was used to assess the flood risk under four SSTs climate change scenarios in Tainan, Taiwan. The results show the uncertainty of future flood risk assessment is significantly decreased in Tainan, Taiwan in late 21th century. Four SSTs could efficiently improve the problems of limited typhoon numbers in single model simulation.

Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China

Science.gov (United States)

Duan, Limin; Fan, Keke; Li, Wei; Liu, Tingxi

2017-12-01

Daily precipitation data from 42 stations in Inner Mongolia, China for the 10 years period from 1 January 2001 to 31 December 2010 was utilized along with downscaled data from the Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25° × 0.25° for the same period based on the statistical relationships between the normalized difference vegetation index (NDVI), meteorological variables, and digital elevation models (https://en.wikipedia.org/wiki/Digital_elevation_model) (DEM) using the leave-one-out (LOO) cross validation method and multivariate step regression. The results indicate that (1) TRMM data can indeed be used to estimate annual precipitation in Inner Mongolia and there is a linear relationship between annual TRMM and observed precipitation; (2) there is a significant relationship between TRMM-based precipitation and predicted precipitation, with a spatial resolution of 0.50° × 0.50°; (3) NDVI and temperature are important factors influencing the downscaling of TRMM precipitation data for DEM and the slope is not the most significant factor affecting the downscaled TRMM data; and (4) the downscaled TRMM data reflects spatial patterns in annual precipitation reasonably well, showing less precipitation falling in west Inner Mongolia and more in the south and southeast. The new approach proposed here provides a useful alternative for evaluating spatial patterns in precipitation and can thus be applied to generate a more accurate precipitation dataset to support both irrigation management and the conservation of this fragile grassland ecosystem.

Downscaling biogeochemistry in the Benguela eastern boundary current

Science.gov (United States)

Machu, E.; Goubanova, K.; Le Vu, B.; Gutknecht, E.; Garçon, V.

2015-06-01

Dynamical downscaling is developed to better predict the regional impact of global changes in the framework of scenarios. As an intermediary step towards this objective we used the Regional Ocean Modeling System (ROMS) to downscale a low resolution coupled atmosphere-ocean global circulation model (AOGCM; IPSL-CM4) for simulating the recent-past dynamics and biogeochemistry of the Benguela eastern boundary current. Both physical and biogeochemical improvements are discussed over the present climate scenario (1980-1999) under the light of downscaling. Despite biases introduced through boundary conditions (atmospheric and oceanic), the physical and biogeochemical processes in the Benguela Upwelling System (BUS) have been improved by the ROMS model, relative to the IPSL-CM4 simulation. Nevertheless, using coarse-resolution AOGCM daily atmospheric forcing interpolated on ROMS grids resulted in a shifted SST seasonality in the southern BUS, a deterioration of the northern Benguela region and a very shallow mixed layer depth over the whole regional domain. We then investigated the effect of wind downscaling on ROMS solution. Together with a finer resolution of dynamical processes and of bathymetric features (continental shelf and Walvis Ridge), wind downscaling allowed correction of the seasonality, the mixed layer depth, and provided a better circulation over the domain and substantial modifications of subsurface biogeochemical properties. It has also changed the structure of the lower trophic levels by shifting large offshore areas from autotrophic to heterotrophic regimes with potential important consequences on ecosystem functioning. The regional downscaling also improved the phytoplankton distribution and the southward extension of low oxygen waters in the Northern Benguela. It allowed simulating low oxygen events in the northern BUS and highlighted a potential upscaling effect related to the nitrogen irrigation from the productive BUS towards the tropical

Assessing the Added Value of Dynamical Downscaling in the Context of Hydrologic Implication

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Lu, M.; IM, E. S.; Lee, M. H.

2017-12-01

There is a scientific consensus that high-resolution climate simulations downscaled by Regional Climate Models (RCMs) can provide valuable refined information over the target region. However, a significant body of hydrologic impact assessment has been performing using the climate information provided by Global Climate Models (GCMs) in spite of a fundamental spatial scale gap. It is probably based on the assumption that the substantial biases and spatial scale gap from GCMs raw data can be simply removed by applying the statistical bias correction and spatial disaggregation. Indeed, many previous studies argue that the benefit of dynamical downscaling using RCMs is minimal when linking climate data with the hydrological model, from the comparison of the impact between bias-corrected GCMs and bias-corrected RCMs on hydrologic simulations. It may be true for long-term averaged climatological pattern, but it is not necessarily the case when looking into variability across various temporal spectrum. In this study, we investigate the added value of dynamical downscaling focusing on the performance in capturing climate variability. For doing this, we evaluate the performance of the distributed hydrological model over the Korean river basin using the raw output from GCM and RCM, and bias-corrected output from GCM and RCM. The impacts of climate input data on streamflow simulation are comprehensively analyzed. [Acknowledgements]This research is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 17AWMP-B083066-04).

Identifying added value in high-resolution climate simulations over Scandinavia

DEFF Research Database (Denmark)

Mayer, Stephania; Fox Maule, Cathrine; Sobolowski, Stefan

2015-01-01

High-resolution data are needed in order to assess potential impacts of extreme events on infrastructure in the mid-latitudes. Dynamical downscaling offers one way to obtain this information. However, prior to implementation in any impacts assessment scheme, model output must be validated and det...

Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate Downscaling within the WRF Model

Science.gov (United States)

Fan, X.; Chen, L.; Ma, Z.

2010-12-01

Climate downscaling has been an active research and application area in the past several decades focusing on regional climate studies. Dynamical downscaling, in addition to statistical methods, has been widely used in downscaling as the advanced modern numerical weather and regional climate models emerge. The utilization of numerical models enables that a full set of climate variables are generated in the process of downscaling, which are dynamically consistent due to the constraints of physical laws. While we are generating high resolution regional climate, the large scale climate patterns should be retained. To serve this purpose, nudging techniques, including grid analysis nudging and spectral nudging, have been used in different models. There are studies demonstrating the benefit and advantages of each nudging technique; however, the results are sensitive to many factors such as nudging coefficients and the amount of information to nudge to, and thus the conclusions are controversy. While in a companion work of developing approaches for quantitative assessment of the downscaled climate, in this study, the two nudging techniques are under extensive experiments in the Weather Research and Forecasting (WRF) model. Using the same model provides fair comparability. Applying the quantitative assessments provides objectiveness of comparison. Three types of downscaling experiments were performed for one month of choice. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using the grid analysis nudging; and the third is using spectral nudging. Emphases are given to the experiments of different nudging coefficients and nudging to different variables in the grid analysis nudging; while in spectral nudging, we focus on testing the nudging coefficients, different wave numbers on different model levels to nudge.

High Resolution Simulations of Future Climate in West Africa Using a Variable-Resolution Atmospheric Model

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Adegoke, J. O.; Engelbrecht, F.; Vezhapparambu, S.

2013-12-01

In previous work demonstrated the application of a var¬iable-resolution global atmospheric model, the conformal-cubic atmospheric model (CCAM), across a wide range of spatial and time scales to investigate the ability of the model to provide realistic simulations of present-day climate and plausible projections of future climate change over sub-Saharan Africa. By applying the model in stretched-grid mode the versatility of the model dynamics, numerical formulation and physical parameterizations to function across a range of length scales over the region of interest, was also explored. We primarily used CCAM to illustrate the capability of the model to function as a flexible downscaling tool at the climate-change time scale. Here we report on additional long term climate projection studies performed by downscaling at much higher resolutions (8 Km) over an area that stretches from just south of Sahara desert to the southern coast of the Niger Delta and into the Gulf of Guinea. To perform these simulations, CCAM was provided with synoptic-scale forcing of atmospheric circulation from 2.5 deg resolution NCEP reanalysis at 6-hourly interval and SSTs from NCEP reanalysis data uses as lower boundary forcing. CCAM 60 Km resolution downscaled to 8 Km (Schmidt factor 24.75) then 8 Km resolution simulation downscaled to 1 Km (Schmidt factor 200) over an area approximately 50 Km x 50 Km in the southern Lake Chad Basin (LCB). Our intent in conducting these high resolution model runs was to obtain a deeper understanding of linkages between the projected future climate and the hydrological processes that control the surface water regime in this part of sub-Saharan Africa.

Wave model downscaling for coastal applications

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Valchev, Nikolay; Davidan, Georgi; Trifonova, Ekaterina; Andreeva, Nataliya

2010-05-01

Downscaling is a suitable technique for obtaining high-resolution estimates from relatively coarse-resolution global models. Dynamical and statistical downscaling has been applied to the multidecadal simulations of ocean waves. Even as large-scale variability might be plausibly estimated from these simulations, their value for the small scale applications such as design of coastal protection structures and coastal risk assessment is limited due to their relatively coarse spatial and temporal resolutions. Another advantage of the high resolution wave modeling is that it accounts for shallow water effects. Therefore, it can be used for both wave forecasting at specific coastal locations and engineering applications that require knowledge about extreme wave statistics at or near the coastal facilities. In the present study downscaling is applied to both ECMWF and NCEP/NCAR global reanalysis of atmospheric pressure over the Black Sea with 2.5 degrees spatial resolution. A simplified regional atmospheric model is employed for calculation of the surface wind field at 0.5 degrees resolution that serves as forcing for the wave models. Further, a high-resolution nested WAM/SWAN wave model suite of nested wave models is applied for spatial downscaling. It aims at resolving the wave conditions in a limited area at the close proximity to the shore. The pilot site is located in the northern part the Bulgarian Black Sea shore. The system involves the WAM wave model adapted for basin scale simulation at 0.5 degrees spatial resolution. The WAM output for significant wave height, mean wave period and mean angle of wave approach is used in terms of external boundary conditions for the SWAN wave model, which is set up for the western Black Sea shelf at 4km resolution. The same model set up on about 400m resolution is nested to the first SWAN run. In this case the SWAN 2D spectral output provides boundary conditions for the high-resolution model run. The models are implemented for a

VALUE - Validating and Integrating Downscaling Methods for Climate Change Research

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Maraun, Douglas; Widmann, Martin; Benestad, Rasmus; Kotlarski, Sven; Huth, Radan; Hertig, Elke; Wibig, Joanna; Gutierrez, Jose

2013-04-01

Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by downscaling. Several projects have been carried out over the last years to validate the performance of statistical and dynamical downscaling, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different downscaling approaches such as dynamical downscaling, statistical downscaling and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical downscalers and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop downscaling methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every downscaling method can be validated and compared with competing methods. The results of

Variational data assimilation system with nesting model for high resolution ocean circulation

Energy Technology Data Exchange (ETDEWEB)

Ishikawa, Yoichi; Igarashi, Hiromichi; Hiyoshi, Yoshimasa; Sasaki, Yuji; Wakamatsu, Tsuyoshi; Awaji, Toshiyuki [Center for Earth Information Science and Technology, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-Ku, Yokohama 236-0001 (Japan); In, Teiji [Japan Marine Science Foundation, 4-24, Minato-cho, Mutsu, Aomori, 035-0064 (Japan); Nakada, Satoshi [Graduate School of Maritime Science, Kobe University, 5-1-1, Fukae-minamimachi, Higashinada-Ku, Kobe, 658-0022 (Japan); Nishina, Kei, E-mail: ishikaway@jamstec.go.jp [Graduate School of Science, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-Ku, Kyoto, 606-8502 (Japan)

2015-10-15

To obtain the high-resolution analysis fields for ocean circulation, a new incremental approach is developed using a four-dimensional variational data assimilation system with nesting models. The results show that there are substantial biases when using a classical method combined with data assimilation and downscaling, caused by different dynamics resulting from the different resolutions of the models used within the nesting models. However, a remarkable reduction in biases of the low-resolution model relative to the high-resolution model was observed using our new approach in narrow strait regions, such as the Tsushima and Tsugaru straits, where the difference in the dynamics represented by the high- and low-resolution models is substantial. In addition, error reductions are demonstrated in the downstream region of these narrow channels associated with the propagation of information through the model dynamics. (paper)

Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China

Directory of Open Access Journals (Sweden)

Peng Liu

2018-03-01

Full Text Available This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF land surface data (OLD and accurate dynamically accordant MODIS data (NEW. Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT, spectral nudging (SN, and re-initialization (Re experiments. The Weather Research and Forecasting (WRF model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error. A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident.

A Dynamical Downscaling study over the Great Lakes Region Using WRF-Lake: Historical Simulation

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Xiao, C.; Lofgren, B. M.

2014-12-01

As the largest group of fresh water bodies on Earth, the Laurentian Great Lakes have significant influence on local and regional weather and climate through their unique physical features compared with the surrounding land. Due to the limited spatial resolution and computational efficiency of general circulation models (GCMs), the Great Lakes are geometrically ignored or idealized into several grid cells in GCMs. Thus, the nested regional climate modeling (RCM) technique, known as dynamical downscaling, serves as a feasible solution to fill the gap. The latest Weather Research and Forecasting model (WRF) is employed to dynamically downscale the historical simulation produced by the Geophysical Fluid Dynamics Laboratory-Coupled Model (GFDL-CM3) from 1970-2005. An updated lake scheme originated from the Community Land Model is implemented in the latest WRF version 3.6. It is a one-dimensional mass and energy balance scheme with 20-25 model layers, including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil layers on the lake bottom. The lake scheme is used with actual lake points and lake depth. The preliminary results show that WRF-Lake model, with a fine horizontal resolution and realistic lake representation, provides significantly improved hydroclimates, in terms of lake surface temperature, annual cycle of precipitation, ice content, and lake-effect snowfall. Those improvements suggest that better resolution of the lakes and the mesoscale process of lake-atmosphere interaction are crucial to understanding the climate and climate change in the Great Lakes region.

Seasonal Prediction of Regional Surface Air Temperature and First-flowering Date in South Korea using Dynamical Downscaling

Science.gov (United States)

Ahn, J. B.; Hur, J.

2015-12-01

The seasonal prediction of both the surface air temperature and the first-flowering date (FFD) over South Korea are produced using dynamical downscaling (Hur and Ahn, 2015). Dynamical downscaling is performed using Weather Research and Forecast (WRF) v3.0 with the lateral forcing from hourly outputs of Pusan National University (PNU) coupled general circulation model (CGCM) v1.1. Gridded surface air temperature data with high spatial (3km) and temporal (daily) resolution are obtained using the physically-based dynamical models. To reduce systematic bias, simple statistical correction method is then applied to the model output. The FFDs of cherry, peach and pear in South Korea are predicted for the decade of 1999-2008 by applying the corrected daily temperature predictions to the phenological thermal-time model. The WRF v3.0 results reflect the detailed topographical effect, despite having cold and warm biases for warm and cold seasons, respectively. After applying the correction, the mean temperature for early spring (February to April) well represents the general pattern of observation, while preserving the advantages of dynamical downscaling. The FFD predictabilities for the three species of trees are evaluated in terms of qualitative, quantitative and categorical estimations. Although FFDs derived from the corrected WRF results well predict the spatial distribution and the variation of observation, the prediction performance has no statistical significance or appropriate predictability. The approach used in the study may be helpful in obtaining detailed and useful information about FFD and regional temperature by accounting for physically-based atmospheric dynamics, although the seasonal predictability of flowering phenology is not high enough. Acknowledgements This work was carried out with the support of the Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953 and

The added value of dynamical downscaling in a climate change scenario simulation:A case study for European Alps and East Asia

Science.gov (United States)

Im, Eun-Soon; Coppola, Erika; Giorgi, Filippo

2010-05-01

Since anthropogenic climate change is a rather important factor for the future human life all over the planet and its effects are not globally uniform, climate information at regional or local scales become more and more important for an accurate assessment of the potential impact of climate change on societies and ecosystems. High resolution information with suitably fine-scale for resolving complex geographical features could be a critical factor for successful linkage between climate models and impact assessment studies. However, scale mismatch between them still remains major problem. One method for overcoming the resolution limitations of global climate models and for adding regional details to coarse-grid global projections is to use dynamical downscaling by means of a regional climate model. In this study, the ECHAM5/MPI-OM (1.875 degree) A1B scenario simulation has been dynamically downscaled by using two different approaches within the framework of RegCM3 modeling system. First, a mosaic-type parameterization of subgrid-scale topography and land use (Sub-BATS) is applied over the European Alpine region. The Sub-BATS system is composed of 15 km coarse-grid cell and 3 km sub-grid cell. Second, we developed the RegCM3 one-way double-nested system, with the mother domain encompassing the eastern regions of Asia at 60 km grid spacing and the nested domain covering the Korean Peninsula at 20 km grid spacing. By comparing the regional climate model output and the driving global model ECHAM5/MPI-OM output, it is possible to estimate the added value of physically-based dynamical downscaling when for example impact studies at hydrological scale are performed.

Downscale cascades in tracer transport test cases: an intercomparison of the dynamical cores in the Community Atmosphere Model CAM5

Directory of Open Access Journals (Sweden)

J. Kent

2012-12-01

Full Text Available The accurate modeling of cascades to unresolved scales is an important part of the tracer transport component of dynamical cores of weather and climate models. This paper aims to investigate the ability of the advection schemes in the National Center for Atmospheric Research's Community Atmosphere Model version 5 (CAM5 to model this cascade. In order to quantify the effects of the different advection schemes in CAM5, four two-dimensional tracer transport test cases are presented. Three of the tests stretch the tracer below the scale of coarse resolution grids to ensure the downscale cascade of tracer variance. These results are compared with a high resolution reference solution, which is simulated on a resolution fine enough to resolve the tracer during the test. The fourth test has two separate flow cells, and is designed so that any tracer in the western hemisphere should not pass into the eastern hemisphere. This is to test whether the diffusion in transport schemes, often in the form of explicit hyper-diffusion terms or implicit through monotonic limiters, contains unphysical mixing.

An intercomparison of three of the dynamical cores of the National Center for Atmospheric Research's Community Atmosphere Model version 5 is performed. The results show that the finite-volume (CAM-FV and spectral element (CAM-SE dynamical cores model the downscale cascade of tracer variance better than the semi-Lagrangian transport scheme of the Eulerian spectral transform core (CAM-EUL. Each scheme tested produces unphysical mass in the eastern hemisphere of the separate cells test.

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

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Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on

Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling

Energy Technology Data Exchange (ETDEWEB)

Wang, Jiali [Environmental Science Division, Argonne National Laboratory, Argonne Illinois USA; Kotamarthi, Veerabhadra R. [Environmental Science Division, Argonne National Laboratory, Argonne Illinois USA

2014-07-27

The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures the main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.

High resolution present climate and surface mass balance (SMB) of Svalbard modelled by MAR and implementation of a new online SMB downscaling method

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Lang, C.; Fettweis, X.; Kittel, C.; Erpicum, M.

2017-12-01

We present the results of high resolution simulations of the climate and SMB of Svalbard with the regional climate model MAR forced by ERA-40 then ERA-Interim, as well as an online downscaling method allowing us to model the SMB and its components at a resolution twice as high (2.5 vs 5 km here) using only about 25% more CPU time. Spitsbergen, the largest island in Svalbard, has a very hilly topography and a high spatial resolution is needed to correctly represent the local topography and the complex pattern of ice distribution and precipitation. However, high resolution runs with an RCM fully coupled to an energy balance module like MAR require a huge amount of computation time. The hydrostatic equilibrium hypothesis used in MAR also becomes less valid as the spatial resolution increases. We therefore developed in MAR a method to run the snow module at a resolution twice as high as the atmospheric module. Near-surface temperature and humidity are corrected on a grid with a resolution twice as high, as a function of their local gradients and the elevation difference between the corresponding pixels in the 2 grids. We compared the results of our runs at 5 km and with SMB downscaled at 2.5 km over 1960 — 2016 and compared those to previous 10 km runs. On Austfonna, where the slopes are gentle, the agreement between observations and the 5 km SMB is better than with the 10 km SMB. It is again improved at 2.5 km but the gain is relatively small, showing the interest of our method rather than running a time consuming classic 2.5 km resolution simulation. On Spitsbergen, we show that a spatial resolution of 2.5 km is still not enough to represent the complex pattern of topography, precipitation and SMB. Due to a change in the summer atmospheric circulation, from a westerly flow over Svalbard to a northwesterly flow bringing colder air, the SMB of Svalbard was stable between 2006 and 2012, while several melt records were broken in Greenland, due to conditions more

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

Science.gov (United States)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area

KAUST Repository

Pizzigalli, Claudia

2012-03-28

In this paper we present and discuss a comparison between statistical and regional climate modeling techniques for downscaling GCM prediction . The comparison is carried out over the “Capitanata” region, an area of agricultural interest in south-eastern Italy, for current (1961-1990) and future (2071–2100) climate. The statistical model is based on Canonical Correlation Analysis (CCA), associated with a data pre-filtering obtained by a Principal Component Analysis (PCA), whereas the Regional Climate Model REGCM3 was used for dynamical downscaling. Downscaling techniques were applied to estimate rainfall, maximum and minimum temperatures and average number of consecutive wet and dry days. Both methods have comparable skills in estimating stations data. They show good results for spring, the most important season for agriculture. Both statistical and dynamical models reproduce the statistical properties of precipitation well, the crucial variable for the growth of crops.

Downscaling Coarse Actual ET Data Using Land Surface Resistance

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Shen, T.

2017-12-01

This study proposed a new approach of downscaling ETWATCH 1km actual evapotranspiration (ET) product to a spatial resolution of 30m using land surface resistance that simulated mainly from monthly Landsat8 data and Jarvis method, which combined the benefits of both high temporal resolution of ETWATCH product and fine spatial resolution of Landsat8. The driving factor, surface resistance (Rs), was chosen for the reason that could reflect the transfer ability of vapor flow over canopy. Combined resistance Rs both upon canopy conditions, atmospheric factors and available water content of soil, which remains stable inside one ETWATCH pixel (1km). In this research, we used ETWATCH 1km ten-day actual ET product from April to October in a total of twenty-one images and monthly 30 meters cloud-free NDVI of 2013 (two images from HJ as a substitute due to cloud contamination) combined meteorological indicators for downscaling. A good agreement and correlation were obtained between the downscaled data and three flux sites observation in the middle reach of Heihe basin. The downscaling results show good consistency with the original ETWATCH 1km data both temporal and spatial scale over different land cover types with R2 ranged from 0.8 to 0.98. Besides, downscaled result captured the progression of vegetation transpiration well. This study proved the practicability of new downscaling method in the water resource management.

Precipitation, temperature and wind in Norway: dynamical downscaling of ERA40

Energy Technology Data Exchange (ETDEWEB)

Barstad, I.; Sorteberg, A.; Flatoey, F. [Bjerknes Centre for Climate Research, Bergen (Norway); Deque, M. [Meteo France, EAC/GMGEC/CNRM, Toulouse (France)

2009-11-15

A novel downscaling approach of the ERA40 (ECMWF 40-years reanalysis) data set has been taken and results for comparison with observations in Norway are shown. The method applies a nudging technique in a stretched global model, focused in the Norwegian Sea (67 N, 5 W). The effective resolution is three times the one of the ERA40, equivalent to about 30 km grid spacing in the area of focus. Longer waves (downscaled solution are nudged towards the ERA40 solution, and thus the large-scale circulation is similar in the two data sets. The shorter waves are free to evolve, and produce high intensities of winds and precipitation. The comparison to observations incorporate numerous station data points of (1) precipitation (357), (2) temperature (98) and (3) wind (10), and for the period 1961-1990, the downscaled data set shows large improvements over ERA40. The daily precipitation shows considerable reduction in bias (from 50 to 11%), and twofold reduction at the 99.9 percentile (from -59 to -29%). The daily temperature showed a bias reduction of about a degree in most areas, and relative large RMSE reduction (from 7.5 to 5.0 C except winter). The wind comparison showed a slight improvement in bias, and significant improvements in RMSE. (orig.)

Dynamical Downscaling of GCM Simulations: Toward the Improvement of Forecast Bias over California

Energy Technology Data Exchange (ETDEWEB)

Chin, H S

2008-09-24

The effects of climate change will mostly be felt on local to regional scales. However, global climate models (GCMs) are unable to produce reliable climate information on the scale needed to assess regional climate-change impacts and variability as a result of coarse grid resolution and inadequate model physics though their capability is improving. Therefore, dynamical and statistical downscaling (SD) methods have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these downscaling techniques show that both downscaling methods have similar skill in simulating the mean and variability of present climate conditions while they show significant differences for future climate conditions (Leung et al., 2003). One difficulty with the SD method is that it relies on predictor-predict and relationships, which may not hold in future climate conditions. In addition, it is now commonly accepted that the dynamical downscaling with the regional climate model (RCM) is more skillful at the resolving orographic climate effect than the driving coarser-grid GCM simulations. To assess the possible societal impacts of climate changes, many RCMs have been developed and used to provide a better projection of future regional-scale climates for guiding policies in economy, ecosystem, water supply, agriculture, human health, and air quality (Giorgi et al., 1994; Leung and Ghan, 1999; Leung et al., 2003; Liang et al., 2004; Kim, 2004; Duffy et al., 2006). Although many regional climate features, such as seasonal mean and extreme precipitation have been successfully captured in these RCMs, obvious biases of simulated precipitation remain, particularly the winter wet bias commonly seen in mountain regions of the Western United States. The importance of regional climate research over California is not only because California has the largest population in the nation, but California has one of the most

Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

Science.gov (United States)

Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

2017-08-01

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

How does dynamical downscaling affect model biases and future projections of explosive extratropical cyclones along North America's Atlantic coast?

Science.gov (United States)

Seiler, C.; Zwiers, F. W.; Hodges, K. I.; Scinocca, J. F.

2018-01-01

Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America's Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America's Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (-0.1 day^{-1}). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to -22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (-15%, -18%) and Eady growth rate (-0.2 day^{-1}, -0.2 day^{-1}), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.

Optimizing dynamic downscaling in one-way nesting using a regional ocean model

Science.gov (United States)

Pham, Van Sy; Hwang, Jin Hwan; Ku, Hyeyun

2016-10-01

Dynamical downscaling with nested regional oceanographic models has been demonstrated to be an effective approach for both operationally forecasted sea weather on regional scales and projections of future climate change and its impact on the ocean. However, when nesting procedures are carried out in dynamic downscaling from a larger-scale model or set of observations to a smaller scale, errors are unavoidable due to the differences in grid sizes and updating intervals. The present work assesses the impact of errors produced by nesting procedures on the downscaled results from Ocean Regional Circulation Models (ORCMs). Errors are identified and evaluated based on their sources and characteristics by employing the Big-Brother Experiment (BBE). The BBE uses the same model to produce both nesting and nested simulations; so it addresses those error sources separately (i.e., without combining the contributions of errors from different sources). Here, we focus on discussing errors resulting from the spatial grids' differences, the updating times and the domain sizes. After the BBE was separately run for diverse cases, a Taylor diagram was used to analyze the results and recommend an optimal combination of grid size, updating period and domain sizes. Finally, suggested setups for the downscaling were evaluated by examining the spatial correlations of variables and the relative magnitudes of variances between the nested model and the original data.

Dynamical Downscaling of NASA/GISS ModelE: Continuous, Multi-Year WRF Simulations

Science.gov (United States)

Otte, T.; Bowden, J. H.; Nolte, C. G.; Otte, M. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D. T.

2010-12-01

The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF downscaling simulations have been performed. WRF will be used to downscale decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available. This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.

Statistical and dynamical downscaling assessments of precipitation extremes in the Mediterranean area

Energy Technology Data Exchange (ETDEWEB)

Hertig, Elke; Seubert, Stefanie; Jacobeit, Jucundus [Augsburg Univ. (Germany). Inst. of Geography; Paxian, Andreas; Vogt, Gernot; Paeth, Heiko [Wuerzburg Univ. (Germany). Inst. of Geography and Geology

2012-02-15

Extreme precipitation events in the Mediterranean area have been defined by different percentile-based indices of extreme precipitation for autumn and winter: the number of events exceeding the 95{sup th} percentile of daily precipitation, percentage, total amount, and mean daily intensity of precipitation from these events. Results from statistical downscaling applying canonical correlation analysis as well as from dynamical downscaling using the regional climate model REMO are mapped for the 1961-1990 baseline period as well as for the magnitude of change for the future time slice 2021-2050 in relation to the former period. Direct output of the coupled global circulation model ECHAM5 is used as an additional source of information. A qualitative comparison of the two different downscaling techniques indicates that under the present climate both the dynamical and the statistical techniques have skill to reproduce extreme precipitation in the Mediterranean area. A good representation of the frequency of extreme precipitation events arises from the statistical downscaling approach, whereas the intensity of such events is adequately modelled by the dynamical downscaling. Concerning the change of extreme precipitation in the Mediterranean area until the mid-21{sup st} century, it is projected that the frequency of extreme precipitation events will decrease in most parts of the Mediterranean area in autumn and winter. The change of the mean intensity of such events shows a rather heterogeneous pattern with intensity increases in winter most likely at topographical elevations exposed to the West, where the uplift of humid air profits by the increase of atmospheric moisture under climate change conditions. For the precipitation total from events exceeding the 95{sup th} percentile of daily precipitation, widespread decreases are indicated in autumn, whereas in winter increases occur over the western part of the Iberian Peninsula and southern France, and reductions over

The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

NARCIS (Netherlands)

Bonekamp, P.N.J.; Collier, S.E.; Immerzeel, W.W.

2017-01-01

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy

Spatial Downscaling of Alien Species Presences Using Machine Learning

Directory of Open Access Journals (Sweden)

Ioannis N. Daliakopoulos

2017-07-01

Full Text Available Spatially explicit assessments of alien species environmental and socio-economic impacts, and subsequent management interventions for their mitigation, require large scale, high-resolution data on species presence distribution. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A bootstrapping scheme is designed to account for sub-setting uncertainty, and subsets are used to train a sufficiently large number of RF models. RF results are processed to estimate variable importance and model performance. The method is tested with an ~8 × 8 km2 grid containing floral alien species presence and several potentially exploratory indices of climatic, habitat, land use, and soil property covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16 × 16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Uncertainty assessment of the spatial downscaling of alien species' occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution. Furthermore, the RF architecture allows for tuning toward operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Moving towards Hyper-Resolution Hydrologic Modeling

Science.gov (United States)

Rouf, T.; Maggioni, V.; Houser, P.; Mei, Y.

2017-12-01

Developing a predictive capability for terrestrial hydrology across landscapes, with water, energy and nutrients as the drivers of these dynamic systems, faces the challenge of scaling meter-scale process understanding to practical modeling scales. Hyper-resolution land surface modeling can provide a framework for addressing science questions that we are not able to answer with coarse modeling scales. In this study, we develop a hyper-resolution forcing dataset from coarser resolution products using a physically based downscaling approach. These downscaling techniques rely on correlations with landscape variables, such as topography, roughness, and land cover. A proof-of-concept has been implemented over the Oklahoma domain, where high-resolution observations are available for validation purposes. Hourly NLDAS (North America Land Data Assimilation System) forcing data (i.e., near-surface air temperature, pressure, and humidity) have been downscaled to 500m resolution over the study area for 2015-present. Results show that correlation coefficients between the downscaled temperature dataset and ground observations are consistently higher than the ones between the NLDAS temperature data at their native resolution and ground observations. Not only correlation coefficients are higher, but also the deviation around the 1:1 line in the density scatterplots is smaller for the downscaled dataset than the original one with respect to the ground observations. Results are therefore encouraging as they demonstrate that the 500m temperature dataset has a good agreement with the ground information and can be adopted to force the land surface model for soil moisture estimation. The study has been expanded to wind speed and direction, incident longwave and shortwave radiation, pressure, and precipitation. Precipitation is well known to vary dramatically with elevation and orography. Therefore, we are pursuing a downscaling technique based on both topographical and vegetation

SAGA GIS based processing of spatial high resolution temperature data

International Nuclear Information System (INIS)

Gerlitz, Lars; Bechtel, Benjamin; Kawohl, Tobias; Boehner, Juergen; Zaksek, Klemen

2013-01-01

Many climate change impact studies require surface and near surface temperature data with high spatial and temporal resolution. The resolution of state of the art climate models and remote sensing data is often by far to coarse to represent the meso- and microscale distinctions of temperatures. This is particularly the case for regions with a huge variability of topoclimates, such as mountainous or urban areas. Statistical downscaling techniques are promising methods to refine gridded temperature data with limited spatial resolution, particularly due to their low demand for computer capacity. This paper presents two downscaling approaches - one for climate model output and one for remote sensing data. Both are methodically based on the FOSS-GIS platform SAGA. (orig.)

The selective dynamical downscaling method for extreme-wind atlases

DEFF Research Database (Denmark)

Larsén, Xiaoli Guo; Badger, Jake; Hahmann, Andrea N.

2012-01-01

A selective dynamical downscaling method is developed to obtain extreme-wind atlases for large areas. The method is general, efficient and flexible. The method consists of three steps: (i) identifying storm episodes for a particular area, (ii) downscaling of the storms using mesoscale modelling...... and (iii) post-processing. The post-processing generalizes the winds from the mesoscale modelling to standard conditions, i.e. 10-m height over a homogeneous surface with roughness length of 5 cm. The generalized winds are then used to calculate the 50-year wind using the annual maximum method for each...... mesoscale grid point. The generalization of the mesoscale winds through the post-processing provides a framework for data validation and for applying further the mesoscale extreme winds at specific places using microscale modelling. The results are compared with measurements from two areas with different...

WCRP COordinated Regional Downscaling EXperiment (CORDEX: a diagnostic MIP for CMIP6

Directory of Open Access Journals (Sweden)

W. J. Gutowski Jr.

2016-11-01

Full Text Available The COordinated Regional Downscaling EXperiment (CORDEX is a diagnostic model intercomparison project (MIP in CMIP6. CORDEX builds on a foundation of previous downscaling intercomparison projects to provide a common framework for downscaling activities around the world. The CORDEX Regional Challenges provide a focus for downscaling research and a basis for making use of CMIP6 global climate model (GCM output to produce downscaled projected changes in regional climates and assess sources of uncertainties in the projections, all of which can potentially be distilled into climate change information for vulnerability, impacts and adaptation studies. CORDEX Flagship Pilot Studies advance regional downscaling by targeting one or more of the CORDEX Regional Challenges. A CORDEX-CORE framework is planned that will produce a baseline set of homogeneous high-resolution, downscaled projections for regions worldwide. In CMIP6, CORDEX coordinates with ScenarioMIP and is structured to allow cross comparisons with HighResMIP and interaction with the CMIP6 VIACS Advisory Board.

Added value of dynamical downscaling of winter seasonal forecasts over North America

Science.gov (United States)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

High-resolution regional climate model evaluation using variable-resolution CESM over California

Science.gov (United States)

Huang, X.; Rhoades, A.; Ullrich, P. A.; Zarzycki, C. M.

2015-12-01

Understanding the effect of climate change at regional scales remains a topic of intensive research. Though computational constraints remain a problem, high horizontal resolution is needed to represent topographic forcing, which is a significant driver of local climate variability. Although regional climate models (RCMs) have traditionally been used at these scales, variable-resolution global climate models (VRGCMs) have recently arisen as an alternative for studying regional weather and climate allowing two-way interaction between these domains without the need for nudging. In this study, the recently developed variable-resolution option within the Community Earth System Model (CESM) is assessed for long-term regional climate modeling over California. Our variable-resolution simulations will focus on relatively high resolutions for climate assessment, namely 28km and 14km regional resolution, which are much more typical for dynamically downscaled studies. For comparison with the more widely used RCM method, the Weather Research and Forecasting (WRF) model will be used for simulations at 27km and 9km. All simulations use the AMIP (Atmospheric Model Intercomparison Project) protocols. The time period is from 1979-01-01 to 2005-12-31 (UTC), and year 1979 was discarded as spin up time. The mean climatology across California's diverse climate zones, including temperature and precipitation, is analyzed and contrasted with the Weather Research and Forcasting (WRF) model (as a traditional RCM), regional reanalysis, gridded observational datasets and uniform high-resolution CESM at 0.25 degree with the finite volume (FV) dynamical core. The results show that variable-resolution CESM is competitive in representing regional climatology on both annual and seasonal time scales. This assessment adds value to the use of VRGCMs for projecting climate change over the coming century and improve our understanding of both past and future regional climate related to fine

Spatial Downscaling of Alien Species Presences using Machine Learning

Science.gov (United States)

Daliakopoulos, Ioannis N.; Katsanevakis, Stelios; Moustakas, Aristides

2017-07-01

Large scale, high-resolution data on alien species distributions are essential for spatially explicit assessments of their environmental and socio-economic impacts, and management interventions for mitigation. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The method is tested with an approximately 8×8 km2 grid containing floral alien species presence and several indices of climatic, habitat, land use covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16×16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Potential explanatory variables included habitat types, land cover richness, endemic species richness, soil type, temperature, precipitation, and freshwater availability. Uncertainty assessment of the spatial downscaling of alien species’ occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution than the alien species data. Furthermore, the RF architecture allows for tuning towards operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Inundation downscaling for the development of a long-term and global inundation database compatible to SWOT mission

Science.gov (United States)

Aires, Filipe; Prigent, Catherine; Papa, Fabrice

2014-05-01

-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. - Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012. - Frappart, F.; F. Papa, A. Guntner, S. Werth, J. Santos da Silva, J. Tomasella, F. Seyler, C. Prigent, W.B. Rossow, S. Calmant, and M.-P. Bonnet. Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains. Remote Sens. Environ., 115 :1588-1594, 2011.

Quasistatic zooming of FDTD E-field computations: the impact of down-scaling techniques

Energy Technology Data Exchange (ETDEWEB)

Van de Kamer, J.B.; Kroeze, H.; De Leeuw, A.A.C.; Lagendijk, J.J.W. [Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht (Netherlands)

2001-05-01

Due to current computer limitations, regional hyperthermia treatment planning (HTP) is practically limited to a resolution of 1 cm, whereas a millimetre resolution is desired. Using the centimetre resolution E-vector-field distribution, computed with, for example, the finite-difference time-domain (FDTD) method and the millimetre resolution patient anatomy it is possible to obtain a millimetre resolution SAR distribution in a volume of interest (VOI) by means of quasistatic zooming. To compute the required low-resolution E-vector-field distribution, a low-resolution dielectric geometry is needed which is constructed by down-scaling the millimetre resolution dielectric geometry. In this study we have investigated which down-scaling technique results in a dielectric geometry that yields the best low-resolution E-vector-field distribution as input for quasistatic zooming. A segmented 2 mm resolution CT data set of a patient has been down-scaled to 1 cm resolution using three different techniques: 'winner-takes-all', 'volumetric averaging' and 'anisotropic volumetric averaging'. The E-vector-field distributions computed for those low-resolution dielectric geometries have been used as input for quasistatic zooming. The resulting zoomed-resolution SAR distributions were compared with a reference: the 2 mm resolution SAR distribution computed with the FDTD method. The E-vector-field distribution for both a simple phantom and the complex partial patient geometry down-scaled using 'anisotropic volumetric averaging' resulted in zoomed-resolution SAR distributions that best approximate the corresponding high-resolution SAR distribution (correlation 97, 96% and absolute averaged difference 6, 14% respectively). (author)

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

Science.gov (United States)

Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.

2012-04-01

Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.

Science.gov (United States)

Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W

2016-07-05

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.

Detectors for high resolution dynamic pet

International Nuclear Information System (INIS)

Derenzo, S.E.; Budinger, T.F.; Huesman, R.H.

1983-05-01

This report reviews the motivation for high spatial resolution in dynamic positron emission tomography of the head and the technical problems in realizing this objective. We present recent progress in using small silicon photodiodes to measure the energy deposited by 511 keV photons in small BGO crystals with an energy resolution of 9.4% full-width at half-maximum. In conjunction with a suitable phototube coupled to a group of crystals, the photodiode signal to noise ratio is sufficient for the identification of individual crystals both for conventional and time-of-flight positron tomography

Comparison of two down-scaling methods for climate study and climate change on the mountain areas in France

International Nuclear Information System (INIS)

Piazza, Marie; Page, Christian; Sanchez-Gomez, Emilia; Terray, Laurent; Deque, Michel

2013-01-01

Mountain regions are highly vulnerable to climate change and are likely to be among the areas most impacted by global warming. But climate projections for the end of the 21. century are developed with general circulation models of climate, which do not present a sufficient horizontal resolution to accurately evaluate the impacts of warming on these regions. Several techniques are then used to perform a spatial down-scaling (on the order of 10 km). There are two categories of down-scaling methods: dynamical methods that require significant computational resources for the achievement of regional climate simulations at high resolution, and statistical methods that require few resources but an observation dataset over a long period and of good quality. In this study, climate simulations of the global atmospheric model ARPEGE projections over France are down-scaled according to a dynamical method, performed with the ALADIN-Climate regional model, and a statistical method performed with the software DSClim developed at CERFACS. The two down-scaling methods are presented and the results on the climate of the French mountains are evaluated for the current climate. Both methods give similar results for average snowfall. However extreme events of total precipitation (droughts, intense precipitation events) are largely underestimated by the statistical method. Then, the results of both methods are compared for two future climate projections, according to the greenhouse gas emissions scenario A1B of IPCC. The two methods agree on fewer frost days, a significant decrease in the amounts of solid precipitation and an average increase in the percentage of dry days of more than 10%. The results obtained on Corsica are more heterogeneous but they are questionable because the reduced spatial domain is probably not very relevant regarding statistical sampling. (authors)

A comparison of dynamical and statistical downscaling methods for regional wave climate projections along French coastlines.

Science.gov (United States)

Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando

2013-04-01

Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical downscaling of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical downscaling method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at

Downscaling the climate change for oceans around Australia

Directory of Open Access Journals (Sweden)

M. A. Chamberlain

2012-09-01

Full Text Available At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere.

A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model.

The downscaling model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air–sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. Also, the spatial differences become established rapidly (< 3 yr, indicating the importance of mesoscale resolution. However, the differences in the magnitude of the difference between experiments show that feedback of the ocean onto the air–sea fluxes is still important in determining the state of the ocean in these projections.

Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change

High-Resolution Regional Reanalysis in China: Evaluation of 1 Year Period Experiments

Science.gov (United States)

Zhang, Qi; Pan, Yinong; Wang, Shuyu; Xu, Jianjun; Tang, Jianping

2017-10-01

Globally, reanalysis data sets are widely used in assessing climate change, validating numerical models, and understanding the interactions between the components of a climate system. However, due to the relatively coarse resolution, most global reanalysis data sets are not suitable to apply at the local and regional scales directly with the inadequate descriptions of mesoscale systems and climatic extreme incidents such as mesoscale convective systems, squall lines, tropical cyclones, regional droughts, and heat waves. In this study, by using a data assimilation system of Gridpoint Statistical Interpolation, and a mesoscale atmospheric model of Weather Research and Forecast model, we build a regional reanalysis system. This is preliminary and the first experimental attempt to construct a high-resolution reanalysis for China main land. Four regional test bed data sets are generated for year 2013 via three widely used methods (classical dynamical downscaling, spectral nudging, and data assimilation) and a hybrid method with data assimilation coupled with spectral nudging. Temperature at 2 m, precipitation, and upper level atmospheric variables are evaluated by comparing against observations for one-year-long tests. It can be concluded that the regional reanalysis with assimilation and nudging methods can better produce the atmospheric variables from surface to upper levels, and regional extreme events such as heat waves, than the classical dynamical downscaling. Compared to the ERA-Interim global reanalysis, the hybrid nudging method performs slightly better in reproducing upper level temperature and low-level moisture over China, which improves regional reanalysis data quality.

Comparing the urbanization and global warming impacts on extreme rainfall characteristics in Southern China Pearl River Delta megacity based on dynamical downscaling

Science.gov (United States)

Fung, K. Y.; Tam, C. Y.; Wang, Z.

2017-12-01

It is well known that urban land use can significantly influence the local temperature, precipitation and meteorology through altering land-atmosphere exchange of momentum, moisture and heat in urban areas. In recent decades, there has been a substantial increase ( 5-10%) on the intensity of extreme rainfall over Southeast China; it is projected to increase further according to the latest IPCC reports. In this study, we assess how urbanization and global warming together might impact on heavy precipitation characteristics over the highly urbanized Pearl River Delta (PRD) megacity, located in southern China. This is done by dynamically downscaling GFDL-ESM2M simulations for the present and future (RCP8.5) climate scenarios, using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model (UCM). Over the PRD area, the WRF model is integrated at a resolution of 2km x 2km. To focus on extreme events, episodes covering daily rainfall intensity above the 99th percentile in Southeast China in the GFDL-ESM2M daily precipitation datasets were first identified. These extreme episodes were then dynamically downscaled in two parallel experiments with the following model designs: one with anthropogenic heat flux (AH) = 0 Wm-2 and the other with peak AH = 300 Wm-2 in the AH diurnal cycle over the urban domain. Results show that, with AH in urban area, the urban 2m-temperature can rise by about 2oC. This in turn leads to an increase of the mean as well as the extreme rain rates by 10-15% in urban domain. The latter is comparable to the impact of global warming alone, according to downscaling experiments for the RCP8.5 scenario. Implications of our results on urban effects on extreme rainfall under a warming background climate will be discussed.

Dynamic high resolution imaging of rats

International Nuclear Information System (INIS)

Miyaoka, R.S.; Lewellen, T.K.; Bice, A.N.

1990-01-01

A positron emission tomography with the sensitivity and resolution to do dynamic imaging of rats would be an invaluable tool for biological researchers. In this paper, the authors determine the biological criteria for dynamic positron emission imaging of rats. To be useful, 3 mm isotropic resolution and 2-3 second time binning were necessary characteristics for such a dedicated tomograph. A single plane in which two objects of interest could be imaged simultaneously was considered acceptable. Multi-layered detector designs were evaluated as a possible solution to the dynamic imaging and high resolution imaging requirements. The University of Washington photon history generator was used to generate data to investigate a tomograph's sensitivity to true, scattered and random coincidences for varying detector ring diameters. Intrinsic spatial uniformity advantages of multi-layered detector designs over conventional detector designs were investigated using a Monte Carlo program. As a result, a modular three layered detector prototype is being developed. A module will consist of a layer of five 3.5 mm wide crystals and two layers of six 2.5 mm wide crystals. The authors believe adequate sampling can be achieved with a stationary detector system using these modules. Economical crystal decoding strategies have been investigated and simulations have been run to investigate optimum light channeling methods for block decoding strategies. An analog block decoding method has been proposed and will be experimentally evaluated to determine whether it can provide the desired performance

TopoSCALE v.1.0: downscaling gridded climate data in complex terrain

Science.gov (United States)

Fiddes, J.; Gruber, S.

2014-02-01

Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of

An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations

KAUST Repository

Xu, Zhongfeng

2012-09-01

An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research\\'s (NCAR\\'s) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)-NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.-Canada region as well as in summer and winter daily precipitation over the middle and eastern United States. © 2012 American Meteorological Society.

Estimation of the high-spatial-resolution variability in extreme wind speeds for forestry applications

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A. Venäläinen

2017-07-01

Full Text Available The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount of wind damage for certain forest stand configurations.

On the downscaling of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates

Science.gov (United States)

Singh, Ramesh K.; Senay, Gabriel B.; Velpuri, Naga Manohar; Bohms, Stefanie; Verdin, James P.

2014-01-01

 Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

Downscaling global precipitation for local applications - a case for the Rhine basin

Science.gov (United States)

Sperna Weiland, Frederiek; van Verseveld, Willem; Schellekens, Jaap

2017-04-01

Within the EU FP7 project eartH2Observe a global Water Resources Re-analysis (WRR) is being developed. This re-analysis consists of meteorological and hydrological water balance variables with global coverage, spanning the period 1979-2014 at 0.25 degrees resolution (Schellekens et al., 2016). The dataset can be of special interest in regions with limited in-situ data availability, yet for local scale analysis particularly in mountainous regions, a resolution of 0.25 degrees may be too coarse and downscaling the data to a higher resolution may be required. A downscaling toolbox has been made that includes spatial downscaling of precipitation based on the global WorldClim dataset that is available at 1 km resolution as a monthly climatology (Hijmans et al., 2005). The input of the down-scaling tool are either the global eartH2Observe WRR1 and WRR2 datasets based on the WFDEI correction methodology (Weedon et al., 2014) or the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2016). Here we present a validation of the datasets over the Rhine catchment by means of a distributed hydrological model (wflow, Schellekens et al., 2014) using a number of precipitation scenarios. (1) We start by running the model using the local reference dataset derived by spatial interpolation of gauge observations. Furthermore we use (2) the MSWEP dataset at the native 0.25-degree resolution followed by (3) MSWEP downscaled with the WorldClim dataset and final (4) MSWEP downscaled with the local reference dataset. The validation will be based on comparison of the modeled river discharges as well as rainfall statistics. We expect that down-scaling the MSWEP dataset with the WorldClim data to higher resolution will increase its performance. To test the performance of the down-scaling routine we have added a run with MSWEP data down-scaled with the local dataset and compare this with the run based on the local dataset itself. - Beck, H. E. et al., 2016. MSWEP

The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

OpenAIRE

Bonekamp, P.N.J.; Collier, S.E.; Immerzeel, W.W.

2017-01-01

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields, as simulations at higher spatial resolution are more capable of resolving the interaction between the topography and the atmosphere. However, most physics par...

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

Science.gov (United States)

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

A high-resolution regional reanalysis for Europe

Science.gov (United States)

Ohlwein, C.

2015-12-01

Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. The regional reanalysis for Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km) and comprises the assimilation of observational data using the existing nudging scheme of COSMO complemented by a special soil moisture analysis with boundary conditions provided by ERA-Interim data. The reanalysis data set covers the past 20 years. Extensive evaluation of the reanalysis is performed using independent observations with special emphasis on precipitation and high-impact weather situations indicating a better representation of small scale variability. Further, the evaluation shows an added value of the regional reanalysis with respect to the forcing ERA Interim reanalysis and compared to a pure high-resolution dynamical downscaling approach without data assimilation.

Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

Science.gov (United States)

Dairaku, K.

2017-12-01

The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.

An Observation-base investigation of nudging in WRF for downscaling surface climate information to 12-km Grid Spacing

Science.gov (United States)

Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...

Data Driven Approach for High Resolution Population Distribution and Dynamics Models

Energy Technology Data Exchange (ETDEWEB)

Bhaduri, Budhendra L [ORNL; Bright, Eddie A [ORNL; Rose, Amy N [ORNL; Liu, Cheng [ORNL; Urban, Marie L [ORNL; Stewart, Robert N [ORNL

2014-01-01

High resolution population distribution data are vital for successfully addressing critical issues ranging from energy and socio-environmental research to public health to human security. Commonly available population data from Census is constrained both in space and time and does not capture population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event-based simulation models with sensitive space-time resolution. This paper describes ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. Integration of such population data provides the opportunity to develop simulations and applications in critical infrastructure management from local to global scales.

Evaluation of downscaled, gridded climate data for the conterminous United States

Science.gov (United States)

Robert J. Behnke,; Stephen J. Vavrus,; Andrew Allstadt,; Thomas P. Albright,; Thogmartin, Wayne E.; Volker C. Radeloff,

2016-01-01

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model

Energy Technology Data Exchange (ETDEWEB)

Heikkilae, U. [Bjerknes Centre for Climate Research, Uni Bjerknes Centre, Bergen (Norway); Sandvik, A. [Bjerknes Centre for Climate Research, Institute for Marine Research (IMR), Bergen (Norway); Sorteberg, A. [University of Bergen, Geophysical Institute, Bergen (Norway)

2011-10-15

Results from a first-time employment of the WRF regional climate model to climatological simulations in Europe are presented. The ERA-40 reanalysis (resolution 1 ) has been downscaled to a horizontal resolution of 30 and 10 km for the period of 1961-1990. This model setup includes the whole North Atlantic in the 30 km domain and spectral nudging is used to keep the large scales consistent with the driving ERA-40 reanalysis. The model results are compared against an extensive observational network of surface variables in complex terrain in Norway. The comparison shows that the WRF model is able to add significant detail to the representation of precipitation and 2-m temperature of the ERA-40 reanalysis. Especially the geographical distribution, wet day frequency and extreme values of precipitation are highly improved due to the better representation of the orography. Refining the resolution from 30 to 10 km further increases the skill of the model, especially in case of precipitation. Our results indicate that the use of 10-km resolution is advantageous for producing regional future climate projections. Use of a large domain and spectral nudging seems to be useful in reproducing the extreme precipitation events due to the better resolved synoptic scale features over the North Atlantic, and also helps to reduce the large regional temperature biases over Norway. This study presents a high-resolution, high-quality climatological data set useful for reference climate impact studies. (orig.)

Temperature Changes In Poland In 21st Century – Results Of Global Simulation And Regional Downscaling

Directory of Open Access Journals (Sweden)

Pilarski Michał

2015-09-01

Full Text Available The main source of information about future climate changes are the results of numerical simulations performed in scientific institutions around the world. Present projections from global circulation models (GCMs are too coarse and are only usefulness for the world, hemisphere or continent spatial analysis. The low horizontal resolution of global models (100–200 km, does not allow to assess climate changes at regional or local scales. Therefore it is necessary to lead studies concerning how to detail the GCMs information. The problem of information transfer from the GCMs to higher spatial scale solve: dynamical and statistical downscaling. The dynamical downscaling method based on “nesting” global information in a regional models (RCMs, which solve the equations of motion and the thermodynamic laws in a small spatial scale (10–50 km. However, the statistical downscaling models (SDMs identify the relationship between large-scale variable (predictor and small-scale variable (predictand implementing linear regression. The main goal of the study was to compare the global model scenarios of thermal condition in Poland in XXI century with the more accurate statistical and dynamical regional models outcomes. Generally studies confirmed usefulness of statistical downscaling to detail information from GCMs. Basic results present that regional models captured local aspects of thermal conditions variability especially in coastal zone.

Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach

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Rihab Mechri

2016-07-01

Full Text Available A wide range of environmental applications require the monitoring of land surface temperature (LST at frequent intervals and fine spatial resolutions, but these conditions are not offered nowadays by the available space sensors. To overcome these shortcomings, LST downscaling methods have been developed to derive higher resolution LST from the available satellite data. This research concerns the application of a data assimilation (DA downscaling approach, the genetic particle smoother (GPS, to disaggregate Meteosat 8 LST time series (3 km × 5 km at finer spatial resolutions. The methodology was applied over the Crau-Camargue region in Southeastern France for seven months in 2009. The evaluation of the downscaled LSTs has been performed at a moderate resolution using a set of coincident clear-sky MODIS LST images from Aqua and Terra platforms (1 km × 1 km and at a higher resolution using Landsat 7 data (60 m × 60 m. The performance of the downscaling has been assessed in terms of reduction of the biases and the root mean square errors (RMSE compared to prior model-simulated LSTs. The results showed that GPS allows downscaling the Meteosat LST product from 3 × 5 km2 to 1 × 1 km2 scales with a RMSE less than 2.7 K. Finer scale downscaling at Landsat 7 resolution showed larger errors (RMSE around 5 K explained by land cover errors and inter-calibration issues between sensors. Further methodology improvements are finally suggested.

Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods

Directory of Open Access Journals (Sweden)

A. Casanueva

2013-08-01

Full Text Available The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles. In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th and low (5th percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different downscaling methods (statistical and dynamical. First, we analyse the performance of reanalysis-driven downscaling methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an ensemble of methodologies.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

Science.gov (United States)

Duffaut Espinosa, L A; Posadas, A N; Carbajal, M; Quiroz, R

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

Science.gov (United States)

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the

Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

Science.gov (United States)

Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

2018-01-01

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970-2000 as well as future period of 2010-2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.

The high-resolution regional reanalysis COSMO-REA6

Science.gov (United States)

Ohlwein, C.

2016-12-01

Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. The regional reanalysis for Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km) and comprises the assimilation of observational data using the existing nudging scheme of COSMO complemented by a special soil moisture analysis with boundary conditions provided by ERA-Interim data. The reanalysis data set covers the past 20 years. Extensive evaluation of the reanalysis is performed using independent observations with special emphasis on precipitation and high-impact weather situations indicating a better representation of small scale variability. Further, the evaluation shows an added value of the regional reanalysis with respect to the forcing ERA Interim reanalysis and compared to a pure high-resolution dynamical downscaling approach without data assimilation.

Representation of spatial and temporal variability of daily wind speed and of intense wind events over the Mediterranean Sea using dynamical downscaling: impact of the regional climate model configuration

Directory of Open Access Journals (Sweden)

M. Herrmann

2011-07-01

Full Text Available Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea.

First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ~0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology.

Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally improves the representation of spatial

Downscaling the Local Weather Above Glaciers in Complex Topography

Science.gov (United States)

Horak, Johannes; Hofer, Marlis; Gutmann, Ethan; Gohm, Alexander; Rotach, Mathias

2017-04-01

Glaciers have experienced a substantial ice-volume loss during the 20th century. To study their response to climate change, process-based glacier mass-balance models (PBGMs) are employed, which require a faithful representation of the state of the atmosphere above the glacier at high spatial and temporal resolution. Glaciers are usually located in complex topography where weather stations are scarce or not existent at all due to the remoteness of such sites and the associated high cost of maintenance. Furthermore. the effective resolution of global circulation models is too large to adequately capture the local topography and represent local weather, which is prerequisite for atmospheric input used by PBGMs. Dynamical downscaling is a physically consistent but computationally expensive approach to bridge the scale gap between GCM output and input needed by PBGMs, while statistical downscaling is faster but requires measurements for training. Both methods have their merits, however, a computationally frugal approach that does not rely on measurements is desirable, especially for long term studies of glacier response to future climate. In this study the intermediate complexity atmospheric research model (ICAR) is employed (Gutmann et al., 2016). It simplifies the wind field physics by relying on analytical solutions derived with linear theory. ICAR then advects atmospheric quantities within this wind field. This allows for computationally fast downscaling and yields a physically consistent set of atmospheric variables. First results obtained from downscaling air temperature, precipitation amount, relative humidity and wind speed to 4 × 4 km2 are presented. Preliminary ICAR is applied for a six month simulation period during five years and evaluated for three domains located in very distinct climates, namely the Southern Alps of New Zealand, the Cordillera Blanca in Peru and the European Alps using ERA Interim reanalysis data (ERAI) as forcing data set. The

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

Science.gov (United States)

Boyte, Stephen; Wylie, Bruce K.; Rigge, Matthew B.; Dahal, Devendra

2018-01-01

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

Science.gov (United States)

Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

2013-01-01

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

Evaluation of a High-Resolution Regional Reanalysis for Europe

Science.gov (United States)

Ohlwein, C.; Wahl, S.; Keller, J. D.; Bollmeyer, C.

2014-12-01

Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. The regional reanalysis for Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km) and comprises the assimilation of observational data using the existing nudging scheme of COSMO complemented by a special soil moisture analysis with boundary conditions provided by ERA-Interim data. The reanalysis data set covers 6 years (2007-2012) and is currently extended to 16 years. Extensive evaluation of the reanalysis is performed using independent observations with special emphasis on precipitation and high-impact weather situations indicating a better representation of small scale variability. Further, the evaluation shows an added value of the regional reanalysis with respect to the forcing ERA Interim reanalysis and compared to a pure high-resolution dynamical downscaling approach without data assimilation.

Dynamically downscaled climate simulations over North America: Methods, evaluation, and supporting documentation for users

Science.gov (United States)

Hostetler, S.W.; Alder, J.R.; Allan, A.M.

2011-01-01

We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968-1999), common periods of the future (2040-2069), and two simulations continuously cover 2010-2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968-1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and

Statistical downscaling of rainfall: a non-stationary and multi-resolution approach

Science.gov (United States)

Rashid, Md. Mamunur; Beecham, Simon; Chowdhury, Rezaul Kabir

2016-05-01

A novel downscaling technique is proposed in this study whereby the original rainfall and reanalysis variables are first decomposed by wavelet transforms and rainfall is modelled using the semi-parametric additive model formulation of Generalized Additive Model in Location, Scale and Shape (GAMLSS). The flexibility of the GAMLSS model makes it feasible as a framework for non-stationary modelling. Decomposition of a rainfall series into different components is useful to separate the scale-dependent properties of the rainfall as this varies both temporally and spatially. The study was conducted at the Onkaparinga river catchment in South Australia. The model was calibrated over the period 1960 to 1990 and validated over the period 1991 to 2010. The model reproduced the monthly variability and statistics of the observed rainfall well with Nash-Sutcliffe efficiency (NSE) values of 0.66 and 0.65 for the calibration and validation periods, respectively. It also reproduced well the seasonal rainfall over the calibration (NSE = 0.37) and validation (NSE = 0.69) periods for all seasons. The proposed model was better than the tradition modelling approach (application of GAMLSS to the original rainfall series without decomposition) at reproducing the time-frequency properties of the observed rainfall, and yet it still preserved the statistics produced by the traditional modelling approach. When downscaling models were developed with general circulation model (GCM) historical output datasets, the proposed wavelet-based downscaling model outperformed the traditional downscaling model in terms of reproducing monthly rainfall for both the calibration and validation periods.

Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

DEFF Research Database (Denmark)

Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

2014-01-01

energy. The present study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by downscaling global numerical weather prediction (NWP) data from the National Centers for Environmental Prediction Global Forecast System (GFS) using the limited-area NWP model described...

High-resolution dynamic downscaling of CMIP5 output over the Tropical Andes

Science.gov (United States)

Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

2015-04-01

Our project is targeted towards making robust predictions of future changes in climate over the tropical part of the South American Andes. This goal is challenging, since tropical lowlands, steep mountains, and snow covered subarctic surfaces meet over relatively short distances, leading to distinct climate regimes within the same domain and pronounced spatial gradients in virtually every climate quantity. We use an innovative approach to solve this problem, including several quadruple nested versions of WRF, a systematic validation strategy to find the version of WRF that best fits our study region, spatial resolutions at the kilometer scale, 20-year-long simulation periods, and bias-corrected output from various CMIP5 simulations that also include the multi-model mean of all CMIP5 models. We show that the simulated changes in climate are consistent with the results from the global climate models and also consistent with two different versions of WRF. We also discuss the expected changes in snow and ice, derived from off-line coupling the regional simulations to a carefully calibrated snow and ice model.

Bioclim Deliverable D8b: development of the physical/statistical down-scaling methodology and application to climate model Climber for BIOCLIM Work-package 3

International Nuclear Information System (INIS)

2003-01-01

The overall aim of BIOCLIM is to assess the possible long term impacts due to climate change on the safety of radioactive waste repositories in deep formations. The main aim of this deliverable is to provide time series of climatic variables at the high resolution as needed by performance assessment (PA) of radioactive waste repositories, on the basis of coarse output from the CLIMBER-GREMLINS climate model. The climatological variables studied here are long-term (monthly) mean temperature and precipitation, as these are the main variables of interest for performance assessment. CLIMBER-GREMLINS is an earth-system model of intermediate complexity (EMIC), designed for long climate simulations (glacial cycles). Thus, this model has a coarse resolution (about 50 degrees in longitude) and other limitations which are sketched in this report. For the purpose of performance assessment, the climatological variables are required at scales pertinent for the knowledge of the conditions at the depository site. In this work, the final resolution is that of the best available global gridded present-day climatology, which is 1/6 degree in both longitude and latitude. To obtain climate-change information at this high resolution on the basis of the climate model outputs, a 2-step down-scaling method is designed. First, physical considerations are used to define variables which are expected to have links which climatological values; secondly a statistical model is used to find the links between these variables and the high-resolution climatology of temperature and precipitation. Thus the method is termed as 'physical/statistical': it involves physically based assumptions to compute predictors from model variables and then relies on statistics to find empirical links between these predictors and the climatology. The simple connection of coarse model results to regional values can not be done on a purely empirical way because the model does not provide enough information - it is both

Spatial Downscaling of TRMM Precipitation using MODIS product in the Korean Peninsula

Science.gov (United States)

Cho, H.; Choi, M.

2013-12-01

Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a downscaling technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the downscaled precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The downscaled precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial downscaling of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.

Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

Science.gov (United States)

Khan, Firdos; Pilz, Jürgen

2016-04-01

South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological

Sensitivity Evaluation of Spectral Nudging Schemes in Historical Dynamical Downscaling for South Asia

Directory of Open Access Journals (Sweden)

Mehwish Ramzan

2017-01-01

Full Text Available Sensitivity experiments testing two scale-selective bias correction (SSBC methods have been carried out to identify an optimal spectral nudging scheme for historical dynamically downscaled simulations of South Asia, using the coordinated regional climate downscaling experiment (CORDEX protocol and the regional spectral model (RSM. Two time periods were selected under the category of short-term extreme summer and long-term decadal analysis. The new SSBC version applied nudging to full wind components, with an increased relaxation time in the lower model layers, incorporating a vertical weighted damping coefficient. An evaluation of the extraordinary weather conditions experienced in South Asia in the summer of 2005 confirmed the advantages of the new SSBC when modeling monsoon precipitation. Furthermore, the new SSBC scheme was found to predict precipitation and wind patterns more accurately than the older version in decadal analysis, which applies nudging only to the rotational wind field, with a constant strength at all heights.

The simulation of medicanes in a high-resolution regional climate model

Energy Technology Data Exchange (ETDEWEB)

Cavicchia, Leone [Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna (Italy); Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, Geesthacht (Germany); Ca' Foscari University, Venice (Italy); Storch, Hans von [Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, Geesthacht (Germany); University of Hamburg, Meteorological Institute, Hamburg (Germany)

2012-11-15

Medicanes, strong mesoscale cyclones with tropical-like features, develop occasionally over the Mediterranean Sea. Due to the scarcity of observations over sea and the coarse resolution of the long-term reanalysis datasets, it is difficult to study systematically the multidecadal statistics of sub-synoptic medicanes. Our goal is to assess the long-term variability and trends of medicanes, obtaining a long-term climatology through dynamical downscaling of the NCEP/NCAR reanalysis data. In this paper, we examine the robustness of this method and investigate the value added for the study of medicanes. To do so, we performed several climate mode simulations with a high resolution regional atmospheric model (CCLM) for a number of test cases described in the literature. We find that the medicanes are formed in the simulations, with deeper pressures and stronger winds than in the driving global NCEP reanalysis. The tracks are adequately reproduced. We conclude that our methodology is suitable for constructing multi-decadal statistics and scenarios of current and possible future medicane activities. (orig.)

Towards high resolution mapping of 3-D mesoscale dynamics from observations

Directory of Open Access Journals (Sweden)

B. Buongiorno Nardelli

2012-10-01

Full Text Available The MyOcean R&D project MESCLA (MEsoSCaLe dynamical Analysis through combined model, satellite and in situ data was devoted to the high resolution 3-D retrieval of tracer and velocity fields in the oceans, based on the combination of in situ and satellite observations and quasi-geostrophic dynamical models. The retrieval techniques were also tested and compared with the output of a primitive equation model, with particular attention to the accuracy of the vertical velocity field as estimated through the Q vector formulation of the omega equation. The project focused on a test case, covering the region where the Gulf Stream separates from the US East Coast. This work demonstrated that innovative methods for the high resolution mapping of 3-D mesoscale dynamics from observations can be used to build the next generations of operational observation-based products.

The response of future projections of the North American monsoon when combining dynamical downscaling and bias correction of CCSM4 output

Science.gov (United States)

Meyer, Jonathan D. D.; Jin, Jiming

2017-07-01

A 20-km regional climate model (RCM) dynamically downscaled the Community Climate System Model version 4 (CCSM4) to compare 32-year historical and future "end-of-the-century" climatologies of the North American Monsoon (NAM). CCSM4 and other phase 5 Coupled Model Intercomparison Project models have indicated a delayed NAM and overall general drying trend. Here, we test the suggested mechanism for this drier NAM where increasing atmospheric static stability and reduced early-season evapotranspiration under global warming will limit early-season convection and compress the mature-season of the NAM. Through our higher resolution RCM, we found the role of accelerated evaporation under a warmer climate is likely understated in coarse resolution models such as CCSM4. Improving the representation of mesoscale interactions associated with the Gulf of California and surrounding topography produced additional surface evaporation, which overwhelmed the convection-suppressing effects of a warmer troposphere. Furthermore, the improved land-sea temperature gradient helped drive stronger southerly winds and greater moisture transport. Finally, we addressed limitations from inherent CCSM4 biases through a form of mean bias correction, which resulted in a more accurate seasonality of the atmospheric thermodynamic profile. After bias correction, greater surface evaporation from average peak GoC SSTs of 32 °C compared to 29 °C from the original CCSM4 led to roughly 50 % larger changes to low-level moist static energy compared to that produced by the downscaled original CCSM4. The increasing destabilization of the NAM environment produced onset dates that were one to 2 weeks earlier in the core of the NAM and northern extent, respectively. Furthermore, a significantly more vigorous NAM signal was produced after bias correction, with >50 mm month-1 increases to the June-September precipitation found along east and west coasts of Mexico and into parts of Texas. A shift towards more

Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale

Energy Technology Data Exchange (ETDEWEB)

Teutschbein, Claudia [Stockholm University, Department of Physical Geography and Quaternary Geology, Stockholm (Sweden); Wetterhall, Fredrik [King' s College London, Department of Geography, Strand, London (United Kingdom); Swedish Meteorological and Hydrological Institute, Norrkoeping (Sweden); Seibert, Jan [Stockholm University, Department of Physical Geography and Quaternary Geology, Stockholm (Sweden); Uppsala University, Department of Earth Sciences, Uppsala (Sweden); University of Zurich, Department of Geography, Zurich (Switzerland)

2011-11-15

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961-1990) and for two future emission scenarios (2071-2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions. (orig.)

Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system

International Nuclear Information System (INIS)

Gao, Y; Fu, J S; Drake, J B; Liu, Y; Lamarque, J-F

2012-01-01

This study is the first evaluation of dynamical downscaling using the Weather Research and Forecasting (WRF) Model on a 4 km × 4 km high resolution scale in the eastern US driven by the new Community Earth System Model version 1.0 (CESM v1.0). First we examined the global and regional climate model results, and corrected an inconsistency in skin temperature during the downscaling process by modifying the land/sea mask. In comparison with observations, WRF shows statistically significant improvement over CESM in reproducing extreme weather events, with improvement for heat wave frequency estimation as high as 98%. The fossil fuel intensive scenario Representative Concentration Pathway (RCP) 8.5 was used to study a possible future mid-century climate extreme in 2057–9. Both the heat waves and the extreme precipitation in 2057–9 are more severe than the present climate in the Eastern US. The Northeastern US shows large increases in both heat wave intensity (3.05 °C higher) and annual extreme precipitation (107.3 mm more per year). (letter)

An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations

KAUST Repository

Xu, Zhongfeng; Yang, Zong-Liang

2012-01-01

An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model

A new dynamical downscaling approach with GCM bias corrections and spectral nudging

Science.gov (United States)

Xu, Zhongfeng; Yang, Zong-Liang

2015-04-01

To improve confidence in regional projections of future climate, a new dynamical downscaling (NDD) approach with both general circulation model (GCM) bias corrections and spectral nudging is developed and assessed over North America. GCM biases are corrected by adjusting GCM climatological means and variances based on reanalysis data before the GCM output is used to drive a regional climate model (RCM). Spectral nudging is also applied to constrain RCM-based biases. Three sets of RCM experiments are integrated over a 31 year period. In the first set of experiments, the model configurations are identical except that the initial and lateral boundary conditions are derived from either the original GCM output, the bias-corrected GCM output, or the reanalysis data. The second set of experiments is the same as the first set except spectral nudging is applied. The third set of experiments includes two sensitivity runs with both GCM bias corrections and nudging where the nudging strength is progressively reduced. All RCM simulations are assessed against North American Regional Reanalysis. The results show that NDD significantly improves the downscaled mean climate and climate variability relative to other GCM-driven RCM downscaling approach in terms of climatological mean air temperature, geopotential height, wind vectors, and surface air temperature variability. In the NDD approach, spectral nudging introduces the effects of GCM bias corrections throughout the RCM domain rather than just limiting them to the initial and lateral boundary conditions, thereby minimizing climate drifts resulting from both the GCM and RCM biases.

A hybrid downscaling procedure for estimating the vertical distribution of ambient temperature in local scale

Science.gov (United States)

Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.

2012-04-01

The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical downscaling procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic downscaling of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical downscaling of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic downscaling element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical downscaling element of the methodology consists of an ensemble of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input

Detailed Urban Heat Island Projections for Cities Worldwide: Dynamical Downscaling CMIP5 Global Climate Models

Directory of Open Access Journals (Sweden)

Dirk Lauwaet

2015-06-01

Full Text Available A new dynamical downscaling methodology to analyze the impact of global climate change on the local climate of cities worldwide is presented. The urban boundary layer climate model UrbClim is coupled to 11 global climate models contained in the Coupled Model Intercomparison Project 5 archive, conducting 20-year simulations for present (1986–2005 and future (2081–2100 climate conditions, considering the Representative Concentration Pathway 8.5 climate scenario. The evolution of the urban heat island of eight different cities, located on three continents, is quantified and assessed, with an unprecedented horizontal resolution of a few hundred meters. For all cities, urban and rural air temperatures are found to increase strongly, up to 7 °C. However, the urban heat island intensity in most cases increases only slightly, often even below the range of uncertainty. A potential explanation, focusing on the role of increased incoming longwave radiation, is put forth. Finally, an alternative method for generating urban climate projections is proposed, combining the ensemble temperature change statistics and the results of the present-day urban climate.

Detectors for high resolution dynamic positron emission tomography

International Nuclear Information System (INIS)

Derenzo, S.E.; Budinger, T.F.; Huesman, R.H.

1985-01-01

Tomography is the technique of producing a photographic image of an opaque specimen by transmitting a beam of x-rays or gamma rays through the specimen onto an adjacent photographic film. The image results from variations in thickness, density, and chemical composition, of the specimen. This technique is used to study the metabolism of the human brain. This article examines the design of equipment used for high resolution dynamic positron emission tomography. 27 references, 5 figures, 3 tables

Dynamic Raman imaging system with high spatial and temporal resolution

Science.gov (United States)

Wang, Lei; Dai, Yinzhen; He, Hao; Lv, Ruiqi; Zong, Cheng; Ren, Bin

2017-09-01

There is an increasing need to study dynamic changing systems with significantly high spatial and temporal resolutions. In this work, we integrated point-scanning, line-scanning, and wide-field Raman imaging techniques into a single system. By using an Electron Multiplying CCD (EMCCD) with a high gain and high frame rate, we significantly reduced the time required for wide-field imaging, making it possible to monitor the electrochemical reactions in situ. The highest frame rate of EMCDD was ˜50 fps, and the Raman images for a specific Raman peak can be obtained by passing the signal from the sample through the Liquid Crystal Tunable Filter. The spatial resolutions of scanning imaging and wide-field imaging with a 100× objective (NA = 0.9) are 0.5 × 0.5 μm2 and 0.36 × 0.36 μm2, respectively. The system was used to study the surface plasmon resonance of Au nanorods, the surface-enhanced Raman scattering signal distribution for Au Nanoparticle aggregates, and dynamic Raman imaging of an electrochemical reacting system.

Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model

KAUST Repository

El-Samra, R.

2017-02-15

A set of ten downscaling simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each downscaling simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model’s ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The downscaled data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.

Drought episodes over Greece as simulated by dynamical and statistical downscaling approaches

Science.gov (United States)

Anagnostopoulou, Christina

2017-07-01

Drought over the Greek region is characterized by a strong seasonal cycle and large spatial variability. Dry spells longer than 10 consecutive days mainly characterize the duration and the intensity of Greek drought. Moreover, an increasing trend of the frequency of drought episodes has been observed, especially during the last 20 years of the 20th century. Moreover, the most recent regional circulation models (RCMs) present discrepancies compared to observed precipitation, while they are able to reproduce the main patterns of atmospheric circulation. In this study, both a statistical and a dynamical downscaling approach are used to quantify drought episodes over Greece by simulating the Standardized Precipitation Index (SPI) for different time steps (3, 6, and 12 months). A statistical downscaling technique based on artificial neural network is employed for the estimation of SPI over Greece, while this drought index is also estimated using the RCM precipitation for the time period of 1961-1990. Overall, it was found that the drought characteristics (intensity, duration, and spatial extent) were well reproduced by the regional climate models for long term drought indices (SPI12) while ANN simulations are better for the short-term drought indices (SPI3).

Coupled ice sheet - climate simulations of the last glacial inception and last glacial maximum with a model of intermediate complexity that includes a dynamical downscaling of heat and moisture

Science.gov (United States)

Quiquet, Aurélien; Roche, Didier M.

2017-04-01

Comprehensive fully coupled ice sheet - climate models allowing for multi-millenia transient simulations are becoming available. They represent powerful tools to investigate ice sheet - climate interactions during the repeated retreats and advances of continental ice sheets of the Pleistocene. However, in such models, most of the time, the spatial resolution of the ice sheet model is one order of magnitude lower than the one of the atmospheric model. As such, orography-induced precipitation is only poorly represented. In this work, we briefly present the most recent improvements of the ice sheet - climate coupling within the model of intermediate complexity iLOVECLIM. On the one hand, from the native atmospheric resolution (T21), we have included a dynamical downscaling of heat and moisture at the ice sheet model resolution (40 km x 40 km). This downscaling accounts for feedbacks of sub-grid precipitation on large scale energy and water budgets. From the sub-grid atmospheric variables, we compute an ice sheet surface mass balance required by the ice sheet model. On the other hand, we also explicitly use oceanic temperatures to compute sub-shelf melting at a given depth. Based on palaeo evidences for rate of change of eustatic sea level, we discuss the capability of our new model to correctly simulate the last glacial inception ( 116 kaBP) and the ice volume of the last glacial maximum ( 21 kaBP). We show that the model performs well in certain areas (e.g. Canadian archipelago) but some model biases are consistent over time periods (e.g. Kara-Barents sector). We explore various model sensitivities (e.g. initial state, vegetation, albedo) and we discuss the importance of the downscaling of precipitation for ice nucleation over elevated area and for the surface mass balance of larger ice sheets.

Fundamental statistical relationships between monthly and daily meteorological variables: Temporal downscaling of weather based on a global observational dataset

Science.gov (United States)

Sommer, Philipp; Kaplan, Jed

2016-04-01

Accurate modelling of large-scale vegetation dynamics, hydrology, and other environmental processes requires meteorological forcing on daily timescales. While meteorological data with high temporal resolution is becoming increasingly available, simulations for the future or distant past are limited by lack of data and poor performance of climate models, e.g., in simulating daily precipitation. To overcome these limitations, we may temporally downscale monthly summary data to a daily time step using a weather generator. Parameterization of such statistical models has traditionally been based on a limited number of observations. Recent developments in the archiving, distribution, and analysis of "big data" datasets provide new opportunities for the parameterization of a temporal downscaling model that is applicable over a wide range of climates. Here we parameterize a WGEN-type weather generator using more than 50 million individual daily meteorological observations, from over 10'000 stations covering all continents, based on the Global Historical Climatology Network (GHCN) and Synoptic Cloud Reports (EECRA) databases. Using the resulting "universal" parameterization and driven by monthly summaries, we downscale mean temperature (minimum and maximum), cloud cover, and total precipitation, to daily estimates. We apply a hybrid gamma-generalized Pareto distribution to calculate daily precipitation amounts, which overcomes much of the inability of earlier weather generators to simulate high amounts of daily precipitation. Our globally parameterized weather generator has numerous applications, including vegetation and crop modelling for paleoenvironmental studies.

Downscaling soil moisture over East Asia through multi-sensor data fusion and optimization of regression trees

Science.gov (United States)

Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung

2017-04-01

Soil moisture is one of the most important keys for understanding regional and global climate systems. Soil moisture is directly related to agricultural processes as well as hydrological processes because soil moisture highly influences vegetation growth and determines water supply in the agroecosystem. Accurate monitoring of the spatiotemporal pattern of soil moisture is important. Soil moisture has been generally provided through in situ measurements at stations. Although field survey from in situ measurements provides accurate soil moisture with high temporal resolution, it requires high cost and does not provide the spatial distribution of soil moisture over large areas. Microwave satellite (e.g., advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR2), the Advanced Scatterometer (ASCAT), and Soil Moisture Active Passive (SMAP)) -based approaches and numerical models such as Global Land Data Assimilation System (GLDAS) and Modern- Era Retrospective Analysis for Research and Applications (MERRA) provide spatial-temporalspatiotemporally continuous soil moisture products at global scale. However, since those global soil moisture products have coarse spatial resolution ( 25-40 km), their applications for agriculture and water resources at local and regional scales are very limited. Thus, soil moisture downscaling is needed to overcome the limitation of the spatial resolution of soil moisture products. In this study, GLDAS soil moisture data were downscaled up to 1 km spatial resolution through the integration of AMSR2 and ASCAT soil moisture data, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Moderate Resolution Imaging Spectroradiometer (MODIS) data—Land Surface Temperature, Normalized Difference Vegetation Index, and Land cover—using modified regression trees over East Asia from 2013 to 2015. Modified regression trees were implemented using Cubist, a commercial software tool based on machine learning. An

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

Science.gov (United States)

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-06-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

High-Resolution Climate Data Visualization through GIS- and Web-based Data Portals

Science.gov (United States)

WANG, X.; Huang, G.

2017-12-01

Sound decisions on climate change adaptation rely on an in-depth assessment of potential climate change impacts at regional and local scales, which usually requires finer resolution climate projections at both spatial and temporal scales. However, effective downscaling of global climate projections is practically difficult due to the lack of computational resources and/or long-term reference data. Although a large volume of downscaled climate data has been make available to the public, how to understand and interpret the large-volume climate data and how to make use of the data to drive impact assessment and adaptation studies are still challenging for both impact researchers and decision makers. Such difficulties have become major barriers preventing informed climate change adaptation planning at regional scales. Therefore, this research will explore new GIS- and web-based technologies to help visualize the large-volume regional climate data with high spatiotemporal resolutions. A user-friendly public data portal, named Climate Change Data Portal (CCDP, http://ccdp.network), will be established to allow intuitive and open access to high-resolution regional climate projections at local scales. The CCDP offers functions of visual representation through geospatial maps and data downloading for a variety of climate variables (e.g., temperature, precipitation, relative humidity, solar radiation, and wind) at multiple spatial resolutions (i.e., 25 - 50 km) and temporal resolutions (i.e., annual, seasonal, monthly, daily, and hourly). The vast amount of information the CCDP encompasses can provide a crucial basis for assessing impacts of climate change on local communities and ecosystems and for supporting better decision making under a changing climate.

Rainfall downscaling of weekly ensemble forecasts using self-organising maps

Directory of Open Access Journals (Sweden)

Masamichi Ohba

2016-03-01

Full Text Available This study presents an application of self-organising maps (SOMs to downscaling medium-range ensemble forecasts and probabilistic prediction of local precipitation in Japan. SOM was applied to analyse and connect the relationship between atmospheric patterns over Japan and local high-resolution precipitation data. Multiple SOM was simultaneously employed on four variables derived from the JRA-55 reanalysis over the area of study (south-western Japan, and a two-dimensional lattice of weather patterns (WPs was obtained. Weekly ensemble forecasts can be downscaled to local precipitation using the obtained multiple SOM. The downscaled precipitation is derived by the five SOM lattices based on the WPs of the global model ensemble forecasts for a particular day in 2009–2011. Because this method effectively handles the stochastic uncertainties from the large number of ensemble members, a probabilistic local precipitation is easily and quickly obtained from the ensemble forecasts. This downscaling of ensemble forecasts provides results better than those from a 20-km global spectral model (i.e. capturing the relatively detailed precipitation distribution over the region. To capture the effect of the detailed pattern differences in each SOM node, a statistical model is additionally concreted for each SOM node. The predictability skill of the ensemble forecasts is significantly improved under the neural network-statistics hybrid-downscaling technique, which then brings a much better skill score than the traditional method. It is expected that the results of this study will provide better guidance to the user community and contribute to the future development of dam-management models.

A survey of statistical downscaling techniques

Energy Technology Data Exchange (ETDEWEB)

Zorita, E.; Storch, H. von [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Hydrophysik

1997-12-31

The derivation of regional information from integrations of coarse-resolution General Circulation Models (GCM) is generally referred to as downscaling. The most relevant statistical downscaling techniques are described here and some particular examples are worked out in detail. They are classified into three main groups: linear methods, classification methods and deterministic non-linear methods. Their performance in a particular example, winter rainfall in the Iberian peninsula, is compared to a simple downscaling analog method. It is found that the analog method performs equally well than the more complicated methods. Downscaling analysis can be also used as a tool to validate regional performance of global climate models by analyzing the covariability of the simulated large-scale climate and the regional climates. (orig.) [Deutsch] Die Ableitung regionaler Information aus Integrationen grob aufgeloester Klimamodelle wird als `Regionalisierung` bezeichnet. Dieser Beitrag beschreibt die wichtigsten statistischen Regionalisierungsverfahren und gibt darueberhinaus einige detaillierte Beispiele. Regionalisierungsverfahren lassen sich in drei Hauptgruppen klassifizieren: lineare Verfahren, Klassifikationsverfahren und nicht-lineare deterministische Verfahren. Diese Methoden werden auf den Niederschlag auf der iberischen Halbinsel angewandt und mit den Ergebnissen eines einfachen Analog-Modells verglichen. Es wird festgestellt, dass die Ergebnisse der komplizierteren Verfahren im wesentlichen auch mit der Analog-Methode erzielt werden koennen. Eine weitere Anwendung der Regionalisierungsmethoden besteht in der Validierung globaler Klimamodelle, indem die simulierte und die beobachtete Kovariabilitaet zwischen dem grosskaligen und dem regionalen Klima miteinander verglichen wird. (orig.)

A survey of statistical downscaling techniques

Energy Technology Data Exchange (ETDEWEB)

Zorita, E; Storch, H von [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Hydrophysik

1998-12-31

The derivation of regional information from integrations of coarse-resolution General Circulation Models (GCM) is generally referred to as downscaling. The most relevant statistical downscaling techniques are described here and some particular examples are worked out in detail. They are classified into three main groups: linear methods, classification methods and deterministic non-linear methods. Their performance in a particular example, winter rainfall in the Iberian peninsula, is compared to a simple downscaling analog method. It is found that the analog method performs equally well than the more complicated methods. Downscaling analysis can be also used as a tool to validate regional performance of global climate models by analyzing the covariability of the simulated large-scale climate and the regional climates. (orig.) [Deutsch] Die Ableitung regionaler Information aus Integrationen grob aufgeloester Klimamodelle wird als `Regionalisierung` bezeichnet. Dieser Beitrag beschreibt die wichtigsten statistischen Regionalisierungsverfahren und gibt darueberhinaus einige detaillierte Beispiele. Regionalisierungsverfahren lassen sich in drei Hauptgruppen klassifizieren: lineare Verfahren, Klassifikationsverfahren und nicht-lineare deterministische Verfahren. Diese Methoden werden auf den Niederschlag auf der iberischen Halbinsel angewandt und mit den Ergebnissen eines einfachen Analog-Modells verglichen. Es wird festgestellt, dass die Ergebnisse der komplizierteren Verfahren im wesentlichen auch mit der Analog-Methode erzielt werden koennen. Eine weitere Anwendung der Regionalisierungsmethoden besteht in der Validierung globaler Klimamodelle, indem die simulierte und die beobachtete Kovariabilitaet zwischen dem grosskaligen und dem regionalen Klima miteinander verglichen wird. (orig.)

Creating Dynamically Downscaled Seasonal Climate Forecast and Climate Change Projection Information for the North American Monsoon Region Suitable for Decision Making Purposes

Science.gov (United States)

Castro, C. L.; Dominguez, F.; Chang, H.

2010-12-01

Current seasonal climate forecasts and climate change projections of the North American monsoon are based on the use of course-scale information from a general circulation model. The global models, however, have substantial difficulty in resolving the regional scale forcing mechanisms of precipitation. This is especially true during the period of the North American Monsoon in the warm season. Precipitation is driven primarily due to the diurnal cycle of convection, and this process cannot be resolve in coarse-resolution global models that have a relatively poor representation of terrain. Though statistical downscaling may offer a relatively expedient method to generate information more appropriate for the regional scale, and is already being used in the resource decision making processes in the Southwest U.S., its main drawback is that it cannot account for a non-stationary climate. Here we demonstrate the use of a regional climate model, specifically the Weather Research and Forecast (WRF) model, for dynamical downscaling of the North American Monsoon. To drive the WRF simulations, we use retrospective reforecasts from the Climate Forecast System (CFS) model, the operational model used at the U.S. National Center for Environmental Prediction, and three select “well performing” IPCC AR 4 models for the A2 emission scenario. Though relatively computationally expensive, the use of WRF as a regional climate model in this way adds substantial value in the representation of the North American Monsoon. In both cases, the regional climate model captures a fairly realistic and reasonable monsoon, where none exists in the driving global model, and captures the dominant modes of precipitation anomalies associated with ENSO and the Pacific Decadal Oscillation (PDO). Long-term precipitation variability and trends in these simulations is considered via the standardized precipitation index (SPI), a commonly used metric to characterize long-term drought. Dynamically

Multisite rainfall downscaling and disaggregation in a tropical urban area

Science.gov (United States)

Lu, Y.; Qin, X. S.

2014-02-01

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.

Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations

KAUST Repository

Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason P.; McCabe, Matthew

2013-01-01

A downscaling approach based on multiple-point geostatistics (MPS) is presented. The key concept underlying MPS is to sample spatial patterns from within training images, which can then be used in characterizing the relationship between different variables across multiple scales. The approach is used here to downscale climate variables including skin surface temperature (TSK), soil moisture (SMOIS), and latent heat flux (LH). The performance of the approach is assessed by applying it to data derived from a regional climate model of the Murray-Darling basin in southeast Australia, using model outputs at two spatial resolutions of 50 and 10 km. The data used in this study cover the period from 1985 to 2006, with 1985 to 2005 used for generating the training images that define the relationships of the variables across the different spatial scales. Subsequently, the spatial distributions for the variables in the year 2006 are determined at 10 km resolution using the 50 km resolution data as input. The MPS geostatistical downscaling approach reproduces the spatial distribution of TSK, SMOIS, and LH at 10 km resolution with the correct spatial patterns over different seasons, while providing uncertainty estimates through the use of multiple realizations. The technique has the potential to not only bridge issues of spatial resolution in regional and global climate model simulations but also in feature sharpening in remote sensing applications through image fusion, filling gaps in spatial data, evaluating downscaled variables with available remote sensing images, and aggregating/disaggregating hydrological and groundwater variables for catchment studies.

Bioclim Deliverable D6b: application of statistical down-scaling within the BIOCLIM hierarchical strategy: methods, data requirements and underlying assumptions

International Nuclear Information System (INIS)

2004-01-01

The overall aim of BIOCLIM is to assess the possible long term impacts due to climate change on the safety of radioactive waste repositories in deep formations. The coarse spatial scale of the Earth-system Models of Intermediate Complexity (EMICs) used in BIOCLIM compared with the BIOCLIM study regions and the needs of performance assessment creates a need for down-scaling. Most of the developmental work on down-scaling methodologies undertaken by the international research community has focused on down-scaling from the general circulation model (GCM) scale (with a typical spatial resolution of 400 km by 400 km over Europe in the current generation of models) using dynamical down-scaling (i.e., regional climate models (RCMs), which typically have a spatial resolution of 50 km by 50 km for models whose domain covers the European region) or statistical methods (which can provide information at the point or station scale) in order to construct scenarios of anthropogenic climate change up to 2100. Dynamical down-scaling (with the MAR RCM) is used in BIOCLIM WP2 to down-scale from the GCM (i.e., IPSL C M4 D ) scale. In the original BIOCLIM description of work, it was proposed that UEA would apply statistical down-scaling to IPSL C M4 D output in WP2 as part of the hierarchical strategy. Statistical down-scaling requires the identification of statistical relationships between the observed large-scale and regional/local climate, which are then applied to large-scale GCM output, on the assumption that these relationships remain valid in the future (the assumption of stationarity). Thus it was proposed that UEA would investigate the extent to which it is possible to apply relationships between the present-day large-scale and regional/local climate to the relatively extreme conditions of the BIOCLIM WP2 snapshot simulations. Potential statistical down-scaling methodologies were identified from previous work performed at UEA. Appropriate station data from the case

Improving Chemical EOR Simulations and Reducing the Subsurface Uncertainty Using Downscaling Conditioned to Tracer Data

KAUST Repository

Torrealba, Victor A.

2017-10-02

Recovery mechanisms are more likely to be influenced by grid-block size and reservoir heterogeneity in Chemical EOR (CEOR) than in conventional Water Flood (WF) simulations. Grid upscaling based on single-phase flow is a common practice in WF simulation models, where simulation grids are coarsened to perform history matching and sensitivity analyses within affordable computational times. This coarse grid resolution (typically about 100 ft.) could be sufficient in WF, however, it usually fails to capture key physical mechanisms in CEOR. In addition to increased numerical dispersion in coarse models, these models tend to artificially increase the level of mixing between the fluids and may not have enough resolution to capture different length scales of geological features to which EOR processes can be highly sensitive. As a result of which, coarse models usually overestimate the sweep efficiency, and underestimate the displacement efficiency. Grid refinement (simple downscaling) can resolve artificial mixing but appropriately re-creating the fine-scale heterogeneity, without degrading the history-match conducted on the coarse-scale, remains a challenge. Because of the difference in recovery mechanisms involved in CEOR, such as miscibility and thermodynamic phase split, the impact of grid downscaling on CEOR simulations is not well understood. In this work, we introduce a geostatistical downscaling method conditioned to tracer data to refine a coarse history-matched WF model. This downscaling process is necessary for CEOR simulations when the original (fine) earth model is not available or when major disconnects occur between the original earth model and the history-matched coarse WF model. The proposed downscaling method is a process of refining the coarse grid, and populating the relevant properties in the newly created finer grid cells. The method considers the values of rock properties in the coarse grid as hard data, and the corresponding variograms and property

The dynamic range of ultra-high-resolution cryogenic gamma-ray spectrometers

Energy Technology Data Exchange (ETDEWEB)

Ali, Shafinaz [Advanced Detector Group, Lawrence Livermore National Laboratory, 7000 East Avenue, L-270, Livermore, CA 94550 (United States); Terracol, Stephane F. [Advanced Detector Group, Lawrence Livermore National Laboratory, 7000 East Avenue, L-270, Livermore, CA 94550 (United States); Drury, Owen B. [Advanced Detector Group, Lawrence Livermore National Laboratory, 7000 East Avenue, L-270, Livermore, CA 94550 (United States); Friedrich, Stephan [Advanced Detector Group, Lawrence Livermore National Laboratory, 7000 East Avenue, L-270, Livermore, CA 94550 (United States)]. E-mail: friedrich1@llnl.gov

2006-04-15

We are developing high-resolution cryogenic gamma-ray spectrometers for nuclear science and non-proliferation applications. The gamma-ray detectors are composed of a bulk superconducting Sn foil absorber attached to a multilayer Mo/Cu transition-edge sensor (TES). The energy resolution of a detector with a 1x1x0.25 mm{sup 3} Sn absorber is 50-90 eV FWHM for {gamma}-rays up to 100 keV, and it decreases for larger absorbers. Here, we present the detector performance for different absorber volumes, and discuss the trade-offs between energy resolution and dynamic range.

The Dynamic Range of Ultra-High Resolution Cryogenic Gamma-ray Spectrometers

International Nuclear Information System (INIS)

Ali, S; Terracol, S F; Drury, O B; Friedrich, S

2005-01-01

We are developing high-resolution cryogenic gamma-ray spectrometers for nuclear science and non-proliferation applications. The gamma-ray detectors are composed of a bulk superconducting Sn foil absorber attached to multilayer Mo/Cu transition-edge sensors (TES). The energy resolution achieved with a 1 x 1 x 0.25 mm 3 Sn absorber is 50 -90eV for γ-rays up to 100 keV and it decreases for large absorber sizes. We discuss the trade-offs between energy resolution and dynamic range, as well as development of TES arrays for higher count rates and better sensitivity

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.

Projected change in East Asian summer monsoon by dynamic downscaling: Moisture budget analysis

Science.gov (United States)

Jung, Chun-Yong; Shin, Ho-Jeong; Jang, Chan Joo; Kim, Hyung-Jin

2015-02-01

The summer monsoon considerably affects water resource and natural hazards including flood and drought in East Asia, one of the world's most densely populated area. In this study, we investigate future changes in summer precipitation over East Asia induced by global warming through dynamical downscaling with the Weather Research and Forecast model. We have selected a global model from the Coupled Model Intercomparison Project Phase 5 based on an objective evaluation for East Asian summer monsoon and applied its climate change under Representative Concentration Pathway 4.5 scenario to a pseudo global warming method. Unlike the previous studies that focused on a qualitative description of projected precipitation changes over East Asia, this study tried to identify the physical causes of the precipitation changes by analyzing a local moisture budget. Projected changes in precipitation over the eastern foothills area of Tibetan Plateau including Sichuan Basin and Yangtze River displayed a contrasting pattern: a decrease in its northern area and an increase in its southern area. A local moisture budget analysis indicated the precipitation increase over the southern area can be mainly attributed to an increase in horizontal wind convergence and surface evaporation. On the other hand, the precipitation decrease over the northern area can be largely explained by horizontal advection of dry air from the northern continent and by divergent wind flow. Regional changes in future precipitation in East Asia are likely to be attributed to different mechanisms which can be better resolved by regional dynamical downscaling.

Application of statistical downscaling technique for the production of wine grapes (Vitis vinifera L.) in Spain

Science.gov (United States)

Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, J.

2012-04-01

Climate and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. Downscaling techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical downscaling technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical downscaling technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

Directory of Open Access Journals (Sweden)

Yuli Shi

2015-05-01

Full Text Available Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI from the Moderate Resolution Imaging Spectroradiometer (MODIS, the Global Digital Elevation Model (GDEM from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM. We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution onto the finer grids (1 × 1 km2 of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010 against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors

Regional reanalysis without local data: Exploiting the downscaling paradigm

Science.gov (United States)

von Storch, Hans; Feser, Frauke; Geyer, Beate; Klehmet, Katharina; Li, Delei; Rockel, Burkhardt; Schubert-Frisius, Martina; Tim, Nele; Zorita, Eduardo

2017-08-01

This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large-scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large-scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large-scale state of the reanalyses with regional detail—wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which's large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks—in particular marine risks—was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

Science.gov (United States)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

Energy Technology Data Exchange (ETDEWEB)

Armstrong, Michael R. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States)], E-mail: armstrong30@llnl.gov; Boyden, Ken [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Browning, Nigel D. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Department of Chemical Engineering and Materials Science, University of California-Davis, One Shields Avenue, Davis, CA 95616 (United States); Campbell, Geoffrey H.; Colvin, Jeffrey D.; De Hope, William J.; Frank, Alan M. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Gibson, David J.; Hartemann, Fred [N Division, Physics and Advanced Technologies Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-280, Livermore, CA 94550 (United States); Kim, Judy S. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Department of Chemical Engineering and Materials Science, University of California-Davis, One Shields Avenue, Davis, CA 95616 (United States); King, Wayne E.; La Grange, Thomas B.; Pyke, Ben J.; Reed, Bryan W.; Shuttlesworth, Richard M.; Stuart, Brent C.; Torralva, Ben R. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States)

2007-04-15

Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5x10{sup 7} electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution <10{sup -6} s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed.

Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

International Nuclear Information System (INIS)

Armstrong, Michael R.; Boyden, Ken; Browning, Nigel D.; Campbell, Geoffrey H.; Colvin, Jeffrey D.; De Hope, William J.; Frank, Alan M.; Gibson, David J.; Hartemann, Fred; Kim, Judy S.; King, Wayne E.; La Grange, Thomas B.; Pyke, Ben J.; Reed, Bryan W.; Shuttlesworth, Richard M.; Stuart, Brent C.; Torralva, Ben R.

2007-01-01

Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5x10 7 electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution -6 s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed

Downscaling of the global climate model data for the mass balance calculation of mountain glaciers

Directory of Open Access Journals (Sweden)

P. A. Morozova

2017-01-01

Full Text Available In this paper, we consider a hybrid method of downscaling of the GCM‑generated meteorological fields to the characteristic spatial resolution which is usually used for modeling of a single mountain glacier mass balance. The main purpose of the study is to develop a reliable forecasting method to evaluate future state of moun‑ tain glaciation under changing climatic conditions. The method consists of two stages. In the first or dynamical stage, we use results of calculations of the regional numerical model HadRM3P for the Black Sea‑Caspian region with a spatial resolution of 25 km [22]. Initial conditions for the HadRM3P were provided by the GCM devel‑ oped in the Institute of Numerical Mathematics of RAS (INMCM4 [18]. Calculations were carried out for two time periods: the present climate (1971–2000 and climate in the late 21st century (2071–2100 according to the scenario of greenhouse gas emissions RCP 8.5. On the second stage of downscaling, further regionalization is achieved by projecting of RCM‑generated data to the high‑resolution (25 m digital altitude model in a domain enclosing a target glacier. Altitude gradients of the surface air temperature and precipitation were derived from the model data. Further on, both were corrected using data of observations. Incoming shortwave radiation was calculated in the mass balance model separately, taking into account characteristics of the slope, i.e. exposition and shading of each cell. Then, the method was tested for glaciers Marukh (Western Caucasus and Jankuat (Central Caucasus, both for the present‑day and for future climates. At the end of the 21st century, the air tem‑ perature rise predicted for the summer months was calculated to be about 5–6 °C, and the result for the winter to be minus 2–3 °C. Change in annual precipitation is not significant, less than 10%. Increase in the total short‑ wave radiation will be about 5%. These changes will result in the fact that

Development of high-resolution multi-scale modelling system for simulation of coastal-fluvial urban flooding

Science.gov (United States)

Comer, Joanne; Indiana Olbert, Agnieszka; Nash, Stephen; Hartnett, Michael

2017-02-01

Urban developments in coastal zones are often exposed to natural hazards such as flooding. In this research, a state-of-the-art, multi-scale nested flood (MSN_Flood) model is applied to simulate complex coastal-fluvial urban flooding due to combined effects of tides, surges and river discharges. Cork city on Ireland's southwest coast is a study case. The flood modelling system comprises a cascade of four dynamically linked models that resolve the hydrodynamics of Cork Harbour and/or its sub-region at four scales: 90, 30, 6 and 2 m. Results demonstrate that the internalization of the nested boundary through the use of ghost cells combined with a tailored adaptive interpolation technique creates a highly dynamic moving boundary that permits flooding and drying of the nested boundary. This novel feature of MSN_Flood provides a high degree of choice regarding the location of the boundaries to the nested domain and therefore flexibility in model application. The nested MSN_Flood model through dynamic downscaling facilitates significant improvements in accuracy of model output without incurring the computational expense of high spatial resolution over the entire model domain. The urban flood model provides full characteristics of water levels and flow regimes necessary for flood hazard identification and flood risk assessment.

Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

Science.gov (United States)

Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping

2017-10-01

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3

High-resolution dynamic pressure sensor array based on piezo-phototronic effect tuned photoluminescence imaging.

Science.gov (United States)

Peng, Mingzeng; Li, Zhou; Liu, Caihong; Zheng, Qiang; Shi, Xieqing; Song, Ming; Zhang, Yang; Du, Shiyu; Zhai, Junyi; Wang, Zhong Lin

2015-03-24

A high-resolution dynamic tactile/pressure display is indispensable to the comprehensive perception of force/mechanical stimulations such as electronic skin, biomechanical imaging/analysis, or personalized signatures. Here, we present a dynamic pressure sensor array based on pressure/strain tuned photoluminescence imaging without the need for electricity. Each sensor is a nanopillar that consists of InGaN/GaN multiple quantum wells. Its photoluminescence intensity can be modulated dramatically and linearly by small strain (0-0.15%) owing to the piezo-phototronic effect. The sensor array has a high pixel density of 6350 dpi and exceptional small standard deviation of photoluminescence. High-quality tactile/pressure sensing distribution can be real-time recorded by parallel photoluminescence imaging without any cross-talk. The sensor array can be inexpensively fabricated over large areas by semiconductor product lines. The proposed dynamic all-optical pressure imaging with excellent resolution, high sensitivity, good uniformity, and ultrafast response time offers a suitable way for smart sensing, micro/nano-opto-electromechanical systems.

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Science.gov (United States)

Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius

2016-01-01

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural

Using Random Forest to Improve the Downscaling of Global Livestock Census Data.

Directory of Open Access Journals (Sweden)

Gaëlle Nicolas

Full Text Available Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version. The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2. RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0 will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

Directory of Open Access Journals (Sweden)

Yinghai Ke

2016-03-01

Full Text Available This study presented a MODIS 8-day 1 km evapotranspiration (ET downscaling method based on Landsat 8 data (30 m and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST, and vegetation indices (VIs derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR, Cubist, and Random Forest (RF were used to model the relationship between the Landsat indicators and MODIS 8-day 1 km ET. The models were then used to predict 30 m ET based on Landsat 8 indicators. A total of thirty-two pairs of Landsat 8 images/MODIS ET data were evaluated at four study sites including two in United States and two in South Korea. Among the three models, RF produced the lowest error, with relative Root Mean Square Error (rRMSE less than 20%. Vegetation greenness related indicators such as Normalized Difference Vegetation Index (NDVI, Enhanced Vegetation Index (EVI, Soil Adjusted Vegetation Index (SAVI, and vegetation moisture related indicators such as Normalized Difference Infrared Index—Landsat 8 OLI band 7 (NDIIb7 and Normalized Difference Water Index (NDWI were the five most important features used in RF model. Temperature-based indicators were less important than vegetation greenness and moisture-related indicators because LST could have considerable variation during each 8-day period. The predicted Landsat downscaled ET had good overall agreement with MODIS ET (average rRMSE = 22% and showed a similar temporal trend as MODIS ET. Compared to the MODIS ET product, the downscaled product demonstrated more spatial details, and had better agreement with in situ ET observations (R2 = 0.56. However, we found that the accuracy of MODIS ET was the main control factor of the accuracy of the downscaled product. Improved coarse-resolution ET estimation would result in better finer-resolution estimation. This study proved the potential of using machine learning

Objective high Resolution Analysis over Complex Terrain with VERA

Science.gov (United States)

Mayer, D.; Steinacker, R.; Steiner, A.

2012-04-01

VERA (Vienna Enhanced Resolution Analysis) is a model independent, high resolution objective analysis of meteorological fields over complex terrain. This system consists of a special developed quality control procedure and a combination of an interpolation and a downscaling technique. Whereas the so called VERA-QC is presented at this conference in the contribution titled "VERA-QC, an approved Data Quality Control based on Self-Consistency" by Andrea Steiner, this presentation will focus on the method and the characteristics of the VERA interpolation scheme which enables one to compute grid point values of a meteorological field based on irregularly distributed observations and topography related aprior knowledge. Over a complex topography meteorological fields are not smooth in general. The roughness which is induced by the topography can be explained physically. The knowledge about this behavior is used to define the so called Fingerprints (e.g. a thermal Fingerprint reproducing heating or cooling over mountainous terrain or a dynamical Fingerprint reproducing positive pressure perturbation on the windward side of a ridge) under idealized conditions. If the VERA algorithm recognizes patterns of one or more Fingerprints at a few observation points, the corresponding patterns are used to downscale the meteorological information in a greater surrounding. This technique allows to achieve an analysis with a resolution much higher than the one of the observational network. The interpolation of irregularly distributed stations to a regular grid (in space and time) is based on a variational principle applied to first and second order spatial and temporal derivatives. Mathematically, this can be formulated as a cost function that is equivalent to the penalty function of a thin plate smoothing spline. After the analysis field has been divided into the Fingerprint components and the unexplained part respectively, the requirement of a smooth distribution is applied to the

Examining South Atlantic Subtropical Cyclone Anita using the Satellite-Enhanced Regional Downscaling for Applied Studies Hourly Outputs

Science.gov (United States)

Vaicberg, H.; Palmeira, A. C. P. A.; Nunes, A.

2017-12-01

Studies on South Atlantic cyclones are mainly compromised by scarcity of observations. Therefore, remote sensing and global (re) analysis products are usually employed in investigations of their evolution. However, the frequent use of global reanalysis might difficult the assessment of the characteristics of the cyclones found in South Atlantic. In that regard, studies on "subtropical" cyclones have been performed using the 25-km resolution, Satellite-enhanced Regional Downscaling for Applied Studies (SRDAS), a product developed at the Federal University of Rio de Janeiro in Brazil. In SRDAS, the Regional Spectral Model assimilates precipitation estimates from environmental satellites, while dynamically downscaling a global reanalysis using the spectral nudging technique to maintain the large-scale features in agreement with the regional model solution. The use of regional models in the downscaling of general circulation models provides more detailed information on weather and climate. As a way of illustrating the usefulness of SRDAS in the study of the subtropical South Atlantic cyclones, the subtropical cyclone Anita was selected because of its intensity. Anita developed near Brazilian south/southeast coast, with damages to local communities. Comparisons with available observations demonstrated the skill of SRDAS in simulating such an extreme event.

Development and evaluation of climatologically-downscaled AFWA AGRMET precipitation products over the continental U.S.

Science.gov (United States)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Gibson, W.; Tian, Y.; Zeng, J.; Kato, H.

2008-05-01

Collaborations between the Air Force Weather Agency (AFWA), the Hydrological Sciences Branch at NASA-GSFC, and the PRISM Group at Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS- based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation

Temporal Scalability of Dynamic Volume Data using Mesh Compensated Wavelet Lifting.

Science.gov (United States)

Schnurrer, Wolfgang; Pallast, Niklas; Richter, Thomas; Kaup, Andre

2017-10-12

Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation. We propose an approach based on compensated wavelet lifting for obtaining a scalable representation of dynamic CT and MR volumes with very high quality. The mesh compensation is feasible to model the displacement in dynamic volumes which is mainly given by expansion and contraction of tissue over time. To achieve this, we propose an optimized estimation of the mesh compensation parameters to optimally fit for dynamic volumes. Within the lifting structure, the inversion of the motion compensation is crucial in the update step. We propose to take this inversion directly into account during the estimation step and can improve the quality of the lowpass sub-band by 0.63 dB and 0.43 dB on average for our tested dynamic CT and MR volumes at the cost of an increase of the rate by 2.4% and 1.2% on average.

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

Directory of Open Access Journals (Sweden)

Hongrui Zheng

2017-12-01

Full Text Available Land Use and Land Cover (LULC classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS, nearest neighbor diffusion (NNDiffusion, PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF and high-pass filter fusion (HPF. Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM and extended attribute profiles (EAPs, are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet

Dynamics of High-Resolution Networks

DEFF Research Database (Denmark)

Sekara, Vedran

the unprecedented amounts of information collected by mobile phones to gain detailed insight into the dynamics of social systems. This dissertation presents an unparalleled data collection campaign, collecting highly detailed traces for approximately 1000 people over the course of multiple years. The availability...... are we all affected by an ever changing network structure? Answering these questions will enrich our understanding of ourselves, our organizations, and our societies. Yet, mapping the dynamics of social networks has traditionally been an arduous undertaking. Today, however, it is possible to use...... of such dynamic maps allows us to probe the underlying social network and understand how individuals interact and form lasting friendships. More importantly, these highly detailed dynamic maps provide us new perspectives at traditional problems and allow us to quantify and predict human life....

Mass Balance Modelling of Saskatchewan Glacier, Canada Using Empirically Downscaled Reanalysis Data

Science.gov (United States)

Larouche, O.; Kinnard, C.; Demuth, M. N.

2017-12-01

Observations show that glaciers around the world are retreating. As sites with long-term mass balance observations are scarce, models are needed to reconstruct glacier mass balance and assess its sensitivity to climate. In regions with discontinuous and/or sparse meteorological data, high-resolution climate reanalysis data provide a convenient alternative to in situ weather observations, but can also suffer from strong bias due to the spatial and temporal scale mismatch. In this study we used data from the North American Regional Reanalysis (NARR) project with a 30 x 30 km spatial resolution and 3-hour temporal resolution to produce the meteorological forcings needed to drive a physically-based, distributed glacier mass balance model (DEBAM, Hock and Holmgren 2005) for the historical period 1979-2016. A two-year record from an automatic weather station (AWS) operated on Saskatchewan Glacier (2014-2016) was used to downscale air temperature, relative humidity, wind speed and incoming solar radiation from the nearest NARR gridpoint to the glacier AWS site. An homogenized historical precipitation record was produced using data from two nearby, low-elevation weather stations and used to downscale the NARR precipitation data. Three bias correction methods were applied (scaling, delta and empirical quantile mapping - EQM) and evaluated using split sample cross-validation. The EQM method gave better results for precipitation and for air temperature. Only a slight improvement in the relative humidity was obtained using the scaling method, while none of the methods improved the wind speed. The later correlates poorly with AWS observations, probably because the local glacier wind is decoupled from the larger scale NARR wind field. The downscaled data was used to drive the DEBAM model in order to reconstruct the mass balance of Saskatchewan Glacier over the past 30 years. The model was validated using recent snow thickness measurements and previously published geodetic mass

Inverse stochastic–dynamic models for high-resolution Greenland ice core records

Directory of Open Access Journals (Sweden)

N. Boers

2017-12-01

Full Text Available Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic–dynamic models from δ18O and dust records of unprecedented, subdecadal temporal resolution. The records stem from the North Greenland Ice Core Project (NGRIP, and we focus on the time interval 59–22 ka b2k. Our model reproduces the dynamical characteristics of both the δ18O and dust proxy records, including the millennial-scale Dansgaard–Oeschger variability, as well as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i high-resolution training data, (ii cubic drift terms, (iii nonlinear coupling terms between the δ18O and dust time series, and (iv non-Markovian contributions that represent short-term memory effects.

Inverse stochastic-dynamic models for high-resolution Greenland ice core records

Science.gov (United States)

Boers, Niklas; Chekroun, Mickael D.; Liu, Honghu; Kondrashov, Dmitri; Rousseau, Denis-Didier; Svensson, Anders; Bigler, Matthias; Ghil, Michael

2017-12-01

Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic-dynamic models from δ18O and dust records of unprecedented, subdecadal temporal resolution. The records stem from the North Greenland Ice Core Project (NGRIP), and we focus on the time interval 59-22 ka b2k. Our model reproduces the dynamical characteristics of both the δ18O and dust proxy records, including the millennial-scale Dansgaard-Oeschger variability, as well as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i) high-resolution training data, (ii) cubic drift terms, (iii) nonlinear coupling terms between the δ18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.

Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

Science.gov (United States)

O'Neill, Andrea; Erikson, Li; Barnard, Patrick

2017-01-01

While global climate models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues provide daily averaged near-surface winds at an appropriate spatial resolution for wave modeling within the orographically complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short-duration high wind speeds, on the order of hours, are usually excluded in statistically downscaled climate models and are of key importance in wave and subsequent coastal flood modeling. Here we present a temporal downscaling approach, similar to constructed analogues, for near-surface winds suitable for use in local wave models and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975–2004) recreate important extreme wind values within San Francisco Bay. A computationally efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over-water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but 21th century projections show little change in the overall magnitude of extreme winds and locally generated waves.

Downscaling CESM1 climate change projections for the MENA-CORDEX domain using WRF

Science.gov (United States)

Zittis, George; Hadjinicolaou, Panos; Lelieveld, Jos

2017-04-01

According to analysis of observations and global climate model projections, the broader Middle East, North Africa and Mediterranean region is found to be a climate change hotspot. Substantial changes in precipitation amounts and patterns and strong summer warming (including an intensification of heat extremes) is a likely future scenario for the region, but a recent uncertainty analysis indicated good model agreement for temperature but much less for precipitation. Although the horizontal resolution of global models has increased over the last years, it is still not adequate for impact and adaptation assessments of regional or national level and further downscaling of the climate information is required. The region is now studied within the CORDEX initiative (Coordinated Regional Climate Downscaling Experiment) with the establishment of a domain covering the Middle East - North Africa (MENA-CORDEX) region (http://mena-cordex.cyi.ac.cy/). In this study, we present the first climate change projections for the MENA produced by dynamically downscaling a bias-corrected output of the CESM1 global earth system model. For the downscaling, we use a climate configuration of the Weather, Research and Forecasting model (WRF). Our simulations use a standard CORDEX Phase I 50-km grid in three simulations, a historical (1950-2005) and two scenario runs (2006-2100) with the greenhouse gas forcing following the RCP 4.5 and 8.5. We evaluate precipitation, temperature and other surface meteorological variables from the historical using gridded and station observational datasets. Maps of projected changes are constructed for different periods in the future as differences of the two scenarios model output against the data from the historical run. The main spatial and temporal patterns of change are discussed, especially in the context of the United Nations Framework Convention on Climate Change agreement in Paris to limit the global average temperature increase to 1.5 degrees above pre

A Method for Retrieving Daily Land Surface Albedo from Space at 30-m Resolution

Directory of Open Access Journals (Sweden)

Bo Gao

2015-08-01

Full Text Available Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and spatial resolution that have better spatio-temporal resolution than existing albedo data, but do not completely satisfy the requirements for monitoring high-frequency land surface changes at the small scale. Downscaling technology provides a chance to further improve the albedo data spatio-temporal resolution and accuracy. This paper introduces a method that combines downscaling technology for land surface reflectance with an empirical method of deriving land surface albedo. Firstly, downscaling daily MODIS land surface reflectance data (MOD09GA from 500 m to 30 m on the basis of HJ-1A/B BRDF data with 2–4-day and 30-m temporal and spatial resolution is performed: this is the key step in the improved method. Subsequently, the daily 30-m land surface albedo data are derived by an empirical method combining prior knowledge of the MODIS BRDF product and the downscaled daily 30-m reflectance. Validation of albedo data obtained using the proposed method shows that the new method has both improved spatio-temporal resolution and good accuracy (a total absolute accuracy of 0.022 and a total root mean squared error at six sites of 0.028.

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

Science.gov (United States)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2013-01-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case

Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

Science.gov (United States)

Pervez, Md Shahriar; Henebry, Geoffrey M.

2014-01-01

Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change

A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Directory of Open Access Journals (Sweden)

Yinping Long

2016-07-01

Full Text Available Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM. The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.

Validation of two high‐resolution climate simulations over Scandinavia

DEFF Research Database (Denmark)

Mayer, Stephanie; Maule, Cathrine Fox; Sobolowski, Stefan

2014-01-01

., 2007) and to evaluate to what degree the models simulate observed weather. This is done by performing a so‐called perfect boundary experiment by dynamically downscaling ERA interim data. The atmospheric models WRF and HIRHAM5 were used as regional climate models (RCMs) in this study. Both models were...... are employed to examine the performance of the RCMs behaviour on a seasonal to sub‐daily time scale. Both models exhibit a wet bias of 50‐100 % (1‐3 mm) in seasonal precipitation. This bias is most pronounced during winter. The lower‐resolution reanalysis data underestimates wet‐day precipitation in all four...... season by 13‐36 % over the selected cities Bergen, Oslo and Copenhagen. The RCM simulations show a reduction of this underestimation and even indicate a sign change in some seasons/locations. A spatio‐temporal evaluation of downscaled precipitation extremes shows that both RCM downscalings are much...

Constraining the dynamics of the water budget at high spatial resolution in the world's water towers using models and remote sensing data; Snake River Basin, USA

Science.gov (United States)

Watson, K. A.; Masarik, M. T.; Flores, A. N.

2016-12-01

Mountainous, snow-dominated basins are often referred to as the water towers of the world because they store precipitation in seasonal snowpacks, which gradually melt and provide water supplies to downstream communities. Yet significant uncertainties remain in terms of quantifying the stores and fluxes of water in these regions as well as the associated energy exchanges. Constraining these stores and fluxes is crucial for advancing process understanding and managing these water resources in a changing climate. Remote sensing data are particularly important to these efforts due to the remoteness of these landscapes and high spatial variability in water budget components. We have developed a high resolution regional climate dataset extending from 1986 to the present for the Snake River Basin in the northwestern USA. The Snake River Basin is the largest tributary of the Columbia River by volume and a critically important basin for regional economies and communities. The core of the dataset was developed using a regional climate model, forced by reanalysis data. Specifically the Weather Research and Forecasting (WRF) model was used to dynamically downscale the North American Regional Reanalysis (NARR) over the region at 3 km horizontal resolution for the period of interest. A suite of satellite remote sensing products provide independent, albeit uncertain, constraint on a number of components of the water and energy budgets for the region across a range of spatial and temporal scales. For example, GRACE data are used to constrain basinwide terrestrial water storage and MODIS products are used to constrain the spatial and temporal evolution of evapotranspiration and snow cover. The joint use of both models and remote sensing products allows for both better understanding of water cycle dynamics and associated hydrometeorologic processes, and identification of limitations in both the remote sensing products and regional climate simulations.

Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

Science.gov (United States)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

2007-05-01

The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and

Projections of rising heat stress over the western Maritime Continent from dynamically downscaled climate simulations

Science.gov (United States)

Im, Eun-Soon; Kang, Suchul; Eltahir, Elfatih A. B.

2018-06-01

This study assesses the future changes in heat stress in response to different emission scenarios over the western Maritime Continent. To better resolve the region-specific changes and to enhance the performance in simulating extreme events, the MIT Regional Climate Model with a 12-km horizontal resolution is used for the dynamical downscaling of three carefully selected CMIP5 global projections forced by two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Daily maximum wet-bulb temperature (TWmax), which includes the effect of humidity, is examined to describe heat stress as regulated by future changes in temperature and humidity. An ensemble of projections reveals robust pattern in which a large increase in temperature is accompanied by a reduction in relative humidity but a significant increase in wet-bulb temperature. This increase in TWmax is relatively smaller over flat and coastal regions than that over mountainous region. However, the flat and coastal regions characterized by warm and humid present-day climate will be at risk even under modest increase in TWmax. The regional extent exposed to higher TWmax and the number of days on which TWmax exceeds its threshold value are projected to be much higher in RCP8.5 scenario than those in RCP4.5 scenario, thus highlighting the importance of controlling greenhouse gas emissions to reduce the adverse impacts on human health and heat-related mortality.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

Science.gov (United States)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

2016-03-01

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959-2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in

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.

Downscaling an Eddy-Resolving Global Model for the Continental Shelf off South Eastern Australia

Science.gov (United States)

Roughan, M.; Baird, M.; MacDonald, H.; Oke, P.

2008-12-01

The Australian Bluelink collaboration between CSIRO, the Bureau of Meteorology and the Royal Australian Navy has made available to the research community the output of BODAS (Bluelink ocean data assimilation system), an ensemble optimal interpolation reanalysis system with ~10 km resolution around Australia. Within the Bluelink project, BODAS fields are assimilated into a dynamic ocean model of the same resolution to produce BRAN (BlueLink ReANalysis, a hindcast of water properties around Australia from 1992 to 2004). In this study, BODAS hydrographic fields are assimilated into a ~ 3 km resolution Princeton Ocean Model (POM) configuration of the coastal ocean off SE Australia. Experiments were undertaken to establish the optimal strength and duration of the assimilation of BODAS fields into the 3 km resolution POM configuration for the purpose of producing hindcasts of ocean state. It is shown that the resultant downscaling of Bluelink products is better able to reproduce coastal features, particularly velocities and hydrography over the continental shelf off south eastern Australia. The BODAS-POM modelling system is used to provide a high-resolution simulation of the East Australian Current over the period 1992 to 2004. One of the applications that we will present is an investigation of the seasonal and inter-annual variability in the dispersion of passive particles in the East Australian Current. The practical outcome is an estimate of the connectivity of estuaries along the coast of southeast Australia, which is relevant for the dispersion of marine pests.

Dynamic high-resolution ultrasound of the shoulder: How we do it

Energy Technology Data Exchange (ETDEWEB)

Corazza, Angelo, E-mail: angelcoraz@libero.it [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Orlandi, Davide, E-mail: theabo@libero.it [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Fabbro, Emanuele, E-mail: emanuele.fabbro@gmail.com [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Ferrero, Giulio, E-mail: giulio.ferrero@gmail.com [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Messina, Carmelo, E-mail: carmelomessina.md@gmail.com [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Piazza Malan 2, 20097 San Donato Milanese (Italy); Sartoris, Riccardo, E-mail: riccardo.sartoris@hotmail.it [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Perugin Bernardi, Silvia, E-mail: silvy-86-@hotmail.it [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Arcidiacono, Alice, E-mail: a.arcidiacono84@gmail.com [Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Genova, Via Alberti 4, 16132 Genova (Italy); Silvestri, Enzo, E-mail: silvi.enzo@gmail.com [Dipartimento di Radiologia, Ospedale Evangelico Internazionale, Corso Solferino 29A, 16121 Genova (Italy); and others

2015-02-15

Highlights: • This paper shows how to apply US technique to image soft tissues around the shoulder. • Readers will learn to recognize normal US anatomy of tendons of the shoulder. • Readers will learn to apply dynamic maneuvers to improve rotator cuff visibility. - Abstract: Ultrasonography (US) is an established and well-accepted modality that can be used to evaluate articular and peri-articular structures around the shoulder. US has been proven to be useful in a wide range of rotator cuff diseases (tendon tears, tendinosis, and bursitis) as well as non-rotator cuff abnormalities (instability problems, synovial joint diseases, and nerve entrapment syndromes). Diagnostic accuracy of shoulder US when evaluating rotator cuff tears can reach 91–100% for partial and full thickness tears detection, respectively, having been reported to be as accurate as magnetic resonance imaging in experienced hands. US is cheap, readily available, capable to provide high-resolution images, and does not use ionizing radiations. In addition, US is the only imaging modality that allows performing dynamic evaluation of musculoskeletal structures, that may help to further increase diagnostic performance. In this setting, a standardized imaging protocol is essential for an exhaustive and efficient examination, also helping reducing the intrinsic dependence from operators of US. Furthermore, knowledge of pitfalls that can be encountered when examining the shoulder may help to avoid erroneous images interpretation. In this article we use detailed anatomic schemes and high-resolution US images to describe the normal US anatomy of soft tissues, articular, and para-articular structures located in and around the shoulder. Short video clips emphasizing the crucial role of dynamic maneuvers and dynamic real-time US examination of these structures are included as supplementary material.

Dynamic high-resolution ultrasound of the shoulder: How we do it

International Nuclear Information System (INIS)

Corazza, Angelo; Orlandi, Davide; Fabbro, Emanuele; Ferrero, Giulio; Messina, Carmelo; Sartoris, Riccardo; Perugin Bernardi, Silvia; Arcidiacono, Alice; Silvestri, Enzo

2015-01-01

Highlights: • This paper shows how to apply US technique to image soft tissues around the shoulder. • Readers will learn to recognize normal US anatomy of tendons of the shoulder. • Readers will learn to apply dynamic maneuvers to improve rotator cuff visibility. - Abstract: Ultrasonography (US) is an established and well-accepted modality that can be used to evaluate articular and peri-articular structures around the shoulder. US has been proven to be useful in a wide range of rotator cuff diseases (tendon tears, tendinosis, and bursitis) as well as non-rotator cuff abnormalities (instability problems, synovial joint diseases, and nerve entrapment syndromes). Diagnostic accuracy of shoulder US when evaluating rotator cuff tears can reach 91–100% for partial and full thickness tears detection, respectively, having been reported to be as accurate as magnetic resonance imaging in experienced hands. US is cheap, readily available, capable to provide high-resolution images, and does not use ionizing radiations. In addition, US is the only imaging modality that allows performing dynamic evaluation of musculoskeletal structures, that may help to further increase diagnostic performance. In this setting, a standardized imaging protocol is essential for an exhaustive and efficient examination, also helping reducing the intrinsic dependence from operators of US. Furthermore, knowledge of pitfalls that can be encountered when examining the shoulder may help to avoid erroneous images interpretation. In this article we use detailed anatomic schemes and high-resolution US images to describe the normal US anatomy of soft tissues, articular, and para-articular structures located in and around the shoulder. Short video clips emphasizing the crucial role of dynamic maneuvers and dynamic real-time US examination of these structures are included as supplementary material

Time-resolved PIV technique for high temporal resolution measurement of mechanical prosthetic aortic valve fluid dynamics.

Science.gov (United States)

Kaminsky, R; Morbiducci, U; Rossi, M; Scalise, L; Verdonck, P; Grigioni, M

2007-02-01

Prosthetic heart valves (PHVs) have been used to replace diseased native valves for more than five decades. Among these, mechanical PHVs are the most frequently implanted. Unfortunately, these devices still do not achieve ideal behavior and lead to many complications, many of which are related to fluid mechanics. The fluid dynamics of mechanical PHVs are particularly complex and the fine-scale characteristics of such flows call for very accurate experimental techniques. Adequate temporal resolution can be reached by applying time-resolved PIV, a high-resolution dynamic technique which is able to capture detailed chronological changes in the velocity field. The aim of this experimental study is to investigate the evolution of the flow field in a detailed time domain of a commercial bileaflet PHV in a mock-loop mimicking unsteady conditions, by means of time-resolved 2D Particle Image Velocimetry (PIV). The investigated flow field corresponded to the region immediately downstream of the valve plane. Spatial resolution as in "standard" PIV analysis of prosthetic valve fluid dynamics was used. The combination of a Nd:YLF high-repetition-rate double-cavity laser with a high frame rate CMOS camera allowed a detailed, highly temporally resolved acquisition (up to 10000 fps depending on the resolution) of the flow downstream of the PHV. Features that were observed include the non-homogeneity and unsteadiness of the phenomenon and the presence of large-scale vortices within the field, especially in the wake of the valve leaflets. Furthermore, we observed that highly temporally cycle-resolved analysis allowed the different behaviors exhibited by the bileaflet valve at closure to be captured in different acquired cardiac cycles. By accurately capturing hemodynamically relevant time scales of motion, time-resolved PIV characterization can realistically be expected to help designers in improving PHV performance and in furnishing comprehensive validation with experimental data

High-resolution polypeptide structure and dynamics in anisotropic environments: The gramicidin channel

Energy Technology Data Exchange (ETDEWEB)

Cross, T.A.; Lee, K.C.; Ketchem, R.R.; Hu, W.; Lazo, N.D.; Huo, S. [Florida State Univ., Tallahassee, FL (United States)

1994-12-01

To understand the details of macromolecular function, high-resolution structural and dynamic detail is essential. The polypeptide fold of the gramicidin channel has been effectively modeled for the past 20 years, yet the functional changes in conductance and channel lifetime associated with amino acid substitutions cannot be predicted. To accomplish this goal, high-resolution electrostatic modeling and the precise orientation of all dipoles are required. Furthermore, an enhanced knowledge of the complex molecular environment of this membrane-bound peptide is needed. An aqueous environment is relatively uniform and achiral. The membrane environment is very heterogenous and chiral. A knowledge of the interactions, specific and nonspecific, between peptide and lipid will aid in developing a better understanding of this environment. To accomplish this goal, it is necessary to study the peptide in an extended lipid bilayer, rather than in a vesicular or micellar form. These latter environments are likely to possess increased dynamics, increased water penetration, and distorted interactions between the polypeptide and membrane surface. To perform NMR studies on bilayer bound peptides, solid state NMR methods are required, and for specific site information, isotopic labels are incorporated using solid phase peptide synthesis.

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational ℓ1-Norm Regularization in the Derivative Domain

Science.gov (United States)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2014-05-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ℓ1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case

Dynamic frequency-domain interferometer for absolute distance measurements with high resolution

International Nuclear Information System (INIS)

Weng, Jidong; Liu, Shenggang; Ma, Heli; Tao, Tianjiong; Wang, Xiang; Liu, Cangli; Tan, Hua

2014-01-01

A unique dynamic frequency-domain interferometer for absolute distance measurement has been developed recently. This paper presents the working principle of the new interferometric system, which uses a photonic crystal fiber to transmit the wide-spectrum light beams and a high-speed streak camera or frame camera to record the interference stripes. Preliminary measurements of harmonic vibrations of a speaker, driven by a radio, and the changes in the tip clearance of a rotating gear wheel show that this new type of interferometer has the ability to perform absolute distance measurements both with high time- and distance-resolution

Dynamic frequency-domain interferometer for absolute distance measurements with high resolution

Science.gov (United States)

Weng, Jidong; Liu, Shenggang; Ma, Heli; Tao, Tianjiong; Wang, Xiang; Liu, Cangli; Tan, Hua

2014-11-01

A unique dynamic frequency-domain interferometer for absolute distance measurement has been developed recently. This paper presents the working principle of the new interferometric system, which uses a photonic crystal fiber to transmit the wide-spectrum light beams and a high-speed streak camera or frame camera to record the interference stripes. Preliminary measurements of harmonic vibrations of a speaker, driven by a radio, and the changes in the tip clearance of a rotating gear wheel show that this new type of interferometer has the ability to perform absolute distance measurements both with high time- and distance-resolution.

Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

Science.gov (United States)

Shanlei Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter V. Caldwell; Kai Duan; Yang Zhang

2016-01-01

Quantifying the potential impacts of climatechange on water yield and ecosystem productivity is essential to developing sound watershed restoration plans, andecosystem adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and StressIndex, WaSSI) with WRF (Weather Research and Forecasting Model) using dynamically downscaled...

Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator.

Science.gov (United States)

Jiang, Wenwen; Ong, Frank; Johnson, Kevin M; Nagle, Scott K; Hope, Thomas A; Lustig, Michael; Larson, Peder E Z

2018-06-01

To achieve motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data. Five-minute free-breathing scans were acquired with a 3D ultrashort echo time (UTE) sequence with 1.25 mm isotropic resolution. From this data, dynamic 3D self-navigating images were reconstructed under locally low rank (LLR) constraints and used for motion compensation with one of two methods: a soft-gating technique to penalize the respiratory motion induced data inconsistency, and a respiratory motion-resolved technique to provide images of all respiratory motion states. Respiratory motion estimation derived from the proposed dynamic 3D self-navigator of 7.5 mm isotropic reconstruction resolution and a temporal resolution of 300 ms was successful for estimating complex respiratory motion patterns. This estimation improved image quality compared to respiratory belt and DC-based navigators. Respiratory motion compensation with soft-gating and respiratory motion-resolved techniques provided good image quality from highly undersampled data in volunteers and clinical patients. An optimized 3D UTE sequence combined with the proposed reconstruction methods can provide high-resolution motion robust pulmonary MRI. Feasibility was shown in patients who had irregular breathing patterns in which our approach could depict clinically relevant pulmonary pathologies. Magn Reson Med 79:2954-2967, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

A low-jitter RF PLL frequency synthesizer with high-speed mixed-signal down-scaling circuits

International Nuclear Information System (INIS)

Tang Lu; Wang Zhigong; Xue Hong; He Xiaohu; Xu Yong; Sun Ling

2010-01-01

A low-jitter RF phase locked loop (PLL) frequency synthesizer with high-speed mixed-signal down-scaling circuits is proposed. Several techniques are proposed to reduce the design complexity and improve the performance of the mixed-signal down-scaling circuit in the PLL. An improved D-latch is proposed to increase the speed and the driving capability of the DMP in the down-scaling circuit. Through integrating the D-latch with 'OR' logic for dual-modulus operation, the delays associated with both the 'OR' and D-flip-flop (DFF) operations are reduced, and the complexity of the circuit is also decreased. The programmable frequency divider of the down-scaling circuit is realized in a new method based on deep submicron CMOS technology standard cells and a more accurate wire-load model. The charge pump in the PLL is also realized with a novel architecture to improve the current matching characteristic so as to reduce the jitter of the system. The proposed RF PLL frequency synthesizer is realized with a TSMC 0.18-μm CMOS process. The measured phase noise of the PLL frequency synthesizer output at 100 kHz offset from the center frequency is only -101.52 dBc/Hz. The circuit exhibits a low RMS jitter of 3.3 ps. The power consumption of the PLL frequency synthesizer is also as low as 36 mW at a 1.8 V power supply. (semiconductor integrated circuits)

Downscaling global land cover projections from an integrated assessment model for use in regional analyses: results and evaluation for the US from 2005 to 2095

International Nuclear Information System (INIS)

West, Tristram O; Le Page, Yannick; Wolf, Julie; Thomson, Allison M; Huang, Maoyi

2014-01-01

Projections of land cover change generated from integrated assessment models (IAM) and other economic-based models can be applied for analyses of environmental impacts at sub-regional and landscape scales. For those IAM and economic models that project land cover change at the continental or regional scale, these projections must be downscaled and spatially distributed prior to use in climate or ecosystem models. Downscaling efforts to date have been conducted at the national extent with relatively high spatial resolution (30 m) and at the global extent with relatively coarse spatial resolution (0.5°). We revised existing methods to downscale global land cover change projections for the US to 0.05° resolution using MODIS land cover data as the initial proxy for land class distribution. Land cover change realizations generated here represent a reference scenario and two emissions mitigation pathways (MPs) generated by the global change assessment model (GCAM). Future gridded land cover realizations are constructed for each MODIS plant functional type (PFT) from 2005 to 2095, commensurate with the community land model PFT land classes, and archived for public use. The GCAM land cover realizations provide spatially explicit estimates of potential shifts in croplands, grasslands, shrublands, and forest lands. Downscaling of the MPs indicate a net replacement of grassland by cropland in the western US and by forest in the eastern US. An evaluation of the downscaling method indicates that it is able to reproduce recent changes in cropland and grassland distributions in respective areas in the US, suggesting it could provide relevant insights into the potential impacts of socio-economic and environmental drivers on future changes in land cover. (letters)

Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

Science.gov (United States)

Pan, Xin; Cao, Chen; Yang, Yingbao; Li, Xiaolong; Shan, Liangliang; Zhu, Xi

2018-04-01

The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of +/-0.02 predictor perturbances, respectively.

Downscaling of Short-Term Precipitation from Regional Climate Models for Sustainable Urban Planning

Directory of Open Access Journals (Sweden)

Holger Hoppe

2012-05-01

Full Text Available A framework for downscaling precipitation from RCM projections to the high resolutions in time and space required in the urban hydrological climate change impact assessment is outlined and demonstrated. The basic approach is that of Delta Change, developed for both continuous and event-based applications. In both cases, Delta Change Factors (DCFs are calculated which represent the expected future change of some key precipitation statistics. In the continuous case, short-term precipitation from climate projections are analysed in order to estimate DCFs associated with different percentiles in the frequency distribution of non-zero intensities. The DCFs may then be applied to an observed time series, producing a realisation of a future time series. The event-based case involves downscaling of Intensity-Duration-Frequency (IDF curves based on extreme value analysis of annual maxima using the Gumbel distribution. The resulting DCFs are expressed as a function of duration and frequency (i.e., return period and may be used to estimate future design storms. The applications are demonstrated in case studies focusing on the expected changes in short-term precipitation statistics until 2100 in the cities of Linz (Austria and Wuppertal (Germany. The downscaling framework is implemented in the climate service developed within the EU-project SUDPLAN.

Empirical downscaling of atmospheric key variables above a tropical glacier surface (Cordillera Blanca, Peru)

Science.gov (United States)

Hofer, M.; Kaser, G.; Mölg, T.; Juen, I.; Wagnon, P.

2009-04-01

Glaciers in the outer tropical Cordillera Blanca (Peru, South America) are of major socio-economic importance, since glacier runoff represents the primary water source during the dry season, when little or no rainfall occurs. Due to their location at high elevations, the glaciers moreover provide important information about climate change in the tropical troposphere, where measurements are sparse. This study targets the local reconstruction of air temperature, specific humidity and wind speed above the surface of an outer tropical glacier from NCEP/NCAR reanalysis data as large scale predictors. Since a farther scope is to provide input data for process based glacier mass balance modelling, the reconstruction pursues a high temporal resolution. Hence an empirical downscaling scheme is developed, based on a few years' time series of hourly observations from automatic weather stations, located at the glacier Artesonraju and nearby moraines (Northern Cordillera Blanca). Principal component and multiple regression analyses are applied to define the appropriate spatial downscaling domain, suitable predictor variables, and the statistical transfer functions. The model performance is verified using an independent data set. The best predictors are lower tropospheric air temperature and specific humidity, at reanalysis model grid points that represent the Bolivian Altiplano, located in the South of the Cordillera Blanca. The developed downscaling model explaines a considerable portion (more than 60%) of the diurnal variance of air temperature and specific humidity at the moraine stations, and air temperature above the glacier surface. Specific humidity above the glacier surface, however, can be reconstructed well in the seasonal, but not in the required diurnal time resolution. Wind speed can only be poorly determined by the large scale predictors (r² lower than 0.3) at both sites. We assume a complex local interaction between valley and glacier wind system to be the main

High Resolution Nature Runs and the Big Data Challenge

Science.gov (United States)

Webster, W. Phillip; Duffy, Daniel Q.

2015-01-01

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility

STATISTICAL DOWNSCALING DENGAN PERGESERAN WAKTU BERDASARKAN KORELASI SILANG

Directory of Open Access Journals (Sweden)

Aji Hamim Wigena

2015-09-01

Full Text Available Pergeseran waktu (time lag dalam analisis data deret waktu diperlukan terutama untuk analisis hubungan dua peubah (variable, seperti dalam statistical downscaling. Pergeseran waktu ini ditentukan berdasarkan korelasi silang tinggi yang setara dengan hubungan yang kuat antar kedua peubah tersebut sehingga dapat digunakan dalam pemodelan untuk prakiraan yang lebih akurat. Makalah ini mengenai statistical downscaling dengan memperhatikan korelasi silang antara data curah hujan dengan data presipitasi Global Circulation Model (GCM dari Climate Model Inter Comparison Project (CMIP5. Salah satu syarat dalam statistical downscaling adalah peubah  skala lokal dan global berkorelasi tinggi. Kedua tipe peubah tersebut berupa data deret waktu sehingga fungsi korelasi silang diterapkan untuk memperoleh pergeseran waktu. Korelasi silang yang tinggi menentukan pergeseran waktu pada luaran GCM yang menghasilkan hubungan fungsional lebih kuat antara kedua tipe peubah. Model regresi komponen utama dan regresi kuadrat terkecil parsial digunakan dalam makalah ini. Model-model dengan pergeseran waktu menduga curah hujan lebih baik daripada model-model tanpa pergeseran waktu.   Time lag in time series data analysis is required especially to analyze the relationship of two variables, such as in statistical downscaling. Time lag is determined based on high cross correlation which is equivalent to strong relationship between the two variables and can be used in modeling for a more accurate forecast. This paper is about  statistical downscaling by considering the cross correlation between rainfall data and precipitation data from Global Circulation Model (GCM of Climate Model Inter Comparison Project (CMIP5. One of the conditions in statistical downscaling is that local scale and global scale variables are highly correlated. Both types of variables are time series data, thus cross correlation function is applied to find time lags. High cross correlation determines

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

Inverse stochastic-dynamic models for high-resolution Greenland ice core records

DEFF Research Database (Denmark)

Boers, Niklas; Chekroun, Mickael D.; Liu, Honghu

2017-01-01

as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i) high-resolution training data, (ii) cubic drift terms, (iii) nonlinear coupling terms between the 18O and dust......Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic-dynamic models from 18O and dust records of unprecedented, subdecadal...

Differences between downscaling with spectral and grid nudging using WRF

Directory of Open Access Journals (Sweden)

P. Liu

2012-04-01

Full Text Available Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields and the small-scale features (from the RCMs has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF Model. The simulations are compared against the results with North America Regional Reanalysis (NARR data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

Statistical-dynamical downscaling of wind roses over the Czech Republic

Czech Academy of Sciences Publication Activity Database

Svoboda, Jaroslav; Chládová, Zuzana; Hošek, Jiří; Pop, Lukáš

2013-01-01

Roč. 112, 3-4 (2013), s. 713-722 ISSN 0177-798X R&D Projects: GA AV ČR KJB300420905 Institutional support: RVO:68378289 Keywords : downscaling * Czech Republic Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.742, year: 2013 http://link.springer.com/content/pdf/10.1007%2Fs00704-012-0759-y.pdf

Statistically downscaled climate projections to support evaluating climate change risks for hydropower

International Nuclear Information System (INIS)

Brekke, L.

2008-01-01

This paper described a web-served public access archive of down-scaled climate projections developed as a tool for water managers of river and hydropower systems. The archive provided access to climate projection data at basin-relevant resolution and included an extensive compilation of down-scale climate projects designed to support risk-based adaptation planning. Downscaled translations of 112 contemporary climate projections produced using the World Climate Research Program's coupled model intercomparison project were also included. Datasets for the coupled model included temperature and precipitation, monthly time-steps, and geographic coverage for the United States and portions of Mexico and Canada. It was concluded that the archive will be used to develop risk-based studies on shifts in seasonal patterns, changes in mean annual runoff, and associated responses in water resources and hydroelectric power management. Case studies demonstrating reclamation applications of archive content and potential applications for hydroelectric power production impacts were included. tabs., figs

Comparison of elastic-viscous-plastic and viscous-plastic dynamics models using a high resolution Arctic sea ice model

Energy Technology Data Exchange (ETDEWEB)

Hunke, E.C. [Los Alamos National Lab., NM (United States); Zhang, Y. [Naval Postgraduate School, Monterey, CA (United States)

1997-12-31

A nonlinear viscous-plastic (VP) rheology proposed by Hibler (1979) has been demonstrated to be the most suitable of the rheologies commonly used for modeling sea ice dynamics. However, the presence of a huge range of effective viscosities hinders numerical implementations of this model, particularly on high resolution grids or when the ice model is coupled to an ocean or atmosphere model. Hunke and Dukowicz (1997) have modified the VP model by including elastic waves as a numerical regularization in the case of zero strain rate. This modification (EVP) allows an efficient, fully explicit discretization that adapts well to parallel architectures. The authors present a comparison of EVP and VP dynamics model results from two 5-year simulations of Arctic sea ice, obtained with a high resolution sea ice model. The purpose of the comparison is to determine how differently the two dynamics models behave, and to decide whether the elastic-viscous-plastic model is preferable for high resolution climate simulations, considering its high efficiency in parallel computation. Results from the first year of this experiment (1990) are discussed in detail in Hunke and Zhang (1997).

Effects on Storm-Water Management for Three Major US Cities Using Location Specific Extreme Precipitation Dynamical Downscaling

Science.gov (United States)

Pelle, A.; Allen, M.; Fu, J. S.

2013-12-01

With rising population and increasing urban density, it is of pivotal importance for urban planners to plan for increasing extreme precipitation events. Climate models indicate that an increase in global mean temperature will lead to increased frequency and intensity of storms of a variety of types. Analysis of results from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) has demonstrated that global climate models severely underestimate precipitation, however. Preliminary results from dynamical downscaling indicate that Philadelphia, Pennsylvania is expected to experience the greatest increase of precipitation due to an increase in annual extreme events in the US. New York City, New York and Chicago, Illinois are anticipated to have similarly large increases in annual extreme precipitation events. In order to produce more accurate results, we downscale Philadelphia, Chicago, and New York City using the Weather Research and Forecasting model (WRF). We analyze historical precipitation data and WRF output utilizing a Log Pearson Type III (LP3) distribution for frequency of extreme precipitation events. This study aims to determine the likelihood of extreme precipitation in future years and its effect on the of cost of stormwater management for these three cities.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

Science.gov (United States)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This

Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments

OpenAIRE

Magali Troin; Daniel Caya; Juan Alberto Velázquez; François Brissette

2015-01-01

Study region: An analysis of hydrological response to a dynamically downscaled multi-member multi-model global climate model (GCM) ensemble of simulations based on the Canadian Regional Climate Model (CRCM) is presented for three snowmelt-dominated basins in Canada. The basins are situated in the western mountainous (British Columbia) and eastern level (Quebec) regions in Canada, providing comprehensive experiments to validate the CRCM over various topographic features. Study focus: The ev...

Impacts of spectral nudging on the simulated surface air temperature in summer compared with the selection of shortwave radiation and land surface model physics parameterization in a high-resolution regional atmospheric model

Science.gov (United States)

Park, Jun; Hwang, Seung-On

2017-11-01

The impact of a spectral nudging technique for the dynamical downscaling of the summer surface air temperature in a high-resolution regional atmospheric model is assessed. The performance of this technique is measured by comparing 16 analysis-driven simulation sets of physical parameterization combinations of two shortwave radiation and four land surface model schemes of the model, which are known to be crucial for the simulation of the surface air temperature. It is found that the application of spectral nudging to the outermost domain has a greater impact on the regional climate than any combination of shortwave radiation and land surface model physics schemes. The optimal choice of two model physics parameterizations is helpful for obtaining more realistic spatiotemporal distributions of land surface variables such as the surface air temperature, precipitation, and surface fluxes. However, employing spectral nudging adds more value to the results; the improvement is greater than using sophisticated shortwave radiation and land surface model physical parameterizations. This result indicates that spectral nudging applied to the outermost domain provides a more accurate lateral boundary condition to the innermost domain when forced by analysis data by securing the consistency with large-scale forcing over a regional domain. This consequently indirectly helps two physical parameterizations to produce small-scale features closer to the observed values, leading to a better representation of the surface air temperature in a high-resolution downscaled climate.

Testing the efficacy of downscaling in species distribution modelling: a comparison between MaxEnt and Favourability Function models

Energy Technology Data Exchange (ETDEWEB)

Olivero, J.; Toxopeus, A.G.; Skidmore, A.K.; Real, R.

2016-07-01

Statistical downscaling is used to improve the knowledge of spatial distributions from broad–scale to fine–scale maps with higher potential for conservation planning. We assessed the effectiveness of downscaling in two commonly used species distribution models: Maximum Entropy (MaxEnt) and the Favourability Function (FF). We used atlas data (10 x 10 km) of the fire salamander Salamandra salamandra distribution in southern Spain to derive models at a 1 x 1 km resolution. Downscaled models were assessed using an independent dataset of the species’ distribution at 1 x 1 km. The Favourability model showed better downscaling performance than the MaxEnt model, and the models that were based on linear combinations of environmental variables performed better than models allowing higher flexibility. The Favourability model minimized model overfitting compared to the MaxEnt model. (Author)

Effect of model resolution on a regional climate model simulation over southeast Australia

KAUST Repository

Evans, J. P.; McCabe, Matthew

2013-01-01

Dynamically downscaling climate projections from global climate models (GCMs) for use in impacts and adaptation research has become a common practice in recent years. In this study, the CSIRO Mk3.5 GCM is downscaled using the Weather Research and Forecasting (WRF) regional climate model (RCM) to medium (50 km) and high (10 km) resolution over southeast Australia. The influence of model resolution on the present-day (1985 to 2009) modelled regional climate and projected future (2075 to 2099) changes are examined for both mean climate and extreme precipitation characteristics. Increasing model resolution tended to improve the simulation of present day climate, with larger improvements in areas affected by mountains and coastlines. Examination of circumstances under which increasing the resolution decreased performance revealed an error in the GCM circulation, the effects of which had been masked by the coarse GCM topography. Resolution modifications to projected changes were largest in regions with strong topographic and coastline influences, and can be large enough to change the sign of the climate change projected by the GCM. Known physical mechanisms for these changes included orographic uplift and low-level blocking of air-masses caused by mountains. In terms of precipitation extremes, the GCM projects increases in extremes even when the projected change in the mean was a decrease: but this was not always true for the higher resolution models. Thus, while the higher resolution RCM climate projections often concur with the GCM projections, there are times and places where they differ significantly due to their better representation of physical processes. It should also be noted that the model resolution can modify precipitation characteristics beyond just its mean value.

Effect of model resolution on a regional climate model simulation over southeast Australia

KAUST Repository

Evans, J. P.

2013-03-26

Dynamically downscaling climate projections from global climate models (GCMs) for use in impacts and adaptation research has become a common practice in recent years. In this study, the CSIRO Mk3.5 GCM is downscaled using the Weather Research and Forecasting (WRF) regional climate model (RCM) to medium (50 km) and high (10 km) resolution over southeast Australia. The influence of model resolution on the present-day (1985 to 2009) modelled regional climate and projected future (2075 to 2099) changes are examined for both mean climate and extreme precipitation characteristics. Increasing model resolution tended to improve the simulation of present day climate, with larger improvements in areas affected by mountains and coastlines. Examination of circumstances under which increasing the resolution decreased performance revealed an error in the GCM circulation, the effects of which had been masked by the coarse GCM topography. Resolution modifications to projected changes were largest in regions with strong topographic and coastline influences, and can be large enough to change the sign of the climate change projected by the GCM. Known physical mechanisms for these changes included orographic uplift and low-level blocking of air-masses caused by mountains. In terms of precipitation extremes, the GCM projects increases in extremes even when the projected change in the mean was a decrease: but this was not always true for the higher resolution models. Thus, while the higher resolution RCM climate projections often concur with the GCM projections, there are times and places where they differ significantly due to their better representation of physical processes. It should also be noted that the model resolution can modify precipitation characteristics beyond just its mean value.

Assessment of the Suitability of High Resolution Numerical Weather Model Outputs for Hydrological Modelling in Mountainous Cold Regions

Science.gov (United States)

Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.

2017-12-01

The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.

Which complexity of regional climate system models is essential for downscaling anthropogenic climate change in the Northwest European Shelf?

Science.gov (United States)

Mathis, Moritz; Elizalde, Alberto; Mikolajewicz, Uwe

2018-04-01

Climate change impact studies for the Northwest European Shelf (NWES) make use of various dynamical downscaling strategies in the experimental setup of regional ocean circulation models. Projected change signals from coupled and uncoupled downscalings with different domain sizes and forcing global and regional models show substantial uncertainty. In this paper, we investigate influences of the downscaling strategy on projected changes in the physical and biogeochemical conditions of the NWES. Our results indicate that uncertainties due to different downscaling strategies are similar to uncertainties due to the choice of the parent global model and the downscaling regional model. Downscaled change signals reveal to depend stronger on the downscaling strategy than on the model skills in simulating present-day conditions. Uncoupled downscalings of sea surface temperature (SST) changes are found to be tightly constrained by the atmospheric forcing. The incorporation of coupled air-sea interaction, by contrast, allows the regional model system to develop independently. Changes in salinity show a higher sensitivity to open lateral boundary conditions and river runoff than to coupled or uncoupled atmospheric forcings. Dependencies on the downscaling strategy for changes in SST, salinity, stratification and circulation collectively affect changes in nutrient import and biological primary production.

Statistical dynamic image reconstruction in state-of-the-art high-resolution PET

International Nuclear Information System (INIS)

Rahmim, Arman; Cheng, J-C; Blinder, Stephan; Camborde, Maurie-Laure; Sossi, Vesna

2005-01-01

Modern high-resolution PET is now more than ever in need of scrutiny into the nature and limitations of the imaging modality itself as well as image reconstruction techniques. In this work, we have reviewed, analysed and addressed the following three considerations within the particular context of state-of-the-art dynamic PET imaging: (i) the typical average numbers of events per line-of-response (LOR) are now (much) less than unity (ii) due to the physical and biological decay of the activity distribution, one requires robust and efficient reconstruction algorithms applicable to a wide range of statistics and (iii) the computational considerations in dynamic imaging are much enhanced (i.e., more frames to be stored and reconstructed). Within the framework of statistical image reconstruction, we have argued theoretically and shown experimentally that the sinogram non-negativity constraint (when using the delayed-coincidence and/or scatter-subtraction techniques) is especially expected to result in an overestimation bias. Subsequently, two schemes are considered: (a) subtraction techniques in which an image non-negativity constraint has been imposed and (b) implementation of random and scatter estimates inside the reconstruction algorithms, thus enabling direct processing of Poisson-distributed prompts. Both techniques are able to remove the aforementioned bias, while the latter, being better conditioned theoretically, is able to exhibit superior noise characteristics. We have also elaborated upon and verified the applicability of the accelerated list-mode image reconstruction method as a powerful solution for accurate, robust and efficient dynamic reconstructions of high-resolution data (as well as a number of additional benefits in the context of state-of-the-art PET)

Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area

KAUST Repository

Pizzigalli, Claudia; Palatella, L.; Zampieri, M.; Lionello, P.; Miglietta, M.M.; Paradisi, P.

2012-01-01

In this paper we present and discuss a comparison between statistical and regional climate modeling techniques for downscaling GCM prediction . The comparison is carried out over the “Capitanata” region, an area of agricultural interest in south

Downscaling Climate Projections to a Mountainous Landscape: A Climate Impact Assessment for the U.S. Northern Rockies Crown of the Continent Ecosystem

Science.gov (United States)

Oyler, J.; Anderson, R.; Running, S. W.

2010-12-01

In topographically complex landscapes, there is often a mismatch in scale between global climate model projections and more local climate-forcing factors and related ecological/hydrological processes. To overcome this limitation, the objective of this study was to downscale climate projections to the rugged Crown of the Continent Ecosystem (CCE) within the U.S. Northern Rockies and assess future impacts on water balances, vegetation dynamics, and carbon fluxes. A 40-year (1970-2009) spatial historical climate dataset (800m resolution, daily timestep) was generated for the CCE and modified for terrain influences. Regional climate projections were downscaled by applying them to the fine-scale historical dataset using a modified delta downscaling method and stochastic weather generator. The downscaled projections were used to drive the Biome-BGC ecosystem model. Overall CCE impacts included decreases in April 1 snow water equivalent, less days with snow on the ground, increased vegetation water stress, and increased growing degree days. The relaxing of temperature constraints increased annual net primary productivity (NPP) throughout most of the CCE landscape. However, an increase in water stress seems to have limited the growth in NPP and, in some areas, NPP actually decreased. Thus, CCE vegetation productivity trends under increasing temperatures will likely be determined by local changes in hydrologic function. Given the greater uncertainty in precipitation projections, future work should concentrate on determining thresholds in water constraints that greatly modify the magnitude and direction of carbon accumulation within the CCE under a warming climate.

Testing the efficacy of downscaling in species distribution modelling: a comparison between MaxEnt and Favourability Function models

Directory of Open Access Journals (Sweden)

Olivero, J.

2016-03-01

Full Text Available Statistical downscaling is used to improve the knowledge of spatial distributions from broad–scale to fine–scale maps with higher potential for conservation planning. We assessed the effectiveness of downscaling in two commonly used species distribution models: Maximum Entropy (MaxEnt and the Favourability Function (FF. We used atlas data (10 x 10 km of the fire salamander Salamandra salamandra distribution in southern Spain to derive models at a 1 x 1 km resolution. Downscaled models were assessed using an independent dataset of the species’ distribution at 1 x 1 km. The Favourability model showed better downscaling performance than the MaxEnt model, and the models that were based on linear combinations of environmental variables performed better than models allowing higher flexibility. The Favourability model minimized model overfitting compared to the MaxEnt model.

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

Directory of Open Access Journals (Sweden)

Lei Cai

2018-01-01

Full Text Available Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research and Forecast (Polar WRF model was forced by three sources: The ERA-interim reanalysis data (for 1979–2014, the Community Earth System Model 1.0 (CESM1.0 historical simulation (for 1950–2005, and the CESM1.0 projected (for 2006–2100 simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5 scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-h interval. The ERA-interim forced WRF (ERA-WRF proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP 8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

The polar WRF downscaled historical and projected 21st century climate for the coast and foothills of Arctic Alaska

Science.gov (United States)

Cai, Lei; Alexeev, Vladimir A.; Arp, Christopher D.; Jones, Benjamin M.; Liljedahl, Anna K.; Gädeke, Anne

2018-01-01

Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research & Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979-2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950-2005), and the CESM1.0 projected (for 2006-2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-hour interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

Advances in High-Resolution Microscale Impedance Sensors

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Marco Carminati

2017-01-01

Full Text Available Sensors based on impedance transduction have been well consolidated in the industry for decades. Today, the downscaling of the size of sensing elements to micrometric and submicrometric dimensions is enabled by the diffusion of lithographic processes and fostered by the convergence of complementary disciplines such as microelectronics, photonics, biology, electrochemistry, and material science, all focusing on energy and information manipulation at the micro- and nanoscale. Although such a miniaturization trend is pivotal in supporting the pervasiveness of sensors (in the context of mass deployment paradigms such as smart city, home and body monitoring networks, and Internet of Things, it also presents new challenges for the detection electronics, reaching the zeptoFarad domain. In this tutorial review, a selection of examples is illustrated with the purpose of distilling key indications and guidelines for the design of high-resolution impedance readout circuits and sensors. The applications span from biological cells to inertial and ultrasonic MEMS sensors, environmental monitoring, and integrated photonics.

High resolution kinetic beam schemes in generalized coordinates for ideal quantum gas dynamics

International Nuclear Information System (INIS)

Shi, Yu-Hsin; Huang, J.C.; Yang, J.Y.

2007-01-01

A class of high resolution kinetic beam schemes in multiple space dimensions in general coordinates system for the ideal quantum gas is presented for the computation of quantum gas dynamical flows. The kinetic Boltzmann equation approach is adopted and the local equilibrium quantum statistics distribution is assumed. High-order accurate methods using essentially non-oscillatory interpolation concept are constructed. Computations of shock wave diffraction by a circular cylinder in an ideal quantum gas are conducted to illustrate the present method. The present method provides a viable means to explore various practical ideal quantum gas flows

Statistical downscaling of IPCC sea surface wind and wind energy predictions for U.S. east coastal ocean, Gulf of Mexico and Caribbean Sea

Science.gov (United States)

Yao, Zhigang; Xue, Zuo; He, Ruoying; Bao, Xianwen; Song, Jun

2016-08-01

A multivariate statistical downscaling method is developed to produce regional, high-resolution, coastal surface wind fields based on the IPCC global model predictions for the U.S. east coastal ocean, the Gulf of Mexico (GOM), and the Caribbean Sea. The statistical relationship is built upon linear regressions between the empirical orthogonal function (EOF) spaces of a cross- calibrated, multi-platform, multi-instrument ocean surface wind velocity dataset (predictand) and the global NCEP wind reanalysis (predictor) over a 10 year period from 2000 to 2009. The statistical relationship is validated before applications and its effectiveness is confirmed by the good agreement between downscaled wind fields based on the NCEP reanalysis and in-situ surface wind measured at 16 National Data Buoy Center (NDBC) buoys in the U.S. east coastal ocean and the GOM during 1992-1999. The predictand-predictor relationship is applied to IPCC GFDL model output (2.0°×2.5°) of downscaled coastal wind at 0.25°×0.25° resolution. The temporal and spatial variability of future predicted wind speeds and wind energy potential over the study region are further quantified. It is shown that wind speed and power would significantly be reduced in the high CO2 climate scenario offshore of the mid-Atlantic and northeast U.S., with the speed falling to one quarter of its original value.

High Resolution Modeling of Hurricanes in a Climate Context

Science.gov (United States)

Knutson, T. R.

2007-12-01

Modeling of tropical cyclone activity in a climate context initially focused on simulation of relatively weak tropical storm-like disturbances as resolved by coarse grid (200 km) global models. As computing power has increased, multi-year simulations with global models of grid spacing 20-30 km have become feasible. Increased resolution also allowed for simulation storms of increasing intensity, and some global models generate storms of hurricane strength, depending on their resolution and other factors, although detailed hurricane structure is not simulated realistically. Results from some recent high resolution global model studies are reviewed. An alternative for hurricane simulation is regional downscaling. An early approach was to embed an operational (GFDL) hurricane prediction model within a global model solution, either for 5-day case studies of particular model storm cases, or for "idealized experiments" where an initial vortex is inserted into an idealized environments derived from global model statistics. Using this approach, hurricanes up to category five intensity can be simulated, owing to the model's relatively high resolution (9 km grid) and refined physics. Variants on this approach have been used to provide modeling support for theoretical predictions that greenhouse warming will increase the maximum intensities of hurricanes. These modeling studies also simulate increased hurricane rainfall rates in a warmer climate. The studies do not address hurricane frequency issues, and vertical shear is neglected in the idealized studies. A recent development is the use of regional model dynamical downscaling for extended (e.g., season-length) integrations of hurricane activity. In a study for the Atlantic basin, a non-hydrostatic model with grid spacing of 18km is run without convective parameterization, but with internal spectral nudging toward observed large-scale (basin wavenumbers 0-2) atmospheric conditions from reanalyses. Using this approach, our

Galactic and stellar dynamics in the era of high resolution surveys

Science.gov (United States)

Boily, C. M.; Combes, F.; Hensler, G.; Spurzem, R.

2008-12-01

The conference Galactic and Stellar Dynamics in the Era of High Resolution Surveys took place at the European Doctoral College (EDC) in Strasbourg from 2008 March 16 to 20. The event was co-sponsored by the Astronomische Gesellschaft (AG) and the Société Fran\\c{c}aise d'Astronomie et d'Astrophysique (SF2A), a joint venture aiming to set a new trend of regular thematic meetings in specific areas of research. This special issue of the Astronomische Nachrichten is a compilation of the papers presented at the meeting. We give an outline of the meeting together with a short history of the relations of the two societies.

Numerical modeling of permafrost dynamics in Alaska using a high spatial resolution dataset

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E. E. Jafarov

2012-06-01

Full Text Available Climate projections for the 21st century indicate that there could be a pronounced warming and permafrost degradation in the Arctic and sub-Arctic regions. Climate warming is likely to cause permafrost thawing with subsequent effects on surface albedo, hydrology, soil organic matter storage and greenhouse gas emissions.

To assess possible changes in the permafrost thermal state and active layer thickness, we implemented the GIPL2-MPI transient numerical model for the entire Alaska permafrost domain. The model input parameters are spatial datasets of mean monthly air temperature and precipitation, prescribed thermal properties of the multilayered soil column, and water content that are specific for each soil class and geographical location. As a climate forcing, we used the composite of five IPCC Global Circulation Models that has been downscaled to 2 by 2 km spatial resolution by Scenarios Network for Alaska Planning (SNAP group.

In this paper, we present the modeling results based on input of a five-model composite with A1B carbon emission scenario. The model has been calibrated according to the annual borehole temperature measurements for the State of Alaska. We also performed more detailed calibration for fifteen shallow borehole stations where high quality data are available on daily basis. To validate the model performance, we compared simulated active layer thicknesses with observed data from Circumpolar Active Layer Monitoring (CALM stations. The calibrated model was used to address possible ground temperature changes for the 21st century. The model simulation results show widespread permafrost degradation in Alaska could begin between 2040–2099 within the vast area southward from the Brooks Range, except for the high altitude regions of the Alaska Range and Wrangell Mountains.

Development of High-Resolution Dynamic Dust Source Function - A Case Study with a Strong Dust Storm in a Regional Model

Science.gov (United States)

Kim, Dongchul; Chin, Mian; Kemp, Eric M.; Tao, Zhining; Peters-Lidard, Christa D.; Ginoux, Paul

2017-01-01

A high-resolution dynamic dust source has been developed in the NASA Unified-Weather Research and Forecasting (NU-WRF) model to improve the existing coarse static dust source. In the new dust source map, topographic depression is in 1-km resolution and surface bareness is derived using the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS). The new dust source better resolves the complex topographic distribution over the Western United States where its magnitude is higher than the existing, coarser resolution static source. A case study is conducted with an extreme dust storm that occurred in Phoenix, Arizona in 0203 UTC July 6, 2011. The NU-WRF model with the new high-resolution dynamic dust source is able to successfully capture the dust storm, which was not achieved with the old source identification. However the case study also reveals several challenges in reproducing the time evolution of the short-lived, extreme dust storm events.

Development of High-Resolution Dynamic Dust Source Function -A Case Study with a Strong Dust Storm in a Regional Model.

Science.gov (United States)

Kim, Dongchul; Chin, Mian; Kemp, Eric M; Tao, Zhining; Peters-Lidard, Christa D; Ginoux, Paul

2017-06-01

A high-resolution dynamic dust source has been developed in the NASA Unified-Weather Research and Forecasting (NU-WRF) model to improve the existing coarse static dust source. In the new dust source map, topographic depression is in 1-km resolution and surface bareness is derived using the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS). The new dust source better resolves the complex topographic distribution over the Western United States where its magnitude is higher than the existing, coarser resolution static source. A case study is conducted with an extreme dust storm that occurred in Phoenix, Arizona in 02-03 UTC July 6, 2011. The NU-WRF model with the new high-resolution dynamic dust source is able to successfully capture the dust storm, which was not achieved with the old source identification. However the case study also reveals several challenges in reproducing the time evolution of the short-lived, extreme dust storm events.

High resolution neutron spectroscopy - a tool for the investigation of dynamics of polymers and soft matter

International Nuclear Information System (INIS)

Monkenbusch, M.; Richter, D.

2007-01-01

Neutron scattering, with the ability to vary the contrast of molecular items by hydrogen/deuterium exchanges, is an invaluable tool for soft matter research. Besides the structural information on the mesoscopic scale that is obtained by diffraction methods like small angle neutron scattering, the slow dynamics of molecular motion on mesoscopic scale is accessible by high resolution neutron spectroscopy. The basic features of neutron backscattering spectroscopy, and in particular neutron spin-echo spectroscopy, are presented, in combination with illustrations of results from polymer melt dynamics to protein dynamics which are obtained by these techniques. (authors)

On the impact of using downscaled reanalysis data instead of direct measurements for modeling the mass balance of a tropical glacier (Cordillera Blanca, Peru)

Science.gov (United States)

Galos, Stephan; Hofer, Marlis; Marzeion, Ben; Mölg, Thomas; Großhauser, Martin

2013-04-01

Due to their setting, tropical glaciers are sensitive indicators of mid-tropospheric meteorological variability and climate change. Furthermore these glaciers are of particular interest because they respond faster to climatic changes than glaciers located in mid- or high-latitudes. As long-term direct meteorological measurements in such remote environments are scarce, reanalysis data (e.g. ERA-Interim) provide a highly valuable source of information. Reanalysis datasets (i) enable a temporal extension of data records gained by direct measurements and (ii) provide information from regions where direct measurements are not available. In order to properly derive the physical exchange processes between glaciers and atmosphere from reanalysis data, downscaling procedures are required. In the present study we investigate if downscaled atmospheric variables (air temperature and relative humidity) from a reanalysis dataset can be used as input for a physically based, high resolution energy and mass balance model. We apply a well validated empirical-statistical downscaling model, fed with ERA-Interim data, to an automated weather station (AWS) on the surface of Glaciar Artesonraju (8.96° S | 77.63° W). The downscaled data is then used to replace measured air temperature and relative humidity in the input for the energy and mass balance model, which was calibrated using ablation data from stakes and a sonic ranger. In order to test the sensitivity of the modeled mass balance to the downscaled data, the results are compared to a reference model run driven solely with AWS data as model input. We finally discuss the results and present future perspectives for further developing this method.

Dynamical Downscaling over Siberia: Is there an added value in representing recent climate conditions?

Science.gov (United States)

Klehmet, K.; Rockel, B.

2012-04-01

The analysis of long-term changes and variability of climate variables for the large areal extent of Siberia - covering arctic, subarctic and temperate northern latitudes - is hampered by the sparseness of in-situ observations. To counteract this deficiency we aimed to provide a reconstruction of regional climate for the period 1948-2010 getting homogenous, consistent fields of various terrestrial and atmospheric parameters for Siberia. In order to obtain in addition a higher temporal and spatial resolution than global datasets can provide, we performed the reconstruction using the regional climate model COSMO-CLM (climate mode of the limited area model COSMO developed by the German weather service). However, the question arises whether the dynamically downscaled data of reanalysis can improve the representation of recent climate conditions. As global forcing for the initialization and the regional boundaries we use NCEP-1 Reanalysis of the National Centers for Environmental Prediction since it has the longest temporal data coverage among the reanalysis products. Additionally, spectral nudging is applied to prevent the regional model from deviating from the prescribed large-scale circulation within the whole simulation domain. The area of interest covers a region in Siberia, spanning from the Laptev Sea and Kara Sea to Northern Mongolia and from the West Siberian Lowland to the border of Sea of Okhotsk. The current horizontal resolution is of about 50 km which is planned to be increased to 25 km. To answer the question, we investigate spatial and temporal characteristics of temperature and precipitation of the model output in comparison to global reanalysis data (NCEP-1, ERA40, ERA-Interim). As reference Russian station data from the "Global Summary of the Day" data set, provided by NCDC, is used. Temperature is analyzed with respect to its climatologically spatial patterns across the model domain and its variability of extremes based on climate indices derived

Using dynamical downscaling to close the gap between global change scenarios and local permafrost dynamics

DEFF Research Database (Denmark)

Stendel, Martin; Romanovsky, Vladimir E.; Christensen, Jens H.

2007-01-01

Even though we can estimate the zonation of present-day permafrost from deep-soil temperatures obtained from global coupled atmosphere-ocean general circulation models (GCMs) by accounting for heat conduction in the frozen soil, it is impossible to explicitly resolve soil properties, vegetation......, in particular in mountainous regions. By using global climate change scenarios as driving fields, one can obtain permafrost dynamics in high temporal resolution on the order of years. For the 21st century under the IPCC SRES scenarios A2 and B2, we find an increase of mean annual ground temperature by up to 6 K...

Assessing drought risk under climate change in the US Great Plains via evaporative demand from downscaled GCM projections

Science.gov (United States)

Dewes, C.; Rangwala, I.; Hobbins, M.; Barsugli, J. J.

2016-12-01

Drought conditions in the US Great Plains occur primarily in response to periods of low precipitation, but they can be exacerbated by enhanced evaporative demand (E0) during periods of elevated temperatures, radiation, advection, and/or decreased humidity. A number of studies project severe to unprecedented drought conditions for this region later in the 21st century. Yet, we have found that methodological choices in the estimation of E0 and the selection of global climate model (GCM) output account for large uncertainties in projections of drought risk. Furthermore, the coarse resolution of GCMs offers little usability for drought risk assessments applied to socio-ecological systems, and users of climate data for that purpose tend to prefer existing downscaled products. Here we derive a physically based estimation of E0 - the FAO56 Penman-Monteith reference evapotranspiration - using driving variables from the Multivariate Adaptive Constructed Analogs (MACA) dataset, which have a spatial resolution of approximately 4 km. We select downscaled outputs from five CMIP5 GCMs, whereby we aim to represent different scenarios for the future of the Great Plains region (e.g. warm/wet, hot/dry, etc.). While this downscaling methodology removes GCM bias relative to a gridded product for historical data (METDATA), we first examine the remaining bias relative to ground (point) estimates of E0. Next we assess whether the downscaled products preserve the variability of their parent GCMs, in both historical and future (RCP8.5) projections. We then use the E0 estimates to compute multi-scale time series of drought indices such as the Evaporative Demand Drought Index (EDDI) and the Standardized Precipitation-Evaporation Index (SPEI) over the Great Plains region. We also attribute variability and drought anomalies to each of the driving parameters, to tease out the influence of specific model biases and evaluate geographical nuances of E0 drivers. Aside from improved understanding of

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

Science.gov (United States)

Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.

2016-01-01

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

Science.gov (United States)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

PDF added value of a high resolution climate simulation for precipitation

Science.gov (United States)

Soares, Pedro M. M.; Cardoso, Rita M.

2015-04-01

dynamical downscaling, based on simple PDF skill scores. The measure can assess the full quality of the PDFs and at the same time integrates a flexible manner to weight differently the PDF tails. In this study we apply the referred method to characaterize the PDF added value of a high resolution simulation with the WRF model. Results from a WRF climate simulation centred at the Iberian Penisnula with two nested grids, a larger one at 27km and a smaller one at 9km. This simulation is forced by ERA-Interim. The observational data used covers from rain gauges precipitation records to observational regular grids of daily precipitation. Two regular gridded precipitation datasets are used. A Portuguese grid precipitation dataset developed at 0.2°× 0.2°, from observed rain gauges daily precipitation. A second one corresponding to the ENSEMBLES observational gridded dataset for Europe, which includes daily precipitation values at 0.25°. The analisys shows an important PDF added value from the higher resolution simulation, regarding the full PDF and the extremes. This method shows higher potential to be applied to other simulation exercises and to evaluate other variables.

User's Manual for Downscaler Fusion Software

Science.gov (United States)

Recently, a series of 3 papers has been published in the statistical literature that details the use of downscaling to obtain more accurate and precise predictions of air pollution across the conterminous U.S. This downscaling approach combines CMAQ gridded numerical model output...

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

Science.gov (United States)

Stoy, Paul C; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Indirect downscaling of global circulation model data based on atmospheric circulation and temperature for projections of future precipitation in hourly resolution

Science.gov (United States)

Beck, F.; Bárdossy, A.

2013-07-01

Many hydraulic applications like the design of urban sewage systems require projections of future precipitation in high temporal resolution. We developed a method to predict the regional distribution of hourly precipitation sums based on daily mean sea level pressure and temperature data from a Global Circulation Model. It is an indirect downscaling method avoiding uncertain precipitation data from the model. It is based on a fuzzy-logic classification of atmospheric circulation patterns (CPs) that is further subdivided by means of the average daily temperature. The observed empirical distributions at 30 rain gauges to each CP-temperature class are assumed as constant and used for projections of the hourly precipitation sums in the future. The method was applied to the CP-temperature sequence derived from the 20th century run and the scenario A1B run of ECHAM5. According to ECHAM5, the summers in southwest Germany will become progressively drier. Nevertheless, the frequency of the highest hourly precipitation sums will increase. According to the predictions, estival water stress and the risk of extreme hourly precipitation will both increase simultaneously during the next decades.

The impact of wave number selection and spin up time when using spectral nudging for dynamical downscaling applications

Science.gov (United States)

Gómez, Breogán; Miguez-Macho, Gonzalo

2017-04-01

Nudging techniques are commonly used to constrain the evolution of numerical models to a reference dataset that is typically of a lower resolution. The nudged model retains some of the features of the reference field while incorporating its own dynamics to the solution. These characteristics have made nudging very popular in dynamic downscaling applications that cover from shot range, single case studies, to multi-decadal regional climate simulations. Recently, a variation of this approach called Spectral Nudging, has gained popularity for its ability to maintain the higher temporal and spatial variability of the model results, while forcing the large scales in the solution with a coarser resolution field. In this work, we focus on a not much explored aspect of this technique: the impact of selecting different cut-off wave numbers and spin-up times. We perform four-day long simulations with the WRF model, daily for three different one-month periods that include a free run and several Spectral Nudging experiments with cut-off wave numbers ranging from the smallest to the largest possible (full Grid Nudging). Results show that Spectral Nudging is very effective at imposing the selected scales onto the solution, while allowing the limited area model to incorporate finer scale features. The model error diminishes rapidly as the nudging expands over broader parts of the spectrum, but this decreasing trend ceases sharply at cut-off wave numbers equivalent to a length scale of about 1000 km, and the error magnitude changes minimally thereafter. This scale corresponds to the Rossby Radius of deformation, separating synoptic from convective scales in the flow. When nudging above this value is applied, a shifting of the synoptic patterns can occur in the solution, yielding large model errors. However, when selecting smaller scales, the fine scale contribution of the model is damped, thus making 1000 km the appropriate scale threshold to nudge in order to balance both effects

High resolution backscattering instruments

International Nuclear Information System (INIS)

Coldea, R.

2001-01-01

The principle of operation of indirect-geometry time-of-flight spectrometers are presented, including the IRIS at the ISIS spallation neutron source. The key features that make those types of spectrometers ideally suited for low-energy spectroscopy are: high energy resolution over a wide dynamic range, and simultaneous measurement over a large momentum transfer range provided by the wide angular detector coverage. To exemplify these features are discussed of single-crystal experiments of the spin dynamics in the two-dimensional frustrated quantum magnet Cs 2 CuCl 4 . (R.P.)

Identification of robust statistical downscaling methods based on a comprehensive suite of performance metrics for South Korea

Science.gov (United States)

Eum, H. I.; Cannon, A. J.

2015-12-01

Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a downscaling procedure is an essential to assess regional impacts of climate change. Numerous statistical downscaling methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical downscaling methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to downscale the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the downscaling methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical downscaling methods based on the performance metrics for each season. The results show that downscaling skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the

Expanding the linear dynamic range for quantitative liquid chromatography-high resolution mass spectrometry utilizing natural isotopologue signals

International Nuclear Information System (INIS)

Liu, Hanghui; Lam, Lily; Yan, Lin; Chi, Bert; Dasgupta, Purnendu K.

2014-01-01

Highlights: • Less abundant isotopologue ions were utilized to decrease detector saturation. • A 25–50 fold increase in the upper limit of dynamic range was demonstrated. • Linear dynamic range was expanded without compromising mass resolution. - Abstract: The linear dynamic range (LDR) for quantitative liquid chromatography–mass spectrometry can be extended until ionization saturation is reached by using a number of target isotopologue ions in addition to the normally used target ion that provides the highest sensitivity. Less abundant isotopologue ions extend the LDR: the lower ion abundance decreases the probability of ion detector saturation. Effectively the sensitivity decreases and the upper limit of the LDR increases. We show in this paper that the technique is particularly powerful with a high resolution time of flight mass spectrometer because the data for all ions are automatically acquired, and we demonstrated this for four small organic molecules; the upper limits of LDRs increased by 25–50 times

Temporal dynamics of motivation-cognitive control interactions revealed by high-resolution pupillometry

Directory of Open Access Journals (Sweden)

Kimberly Sarah Chiew

2013-01-01

Full Text Available Motivational manipulations, such as the presence of performance-contingent reward incentives, can have substantial influences on cognitive control. Previous evidence suggests that reward incentives may enhance cognitive performance specifically through increased preparatory, or proactive, control processes. The present study examined reward influences on cognitive control dynamics in the AX-Continuous Performance Task (AX-CPT, using high-resolution pupillometry. In the AX-CPT, contextual cues must be actively maintained over a delay in order to appropriately respond to ambiguous target probes. A key feature of the task is that it permits dissociable characterization of preparatory, proactive control processes (i.e., utilization of context and reactive control processes (i.e., target-evoked interference resolution. Task performance profiles suggested that reward incentives enhanced proactive control (context utilization. Critically, pupil dilation was also increased on reward incentive trials during context maintenance periods, suggesting trial-specific shifts in proactive control, particularly when context cues indicated the need to overcome the dominant target response bias. Reward incentives had both transient (i.e., trial-by-trial and sustained (i.e., block-based effects on pupil dilation, which may reflect distinct underlying processes. The transient pupillary effects were present even when comparing against trials matched in task performance, suggesting a unique motivational influence of reward incentives. These results suggest that pupillometry may be a useful technique for investigating reward motivational signals and their dynamic influence on cognitive control.

Application of physical scaling towards downscaling climate model precipitation data

Science.gov (United States)

Gaur, Abhishek; Simonovic, Slobodan P.

2018-04-01

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected.

High-resolution dynamic angiography using flat-panel volume CT: feasibility demonstration for neuro and lower limb vascular applications

International Nuclear Information System (INIS)

Mehndiratta, Amit; Rabinov, James D.; Grasruck, Michael; Liao, Eric C.; Crandell, David; Gupta, Rajiv

2015-01-01

This paper evaluates a prototype flat-panel volume CT (fpVCT) for dynamic in vivo imaging in a variety of neurovascular and lower limb applications. Dynamic CTA was performed on 12 patients (neuro = 8, lower limb = 4) using an fpVCT with 120 kVp, 50 mA, rotation time varying from 8 to 19 s, and field of view of 25 x 25 x 18 cm 3 . Four-dimensional data sets (i.e. 3D images over time) were reconstructed and reviewed. Dynamic CTA demonstrated sufficient spatio-temporal resolution to elucidate first-pass and recirculation dynamics of contrast bolus through neurovasclar pathologies and phasic blood flow though lower-limb vasculature and grafts. The high spatial resolution of fpVCT resulted in reduced partial volume and metal beam-hardening artefacts. This facilitated assessment of vascular lumen in the presence of calcified plaque and evaluation of fractures, especially in the presence of fixation hardware. Evaluation of arteriovenous malformation using dynamic fpVCT angiography was of limited utility. Dynamic CTA using fpVCT can visualize time-varying phenomena in neuro and lower limb vascular applications and has sufficient diagnostic imaging quality to evaluate a number of pathologies affecting these regions. (orig.)

GPCC - A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts

Science.gov (United States)

Resolution of climate model outputs are too coarse to be used as direct inputs to impact models for assessing climate change impacts on agricultural production, water resources, and eco-system services at local or site-specific scales. Statistical downscaling approaches are usually used to bridge th...

A characteristics of East Asian climate using high-resolution regional climate model

Science.gov (United States)

Yhang, Y.

2013-12-01

Climate research, particularly application studies for water, agriculture, forestry, fishery and energy management require fine scale multi-decadal information of meteorological, oceanographic and land states. Unfortunately, spatially and temporally homogeneous multi-decadal observations of these variables in high horizontal resolution are non-existent. Some long term surface records of temperature and precipitation exist, but the number of observation is very limited and the measurements are often contaminated by changes in instrumentation over time. Some climatologically important variables, such as soil moisture, surface evaporation, and radiation are not even measured over most of East Asia. Reanalysis is one approach to obtaining long term homogeneous analysis of needed variables. However, the horizontal resolution of global reanalysis is of the order of 100 to 200 km, too coarse for many application studies. Regional climate models (RCMs) are able to provide valuable regional finescale information, especially in regions where the climate variables are strongly regulated by the underlying topography and the surface heterogeneity. In this study, we will provide accurately downscaled regional climate over East Asia using the Global/Regional Integrated Model system [GRIMs; Hong et al. 2013]. A mixed layer model is embedded within the GRIMs in order to improve air-sea interaction. A detailed description of the characteristics of the East Asian summer and winter climate will be presented through the high-resolution numerical simulations. The increase in horizontal resolution is expected to provide the high-quality data that can be used in various application areas such as hydrology or environmental model forcing.

Dynamic rupture scenarios from Sumatra to Iceland - High-resolution earthquake source physics on natural fault systems

Science.gov (United States)

Gabriel, Alice-Agnes; Madden, Elizabeth H.; Ulrich, Thomas; Wollherr, Stephanie

2017-04-01

Capturing the observed complexity of earthquake sources in dynamic rupture simulations may require: non-linear fault friction, thermal and fluid effects, heterogeneous fault stress and fault strength initial conditions, fault curvature and roughness, on- and off-fault non-elastic failure. All of these factors have been independently shown to alter dynamic rupture behavior and thus possibly influence the degree of realism attainable via simulated ground motions. In this presentation we will show examples of high-resolution earthquake scenarios, e.g. based on the 2004 Sumatra-Andaman Earthquake, the 1994 Northridge earthquake and a potential rupture of the Husavik-Flatey fault system in Northern Iceland. The simulations combine a multitude of representations of source complexity at the necessary spatio-temporal resolution enabled by excellent scalability on modern HPC systems. Such simulations allow an analysis of the dominant factors impacting earthquake source physics and ground motions given distinct tectonic settings or distinct focuses of seismic hazard assessment. Across all simulations, we find that fault geometry concurrently with the regional background stress state provide a first order influence on source dynamics and the emanated seismic wave field. The dynamic rupture models are performed with SeisSol, a software package based on an ADER-Discontinuous Galerkin scheme for solving the spontaneous dynamic earthquake rupture problem with high-order accuracy in space and time. Use of unstructured tetrahedral meshes allows for a realistic representation of the non-planar fault geometry, subsurface structure and bathymetry. The results presented highlight the fact that modern numerical methods are essential to further our understanding of earthquake source physics and complement both physic-based ground motion research and empirical approaches in seismic hazard analysis.

Satellite-Enhanced Regional Downscaling for Applied Studies: Extreme Precipitation Events in Southeastern South America

Science.gov (United States)

Nunes, A.; Gomes, G.; Ivanov, V. Y.

2016-12-01

Frequently found in southeastern South America during the warm season from October through May, strong and localized precipitation maxima are usually associated with the presence of mesoscale convective complexes (MCCs) travelling across the region. Flashfloods and landslides can be caused by these extremes in precipitation, with damages to the local communities. Heavily populated, southeastern South America hosts many agricultural activities and hydroelectric production. It encompasses one of the most important river basins in South America, the La Plata River Basin. Therefore, insufficient precipitation is equally prejudicial to the region socio-economic activities. MCCs are originated in the warm season of many regions of the world, however South American MCCs are related to the most severe thunderstorms, and have significantly contributed to the precipitation regime. We used the hourly outputs of Satellite-enhanced Regional Downscaling for Applied Studies (SRDAS), developed at the Federal University of Rio de Janeiro in Brazil, in the analysis of the dynamics and physical characteristics of MCCs in South America. SRDAS is the 25-km resolution downscaling of a global reanalysis available from January 1998 through December 2010. The Regional Spectral Model is the SRDAS atmospheric component and assimilates satellite-based precipitation estimates from the NOAA/Climate Prediction Center MORPHing technique global precipitation analyses. In this study, the SRDAS atmospheric and land-surface variables, global reanalysis products, infrared satellite imagery, and the physical retrievals from the Atmospheric Infrared Sounder (AIRS), on board of the NASA's Aqua satellite, were used in the evaluation of the MCCs developed in southeastern South America from 2008 and 2010. Low-level circulations and vertical profiles were analyzed together to establish the relevance of the moisture transport in connection with the upper-troposphere dynamics to the development of those MCCs.

Requirements on high resolution detectors

Energy Technology Data Exchange (ETDEWEB)

Koch, A. [European Synchrotron Radiation Facility, Grenoble (France)

1997-02-01

For a number of microtomography applications X-ray detectors with a spatial resolution of 1 {mu}m are required. This high spatial resolution will influence and degrade other parameters of secondary importance like detective quantum efficiency (DQE), dynamic range, linearity and frame rate. This note summarizes the most important arguments, for and against those detector systems which could be considered. This article discusses the mutual dependencies between the various figures which characterize a detector, and tries to give some ideas on how to proceed in order to improve present technology.

Downscaling Ocean Conditions: Initial Results using a Quasigeostrophic and Realistic Ocean Model

Science.gov (United States)

Katavouta, Anna; Thompson, Keith

2014-05-01

Previous theoretical work (Henshaw et al, 2003) has shown that the small-scale modes of variability of solutions of the unforced, incompressible Navier-Stokes equation, and Burgers' equation, can be reconstructed with surprisingly high accuracy from the time history of a few of the large-scale modes. Motivated by this theoretical work we first describe a straightforward method for assimilating information on the large scales in order to recover the small scale oceanic variability. The method is based on nudging in specific wavebands and frequencies and is similar to the so-called spectral nudging method that has been used successfully for atmospheric downscaling with limited area models (e.g. von Storch et al., 2000). The validity of the method is tested using a quasigestrophic model configured to simulate a double ocean gyre separated by an unstable mid-ocean jet. It is shown that important features of the ocean circulation including the position of the meandering mid-ocean jet and associated pinch-off eddies can indeed be recovered from the time history of a small number of large-scales modes. The benefit of assimilating additional time series of observations from a limited number of locations, that alone are too sparse to significantly improve the recovery of the small scales using traditional assimilation techniques, is also demonstrated using several twin experiments. The final part of the study outlines the application of the approach using a realistic high resolution (1/36 degree) model, based on the NEMO (Nucleus for European Modelling of the Ocean) modeling framework, configured for the Scotian Shelf of the east coast of Canada. The large scale conditions used in this application are obtained from the HYCOM (HYbrid Coordinate Ocean Model) + NCODA (Navy Coupled Ocean Data Assimilation) global 1/12 degree analysis product. Henshaw, W., Kreiss, H.-O., Ystrom, J., 2003. Numerical experiments on the interaction between the larger- and the small-scale motion of

Characterising Dynamic Instability in High Water-Cut Oil-Water Flows Using High-Resolution Microwave Sensor Signals

Science.gov (United States)

Liu, Weixin; Jin, Ningde; Han, Yunfeng; Ma, Jing

2018-06-01

In the present study, multi-scale entropy algorithm was used to characterise the complex flow phenomena of turbulent droplets in high water-cut oil-water two-phase flow. First, we compared multi-scale weighted permutation entropy (MWPE), multi-scale approximate entropy (MAE), multi-scale sample entropy (MSE) and multi-scale complexity measure (MCM) for typical nonlinear systems. The results show that MWPE presents satisfied variability with scale and anti-noise ability. Accordingly, we conducted an experiment of vertical upward oil-water two-phase flow with high water-cut and collected the signals of a high-resolution microwave resonant sensor, based on which two indexes, the entropy rate and mean value of MWPE, were extracted. Besides, the effects of total flow rate and water-cut on these two indexes were analysed. Our researches show that MWPE is an effective method to uncover the dynamic instability of oil-water two-phase flow with high water-cut.

Intramolecular diffusive motion in alkane monolayers studied by high-resolution quasielastic neutron scattering and molecular dynamics simulations

DEFF Research Database (Denmark)

Hansen, Flemming Yssing; Criswell, L.; Fuhrmann, D

2004-01-01

Molecular dynamics simulations of a tetracosane (n-C24H50) monolayer adsorbed on a graphite basal-plane surface show that there are diffusive motions associated with the creation and annihilation of gauche defects occurring on a time scale of similar to0.1-4 ns. We present evidence...... that these relatively slow motions are observable by high-energy-resolution quasielastic neutron scattering (QNS) thus demonstrating QNS as a technique, complementary to nuclear magnetic resonance, for studying conformational dynamics on a nanosecond time scale in molecular monolayers....

A high resolution large dynamic range TDC circuit implementation

International Nuclear Information System (INIS)

Lei Wuhu; Liu Songqiu; Ye Weiguo; Han Hui; Li Pengyu

2003-01-01

Time measurement technology is usually used in nuclear experimentation. There are many methods of time measurement. The implementation method of Time to Digital Conversion (TDC) by means of electronic is a classical technology. The range and resolution of TDC is different according with different usage. A wide range and high resolution TDC circuit, including its theory and implementation way, is introduced in this paper. The test result is also given. (authors)

A high resolution large dynamic range TDC circuit implementation

International Nuclear Information System (INIS)

Lei Wuhu; Liu Songqiu; Li Pengyu; Han Hui; Ye Yanlin

2005-01-01

Time measurement technology is usually used in nuclear experimentation. There are many methods of time measurement. The implementation method of Time to Digital Conversion (TDC) by means of electronics is a classical technology. The range and resolution of TDC is different according with different usage. A wide range and high resolution TDC circuit, including its theory and implementation way, is introduced in this paper. The test result is also given. (authors)

Comparison among different downscaling approaches in building water scarcity scenarios in an Alpine basin.

Science.gov (United States)

Guyennon, Nicolas; Romano, Emanuele; Mariani, Davide; Bruna Petrangeli, Anna; Portoghese, Ivan

2014-05-01

Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Although statistical downscaling (SD) has been traditionally seen as an alternative to dynamical downscaling (DD), recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD is able to provide more reliable climate forcing for crop water demand models. The case study presented here focuses on the Maggiore Lake (Alpine region), with a watershed of approximately 4750 km2 and whose waters are mainly used for irrigation purposes in the Lombardia and Piemonte regions. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction of the precipitation data collected in the period 1950-2012 by the 19 rainfall gauges located in the watershed area (some of them operating not continuously during the study period). The relationship between the precipitation regime and the inflow to the reservoir is obtained through a simple multilinear regression model, validated using both precipitation data and inflow measurements to the lake in the period 1996-2012 then, the same relation has been applied to the control (20c) and scenario (a1b) simulations downscaled by means of the different downscaling approaches (DD, SD and combined DD-SD). The resulting forcing has been used as input to a daily water balance model taking into account the inflow to the lake, the demand for irrigation and the reservoir management policies. The impact of the different downscaling approaches on the water budget scenarios has been evaluated in terms of occurrence, duration and intensity of water scarcity periods.

Super-resolution optical microscopy for studying membrane structure and dynamics.

Science.gov (United States)

Sezgin, Erdinc

2017-07-12

Investigation of cell membrane structure and dynamics requires high spatial and temporal resolution. The spatial resolution of conventional light microscopy is limited due to the diffraction of light. However, recent developments in microscopy enabled us to access the nano-scale regime spatially, thus to elucidate the nanoscopic structures in the cellular membranes. In this review, we will explain the resolution limit, address the working principles of the most commonly used super-resolution microscopy techniques and summarise their recent applications in the biomembrane field.

A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

KAUST Repository

Jha, Sanjeev Kumar

2015-07-21

A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here, the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 km and 10 km resolution for a twenty year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference dataset indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local scale estimates of precipitation and temperature from General Circulation Models. This article is protected by copyright. All rights reserved.

On the improvement of wave and storm surge hindcasts by downscaled atmospheric forcing: application to historical storms

Science.gov (United States)

Bresson, Émilie; Arbogast, Philippe; Aouf, Lotfi; Paradis, Denis; Kortcheva, Anna; Bogatchev, Andrey; Galabov, Vasko; Dimitrova, Marieta; Morvan, Guillaume; Ohl, Patrick; Tsenova, Boryana; Rabier, Florence

2018-04-01

Winds, waves and storm surges can inflict severe damage in coastal areas. In order to improve preparedness for such events, a better understanding of storm-induced coastal flooding episodes is necessary. To this end, this paper highlights the use of atmospheric downscaling techniques in order to improve wave and storm surge hindcasts. The downscaling techniques used here are based on existing European Centre for Medium-Range Weather Forecasts reanalyses (ERA-20C, ERA-40 and ERA-Interim). The results show that the 10 km resolution data forcing provided by a downscaled atmospheric model gives a better wave and surge hindcast compared to using data directly from the reanalysis. Furthermore, the analysis of the most extreme mid-latitude cyclones indicates that a four-dimensional blending approach improves the whole process, as it assimilates more small-scale processes in the initial conditions. Our approach has been successfully applied to ERA-20C (the 20th century reanalysis).

High-resolution dynamic angiography using flat-panel volume CT: feasibility demonstration for neuro and lower limb vascular applications

Energy Technology Data Exchange (ETDEWEB)

Mehndiratta, Amit [Massachusetts General Hospital, Department of Radiology, Harvard Medical School, Boston, MA (United States); University of Oxford, Institute of Biomedical Engineering and Keble College, Oxford (United Kingdom); Indian Institute of Technology Delhi and All India Institute of Medical Science, Centre for Biomedical Engineering, New Delhi (India); Rabinov, James D. [Massachusetts General Hospital, Interventional Neuroradiology, Harvard Medical School, Boston, MA (United States); Grasruck, Michael [Siemens Medical Solutions, Forchheim (Germany); Liao, Eric C. [Massachusetts General Hospital, Department of Plastic and Reconstructive Surgery and Center for Regenerative Medicine, Harvard Medical School, Boston, MA (United States); Crandell, David [Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Charlestown, MA (United States); Gupta, Rajiv [Massachusetts General Hospital, Department of Radiology, Harvard Medical School, Boston, MA (United States)

2015-07-15

This paper evaluates a prototype flat-panel volume CT (fpVCT) for dynamic in vivo imaging in a variety of neurovascular and lower limb applications. Dynamic CTA was performed on 12 patients (neuro = 8, lower limb = 4) using an fpVCT with 120 kVp, 50 mA, rotation time varying from 8 to 19 s, and field of view of 25 x 25 x 18 cm{sup 3}. Four-dimensional data sets (i.e. 3D images over time) were reconstructed and reviewed. Dynamic CTA demonstrated sufficient spatio-temporal resolution to elucidate first-pass and recirculation dynamics of contrast bolus through neurovasclar pathologies and phasic blood flow though lower-limb vasculature and grafts. The high spatial resolution of fpVCT resulted in reduced partial volume and metal beam-hardening artefacts. This facilitated assessment of vascular lumen in the presence of calcified plaque and evaluation of fractures, especially in the presence of fixation hardware. Evaluation of arteriovenous malformation using dynamic fpVCT angiography was of limited utility. Dynamic CTA using fpVCT can visualize time-varying phenomena in neuro and lower limb vascular applications and has sufficient diagnostic imaging quality to evaluate a number of pathologies affecting these regions. (orig.)

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.

High resolution mapping of riffle-pool dynamics based on ADCP and close-range remote sensing data

Science.gov (United States)

Salmela, Jouni; Kasvi, Elina; Alho, Petteri

2017-04-01

Present development of mobile laser scanning (MLS) and close-range photogrammetry with unmanned aerial vehicle (UAV) enable us to create seamless digital elevation models (DEMs) of the riverine environment. Remote-controlled flow measurement platforms have also improved spatio-temporal resolution of the flow field data. In this study, acoustic Doppler current profiler (ADCP) attached to remote-controlled mini-boat, UAV-based bathymetry and MLS techniques were utilized to create the high-resolution DEMs of the river channel. These high-resolution measurements can be used in many fluvial applications such as computational fluid dynamics, channel change detection, habitat mapping or hydro-electric power plant planning. In this study we aim: 1) to analyze morphological changes of river channel especially riffle and pool formations based on fine-scale DEMs and ADCP measurements, 2) to analyze flow fields and their effect on morphological changes. The interest was mainly focused on reach-scale riffle-pool dynamics within two-year period of 2013 and 2014. The study was performed in sub-arctic meandering Pulmankijoki River located in Northern Finland. The river itself has shallow and clear water and sandy bed sediment. Discharge remains typically below 10 m3s-1 most of the year but during snow melt period in spring the discharge may exceed 70 m3s-1. We compared DEMs and ADCP measurements to understand both magnitude and spatio-temporal change of the river bed. Models were accurate enough to study bed form changes and locations and persistence of riffles and pools. We analyzed their locations with relation to flow during the peak and low discharge. Our demonstrated method has improved significantly spatio-temporal resolution of riverine DEMs compared to other cross-sectional and photogrammetry based models. Together with flow field measurements we gained better understanding of riverbed-water interaction

Simulating European wind power generation applying statistical downscaling to reanalysis data

International Nuclear Information System (INIS)

González-Aparicio, I.; Monforti, F.; Volker, P.; Zucker, A.; Careri, F.; Huld, T.; Badger, J.

2017-01-01

Highlights: •Wind speed spatial resolution highly influences calculated wind power peaks and ramps. •Reduction of wind power generation uncertainties using statistical downscaling. •Publicly available dataset of wind power generation hourly time series at NUTS2. -- Abstract: The growing share of electricity production from solar and mainly wind resources constantly increases the stochastic nature of the power system. Modelling the high share of renewable energy sources – and in particular wind power – crucially depends on the adequate representation of the intermittency and characteristics of the wind resource which is related to the accuracy of the approach in converting wind speed data into power values. One of the main factors contributing to the uncertainty in these conversion methods is the selection of the spatial resolution. Although numerical weather prediction models can simulate wind speeds at higher spatial resolution (up to 1 × 1 km) than a reanalysis (generally, ranging from about 25 km to 70 km), they require high computational resources and massive storage systems: therefore, the most common alternative is to use the reanalysis data. However, local wind features could not be captured by the use of a reanalysis technique and could be translated into misinterpretations of the wind power peaks, ramping capacities, the behaviour of power prices, as well as bidding strategies for the electricity market. This study contributes to the understanding what is captured by different wind speeds spatial resolution datasets, the importance of using high resolution data for the conversion into power and the implications in power system analyses. It is proposed a methodology to increase the spatial resolution from a reanalysis. This study presents an open access renewable generation time series dataset for the EU-28 and neighbouring countries at hourly intervals and at different geographical aggregation levels (country, bidding zone and administrative

High-Resolution Monitoring of Himalayan Glacier Dynamics Using Unmanned Aerial Vehicles

Science.gov (United States)

Immerzeel, W.; Kraaijenbrink, P. D. A.; Shea, J.; Shrestha, A. B.; Pellicciotti, F.; Bierkens, M. F.; de Jong, S. M.

2014-12-01

Himalayan glacier tongues are commonly debris covered and play an important role in modulating the glacier response to climate . However, they remain relatively unstudied because of the inaccessibility of the terrain and the difficulties in field work caused by the thick debris mantles. Observations of debris-covered glaciers are therefore limited to point locations and airborne remote sensing may bridge the gap between scarce, point field observations and coarse resolution space-borne remote sensing. In this study we deploy an Unmanned Airborne Vehicle (UAV) on two debris covered glaciers in the Nepalese Himalayas: the Lirung and Langtang glacier during four field campaigns in 2013 and 2014. Based on stereo-imaging and the structure for motion algorithm we derive highly detailed ortho-mosaics and digital elevation models (DEMs), which we geometrically correct using differential GPS observations collected in the field. Based on DEM differencing and manual feature tracking we derive the mass loss and the surface velocity of the glacier at a high spatial resolution and accuracy. We also assess spatiotemporal changes in supra-glacial lakes and ice cliffs based on the imagery. On average, mass loss is limited and the surface velocity is very small. However, the spatial variability of melt rates is very high, and ice cliffs and supra-glacial ponds show mass losses that can be an order of magnitude higher than the average. We suggest that future research should focus on the interaction between supra-glacial ponds, ice cliffs and englacial hydrology to further understand the dynamics of debris-covered glaciers. Finally, we conclude that UAV deployment has large potential in glaciology and it represents a substantial advancement over methods currently applied in studying glacier surface features.

Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

Directory of Open Access Journals (Sweden)

Nam Do Hoai

2011-01-01

Full Text Available Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

Science.gov (United States)

Guentchev, Galina S.; Rood, Richard B.; Ammann, Caspar M.; Barsugli, Joseph J.; Ebi, Kristie; Berrocal, Veronica; O’Neill, Marie S.; Gronlund, Carina J.; Vigh, Jonathan L.; Koziol, Ben; Cinquini, Luca

2016-01-01

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544

Achieving accurate simulations of urban impacts on ozone at high resolution

International Nuclear Information System (INIS)

Li, J; Georgescu, M; Mahalov, A; Moustaoui, M; Hyde, P

2014-01-01

The effects of urbanization on ozone levels have been widely investigated over cities primarily located in temperate and/or humid regions. In this study, nested WRF-Chem simulations with a finest grid resolution of 1 km are conducted to investigate ozone concentrations [O 3 ] due to urbanization within cities in arid/semi-arid environments. First, a method based on a shape preserving Monotonic Cubic Interpolation (MCI) is developed and used to downscale anthropogenic emissions from the 4 km resolution 2005 National Emissions Inventory (NEI05) to the finest model resolution of 1 km. Using the rapidly expanding Phoenix metropolitan region as the area of focus, we demonstrate the proposed MCI method achieves ozone simulation results with appreciably improved correspondence to observations relative to the default interpolation method of the WRF-Chem system. Next, two additional sets of experiments are conducted, with the recommended MCI approach, to examine impacts of urbanization on ozone production: (1) the urban land cover is included (i.e., urbanization experiments) and, (2) the urban land cover is replaced with the region’s native shrubland. Impacts due to the presence of the built environment on [O 3 ] are highly heterogeneous across the metropolitan area. Increased near surface [O 3 ] due to urbanization of 10–20 ppb is predominantly a nighttime phenomenon while simulated impacts during daytime are negligible. Urbanization narrows the daily [O 3 ] range (by virtue of increasing nighttime minima), an impact largely due to the region’s urban heat island. Our results demonstrate the importance of the MCI method for accurate representation of the diurnal profile of ozone, and highlight its utility for high-resolution air quality simulations for urban areas. (letter)

Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

Czech Academy of Sciences Publication Activity Database

Hirschi, M.; Stoeckli, S.; Dubrovský, Martin; Spirig, C.; Calanca, P.; Rotach, M. V.; Fischer, A. M.; Duffy, B.; Samietz, J.

2012-01-01

Roč. 3, č. 1 (2012), s. 33-47 ISSN 2190-4979 R&D Projects: GA AV ČR IAA300420806 Institutional research plan: CEZ:AV0Z30420517 Keywords : Climate change * downscaling * weather generator * pest and disease model ling * Switzerland Subject RIV: DG - Athmosphere Sciences, Meteorology http://www.earth-syst-dynam.net/3/33/2012/esd-3-33-2012.pdf

High-resolution inelastic X-ray scattering to study the high-frequency atomic dynamics of disordered systems

International Nuclear Information System (INIS)

Monaco, G.

2008-01-01

The use of momentum-resolved inelastic X-ray scattering with meV energy resolution to study the high-frequency atomic dynamics in disordered systems is here reviewed. The typical realization of this experiment is described together with some common models used to interpret the measured spectra and to extract parameters of interest for the investigation of disordered systems. With the help of some selected examples, the present status of the field is discussed. Particular attention is given to those results which are still open for discussion or controversial, and which will require further development of the technique to be fully solved. Such an instrumental development seems nowadays possible at the light of recently proposed schemes for advanced inelastic X-ray scattering spectrometers. (author)

Downscaling a Global Climate Model to Simulate Climate Change Impacts on U.S. Regional and Urban Air Quality

Science.gov (United States)

Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, K.; Hu, Y.; Nenes, A.; Russell, A. G.

2013-01-01

Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12km by 12km resolution, as well as the effect of evolving climate conditions on the air quality at major U.S. cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the U.S. during fall (Western U.S., Texas, Northeastern, and Southeastern U.S), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). We also find that daily peak temperatures tend to increase in most major cities in the U.S. which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

Method of Obtaining High Resolution Intrinsic Wire Boom Damping Parameters for Multi-Body Dynamics Simulations

Science.gov (United States)

Yew, Alvin G.; Chai, Dean J.; Olney, David J.

2010-01-01

The goal of NASA's Magnetospheric MultiScale (MMS) mission is to understand magnetic reconnection with sensor measurements from four spinning satellites flown in a tight tetrahedron formation. Four of the six electric field sensors on each satellite are located at the end of 60- meter wire booms to increase measurement sensitivity in the spin plane and to minimize motion coupling from perturbations on the main body. A propulsion burn however, might induce boom oscillations that could impact science measurements if oscillations do not damp to values on the order of 0.1 degree in a timely fashion. Large damping time constants could also adversely affect flight dynamics and attitude control performance. In this paper, we will discuss the implementation of a high resolution method for calculating the boom's intrinsic damping, which was used in multi-body dynamics simulations. In summary, experimental data was obtained with a scaled-down boom, which was suspended as a pendulum in vacuum. Optical techniques were designed to accurately measure the natural decay of angular position and subsequently, data processing algorithms resulted in excellent spatial and temporal resolutions. This method was repeated in a parametric study for various lengths, root tensions and vacuum levels. For all data sets, regression models for damping were applied, including: nonlinear viscous, frequency-independent hysteretic, coulomb and some combination of them. Our data analysis and dynamics models have shown that the intrinsic damping for the baseline boom is insufficient, thereby forcing project management to explore mitigation strategies.

High resolution polarimeter-interferometer system for fast equilibrium dynamics and MHD instability studies on Joint-TEXT tokamak (invited)

Energy Technology Data Exchange (ETDEWEB)

Chen, J.; Zhuang, G., E-mail: ge-zhuang@hust.edu.cn; Li, Q.; Liu, Y.; Gao, L.; Zhou, Y. N.; Jian, X.; Xiong, C. Y.; Wang, Z. J. [State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Brower, D. L.; Ding, W. X. [Department of Physics and Astronomy, University of California Los Angeles, Los Angeles, California 90095 (United States)

2014-11-15

A high-performance Faraday-effect polarimeter-interferometer system has been developed for the J-TEXT tokamak. This system has time response up to 1 μs, phase resolution < 0.1° and minimum spatial resolution ∼15 mm. High resolution permits investigation of fast equilibrium dynamics as well as magnetic and density perturbations associated with intrinsic Magneto-Hydro-Dynamic (MHD) instabilities and external coil-induced Resonant Magnetic Perturbations (RMP). The 3-wave technique, in which the line-integrated Faraday angle and electron density are measured simultaneously by three laser beams with specific polarizations and frequency offsets, is used. In order to achieve optimum resolution, three frequency-stabilized HCOOH lasers (694 GHz, >35 mW per cavity) and sensitive Planar Schottky Diode mixers are used, providing stable intermediate-frequency signals (0.5–3 MHz) with S/N > 50. The collinear R- and L-wave probe beams, which propagate through the plasma poloidal cross section (a = 0.25–0.27 m) vertically, are expanded using parabolic mirrors to cover the entire plasma column. Sources of systematic errors, e.g., stemming from mechanical vibration, beam non-collinearity, and beam polarization distortion are individually examined and minimized to ensure measurement accuracy. Simultaneous density and Faraday measurements have been successfully achieved for 14 chords. Based on measurements, temporal evolution of safety factor profile, current density profile, and electron density profile are resolved. Core magnetic and density perturbations associated with MHD tearing instabilities are clearly detected. Effects of non-axisymmetric 3D RMP in ohmically heated plasmas are directly observed by polarimetry for the first time.

Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

Science.gov (United States)

Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

2015-04-01

regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested downscaling scheme. To our knowledge, this is the first time that climate model rainfall and statistically downscaled precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested downscaling scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.

Carcinoma of the uterine cervix. High-resolution turbo spin-echo MR imaging with contrast-enhanced dynamic scanning and T2-weighting

International Nuclear Information System (INIS)

Abe, Y.; Yamashita, Y.; Namimoto, T.; Takahashi, M.; Katabuchi, H.; Tanaka, N.; Okamura, H.

1998-01-01

Purpose: To compare high-resolution contrast-enhanced (Gd-DTPA) dynamic MR imaging with T2-weighted turbo spin-echo (TSE) imaging in the evaluation of uterine cervical carcinoma. Material and Methods: Thirty-two patients with cervical carcinoma underwent MR imaging on a 1.5 T superconductive unit to have the extension of the disease assessed before treatment. A phased-array coil was used in all patients. In 25 patients, surgical confirmation of the diagnosis was obtained after imaging. Radiation therapy was selected for the remaining 7 patients with advanced carcinoma. Qualitative and quantitative image analyses were also performed. Results: The cervical carcinomas showed maximum contrast in the cervical stroma and myometrium in the early dynamic phase. The tumor/cervical-stroma contrast in the early dynamic phase obtained with the T1-weighted TSE technique (contrast-to-noise ratio 22.6) was significantly higher than that obtained in T2-weighted TSE imaging (contrast-to-noise ratio 4.3). In the evaluation of parametrial invasion, the accuracy of T2-weighted imaging was 71.8% and contrast-enhanced dynamic imaging 81.2%. Conclusion: High-resolution contrast-enhanced (Gd-DTPA) dynamic MR imaging in cervical cancer offers improved tumor/cervical-stroma contrast and provides useful information on parametrial invasion. (orig.)

Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling

Science.gov (United States)

Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal

2014-05-01

Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is

Tidal downscaling from the open ocean to the coast: a new approach applied to the Bay of Biscay

Science.gov (United States)

Toublanc, F.; Ayoub, N. K.; Lyard, F.; Marsaleix, P.; Allain, D. J.

2018-04-01

Downscaling physical processes from a large scale to a regional scale 3D model is a recurrent issue in coastal processes studies. The choice of boundary conditions will often greatly influence the solution within the 3D circulation model. In some regions, tides play a key role in coastal dynamics and must be accurately represented. The Bay of Biscay is one of these regions, with highly energetic tides influencing coastal circulation and river plume dynamics. In this study, three strategies are tested to force with barotropic tides a 3D circulation model with a variable horizontal resolution. The tidal forcings, as well as the tidal elevations and currents resulting from the 3D simulations, are compared to tidal harmonics extracted from satellite altimetry and tidal gauges, and tidal currents harmonics obtained from ADCP data. The results show a strong improvement of the M2 solution within the 3D model with a "tailored" tidal forcing generated on the same grid and bathymetry as the 3D configuration, compared to a global tidal atlas forcing. Tidal harmonics obtained from satellite altimetry data are particularly valuable to assess the performance of each simulation. Comparisons between sea surface height time series, a sea surface salinity database, and daily averaged 2D currents also show a better agreement with this tailored forcing.

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps.

Science.gov (United States)

Pittiglio, Claudia; Khomenko, Sergei; Beltran-Alcrudo, Daniel

2018-01-01

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its

Evaluation of the WRF model for precipitation downscaling on orographic complex islands

Science.gov (United States)

Díaz, Juan P.; González, Albano; Expósito, Francisco; Pérez, Juan C.

2010-05-01

General Circulation Models (GCMs) have proven to be an effective tool to simulate many aspects of large-scale and global climate. However, their applicability to climate impact studies is limited by their capabilities to resolve regional scale situations. In this sense, dynamical downscaling techniques are an appropriate alternative to estimate high resolution regional climatologies. In this work, the Weather Research and Forecasting model (WRF) has been used to simulate precipitations over the Canary Islands region during 2009. The precipitation patterns over Canary Islands, located at North Atlantic region, show large gradients over a relatively small geographical area due to large scale factors such as Trade Winds regime predominant in the area and mesoscale factors mainly due to the complex terrain. Sensitivity study of simulated WRF precipitations to variations in model setup and parameterizations was carried out. Thus, WRF experiments were performed using two way nesting at 3 km horizontal grid spacing and 28 vertical levels in the Canaries inner domain. The initial and lateral and lower boundary conditions for the outer domain were provided at 6 hourly intervals by NCEP FNL (Final) Operational Global Analysis data on 1.0x1.0 degree resolution interpolated onto the WRF model grid. Numerous model options have been tested, including different microphysics schemes, cumulus parameterizations and nudging configuration Positive-definite moisture advection condition was also checked. Two integration approaches were analyzed: a 1-year continuous long-term integration and a consecutive short-term monthly reinitialized integration. To assess the accuracy of our simulations, model results are compared against observational datasets obtained from a network of meteorological stations in the region. In general, we can observe that the regional model is able to reproduce the spatial distribution of precipitation, but overestimates rainfall, mainly during strong

Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

Science.gov (United States)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All

High-resolution intravital microscopy.

Directory of Open Access Journals (Sweden)

Volker Andresen

Full Text Available Cellular communication constitutes a fundamental mechanism of life, for instance by permitting transfer of information through synapses in the nervous system and by leading to activation of cells during the course of immune responses. Monitoring cell-cell interactions within living adult organisms is crucial in order to draw conclusions on their behavior with respect to the fate of cells, tissues and organs. Until now, there is no technology available that enables dynamic imaging deep within the tissue of living adult organisms at sub-cellular resolution, i.e. detection at the level of few protein molecules. Here we present a novel approach called multi-beam striped-illumination which applies for the first time the principle and advantages of structured-illumination, spatial modulation of the excitation pattern, to laser-scanning-microscopy. We use this approach in two-photon-microscopy--the most adequate optical deep-tissue imaging-technique. As compared to standard two-photon-microscopy, it achieves significant contrast enhancement and up to 3-fold improved axial resolution (optical sectioning while photobleaching, photodamage and acquisition speed are similar. Its imaging depth is comparable to multifocal two-photon-microscopy and only slightly less than in standard single-beam two-photon-microscopy. Precisely, our studies within mouse lymph nodes demonstrated 216% improved axial and 23% improved lateral resolutions at a depth of 80 µm below the surface. Thus, we are for the first time able to visualize the dynamic interactions between B cells and immune complex deposits on follicular dendritic cells within germinal centers (GCs of live mice. These interactions play a decisive role in the process of clonal selection, leading to affinity maturation of the humoral immune response. This novel high-resolution intravital microscopy method has a huge potential for numerous applications in neurosciences, immunology, cancer research and

High-Resolution Intravital Microscopy

Science.gov (United States)

Andresen, Volker; Pollok, Karolin; Rinnenthal, Jan-Leo; Oehme, Laura; Günther, Robert; Spiecker, Heinrich; Radbruch, Helena; Gerhard, Jenny; Sporbert, Anje; Cseresnyes, Zoltan; Hauser, Anja E.; Niesner, Raluca

2012-01-01

Cellular communication constitutes a fundamental mechanism of life, for instance by permitting transfer of information through synapses in the nervous system and by leading to activation of cells during the course of immune responses. Monitoring cell-cell interactions within living adult organisms is crucial in order to draw conclusions on their behavior with respect to the fate of cells, tissues and organs. Until now, there is no technology available that enables dynamic imaging deep within the tissue of living adult organisms at sub-cellular resolution, i.e. detection at the level of few protein molecules. Here we present a novel approach called multi-beam striped-illumination which applies for the first time the principle and advantages of structured-illumination, spatial modulation of the excitation pattern, to laser-scanning-microscopy. We use this approach in two-photon-microscopy - the most adequate optical deep-tissue imaging-technique. As compared to standard two-photon-microscopy, it achieves significant contrast enhancement and up to 3-fold improved axial resolution (optical sectioning) while photobleaching, photodamage and acquisition speed are similar. Its imaging depth is comparable to multifocal two-photon-microscopy and only slightly less than in standard single-beam two-photon-microscopy. Precisely, our studies within mouse lymph nodes demonstrated 216% improved axial and 23% improved lateral resolutions at a depth of 80 µm below the surface. Thus, we are for the first time able to visualize the dynamic interactions between B cells and immune complex deposits on follicular dendritic cells within germinal centers (GCs) of live mice. These interactions play a decisive role in the process of clonal selection, leading to affinity maturation of the humoral immune response. This novel high-resolution intravital microscopy method has a huge potential for numerous applications in neurosciences, immunology, cancer research and developmental biology

A high resolution hydrodynamic 3-D model simulation of the malta shelf area

Directory of Open Access Journals (Sweden)

A. F. Drago

2003-01-01

Full Text Available The seasonal variability of the water masses and transport in the Malta Channel and proximity of the Maltese Islands have been simulated by a high resolution (1.6 km horizontal grid on average, 15 vertical sigma layers eddy resolving primitive equation shelf model (ROSARIO-I. The numerical simulation was run with climatological forcing and includes thermohaline dynamics with a turbulence scheme for the vertical mixing coefficients on the basis of the Princeton Ocean Model (POM. The model has been coupled by one-way nesting along three lateral boundaries (east, south and west to an intermediate coarser resolution model (5 km implemented over the Sicilian Channel area. The fields at the open boundaries and the atmospheric forcing at the air-sea interface were applied on a repeating "perpetual" year climatological cycle. The ability of the model to reproduce a realistic circulation of the Sicilian-Maltese shelf area has been demonstrated. The skill of the nesting procedure was tested by model-modelc omparisons showing that the major features of the coarse model flow field can be reproduced by the fine model with additional eddy space scale components. The numerical results included upwelling, mainly in summer and early autumn, along the southern coasts of Sicily and Malta; a strong eastward shelf surface flow along shore to Sicily, forming part of the Atlantic Ionian Stream, with a presence throughout the year and with significant seasonal modulation, and a westward winter intensified flow of LIW centered at a depth of around 280 m under the shelf break to the south of Malta. The seasonal variability in the thermohaline structure of the domain and the associated large-scale flow structures can be related to the current knowledge on the observed hydrography of the area. The level of mesoscale resolution achieved by the model allowed the spatial and temporal evolution of the changing flow patterns, triggered by internal dynamics, to be followed in

A high resolution hydrodynamic 3-D model simulation of the malta shelf area

Directory of Open Access Journals (Sweden)

A. F. Drago

Full Text Available The seasonal variability of the water masses and transport in the Malta Channel and proximity of the Maltese Islands have been simulated by a high resolution (1.6 km horizontal grid on average, 15 vertical sigma layers eddy resolving primitive equation shelf model (ROSARIO-I. The numerical simulation was run with climatological forcing and includes thermohaline dynamics with a turbulence scheme for the vertical mixing coefficients on the basis of the Princeton Ocean Model (POM. The model has been coupled by one-way nesting along three lateral boundaries (east, south and west to an intermediate coarser resolution model (5 km implemented over the Sicilian Channel area. The fields at the open boundaries and the atmospheric forcing at the air-sea interface were applied on a repeating "perpetual" year climatological cycle.

The ability of the model to reproduce a realistic circulation of the Sicilian-Maltese shelf area has been demonstrated. The skill of the nesting procedure was tested by model-modelc omparisons showing that the major features of the coarse model flow field can be reproduced by the fine model with additional eddy space scale components. The numerical results included upwelling, mainly in summer and early autumn, along the southern coasts of Sicily and Malta; a strong eastward shelf surface flow along shore to Sicily, forming part of the Atlantic Ionian Stream, with a presence throughout the year and with significant seasonal modulation, and a westward winter intensified flow of LIW centered at a depth of around 280 m under the shelf break to the south of Malta. The seasonal variability in the thermohaline structure of the domain and the associated large-scale flow structures can be related to the current knowledge on the observed hydrography of the area. The level of mesoscale resolution achieved by the model allowed the spatial and temporal evolution of the changing flow patterns, triggered by

Spatial dynamics of thermokarst and thermo-erosion at lakes and ponds in North Siberia and Northwest Alaska using high-resolution remote sensing

Science.gov (United States)

Grosse, G.; Tillapaugh, M.; Romanovsky, V. E.; Walter, K. M.; Plug, L. J.

2008-12-01

Formation, growth, and drainage of thermokarst lakes in ice-rich permafrost deposits are important factors of landscape dynamics in extent Arctic lowlands. Monitoring of spatial and temporal dynamics of such lakes will allow an assessment of permafrost stability and enhance the capabilities for modelling and quantifying biogeochemical processes related to permafrost degradation in a warming Arctic. In this study we use high-resolution remote sensing and GIS to analyze the development of thermokarst lakes and ponds in two study regions in North Siberia and Northwest Alaska. The sites are 1) the Cherskii region in the Kolyma lowland (Siberia) and 2) the Kitluk River area on the northern Seward Peninsula (Alaska). Both regions are characterized by continuous permafrost, a highly dissected and dynamic thermokarst landscape, uplands of Late Pleistocene permafrost deposits with high excess ice contents, and a large total volume of permafrost-stored carbon. These ice-rich Yedoma or Yedoma-like deposits are highly vulnerable to permafrost degradation forced by climate warming or other surface disturbance. Time series of high- resolution imagery (aerial, Corona, Ikonos, Alos Prism) covering more than 50 years of lake dynamics allow detailed assessments of processes and spatial patterns of thermokarst lake expansion and drainage in continuous permafrost. Time series of high-resolution imagery (aerial, Corona, Ikonos, Alos Prism) covering more than 50 years of lake dynamics allow detailed assessments of processes and spatial patterns of thermokarst lake expansion and drainage in continuous permafrost. Processes identified include thaw slumping, wave undercutting of frozen sediments or peat blocks and subsequent mass wasting, thaw collapse of near-shore zones, sinkhole formation and ice-wedge tunnelling, and gully formation by thermo-erosion. We use GIS-based tools to relate the remote sensing results to field data (ground ice content, topography, lithology, and relative age

Projection of Climate Change Based on Multi-Site Statistical Downscaling over Gilan area, Iran

Directory of Open Access Journals (Sweden)

Vesta Afzali

2017-01-01

Full Text Available Introduction: The phenomenon of climate change and its consequences is a familiar topic which is associated with natural disasters such as, flooding, hurricane, drought that cause water crisis and irreparable damages. Studying this phenomenon is a serious warning regarding the earth’s weather change for a long period of time. Materials and Methods: In order to understand and survey the impacts of climate change on water resources, Global Circulation Models, GCMs, are used; their main role is analyzing the current climate and projecting the future climate. Climate change scenarios developing from GCMs are the initial source of information to estimate plausible future climate. For transforming coarse resolution outputs of the GCMs into finer resolutions influenced by local variables, there is a need for reliable downscaling techniques in order to analyze climate changes in a region. The classical statistical methods run the model and generate the future climate just with considering the time variable. Multi-site daily rainfall and temperature time series are the primary inputs in most hydrological analyses such as rainfall-runoff modeling. Water resource management is directly influenced by the spatial and temporal variation of rainfall and temperature. Therefore, spatial-temporal modeling of daily rainfall or temperature including climate change effects is required for sustainable planning of water resources. Results and Discussion: For the first time, in this study by ASD model (Automated regression-based Statistical Downscaling tool developed by M. Hessami et al., multi-site downscaling of temperature and precipitation was done with CGCM3.1A2 outputs and two synoptic stations (Rasht and Bandar Anzali simultaneously by considering the correlations of multiple sites. The model can process conditionally on the occurrence of precipitation or unconditionally for temperature. Hence, the modeling of daily precipitation involves two steps: one step

SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)

Science.gov (United States)

Wilby, R. L.; Dawson, C. W.

2013-12-01

General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.

Climate Downscaling over Nordeste, Brazil, Using the NCEP RSM97.

Science.gov (United States)

Sun, Liqiang; Ferran Moncunill, David; Li, Huilan; Divino Moura, Antonio; de Assis de Souza Filho, Francisco

2005-02-01

The NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to downscale the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An ensemble of 10 runs for the period January-June 1971-2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of observed seasonal precipitation as well. The AGCM bias in displacement of the Atlantic ITCZ is partially corrected in the RSM. The RSM probability distribution function of seasonal precipitation anomalies is in better agreement with observations than that of the driving AGCM. Good potential prediction skills are demonstrated by the RSM in predicting the interannual variability of regional seasonal precipitation. The RSM can also capture the interannual variability of observed precipitation at intraseasonal time scales, such as precipitation intensity distribution and dry spells. A drought index and a flooding index were adopted to indicate the severity of drought and flooding conditions, and their interannual variability was reproduced by the RSM. The overall RSM performance in the downscaled climate of the ECHAM4.5 AGCM is satisfactory over Nordeste. The primary deficiency is a systematic dry bias for precipitation simulation.

A High-Resolution Sensor Network for Monitoring Glacier Dynamics

Science.gov (United States)

Edwards, S.; Murray, T.; O'Farrell, T.; Rutt, I. C.; Loskot, P.; Martin, I.; Selmes, N.; Aspey, R.; James, T.; Bevan, S. L.; Baugé, T.

2013-12-01

Changes in Greenland and Antarctic ice sheets due to ice flow/ice-berg calving are a major uncertainty affecting sea-level rise forecasts. Latterly GNSS (Global Navigation Satellite Systems) have been employed extensively to monitor such glacier dynamics. Until recently however, the favoured methodology has been to deploy sensors onto the glacier surface, collect data for a period of time, then retrieve and download the sensors. This approach works well in less dynamic environments where the risk of sensor loss is low. In more extreme environments e.g. approaching the glacial calving front, the risk of sensor loss and hence data loss increases dramatically. In order to provide glaciologists with new insights into flow dynamics and calving processes we have developed a novel sensor network to increase the robustness of data capture. We present details of the technological requirements for an in-situ Zigbee wireless streaming network infrastructure supporting instantaneous data acquisition from high resolution GNSS sensors thereby increasing data capture robustness. The data obtained offers new opportunities to investigate the interdependence of mass flow, uplift, velocity and geometry and the network architecture has been specifically designed for deployment by helicopter close to the calving front to yield unprecedented detailed information. Following successful field trials of a pilot three node network during 2012, a larger 20 node network was deployed on the fast-flowing Helheim glacier, south-east Greenland over the summer months of 2013. The utilisation of dual wireless transceivers in each glacier node, multiple frequencies and four ';collector' stations located on the valley sides creates overlapping networks providing enhanced capacity, diversity and redundancy of data 'back-haul', even close to ';floor' RSSI (Received Signal Strength Indication) levels around -100 dBm. Data loss through radio packet collisions within sub-networks are avoided through the

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

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Werner, Arelia T.; Cannon, Alex J.

2016-04-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI), International Journal of Applied Earth Observation and Geoinformation

KAUST Repository

Houborg, Rasmus

2015-12-12

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r2 = 0

Improved High Resolution Models of Subduction Dynamics: Use of transversely isotropic viscosity with a free-surface

Science.gov (United States)

Liu, X.; Gurnis, M.; Stadler, G.; Rudi, J.; Ratnaswamy, V.; Ghattas, O.

2017-12-01

Dynamic topography, or uncompensated topography, is controlled by internal dynamics, and provide constraints on the buoyancy structure and rheological parameters in the mantle. Compared with other surface manifestations such as the geoid, dynamic topography is very sensitive to shallower and more regional mantle structure. For example, the significant dynamic topography above the subduction zone potentially provides a rich mine for inferring the rheological and mechanical properties such as plate coupling, flow, and lateral viscosity variations, all critical in plate tectonics. However, employing subduction zone topography in the inversion study requires that we have a better understanding of the topography from forward models, especially the influence of the viscosity formulation, numerical resolution, and other factors. One common approach to formulating a fault between the subducted slab and the overriding plates in viscous flow models assumes a thin weak zone. However, due to the large lateral variation in viscosity, topography from free-slip numerical models typically has artificially large magnitude as well as high-frequency undulations over subduction zone, which adds to the difficulty in making comparisons between model results and observations. In this study, we formulate a weak zone with the transversely isotropic viscosity (TI) where the tangential viscosity is much smaller than the viscosity in the normal direction. Similar with isotropic weak zone models, TI models effectively decouple subducted slabs from the overriding plates. However, we find that the topography in TI models is largely reduced compared with that in weak zone models assuming an isotropic viscosity. Moreover, the artificial `tooth paste' squeezing effect observed in isotropic weak zone models vanishes in TI models, although the difference becomes less significant when the dip angle is small. We also implement a free-surface condition in our numerical models, which has a smoothing

Progress in high-resolution x-ray holographic microscopy

International Nuclear Information System (INIS)

Jacobsen, C.; Kirz, J.; Howells, M.; McQuaid, K.; Rothman, S.; Feder, R.; Sayre, D.

1987-07-01

Among the various types of x-ray microscopes that have been demonstrated, the holographic microscope has had the largest gap between promise and performance. The difficulties of fabricating x-ray optical elements have led some to view holography as the most attractive method for obtaining the ultimate in high resolution x-ray micrographs; however, we know of no investigations prior to 1987 that clearly demonstrated submicron resolution in reconstructed images. Previous efforts suffered from problems such as limited resolution and dynamic range in the recording media, low coherent x-ray flux, and aberrations and diffraction limits in visible light reconstruction. We have addressed the recording limitations through the use of an undulator x-ray source and high-resolution photoresist recording media. For improved results in the readout and reconstruction steps, we have employed metal shadowing and transmission electron microscopy, along with numerical reconstruction techniques. We believe that this approach will allow holography to emerge as a practical method of high-resolution x-ray microscopy. 30 refs., 4 figs

Progress in high-resolution x-ray holographic microscopy

Energy Technology Data Exchange (ETDEWEB)

Jacobsen, C.; Kirz, J.; Howells, M.; McQuaid, K.; Rothman, S.; Feder, R.; Sayre, D.

1987-07-01

Among the various types of x-ray microscopes that have been demonstrated, the holographic microscope has had the largest gap between promise and performance. The difficulties of fabricating x-ray optical elements have led some to view holography as the most attractive method for obtaining the ultimate in high resolution x-ray micrographs; however, we know of no investigations prior to 1987 that clearly demonstrated submicron resolution in reconstructed images. Previous efforts suffered from problems such as limited resolution and dynamic range in the recording media, low coherent x-ray flux, and aberrations and diffraction limits in visible light reconstruction. We have addressed the recording limitations through the use of an undulator x-ray source and high-resolution photoresist recording media. For improved results in the readout and reconstruction steps, we have employed metal shadowing and transmission electron microscopy, along with numerical reconstruction techniques. We believe that this approach will allow holography to emerge as a practical method of high-resolution x-ray microscopy. 30 refs., 4 figs.

Statistical Downscaling of Temperature with the Random Forest Model

Directory of Open Access Journals (Sweden)

Bo Pang

2017-01-01

Full Text Available The issues with downscaling the outputs of a global climate model (GCM to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA and partial correlation analysis (PAR were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

Identification of reliable gridded reference data for statistical downscaling methods in Alberta

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Eum, H. I.; Gupta, A.

2017-12-01

Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system

Dynamic functional coupling of high resolution EEG potentials related to unilateral internally triggered one-digit movements.

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Urbano, A; Babiloni, C; Onorati, P; Babiloni, F

1998-06-01

Between-electrode cross-covariances of delta (0-3 Hz)- and theta (4-7 Hz)-filtered high resolution EEG potentials related to preparation, initiation. and execution of human unilateral internally triggered one-digit movements were computed to investigate statistical dynamic coupling between these potentials. Significant (P planning, starting, and performance of unilateral movement. The involvement of these cortical areas is supported by the observation that averaged spatially enhanced delta- and theta-bandpassed potentials were computed from the scalp regions where task-related electrical activation of primary sensorimotor areas and supplementary motor area was roughly represented.

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

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Fenta Mekonnen, Dagnenet; Disse, Markus

2018-04-01

Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 �

Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

Science.gov (United States)

Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

2016-01-01

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

Science.gov (United States)

Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

2018-02-01

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

Science.gov (United States)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

Science.gov (United States)

Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

2010-05-01

The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

Detection and Extraction of Roads from High Resolution Satellites Images with Dynamic Programming

Science.gov (United States)

Benzouai, Siham; Smara, Youcef

2010-12-01

The advent of satellite images allows now a regular and a fast digitizing and update of geographic data, especially roads which are very useful for Geographic Information Systems (GIS) applications such as transportation, urban pollution, geomarketing, etc. For this, several studies have been conducted to automate roads extraction in order to minimize the manual processes [4]. In this work, we are interested in roads extraction from satellite imagery with high spatial resolution (at best equal to 10 m). The method is semi automatic and follows a linear approach where road is considered as a linear object. As roads extraction is a pattern recognition problem, it is useful, above all, to characterize roads. After, we realize a pre-processing by applying an Infinite Size Edge Filter -ISEF- and processing method based on dynamic programming concept, in particular, Fishler algorithm designed by F*.

Downscaling a global climate model to simulate climate change over the US and the implication on regional and urban air quality

Directory of Open Access Journals (Sweden)

M. Trail

2013-09-01

Full Text Available Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with the Weather Research and Forecasting (WRF model to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the contiguous United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF regional climate model (RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high-resolution simulations produce somewhat different results than the coarse-resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (western US, Texas, northeastern, and southeastern US, one region during summer (Texas, and one region where changes potentially would lead to better air quality during spring (Northeast. Changes in regional climate that would enhance ozone levels are increased temperatures and stagnation along with decreased precipitation and ventilation. We also find that daily peak temperatures tend to increase in most major cities in the US, which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

Science.gov (United States)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state

High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision and hobbyist unmanned aerial vehicles

Science.gov (United States)

Dandois, J. P.; Ellis, E. C.

2013-12-01

High spatial resolution three-dimensional (3D) measurements of vegetation by remote sensing are advancing ecological research and environmental management. However, substantial economic and logistical costs limit this application, especially for observing phenological dynamics in ecosystem structure and spectral traits. Here we demonstrate a new aerial remote sensing system enabling routine and inexpensive aerial 3D measurements of canopy structure and spectral attributes, with properties similar to those of LIDAR, but with RGB (red-green-blue) spectral attributes for each point, enabling high frequency observations within a single growing season. This 'Ecosynth' methodology applies photogrammetric ''Structure from Motion'' computer vision algorithms to large sets of highly overlapping low altitude (USA. Ecosynth canopy height maps (CHMs) were strong predictors of field-measured tree heights (R2 0.63 to 0.84) and were highly correlated with a LIDAR CHM (R 0.87) acquired 4 days earlier, though Ecosynth-based estimates of aboveground biomass densities included significant errors (31 - 36% of field-based estimates). Repeated scanning of a 0.25 ha forested area at six different times across a 16 month period revealed ecologically significant dynamics in canopy color at different heights and a structural shift upward in canopy density, as demonstrated by changes in vertical height profiles of point density and relative RGB brightness. Changes in canopy relative greenness were highly correlated (R2 = 0.88) with MODIS NDVI time series for the same area and vertical differences in canopy color revealed the early green up of the dominant canopy species, Liriodendron tulipifera, strong evidence that Ecosynth time series measurements capture vegetation structural and spectral dynamics at the spatial scale of individual trees. Observing canopy phenology in 3D at high temporal resolutions represents a breakthrough in forest ecology. Inexpensive user-deployed technologies for

Statistical downscaling of daily precipitation over Llobregat river basin in Catalonia (Spain) using three downscaling methods.

Science.gov (United States)

Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.

2009-09-01

Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and

Accelerated high-resolution photoacoustic tomography via compressed sensing

Science.gov (United States)

Arridge, Simon; Beard, Paul; Betcke, Marta; Cox, Ben; Huynh, Nam; Lucka, Felix; Ogunlade, Olumide; Zhang, Edward

2016-12-01

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Pérot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.

Downscaling the 2D Bénard convection equations using continuous data assimilation

KAUST Repository

Altaf, Muhammad; Titi, E. S.; Gebrael, T.; Knio, Omar; Zhao, L.; McCabe, Matthew; Hoteit, Ibrahim

2017-01-01

We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Bénard convection equations into a finer grid. In this CDA, a nudging term, estimated as the misfit between some interpolants of the assimilated coarse-grid measurements and the fine-grid model solution, is added to the model equations to constrain the model. The main contribution of this study is a performance analysis of CDA for downscaling measurements of temperature and velocity. These measurements are assimilated either separately or simultaneously, and the results are compared against those resulting from the standard point-to-point nudging approach (NA). Our numerical results suggest that the CDA solution outperforms that of NA, always converging to the true solution when the velocity is assimilated as has been theoretically proven. Assimilation of temperature measurements only may not always recover the true state as demonstrated in the case study. Various runs are conducted to evaluate the sensitivity of CDA to noise in the measurements, the size, and the time frequency of the measured grid, suggesting a more robust behavior of CDA compared to that of NA.

Downscaling the 2D Bénard convection equations using continuous data assimilation

KAUST Repository

Altaf, Muhammad

2017-02-27

We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Bénard convection equations into a finer grid. In this CDA, a nudging term, estimated as the misfit between some interpolants of the assimilated coarse-grid measurements and the fine-grid model solution, is added to the model equations to constrain the model. The main contribution of this study is a performance analysis of CDA for downscaling measurements of temperature and velocity. These measurements are assimilated either separately or simultaneously, and the results are compared against those resulting from the standard point-to-point nudging approach (NA). Our numerical results suggest that the CDA solution outperforms that of NA, always converging to the true solution when the velocity is assimilated as has been theoretically proven. Assimilation of temperature measurements only may not always recover the true state as demonstrated in the case study. Various runs are conducted to evaluate the sensitivity of CDA to noise in the measurements, the size, and the time frequency of the measured grid, suggesting a more robust behavior of CDA compared to that of NA.

Sub-Airy disk angular resolution with high dynamic range in the near-infrared

Directory of Open Access Journals (Sweden)

Richichi A.

2011-07-01

Full Text Available Lunar occultations (LO are a simple and effective high angular resolution method, with minimum requirements in instrumentation and telescope time. They rely on the analysis of the diffraction fringes created by the lunar limb. The diffraction phenomen occurs in space, and as a result LO are highly insensitive to most of the degrading effects that limit the performance of traditional single telescope and long-baseline interferometric techniques used for direct detection of faint, close companions to bright stars. We present very recent results obtained with the technique of lunar occultations in the near-IR, showing the detection of companions with very high dynamic range as close as few milliarcseconds to the primary star. We discuss the potential improvements that could be made, to increase further the current performance. Of course, LO are fixed-time events applicable only to sources which happen to lie on the Moon’s apparent orbit. However, with the continuously increasing numbers of potential exoplanets and brown dwarfs beign discovered, the frequency of such events is not negligible. I will list some of the most favorable potential LO in the near future, to be observed from major observatories.

An effective drift correction for dynamical downscaling of decadal global climate predictions

Science.gov (United States)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

Fast downscaled inverses for images compressed with M-channel lapped transforms.

Science.gov (United States)

de Queiroz, R L; Eschbach, R

1997-01-01

Compressed images may be decompressed and displayed or printed using different devices at different resolutions. Full decompression and rescaling in space domain is a very expensive method. We studied downscaled inverses where the image is decompressed partially, and a reduced inverse transform is used to recover the image. In this fashion, fewer transform coefficients are used and the synthesis process is simplified. We studied the design of fast inverses, for a given forward transform. General solutions are presented for M-channel finite impulse response (FIR) filterbanks, of which block and lapped transforms are a subset. Designs of faster inverses are presented for popular block and lapped transforms.

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

Directory of Open Access Journals (Sweden)

D. Fenta Mekonnen

2018-04-01

Full Text Available Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG and the Statistical Downscaling Model (SDSM, and (ii to downscale future climate scenarios of precipitation, maximum temperature (Tmax and minimum temperature (Tmin of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase

Dynamically-downscaled projections of changes in temperature extremes over China

Science.gov (United States)

Guo, Junhong; Huang, Guohe; Wang, Xiuquan; Li, Yongping; Lin, Qianguo

2018-02-01

In this study, likely changes in extreme temperatures (including 16 indices) over China in response to global warming throughout the twenty-first century are investigated through the PRECIS regional climate modeling system. The PRECIS experiment is conducted at a spatial resolution of 25 km and is driven by a perturbed-physics ensemble to reflect spatial variations and model uncertainties. Simulations of present climate (1961-1990) are compared with observations to validate the model performance in reproducing historical climate over China. Results indicate that the PRECIS demonstrates reasonable skills in reproducing the spatial patterns of observed extreme temperatures over the most regions of China, especially in the east. Nevertheless, the PRECIS shows a relatively poor performance in simulating the spatial patterns of extreme temperatures in the western mountainous regions, where its driving GCM exhibits more uncertainties due to lack of insufficient observations and results in more errors in climate downscaling. Future spatio-temporal changes of extreme temperature indices are then analyzed for three successive periods (i.e., 2020s, 2050s and 2080s). The projected changes in extreme temperatures by PRECIS are well consistent with the results of the major global climate models in both spatial and temporal patterns. Furthermore, the PRECIS demonstrates a distinct superiority in providing more detailed spatial information of extreme indices. In general, all extreme indices show similar changes in spatial pattern: large changes are projected in the north while small changes are projected in the south. In contrast, the temporal patterns for all indices vary differently over future periods: the warm indices, such as SU, TR, WSDI, TX90p, TN90p and GSL are likely to increase, while the cold indices, such as ID, FD, CSDI, TX10p and TN10p, are likely to decrease with time in response to global warming. Nevertheless, the magnitudes of changes in all indices tend to

Effect of Downscaled Forcings and Soil Texture Properties on Hyperresolution Hydrologic Simulations in a Regional Basin in Northwest Mexico

Science.gov (United States)

Ko, A.; Mascaro, G.; Vivoni, E. R.

2017-12-01

Hyper-resolution ( 10 km) scales. In this study, we address some of the challenges by applying a parallel version of the Triangulated Irregular Network (TIN)-based Real Time Integrated Basin Simulator (tRIBS) to the Rio Sonora Basin (RSB) in northwest Mexico. The RSB is a large, semiarid watershed ( 21,000 km2) characterized by complex topography and a strong seasonality in vegetation conditions, due to the North American monsoon. We conducted simulations at an average spatial resolution of 88 m over a decadal (2004-2013) period using spatially-distributed forcings from remotely-sensed and reanalysis products. Meteorological forcings were derived from the North American Land Data Assimilation System (NLDAS) at the original resolution of 12 km and were downscaled at 1 km with techniques accounting for terrain effects. Two grids of soil properties were created from different sources, including: (i) CONABIO (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad) at 6 km resolution; and (ii) ISRIC (International Soil Reference Information Centre) at 250 m. Time-varying vegetation parameters were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) composite products. The model was first calibrated and validated through distributed soil moisture data from a network of 20 soil moisture stations during the monsoon season. Next, hydrologic simulations were conducted with five different combinations of coarse and downscaled forcings and soil properties. Outputs in the different configurations were then compared with independent observations of soil moisture, and with estimates of land surface temperature (1 km, daily) and evapotranspiration (1 km, monthly) from MODIS. This study is expected to support the community involved in hyper-resolution hydrologic modeling by identifying the crucial factors that, if available at higher resolution, lead to the largest improvement of the simulation prognostic capability.

Magnetic Particle Imaging for High Temporal Resolution Assessment of Aneurysm Hemodynamics.

Directory of Open Access Journals (Sweden)

Jan Sedlacik

Full Text Available The purpose of this work was to demonstrate the capability of magnetic particle imaging (MPI to assess the hemodynamics in a realistic 3D aneurysm model obtained by additive manufacturing. MPI was compared with magnetic resonance imaging (MRI and dynamic digital subtraction angiography (DSA.The aneurysm model was of saccular morphology (7 mm dome height, 5 mm cross-section, 3-4 mm neck, 3.5 mm parent artery diameter and connected to a peristaltic pump delivering a physiological flow (250 mL/min and pulsation rate (70/min. High-resolution (4 h long 4D phase contrast flow quantification (4D pc-fq MRI was used to directly assess the hemodynamics of the model. Dynamic MPI, MRI, and DSA were performed with contrast agent injections (3 mL volume in 3 s through a proximally placed catheter.4D pc-fq measurements showed distinct pulsatile flow velocities (20-80 cm/s as well as lower flow velocities and a vortex inside the aneurysm. All three dynamic methods (MPI, MRI, and DSA also showed a clear pulsation pattern as well as delayed contrast agent dynamics within the aneurysm, which is most likely caused by the vortex within the aneurysm. Due to the high temporal resolution of MPI and DSA, it was possible to track the contrast agent bolus through the model and to estimate the average flow velocity (about 60 cm/s, which is in accordance with the 4D pc-fq measurements.The ionizing radiation free, 4D high resolution MPI method is a very promising tool for imaging and characterization of hemodynamics in human. It carries the possibility of overcoming certain disadvantages of other modalities like considerably lower temporal resolution of dynamic MRI and limited 2D characteristics of DSA. Furthermore, additive manufacturing is the key for translating powerful pre-clinical techniques into the clinic.

Imaging collagen type I fibrillogenesis with high spatiotemporal resolution

International Nuclear Information System (INIS)

Stamov, Dimitar R; Stock, Erik; Franz, Clemens M; Jähnke, Torsten; Haschke, Heiko

2015-01-01

Fibrillar collagens, such as collagen type I, belong to the most abundant extracellular matrix proteins and they have received much attention over the last five decades due to their large interactome, complex hierarchical structure and high mechanical stability. Nevertheless, the collagen self-assembly process is still incompletely understood. Determining the real-time kinetics of collagen type I formation is therefore pivotal for better understanding of collagen type I structure and function, but visualising the dynamic self-assembly process of collagen I on the molecular scale requires imaging techniques offering high spatiotemporal resolution. Fast and high-speed scanning atomic force microscopes (AFM) provide the means to study such processes on the timescale of seconds under near-physiological conditions. In this study we have applied fast AFM tip scanning to study the assembly kinetics of fibrillar collagen type I nanomatrices with a temporal resolution reaching eight seconds for a frame size of 500 nm. By modifying the buffer composition and pH value, the kinetics of collagen fibrillogenesis can be adjusted for optimal analysis by fast AFM scanning. We furthermore show that amplitude-modulation imaging can be successfully applied to extract additional structural information from collagen samples even at high scan rates. Fast AFM scanning with controlled amplitude modulation therefore provides a versatile platform for studying dynamic collagen self-assembly processes at high resolution. - Highlights: • Continuous non-invasive time-lapse investigation of collagen I fibrillogenesis in situ. • Imaging of collagen I self-assembly with high spatiotemporal resolution. • Application of setpoint modulation to study the hierarchical structure of collagen I. • Observing real-time formation of the D-banding pattern in collagen I

Imaging collagen type I fibrillogenesis with high spatiotemporal resolution

Energy Technology Data Exchange (ETDEWEB)

Stamov, Dimitar R, E-mail: stamov@jpk.com [JPK Instruments AG, Bouchéstrasse 12, 12435 Berlin (Germany); Stock, Erik [JPK Instruments AG, Bouchéstrasse 12, 12435 Berlin (Germany); Franz, Clemens M [DFG-Center for Functional Nanostructures (CFN), Karlsruhe Institute of Technology (KIT), Wolfgang-Gaede-Strasse 1a, 76131 Karlsruhe (Germany); Jähnke, Torsten; Haschke, Heiko [JPK Instruments AG, Bouchéstrasse 12, 12435 Berlin (Germany)

2015-02-15

Fibrillar collagens, such as collagen type I, belong to the most abundant extracellular matrix proteins and they have received much attention over the last five decades due to their large interactome, complex hierarchical structure and high mechanical stability. Nevertheless, the collagen self-assembly process is still incompletely understood. Determining the real-time kinetics of collagen type I formation is therefore pivotal for better understanding of collagen type I structure and function, but visualising the dynamic self-assembly process of collagen I on the molecular scale requires imaging techniques offering high spatiotemporal resolution. Fast and high-speed scanning atomic force microscopes (AFM) provide the means to study such processes on the timescale of seconds under near-physiological conditions. In this study we have applied fast AFM tip scanning to study the assembly kinetics of fibrillar collagen type I nanomatrices with a temporal resolution reaching eight seconds for a frame size of 500 nm. By modifying the buffer composition and pH value, the kinetics of collagen fibrillogenesis can be adjusted for optimal analysis by fast AFM scanning. We furthermore show that amplitude-modulation imaging can be successfully applied to extract additional structural information from collagen samples even at high scan rates. Fast AFM scanning with controlled amplitude modulation therefore provides a versatile platform for studying dynamic collagen self-assembly processes at high resolution. - Highlights: • Continuous non-invasive time-lapse investigation of collagen I fibrillogenesis in situ. • Imaging of collagen I self-assembly with high spatiotemporal resolution. • Application of setpoint modulation to study the hierarchical structure of collagen I. • Observing real-time formation of the D-banding pattern in collagen I.

Expansion of the On-line Archive "Statistically Downscaled WCRP CMIP3 Climate Projections"

Science.gov (United States)

Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Das, T.; Duffy, P.; White, K.

2009-12-01

Presentation highlights status and plans for a public-access archive of downscaled CMIP3 climate projections. Incorporating climate projection information into long-term evaluations of water and energy resources requires analysts to have access to projections at "basin-relevant" resolution. Such projections would ideally be bias-corrected to account for climate model tendencies to systematically simulate historical conditions different than observed. In 2007, the U.S. Bureau of Reclamation, Santa Clara University and Lawrence Livermore National Laboratory (LLNL) collaborated to develop an archive of 112 bias-corrected and spatially disaggregated (BCSD) CMIP3 temperature and precipitation projections. These projections were generated using 16 CMIP3 models to simulate three emissions pathways (A2, A1b, and B1) from one or more initializations (runs). Projections are specified on a monthly time step from 1950-2099 and at 0.125 degree spatial resolution within the North American Land Data Assimilation System domain (i.e. contiguous U.S., southern Canada and northern Mexico). Archive data are freely accessible at LLNL Green Data Oasis (url). Since being launched, the archive has served over 3500 data requests by nearly 500 users in support of a range of planning, research and educational activities. Archive developers continue to look for ways to improve the archive and respond to user needs. One request has been to serve the intermediate datasets generated during the BCSD procedure, helping users to interpret the relative influences of the bias-correction and spatial disaggregation on the transformed CMIP3 output. This request has been addressed with intermediate datasets now posted at the archive web-site. Another request relates closely to studying hydrologic and ecological impacts under climate change, where users are asking for projected diurnal temperature information (e.g., projected daily minimum and maximum temperature) and daily time step resolution. In

Statistical downscaling and future scenario generation of temperatures for Pakistan Region

Science.gov (United States)

Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas

2015-04-01

Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical downscaling) has been adopted. Statistical DownScaling Model (SDSM) employed for downscaling of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The downscaled data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.

Developing a high-resolution regional atmospheric reanalysis for Australia

Science.gov (United States)

White, Christopher; Fox-Hughes, Paul; Su, Chun-Hsu; Jakob, Dörte; Kociuba, Greg; Eisenberg, Nathan; Steinle, Peter; Harris, Rebecca; Corney, Stuart; Love, Peter; Remenyi, Tomas; Chladil, Mark; Bally, John; Bindoff, Nathan

2017-04-01

A dynamically consistent, long-term atmospheric reanalysis can be used to support high-quality assessments of environmental risk and likelihood of extreme events. Most reanalyses are presently based on coarse-scale global systems that are not suitable for regional assessments in fire risk, water and natural resources, amongst others. The Australian Bureau of Meteorology is currently working to close this gap by producing a high-resolution reanalysis over the Australian and New Zealand region to construct a sequence of atmospheric conditions at sub-hourly intervals over the past 25 years from 1990. The Australia reanalysis consists of a convective-scale analysis nested within a 12 km regional-scale reanalysis, which is bounded by a coarse-scale ERA-Interim reanalysis that provides the required boundary and initial conditions. We use an unchanging atmospheric modelling suite based on the UERRA system used at the UK Met Office and the more recent version of the Bureau of Meteorology's operational numerical prediction model used in ACCESS-R (Australian Community Climate and Earth-System Simulator-Regional system). An advanced (4-dimensional variational) data assimilation scheme is used to optimally combine model physics with multiple observations from aircrafts, sondes, surface observations and satellites to create a best estimate of state of the atmosphere over a 6-hour moving window. This analysis is in turn used to drive a higher-resolution (1.5 km) downscaling model over selected subdomains within Australia, currently eastern New South Wales and Tasmania, with the capability to support this anywhere in the Australia-New Zealand domain. The temporal resolution of the gridded analysis fields for both the regional and higher-resolution subdomains are generally one hour, with many fields such as 10 m winds and 2 m temperatures available every 10 minutes. The reanalysis also produces many other variables that include wind, temperature, moisture, pressure, cloud cover

High-resolution spectroscopic probes of collisions and half-collisions

Energy Technology Data Exchange (ETDEWEB)

Hall, G.E. [Brookhaven National Laboratory, Upton, NY (United States)

1993-12-01

Research in this program explores the dynamics of gas phase collisions and photodissociation by high-resolution laser spectroscopy. Simultaneous state and velocity detection frequently permits a determination of scalar or vector correlations among products. The correlated product distributions are always more informative, and often easier to interpret than the uncorrelated product state distributions. The authors have recently built an apparatus to record transient absorption spectra with 50 nS time resolution and 20 MHz frequency resolution using a single frequency Ti:sapphire laser. The photodissociation of NCCN and C{sub 2}H{sub 5}SCN at 193 nm is discussed.

Satellite microwave remote sensing of North Eurasian inundation dynamics: development of coarse-resolution products and comparison with high-resolution synthetic aperture radar data

International Nuclear Information System (INIS)

Schroeder, R; Rawlins, M A; McDonald, K C; Podest, E; Zimmermann, R; Kueppers, M

2010-01-01

Wetlands are not only primary producers of atmospheric greenhouse gases but also possess unique features that are favourable for application of satellite microwave remote sensing to monitoring their status and trend. In this study we apply combined passive and active microwave remote sensing data sets from the NASA sensors AMSR-E and QuikSCAT to map surface water dynamics over Northern Eurasia. We demonstrate our method on the evolution of large wetland complexes for two consecutive years from January 2006 to December 2007. We apply river discharge measurements from the Ob River along with land surface runoff simulations derived from the Pan-Arctic Water Balance Model during and after snowmelt in 2006 and 2007 to interpret the abundance of widespread flooding along the River Ob in early summer of 2007 observed in the remote sensing products. The coarse-resolution, 25 km, surface water product is compared to a high-resolution, 30 m, inundation map derived from ALOS PALSAR (Advanced Land Observation Satellite phased array L-band synthetic aperture radar) imagery acquired for 11 July 2006, and extending along a transect in the central Western Siberian Plain. We found that the surface water fraction derived from the combined AMSR-E/QuikSCAT data sets closely tracks the inundation mapped using higher-resolution ALOS PALSAR data.

Actor groups, related needs, and challenges at the climate downscaling interface

Science.gov (United States)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are

Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

Directory of Open Access Journals (Sweden)

Yangxiaoyue Liu

2017-12-01

Full Text Available Although numerous satellite-based soil moisture (SM products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART, K-nearest neighbors (KNN, Bayesian (BAYE, and random forests (RF, were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature, normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band, and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values.

From a Real Deployment to a Downscaled Testbed : A Methodological Approach

NARCIS (Netherlands)

Stajkic, Andrea; Abrignani, Melchiorre Danilo; Buratti, Chiara; Bettinelli, Andrea; Vigo, Daniele; Verdone, Roberto

2016-01-01

This paper proposes a novel methodology for the spatial downscaling of real-world deployments of wireless networks, running protocols, and/or applications for the Internet of Things (IoT). These networks are often deployed in environments not easily accessible and highly unpredictable, where doing

Transferability in the future climate of a statistical downscaling method for precipitation in France

Science.gov (United States)

Dayon, G.; Boé, J.; Martin, E.

2015-02-01

A statistical downscaling approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with focus on the transferability of the method to the future climate. First, the realism of downscaled present-day precipitation climatology and interannual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained, but elaborated predictors do not lead to major and consistent across-reanalyses improvements. The downscaling method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in downscaled trends due to the choice of the reanalysis are large and observed trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect model framework. The statistical downscaling relationship is built using present-day predictors and precipitation simulated by 12 regional climate models. The entire projections are then downscaled, and future downscaled and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are downscaled, thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the downscaling method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.

Two-Step Downscaling of Trmm 3b43 V7 Precipitation in Contrasting Climatic Regions With Sparse Monitoring: The Case of Ecuador in Tropical South America

Directory of Open Access Journals (Sweden)

Jacinto Ulloa

2017-07-01

Full Text Available Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of precipitation are available at coarse resolutions, and require adequate methods for spatial downscaling and calibration. The objective of this paper is to introduce and evaluate a 2-step spatial downscaling approach for monthly precipitation applied to TRMM 3B43 (from 0 . 25 ∘ ≈ 27 km to 5 km resolution, resulting in 5 downscaled products for the period 01-2001/12-2011. The methodology was evaluated in 3 contrasting climatic regions of Ecuador. In step 1, bilinear resampling was applied over TRMM, and used as a reference product. The second step introduces further variability, and consists of four alternative gauge-satellite merging methods: (1 regression with in situ stations, (2 regression kriging with in situ stations, (3 regression with in situ stations and auxiliary variables, and (4 regression kriging with in situ stations and auxiliary variables. The first 2 methods only use the resampled TRMM data set as an independent variable. The last 2 methods enrich these models with auxiliary environmental factors, incorporating atmospheric and land variables. The results showed that no product outperforms the others in every region. In general, the methods with residual kriging correction outperformed the regression models. Regression kriging with situ data provided the best representation in the Coast, while regression kriging with in situ and auxiliary data generated the best results in the Andes. In the Amazon, no product outperformed the resampled TRMM images, probably due to the low density of in situ stations. These results are relevant to enhance satellite precipitation, depending on the availability of in situ data, auxiliary satellite

Dynamic Downscaling of Seasonal Simulations over South America.

Science.gov (United States)

Misra, Vasubandhu; Dirmeyer, Paul A.; Kirtman, Ben P.

2003-01-01

resolution. It is seen in this study that the summer precipitation over tropical and subtropical South America is highly unpredictable in both models.

High-resolution X-ray television and high-resolution video recorders

International Nuclear Information System (INIS)

Haendle, J.; Horbaschek, H.; Alexandrescu, M.

1977-01-01

The improved transmission properties of the high-resolution X-ray television chain described here make it possible to transmit more information per television image. The resolution in the fluoroscopic image, which is visually determined, depends on the dose rate and the inertia of the television pick-up tube. This connection is discussed. In the last few years, video recorders have been increasingly used in X-ray diagnostics. The video recorder is a further quality-limiting element in X-ray television. The development of function patterns of high-resolution magnetic video recorders shows that this quality drop may be largely overcome. The influence of electrical band width and number of lines on the resolution in the X-ray television image stored is explained in more detail. (orig.) [de

A regional climate model for northern Europe: model description and results from the downscaling of two GCM control simulations

Science.gov (United States)

Rummukainen, M.; Räisänen, J.; Bringfelt, B.; Ullerstig, A.; Omstedt, A.; Willén, U.; Hansson, U.; Jones, C.

This work presents a regional climate model, the Rossby Centre regional Atmospheric model (RCA1), recently developed from the High Resolution Limited Area Model (HIRLAM). The changes in the HIRLAM parametrizations, necessary for climate-length integrations, are described. A regional Baltic Sea ocean model and a modeling system for the Nordic inland lake systems have been coupled with RCA1. The coupled system has been used to downscale 10-year time slices from two different general circulation model (GCM) simulations to provide high-resolution regional interpretation of large-scale modeling. A selection of the results from the control runs, i.e. the present-day climate simulations, are presented: large-scale free atmospheric fields, the surface temperature and precipitation results and results for the on-line simulated regional ocean and lake surface climates. The regional model modifies the surface climate description compared to the GCM simulations, but it is also substantially affected by the biases in the GCM simulations. The regional model also improves the representation of the regional ocean and the inland lakes, compared to the GCM results.

A regional climate model for northern Europe: model description and results from the downscaling of two GCM control simulations

Energy Technology Data Exchange (ETDEWEB)

Rummukainen, M.; Raeisaenen, J.; Bringfelt, B.; Ullerstig, A.; Omstedt, A.; Willen, U.; Hansson, U.; Jones, C. [Rossby Centre, Swedish Meteorological and Hydrological Inst., Norrkoeping (Sweden)

2001-03-01

This work presents a regional climate model, the Rossby Centre regional Atmospheric model (RCA1), recently developed from the High Resolution Limited Area Model (HIRLAM). The changes in the HIRLAM parametrizations, necessary for climate-length integrations, are described. A regional Baltic Sea ocean model and a modeling system for the Nordic inland lake systems have been coupled with RCA1. The coupled system has been used to downscale 10-year time slices from two different general circulation model (GCM) simulations to provide high-resolution regional interpretation of large-scale modeling. A selection of the results from the control runs, i.e. the present-day climate simulations, are presented: large-scale free atmospheric fields, the surface temperature and precipitation results and results for the on-line simulated regional ocean and lake surface climates. The regional model modifies the surface climate description compared to the GCM simulations, but it is also substantially affected by the biases in the GCM simulations. The regional model also improves the representation of the regional ocean and the inland lakes, compared to the GCM results. (orig.)

High-resolution He beam scattering as a tool for the investigation of the structural and dynamical properties of surface soliton dislocations

International Nuclear Information System (INIS)

El-Batanouny, M.; Martini, K.M.

1986-01-01

We discuss the applicability of high-resolution-He-beam/surface scattering to the investigation of the structural and dynamic properties of soliton-like surface misfit dislocations and associated phase transitions. We present evidence, based on recent He diffraction measurements, for the existence of double-sine-Gordon soliton-like dislocations on the reconstructed Au(111) surface. 18 refs., 3 figs., 1 tab

High resolution field study of sediment dynamics on a strongly heterogeneous bed

Science.gov (United States)

Bailly Du Bois, P.; Blanpain, O.; Lafite, R.; Cugier, P.; Lunven, M.

2010-12-01

Extensive field measurements have been carried out at several stations in a macrotidal inner continental shelf in the English Channel (around 25 m depth) during spring tide period. The strong tidal current measured (up to 1.6 m.s-1) allowed sediment dynamics on a bed characterised by a mixture of size with coarse grains to be dominant. Data acquired in such hydro-sedimentary conditions are scarce. A new instrument, the DYnamic Sediment Profile Imagery (DySPI) system, was specifically conceived and implemented in-situ to observe and measure, with a high temporal resolution, the dynamics of a strongly heterogeneous mixture of particles in a grain-size scale. The data collected covered: 1) grain size range (side scan sonar, video observations, Shipeck grab samples, DySPI images) and vertical sorting (stratigraphic sampling by divers) of sediment cover, 2) hydrodynamic features (acoustic Doppler velocimeter, acoustic Doppler profiler), 3) suspended load nature and dynamics (optical backscatter, chlorophyll fluorometer, particle size analyser, Niskin bottles, scanning electron microscopy), 4) sand and gravel bedload transport estimates (DySPI image processing), 5) transfer dynamics of fine grains within a coarse matrix and their depth of penetration (radionuclides measurements in stratigraphic samples). The four stations present different grain size vertical sorting from a quasi-permanent armouring to a homogenous distribution. The sediment cover condition is directly linked to hydrodynamic capacity and sediment availability. Fine grain ratio within deep sediment layers (up to 10 cm) is higher when the bed armouring is durable. However, fine sediments are not permanently depth trapped: deep layers are composed of few years-old radionuclide tracers fixed on fine grains and a vertical mixing coefficient has been evaluated for each sediment cover. Fine grain dynamics within a coarse matrix is inversely proportional to the robustness of the armour layer. For current

Two millennia of soil dynamics derived from ancient desert terraces using high resolution 3-D data

Science.gov (United States)

Filin, Sagi; Arav, Reuma; Avni, Yoav

2017-04-01

Large areas in the arid southern Levant are dotted with ancient terrace-based agriculture systems which were irrigated by runoff harvesting techniques. They were constructed and maintained between the 3rd - 9th centuries AD and abandoned in the 10th century AD. During their 600 years of cultivation, these terraces documented the gradual aggradation of alluvial soils, erosion processes within the drainage basins, as well as flashflood damage. From their abandonment and onwards, they documented 1000 years and more of land degradation and soil erosion processes. Examination of these installations presents an opportunity to study natural and anthropogenic induced changes over almost two millennia. On a global scale, such an analysis is unique as it is rare to find intact manifestations of anthropogenic influences over such time-scales because of landscape dynamics. It is also rare to find a near millennia documentation of soil erosion processes. We study in this paper the aggradation processes within intact agriculture plots in the region surrounding the world heritage Roman-Byzantine ancient city of Avdat, Negev Highlands. We follow the complete cycle of the historical desert agriculture, from the configuration pre-dating the first anthropogenic intervention, through the centuries of cultivation, and up to the present erosion phase, which spans over more than a millennium. We use high resolution 3-D laser scans to document the erosion and the environmental dynamics during these two millennia. The high-resolution data is then utilized to compute siltation rates as well as erosion rates. The long-term measures of soil erosion and land degradation we present here significantly improve our understanding of the mechanism of long-term environmental change acting in arid environments. For sustainable desert inhabitation, the study offers insights into better planning of modern agriculture in similar zones as well as insights on strategies needed to protect such historical

Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes

Science.gov (United States)

Vallam, P.; Qin, X. S.

2017-10-01

Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.

Quantitative analysis of localized stresses in irradiated stainless steels using high resolution electron backscatter diffraction and molecular dynamics modeling

International Nuclear Information System (INIS)

Johnson, D.C.; Kuhr, B.; Farkas, D.; Was, G.S.

2016-01-01

Quantitative measurements of stress near dislocation channel–grain boundary (DC–GB) interaction sites were made using high resolution electron backscatter diffraction (HREBSD) and have been compared with molecular dynamics (MD) simulations. Tensile stress normal to the grain boundary was significantly elevated at discontinuous DC–GB intersections with peak magnitudes roughly an order of magnitude greater than at sites where slip transfer occurred. These results constitute the first measurement of stress amplification at DC–GB intersections and provide support to the theory that high normal stress at the grain boundary may be a key driver for the initiation of irradiation assisted stress corrosion cracks.

High-resolution Mapping of Permafrost and Soil Freeze/thaw Dynamics in the Tibetan Plateau Based on Multi-sensor Satellite Observations

Science.gov (United States)

Zhang, W.; Yi, Y.; Yang, K.; Kimball, J. S.

2016-12-01

The Tibetan Plateau (TP) is underlain by the world's largest extent of alpine permafrost ( 2.5×106 km2), dominated by sporadic and discontinuous permafrost with strong sensitivity to climate warming. Detailed permafrost distributions and patterns in most of the TP region are still unknown due to extremely sparse in-situ observations in this region characterized by heterogeneous land cover and large temporal dynamics in surface soil moisture conditions. Therefore, satellite-based temperature and moisture observations are essential for high-resolution mapping of permafrost distribution and soil active layer changes in the TP region. In this study, we quantify the TP regional permafrost distribution at 1-km resolution using a detailed satellite data-driven soil thermal process model (GIPL2). The soil thermal model is calibrated and validated using in-situ soil temperature/moisture observations from the CAMP/Tibet field campaign (9 sites: 0-300 cm soil depth sampling from 1997-2007), a multi-scale soil moisture and temperature monitoring network in the central TP (CTP-SMTMN, 57 sites: 5-40 cm, 2010-2014) and across the whole plateau (China Meteorology Administration, 98 sites: 0-320 cm, 2000-2015). Our preliminary results using the CAMP/Tibet and CTP-SMTMN network observations indicate strong controls of surface thermal and soil moisture conditions on soil freeze/thaw dynamics, which vary greatly with underlying topography, soil texture and vegetation cover. For regional mapping of soil freeze/thaw and permafrost dynamics, we use the most recent soil moisture retrievals from the NASA SMAP (Soil Moisture Active Passive) sensor to account for the effects of temporal soil moisture dynamics on soil thermal heat transfer, with surface thermal conditions defined by MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature records. Our study provides the first 1-km map of spatial patterns and recent changes of permafrost conditions in the TP.

Development of a high-resolution cavity-beam position monitor

Directory of Open Access Journals (Sweden)

Yoichi Inoue

2008-06-01

Full Text Available We have developed a high-resolution cavity-beam position monitor (BPM to be used at the focal point of the ATF2, which is a test beam line that is now being built to demonstrate stable orbit control at ∼nanometer resolution. The design of the cavity structure was optimized for the Accelerator Test Facility (ATF beam in various ways. For example, the cavity has a rectangular shape in order to isolate two dipole modes in orthogonal directions, and a relatively thin gap that is less sensitive to trajectory inclination. A two stage homodyne mixer with highly sensitive electronics and phase-sensitive detection was also developed. Two BPM blocks, each containing two cavity BPMs, were installed in the existing ATF beam line using a rigid support frame. After testing the basic characteristics, we measured the resolution using three BPMs. The system demonstrated 8.7 nm position resolution over a dynamic range of 5  μm.

Development of a high-resolution cavity-beam position monitor

Science.gov (United States)

Inoue, Yoichi; Hayano, Hitoshi; Honda, Yosuke; Takatomi, Toshikazu; Tauchi, Toshiaki; Urakawa, Junji; Komamiya, Sachio; Nakamura, Tomoya; Sanuki, Tomoyuki; Kim, Eun-San; Shin, Seung-Hwan; Vogel, Vladimir

2008-06-01

We have developed a high-resolution cavity-beam position monitor (BPM) to be used at the focal point of the ATF2, which is a test beam line that is now being built to demonstrate stable orbit control at ˜nanometer resolution. The design of the cavity structure was optimized for the Accelerator Test Facility (ATF) beam in various ways. For example, the cavity has a rectangular shape in order to isolate two dipole modes in orthogonal directions, and a relatively thin gap that is less sensitive to trajectory inclination. A two stage homodyne mixer with highly sensitive electronics and phase-sensitive detection was also developed. Two BPM blocks, each containing two cavity BPMs, were installed in the existing ATF beam line using a rigid support frame. After testing the basic characteristics, we measured the resolution using three BPMs. The system demonstrated 8.7 nm position resolution over a dynamic range of 5μm.

Measurement of Dynamic Urethral Pressures with a High Resolution Manometry System in Continent and Incontinent Women

Science.gov (United States)

Kirby, Anna C; Tan-Kim, Jasmine; Nager, Charles W.

2015-01-01

Objectives Female stress urinary incontinence (SUI) is caused by urethral dysfunction during dynamic conditions, but current technology has limitations in measuring urethral pressures under dynamic conditions. An 8-French high resolution manometry catheter (HRM) currently in clinical use in gastroenterology may accurately measure urethral pressures under dynamic conditions because it has a 25ms response rate and circumferential pressure sensors along the length of the catheter (ManoScan® ESO, Given Imaging). We evaluated the concordance, repeatability, and tolerability of this catheter. Methods We measured resting, cough, and strain maximum urethral closure pressures (MUCPs) using HRM and measured resting MUCPs with water perfusion side-hole catheter urethral pressure profilometry (UPP) in 37 continent and 28 stress incontinent subjects. Maneuvers were repeated after moving the HRM catheter along the urethral length to evaluate whether results depend on catheter positioning. Visual analog pain scores evaluated the comfort of HRM compared to UPP. Results The correlation coefficient for resting MUCPs measured by HRM vs. UPP was high (r = 0.79, prest, cough, and strain with HRM: r= 0.92, 0.89, and 0.89. Mean MUCPs (rest, cough, strain) were higher in continent than incontinent subjects (all p continent subjects during cough and strain maneuvers compared to rest. Conclusions This preliminary study shows that HRM is concordant with standard technology, repeatable, and well tolerated in the urethra. Incontinent women have more impairment of their urethral closure pressures during cough and strain than continent women. PMID:25185595

Improving Chemical EOR Simulations and Reducing the Subsurface Uncertainty Using Downscaling Conditioned to Tracer Data

KAUST Repository

Torrealba, Victor A.; Hoteit, Hussein; Chawathe, Adwait

2017-01-01

and thermodynamic phase split, the impact of grid downscaling on CEOR simulations is not well understood. In this work, we introduce a geostatistical downscaling method conditioned to tracer data to refine a coarse history-matched WF model. This downscaling process

Application of a hybrid method for downscaling of the global climate model fields for evaluation of future surface mass balance of mountain glaciers

Science.gov (United States)

Morozova, Polina; Rybak, Oleg; Kaminskaia, Mariia

2017-04-01

Mountain glaciers in the Caucasus have been degrading during the last century. During this time period they lost approximately one-third in area and half of their volume. Prediction of their evolution in changing climate is crucial for the local economy because hydrological regime in the territory north to the Main Caucasus Chain is mainly driven by glacier run-off. For future projections of glaciers' surface mass balance (SMB) we apply a hybrid method of downscaling of GCM-generated meteorological fields from the global scale to the characteristic spatial resolution normally used for modeling of a single mountain glacier SMB. A method consists of two stages. On the first, dynamical stage, we use the results of calculations of regional climate model (RCM) HadRM3P for the Black Sea-Caspian region with a spatial resolution of approximately 25 km. Initial and boundary conditions for HadRM3P are provided by an AO GCM INMCM developed in the Institute of Numerical Mathematics (Moscow, Russia). Calculations were carried out for two time slices: the present (reference) climate (1971-2000 years) and climate in the late 21st century (2071-2100 years) according to scenario of greenhouse gas emissions RCP 8.5. On the second stage of downscaling, further regionalization is achieved by projecting of RCM-generated data to the high-resolution (25 m) digital elevation models in a domain enclosing target glaciers (Marukh in the Western Caucasus and Djankuat in the Central Caucasus, both being typical valley glaciers). Elevation gradient of surface air temperature and precipitation were derived from the model data. Further, results were corrected using data of observations. The incoming shortwave radiation is calculated separately, taking into account slopes, aspects and shade effect. In the end of the current century expected air temperature growth in the Central and Western Caucasus is about 5-6 °C (summer), and 2-3 °C (winter). Reduction in annual precipitation is not

Verification of high resolution simulation of precipitation and wind in Portugal

Science.gov (United States)

Menezes, Isilda; Pereira, Mário; Moreira, Demerval; Carvalheiro, Luís; Bugalho, Lourdes; Corte-Real, João

2017-04-01

Demand of energy and freshwater continues to grow as the global population and demands increase. Precipitation feed the freshwater ecosystems which provides a wealth of goods and services for society and river flow to sustain native species and natural ecosystem functions. The adoption of the wind and hydro-electric power supplies will sustain energy demands/services without restricting the economic growth and accelerated policies scenarios. However, the international meteorological observation network is not sufficiently dense to directly support high resolution climatic research. In this sense, coupled global and regional atmospheric models constitute the most appropriate physical and numerical tool for weather forecasting and downscaling in high resolution grids with the capacity to solve problems resulting from the lack of observed data and measuring errors. Thus, this study aims to calibrate and validate of the WRF regional model from precipitation and wind fields simulation, in high spatial resolution grid cover in Portugal. The simulations were performed in two-way nesting with three grids of increasing resolution (60 km, 20 km and 5 km) and the model performance assessed for the summer and winter months (January and July), using input variables from two different reanalyses and forecasted databases (ERA-Interim and NCEP-FNL) and different forcing schemes. The verification procedure included: (i) the use of several statistics error estimators, correlation based measures and relative errors descriptors; and, (ii) an observed dataset composed by time series of hourly precipitation, wind speed and direction provided by the Portuguese meteorological institute for a comprehensive set of weather stations. Main results suggested the good ability of the WRF to: (i) reproduce the spatial patterns of the mean and total observed fields; (ii) with relatively small values of bias and other errors; and, (iii) and good temporal correlation. These findings are in good

Compact High Resolution SANS using very cold neutrons (VCN-SANS)

International Nuclear Information System (INIS)

Kennedy, S.; Yamada, M.; Iwashita, Y.; Geltenbort, P.; Bleuel, M.; Shimizu, H.

2011-01-01

SANS (Small Angle Neutron Scattering) is a popular method for elucidation of nano-scale structures. However science continually challenges SANS for higher performance, prompting exploration of ever-more exotic and expensive technologies. We propose a compact high resolution SANS, using very cold neutrons, magnetic focusing lens and a wide-angle spherical detector. This system will compete with modern 40 m pinhole SANS in one tenth of the length, matching minimum Q, Q-resolution and dynamic range. It will also probe dynamics using the MIEZE method. Our prototype lens (a rotating permanent-magnet sextupole), focuses a pulsed neutron beam over 3-5 nm wavelength and has measured SANS from micelles and polymer blends. (authors)

High Resolution Forecasts in the Florida Straits: Predicting the Modulations of the Florida Current and Connectivity Around South Florida and Cuba

Science.gov (United States)

Kourafalou, V.; Kang, H.; Perlin, N.; Le Henaff, M.; Lamkin, J. T.

2016-02-01

Connectivity around the South Florida coastal regions and between South Florida and Cuba are largely influenced by a) local coastal processes and b) circulation in the Florida Straits, which is controlled by the larger scale Florida Current variability. Prediction of the physical connectivity is a necessary component for several activities that require ocean forecasts, such as oil spills, fisheries research, search and rescue. This requires a predictive system that can accommodate the intense coastal to offshore interactions and the linkages to the complex regional circulation. The Florida Straits, South Florida and Florida Keys Hybrid Coordinate Ocean Model is such a regional ocean predictive system, covering a large area over the Florida Straits and the adjacent land areas, representing both coastal and oceanic processes. The real-time ocean forecast system is high resolution ( 900m), embedded in larger scale predictive models. It includes detailed coastal bathymetry, high resolution/high frequency atmospheric forcing and provides 7-day forecasts, updated daily (see: http://coastalmodeling.rsmas.miami.edu/). The unprecedented high resolution and coastal details of this system provide value added on global forecasts through downscaling and allow a variety of applications. Examples will be presented, focusing on the period of a 2015 fisheries cruise around the coastal areas of Cuba, where model predictions helped guide the measurements on biophysical connectivity, under intense variability of the mesoscale eddy field and subsequent Florida Current meandering.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

Science.gov (United States)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

2015-04-01

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue Downscaling method for Hydrology) statistical downscaling method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an ensemble of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are

Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables

Directory of Open Access Journals (Sweden)

No-Wook Park

2013-01-01

Full Text Available A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM and normalized difference vegetation index (NDVI, the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.

A Spatial Allocation Procedure to Downscale Regional Crop Production Estimates from an Integrated Assessment Model

Science.gov (United States)

Moulds, S.; Djordjevic, S.; Savic, D.

2017-12-01

The Global Change Assessment Model (GCAM), an integrated assessment model, provides insight into the interactions and feedbacks between physical and human systems. The land system component of GCAM, which simulates land use activities and the production of major crops, produces output at the subregional level which must be spatially downscaled in order to use with gridded impact assessment models. However, existing downscaling routines typically consider cropland as a homogeneous class and do not provide information about land use intensity or specific management practices such as irrigation and multiple cropping. This paper presents a spatial allocation procedure to downscale crop production data from GCAM to a spatial grid, producing a time series of maps which show the spatial distribution of specific crops (e.g. rice, wheat, maize) at four input levels (subsistence, low input rainfed, high input rainfed and high input irrigated). The model algorithm is constrained by available cropland at each time point and therefore implicitly balances extensification and intensification processes in order to meet global food demand. It utilises a stochastic approach such that an increase in production of a particular crop is more likely to occur in grid cells with a high biophysical suitability and neighbourhood influence, while a fall in production will occur more often in cells with lower suitability. User-supplied rules define the order in which specific crops are downscaled as well as allowable transitions. A regional case study demonstrates the ability of the model to reproduce historical trends in India by comparing the model output with district-level agricultural inventory data. Lastly, the model is used to predict the spatial distribution of crops globally under various GCAM scenarios.

High-resolution high-speed dynamic mechanical spectroscopy of cells and other soft materials with the help of atomic force microscopy.

Science.gov (United States)

Dokukin, M; Sokolov, I

2015-07-28

Dynamic mechanical spectroscopy (DMS), which allows measuring frequency-dependent viscoelastic properties, is important to study soft materials, tissues, biomaterials, polymers. However, the existing DMS techniques (nanoindentation) have limited resolution when used on soft materials, preventing them from being used to study mechanics at the nanoscale. The nanoindenters are not capable of measuring cells, nanointerfaces of composite materials. Here we present a highly accurate DMS modality, which is a combination of three different methods: quantitative nanoindentation (nanoDMA), gentle force and fast response of atomic force microscopy (AFM), and Fourier transform (FT) spectroscopy. This new spectroscopy (which we suggest to call FT-nanoDMA) is fast and sensitive enough to allow DMS imaging of nanointerfaces, single cells, while attaining about 100x improvements on polymers in both spatial (to 10-70 nm) and temporal resolution (to 0.7 s/pixel) compared to the current art. Multiple frequencies are measured simultaneously. The use of 10 frequencies are demonstrated here (up to 300 Hz which is a rather relevant range for biological materials and polymers, in both ambient conditions and liquid). The method is quantitatively verified on known polymers and demonstrated on cells and polymers blends. Analysis shows that FT-nanoDMA is highly quantitative. The FT-nanoDMA spectroscopy can easily be implemented in the existing AFMs.

High-Resolution Reciprocal Space Mapping for Characterizing Deformation Structures

DEFF Research Database (Denmark)

Pantleon, Wolfgang; Wejdemann, Christian; Jakobsen, Bo

2014-01-01

With high-angular resolution three-dimensional X-ray diffraction (3DXRD), quantitative information is gained about dislocation structures in individual grains in the bulk of a macroscopic specimen by acquiring reciprocal space maps. In high-resolution 3D reciprocal space maps of tensile......-deformed copper, individual, almost dislocation-free subgrains are identified from high-intensity peaks and distinguished by their unique combination of orientation and elastic strain; dislocation walls manifest themselves as a smooth cloud of lower intensity. The elastic strain shows only minor variations within...... dynamics is followed in situ during varying loading conditions by reciprocal space mapping: during uninterrupted tensile deformation, formation of subgrains is observed concurrently with broadening of Bragg reflections shortly after the onset of plastic deformation. When the traction is terminated, stress...

Breast MR imaging: correlation of high resolution dynamic MR findings with prognostic factors

International Nuclear Information System (INIS)

Lee, Shin Ho; Cho, Nariya; Chung, Hye Kyung; Kim, Seung Ja; Cho, Kyung Soo; Moon, Woo Kyung; Cho, Joo Hee

2005-01-01

We wanted to correlate the kinetic and morphologic MR findings of invasive breast cancer with the classical and molecular prognostic factors. Eighty-seven patients with invasive ductal carcinoma NOS underwent dynamic MR imaging at 1.5 T, and with using the T1-weighted 3D FLASH technique. The morphologic findings (shape, margin, internal enhancement of the mass or the enhancement distribution and the internal enhancement of any non-mass lesion) and the kinetic findings (the initial phase and the delayed phase of the time-signal. Intensity curve) were interpreted using a ACR BI-RADS-MRI lexicon. We correlate MR findings with histopathologic prognostic factors (tumor size, lymph node status and tumor grade) and the immunohistochemically detected biomarkers (ER, PR, ρ 53, c-erbB-2, EGFR and Ki-67). Univariate and multivariate statistical analyses were than performed. Among the MR findings, a spiculated margin, rim enhancement and washout were significantly correlated with the prognostic factors. A spiculated margin was independently associated with the established predictors of a good prognosis (a lower histologic and nuclear grade, positive ER and PR) and rim enhancement was associated with a poor prognosis (a higher histologic and nuclear grade, negative ER and PR). Wash out was a independent predictor of Ki-67 activity. Some of the findings of high resolution dynamic MR imaging were associated with the prognostic factors, and these findings may predict the prognosis of breast cancer

ESSENSE: Ultra high resolution spectroscopy for the ESS

International Nuclear Information System (INIS)

Pasini, Stefano; Monkenbusch, Michael; Kozielewski, Tadeusz

2016-01-01

The instrument concept for a very high intensity neutron spin-echo spectrometer with ultimate resolution properties has been developed and submitted as an instrument proposal to ESS. Effective intensity gain factors up to 30 compared to the best current instruments are anticipated. In addition the resolution will be boosted to the technical limits by newly designed superconducting precession solenoids. The intensity gain results from the use of an optimized guide transporting the high flux from the ESS cold moderator on the one side and from the utilization of an extended wavelength frame of 8 Å yielding a multiplication of information collection rate on the other side. The instrument thus enables novel views on soft matter systems ranging from polymers, functional gels and more to to dynamics of biological molecules with relevance for MD development; the employment of new techniques for surface NSE (GINSE) may contribute to new knowledge in tribology and lubrication and other surface phenomena that currently are hampered by low intensity. New developments in “intelligent” polymers as e.g. self-healing, the properties of which depend on molecular mobility and dynamics, require observation at many 100 ns of correlation times with high intensity, which can be made with ESSENSE. (paper)

Automated method for relating regional pulmonary structure and function: integration of dynamic multislice CT and thin-slice high-resolution CT

Science.gov (United States)

Tajik, Jehangir K.; Kugelmass, Steven D.; Hoffman, Eric A.

1993-07-01

We have developed a method utilizing x-ray CT for relating pulmonary perfusion to global and regional anatomy, allowing for detailed study of structure to function relationships. A thick slice, high temporal resolution mode is used to follow a bolus contrast agent for blood flow evaluation and is fused with a high spatial resolution, thin slice mode to obtain structure- function detail. To aid analysis of blood flow, we have developed a software module, for our image analysis package (VIDA), to produce the combined structure-function image. Color coded images representing blood flow, mean transit time, regional tissue content, regional blood volume, regional air content, etc. are generated and imbedded in the high resolution volume image. A text file containing these values along with a voxel's 3-D coordinates is also generated. User input can be minimized to identifying the location of the pulmonary artery from which the input function to a blood flow model is derived. Any flow model utilizing one input and one output function can be easily added to a user selectable list. We present examples from our physiologic based research findings to demonstrate the strengths of combining dynamic CT and HRCT relative to other scanning modalities to uniquely characterize pulmonary normal and pathophysiology.

High resolution, position sensitive detector for energetic particle beams

International Nuclear Information System (INIS)

Marsh, E.P.; Strathman, M.D.; Reed, D.A.; Odom, R.W.; Morse, D.H.; Pontau, A.E.

1993-01-01

The performance and design of an imaging position sensitive, particle beam detector will be presented. The detector is minimally invasive, operates a wide dynamic range (>10 10 ), and exhibits high spatial resolution. The secondary electrons produced when a particle beam passes through a thin foil are imaged using stigmatic ion optics onto a two-dimensional imaging detector. Due to the low scattering cross section of the 6 nm carbon foil the detector is a minimal perturbation on the primary beam. A prototype detector with an image resolution of approximately 5 μm for a field of view of 1 mm has been reported. A higher resolution detector for imaging small beams (<50 μm) with an image resolution of better than 0.5 μm has since been developed and its design is presented. (orig.)

Ultraprecision motion control technique for high-resolution x-ray instrumentation

Energy Technology Data Exchange (ETDEWEB)

Shu, D.; Toellner, T. S.; Alp, E. E.

2000-07-17

With the availability of third-generation hard x-ray synchrotron radiation sources, such as the Advanced Photon Source (APS) at Argonne National Laboratory, x-ray inelastic scattering and x-ray nuclear resonant scattering provide powerful means for investigating the vibrational dynamics of a variety of materials and condensed matter systems. Novel high-resolution hard x-ray optics with meV energy resolution requires a compact positioning mechanism with 20--50-nrad angular resolution and stability. In this paper, the authors technical approach to this design challenge is presented. Sensitivity and stability test results are also discussed.

Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a downscaling approach using PRECIS

Science.gov (United States)

Bal, Prasanta Kumar; Ramachandran, A.; Geetha, R.; Bhaskaran, B.; Thirumurugan, P.; Indumathi, J.; Jayanthi, N.

2016-02-01

In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments.

A high-resolution multimode digital microscope system.

Science.gov (United States)

Salmon, Edward D; Shaw, Sidney L; Waters, Jennifer C; Waterman-Storer, Clare M; Maddox, Paul S; Yeh, Elaine; Bloom, Kerry

2013-01-01

This chapter describes the development of a high-resolution, multimode digital imaging system based on a wide-field epifluorescent and transmitted light microscope, and a cooled charge-coupled device (CCD) camera. The three main parts of this imaging system are Nikon FXA microscope, Hamamatsu C4880 cooled CCD camera, and MetaMorph digital imaging system. This chapter presents various design criteria for the instrument and describes the major features of the microscope components-the cooled CCD camera and the MetaMorph digital imaging system. The Nikon FXA upright microscope can produce high resolution images for both epifluorescent and transmitted light illumination without switching the objective or moving the specimen. The functional aspects of the microscope set-up can be considered in terms of the imaging optics, the epi-illumination optics, the transillumination optics, the focus control, and the vibration isolation table. This instrument is somewhat specialized for microtubule and mitosis studies, and it is also applicable to a variety of problems in cellular imaging, including tracking proteins fused to the green fluorescent protein in live cells. The instrument is also valuable for correlating the assembly dynamics of individual cytoplasmic microtubules (labeled by conjugating X-rhodamine to tubulin) with the dynamics of membranes of the endoplasmic reticulum (labeled with DiOC6) and the dynamics of the cell cortex (by differential interference contrast) in migrating vertebrate epithelial cells. This imaging system also plays an important role in the analysis of mitotic mutants in the powerful yeast genetic system Saccharomyces cerevisiae. Copyright © 1998 Elsevier Inc. All rights reserved.

Comparative validation of statistical and dynamical downscaling models on a dense grid in central Europe: temperature

Czech Academy of Sciences Publication Activity Database

Huth, Radan; Mikšovský, J.; Štěpánek, P.; Belda, M.; Farda, A.; Chládová, Zuzana; Pišoft, P.

2015-01-01

Roč. 120, 3-4 (2015), s. 533-553 ISSN 0177-798X R&D Projects: GA ČR(CZ) GAP209/11/2405 EU Projects: European Commission(XE) 37005 Institutional support: RVO:68378289 Keywords : statistical downscaling models * regional climate models * central Europe Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.433, year: 2015 http://link.springer.com/ article /10.1007%2Fs00704-014-1190-3

High resolution Moessbauer spectroscopy with 67Zn in metallic systems

International Nuclear Information System (INIS)

Potzel, W.

1985-01-01

Moessbauer experiments on metallic systems are described where the high resolution 93.3 keV resonance in 67 Zn is used. In the first part, the Cu-Zn alloy system is investigated and the high energy resolution of this Moessbauer transition is employed to determine small changes of the s-electron density at the 67 Zn nucleus when the Zn concentration is changed. In the second part, Zn metal is taken as an example to demonstrate that the 93.3 keV transition is also extremely sensitive to small changes of lattice dynamical effects. 7 refs., 18 figs. (author)

Transforming SWAT for continental-scale high-resolution modeling of floodplain dynamics: opportunities and challenges

Science.gov (United States)

Rajib, A.; Merwade, V.; Liu, Z.; Lane, C.; Golden, H. E.; Tavakoly, A. A.; Follum, M. L.

2017-12-01

There have been many initiatives to develop frameworks for continental-scale modeling and mapping floodplain dynamics. The choice of a model for such needs should be governed by its suitability to be executed in high performance cyber platforms, ability to integrate supporting hydraulic/hydrodynamic tools, and ability to assimilate earth observations. Furthermore, disseminating large volume of outputs for public use and interoperability with similar frameworks should be considered. Considering these factors, we have conducted a series of modeling experiments and developed a suite of cyber-enabled platforms that have transformed Soil and Water Assessment Tool (SWAT) into an appropriate model for use in a continental-scale, high resolution, near real-time flood information framework. Our first experiment uses a medium size watershed in Indiana, USA and attempts burning-in a high resolution, National Hydrography Dataset Plus(NHDPlus) into the SWAT model. This is crucial with a view to make the outputs comparable with other global/national initiatives. The second experiment is built upon the first attempt to add a modified landscape representation in the model which differentiates between the upland and floodplain processes. Our third experiment involves two separate efforts: coupling SWAT with a hydrodynamic model LISFLOOD-FP and a new generation, low complexity hydraulic model AutoRoute. We have executed the prototype "loosely-coupled" models for the Upper Mississippi-Ohio River Basin in the USA, encompassing 1 million square km drainage area and nearly 0.2 million NHDPlus river reaches. The preliminary results suggest reasonable accuracy for both streamflow and flood inundation. In this presentation, we will also showcase three cyber-enabled platforms, including SWATShare to run and calibrate large scale SWAT models online using high performance computational resources, HydroGlobe to automatically extract and assimilate multiple remotely sensed earth observations in

Monte-Carlo simulation of a high-resolution inverse geometry spectrometer on the SNS. Long Wavelength Target Station

International Nuclear Information System (INIS)

Bordallo, H.N.; Herwig, K.W.

2001-01-01

Using the Monte-Carlo simulation program McStas, we present the design principles of the proposed high-resolution inverse geometry spectrometer on the SNS-Long Wavelength Target Station (LWTS). The LWTS will provide the high flux of long wavelength neutrons at the requisite pulse rate required by the spectrometer design. The resolution of this spectrometer lies between that routinely achieved by spin echo techniques and the design goal of the high power target station backscattering spectrometer. Covering this niche in energy resolution will allow systematic studies over the large dynamic range required by many disciplines, such as protein dynamics. (author)

Dynamic patterns and ecological impacts of declining ocean pH in a high-resolution multi-year dataset.

Science.gov (United States)

Wootton, J Timothy; Pfister, Catherine A; Forester, James D

2008-12-02

Increasing global concentrations of atmospheric CO(2) are predicted to decrease ocean pH, with potentially severe impacts on marine food webs, but empirical data documenting ocean pH over time are limited. In a high-resolution dataset spanning 8 years, pH at a north-temperate coastal site declined with increasing atmospheric CO(2) levels and varied substantially in response to biological processes and physical conditions that fluctuate over multiple time scales. Applying a method to link environmental change to species dynamics via multispecies Markov chain models reveals strong links between in situ benthic species dynamics and variation in ocean pH, with calcareous species generally performing more poorly than noncalcareous species in years with low pH. The models project the long-term consequences of these dynamic changes, which predict substantial shifts in the species dominating the habitat as a consequence of both direct effects of reduced calcification and indirect effects arising from the web of species interactions. Our results indicate that pH decline is proceeding at a more rapid rate than previously predicted in some areas, and that this decline has ecological consequences for near shore benthic ecosystems.

Downscaling, 2-way Nesting, and Data Assimilative Modeling in Coastal and Shelf Waters of the U.S. Mid-Atlantic Bight and Gulf of Maine

Science.gov (United States)

Wilkin, J.; Levin, J.; Lopez, A.; Arango, H.

2016-02-01

Coastal ocean models that downscale output from basin and global scale models are widely used to study regional circulation at enhanced resolution and locally important ecosystem, biogeochemical, and geomorphologic processes. When operated as now-cast or forecast systems, these models offer predictions that assist decision-making for numerous maritime applications. We describe such a system for shelf waters of the Mid-Atlantic Bight (MAB) and Gulf of Maine (GoM) where the MARACOOS and NERACOOS associations of U.S. IOOS operate coastal ocean observing systems that deliver a dense observation set using CODAR HF-radar, autonomous underwater glider vehicles (AUGV), telemetering moorings, and drifting buoys. Other U.S. national and global observing systems deliver further sustained observations from moorings, ships, profiling floats, and a constellation of satellites. Our MAB and GoM re-analysis and forecast system uses the Regional Ocean Modeling System (ROMS; myroms.org) with 4-dimensional Variational (4D-Var) data assimilation to adjust initial conditions, boundary conditions, and surface forcing in each analysis cycle. Data routinely assimilated include CODAR velocities, altimeter satellite sea surface height (with coastal corrections), satellite temperature, in situ CTD data from AUGV and ships (NMFS Ecosystem Monitoring voyages), and all in situ data reported via the WMO GTS network. A climatological data assimilative analysis of hydrographic and long-term mean velocity observations specifies the regional Mean Dynamic Topography that augments altimeter sea level anomaly data and is also used to adjust boundary condition biases that would otherwise be introduced in the process of downscaling from global models. System performance is described with respect to the impact of satellite, CODAR and in situ observations on analysis skill. Results from a 2-way nested modeling system that adds enhanced resolution over the NSF OOI Pioneer Array in the central MAB are also

High-resolution dynamic imaging and quantitative analysis of lung cancer xenografts in nude mice using clinical PET/CT.

Science.gov (United States)

Wang, Ying Yi; Wang, Kai; Xu, Zuo Yu; Song, Yan; Wang, Chu Nan; Zhang, Chong Qing; Sun, Xi Lin; Shen, Bao Zhong

2017-08-08

Considering the general application of dedicated small-animal positron emission tomography/computed tomography is limited, an acceptable alternative in many situations might be clinical PET/CT. To estimate the feasibility of using clinical PET/CT with [F-18]-fluoro-2-deoxy-D-glucose for high-resolution dynamic imaging and quantitative analysis of cancer xenografts in nude mice. Dynamic clinical PET/CT scans were performed on xenografts for 60 min after injection with [F-18]-fluoro-2-deoxy-D-glucose. Scans were reconstructed with or without SharpIR method in two phases. And mice were sacrificed to extracting major organs and tumors, using ex vivo γ-counting as a reference. Strikingly, we observed that the image quality and the correlation between the all quantitive data from clinical PET/CT and the ex vivo counting was better with the SharpIR reconstructions than without. Our data demonstrate that clinical PET/CT scanner with SharpIR reconstruction is a valuable tool for imaging small animals in preclinical cancer research, offering dynamic imaging parameters, good image quality and accurate data quatification.

Localization-based super-resolution imaging meets high-content screening.

Science.gov (United States)

Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

2017-12-01

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

Downscaling climate model output for water resources impacts assessment (Invited)

Science.gov (United States)

Maurer, E. P.; Pierce, D. W.; Cayan, D. R.

2013-12-01

Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.

Improvement of downscaled rainfall and temperature across generations over the Western Himalayan region of India

Science.gov (United States)

Das, L.; Dutta, M.; Akhter, J.; Meher, J. K.

2016-12-01

It is a challenging task to create station level (local scale) climate change information over the mountainous locations of Western Himalayan Region (WHR) in India because of limited data availability and poor data quality. In the present study, missing values of station data were handled through Multiple Imputation Chained Equation (MICE) technique. Finally 22 numbers of rain gauge and 16 number of temperature station data having continuous record during 1901­2005 and 1969­2009 period respectively were considered as reference stations for developing downscaled rainfall and temperature time series from five commonly available GCMs in the IPCC's different generation assessment reports namely 2nd, 3rd, 4th and 5th hereafter known as SAR, TAR, AR4 and AR5 respectively. Downscaled models were developed using the combined data from the ERA-interim reanalysis and GCMs historical runs (in spite of forcing were not identical in different generation) as predictor and station level rainfall and temperature as predictands. Station level downscaled rainfall and temperature time series were constructed for five GCMs available in each generation. Regional averaged downscaled time series comprising of all stations was prepared for each model and generation and the downscaled results were compared with observed time series. Finally an Overall Model Improvement Index (OMII) was developed using the downscaling results, which was used to investigate the model improvement across generations as well as the improvement of downscaling results obtained from the Empirical Statistical Downscaling (ESD) methods. In case of temperature, models have improved from SAR to AR5 over the study area. In all most all the GCMs TAR is showing worst performance over the WHR by considering the different statistical indices used in this study. In case of precipitation, no model has shown gradual improvement from SAR to AR5 both for interpolated and downscaled values.

High resolution, position sensitive detector for energetic particle beams

Energy Technology Data Exchange (ETDEWEB)

Marsh, E P [Charles Evans and Associates, Redwood City, CA (United States); Strathman, M D [Charles Evans and Associates, Redwood City, CA (United States); Reed, D A [Charles Evans and Associates, Redwood City, CA (United States); Odom, R W [Charles Evans and Associates, Redwood City, CA (United States); Morse, D H [Sandia National Labs., Livermore, CA (United States); Pontau, A E [Sandia National Labs., Livermore, CA (United States)

1993-05-01

The performance and design of an imaging position sensitive, particle beam detector will be presented. The detector is minimally invasive, operates a wide dynamic range (>10[sup 10]), and exhibits high spatial resolution. The secondary electrons produced when a particle beam passes through a thin foil are imaged using stigmatic ion optics onto a two-dimensional imaging detector. Due to the low scattering cross section of the 6 nm carbon foil the detector is a minimal perturbation on the primary beam. A prototype detector with an image resolution of approximately 5 [mu]m for a field of view of 1 mm has been reported. A higher resolution detector for imaging small beams (<50 [mu]m) with an image resolution of better than 0.5 [mu]m has since been developed and its design is presented. (orig.)

ANL high resolution injector

International Nuclear Information System (INIS)

Minehara, E.; Kutschera, W.; Hartog, P.D.; Billquist, P.

1985-01-01

The ANL (Argonne National Laboratory) high-resolution injector has been installed to obtain higher mass resolution and higher preacceleration, and to utilize effectively the full mass range of ATLAS (Argonne Tandem Linac Accelerator System). Preliminary results of the first beam test are reported briefly. The design and performance, in particular a high-mass-resolution magnet with aberration compensation, are discussed. 7 refs., 5 figs., 2 tabs

High-resolution flood modeling of urban areas using MSN_Flood

Directory of Open Access Journals (Sweden)

Michael Hartnett

2017-07-01

Full Text Available Although existing hydraulic models have been used to simulate and predict urban flooding, most of these models are inadequate due to the high spatial resolution required to simulate flows in urban floodplains. Nesting high-resolution subdomains within coarser-resolution models is an efficient solution for enabling simultaneous calculation of flooding due to tides, surges, and high river flows. MSN_Flood has been developed to incorporate moving boundaries around nested domains, permitting alternate flooding and drying along the boundary and in the interior of the domain. Ghost cells adjacent to open boundary cells convert open boundaries, in effect, into internal boundaries. The moving boundary may be multi-segmented and non-continuous, with recirculating flow across the boundary. When combined with a bespoke adaptive interpolation scheme, this approach facilitates a dynamic internal boundary. Based on an alternating-direction semi-implicit finite difference scheme, MSN_Flood was used to hindcast a major flood event in Cork City resulting from the combined pressures of fluvial, tidal, and storm surge processes. The results show that the model is computationally efficient, as the 2-m high-resolution nest is used only in the urban flooded region. Elsewhere, lower-resolution nests are used. The results also show that the model is highly accurate when compared with measured data. The model is capable of incorporating nested sub-domains when the nested boundary is multi-segmented and highly complex with lateral gradients of elevation and velocities. This is a major benefit when modelling urban floodplains at very high resolution.

Correlation between High Resolution Dynamic MR Features and Prognostic Factors in Breast Cancer

International Nuclear Information System (INIS)

Lee, Shin Ho; Cho, Nariya; Kim, Seung Ja; Cho, Kyung Soo; Ko, Eun Sook; Moon, Woo Kyung; Cha, Joo Hee

2008-01-01

To correlate high resolution dynamic MR features with prognostic factors in breast cancer. One hundred and ninety-four women with invasive ductal carcinomas underwent dynamic MR imaging using T1-weighted three dimensional fast low-angle shot (3D-FLASH) sequence within two weeks prior to surgery. Morphological and kinetic MR features were determined based on the breast imaging and reporting data system (BI-RADS) MR imaging lexicon. Histological specimens were analyzed for tumor size, axillary lymph node status, histological grade, expression of estrogen receptor (ER), expression of progesterone receptor (PR), and expression of p53, c-erbB-2, and Ki-67. Correlations between the MR features and prognostic factors were determined using the Pearson x 2 test, linear-by-linear association, and logistic regression analysis. By multivariate analysis, a spiculated margin was a significant, independent predictor of a lower histological grade (p < 0.001), and lower expression of Ki-67 (p = 0.007). Rim enhancement was significant, independent predictor of a higher histological grade (p < 0.001), negative expression of ER (p 0.001), negative expression of PR (p < 0.001) and a larger tumor size (p = 0.006). A washout curve may predict a higher level of Ki-67 (p = 0.05). Most of the parameters of the initial enhancement phase cannot predict the status of the prognostic factors. Only the enhancement ratio may predict a larger tumor size (p 0.05). Of the BI-RADS-MR features, a spiculated margin may predict favorable prognosis, whereas rim enhancement or washout may predict unfavorable prognosis of breast cancer

Performance of a high resolution cavity beam position monitor system

Science.gov (United States)

Walston, Sean; Boogert, Stewart; Chung, Carl; Fitsos, Pete; Frisch, Joe; Gronberg, Jeff; Hayano, Hitoshi; Honda, Yosuke; Kolomensky, Yury; Lyapin, Alexey; Malton, Stephen; May, Justin; McCormick, Douglas; Meller, Robert; Miller, David; Orimoto, Toyoko; Ross, Marc; Slater, Mark; Smith, Steve; Smith, Tonee; Terunuma, Nobuhiro; Thomson, Mark; Urakawa, Junji; Vogel, Vladimir; Ward, David; White, Glen

2007-07-01

It has been estimated that an RF cavity Beam Position Monitor (BPM) could provide a position measurement resolution of less than 1 nm. We have developed a high resolution cavity BPM and associated electronics. A triplet comprised of these BPMs was installed in the extraction line of the Accelerator Test Facility (ATF) at the High Energy Accelerator Research Organization (KEK) for testing with its ultra-low emittance beam. The three BPMs were each rigidly mounted inside an alignment frame on six variable-length struts which could be used to move the BPMs in position and angle. We have developed novel methods for extracting the position and tilt information from the BPM signals including a robust calibration algorithm which is immune to beam jitter. To date, we have demonstrated a position resolution of 15.6 nm and a tilt resolution of 2.1 μrad over a dynamic range of approximately ±20 μm.

Comparison of past and future Mediterranean high and low extremes of precipitation and river flow projected using different statistical downscaling methods

Directory of Open Access Journals (Sweden)

P. Quintana-Seguí

2011-05-01

Full Text Available The extremes of precipitation and river flow obtained using three different statistical downscaling methods applied to the same regional climate simulation have been compared. The methods compared are the anomaly method, quantile mapping and a weather typing. The hydrological model used in the study is distributed and it is applied to the Mediterranean basins of France. The study shows that both quantile mapping and weather typing methods are able to reproduce the high and low precipitation extremes in the region of interest. The study also shows that when the hydrological model is forced with these downscaled data, there are important differences in the outputs. This shows that the model amplifies the differences and that the downscaling of other atmospheric variables might be very relevant when simulating river discharges. In terms of river flow, the method of the anomalies, which is very simple, performs better than expected. The methods produce qualitatively similar future scenarios of the extremes of river flow. However, quantitatively, there are still significant differences between them for each individual gauging station. According to these scenarios, it is expected that in the middle of the 21st century (2035–2064, the monthly low flows will have diminished almost everywhere in the region of our study by as much as 20 %. Regarding high flows, there will be important increases in the area of the Cévennes, which is already seriously affected by flash-floods. For some gauging stations in this area, the frequency of what was a 10-yr return flood at the end of the 20th century is expected to increase, with such return floods then occurring every two years in the middle of the 21st century. Similarly, the 10-yr return floods at that time are expected to carry 100 % more water than the 10-yr return floods experienced at the end of the 20th century. In the northern part of the Rhône basin, these extremes will be reduced.

Ultra-high resolution AMOLED

Science.gov (United States)

Wacyk, Ihor; Prache, Olivier; Ghosh, Amal

2011-06-01

AMOLED microdisplays continue to show improvement in resolution and optical performance, enhancing their appeal for a broad range of near-eye applications such as night vision, simulation and training, situational awareness, augmented reality, medical imaging, and mobile video entertainment and gaming. eMagin's latest development of an HDTV+ resolution technology integrates an OLED pixel of 3.2 × 9.6 microns in size on a 0.18 micron CMOS backplane to deliver significant new functionality as well as the capability to implement a 1920×1200 microdisplay in a 0.86" diagonal area. In addition to the conventional matrix addressing circuitry, the HDTV+ display includes a very lowpower, low-voltage-differential-signaling (LVDS) serialized interface to minimize cable and connector size as well as electromagnetic emissions (EMI), an on-chip set of look-up-tables for digital gamma correction, and a novel pulsewidth- modulation (PWM) scheme that together with the standard analog control provides a total dimming range of 0.05cd/m2 to 2000cd/m2 in the monochrome version. The PWM function also enables an impulse drive mode of operation that significantly reduces motion artifacts in high speed scene changes. An internal 10-bit DAC ensures that a full 256 gamma-corrected gray levels are available across the entire dimming range, resulting in a measured dynamic range exceeding 20-bits. This device has been successfully tested for operation at frame rates ranging from 30Hz up to 85Hz. This paper describes the operational features and detailed optical and electrical test results for the new AMOLED WUXGA resolution microdisplay.

Statistical downscaling of sea-surface wind over the Peru-Chile upwelling region: diagnosing the impact of climate change from the IPSL-CM4 model

Energy Technology Data Exchange (ETDEWEB)

Goubanova, K. [CNES/CNRS/IRD/UPS, Laboratoire d' Etudes en Geophysique et Oceanographie Spatiale, Toulouse (France); Instituto del Mar del Peru, Callao (Peru); Echevin, V.; Terray, P. [IPSL/UPMC/IRD, Laboratoire d' Oceanographie et de Climatologie, Experimentation et Approches Numeriques, Paris (France); Dewitte, B. [CNES/CNRS/IRD/UPS, Laboratoire d' Etudes en Geophysique et Oceanographie Spatiale, Toulouse (France); Instituto del Mar del Peru, Callao (Peru); Instituto Geofisico del Peru, Lima (Peru); Codron, F. [UPMC/CNRS, Laboratoire de Meteorologie Dynamique, Paris (France); Takahashi, K. [Instituto Geofisico del Peru, Lima (Peru); Vrac, M. [IPSL/CNRS/CEA/UVSQ, Laboratoire des Sciences du Climat et de l' Environnement, Gif-sur-Yvette (France)

2011-04-15

The key aspect of the ocean circulation off Peru-Chile is the wind-driven upwelling of deep, cold, nutrient-rich waters that promote a rich marine ecosystem. It has been suggested that global warming may be associated with an intensification of upwelling-favorable winds. However, the lack of high-resolution long-term observations has been a limitation for a quantitative analysis of this process. In this study, we use a statistical downscaling method to assess the regional impact of climate change on the sea-surface wind over the Peru-Chile upwelling region as simulated by the global coupled general circulation model IPSL-CM4. Taking advantage of the high-resolution QuikSCAT wind product and of the NCEP reanalysis data, a statistical model based on multiple linear regressions is built for the daily mean meridional and zonal wind at 10 m for the period 2000-2008. The large-scale 10 m wind components and sea level pressure are used as regional circulation predictors. The skill of the downscaling method is assessed by comparing with the surface wind derived from the ERS satellite measurements, with in situ wind observations collected by ICOADS and through cross-validation. It is then applied to the outputs of the IPSL-CM4 model over stabilized periods of the pre-industrial, 2 x CO{sub 2} and 4 x CO{sub 2} IPCC climate scenarios. The results indicate that surface along-shore winds off central Chile (off central Peru) experience a significant intensification (weakening) during Austral winter (summer) in warmer climates. This is associated with a general decrease in intra-seasonal variability. (orig.)

High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

Science.gov (United States)

Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

2017-12-01

For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China

Science.gov (United States)

Zhao, Yu; Zhou, Yaduan; Mao, Pan; Zhang, Jie

2017-04-01

Improved emission inventories combining detailed source information are crucial for better understanding the atmospheric chemistry and effectively making emission control policies using air quality simulation, particularly at regional or local scales. With the downscaled inventories directly applied, chemical transport model might not be able to reproduce the authentic evolution of atmospheric pollution processes at small spatial scales. Using the bottom-up approach, a high-resolution emission inventory was developed for Jiangsu China, including SO2, NOx, CO, NH3, volatile organic compounds (VOCs), total suspended particulates (TSP), PM10, PM2.5, black carbon (BC), organic carbon (OC), and CO2. The key parameters relevant to emission estimation for over 6000 industrial sources were investigated, compiled and revised at plant level based on various data sources and on-site survey. As a result, the emission fractions of point sources were significantly elevated for most species. The improvement of this provincial inventory was evaluated through comparisons with other inventories at larger spatial scales, using satellite observation and air quality modeling. Compared to the downscaled Multi-resolution Emission Inventory for China (MEIC), the spatial distribution of NOX emissions in our provincial inventory was more consistent with summer tropospheric NO2 VCDs observed from OMI, particularly for the grids with moderate emission levels, implying the improved emission estimation for small and medium industrial plants by this work. Three inventories (national, regional, and provincial by this work) were applied in the Models-3/Community Multi-scale Air Quality (CMAQ) system for southern Jiangsu October 2012, to evaluate the model performances with different emission inputs. The best agreement between available ground observation and simulation was found when the provincial inventory was applied, indicated by the smallest normalized mean bias (NMB) and normalized mean

Analysis of stationary fuel cell dynamic ramping capabilities and ultra capacitor energy storage using high resolution demand data

Science.gov (United States)

Meacham, James R.; Jabbari, Faryar; Brouwer, Jacob; Mauzey, Josh L.; Samuelsen, G. Scott

Current high temperature fuel cell (HTFC) systems used for stationary power applications (in the 200-300 kW size range) have very limited dynamic load following capability or are simply base load devices. Considering the economics of existing electric utility rate structures, there is little incentive to increase HTFC ramping capability beyond 1 kWs -1 (0.4% s -1). However, in order to ease concerns about grid instabilities from utility companies and increase market adoption, HTFC systems will have to increase their ramping abilities, and will likely have to incorporate electrical energy storage (EES). Because batteries have low power densities and limited lifetimes in highly cyclic applications, ultra capacitors may be the EES medium of choice. The current analyses show that, because ultra capacitors have a very low energy storage density, their integration with HTFC systems may not be feasible unless the fuel cell has a ramp rate approaching 10 kWs -1 (4% s -1) when using a worst-case design analysis. This requirement for fast dynamic load response characteristics can be reduced to 1 kWs -1 by utilizing high resolution demand data to properly size ultra capacitor systems and through demand management techniques that reduce load volatility.

High resolution extremity CT for biomechanics modeling

International Nuclear Information System (INIS)

Ashby, A.E.; Brand, H.; Hollerbach, K.; Logan, C.M.; Martz, H.E.

1995-01-01

With the advent of ever more powerful computing and finite element analysis (FEA) capabilities, the bone and joint geometry detail available from either commercial surface definitions or from medical CT scans is inadequate. For dynamic FEA modeling of joints, precise articular contours are necessary to get appropriate contact definition. In this project, a fresh cadaver extremity was suspended in parafin in a lucite cylinder and then scanned with an industrial CT system to generate a high resolution data set for use in biomechanics modeling

High resolution extremity CT for biomechanics modeling

Energy Technology Data Exchange (ETDEWEB)

Ashby, A.E.; Brand, H.; Hollerbach, K.; Logan, C.M.; Martz, H.E.

1995-09-23

With the advent of ever more powerful computing and finite element analysis (FEA) capabilities, the bone and joint geometry detail available from either commercial surface definitions or from medical CT scans is inadequate. For dynamic FEA modeling of joints, precise articular contours are necessary to get appropriate contact definition. In this project, a fresh cadaver extremity was suspended in parafin in a lucite cylinder and then scanned with an industrial CT system to generate a high resolution data set for use in biomechanics modeling.

An intercomparison of a large ensemble of statistical downscaling methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment

Science.gov (United States)

Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of downscaling experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open downscaling intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to downscale precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data

Bioclim Deliverable D6a: regional climatic characteristics for the European sites at specific times: the dynamical down-scaling

International Nuclear Information System (INIS)

2003-01-01

The overall aim of BIOCLIM is to assess the possible long-term impacts due to climate change on the safety of radioactive waste repositories in deep formations. This aim is addressed through the following specific objectives: - Development of practical and innovative strategies for representing sequential climatic changes to the geosphere-biosphere system for existing sites over central Europe, addressing the timescale of one million years, which is relevant to the geological disposal of radioactive waste. - Exploration and evaluation of the potential effects of climate change on the nature of the biosphere systems used to assess the environmental impact. - Dissemination of information on the new methodologies and the results obtained from the project among the international waste management community for use in performance assessments of potential or planned radioactive waste repositories. The BIOCLIM project is designed to advance the state-of-the-art of biosphere modelling for use in Performance Assessments. Therefore, two strategies are developed for representing sequential climatic changes to geosphere-biosphere systems. The hierarchical strategy successively uses a hierarchy of climate models. These models vary from simple 2-D models, which simulate interactions between a few aspects of the Earth system at a rough surface resolution, through General Circulation Model (GCM) and vegetation model, which simulate in great detail the dynamics and physics of the atmosphere, ocean and biosphere, to regional models, which focus on the European regions and sites of interest. Moreover, rule-based and statistical down-scaling procedures are also considered. Comparisons are provided in terms of climate and vegetation cover at the selected times and for the study regions. The integrated strategy consists of using integrated climate models, representing all the physical mechanisms important for long-term continuous climate variations, to simulate the climate evolution over

Ultra-high-resolution inelastic X-ray scattering at high-repetition-rate self-seeded X-ray free-electron lasers

Energy Technology Data Exchange (ETDEWEB)

Chubar, Oleg [Brookhaven National Laboratory, Upton, NY 11973 (United States); Geloni, Gianluca [European X-ray Free-Electron Laser, Albert-Einstein-Ring 19, 22761 Hamburg (Germany); Kocharyan, Vitali [Deutsches Elektronen-Synchrotron, 22761 Hamburg (Germany); Madsen, Anders [European X-ray Free-Electron Laser, Albert-Einstein-Ring 19, 22761 Hamburg (Germany); Saldin, Evgeni; Serkez, Svitozar [Deutsches Elektronen-Synchrotron, 22761 Hamburg (Germany); Shvyd’ko, Yuri, E-mail: shvydko@aps.anl.gov [Argonne National Laboratory, Argonne, IL 60439 (United States); Sutter, John [Diamond Light Source Ltd, Didcot OX11 0DE (United Kingdom)

2016-02-12

This article explores novel opportunities for ultra-high-resolution inelastic X-ray scattering (IXS) at high-repetition-rate self-seeded XFELs. These next-generation light sources are promising a more than three orders of magnitude increase in average spectral flux compared with what is possible with storage-ring-based radiation sources. In combination with the advanced IXS spectrometer described here, this may become a real game-changer for ultra-high-resolution X-ray spectroscopies, and hence for the studies of dynamics in condensed matter systems. Inelastic X-ray scattering (IXS) is an important tool for studies of equilibrium dynamics in condensed matter. A new spectrometer recently proposed for ultra-high-resolution IXS (UHRIX) has achieved 0.6 meV and 0.25 nm{sup −1} spectral and momentum-transfer resolutions, respectively. However, further improvements down to 0.1 meV and 0.02 nm{sup −1} are required to close the gap in energy–momentum space between high- and low-frequency probes. It is shown that this goal can be achieved by further optimizing the X-ray optics and by increasing the spectral flux of the incident X-ray pulses. UHRIX performs best at energies from 5 to 10 keV, where a combination of self-seeding and undulator tapering at the SASE-2 beamline of the European XFEL promises up to a 100-fold increase in average spectral flux compared with nominal SASE pulses at saturation, or three orders of magnitude more than what is possible with storage-ring-based radiation sources. Wave-optics calculations show that about 7 × 10{sup 12} photons s{sup −1} in a 90 µeV bandwidth can be achieved on the sample. This will provide unique new possibilities for dynamics studies by IXS.

High resolution and simultaneous monitoring of airborne radionuclides

International Nuclear Information System (INIS)

Abe, T.; Yamaguchi, Y.; Muguntha Manikandan, N.; Komura, K.

2005-01-01

By using 11 extremely low background Ge detectors at Ogoya Underground Laboratory, it became possible to investigate temporal variations of airborne 212 Pb (T 1/2 =10.6 h) along with 210 Pb and 7 Be with order of magnitude higher time resolution. Then, we have measured airborne nuclides at three monitoring points, (1) roof of our laboratory (LLRL; 40 m ASL), (2) Shinshiku Plateau (640 m ASL) located about 8 km from LLRL as a comparison of vertical distribution, and (3) Hegura Island (10 m ASL) at about 50 km from Wajima located north of Noto Peninsula facing on the Sea of Japan (about 180 km to the north-northeast of LLRL), to investigate influence of Asian continent. Airborne nuclides were collected by high volume air samplers at intervals of a few hours at either two or three points simultaneously. In the same manner, high resolution monitoring was carried out also at the time of passage of typhoon and cold front. In this study, we observed drastic temporal variations of airborne radionuclides and correlations of multiple monitoring points. The results indicate that high resolution and simultaneous monitoring is very useful to understand dynamic state of variations of airborne nuclides due to short and long-term air-mass movement. (author)

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.

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

Science.gov (United States)

Haylock, M. R.

2011-10-01

Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961-2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed. The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

Directory of Open Access Journals (Sweden)

M. R. Haylock

2011-10-01

Full Text Available Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961–2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed.

The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.

Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures

Science.gov (United States)

Dabanlı, İsmail; Şen, Zekai

2018-04-01

The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.

Neutron spin echo and high resolution inelastic spectroscopy

International Nuclear Information System (INIS)

Mezei, F.; Hungarian Academy of Sciences, Budapest. Central Research Inst. for Physics)

1982-01-01

The principles of neutrons spin echo (NSE) technique are considered. It is shown that the basis of NSE principle is a single step measurement of the change of the neutron velocity in the scattering process. The backscattering soectroscopy and the NSE techniques are compared. The NSF spectrometer is described. It is shown that 0.5 MeV energy resolution achieved in the NSE experiment is about 40 times superior to those achieved by the other techniques. The NSE technique has the unique feature that provides high resolution in neutron energy change independently of the monochromatization of the beam. The NSE instrument not only covers a wider dynamic range on a pulsed source that on a continuous one, but also collects data more efficiently

High-Spatial- and High-Temporal-Resolution Dynamic Contrast-enhanced MR Breast Imaging with Sweep Imaging with Fourier Transformation: A Pilot Study

Science.gov (United States)

Benson, John C.; Idiyatullin, Djaudat; Snyder, Angela L.; Snyder, Carl J.; Hutter, Diane; Everson, Lenore I.; Eberly, Lynn E.; Nelson, Michael T.; Garwood, Michael

2015-01-01

Purpose To report the results of sweep imaging with Fourier transformation (SWIFT) magnetic resonance (MR) imaging for diagnostic breast imaging. Materials and Methods Informed consent was obtained from all participants under one of two institutional review board–approved, HIPAA-compliant protocols. Twelve female patients (age range, 19–54 years; mean age, 41.2 years) and eight normal control subjects (age range, 22–56 years; mean age, 43.2 years) enrolled and completed the study from January 28, 2011, to March 5, 2013. Patients had previous lesions that were Breast Imaging Reporting and Data System 4 and 5 based on mammography and/or ultrasonographic imaging. Contrast-enhanced SWIFT imaging was completed by using a 4-T research MR imaging system. Noncontrast studies were completed in the normal control subjects. One of two sized single-breast SWIFT-compatible transceiver coils was used for nine patients and five controls. Three patients and five control subjects used a SWIFT-compatible dual breast coil. Temporal resolution was 5.9–7.5 seconds. Spatial resolution was 1.00 mm isotropic, with later examinations at 0.67 mm isotropic, and dual breast at 1.00 mm or 0.75 mm isotropic resolution. Results Two nonblinded breast radiologists reported SWIFT image findings of normal breast tissue, benign fibroadenomas (six of six lesions), and malignant lesions (10 of 12 lesions) concordant with other imaging modalities and pathologic reports. Two lesions in two patients were not visualized because of coil field of view. The images yielded by SWIFT showed the presence and extent of known breast lesions. Conclusion The SWIFT technique could become an important addition to breast imaging modalities because it provides high spatial resolution at all points during the dynamic contrast-enhanced examination. © RSNA, 2014 PMID:25247405

Navigating Earthquake Physics with High-Resolution Array Back-Projection

Science.gov (United States)

Meng, Lingsen

Understanding earthquake source dynamics is a fundamental goal of geophysics. Progress toward this goal has been slow due to the gap between state-of-art earthquake simulations and the limited source imaging techniques based on conventional low-frequency finite fault inversions. Seismic array processing is an alternative source imaging technique that employs the higher frequency content of the earthquakes and provides finer detail of the source process with few prior assumptions. While the back-projection provides key observations of previous large earthquakes, the standard beamforming back-projection suffers from low resolution and severe artifacts. This thesis introduces the MUSIC technique, a high-resolution array processing method that aims to narrow the gap between the seismic observations and earthquake simulations. The MUSIC is a high-resolution method taking advantage of the higher order signal statistics. The method has not been widely used in seismology yet because of the nonstationary and incoherent nature of the seismic signal. We adapt MUSIC to transient seismic signal by incorporating the Multitaper cross-spectrum estimates. We also adopt a "reference window" strategy that mitigates the "swimming artifact," a systematic drift effect in back projection. The improved MUSIC back projections allow the imaging of recent large earthquakes in finer details which give rise to new perspectives on dynamic simulations. In the 2011 Tohoku-Oki earthquake, we observe frequency-dependent rupture behaviors which relate to the material variation along the dip of the subduction interface. In the 2012 off-Sumatra earthquake, we image the complicated ruptures involving orthogonal fault system and an usual branching direction. This result along with our complementary dynamic simulations probes the pressure-insensitive strength of the deep oceanic lithosphere. In another example, back projection is applied to the 2010 M7 Haiti earthquake recorded at regional distance. The

Mechanical behavior and high-resolution EBSD investigation of the microstructural evolution in AISI 321 stainless steel under dynamic loading condition

International Nuclear Information System (INIS)

Tiamiyu, A.A.; Eskandari, M.; Sanayei, Mohsen; Odeshi, A.G.; Szpunar, J.A.

2016-01-01

The impact response of three regions (top, mid and center) across the thickness of AISI 321 austenitic stainless steel plate at high strain rates (>6000 s −1 ) was studied using the split Hopkinson pressure bar system. The result shows that texture and stored energy heterogeneity across plate thickness influenced the mechanical responses of the investigated steel in these regions. Microstructural evaluation using high-resolution electron backscattered diffraction (HR-EBSD) analysis showed that strengthening in AISI 321 steel originates from the evolution of strain-induced martensite and formation of nano-carbides in addition to plastic deformation by mechanical twinning and slip. This resulted in a desirable combination of high strength and good ductility (approx. 2000 MPa at 0.42 true strain). Phase transformation, dynamic recrystallization and formation of nano-carbides were confirmed within the adiabatic shear band (ASB) region. The average dynamic recrystallized (DRX) grain size in the shear band region is 0.28 µm in comparison to grain size of 15 µm outside the shear bands. The nano-sized grain inside the shear bands is proposed to form by rotational dynamic recrystallization. A comparative study of the alloy's behavior under dynamic and quasi-static compression shows that the stability of austenite is higher at high strain rates and lower at a low strain rate. The strength in the dynamically impacted specimen is compromised as a result of the suppressed evolution of strain-induced martensite and mechanical twin. Martensitic transformation under both loading conditions follows the FCC É£-austenite→BCC ά-martensite kinetic path and both phases obey the Kurdjumov-Sachs' {(111)É£||(110)ά and <−101>É£||<1–11>ά} orientation relationship.

Mechanical behavior and high-resolution EBSD investigation of the microstructural evolution in AISI 321 stainless steel under dynamic loading condition

Energy Technology Data Exchange (ETDEWEB)

Tiamiyu, A.A., E-mail: ahmed.tiamiyu@usask.ca [Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Sask. (Canada); Eskandari, M. [Department of Materials Science & Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz (Iran, Islamic Republic of); Sanayei, Mohsen; Odeshi, A.G.; Szpunar, J.A. [Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Sask. (Canada)

2016-09-15

The impact response of three regions (top, mid and center) across the thickness of AISI 321 austenitic stainless steel plate at high strain rates (>6000 s{sup −1}) was studied using the split Hopkinson pressure bar system. The result shows that texture and stored energy heterogeneity across plate thickness influenced the mechanical responses of the investigated steel in these regions. Microstructural evaluation using high-resolution electron backscattered diffraction (HR-EBSD) analysis showed that strengthening in AISI 321 steel originates from the evolution of strain-induced martensite and formation of nano-carbides in addition to plastic deformation by mechanical twinning and slip. This resulted in a desirable combination of high strength and good ductility (approx. 2000 MPa at 0.42 true strain). Phase transformation, dynamic recrystallization and formation of nano-carbides were confirmed within the adiabatic shear band (ASB) region. The average dynamic recrystallized (DRX) grain size in the shear band region is 0.28 µm in comparison to grain size of 15 µm outside the shear bands. The nano-sized grain inside the shear bands is proposed to form by rotational dynamic recrystallization. A comparative study of the alloy's behavior under dynamic and quasi-static compression shows that the stability of austenite is higher at high strain rates and lower at a low strain rate. The strength in the dynamically impacted specimen is compromised as a result of the suppressed evolution of strain-induced martensite and mechanical twin. Martensitic transformation under both loading conditions follows the FCC É£-austenite→BCC ά-martensite kinetic path and both phases obey the Kurdjumov-Sachs' {(111)É£||(110)ά and <−101>É£||<1–11>ά} orientation relationship.

Assessment of prediction skill in equatorial Pacific Ocean in high resolution model of CFS

Science.gov (United States)

Arora, Anika; Rao, Suryachandra A.; Pillai, Prasanth; Dhakate, Ashish; Salunke, Kiran; Srivastava, Ankur

2018-01-01

The effect of increasing atmospheric resolution on prediction skill of El Niño southern oscillation phenomenon in climate forecast system model is explored in this paper. Improvement in prediction skill for sea surface temperature (SST) and winds at all leads compared to low resolution model in the tropical Indo-Pacific basin is observed. High resolution model is able to capture extreme events reasonably well. As a result, the signal to noise ratio is improved in the high resolution model. However, spring predictability barrier (SPB) for summer months in Nino 3 and Nino 3.4 region is stronger in high resolution model, in spite of improvement in overall prediction skill and dynamics everywhere else. Anomaly correlation coefficient of SST in high resolution model with observations in Nino 3.4 region targeting boreal summer months when predicted at lead times of 3-8 months in advance decreased compared its lower resolution counterpart. It is noted that higher variance of winds predicted in spring season over central equatorial Pacific compared to observed variance of winds results in stronger than normal response on subsurface ocean, hence increases SPB for boreal summer months in high resolution model.

Using High-Resolution Data to Assess Land Use Impact on Nitrate Dynamics in East African Tropical Montane Catchments

Science.gov (United States)

Jacobs, Suzanne R.; Weeser, Björn; Guzha, Alphonce C.; Rufino, Mariana C.; Butterbach-Bahl, Klaus; Windhorst, David; Breuer, Lutz

2018-03-01

Land use change alters nitrate (NO3-N) dynamics in stream water by changing nitrogen cycling, nutrient inputs, uptake and hydrological flow paths. There is little empirical evidence of these processes for East Africa. We collected a unique 2 year high-resolution data set to assess the effects of land use (i.e., natural forest, smallholder agriculture and commercial tea plantations) on NO3-N dynamics in three subcatchments within a headwater catchment in the Mau Forest Complex, Kenya's largest tropical montane forest. The natural forest subcatchment had the lowest NO3-N concentrations (0.44 ± 0.043 mg N L-1) with no seasonal variation. NO3-N concentrations in the smallholder agriculture (1.09 ± 0.11 mg N L-1) and tea plantation (2.13 ± 0.19 mg N L-1) subcatchments closely followed discharge patterns, indicating mobilization of NO3-N during the rainy seasons. Hysteresis patterns of rainfall events indicate a shift from subsurface flow in the natural forest to surface runoff in agricultural subcatchments. Distinct peaks in NO3-N concentrations were observed during rainfall events after a longer dry period in the forest and tea subcatchments. The high-resolution data set enabled us to identify differences in NO3-N transport of catchments under different land use, such as enhanced NO3-N inputs to the stream during the rainy season and higher annual export in agricultural subcatchments (4.9 ± 0.3 to 12.0 ± 0.8 kg N ha-1 yr-1) than in natural forest (2.6 ± 0.2 kg N ha-1 yr-1). This emphasizes the usefulness of our monitoring approach to improve the understanding of land use effects on riverine N exports in tropical landscapes, but also the need to apply such methods in other regions.

Measurement of dynamic urethral pressures with a high-resolution manometry system in continent and incontinent women.

Science.gov (United States)

Kirby, Anna C; Tan-Kim, Jasmine; Nager, Charles W

2015-01-01

Female stress urinary incontinence is caused by urethral dysfunction during dynamic conditions, but current technology has limitations in measuring urethral pressures under these conditions. An 8-French high-resolution manometry (HRM) catheter currently in clinical use in gastroenterology may accurately measure urethral pressures under dynamic conditions because it has a 25-millisecond response rate and circumferential pressure sensors along the length of the catheter (ManoScan ESO; Given Imaging, Yoqneam, Israel). We evaluated the concordance, repeatability, and tolerability of this catheter. We measured resting, cough, and strain maximum urethral closure pressures (MUCPs) using HRM and measured resting MUCPs with water-perfusion side-hole catheter urethral pressure profilometry (UPP) in 37 continent and 28 stress-incontinent subjects. Maneuvers were repeated after moving the HRM catheter along the urethral length to evaluate whether results depend on catheter positioning. Visual analog pain scores evaluated the comfort of HRM compared to UPP. The correlation coefficient for resting MUCPs measured by HRM versus UPP was high (r = 0.79, P rest, cough, and strain with HRM: r = 0.92, 0.89, and 0.89. Mean MUCPs (rest, cough, and strain) were higher in continent than in incontinent subjects (all P continent subjects during cough and strain maneuvers compared to rest. This preliminary study shows that HRM is concordant with standard technology, repeatable, and well tolerated in the urethra. Incontinent women have more impairment of their urethral closure pressures during cough and strain than continent women.

CMIP5 downscaling and its uncertainty in China

Science.gov (United States)

Yue, TianXiang; Zhao, Na; Fan, ZeMeng; Li, Jing; Chen, ChuanFa; Lu, YiMin; Wang, ChenLiang; Xu, Bing; Wilson, John

2016-11-01

A comparison between the Coupled Model Intercomparison Project Phase 5 (CMIP5) data and observations at 735 meteorological stations indicated that mean annual temperature (MAT) was underestimated about 1.8 °C while mean annual precipitation (MAP) was overestimated about 263 mm in general across the whole of China. A statistical analysis of China-CMIP5 data demonstrated that MAT exhibits spatial stationarity, while MAP exhibits spatial non-stationarity. MAT and MAP data from the China-CMIP5 dataset were downscaled by combining statistical approaches with a method for high accuracy surface modeling (HASM). A statistical transfer function (STF) of MAT was formulated using minimized residuals output by HASM with an ordinary least squares (OLS) linear equation that used latitude and elevation as independent variables, abbreviated as HASM-OLS. The STF of MAP under a BOX-COX transformation was derived as a combination of minimized residuals output by HASM with a geographically weight regression (GWR) using latitude, longitude, elevation and impact coefficient of aspect as independent variables, abbreviated as HASM-GB. Cross validation, using observational data from the 735 meteorological stations across China for the period 1976 to 2005, indicates that the largest uncertainty occurred on the Tibet plateau with mean absolute errors (MAEs) of MAT and MAP as high as 4.64 °C and 770.51 mm, respectively. The downscaling processes of HASM-OLS and HASM-GB generated MAEs of MAT and MAP that were 67.16% and 77.43% lower, respectively across the whole of China on average, and 88.48% and 97.09% lower for the Tibet plateau.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

Directory of Open Access Journals (Sweden)

Adam M Wilson

2016-03-01

Full Text Available Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

Estimating the Wind Resource in Uttarakhand: Comparison of Dynamic Downscaling with Doppler Lidar Wind Measurements

Energy Technology Data Exchange (ETDEWEB)

Lundquist, J. K. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Pukayastha, A. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Martin, C. [Univ. of Colorado, Boulder, CO (United States); Newsom, R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2014-03-01

Previous estimates of the wind resources in Uttarakhand, India, suggest minimal wind resources in this region. To explore whether or not the complex terrain in fact provides localized regions of wind resource, the authors of this study employed a dynamic down scaling method with the Weather Research and Forecasting model, providing detailed estimates of winds at approximately 1 km resolution in the finest nested simulation.

Aspect of ECMWF downscaled Regional Climate Modeling in simulating Indian summer monsoon rainfall and dependencies on lateral boundary conditions

Science.gov (United States)

Ghosh, Soumik; Bhatla, R.; Mall, R. K.; Srivastava, Prashant K.; Sahai, A. K.

2018-03-01

Climate model faces considerable difficulties in simulating the rainfall characteristics of southwest summer monsoon. In this study, the dynamical downscaling of European Centre for Medium-Range Weather Forecast's (ECMWF's) ERA-Interim (EIN15) has been utilized for the simulation of Indian summer monsoon (ISM) through the Regional Climate Model version 4.3 (RegCM-4.3) over the South Asia Co-Ordinated Regional Climate Downscaling EXperiment (CORDEX) domain. The complexities of model simulation over a particular terrain are generally influenced by factors such as complex topography, coastal boundary, and lack of unbiased initial and lateral boundary conditions. In order to overcome some of these limitations, the RegCM-4.3 is employed for simulating the rainfall characteristics over the complex topographical conditions. For reliable rainfall simulation, implementations of numerous lower boundary conditions are forced in the RegCM-4.3 with specific horizontal grid resolution of 50 km over South Asia CORDEX domain. The analysis is considered for 30 years of climatological simulation of rainfall, outgoing longwave radiation (OLR), mean sea level pressure (MSLP), and wind with different vertical levels over the specified region. The dependency of model simulation with the forcing of EIN15 initial and lateral boundary conditions is used to understand the impact of simulated rainfall characteristics during different phases of summer monsoon. The results obtained from this study are used to evaluate the activity of initial conditions of zonal wind circulation speed, which causes an increase in the uncertainty of regional model output over the region under investigation. Further, the results showed that the EIN15 zonal wind circulation lacks sufficient speed over the specified region in a particular time, which was carried forward by the RegCM output and leads to a disrupted regional simulation in the climate model.

Utilization of downscaled microwave satellite data and GRACE Total Water Storage anomalies for improving streamflow prediction in the Lower Mekong Basin

Science.gov (United States)

Lakshmi, V.; Gupta, M.; Bolten, J. D.

2016-12-01

The Mekong river is the world's eighth largest in discharge with draining an area of 795,000 km² from the Eastern watershed of the Tibetan Plateau to the Mekong Delta including, Myanmar, Laos PDR, Thailand, Cambodia, Vietnam and three provinces of China. The populations in these countries are highly dependent on the Mekong River and they are vulnerable to the availability and quality of the water resources within the Mekong River Basin. Soil moisture is one of the most important hydrological cycle variables and is available from passive microwave satellite sensors (such as AMSR-E, SMOS and SMAP), but their spatial resolution is frequently too coarse for effective use by land managers and decision makers. The merging of satellite observations with numerical models has led to improved land surface predictions. Although performance of the models have been continuously improving, the laboratory methods for determining key hydraulic parameters are time consuming and expensive. The present study assesses a method to determine the effective soil hydraulic parameters using a downscaled microwave remote sensing soil moisture product based on the NASA Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture downscaling algorithm is based on a regression relationship between 1-km MODIS land surface temperature and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) to produce an enhanced spatial resolution ASMR-E-based soil moisture product. Since the optimized parameters are based on the near surface soil moisture information, further constraints are applied during the numerical simulation through the assimilation of GRACE Total Water Storage (TWS) within the land surface model. This work improves the hydrological fluxes and the state variables are optimized and the optimal parameter values are then transferred for retrieving hydrological fluxes. To evaluate the performance of the system in helping improve

Ultra-high resolution flat-panel volume CT: fundamental principles, design architecture, and system characterization

International Nuclear Information System (INIS)

Gupta, Rajiv; Brady, Tom; Grasruck, Michael; Suess, Christoph; Schmidt, Bernhard; Stierstorfer, Karl; Popescu, Stefan; Flohr, Thomas; Bartling, Soenke H.

2006-01-01

Digital flat-panel-based volume CT (VCT) represents a unique design capable of ultra-high spatial resolution, direct volumetric imaging, and dynamic CT scanning. This innovation, when fully developed, has the promise of opening a unique window on human anatomy and physiology. For example, the volumetric coverage offered by this technology enables us to observe the perfusion of an entire organ, such as the brain, liver, or kidney, tomographically (e.g., after a transplant or ischemic event). By virtue of its higher resolution, one can directly visualize the trabecular structure of bone. This paper describes the basic design architecture of VCT. Three key technical challenges, viz., scatter correction, dynamic range extension, and temporal resolution improvement, must be addressed for successful implementation of a VCT scanner. How these issues are solved in a VCT prototype and the modifications necessary to enable ultra-high resolution volumetric scanning are described. The fundamental principles of scatter correction and dose reduction are illustrated with the help of an actual prototype. The image quality metrics of this prototype are characterized and compared with a multi-detector CT (MDCT). (orig.)

Ultra-high resolution flat-panel volume CT: fundamental principles, design architecture, and system characterization

Energy Technology Data Exchange (ETDEWEB)

Gupta, Rajiv; Brady, Tom [Massachusetts General Hospital, Department of Radiology, Founders House, FND-2-216, Boston, MA (United States); Grasruck, Michael; Suess, Christoph; Schmidt, Bernhard; Stierstorfer, Karl; Popescu, Stefan; Flohr, Thomas [Siemens Medical Solutions, Forchheim (Germany); Bartling, Soenke H. [Hannover Medical School, Department of Neuroradiology, Hannover (Germany)

2006-06-15

Digital flat-panel-based volume CT (VCT) represents a unique design capable of ultra-high spatial resolution, direct volumetric imaging, and dynamic CT scanning. This innovation, when fully developed, has the promise of opening a unique window on human anatomy and physiology. For example, the volumetric coverage offered by this technology enables us to observe the perfusion of an entire organ, such as the brain, liver, or kidney, tomographically (e.g., after a transplant or ischemic event). By virtue of its higher resolution, one can directly visualize the trabecular structure of bone. This paper describes the basic design architecture of VCT. Three key technical challenges, viz., scatter correction, dynamic range extension, and temporal resolution improvement, must be addressed for successful implementation of a VCT scanner. How these issues are solved in a VCT prototype and the modifications necessary to enable ultra-high resolution volumetric scanning are described. The fundamental principles of scatter correction and dose reduction are illustrated with the help of an actual prototype. The image quality metrics of this prototype are characterized and compared with a multi-detector CT (MDCT). (orig.)

Projected future vegetation changes for the northwest United States and southwest Canada at a fine spatial resolution using a dynamic global vegetation model.

Science.gov (United States)

Shafer, Sarah; Bartlein, Patrick J.; Gray, Elizabeth M.; Pelltier, Richard T.

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.

High resolution transmission spectroscopy as a diagnostic for Jovian exoplanet atmospheres: constraints from theoretical models

Energy Technology Data Exchange (ETDEWEB)

Kempton, Eliza M.-R. [Department of Physics, Grinnell College, Grinnell, IA 50112 (United States); Perna, Rosalba [Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794 (United States); Heng, Kevin, E-mail: kemptone@grinnell.edu [University of Bern, Center for Space and Habitability, Sidlerstrasse 5, CH-3012 Bern (Switzerland)

2014-11-01

We present high resolution transmission spectra of giant planet atmospheres from a coupled three-dimensional (3D) atmospheric dynamics and transmission spectrum model that includes Doppler shifts which arise from winds and planetary motion. We model Jovian planets covering more than two orders of magnitude in incident flux, corresponding to planets with 0.9-55 day orbital periods around solar-type stars. The results of our 3D dynamical models reveal certain aspects of high resolution transmission spectra that are not present in simple one-dimensional (1D) models. We find that the hottest planets experience strong substellar to anti-stellar (SSAS) winds, resulting in transmission spectra with net blueshifts of up to 3 km s{sup –1}, whereas less irradiated planets show almost no net Doppler shifts. We find only minor differences between transmission spectra for atmospheres with temperature inversions and those without. Compared to 1D models, peak line strengths are significantly reduced for the hottest atmospheres owing to Doppler broadening from a combination of rotation (which is faster for close-in planets under the assumption of tidal locking) and atmospheric winds. Finally, high resolution transmission spectra may be useful in studying the atmospheres of exoplanets with optically thick clouds since line cores for very strong transitions should remain optically thick to very high altitude. High resolution transmission spectra are an excellent observational test for the validity of 3D atmospheric dynamics models, because they provide a direct probe of wind structures and heat circulation. Ground-based exoplanet spectroscopy is currently on the verge of being able to verify some of our modeling predictions, most notably the dependence of SSAS winds on insolation. We caution that interpretation of high resolution transmission spectra based on 1D atmospheric models may be inadequate, as 3D atmospheric motions can produce a noticeable effect on the absorption

Ultra high spatial and temporal resolution breast imaging at 7T.

Science.gov (United States)

van de Bank, B L; Voogt, I J; Italiaander, M; Stehouwer, B L; Boer, V O; Luijten, P R; Klomp, D W J

2013-04-01

There is a need to obtain higher specificity in the detection of breast lesions using MRI. To address this need, Dynamic Contrast-Enhanced (DCE) MRI has been combined with other structural and functional MRI techniques. Unfortunately, owing to time constraints structural images at ultra-high spatial resolution can generally not be obtained during contrast uptake, whereas the relatively low spatial resolution of functional imaging (e.g. diffusion and perfusion) limits the detection of small lesions. To be able to increase spatial as well as temporal resolution simultaneously, the sensitivity of MR detection needs to increase as well as the ability to effectively accelerate the acquisition. The required gain in signal-to-noise ratio (SNR) can be obtained at 7T, whereas acceleration can be obtained with high-density receiver coil arrays. In this case, morphological imaging can be merged with DCE-MRI, and other functional techniques can be obtained at higher spatial resolution, and with less distortion [e.g. Diffusion Weighted Imaging (DWI)]. To test the feasibility of this concept, we developed a unilateral breast coil for 7T. It comprises a volume optimized dual-channel transmit coil combined with a 30-channel receive array coil. The high density of small coil elements enabled efficient acceleration in any direction to acquire ultra high spatial resolution MRI of close to 0.6 mm isotropic detail within a temporal resolution of 69 s, high spatial resolution MRI of 1.5 mm isotropic within an ultra high temporal resolution of 6.7 s and low distortion DWI at 7T, all validated in phantoms, healthy volunteers and a patient with a lesion in the right breast classified as Breast Imaging Reporting and Data System (BI-RADS) IV. Copyright © 2012 John Wiley & Sons, Ltd.

MiKlip-PRODEF: Probabilistic Decadal Forecast for Central and Western Europe

Science.gov (United States)

Reyers, Mark; Haas, Rabea; Ludwig, Patrick; Pinto, Joaquim

2013-04-01

The demand for skilful climate predictions on time-scales of several years to decades has increased in recent years, in particular for economic, societal and political terms. Within the BMBF MiKlip consortium, a decadal prediction system on the global to local scale is currently being developed. The subproject PRODEF is part of the MiKlip-Module C, which aims at the regionalisation of decadal predictability for Central and Western Europe. In PRODEF, a combined statistical-dynamical downscaling (SDD) and a probabilistic forecast tool are developed and applied to the new Earth system model of the Max-Planck Institute Hamburg (MPI-ESM), which is part of the CMIP5 experiment. Focus is given on the decadal predictability of windstorms, related wind gusts as well as wind energy potentials. SDD combines the benefits of both high resolution dynamical downscaling and purely statistical downscaling of GCM output. Hence, the SDD approach is used to obtain a very large ensemble of highly resolved decadal forecasts. With respect to the focal points of PRODEF, a clustering of temporal evolving atmospheric fields, a circulation weather type (CWT) analysis, and a storm damage indices analysis is applied to the full ensemble of the decadal hindcast experiments of the MPI-ESM in its lower resolution (MPI-ESM-LR). The ensemble consists of up to ten realisations per yearly initialised decadal hindcast experiments for the period 1960-2010 (altogether 287 realisations). Representatives of CWTs / clusters and single storm episodes are dynamical downscaled with the regional climate model COSMO-CLM with a horizontal resolution of 0.22°. For each model grid point, the distributions of the local climate parameters (e.g. surface wind gusts) are determined for different periods (e.g. each decades) by recombining dynamical downscaled episodes weighted with the respective weather type frequencies. The applicability of the SDD approach is illustrated with examples of decadal forecasts of the MPI

Ultra-high resolution protein crystallography

International Nuclear Information System (INIS)

Takeda, Kazuki; Hirano, Yu; Miki, Kunio

2010-01-01

Many protein structures have been determined by X-ray crystallography and deposited with the Protein Data Bank. However, these structures at usual resolution (1.5< d<3.0 A) are insufficient in their precision and quantity for elucidating the molecular mechanism of protein functions directly from structural information. Several studies at ultra-high resolution (d<0.8 A) have been performed with synchrotron radiation in the last decade. The highest resolution of the protein crystals was achieved at 0.54 A resolution for a small protein, crambin. In such high resolution crystals, almost all of hydrogen atoms of proteins and some hydrogen atoms of bound water molecules are experimentally observed. In addition, outer-shell electrons of proteins can be analyzed by the multipole refinement procedure. However, the influence of X-rays should be precisely estimated in order to derive meaningful information from the crystallographic results. In this review, we summarize refinement procedures, current status and perspectives for ultra high resolution protein crystallography. (author)

Statistical downscaling and projection of future temperature and ...