The Experts below are selected from a list of 47046 Experts worldwide ranked by ideXlab platform
Yann Kerr - One of the best experts on this subject based on the ideXlab platform.
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towards deterministic Downscaling of smos soil moisture using modis derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:Abstract A deterministic approach for Downscaling ∼ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic Downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four Downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
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Towards deterministic Downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:A deterministic approach for Downscaling 40km resolution Soil Moisture and Ocean Salinity (SMOS) observation is developed from 1km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is xed to 10 times the spatial resolution of MODIS thermal data (10km). Four dierent analytic Downscaling relationships are derived from MODIS derived and physically-based model predictions of soil evaporative eciency (). The four Downscaling algorithms dier with regards to i) the assumed relationship (linear or nonlinear) between and near-surface soil moisture, and ii) the scale at which soil parameters are available (40km or 10km). The 1km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40km by 60km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square dierence between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
Paulin Coulibaly - One of the best experts on this subject based on the ideXlab platform.
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Assessing the Need for Downscaling RCM Data for Hydrologic Impact Study
Journal of Hydrologic Engineering, 2011Co-Authors: Manu Sharma, Paulin Coulibaly, Yonas DibikeAbstract:Climate change impact studies have generally downscaled large-scale global climate model (GCM) output data; however, few studies have considered Downscaling regional climate model (RCM) data. It is unclear whether further Downscaling raw RCM data could be beneficial or not in a hydrologic impact study. This study provides some experimental results to address that question. Raw Canadian regional climate model (CRCM4.2) data are downscaled by using a common statistical Downscaling method (SDSM) and a data-driven technique called a time-lagged feedforward network (TLFN). Regardless of the Downscaling methods and the predictands (e.g., precipitation, temperature), the downscaled CRCM4.2 data are found to be much closer to the observed data than the raw CRCM4.2 data. When the downscaled CRCM4.2 data are used in a hydrologic model (HBV), the model’s ability to accurately simulate streamflow and reservoir inflow is significantly improved as compared to the use of the raw CRCM4.2 data. Simulations of future river...
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Comparison of data-driven methods for Downscaling ensemble weather forecasts
Hydrology and Earth System Sciences, 2008Co-Authors: Xiaoli Liu, Paulin Coulibaly, N. EvoraAbstract:This study investigates dynamically different data- driven methods, specifically a statistical Downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for Downscaling numerical weather ensemble forecasts gen- erated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate Downscaling tech- niques. The selected methods are applied for Downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The Downscaling results show that the TLFN and EPR have simi- lar performance in Downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more ef- ficient Downscaling techniques than SDSM for both the en- semble daily precipitation and temperature.
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Downscaling Precipitation and Temperature with Temporal Neural Networks
Journal of Hydrometeorology, 2005Co-Authors: Paulin Coulibaly, Yonas Dibike, François AnctilAbstract:The issues of Downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for Downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The Downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP– NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the Downscaling models. The performance of the TLFN Downscaling model is also compared to a statistical Downscaling model (SDSM). The Downscaling results suggest that the TLFN is an efficient method for Downscaling both daily precipitation and temperature series. The best Downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate Downscaling tools for impact studies by showing how a future climate scenario downscaled with different Downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed.
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Downscaling daily extreme temperatures with genetic programming
Geophysical Research Letters, 2004Co-Authors: Paulin CoulibalyAbstract:[1] A context-free genetic programming (GP) method is presented that simulated local scale daily extreme (maximum and minimum) temperatures based on large scale atmospheric variables. The method evolves simple and optimal models for Downscaling daily temperature at a station. The advantage of the context-free GP method is that both the variables and constants of the candidate models are optimized and consequently the selection of the optimal model. The method is applied to the Chute-du-Diable weather station in Northeastern Canada along with the National Center for Environmental Prediction (NCEP) reanalysis datasets. The performance of the GP based Downscaling models is compared to benchmarks from a commonly used statistical Downscaling model. The experiment results show that the models evolved by the GP are simpler and more efficient for Downscaling daily extreme temperature than the common statistical method. The different model test results indicate that the GP approach significantly outperforms the statistical method for the Downscaling of daily minimum temperature, while for the maximum temperature the two methods are almost equivalent. However, the GP method remains slightly more effective for maximum temperature Downscaling than the statistical method.
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Temporal neural networks for Downscaling climate variability and extremes
Proceedings. 2005 IEEE International Joint Conference on Neural Networks 2005., 1Co-Authors: Yonas Dibike, Paulin CoulibalyAbstract:Global climate models (GCMs) are inherently unable to present local subgrid-scale features and dynamics and consequently, outputs from these models cannot be directly applied in many impact studies. This paper presents the issues of Downscaling the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for Downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The performance of the temporal neural network Downscaling model is compared to a regression-based statistical Downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The Downscaling results for the base period (1961- 2000) suggest that the TNN is an efficient method for Downscaling both daily precipitation as well as daily temperature series. Furthermore, the different model test results indicate that the TNN model significantly outperforms the statistical models for the Downscaling of daily precipitation extremes and variability.
O. Merlin - One of the best experts on this subject based on the ideXlab platform.
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towards deterministic Downscaling of smos soil moisture using modis derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:Abstract A deterministic approach for Downscaling ∼ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic Downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four Downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
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Towards deterministic Downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:A deterministic approach for Downscaling 40km resolution Soil Moisture and Ocean Salinity (SMOS) observation is developed from 1km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is xed to 10 times the spatial resolution of MODIS thermal data (10km). Four dierent analytic Downscaling relationships are derived from MODIS derived and physically-based model predictions of soil evaporative eciency (). The four Downscaling algorithms dier with regards to i) the assumed relationship (linear or nonlinear) between and near-surface soil moisture, and ii) the scale at which soil parameters are available (40km or 10km). The 1km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40km by 60km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square dierence between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
A. Chehbouni - One of the best experts on this subject based on the ideXlab platform.
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towards deterministic Downscaling of smos soil moisture using modis derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:Abstract A deterministic approach for Downscaling ∼ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic Downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four Downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
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Towards deterministic Downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:A deterministic approach for Downscaling 40km resolution Soil Moisture and Ocean Salinity (SMOS) observation is developed from 1km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is xed to 10 times the spatial resolution of MODIS thermal data (10km). Four dierent analytic Downscaling relationships are derived from MODIS derived and physically-based model predictions of soil evaporative eciency (). The four Downscaling algorithms dier with regards to i) the assumed relationship (linear or nonlinear) between and near-surface soil moisture, and ii) the scale at which soil parameters are available (40km or 10km). The 1km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40km by 60km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square dierence between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
J.p. Walker - One of the best experts on this subject based on the ideXlab platform.
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towards deterministic Downscaling of smos soil moisture using modis derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:Abstract A deterministic approach for Downscaling ∼ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic Downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four Downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
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Towards deterministic Downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency
Remote Sensing of Environment, 2008Co-Authors: O. Merlin, J.p. Walker, A. Chehbouni, Yann KerrAbstract:A deterministic approach for Downscaling 40km resolution Soil Moisture and Ocean Salinity (SMOS) observation is developed from 1km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is xed to 10 times the spatial resolution of MODIS thermal data (10km). Four dierent analytic Downscaling relationships are derived from MODIS derived and physically-based model predictions of soil evaporative eciency (). The four Downscaling algorithms dier with regards to i) the assumed relationship (linear or nonlinear) between and near-surface soil moisture, and ii) the scale at which soil parameters are available (40km or 10km). The 1km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40km by 60km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square dierence between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the Downscaling algorithm with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the Downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.
