The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Benyang Tang - One of the best experts on this subject based on the ideXlab platform.
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Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression
Remote Sensing of Environment, 2008Co-Authors: Xiaosu Xie, W. Timothy Liu, Benyang TangAbstract:Abstract An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere (Θ) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data (Θ calculated from Rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of Θ derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation compared with rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.
Xiaosu Xie - One of the best experts on this subject based on the ideXlab platform.
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Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression
Remote Sensing of Environment, 2008Co-Authors: Xiaosu Xie, W. Timothy Liu, Benyang TangAbstract:Abstract An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere (Θ) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data (Θ calculated from Rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of Θ derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation compared with rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.
Birgitte Rugaard Furevik - One of the best experts on this subject based on the ideXlab platform.
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near surface marine wind profiles from rawinsonde and nora10 hindcast
Journal of Geophysical Research, 2012Co-Authors: Birgitte Rugaard Furevik, Hilde HaakenstadAbstract:[1] With huge investments going into offshore wind farming and strong focus on offshore safety at all levels, there is an increasing demand for high-resolution wind products in the near-surface boundary layer. The Norwegian Reanalysis Archive (NORA10) is a dynamical downscaling of ERA-40 to a spatial resolution of 10–11 km over the northeastern North Atlantic using the High-Resolution Limited Area Model (HIRLAM). The boundary layer wind speed between 10 and 150 m above the sea surface from NORA10 is used in a large number of applications. In this study, wind speed maps are produced, and the seasonal and decadal variability in wind speed is discussed. The model underestimates the mean wind speed from in situ winds from offshore platforms and 0.5 Hz rawinsonde observations over the sea by 5–10%. One exception is FINO-1, where there is excellent agreement. Part of the discrepancies may be due to the speed-up effects over large platform structures. The high sampling rate of the Rawinsondes gives good quality recordings of wind speed and temperature in approximately 10 m height intervals for a 10 year period. Mean model wind profile shapes below 150 m above sea level favorable compare with mean wind speed profiles for stable, unstable and neutral conditions from rawinsonde at Polarfront (ocean weather ship in the geographical position 66°N, 2°E). However, in 18% of the cases the wind speed is decreasing with height, which is not reproduced by the model. We suggest that these inverse wind profiles may be related to cold air advection and convection cells, e.g., downstream of cold air outbreaks.
W. Timothy Liu - One of the best experts on this subject based on the ideXlab platform.
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Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression
Remote Sensing of Environment, 2008Co-Authors: Xiaosu Xie, W. Timothy Liu, Benyang TangAbstract:Abstract An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere (Θ) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data (Θ calculated from Rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of Θ derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation compared with rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.
Hilde Haakenstad - One of the best experts on this subject based on the ideXlab platform.
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near surface marine wind profiles from rawinsonde and nora10 hindcast
Journal of Geophysical Research, 2012Co-Authors: Birgitte Rugaard Furevik, Hilde HaakenstadAbstract:[1] With huge investments going into offshore wind farming and strong focus on offshore safety at all levels, there is an increasing demand for high-resolution wind products in the near-surface boundary layer. The Norwegian Reanalysis Archive (NORA10) is a dynamical downscaling of ERA-40 to a spatial resolution of 10–11 km over the northeastern North Atlantic using the High-Resolution Limited Area Model (HIRLAM). The boundary layer wind speed between 10 and 150 m above the sea surface from NORA10 is used in a large number of applications. In this study, wind speed maps are produced, and the seasonal and decadal variability in wind speed is discussed. The model underestimates the mean wind speed from in situ winds from offshore platforms and 0.5 Hz rawinsonde observations over the sea by 5–10%. One exception is FINO-1, where there is excellent agreement. Part of the discrepancies may be due to the speed-up effects over large platform structures. The high sampling rate of the Rawinsondes gives good quality recordings of wind speed and temperature in approximately 10 m height intervals for a 10 year period. Mean model wind profile shapes below 150 m above sea level favorable compare with mean wind speed profiles for stable, unstable and neutral conditions from rawinsonde at Polarfront (ocean weather ship in the geographical position 66°N, 2°E). However, in 18% of the cases the wind speed is decreasing with height, which is not reproduced by the model. We suggest that these inverse wind profiles may be related to cold air advection and convection cells, e.g., downstream of cold air outbreaks.
