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RSS FeedsRemote Sensing, Vol. 12, Pages 670: Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager--A Case Study (Remote Sensing)

 
 

18 february 2020 15:03:09

 
Remote Sensing, Vol. 12, Pages 670: Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager--A Case Study (Remote Sensing)
 


The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites, it is possible to quantitatively evaluate the environmental moisture fields from NWP models. Three NWP models—Global Forecast System (GFS), Unified Model (UM), Weather Research and Forecasting (WRF)—are evaluated with brightness temperature (BT) measurements from the three moisture channels of Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite for Typhoon Linfa (2015) case. It is found that the three NWP models have similar performance for lower tropospheric moisture, and GFS has a smaller bias for middle tropospheric moisture. Besides, there is a close relationship between moisture forecasts in the environment and the tropical cyclone (TC) track forecasts in GFS, while regional WRF does not show this pattern. When the infrared and microwave sounder radiance measurements from polar orbit satellite are assimilated in regional WRF, it is clearly shown that the environment moisture fields are improved compared with that with only conventional data are assimilated.


 
178 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 671: Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing (Remote Sensing)
Remote Sensing, Vol. 12, Pages 669: Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques (Remote Sensing)
 
 
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