Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting
AbstractHigh-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction is not a state vector and its observational error is not considered during the assimilation calculation. In this paper, an improved method for wind observation directly assimilates the SAR wind observations in the form of speed and direction (SAR_sd). This method was implemented to assimilate the sea surface wind retrieved from Sentinel-1 synthetic aperture radar (SAR) in the basic three-dimensional variational system for the Weather Research and Forecasting Model (WRF 3DVAR). Furthermore, a new quality control scheme for wind observations is also presented. Typhoon Lionrock in August 2016 is chosen as a case study to investigate and compare both assimilation methods. The experimental results show that the SAR wind observations can increase the number of the effective observations in the area of a typhoon and have a positive impact on the assimilation analysis. The numerical forecast results for this case show better results for the SAR_sd method than for the SAR_uv method. The SAR_sd method looks very promising for winds assimilation under typhoon conditions, but more cases need to be considered to draw final conclusions. View Full-Text
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Yu, Y.; Yang, X.; Zhang, W.; Duan, B.; Cao, X.; Leng, H. Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting. Remote Sens. 2017, 9, 845.
Yu Y, Yang X, Zhang W, Duan B, Cao X, Leng H. Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting. Remote Sensing. 2017; 9(8):845.Chicago/Turabian Style
Yu, Yi; Yang, Xiaofeng; Zhang, Weimin; Duan, Boheng; Cao, Xiaoqun; Leng, Hongze. 2017. "Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting." Remote Sens. 9, no. 8: 845.
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