23 October 2018 Impacts of all-sky assimilation of the FY-3C MWHS-2 observations on analysis and forecasts of typhoon precipitation
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Abstract
The FY-3C MWHS-2 (Microwave Humidity Sounder-2) data have been assimilated in the ECMWF operational forecasting system showing small positive impacts on short-range forecasts. To assess the impacts of all-sky (i.e. clear, cloudy and precipitating) assimilation of MWHS-2 183 GHz channels on the analysis and forecasts of typhoon precipitation caused by Typhoon Nida which made landfall in Guangdong Province, China in 2016, three experiments (without MWHS-2 data, with MWHS-2 data in clear-sky conditions and with MWHS-2 data in all-sky conditions) have been carried out. RTTOV-SCATT, a fast Radiative Transfer Model for simulating cloud- and precipitation- affected microwave radiances, and a symmetric observation error model for all-sky radiance assimilation are implemented within the Weather Research and Forecasting model data assimilation system (WRFDA) and its three-dimension variational data assimilation scheme is used for all experiments. Compared with the experiment run without MWHS-2 data, the assimilation of MWHS-2 observations in clear-sky and all-sky conditions makes more accurate precipitation forecasts over the north-central Guangdong. Furthermore, compared with the clear-sky experiment, the precipitation distribution over the east of the Pearl River and the rainfall amount in the eastern Guangdong both are more close to the observations due to the improved temperature and humidity analysis by assimilating more cloud- and precipitation- affected radiances over the northern South China Sea and Typhoon Nida in all-sky experiment. Since the results in this study are very encouraging, more experiments are to be run to verify the positive impacts of all-sky assimilation on the prediction of severe weather processes.
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Zhipeng Xian, Keyi Chen, and Jiang Zhu "Impacts of all-sky assimilation of the FY-3C MWHS-2 observations on analysis and forecasts of typhoon precipitation", Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 1078019 (23 October 2018); doi: 10.1117/12.2324457; https://doi.org/10.1117/12.2324457
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