Assimilating Sentinel‐3 All‐Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China

Abstract

AbstractWater vapor is a key driver for the evolution of weather system. To investigate the impact of assimilating Sentinel‐3 precipitable water vapor (PWV) on weather forecasting, Sentinel‐3 PWV retrievals over the South China with two different assimilation schemes are assimilated into the Weather Research and Forecasting (WRF) model. In the first assimilation scheme, only Sentinel‐3 clear‐sky PWV are assimilated, while Sentinel‐3 all‐sky PWV are assimilated for the second assimilation scheme. For both data assimilation schemes, we totally conduct 28 WRF data assimilation runs and forecasts for 28 selected days over two periods, that is, 14 days in March 2020 and 14 days in June 2020. The weather condition in June 2020 is much wetter than March 2020. Generally, assimilating Sentinel‐3 PWV improves the WRF forecasting performance, particularly for June 2020. Assimilation of all‐sky PWV outperforms assimilation of clear‐sky PWV. The comparison results with radiosonde profiles show that assimilating Sentinel‐3 PWV appreciably corrects the bias of WRF water vapor mixing ratio forecasting results for June 2020. The rainfall validation results show that both assimilation schemes show a positive impact in June 2020, but a neutral impact in March 2020. For June 2020, assimilating Sentinel‐3 all‐sky PWV improves rainfall forecast skill score by 2.4%, while the rainfall forecast score is improved by 1.0% after assimilating clear‐sky PWV. Additionally, assimilation of Sentinel‐3 PWV can modify the WRF moisture field, which further improves the rainfall spatial pattern.