Assimilating Sentinel‐3 All‐Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China. Issue 8 (25th April 2023)

• Record Type: Journal Article
• Authors: Gong, Yangzhao; Liu, Zhizhao; Chan, Pak Wai; Hon, Kai Kwong
• Is Part Of: Journal of geophysical research. Volume 128:Issue 8(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-25
• Subjects: precipitable water vapor -- data assimilation -- Weather Research and Forecasting -- Sentinel‐3 -- numerical weather prediction; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD037979
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Water 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. Plain Language Summary: Water vapor is a key parameter in weather forecasting models as it plays a crucial role in the formation and evolution of weather process. Incorporating accurate water vapor observations from satellite‐based platforms has been demonstrated to be an effective way to improve the performance of forecasting model. While most of previous studies paid their attention on assimilating water vapor radiance data at infrared or microwave bands, this study assimilates the near infrared (NIR) water vapor data. The NIR data obtained from the Ocean and Land Colour Instrument aboard Sentinel‐3A/3B satellites are assimilated into the Weather Research and Forecasting (WRF) model. The Sentinel‐3A/3B water vapor data assimilation experiments are conducted for two different periods with two different weather conditions, that is, dry period (March 2020) and wet period (June 2020). The experiment results showed that assimilation of Sentinel‐3A/3B water vapor data can improve the WRF water vapor forecasting accuracy and rainfall forecasting performance. The bias of forecast humidity profile has also been considerably corrected after assimilation. The improvements gained from Sentinel‐3A/3B water vapor data are larger for the wet period. Key Points: Assimilating Sentinel‐3 precipitable water vapor (PWV) considerably reduces the bias in Weather Research and Forecasting model's humidity forecasting field during wet period Assimilating Sentinel‐3 PWV improves the probability of detection of 12‐hr accumulated rainfall by 2.4% during wet period Assimilating all‐sky PWV can get a higher rainfall pattern correlation than no data assimilation and assimilating clear‐sky PWV