Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for "7.19" Severe Storm Over North China. Issue 7 (6th April 2018)

• Record Type: Journal Article
• Authors: Wang, Yuanbing; Liu, Zhiquan; Yang, Sen; Min, Jinzhong; Chen, Liqiang; Chen, Yaodeng; Zhang, Tao
• Is Part Of: Journal of geophysical research. Volume 123:Issue 7(2018)
• Page Start: 3374
• Page End: 3394
• Publication Date: 2018-04-06
• Subjects: AHI radiances -- data assimilation -- convection‐permitting -- WRFDA; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027697
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Himawari‐8 is the first launched and operational new‐generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari‐8 provides continuous high‐resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear‐sky radiances from three water vapor channels on convection‐permitting (3 km) analyses and forecasts of the "7.19" severe rainstorm that occurred over north China during 18–21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm. Key Points: This is the first attempt to assimilate AHI radiances using WRFDA at convective scales The added value of hourly AHI water vapor radiances on convection‐permitting analyses and forecasts for "7.19" rainstorm was investigated Assimilation of AHI clearly improved the simulated wind, humidity, and precipitation in analyses and forecasts