In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.

In this paper, the Advanced Weather Research Forecast model (WRF-ARW) is used to investigate the potential impacts of assimilating the FengYun-4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channel data on the prediction of Typhoon In-Fa (2021). In addition, a series of data assimilation experiments are conducted to demonstrate the added value of the FY-4A/GIIRS data assimilation for typhoon forecasts. It is shown that the higher spectral resolution and broader coverage of GIIRS radiance data can positively impact the model analysis and forecasts with larger temperature and moisture increments at the initial time of simulations, thus producing a better simulation for the typhoon’s warm core aloft, vortex wind structure, and spiral rainfall band. Moreover, the assimilation of the GIIRS data can also lead to better storm steering flows and, consequently, better typhoon track forecasts. Overall, the assimilation of FY-4A/GIIRS temperature sounding channel data shows some added values to improve the track and storm structure forecasts of Typhoon In-Fa.

This study conducts a one-month parallel experiment to compare the results of the operational Shanghai Typhoon Model with (WV123) and without (CTL) the assimilation of clear-sky brightness temperature from three Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water-vapor (WV) channels. The results demonstrate a notable improvement (∼3%) in the moisture fields by the WV123 experiment at all atmospheric levels in the forecasts of 24–120 hours when compared to those of the CTL experiment. Besides, results show an improvement in the correlation coefficient of the 500-hPa temperature field from WV123 experiment than CTL experiment after 24 hours. For Typhoon Doksuri (2023), the WV123 experiment shows a reduction of 19.4% in the mean track errors after 24-hour and an improvement of approximately 10% in the forecasted intensities with five different initial forecasting times. In the cases of Typhoons Mawar, Khanun, and Kaihui in 2023, track errors also exhibit some degree of amelioration in forecast experiments at different initial forecasting times. Moreover, the WV123 experiment can improve the forecast accuracy for precipitation exceeding 250 mm in terms of both spatial distribution and intensity. A statistical study over one-month period indicates that the WV123 experiment shows a relatively neutral impact on the precipitation forecasts of 0–48 hours. However, for the forecast lead time of 48–120 hours, there is a considerable improvement in terms of the threat scores of predicting the precipitation exceeding 50 mm, suggesting the positive effects of assimilating clear-sky and future all-sky FY-4B AGRI WV radiances.