All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

The Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) allows for high-spatiotemporal-resolution observations of the local severe weather systems. The capabilities for assimilating the all-sky AGRI infrared radiances are explored with the WRF three-dimensional variational data assimilation system (WRF-3DVAR) to improve the forecast accuracy of an extremely heavy rainfall event over the Henan Province of China in this study. In particular, data assimilation experiments are conducted with different physical parameterization schemes, including microphysical schemes and cumulus parameterizations. The results show that the all-sky AGRI radiance data assimilation experiments based on the Purdue Lin microphysics scheme and the Kain–Fritsch cumulus scheme can lead to better forecasts of the rainfall intensity and location, respectively. Meanwhile, it is found that the analyzed cloud top temperature fits the observations better when cloudy radiances from the AGRI are assimilated using the Purdue Lin scheme. This finding suggests that proper physical processes could facilitate the effective use of AGRI cloud-affected observations. On the other hand, positive forecast impacts from assimilating the all-sky AGRI radiance data have been confirmed when compared to the experiments that assimilate only the clear-sky AGRI radiances or only conventional observations. The assimilation of the all-sky AGRI water vapor channel is conducive to humidifying the initial conditions in the middle and low troposphere with more realistic analysis increments of water vapor, cloud water, and cloud ice, resulting in improved intensity forecasts of the heavy rainfall system and better forecast skill scores.