Impact of EnKF assimilating Himawari-8 all-sky infrared radiance on the forecasting of a warm-sector rainstorm event

Abstract

Warm-sector rainstorms are highly localized events, with weather systems and triggering mechanisms are not obvious, leading to limited forecasting capabilities in numerical models. Based on the ensemble Kalman filter (PSU-EnKF) assimilation system and the regional mesoscale model WRF, this study conducted a simulation experiment assimilating all-sky infrared (IR) radiance for a warm-sector rainstorm in East China and investigated the positive impact of assimilating the Himawari-8 moisture channel all-sky IR radiance on the forecast of the rainstorm. Results indicate that hourly cycling assimilation of all-sky IR radiance can significantly improve the forecast accuracy of this warm-sector rainstorm. There is a notable increase in the Threat Score (TS), with the simulated location and intensity of the 3-hour precipitation aligning more closely with observations. These improvements result from the assimilation of cloud-affected radiance, which introduces more mesoscale convective information into the model’s initial fields. The adjustments include enhancements to the moisture field, such as increased humidity and moisture transport, and modifications to the wind field, including the intrusion of mid-level cold air and the strengthening of low-level convergent shear. These factors are critical in improving the forecast of this warm-sector rainstorm event.