Benefit of assimilating satellite all‐sky infrared radiances on the cloud and precipitation prediction of a long‐lasting mesoscale convective system over the Tibetan plateau

Abstract

Heavy rainfall events in the warm season (May–September) over the Tibetan Plateau (TP) region and its downstream areas are often closely related to eastward‐propagating Tibetan Plateau Vortices (TPVs). Hence, improving the prediction of TPVs and their associated convective activity is of paramount importance, given the significant potential impacts they can have on densely populated downstream regions, including but not limited to flooding and damages. In this study, a typical long‐lived TPV that occurred in July 2008 was used for the first time to explore the benefit of assimilating satellite all‐sky infrared radiances on the cloud and precipitation prediction of the TPV‐induced eastward‐propagating mesoscale convective system (MCS). The all‐sky infrared radiances from the water vapor (WV) channel of the geostationary Meteosat‐7 and other conventional observations were assimilated into a 4‐km grid spacing regional model using the ensemble Kalman filter. The results revealed that the all‐sky infrared data assimilation improved the cloud, precipitation, dynamical, and thermodynamical analyses as well as 0–12‐hr deterministic and ensemble forecasts. Compared with the experiment in which the all‐sky infrared radiances were not assimilated (non‐radiance experiment), the experiment with assimilated all‐sky infrared radiances yielded clearly improved initial wind and cloud fields, 1–12‐hr cloud forecasts, and 1–6‐hr precipitation forecasts. This study indicates that assimilation of all‐sky satellite radiances has the potential for improving the operational cloud and precipitation forecasts over the TP and its downstream areas.

Enhancing Severe Weather Prediction With Microwave All‐Sky Radiance Assimilation: The 10 August 2020 Midwest Derecho

“…One field of all‐sky radiance assimilation research focuses on IR all‐sky radiances from geostationary satellite imagers. The assimilation of IR all‐sky BTs using an ensemble‐based data assimilation technique, such as ensemble Kalman filter (EnKF) (Houtekamer & Zhang, 2016), has been proven to improve the forecasts of TCs (Feng & Pu, 2022; Hartman et al., 2021, 2023; Honda, Miyoshi, et al., 2018; Minamide & Zhang, 2018; F. Zhang et al., 2016, 2019), torrential rainfall (He et al., 2023; Honda, Kotsuki, et al., 2018; Okamoto et al., 2019; Otkin & Potthast, 2019), mid‐latitude convection (Chandramouli et al., 2022; Degelia et al., 2023; Eure et al., 2023; Jones et al., 2020; Sawada et al., 2019; Y. Zhang et al., 2018; L. Zhu et al., 2023), and tropical convection (Chan & Chen, 2022; Chan et al., 2022; Chan, Zhang, et al., 2020). Y. Zhang et al.…”