Enhancing Typhoon Doksuri (2023) Forecasts via Radar Data Assimilation: Evaluation of Momentum Control Variable Schemes with Background-Dependent Hydrometeor Retrieval in WRF-3DVAR - Xinyi Wang; Feifei Shen; Shen Wan; Jing Liu; Haiyan Fei; Changliang Shao; Song Yuan; Jiajun Chen; Xiaolin Yuan (Atmosphere)
This research investigates how incorporating both radar radial velocity (<semantics>Vr</semantics>) and radar reflectivity influences the accuracy of tropical cyclone (TC) prediction. Different control variables are introduced to analyze their roles in <semantics>Vr</semantics> data assimilation, while background-dependent radar reflectivity assimilation methods are also applied. Using Typhoon “Doksuri” (2023) as a primary case study and Typhoon “Kompasu” (2021) as a supplementary case, the Weather Research and Forecasting (WRF) model’s three-dimensional variational assimilation (3DVAR) is utilized to assimilate <semantics>Vr</semantics> and reflectivity observations to improve TC track, intensity, and precipitation forecasts. Three experiments were conducted for each typhoon: one with no assimilation, one with <semantics>Vr</semantics> assimilation using <semantics>ψ−χ</semantics> control variables and background-dependent radar reflectivity assimilation, and one with <semantics>Vr</semantics> assimilation using <semantics>U−V</semantics> control variables and background-dependent radar reflectivity assimilation. The results show that assimilating <semantics>Vr</semantics> enhances small-scale dynamics in the TC core, leading to a more organized and stronger wind field. The experiment involving <semantics>U−V</semantics> control variables consistently showed advantages over the <semantics>ψ−χ</semantics> scheme in aspects such as overall track prediction, initial intensity representation, and producing more stable or physically plausible intensity trends, particularly evident when comparing both typhoon events. These findings highlight the importance of optimizing control variables and assimilation methods to enhance the prediction of TCs.