Combined Assimilation of Radar and AWS Data From Mesoscale Networks for the Record‐Breaking Heavy Rainfall in China

Abstract

Radar data assimilation (DA) alone remains insufficient for precipitation forecasts due to the lack of thermodynamic information in the pre‐storm environment. This study investigates the assimilation of dense automatic weather station (AWS) observations from China's mesoscale network alongside radar during an extreme rainfall event caused by the remnant vortex of typhoon Haikui. Optimal sequence and scale representation of dense AWS observations within the operational two‐step DA framework are examined. Results demonstrate that adding regional AWS data to radar and national‐level observations significantly refines surface wind, temperature, and humidity analyses, directly enhancing precipitation forecasts. Notably, assimilating synoptic‐scale information followed by radar outperforms the reverse approach, as initial large‐scale increments stabilize subsequent convective‐scale updates, including stronger maritime airflow, enhanced low‐level convergence and atmospheric instability. Furthermore, despite ∼10 km spacing, AWS data function optimally as synoptic‐scale observations rather than convective‐scale, as the single‐layer temperature and humidity increments with very small decorrelation scales have limited impact in NWP models.