Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system

Abstract

AbstractHigh‐resolution numerical simulations are regularly used for severe weather forecasts. To improve model initial conditions, a single short localization is commonly applied in the ensemble Kalman filter when assimilating observations. This approach prevents large‐scale corrections from appearing in a high‐resolution analysis. To improve heavy rainfall forecasts associated with a multiscale weather system, analyses must be accurate across a range of spatial scales, a task that is difficult to accomplish using a single localization. This study is the first to apply a dual‐localization (DL) method to improve high‐resolution analyses used to forecast a real‐case heavy rainfall event associated with a Meiyu front on 16 June 2008 in Taiwan. A Meiyu front is a multiscale weather system characterized by storm‐scale convection, a mesoscale front, and large‐scale southwesterly monsoonal flow. The use of the DL method to produce the analyses was able to correct both the synoptic‐scale moisture flux transported by southwesterly monsoonal flow and the mesoscale low‐level convergence offshore of southwestern Taiwan. As a result, the forecasted amount, pattern, and temporal evolution of the heavy rainfall event were improved.