Multi-Scale Assimilation for Predicting Heavy Precipitation in Taiwan During the Mei-Yu Season

The southwesterly monsoonal flow over South China Sea transports large amount of moisture northeastward and sets up a pre-condition for heavy precipitation in Taiwan during the Mei-Yu season (late-May and June). With the orographic effect, the heavy precipitation is characterized in the western part of Taiwan.

The regional data assimilation system based on the Ensemble Kalman filter (EnKF) with the flow-dependent error statistics is expected to better use observations, which leads to better severe weather prediction. To capture the heavy precipitation during the Mei-Yu season in Taiwan, the key elements include representing the prevailing southwesterly flow and the local convergence over coastal area of Taiwan. High-resolution simulation and assimilation are required to improve a multi-scale convective system, including the topography effect. Due to sampling errors, EnKF with high-resolution grids applies a shorter localization scale to limit the influence of the observation. However, applying a shorter localization scale on observations in the high-resolution grids might cut off the large-scale moisture transport, which is essential to storm development. In this study, we applied the dual-localization method proposed by Kondo and Miyoshi (2013) to highlight the importance of multi-scale correction for predicting such kind of multi-scale events with multi-resolution nesting model grids.

Results suggest that the dual-localization scheme helps to better use the flow-dependent error statistics of the regional EnKF system and optimize the corrections from observations like radio sondes and GPS Radio Occultation data. Compared with the prediction using the single-scale localization, the synoptic-scale moisture correction is now better preserved and the intensity and location of heavy precipitation prediction are significantly improved.