Observation and numerical simulations with radar and surface data assimilation for heavy rainfall over central Korea

Abstract

This study investigated the impact of multiple-Doppler radar data and surface data assimilation on forecasts of heavy rainfall over the central Korean Peninsula; the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system (3DVAR) were used for this purpose. During data assimilation, the WRF 3DVAR cycling mode with incremental analysis updates (IAU) was used. A maximum rainfall of 335.0 mm occurred during a 12-h period from 2100 UTC 11 July 2006 to 0900 UTC 12 July 2006. Doppler radar data showed that the heavy rainfall was due to the back-building formation of mesoscale convective systems (MCSs). New convective cells were continuously formed in the upstream region, which was characterized by a strong southwesterly low-level jet (LLJ). The LLJ also facilitated strong convergence due to horizontal wind shear, which resulted in maintenance of the storms. The assimilation of both multiple-Doppler radar and surface data improved the accuracy of precipitation forecasts and had a more positive impact on quantitative forecasting (QPF) than the assimilation of either radar data or surface data only. The back-building characteristic was successfully forecasted when the multiple-Doppler radar data and surface data were assimilated. In data assimilation experiments, the radar data helped forecast the development of convective storms responsible for heavy rainfall, and the surface data contributed to the occurrence of intensified low-level winds. The surface data played a significant role in enhancing the thermal gradient and modulating the planetary boundary layer of the model, which resulted in favorable conditions for convection.