Radar Data Assimilation for the Simulation of Mesoscale Convective Systems  

作　　者：Jo-Han LEE Hyun-Ha LEE Yonghan CHOI Hyung-Woo KIM Dong-Kyou LEE 

机构地区：[1]School of Earth and Environmental Sciences, Seoul National University

出　　处：《Advances in Atmospheric Sciences》2010年第5期1025-1042,共18页大气科学进展（英文版）

基　　金：supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006–2303 and by the Brain Korea 21 Project in 2007

摘　　要：A heavy rainfall case related to Mesoscale Convective Systems （MCSs） over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting （WRF） three-dimensional variational （3DVAR） data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reffectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences （RMSDs） of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.A heavy rainfall case related to Mesoscale Convective Systems （MCSs） over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting （WRF） three-dimensional variational （3DVAR） data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reffectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences （RMSDs） of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.

关 键 词：WRF 3DVAR 3DVAR cycling INITIALIZATION tuning heavy rainfall radar data 

分 类 号：P457.6[天文地球—大气科学及气象学]