Doppler Radar Data Assimilation in KMA's Operational Forecasting
INTRODUCTION TO DOPPLER RADAR DATA ASSIMILATION. Assimilation of highresolution Doppler radar observations has long been recognized as an effi cient way to improve short-range quantitative precipitation forecasting (QPF). Since the Weather Surveillance Radar 88 Doppler (WSR-88D) network in the United States was established, methods to assimilate Doppler radar data have been extensively explored. Although a lot of questions remain, research (including real-time experiments) through the past decade has yielded progress. Doppler radar data assimilation is showing signifi cant promise now compared to its initial research stage in the early 1990s. For Doppler radar data assimilation, 4D-Var and an ensemble Kalman filter have been actively researched in recent years but remain computationally expensive and thus impractical. However, 3D-Var is a feasible and advanced technique for Doppler radar data assimilation in operational applications. During the present decade, 3D-Var has been one of the most widely used techniques for operational data assimilation. Use of 3D-Var is a quick approach to adding Doppler radar data to operational forecasts. In addition to computational efficiency, 3-D Var offers a way to directly assimilate observations (radial velocity and reflectivity) that are not model variables through the use of the observation operators. Direct assimilation of radial velocity and reflectivity is an advantage of the variational method over the Newtonian relaxation nudging and optimal interpolation approaches, in which retrievals are required to produce model variables for assimilation. D uring 2001–03, the Mesoscale and Microscale Meteorology Division (MMM) at the National Center for Atmospheric Research (NCAR) partnered with the Korean Meteorological Administration (KMA) and Seoul National University (SNU) to use Doppler radar data in the MM5 with the Weather Research and Forecasting (WRF) 3-dimensional variational (3D-Var) data-assimilation system. After case studies and one-month comparison experiments with and without radar data assimilation in 2004, the system proceeded to semioperational testing in 2005 and was implemented for full operational production in 2006. The case studies showed benefits of radar data assimilation, and further tests indicated that Doppler radar data assimilation in WRF 3D-Var performed robustly and improved rainfall forecasting. The procedure for KMA Doppler radar data assimilation is rather sophisticated: it includes data preprocessing (quality control, error statistics, and formatting), assimilation with WRF 3D-Var, and analysis update cycling. The algorithms for direct assimilations of radial velocity and reflectivity are advanced and innovative. The transfer of the developed system from research mode at NCAR to operational Doppler Radar Data Assimilation in KMA’s Operational Forecasting