Eunha Lim

Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case

Abstract In this paper, the impact of Doppler radar radial velocity on the prediction of a heavy rainfall event is examined. The three-dimensional variational data assimilation (3DVAR) system for use with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is further developed to enable the assimilation of radial velocity observations. Doppler velocities from the Korean Jindo radar are assimilated into MM5 using the 3DVAR system for a heavy rainfall case that occurred on 10 June 2002. The results show that the assimilation of Doppler velocities has a positive impact on the short-range prediction of heavy rainfall. The dynamic balance between atmospheric wind and thermodynamic fields, based on the Richardson equation, is introduced to the 3DVAR system. Vertical velocity (w) increments are included in the 3DVAR system to enable the assimilation of the vertical velocity component of the Doppler radial velocity observation. The forecast of the hydrometeor variables of cloud water (qc...

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assi...

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

Incremental Analysis Updates Initialization Technique Applied to 10-km MM5 and MM5 3DVAR

Abstract An incremental analysis updates (IAU) technique is implemented for 3-h updates of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) and model system with a 10-km resolution to remove spurious gravity waves. By gradually incorporating analysis increments, IAU affects only the removal of high frequencies, leaving the waves related to diurnal processes. IAU appears to be efficient in reducing the moisture spinup problem in the MM5 3DVAR cycling system. The advantage of the IAU is the most significant in improving precipitation forecasts. Rapid update cycle (RUC) with 1- and 2-h intervals in conjunction with the IAU indicates a rapid minimization and less spinup and -down problems because of greater balancing between the moisture and dynamic variables. Impact studies are performed on a heavy rainfall case that occurred in the Korean Peninsula. Verification results with a 3-h cycling system are presented on operational...

A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the ...