Radar Data Assimilation for the Simulation of Mesoscale Convective Systems

A heavy rainfall case related to Mesoscale Convective Systems (MCSs) overthe Korean Peninsula was selected to investigate the impact of radar dataassimilation on a heavy rainfall forecast. The Weather Research andForecasting (WRF) three-dimensional variational (3DVAR) data assimilationsystem with tuning of the length scale of the background error covarianceand observation error parameters was used to assimilate radar radialvelocity and reflectivity data. The radar data used in the assimilationexperiments were preprocessed using quality-control procedures andinterpolated/thinned into Cartesian coordinates by the SPRINT/CEDRICpackages. Sensitivity experiments were carried out in order to determine theoptimal values of the assimilation window length and the update frequencyused for the rapid update cycle and incremental analysis update experiments.The assimilation of radar data has a positive influence on the heavyrainfall forecast. Quantitative features of the heavy rainfall case, such asthe maximum rainfall amount and Root Mean Squared Differences (RMSDs) ofzonal/meridional wind components, were improved by tuning of the lengthscale 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 positiveeffects of the radar data assimilation and the tuning of the length scaleand observation error parameters were clearly shown by the 3DVAR increment.