Assimilation of Doppler Weather Radar data with a regional WRF-3DVAR system: Impact of control variables on forecasts of a heavy rainfall case.

Highlights • Variational data assimilation used for assimilating Doppler weather data is sensitive to the selection of control variables. • Doppler Weather Radar assimilation using the unconventional control variables of horizontal wind components significantly improve the skill of high intensity precipitation forecast in the first 12 h of the forecast model. • The smaller length scale and larger variance from horizontal wind components product analysis increments that tend to effectively capture small-scale features. Abstract Short-term precipitation forecasts from numerical weather prediction models are a vital source of information for real-time flood forecasting systems. Previous studies show that assimilation of Doppler Weather Radar (DWR) observations significantly improves the forecast skill of short-term precipitation. However, the variational assimilation methods used for DWR assimilation are sensitive to the selection of control variable options in background error statistics. In this study, the impact of control variable choices in assimilating DWR observations for improving the forecast of heavy rainfall event is analysed. For this purpose radar reflectivity and radial velocity, observations are assimilated using stream function velocity potential (ψχ) and horizontal wind components (uv) control variable options in Weather Research and Forecast model – 3DVAR (three-dimensional variational assimilation system). The results show that DWR assimilation using uv control variable option has improved the skill of first 12 h of high intensity precipitation forecasts. [ABSTRACT FROM AUTHOR]