Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.

The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.

Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.