Assimilation of radar observations in mesoscale models using approximate background error covariance matrices

Temporally and spatially highly resolving radar measurements are the only means to continuously observe dynamically evolving meteorological phenomena such as severe thunderstorms. Assimilation of radar data into mesoscale models might be a key factor to improve precipitation forecasting especially at shorter time scales.

However, major obstacles for the assimilation of radar data lie in the strong non-linearity of the observation operators and the intermittent nature of the precipitation processes. These result in a severe violation of the assumption of Gaussian error characteristics in the data assimilation schemes, which manifests itself in unrealistic background error covariance matrices and in unstable solutions.

In this paper, we propose a new method to address this problem and to assimilate radar observations into mesoscale models. The proposed solution includes two steps to obtain a well-conditioned background error covariance matrix: A normalizing step and a rescaling step. We also introduce a new weighting technique to avoid filter divergence, another common issue especially in Ensemble Kalman Filter (EnKF) type assimilation schemes.

The proposed method is applied to simulate an intensive precipitation event near Madison, WI in July 2006 using the Weather Research and Forecasting (WRF) Model in an EnKF mode. The new method results in a significant improvement of short-range forecasting skills for this severe weather event.