Assimilation of radar data based on cloud-dependent background error covariance and its impact on rainfall forecasting

The traditional variational assimilation method uses isotropic and homogeneous background error covariance, which ignores the weather system dependence of the background error covariance, and the introduction of ensemble flow-dependent background error covariance in the variational framework requires additional ensemble forecasts. In order to introduce more reasonable background error covariance in the variational assimilation, a "cloud-dependent" background error covariance is constructed by introducing cloud indices, and a cloud-dependent background error covariance assimilation scheme is proposed and applied to the assimilation of radar and other multi-source observations. Based on the cloud-dependent background error covariance data assimilation scheme, a series of single observation tests and batch cyclic assimilation forecasts during the rainy season as well as detailed diagnostic analysis of rainfall cases are carried out. From the single observation tests, it is found that the cloud-dependent background error covariance can dynamically adjust the background error at each grid point in real time, resulting in significant anisotropy and dependence of the analysis increments on cloud and rain characteristics; the batch cyclic assimilation and forecasting experiments show that the radar assimilation with cloud-dependent background error covariance can steadily improve the precipitation forecasting capability, and the improvement is especially obvious for the large magnitude precipitation scores; The diagnosis of strong convective storms further shows that the application of cloud-dependent background error covariance improves the prediction of dynamical, thermal, water vapor and hydrometeor fields. The assimilation scheme based on cloud-dependent background error covariance can introduce background error covariance information that is more consistent with real-time weather characteristics in the framework of variational assimilation, which provides a basis for better assimilation of high-resolution radar data and effectively improves rainfall forecasting.