On process-oriented conditional targeted covariance inflation (TCI) for 3D-volume radar data assimilation

Abstract. This paper addresses a major challenge in assimilating 3D radar reflectivity data with a localized ensemble transform Kalman filter (LETKF). In the case of observations with significant reflectivity and small or zero corresponding simulated reflectivities for all ensemble members, i.e., when the ensemble spread is vanishing, the filter ignores the observations based on its low-variance estimate for the background uncertainty. For such low-variance cases, the LETKF is insensitive to observations and their contribution to the analysis increment is effectively zero. Targeted covariance inflation (TCI) has been suggested to deal with the ensemble spread deficiency (Yokota et al., 2018; Dowell and Wicker, 2009; Vobig et al., 2021). To actually make TCI work in a fully cycled convective-scale data assimilation framework, here we will introduce a process-oriented approach to the TCI in combination with a conditional approach formulating criteria under which targeted covariance inflation is efficient. The process-oriented conditional TCI addresses the challenge of underrepresented reflectivity in the prior by constructing artificially simulated reflectivities for each ensemble member based on current observations and typical convective processes. Furthermore, certain conditions are used to restrict this spread inflation process to a carefully selected minimal set of eligible observations, reducing the noise introduced into the system. We will describe the theoretical basis of the new TCI approach. Furthermore, we will present numerical results of a case study in an operational framework, for which the TCI is applied to radar observations at each hourly assimilation step throughout a data assimilation cycle. We are able to demonstrate that the TCI is able to clearly improve the assimilation of radar reflectivities, making the system dynamically generate reflectivity that would otherwise be missing. Related to this, we are able to show that the fractional skill score of radar reflectivity forecasts over lead times of up to 6 h is significantly improved by up to 10 %. All of the results are based on the German radar network and the ICON-D2 model covering central Europe.