Assimilating radar radial wind and reflectivity data in an idealized setup of the COSMO-KENDA system

Abstract

An idealized framework for radar data assimilation has been developed based on the operational data assimilation system of the Deutscher Wetterdienst (DWD) for the regional convection-permitting model of COSMO (COnsortium for Small-scale MOdelling), coupled with an Efficient Modular VOlume scanning RADar Operator (EMVORADO). The data assimilation scheme is the Local Ensemble Transform Kalman Filter (LETKF). The idealized framework is used to explore differences between radial wind and reflectivity observations in storm-scale data assimilation by conducting a series of twin experiments. First, it is shown for both types of data that using the estimated observation errors obtained by the Desroziers method help to reduce the model state errors during cycles. Assimilating only radial winds results in smaller errors in wind components and assimilating reflectivities only results in smaller errors in temperature and microphysical variables, and the latter one requires a shorter spinup time to reconstruct precipitating systems. Both radial wind and reflectivity data are useful to reconstruct the dynamical structure of supercells described by the supercell detection index but radial wind data are more important. However, a considerable amount of spurious convective cells arise if only radial winds are assimilated. Assimilating reflectivities is able to efficiently reduce the spurious convection due to the assimilation of no reflectivity data. Additionally, it is shown that the data assimilation could cause significant biased increases in divergence, vorticity and total specific mass of microphysical variables. Assimilation of radial winds leads to lower increases in divergence and vorticity, while assimilation of reflectivities leads to lower increases in total specific mass. The amount of increase depends on the specification of the observation error statistics and on the number of assimilated data. The 6-h forecasts are skillful for both assimilating radial winds only and assimilating reflectivity only, while the latter one is even better since the skills of the former one are heavily penalized for spurious convection. Overall, radial wind and reflectivity data complement each other, assimilation of both data simultaneously results in the smallest state errors and the lowest biased increase in divergence, vorticity and total specific mass during cycles and subsequently the best 6-h forecasts.