4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for rain drop size distribution and vertical wind retrievals

Abstract

Abstract. This paper presents a novel approach for retrieving the vertical raindrop size distribution (DSD) profiles and vertical winds during light rain events. It consists in coupling K band Doppler spectra and ground disdrometer measurements (raindrop fluxes) in a 2-D numerical model propagating the DSD from the clouds to the ground level. The coupling is made via a 4-D-VAR data assimilation algorithm. The model is, up to now, limited to the fall of droplets under gravity, modulated by the effects of vertical winds. Since evaporation, coalescence/break-up and horizontal air motion are not taken into account, we limit the study to light, stratiform rain events in which these phenomena appear negligible. We firstly use simulated data sets (data assimilation twin experiment) to show that the algorithm is able to retrieve the DSD profiles and vertical winds. It also demonstrates the ability of the algorithm to deal with the atmospheric turbulence (broadening of the Doppler spectra) and the instrumental noise. The method is then applied to a real case study which happened in the south-west of France during the autumn 2013. The data set collected during a long, quiet event (6 h duration, rain rate between 2 and 7 mm h−1) comes from an optical disdrometer and a 24 GHz vertically pointing Doppler radar. We show that the algorithm is able to explain the observations and supplies DSD and vertical wind profiles realistic compared to what could be expected for such a rain event. A perspective for this study is to apply it to extended data sets for a more thorough validation. Other data sets would also help to parameterize more phenomena needed in the model (evaporation, coalescence/break-up) to apply the algorithm to convective rain and to evaluate the adequacy of the model's parameterization.