Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale

Abstract

Numerical weather prediction (NWP) systems at the convective scale are operated to gain reliable forecasts for diverse atmospheric variables at high spatial resolution. Especially for the prediction of small-scale weather phenomena such as deep convection including the associated precipitation patterns and wind gusts the high-resolution models provide additional benefit over coarser scale models. In this context the distribution of atmospheric humidity plays an important role, however conventional observations of atmospheric humidity are sparse in space and time. The present work aims at the assimilation of water vapor channel radiances of the satellite instrument SEVIRI in an operational framework based on a Local Ensemble Transform Kalman Filter (LETKF) and a convection permitting NWP model. This article describes all the essential elements for a successful incorporation of this kind of data into the system, from the application of a cloud filtering technique over bias correction and vertical localization of the radiance observation. Data assimilation experiments over two 4-week periods show a neutral to slightly positive impact of SEVIRI radiances on upper-air relative humidity and wind speed forecasts.