Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system

Infrared satellite observations are strongly affected by clouds, which complicates their effective use in data assimilation. While observation minus first-guess (FG departure) statistics for cloud-free data are close to a normal (Gaussian) distribution, the occurrence of clouds leads to strongly increased uncertainty, systematic differences between observations and model forecasts and subsequently a clear deviation of the FG departures from the Gaussianity that is usually assumed in data assimilation. This study aims to classify the cloud impact on Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared brightness temperature observations and model equivalents to mitigate the issues of non-Gaussian FG departure statistics for data assimilation. A threshold brightness temperature is introduced that allows us to quantify the cloud impact and to derive an error estimate for FG departures as a function of the cloud impact. The use of the dynamic error estimate leads to substantially more Gaussian FG departure statistics. Based on the dynamic error estimate, an observation error model is derived for the assimilation of infrared brightness temperature observations in an all-sky approach. The proposed method allows us to treat cloud-free and cloud-affected observations in a uniform way, without the need for cloud screening.