The potential of variational retrieval of temperature and humidity profiles from Meteosat Second Generation observations

Abstract

AbstractThe quality of temperature and humidity retrievals from the infrared Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one‐dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high‐resolution regional‐scale numerical weather prediction (NWP) models. The non‐hydrostatic forecast model COSMO in the ARPA‐SIMC operational configuration is used to provide background fields. Only clear‐sky observations over sea are processed.An optimized one‐dimensional variational set‐up comprised of two water‐vapour and three window channels is selected. It maximizes the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations.The 1Dvar retrieval quality is first quantified in relative terms, employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed by comparing the analysis with independent radiosonde observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the use of the retrieved profiles generated by the 1Dvar in the COSMO nudging scheme can locally reduce forecast errors. Copyright © 2009 Royal Meteorological Society