[10-3]

Characterization of SEVIRI satellite observations and their potential for convective-scale ensemble data assimilation

Florian Harnisch (Hans-Ertel Centre for Weather Research, LMU Munich), Africa Perianez (RIKEN Advanced Institute for Computational Science), Jason Otkin (CIMSS, University Wisconsin), Leonhard Scheck (Hans-Ertel Centre for Weather Research, LMU Munich), Martin Weissmann (Hans-Ertel Centre for Weather Research, LMU Munich)

The limited predictability of convective systems requires the assimilation of frequent and spatially dense observations in convective-scale data assimilation systems. Measurements from geostationary satellites are therefore a potentially powerful data set. While clear-sky radiances of various channels have been used widely and successfully in several modelling systems, the assimilation of cloud-affected data poses significant challenges.

So far, mainly variational methods have been used to assimilate radiances, while using radiances in ensemble data assimilation is still in its infancy. To facilitate the direct assimilation of MSG SEVIRI observations in the experimental ensemble system of Deutscher Wetterdienst, a variety of problems need to be addressed. These are, among others, accurate and fast forward operators, an effective treatment of clear-sky and cloudy areas and the correction of systematic differences between observations and model equivalents.

In this study, observations in the infrared and visible channels of SEVIRI are compared to COSMO-DE forecasts and statistics are assessed to determine suitable assimilation settings. The main focus is the potential classification and screening of cloud-affected observations , the investigation of situation dependent-observation errors and determination of a suitable bias correction. In addition, the complimentary information of the different channels and their interaction is examined.