P-7

Cloud-affected Infrared Brightness Temperature Assimilation using a Local Ensemble Transform Kalman Filter

Perianez, A. (RIKEN/University of Reading), J. A. Otkin (UW/CIMSS), A. Schomburg (DWD), R. Faulwetter (DWD), H. Reich (DWD), C. Schraff (DWD), R. Potthast (DWD/UoR), K. Okamoto (MRI), K. Bessyo (MSC), H. Seko (MRI) and T. Miyoshi (RIKEN)

The accurate modeling of clouds in NWP is substantial to improve the quality of the current weather forecasting systems. Advanced instruments on board geostationary satellites, like Spinning Enhanced Visible and Infrared Imager (SEVIRI) or Advanced Himawari Imager (AHI), provide valuable information about the structure of cloud distribution and atmospheric water vapor. Moreover, due to the high temporal frequency, they are able to detect rapidly changing fields like precipitation or clouds.

The German Weather Service (DWD) and RIKEN/Meteorological Research Institute (MRI) are making efforts to assimilate cloud and water vapor sensitive infrared brightness temperatures in high-resolution models using an LETKF data assimilation scheme. Observations from several infrared channels from the SEVIRI and the AHI instruments are chosen for the assimilation, respectively. However, in order to operationally assimilate cloud-affected brightness temperatures a number of challenges must be addressed.

The experimental work that will be presented includes the development of a cloud-dependent bias correction and the assimilation of water vapor radiances from the SEVIRI sensor with COSMO-DE. The results of these experiments show that the assimilation of cloud-affected brightness temperatures with an LETKF, and with an adequate bias correction, improves the analysis and the skill assimilation cycle. Steps towards experimental AHI radiances assimilation in the NHM-LETKF model at convection-resolving scales will be shown.