First results of the assimilation of AIRS data in Météo-France Numerical Weather Prediction model Thomas Auligné*, Florence Rabier*, Lydie Lavanant**, Mohamed Dahoui*** * Météo-France, Centre National de Recherche Météorologique, 42 ave. Coriolis, 31057 Toulouse Cedex 1, France ** Météo-France, Centre de Météorologie Spatiale, BP 147, 22300 Lannion Cedex, France *** Moroccan Meteorological Service Abstract A subset of channels from AIRS (Atmospheric InfraRed Sounder) aboard AQUA satellite is provided operationally by NOAA/NESDIS to Numerical Weather Prediction (NWP) centers. Studies have been carried out to assimilate this data in the Météo-France NWP suite. They require performance monitoring and bias correction of the observations. The impact of the early assimilation of AIRS on numerical weather forecast is presented. Infrared radiances are contaminated by clouds in most cases. Therefore, there is a need for a cloud detection scheme. This study focuses on the NESDIS method (Goldberg et al., 2003) that has been validated in a comparison study by Lavanant et al. (2003). In order to take more advantage of the available data, the assimilation of cloudy radiances is investigated, using a radiative transfer model in cloudy conditions (RTTOVCLD). Results from monitoring and 1D- Var assimilation experiments are shown. Introduction The Atmospheric InfraRed Sounder (AIRS), launched by NASA in 2002 aboard the AQUA satellite is the first instrument of a new generation called “advanced infrared sounders” (Aumann et al., 2003). 2378 channels are available within the 3.7-15.4 micron range, most of them showing an excellent performance regarding their spectral response and sensitivity. The AQUA polar- orbiting platform provides global coverage of measurements. Thus, an important impact of the assimilation of AIRS data is expected on weather forecast skills. On the other hand, new challenges are directly linked to this generation of instruments and they need to be solved in order to take full benefit of the data. The first and probably main problem is the dramatic increase of the number of channels (about two orders of magnitude). This results in communication, computing and storage issues. Since NWP centers have a “real-time” constraint for data assimilation, it is currently impossible to assimilate all AIRS channels operationally. A constant subset of 328 channels is provided by NOAA/NESDIS for AIRS center pixel of every other AMSU field of view (i.e. 1 AIRS pixel over 18). Using this product, a first assimilation suite has been implemented to study the impact of AIRS data on weather forecast. The potential use of cloudy radiances is investigated in a uni-dimensional, non-linear variational algorithm. A diagnostic cloud scheme linking temperature and humidity to cloud variables has been implemented in order to use selected AIRS cloudy radiances to retrieve temperature and humidity profiles.