New developments for the use of microphysical variables for the assimilation of IASI radiances in convective scale models

Martinet, Pauline ; Fourrié, Nadia ; Rabier, Florence ; Guidard, Vincent ; Montmerle, Thibaut ; Brunel, Pascal ; Lavanant, Lydie

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This paper focuses on the simulation and the assimilation of IASI (Infrared Atmospheric Sounding Interferometer) observations in convective scale numerical weather prediction (NWP) systems. A radiative transfer model that includes profiles for liquid water content, ice water content and cloud fraction was used to simulate cloud-affected radiances as background equivalents. This approach avoids the use of cloud parameters (cloud top pressure and effective cloud fraction) deduced from a CO2 slicing algorithm and the modelling of clouds by single layer clouds. The observation-screening procedure that was developed to improve the selection of usable cloudy scenes led to a good agreement between observations and background equivalents. For that purpose, a radiance analysis of co-located AVHRR (Advanced Very High Resolution Radiometer) pixels inside each IASI field of view was used. The goal of this preliminary work is to assess the feasibility of adding the cloud variables (liquid and ice water contents) to the state vector of the assimilation system. As it is not feasible to assimilate all the 8461 channels of IASI, we decided to evaluate the capability of the 366 channels (Collard and McNally 2009) used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) on cloud profiles. To this selection (called CM2009 hereafter), 134 new channels were added to optimize the retrieval of microphysical variables focussing mainly on the Mediterranean Sea. A linear approach based on the Degrees of Freedom for Signal (DFS) was compared to a non-linear method based on the brightness temperature response to the perturbation of cloud variables. To validate the new selections, observing system simulation experiments (OSSE) were used in the context of one-dimensional variational (1D-Var) retrievals.

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