Future benefits of high-density radiance data from MTG-IRS in the AROME fine-scale weather forecast model [poster]
Guedj, Stephanie ; Rabier, Florence ; Guidard, Vincent ; Desroziers, Gérald
Satellite radiances from geostationary platforms currently provide a large input to meso-scale data assimilation systems for Numerical Weather Prediction (NWP) but the number of used observations is far less than the number of available data (less than 5%). In fact, assimilating high density observations may result in a degradation of the analysis if observation and background error covariances are not correctly specified. Observation errors are supposed to account for errors in : observation operator (RTTOV), representativeness and quality control (cloud screening). Errors are likely to exhibit correlations. However, for technical and computational reasons, covariance matrices are mostly assumed to be diagonal in most NWP centers. For high resolution satellite observation systems, such as MTG-IRS, the assumption of uncorrelated errors is questionable and can lead to sub-optimal systems if the observations are used at full resolution (Liu and Rabier, 2003). Recently, a relevant formulation for spatial error correlation has been proposed and implemented in a real size system (Bormann and Bauer, 2010, ECMWF). It was shown that correlated observations might be less informative than uncorrelated observations (even if correlations are well specified). But, is it valid for meso-scale models since background error correlation length (Lb≈20 km) is usually shorter than the one in global models (Lb≈200km) ? Objective : Enhanced density of assimilated radiances using available data (SEVIRI/ IASI) and simulated data (MTG-IRS). Context : International HyMeX project to improve our understanding of the water cycle, with emphasis on the predictability of intense events.
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