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. This article focuses on the selection of satellite infrared IASI (InfraredAtmospheric Sounding Interferometer) observations in the global numericalweather prediction (NWP) system ARPEGE (Action de Recherche Petite EchelleGrande Echelle). The observation simulation is performed with thesophisticated radiative transfer model RTTOV-CLD, which takes into accountthe cloud scattering and the multilayer clouds from atmospheric profiles andcloud microphysical profiles (liquid water content, ice content and cloudfraction). The aim of this work is to select homogeneous scenes by using theinformation of the collocated Advanced Very High Resolution Radiometer(AVHRR) pixels inside each IASI field of view and to retain the mostfavourable cases for the assimilation of IASI infrared radiances. Two methodsto select homogeneous scenes using homogeneity criteria already proposed inthe literature were adapted: the criteria derived from Martinet et al. ( 2013 ) for cloudy sky selection in the French mesoscalemodel AROME (Applications of Research to Operations at MEsoscale) and thecriteria from Eresmaa ( 2014 ) for clear-sky selection in the globalmodel IFS (Integrated Forecasting System). A comparison between these methodsreveals considerable differences, in both the method to compute the criteriaand the statistical results. From this comparison a revised methodrepresenting a kind of compromise between the different tested methods isproposed and it uses the two infrared AVHRR channels to define thehomogeneity criteria in the brightness temperature space. This revised methodhas a positive impact on the observation minus the simulation statistics,while retaining 36 % of observations for the assimilation. It was thentested in the NWP system ARPEGE for the clear-sky assimilation. Thesecriteria were added to the current data selection based on the McNally andWatts (2003) cloud detection scheme. It appears that the impact on analysesand forecasts is rather neutral.

Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context