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In this paper we describe a general procedure for calculating syntheticsensor radiances from variable output from a global atmospheric forecastmodel. In order to take proper account of the discrepancies between modelresolution and sensor footprint, the algorithm takes explicit account of themodel subgrid variability, in particular its description of the probabilitydensity function of total water (vapor and cloud condensate.) The simulatedsensor radiances are then substituted into an operational remote sensingalgorithm processing chain to produce a variety of remote sensing productsthat would normally be produced from actual sensor output. This output canthen be used for a wide variety of purposes such as model parameterverification, remote sensing algorithm validation, testing of new retrievalmethods and future sensor studies. We show a specific implementation usingthe GEOS-5 model, the MODIS instrument and the MODIS Adaptive ProcessingSystem (MODAPS) Data Collection 5.1 operational remote sensing cloudalgorithm processing chain (including the cloud mask, cloud top propertiesand cloud optical and microphysical properties products). We focus on cloudsbecause they are very important to model development and improvement

Multi-sensor cloud retrieval simulator and remote sensing from model parameters – Part 1: Synthetic sensor radiance formulation