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Modeling uncertainties for passive microwave precipitation retrieval: evaluation of a case study

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6 Author(s)
A. Tassa ; Inst. di Sci. dell'Atmosfera e del Clima, CNR, Roma, Italy ; S. Di Michele ; A. Mugnai ; F. S. Marzano
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Physically based microwave spaceborne techniques for rainfall retrieval are usually trained by simulated cloud-radiation databases (CRDs) composed of cloud profiles and associated brightness temperatures (TBs). When generating the database, the evaluation of the associated modeling uncertainties is crucial for retrieval error estimation. However, this is extremely complex due to the large number of free parameters. In this work, a possible methodology for taking into account CRD-related modeling uncertainties is proposed. The methodology-fairly general-is here applied to a limited dataset (a cloud-model resolved numerical output of a tropical cyclone). The modeling errors are obtained from systematic TB sensitivity tests associated to several parameters: particle sizes, temperature, ice content, sea surface wind speed, viewing angle, footprint size, radiative transfer schemes, melting phase, and particle shape. TB uncertainties are eventually summarized in a modeling error covariance matrix representing the intrinsic variability of the generated CRD. For comparison with real observations, the TBs are simulated at the spatial resolution, viewing geometry and frequencies of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The matrix is evaluated with respect to TMI data in terms of an indicator called database matching index. Since they are based on a single case study and suffer from the lack of direct coupling of the radiative transfer with the cloud-resolving model, the provided results should not be considered an exhaustive evaluation of cloud-radiation modeling errors. Nevertheless, they may be considered a valuable starting point for error characterization, since extensions to larger databases could definitely improve modeling error budgets.

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IEEE Transactions on Geoscience and Remote Sensing  (Volume:44 ,  Issue: 1 )