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Novel assessment of numerical forecasting model relative humidity with satellite probabilistic estimates

Abstract : A novel method of comparison between an atmospheric model and satellite probabilistic estimates of relative humidity (RH) in the tropical atmosphere is presented. The method is developed to assess the Météo- France numerical weather forecasting model ARPEGE (Action de Recherche Petite Echelle Grande Echelle) using probability density functions (PDFs) of RH estimated from the SAPHIR (Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie) microwave sounder. The satellite RH reference is derived by aggregating footprint-scale probabilistic RH to match the spatial and temporal resolution of ARPEGE over the April–May–June 2018 period. The probabilistic comparison is discussed with respect to a classical deterministic comparison confronting each model RH value to the reference average and using a set confidence interval. This study first documents the significant spatial and temporal variability in the reference distribution spread and shape. We demonstrate the need for a finer assessment at the individual case level to characterize specific situations beyond the classical bulk comparison using determinist “best” reference estimates. The probabilistic comparison allows for a more contrasted assessment than the deterministic one. Specifically, it reveals cases where the ARPEGE-simulated values falling within the deterministic confidence range actually correspond to extreme departures in the reference distribution, highlighting the shortcomings of the too-common Gaussian assumption of the reference, on which most current deterministic comparison methods are based.
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Submitted on : Wednesday, March 23, 2022 - 9:13:33 AM
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Chloe Radice, Hélène Brogniez, Pierre-Emmanuel Kirstetter, Philippe Chambon. Novel assessment of numerical forecasting model relative humidity with satellite probabilistic estimates. Atmospheric Chemistry and Physics, European Geosciences Union, 2022, 22 (6), pp.3811-3825. ⟨10.5194/acp-22-3811-2022⟩. ⟨insu-03364623v2⟩



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