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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 using probability density functions (PDF) of RH estimated from the SAPHIR 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. The study first documents the significant spatial and temporal variability of the reference distribution spread and shape. It warrants the need for a finer assessment at the individual case level to characterise 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.
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Contributor : Catherine Cardon Connect in order to contact the contributor
Submitted on : Monday, October 4, 2021 - 6:29:40 PM
Last modification on : Tuesday, January 4, 2022 - 1:47:52 PM
Long-term archiving on: : Wednesday, January 5, 2022 - 7:11:26 PM


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Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : insu-03364623, version 1


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 Discussions, European Geosciences Union, 2022, pp.(Under Review). ⟨insu-03364623⟩



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