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

Radice, Chloé; Brogniez, Hélène; Kirstetter, Pierre-Emmanuel; Chambon, Philippe

doi:https://doi.org/10.5194/acp-22-3811-2022

Articles | Volume 22, issue 6

https://doi.org/10.5194/acp-22-3811-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Analysis of atmospheric water vapour observations and their...

https://doi.org/10.5194/acp-22-3811-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 6

Research article

|

22 Mar 2022

Research article |

| 22 Mar 2022

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

Chloé Radice, Hélène Brogniez, Pierre-Emmanuel Kirstetter, and Philippe Chambon

Supplement

https://doi.org/10.5194/acp-22-3811-2022-supplement

Data sets

SAPHIR-L2-RH: Instantaneous non-precipitating conditions level 2 products Relative Humidity derived from SAPHIR, version 1 R. Sivira, H. Brogniez, C. Dufour, and S. Cloché https://www.icare.univ-lille.fr/product-documentation/?product=SAPHIR-L2B-RH

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Short summary

A novel probabilistic approach is proposed to evaluate relative humidity (RH) profiles simulated by an atmospheric model with respect to satellite-based RH defined from probability distributions. It improves upon deterministic comparisons by enhancing the information content to enable a finer assessment of each model–observation discrepancy, highlighting significant departures within a deterministic confidence range. Geographical and vertical distributions of the model biases are discussed.