High-Resolution Reanalysis of Daily Precipitation using AROME Model Over France

Van Hyfte, Stéphane ; Le Moigne, Patrick ; Bazile, Eric ; Verrelle, Antoine ; Boone, Aaron

Année de publication
2023
Résumé
<p align=justify>Among the various meteorological variables, precipitation is one of significant interest, especially for hydrological studies. However, obtaining a reliable precipitation data set is a difficult challenge as precipitation can be very discontinuous in space and time. In this study, a method to obtain a high resolution precipitation reanalysis over France is purposed based on a study from 01/01/2016 to 31/12/2018. The French operational regional model Application de la Recherche à l'Opérationnel à Méso-Echelle (AROME) is combined with precipitation observations, which have been quality controlled, using an optimum interpolation data assimilation algorithm. To use this technique, some hypotheses have to be verified, such as the Gaussian distribution of the innovations. Since the precipitation distribution is highly asymmetric, a Box-Cox transformation is applied to both background and observations to work with variables which behave like Gaussian variables. Then, the background and observation standard deviation errors are determined thanks to the semi-variogram technique, which provides daily values. Results show that the Box-Cox transformation provides better scores for light precipitation and has the same quality as the reference - analysis in the physical space - for high precipitation amounts.</p>
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