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

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 Meteo-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 Atmospherique du Profil d'Humidite Intertropicale par Radiometrie) 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.

Automated summary

This study presents a novel method of comparing an atmospheric model and satellite probabilistic estimates of relative humidity using probability density functions. It shows the need for a finer assessment at the individual case level to characterize specific situations beyond the classical bulk comparison using deterministic best reference estimates. The probabilistic comparison allows for a more contrasted assessment than the deterministic one, highlighting the shortcomings of the Gaussian assumption of the reference distributions.