Evaluation of precipitation satellite products and ground-based radars during two case studies over France in 2022

Precipitations are a crucial and extremely valuable source of water for humanity’s food production and for its own consumption but droughts or even floods can be potential causes of considerable damages to crops, infrastructures or properties and life-threatening situation. Precipitations are characterised by a high spatial and temporal variabilities. Hence, multiple rain gauges, precipitation radars and satellite-based estimates gridded datasets are necessary to quantify rain accumulation at local, regional and global scales.

Rain gauges observations tend to observe the amount of precipitable water locally providing rain rates for an equivalent surface of less than 1 m². Weather radars estimate the rain fields over a surface at high spatio-temporal resolution (~1km for space and several minutes for the time). Finally, the satellite-related estimates of precipitation, mainly based on the estimates made by infrared (IR) and passive or active microwave (PMW or AMW) sensors, have been developed and studied in the last decades. Moreover, new innovative satellite skills provide rain estimates from the attenuation of broadband communication satellite link signals at Ka-band.

This work evaluated the behaviour of two precipitation radar products: the French radar product PANTHERE and the pan-European radar mosaic product OPERA, and 11 satellite precipitation products: GHE, PDIR, IMERG Early v6, IMERG Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v8, GSMaP-NRT-GC v8, GSMaP-NOW, GSMaP-NOW-GC and Databourg. This study has been performed against the observation made the RADOME rain gauge network with more than 500 stations over the French territory. All datasets are aggregated at hourly temporal resolution and are compared using a point-to-pixel method during two notable case studies: a 3-days April 2022 event and a 4-days June 2022 event. It is shown that radar products tend to be more reliable on the estimation of precipitation accumulation while PMW datasets tend to underestimate rain rates compared to the observations whereas IR datasets has the potential to overestimate these values.