2013: An error model for instantaneous satellite rainfall estimates: Evaluation of BRAINTMI over

Characterising the error associated with satellite rainfall estimates based on space-borne passive and active microwave measurements is a major issue for many applications, such as water budget studies or assessment of natural hazards caused by extreme rainfall events. We focus here on the error structure of the Bayesian Rain retrieval Algorithm Including Neural Network (BRAIN), the algorithm that provides instantaneous quantitative precipitation estimates at the surface based on the MADRAS radiometer on board the Megha-Tropiques satellite. A version of BRAIN using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been compared to reference values derived either from TRMM Precipitation Radar (PR) or from a ground validation (GV) dataset. The ground-based measurements were provided by two densified rain-gauge networks in West Africa, using a geostatistical framework. The comparisons were carried out at the BRAIN retrieval scale for TMI (instantaneous and 12.5 km) and over a ten-year-long period. The primary contribution of this study is to provide some insight into the most significant error sources of satellite rainfall retrieval. This involve