The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias <2.0) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run.

Automated summary

West African food systems and rural socio-economics are heavily reliant on rainfed agriculture, making them highly susceptible to rainfall uncertainty, frequent floods, and droughts. However, reliable rainfall information is currently lacking. This paper proposes a Deep Learning (DL) model that utilizes water vapor (WV) observations and temporal information, in addition to thermal infrared (TIR) data, for satellite rainfall retrieval. The results demonstrate that the incorporation of WV data enhances the detection of convective motions associated with heavy rainfall and allows for the identification of dry air masses from the Sahara Desert that disrupt precipitation events. The developed DL model outperforms the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run method in rainfall binary classification, exhibiting fewer false alarms and lower rainfall overdetection rates (FBias <2.0).