West African food and economic safety are heavily reliant on agriculture, most of which is rainfed. Changing rainfall patterns induced by global warming jeopardize yields by unpredictable water availability. At the same time, a rapidly growing population leads to rising demands for food production. Accurate rainfall information is essential for farmers to adjust their crop management practices and avoid yield, thereby improving the overall resilience of the region. However, this information is largely lacking due to a sparse rain gauge distribution, limited resources and data transmission challenges. Added to this, existing satellite rainfall products show a particularly poor correlation with ground observations in West Africa.
This dissertation aims at improving rainfall information for farmers in the Sudanian Savanna bioclimatic region of West Africa. The Sudanian Savanna stretches across Africa as a broad belt, covering roughly from southern Mali in the north to northern Ghana in the south. Its West African area expands from Senegal in the west to Chad in the easte. Improving rainfall information will support climate resilience and food and economic safety in the region. This research leverages on the unique potential of Earth Observation satellites to provide rainfall information everywhere, due to their global coverage and ability to track atmospheric processes. To tackle the general poor performance of existing rainfall information products, this work proposes an alternative avenue. Particularly, it investigates the potential of Deep Learning (DL) methods to extract relationships between meteorological variables and raw satellite data that might be overlooked by traditional satellite rainfall retrieval methods....

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.

Food and economic security in West Africa rely heavily on rainfed agriculture and are threatened by climate change and demographic growth. Accurate rainfall information is therefore crucial to tackling these challenges. Particularly, information about the occurrence and length of droughts as well as the onset date of the rainy season is essential for agricultural planning. However, existing rainfall models fail to accurately represent the highly variable and sparsely monitored West African rainfall patterns. In this paper, we show the potential of deep learning (DL) to model rainfall in the region and propose a methodology to develop DL models in data-scarce areas. We built two DL models for satellite rainfall (rain/no-rain) detection over northern Ghana from Meteosat TIR data based on standard DL architectures: Convolutional neural networks (CNNs) and convolutional long short-term memory neural networks (ConvLSTM). The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System (PERSIANN-CCS) products are used as benchmarks. We use rain gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) for model development and performance evaluation. We show that our models compare well against existing products despite being considerably simpler, developed with a small training dataset—i.e., 8 stations covering 2.5 years with 20.4% of the data missing—and using TIR data alone. Concretely, our models consistently outperform PERSIANN-CCS for rain/no-rain detection at a sub-daily timescale. While IMERG is the overall best performer, the DL models perform better in the second half of the rainy season despite their simplicity (i.e., up to 120 k parameters). Our results suggest that DL-based regional models are a promising alternative to state-of-the-art global products for providing regional rainfall information, especially in meteorologically complex regions such as the (sub)tropics, which are poorly covered by ground-based rainfall observations.