TY - DATA T1 - Data and code underlying the PhD thesis: Deep Learning and Earth Observation for the Study of West African Rainfall PY - 2024/12/10 AU - Monica Estebanez Camarena UR - DO - 10.4121/0581dd0b-bfe8-466c-b7f7-dffe55ed28b5.v1 KW - Deep Learning KW - Earth Observation KW - Rainfall KW - West Africa KW - rainfall detection KW - precipitation KW - RainRunner KW - Satellite Rainfall Detection N2 -

The PhD thesis "Deep Learning and Earth Observation for the Study of West African Rainfall" develops a Deep Learning-based satellite rainfall retrieval model for West Africa, called "RainRunner". RainRunner classifies 3-hour sequences of Meteosat Second Generation (MSG) WV and TIR images in rain/no-rain. After being trained in Northern Ghana, RainRunner is applied to a wider area in West Africa (the Sudanian Savana), to evaluate generalization capability and understand better the rainfall mechanisms in the wider area. This dataset allows to do a full performance evaluation of the model by downloading and processing MSG data to create the test dataset, applying the model and evaluating the results. More information about the exact goal of each script can be found the README.txt file.

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