Data and code underlying the PhD thesis: Deep Learning and Earth Observation for the Study of West African Rainfall
DOI: 10.4121/0581dd0b-bfe8-466c-b7f7-dffe55ed28b5
Datacite citation style
Dataset
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.
History
- 2024-12-10 first online, published, posted
Publisher
4TU.ResearchDataFormat
scripts/ipynb; compressed dataset (tar): objects/npy and spreadsheets/csvOrganizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water ManagementDATA
Files (17)
- 2,072 bytesMD5:
b47f0580b6b624816db72ca06c3dcc8bREADME.txt - 6,682 bytesMD5:
0bb17d39fe32c5e779fe92615549d63301 - Parallel downloading.ipynb - 13,120 bytesMD5:
d03d73c26130d86bcb8edc7625e856e902 - Datacrop-WVP-2019.ipynb - 15,296 bytesMD5:
8df5e6981878fe003b50e9dfa0a919da02 - Datacrop.ipynb - 4,563 bytesMD5:
42dd413a2017dbbcc7174686ce58e20302_1 - Complete_folders_cropped.ipynb - 19,723,136 bytesMD5:
aef309815da545534dc88540dd6769e802_2 - Rename_folders.ipynb - 1,304,133 bytesMD5:
b1d22acc554d676d150979fb92c8550703 - Create sample files TAHMO.ipynb - 14,858 bytesMD5:
89aeb94953db89417b6337e077935b6204 - PrepareDatasets-sequences.ipynb - 3,948 bytesMD5:
48342ceb7f48d532e80403668800144605-Temporal features.ipynb - 13,482 bytesMD5:
23a39c9096689a486535b9f50cecba9006-GPU-Create test dataset.ipynb - 63,602 bytesMD5:
a29226759e887e5104e08ba73349780a07-Model-general.ipynb - 1,377,816 bytesMD5:
738fe35735acbb05ce11c9378a7c39e808A-Couple TAHMO with output model.ipynb - 20,933 bytesMD5:
e1a7bbc8983d83bb0e98056a19328eda08B-Rainfall_data_preparation.ipynb - 1,437,046 bytesMD5:
c52bb956cddeb38db0eb1abfc0e618db09-Result-analysis.ipynb - 7,671,375 bytesMD5:
0bb58de3ab1989249d5f26d1968c63e810 - (Final code) Final analysis.ipynb - 4,958,955 bytesMD5:
5ba4c2b4d0fb4a7d1144dfef220f25ff10B - Exploratory - Station per station.ipynb - 16,908,733 bytesMD5:
0cb234b19d86f92476aa4cad0271b43711 - Analyse sequences.ipynb -
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