├── Info.md └── README.md /Info.md: -------------------------------------------------------------------------------- 1 | # Events Databases 2 | 3 | * Copernicus Emergency Management Service: https://emergency.copernicus.eu/ 4 | - In particular, please look at the mapping service: https://emergency.copernicus.eu/mapping 5 | * European Severe Weather Database (ESWD): http://www.eswd.eu/ 6 | * The International Disaster Database (EM-DAT): https://www.emdat.be/ 7 | * ECMWF Severe Event Catalogue: https://confluence.ecmwf.int/display/FCST/Severe+Event+Catalogue 8 | * FloodList: http://floodlist.com/ 9 | * For daily news on wildfires, subscribe to http://archercopywriting.com/newsletter.html 10 | 11 | # Useful examples of raster/vector operations 12 | 13 | * https://github.com/cvitolo/GEFF_notebooks/blob/master/GEFF_ERAI_Python3.ipynb 14 | * https://github.com/jwagemann/seasonal_forecasts/blob/master/Workflow_seasonal_fc_processing.ipynb 15 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | The [Summer of Weather Code(ESoWC)](https://www.ecmwf.int/en/learning/workshops/ecmwf-summer-weather-code-2019) programme by the [European Centre for Medium-Range Weather Forecasts (ECMWF)](https://www.ecmwf.int) is a collabrative online programme to promote the development of weather-related open-source software. 2 | 3 | 4 | ### Overview of ESoWC 2019 projects and participants 5 |
6 | 7 | |#|Project|Participant|Mentor| 8 | |------|---------|-------|----------| 9 | |1|Python-based interface to ECMWF's Intergrated Forecasting System single model column|[@tibi77](https://github.com/tibi77)|[@mlange05](https://github.com/mlange05), [@marsdeno](https://github.com/marsdeno)| 10 | |2|GIS-based dissolve functionalities using GRASS GIS|[@mundialis](https://github.com/mundialis)|[@latimau](https://github.com/latimau), [@calumbaugh](https://github.com/calumbaugh)| 11 | |3|BVtk: scientific visualization inside Blender|[@LorenzoCelli](https://github.com/LorenzoCelli), Antonella Guidazzoli, [@simboden](https://github.com/simboden), Gabriele Brizzi, Enzo Papandrea|[@sylvielamythepaut](https://github.com/sylvielamythepaut), [@milanavuckovic](https://github.com/milanavuckovic), [@joaquinval](https://github.com/joaquinval)| 12 | |4|Obtaining online aircraft metadata|[@michiboo](https://github.com/michiboo)|[@bruceingleby](https://github.com/bruceingleby), [@mohameddahoui](https://github.com/mohameddahoui)| 13 | |5|Extend ecPoint-PyCal for conditional verification applications|[@onyb](https://github.com/onyb)|[@fatimapillosu](https://github.com/fatimapillosu)| 14 | 15 |
16 | 17 | #### ESoWC 2019 challenges with a focus on machine-learning (supported by [Copernicus](https://climate.copernicus.eu/)) 18 | 19 | |#|Project|Participant|Mentor| 20 | |------|---------|-------|----------| 21 | |1|Machine learning to better predict and understand drought |[@tommylees112](https://github.com/tommylees112), [@gabrieltseng](https://github.com/gabrieltseng)|[@cvitolo](https://github.com/cvitolo), [@jwagemann](https://github.com/jwagemann), [@StephanSiemen](https://github.com/StephanSiemen)| 22 | |2|MATEHIW - MAchine learning TEchniques for High-Impact Weather|[@lkugler](https://github.com/lkugler), [@seblehner](https://github.com/seblehner) |[@cvitolo](https://github.com/cvitolo), [@jwagemann](https://github.com/jwagemann), [@StephanSiemen](https://github.com/StephanSiemen)| 23 | |3|Data-driven feature selection towards improving forecast-based prediction of wildfire hazard| [@mariajoaosousa](https://github.com/mariajoaosousa), [@ricardomaia300](https://github.com/ricardomaia300), [@eduardogfma](https://github.com/eduardogfma) |[@cvitolo](https://github.com/cvitolo), [@jwagemann](https://github.com/jwagemann), [@StephanSiemen](https://github.com/StephanSiemen)| 24 | 25 |
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