DeepDownscaling
Deep learning approaches for statistical downscaling in climate.
https://github.com/SantanderMetGroup/DeepDownscaling
Keywords from Contributors
climate-change-atlas climate4r cmip6 cordex ipcc-regions warming-levels
Last synced: over 1 year ago
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Acceptance Criteria
- Revelant topics? false
- External users? true
- Open source license? false
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Repository metadata
Deep learning approaches for statistical downscaling in climate
- Host: GitHub
- URL: https://github.com/SantanderMetGroup/DeepDownscaling
- Owner: SantanderMetGroup
- Created: 2019-09-11T11:07:41.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-07-01T04:53:41.000Z (almost 3 years ago)
- Last Synced: 2024-01-21T03:33:06.433Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 15.4 MB
- Stars: 49
- Watchers: 12
- Forks: 22
- Open Issues: 0
- Releases: 0
-
Metadata Files:
- Readme: README.md
README.md
DeepDownscaling
Deep learning approaches for statistical downscaling in climate
Transparency and reproducibility are key ingredients to develop top-quality science. For this reason, this repository is aimed at hosting and maintaining updated versions of the code and notebooks needed to (partly or fully) reproduce the results of the papers developed in the Santander MetGroup dealing with the application of deep learning techniques for statistical dowscaling in climate.
These works build on climate4R, a bundle of R packages developed for transparent climate data access, post processing (including bias correction and downscaling), visualization and model validation. A battery of Jupyter notebooks with worked examples explaining how to use the main functionalities of the core climate4R packages (including downscaleR for standard statistical downscaling methods) can be found at the notebooks' repositoty.
For deep learning impplementations we use keras, an R library which provides an interface to Keras, a high-level neural networks API which supports arbitrary network architectures and is seamlessly integrated with TensorFlow, and a wrapper of this package for the downscaleR package, downscaleR.keras.
The table below lists the documents (Jupyter notebooks, scripts, etc.) contained in this respository along with the information of the corresponding published (or submitted) papers.
| Notebook files | Article Title | Journal | Paper files |
|---|---|---|---|
| 2022_Bano_GMD.ipynb | Downscaling Multi-Model Ensembles of Climate Change Projections with Deep Learning (DeepESD): Contribution to CORDEX EUR-44 | Geoscientific Model Development | - |
| 2020_Bano_CD.ipynb | On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections | Climate Dynamics | https://doi.org/10.1007/s00382-021-05847-0 |
| 2020_Bano_CI.ipynb | Understanding Deep Learning Decisions in Statistical Downscaling Models | International Conference Proceedings Series (ICPS) | https://doi.org/10.1145/3429309.3429321 2020_Bano_CI.pdf |
| 2020_Bano_GMD.ipynb 2020_Bano_GMD_FULL.ipynb | Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling | Geoscientific Model Development | https://doi.org/10.5194/gmd-2019-278 |
| 2019_Bano_CI.ipynb | The Importance of Inductive Bias in Convolutional Models for Statistical Downscaling | Proceedings of the 9th International Workshop on Climate Informatics (CI2019) | http://dx.doi.org/10.5065/y82j-f154 2019_Bano_CI.pdf |
| 2018_Bano_CI.ipynb | Deep Convolutional Networks for Feature Selection in Statistical Downscaling | Proceedings of the 8th International Workshop on Climate Informatics (CI2018) | http://dx.doi.org/10.5065/D6BZ64XQ 2018_Bano_CI.pdf |
Owner metadata
- Name: Santander Meteorology Group (UC-CSIC)
- Login: SantanderMetGroup
- Email:
- Kind: organization
- Description: a multidisciplinary approach to weather & climate
- Website: http://www.meteo.unican.es
- Location: Santander
- Twitter: SantanderMeteo
- Company:
- Icon url: https://avatars.githubusercontent.com/u/5774630?v=4
- Repositories: 73
- Last ynced at: 2023-08-05T04:21:32.469Z
- Profile URL: https://github.com/SantanderMetGroup
GitHub Events
Total
- Create event: 16
- Issues event: 7
- Release event: 14
- Watch event: 49
- Delete event: 2
- Push event: 149
- Fork event: 22
Last Year
- Fork event: 4
- Issues event: 5
- Watch event: 11
Committers metadata
Last synced: over 1 year ago
Total Commits: 152
Total Committers: 4
Avg Commits per committer: 38.0
Development Distribution Score (DDS): 0.191
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| Jorge Baño-Medina | j****a@g****m | 123 |
| Rodrigo Manzanas | r****s | 14 |
| Jose M. Gutierrez | g****m@u****s | 13 |
| Jorge Bano Medina | j****a@M****l | 2 |
Committer domains:
- unican.es: 1
Issue and Pull Request metadata
Last synced: over 1 year ago
Total issues: 3
Total pull requests: 0
Average time to close issues: 24 days
Average time to close pull requests: N/A
Total issue authors: 2
Total pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0
Past year issues: 2
Past year pull requests: 0
Past year average time to close issues: about 1 month
Past year average time to close pull requests: N/A
Past year issue authors: 1
Past year pull request authors: 0
Past year average comments per issue: 0.0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- QuocPhamBao (2)
- omidnabavi (1)
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Score: 5.278114659230517