The Data and Codes for Training, Testing, and Interpreting A ResNet Ensemble for Moist Physics (ResCu-en)
The data and codes for ResCu-enare stored in this repositary. This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook. Please be sure to install all considered python packages in anenvironment. Please read the ReadME.txt. For the ent...
| Main Authors: | , |
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| Format: | Other/Unknown Material |
| Language: | unknown |
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Zenodo
2022
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| Online Access: | https://doi.org/10.5281/zenodo.5510878 |
| Summary: | The data and codes for ResCu-enare stored in this repositary. This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook. Please be sure to install all considered python packages in anenvironment. Please read the ReadME.txt. For the entire training and testing datasets in both thebaseline and +4K SST climates. Please download them fromDryad( https://doi.org/10.6075/J0CZ35PP and https://doi.org/10.6075/J03J3BGF), Onedrive (https://1drv.ms/u/s!ArKTPPs6U_9DjxPJeSReKlbsLzyh?e=PDlWYJ), and Dropbox (https://www.dropbox.com/s/yc4fx35laqwt0fu/SPCAM_ML_4K.tar.gz?dl=0 andhttps://www.dropbox.com/s/4pxahzwt9v55u2m/SPCAM_ML_RAD.tar.gz?dl=0). |
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