Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach
Description
This is the repository for the codes and input/output datasets used for the "Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach" paper published at Geoscientific Model Development Discussion. For a comprehensive description of the methods see (Harder et. al., 2022 and Fallah et. al., 2023 ).
This repository contains:
- A Jupyter Notebook showing the workflow of the work used in the paper "Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach" [Climate_Model_Downscaling_GMD-main.zip].
- List of analysed CMIP6 simulations [model_lists.pdf].
- COSMO-CLM model ste-up files [cclm_setups.zip].
- Snapshot of the code as used in the paper [constrained-downscaling.zip].
- Input/output, as well as, trained CNN models, which could also be downloaded by the Jupyter notebook of Climate_Model_Downscaling_GMD-main.zip as following:
- input_test.pt, target_test.pt, input_train.pt, target_train.pt, input_val.pt, target_val.pt , : test, train and val datasets for training the model.
- my_own_test_generalization.zip: the required data fr generalization test.
- my_own_test_twc_cnn_acadd_constraints_epochs_150_lr_0.00001_alpha_0.99_test.pt: output of the models.
Please note that the Jupyter Notebook will download the original code of Physics-Constrained Deep Learning for Climate Downscaling which is has the following DOI at Zenodo: https://zenodo.org/uploads/8150694.
Given that the complete COSMO-CLM model output is of order of ~100 TB, we could provide them upon individual requests. We aim to standardise and make the model output available according to the CORDEX standards.
Files
cclm_steups.zip
Files
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Additional details
Related works
- Is described by
- Preprint: arXiv:2208.05424v8 (arXiv)
- Is part of
- Preprint: 10.5194/gmd-2023-227 (DOI)
Dates
- Available
-
2023-12-21