Currently, this is the repository for the code of the publication Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data
The code is split into three Jupyter Notebooks:
- preprocess_netcdf_RNN_public.ipynb
This Notebook deals with processing the raw netcdf input data in order for them to be used for training.
- LSTM_MPIGE_nonrandom_4git.ipynb
This notebook contains the sampling of the data and the RNN architecture and its execution.
- postprocess_netcdf_RNN_public.ipynb
This Notebook deals with processing the output of the RNN in order to be compared to other netcdf datasets.
We also added a .csv file with the 140 model architectures evaluated in the testing phase.
If you would like to contact us, you can try the following channels
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Twitter: @fer_jaume
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Twitter: @martin_wegmann
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
