ENSO-GTC 1.0.0

This is the code for this paper https://doi.org/10.1029/2022MS003132

This project can be built and trained on Ubuntu 18.04.3 LTS, with python3.7 and CUDA 10.0/cudnn 7.6.5.

0. Environment

conda create -n enso python=3.7
source activate enso

pip install [some torch-related libs](https://drive.google.com/drive/folders/1hHQC0Ku1Vm4pLd2F3wVb2f5wnxx9ZyH6?usp=sharing)
pip install netCDF4==1.5.3
pip install progress==1.5
pip install loguru==0.3.2
pip install cmaps
pip install pyproj
pip install h5py
conda install -c conda-forge cartopy

1. Download climate dataset

Met Office Hadley Centre observations datasets (HadISST) is used for this model. Download it and put it in ./file/.

The archieved dataset is also in (not the latest!)

2. For independent training and forecasting processes

Firstly, use the following commands to parse and parpare training data.

python -m data.prepare_data

The output training data files are also in ./file/

Then, train the model:

python -m train_multi_gpus

3. Monthly ENSO forecasting

Firstly, download the latest HadISST from the above wetsites and replace the new data for data preprocessing.

Secondly, fine-tune the trained model:

python workflow.py

Finally, make forecasts for the future 18 months:

python forecast.py

The forecast results will be recorded in ./result-{year}-{month}.csv