Stochastic, Recurrent Super-Resolution GAN for Downscaling Time-Evolving Atmospheric Fields

This is a reference implementation of a stochastic, recurrent super-resolution GAN for downscaling time-evolving fields, intended for use in the weather and climate sciences domain. This code supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network", IEEE Transactions in Geoscience and Remote Sensing, 2020, https://doi.org/10.1109/TGRS.2020.3032790, the accepted version is available at https://arxiv.org/abs/2005.10374.

Obtaining the data

The radar precipitation dataset (MCH-RZC in the paper) can be downloaded at https://doi.org/10.7910/DVN/ZDWWMG by following the instructions there. The GOES cloud optical thickness dataset (GOES-COT) can be found in this data repository as "goes-samples-2019-128x128.nc".

Obtaining the trained network

The trained generator weights selected for use in the paper are included in the models directory. The weights for the other time steps can be found here.

Running the code

For training, you'll want a machine with a GPU and around 32 GB of memory (the training procedure for the radar dataset loads the entire dataset into memory). Running the pre-trained model should work just fine on a CPU.

You may want to work with the code interactively; in this case, just start a Python shell in the dsrnngan directory.

If you want the simplest way to run the code, the following two options are available. You may also want to look at what main.py does in order to get an idea of how the training/plotting flow works.

Producing plots

You can replicate the plots in the paper (except for Fig. 7 for which we unfortunately cannot release the source data) by going to the dsrnngan directory and using

python main.py plot --mchrzc_data_file=<mchrzc_data_file> --goescod_data_file=<goescod_data_file>

where <mchrzc_data_file> is the path to the radar precipitation dataset and <goescod_data_file> is the path to the GOES cloud optical thickness dataset. For more control over the plotting process, see the function plot_all in plots.py.

Training the model

Run the following to start the training:

python main.py train --application=<application> --data_file=<data_file> --save_weights_root=<save_weights_root> --log_path=<log_path>

where <application> is either mchrzc (for the MCH-RZC dataset) or goescod (for the GOES-COT dataset), <data_file> is the training data file appropriate for the application, <save_weights_root> is the directory and file name root to which you want to save all model weights, and <log_path> is a path to a log directory where the logs and generator weights over time will be saved.