Conditional Adversarial Networks offer a general machine learning tool to build a model deriving one (image) dataset from another. Originally demonstrated as a tool called pix2pix. Here I'm using a tensorflow port of this technique pix2pix-tensorflow.

First we need a tool to encode a surface weather field (2m air temperature anomaly, mean-sea-level-pressure, and precipitation rate) as an image. Script

Then we need a set of pairs of such images - a source image, and a target image from 6-hours later. Each pair should be separated by at least 5 days, so they are independent states. Script

Then we need to take a training set (400) of those pairs of images and pack them into the 512x256 side-by-side format used by pix2pix (source in the left half, and target in the right half). Script

Alternatively, you can get the set of training and test images I used from Dropbox.

Then train a model on this set for 200 epochs - with a fast GPU this should take about 1 hour, but, CPU-only, it takes a bit over 24 hours on my 4-core iMac. (It took about 2 hours on one gpu-node of Isambard).

python weather2weather.py \
  --mode train \
  --output_dir $SCRATCH/weather2weather/model_train \
  --max_epochs 200 \
  --input_dir $SCRATCH/weather2weather/p2p_format_images_for_training \
  --which_direction AtoB

Now make some more pairs of images (100) to test the model on - same format as the training set, but must be different weather states (times). Script

Use the trained model to make predictions from the validation set sources and compare those predictions to the validation set targets.

python weather2weather.py \
  --mode test \
  --output_dir $SCRATCH/weather2weather/model_test \
  --input_dir $SCRATCH/weather2weather/p2p_format_images_for_validation \
  --checkpoint $SCRATCH/weather2weather/model_train

The test run will output an HTML file at $SCRATCH/weather2weather/model_test/index.html that shows input/output/target image sets. This is good for a first glance, but those images are in a packed analysis form. So we need a tool to convert the packed image pairs to a clearer image format: Script. This shows target weather (top left), model output weather (top right), target pressure increment (bottom left), and model output pressure increment (bottom right).

To postprocess all the test cases run:

./weather2image/replot_all_validation.R \
  --input.dir=$SCRATCH/weather2weather/model_test/images \
  --output.dir=$SCRATCH/weather2weather/model_test/postprocessed

This will produce an HTML file at $SCRATCH/weather2weather/model_test/index.html showing results of all the test cases.

This clearly does have skill at 6-hour weather forecasts - it gets the semi-diurnal oscillation, and some of the extratropical structure. The final step is to use the model on it's own output - by making repeated 6-hour forecasts we can make a forecast as far into the future as we like. This is less successful.

Derived from pix2pix-tensorflow.

Please cite the paper this code is based on: Image-to-Image Translation Using Conditional Adversarial Networks:

@article{pix2pix2016,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  journal={arxiv},
  year={2016}
}