deep_climate_emulator

A Deep Neural Network approach for estimating precipitation fields in Earth System Models.

To install package

pip install git+https://github.com/hutchresearch/deep_climate_emulator

Training models

We provide trainer.py as an example script that utilizes our residual network training package. To train a model using the same architecture and hyperparameters used in our 18-Layer Residual Network, run the following:

# Pull scripts to local machine, if not already available.
git clone https://github.com/hutchresearch/deep_climate_emulator 

# Install the "deep_climate_emulator" package, if not already installed.
pip install git+https://github.com/hutchresearch/deep_climate_emulator

# Navigate to "scripts" directory and run "trainer.py".
cd deep_climate_emulator/scripts
python trainer.py \
--data <PATH_TO_PRECIPITATION_DATA> \
--architecture ../configs/18-layer-ResNet_architecture.json \
--hyperparameters ../configs/18-layer-ResNet_hyperparameters.json

Note: Our package was developed using Python 3. Python 2 compatibility is not guaranteed.

Using pretrained models to generate precipitation forecasts

We provide inference.py as an example script for generating predictions with a pretrained model. To generate predictions with our 18-Layer Residual Network run the following:

# Pull scripts to local machine, if not already available.
git clone https://github.com/hutchresearch/deep_climate_emulator 

# Install the "deep_climate_emulator" package, if not already installed.
pip install git+https://github.com/hutchresearch/deep_climate_emulator

# Navigate to "scripts" directory and run "inference.py".
cd deep_climate_emulator/scripts
python inference.py \
--data <PATH_TO_PRECIPITATION_DATA> \
--window_size 60 \
--num_forecasts 120 \
--num_preforecasts 30 \
--outfile <OUTPUT_FILENAME>

This package comes bundled with a pretrained 18-layer Residual Network (window size = 60), and this will be loaded by default if TensorFlow checkpoint files are not provided with the --model flag.

GPU support

It is highly recommended to train your models with a GPU! To train using a GPU, make sure you have the appropriate versions of TensorFlow and cuDNN installed on your system. For more information on GPU support see: https://www.tensorflow.org/install/gpu

Inference using pretrained models, on the other hand, will run sufficiently fast on CPUs alone (i.e. without GPU support). Our package uses a version of TensorFlow that runs on CPUs by default.

Training Data

All climate model output used in this study as training data is available through the Earth System Grid Federation.