This example is an implementation of the DLWP Cubed-sphere model. The DLWP model can be used to predict the state of the atmosphere given a previous atmospheric state. You can infer a 320-member ensemble set of six-week forecasts at 1.4° resolution within a couple of minutes, demonstrating the potential of AI in developing near real-time digital twins for weather prediction
The goal is to train an AI model that can emulate the state of the atmosphere and predict global weather over a certain time span. The Deep Learning Weather Prediction (DLWP) model uses deep CNNs for globally gridded weather prediction. DLWP CNNs directly map u(t) to its future state u(t+Δt) by learning from historical observations of the weather, with Δt set to 6 hr
The model is trained on 7-channel subset of ERA5 Data that is mapped onto a cubed sphere
grid with a resolution of 64x64 grid cells. The map files were generated using
TempestRemap library.
The model uses years 1980-2015 for training, 2016-2017 for validation
and 2018 for out of sample testing. Some sample scripts for downloading the data and processing
it are provided in the data_curation directory. A larger subset of dataset is hosted
at the National Energy Research Scientific Computing Center (NERSC). For convenience
it is available to all via Globus.
You will need a Globus account and will need to be logged in to your account in order
to access the data. You may also need the Globus Connect
to transfer data.
DLWP uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. The latest DLWP model leverages a U-Net architecture with skip connections to capture multi-scale processes.The model architecture is described in the following papers
Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models
Install the required dependencies by running below:
pip install -r requirements.txtTo train the model, run
python train_dlwp.pyProgress can be monitored using MLFlow. Open a new terminal and navigate to the training directory, then run:
mlflow ui -p 2458View progress in a browser at http://127.0.0.1:2458
Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models
Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps: Part 1
Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps, Part 2