Deep Learning Weather Prediction (DLWP) model for weather forecasting

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

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

Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

Prerequisites

1. Install PhysicsNeMo with required extras:

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   pip install .[launch]
           

2. Install additional dependencies:

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   pip install -r requirements.txt
           

3. Install TempestRemap (required for coordinate transformation):

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   git clone https://github.com/ClimateGlobalChange/tempestremap
   cd tempestremap
   mkdir build && cd build
   cmake ..
   make
   make install
           

There are two methods to prepare the training data for DLWP:

Option 1: Using Dataset Download Example (Recommended)

This is the recommended approach for full model training. It provides more control over variable selection and time periods.

1. First, ensure you have set up your CDS API key as described in the dataset_download README.

2. Use the provided DLWP configuration:

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   python dataset_download/start_mirror.py --config-name="config_dlwp.yaml"
           

   The configuration includes:

   • 7 ERA5 variables mapped to cubed-sphere grid

   • Resolution: 64x64 grid cells per face

   • Years: 1980-2015 (training), 2016-2017 (validation), 2018 (testing)

   • Temporal resolution: 6-hourly

3. Transform the downloaded data to cubed-sphere format:

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   cd data_curation
   python post_processing.py --input-dir /path/to/downloaded/data --output-dir /path/to/output
           

Option 2: Quick Start with Minimal Dataset

For testing or development, you can use the simplified data preparation scripts in the data_curation directory:

1. Download a minimal set of ERA5 variables:

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   cd data_curation
   python data_download_simple.py
           

2. Process the downloaded data:

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   python post_processing.py
           

   See the data_curation/README.md for detailed instructions and parameters.

Data Format

The final dataset should be organized as follows:

            

            
data_dir/
├── train/
│   ├── 1980.h5
│   ├── 1981.h5
│   └── ...
├── test/
│   ├── 2017.h5
│   └── ...
├── out_of_sample/
│   └── 2018.h5
└── stats/
    ├── global_means.npy
    └── global_stds.npy
        

Each HDF5 file contains:

• Shape: (time_steps, channels, faces, height, width)

• Faces: 6 (cubed-sphere)

• Height/Width: 64 (resolution parameter)

• Channels: 7 (atmospheric variables)

To train the model, run:

            

            
python train_dlwp.py
        

Multi-GPU Training

For distributed training:

            

            
mpirun -np <NUM_GPUS> python train_dlwp.py
        

Note: Add --allow-run-as-root if running in a container as root.

Monitoring Training

Progress can be monitored using MLFlow:

            

            
mlflow ui -p 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