mlcast

The MLCast Community is a collaborative effort bringing together meteorological services, research institutions, and academia across Europe to develop a unified Python package for AI-based nowcasting. This is an initiative of the E-AI WG6 (Nowcasting) of EUMETNET.

This repo contains the mlcast package for machine learning-based weather nowcasting.

Project Status

⚠️ Under Development - This package is currently in early development stages and not usable by end users. The API and functionality are subject to change.

Installation

# Install from pypi
pip install mlcast

or

# Install from source
git clone https://github.com/mlcast-community/mlcast
cd mlcast
uv pip install -e .

# For development
uv pip install -e ".[dev]"

Project Structure

mlcast/
├── src/mlcast/          # Main package source code
│   ├── __init__.py      # Package initialization and version
│   ├── data/            # Data loading and preprocessing
│   │   ├── zarr_datamodule.py   # PyTorch Lightning data module for Zarr
│   │   └── zarr_dataset.py      # PyTorch dataset for Zarr arrays
│   ├── models/          # Lightning model implementations
│   │   └── base.py      # Abstract base classes for nowcasting models
│   └── modules/         # Pure PyTorch neural network modules
│       └── convgru_modules.py   # ConvGRU encoder-decoder modules
├── examples/            # Example scripts and notebooks
│   └── scripts/
│       └── simple_train.py      # Basic training example
├── pyproject.toml       # Project metadata and dependencies
├── LICENSE              # Apache 2.0 license
└── README.md            # This file

Development

This project uses uv for dependency management. To set up the development environment:

# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

# Run pre-commit hooks
uv run pre-commit install

Contributing

Please feel free to raise issues or PRs if you have any suggestions or questions.

Links to presentations for discussion about the API

• 2025/02/04 first design discussions

License

This project is dual-licensed under either:

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• BSD 3-Clause License (LICENSE-BSD or https://opensource.org/licenses/BSD-3-Clause)

at your option.

See LICENSE for more details.