$ account openclimatefix
graph_weather is a research-oriented deep learning library for weather forecasting with graph neural networks. It implements multiple model families—GraphCast-inspired encoder/processor/decoder models, the diffusion-based GenCast and GenDA, the ensemble FGN, WeatherMesh, Aurora, FengWu-GHR, and CaFA—along with a substantial set of data loaders, loss functions, training scripts, and evaluation utilities. The package is structured as a Python library with HuggingFace Hub model integration, PyTorch Lightning training support, and a comprehensive test suite, giving it a hybrid character: it is fundamentally research code, but packaged and tested with application-grade discipline. Entry points include GraphWeatherForecaster, GraphWeatherAssimilator, WeatherStationReader, and various dataset classes, while training scripts live under train/.
This repository provides PyTorch implementations of Google Research's MetNet family of models for short-term weather forecasting, including MetNet, MetNet-2, MetNet-3, MetNetPV (photovoltaic), and MetNetSingleShot. The codebase is structured as a Python package with modular layers (ConvGRU, ConvLSTM, CoordConv, FiLM conditioning, MaxViT, partition attention, etc.) and data preprocessing utilities (Gaussian satellite merging, point-to-grid translation, MRMS normalization). The models are designed to ingest satellite imagery and radar data and output precipitation or other weather variables. The repository also includes pretrained weights hosted on HuggingFace Hub and a test suite covering layers, data utilities, and model forward/backward passes. While primarily an application library, it contains reusable data engineering components for geospatial data fusion.
This repository is a PyTorch Lightning implementation of DeepMind's Skillful Nowcasting GAN (DGMR) for precipitation nowcasting. It provides a modular, well-structured library of neural network components including residual blocks (GBlock, DBlock, LBlock), conditioning stacks (ContextConditioningStack, LatentConditioningStack), a ConvGRU-based sampler, spatial and temporal discriminators, and multiple loss functions (hinge, grid cell, SSIM, etc.). The core model (DGMR) wraps these components into a PyTorch Lightning module with manual optimization for the GAN training loop. It supports HuggingFace Hub integration for pretrained weights and includes training scripts for the UK Nimrod radar dataset. The codebase is well-documented, type-annotated, and includes comprehensive unit tests for individual components, making it suitable both as a library for nowcasting research and as a reference implementation for complex GAN architectures.