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 a comprehensive pipeline for processing ECMWF's ERA5 reanalysis data into Analysis-Ready (AR) and Cloud-Optimized (CO) datasets stored in Zarr format on Google Cloud Storage. The workflow includes downloading GRIB data via the CDS API, splitting heterogeneous GRIB files (for soil and precipitation variables), converting to Zarr using Pangeo Forge Recipes, regridding to a uniform 0.25° latitude-longitude grid for AR datasets, and automating monthly updates via Apache Beam pipelines on Dataflow. The repository also includes tools for data sanity checks (comparing ERA5 with ERA5T) and utilities for updating configuration files that track data ingestion progress. While the code is tightly integrated with Google Cloud services (Secret Manager, Dataflow, Cloud Run) and relies on specific system dependencies (ecCodes via apt-get), it demonstrates solid data engineering practices for preparing large climate datasets for machine learning and research use. The included notebooks offer educational walkthroughs for working with the resulting model-level data, showcasing Metview integration for visualization and derived variable computation.