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google-deepmind avatar

google-deepmind/graphcast

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Graphcast

GraphCast is a machine learning model that uses graph neural networks to produce global weather forecasts up to ten days ahead at high spatial resolution. The system represents the Earth's surface as an icosahedral mesh, enabling message passing between mesh nodes to capture atmospheric dynamics, and combines this with a learned multiscale processor that operates across coarse-to-fine mesh resolutions.

The model is trained on historical ERA5 reanalysis data through a supervised learning objective, and its autoregressive rollout loop feeds predictions back as input to generate multi-step forecast trajectories while maintaining end-to-end differentiability. GraphCast includes a grid-to-mesh encoder-decoder that converts regular latitude-longitude grid data into the triangular mesh format for processing and decodes outputs back to grid coordinates.

The repository provides three pretrained model snapshots that can generate forecasts without training from scratch, along with a training pipeline for producing custom forecasting models. Conversion utilities are included to transform gridded climate data into the icosahedral mesh representation required by the graph network.

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Features

  • Weather Forecasting Graph Networks - Uses graph neural networks to forecast global weather conditions up to 10 days ahead at high spatial resolution.
  • Differentiable Forecast Trajectories - Maintains end-to-end differentiability through the autoregressive rollout for gradient-based training.
  • Weather Forecast Rollouts - Feeds model predictions back as inputs to generate a differentiable multi-step forecast trajectory.
  • Autoregressive Forecast Generators - Feeds model predictions back as inputs to produce a differentiable multi-step forecast trajectory.
  • Graph Neural Network Forecast Trainers - Trains graph neural networks from scratch on historical ERA5 reanalysis data to produce custom forecasting models.
  • ERA5 Reanalysis Training Pipelines - Trains the graph neural network on historical ERA5 reanalysis data using supervised learning.
  • Graph Neural Networks - Trains graph neural networks on gridded climate reanalysis data to produce custom forecasting models.
  • Icosahedral Mesh Graph Networks - Represents the Earth's surface as a triangular icosahedral mesh for graph neural network message passing.
  • Multiscale Mesh Processors - Uses a hierarchy of coarse-to-fine mesh resolutions to capture both global and local atmospheric dynamics.
  • ERA5 Reanalysis Training Pipelines - Trains graph neural network parameters from historical ERA5 reanalysis data to produce custom forecasting models.
  • Global Model Forecasts - Predicts global weather conditions days ahead using learned graph neural network models on mesh data.
  • Graph Neural Network Forecasts - Predicts global weather conditions up to 10 days ahead at high spatial resolution using a learned graph neural network.
  • Grid-to-Mesh Encoder-Decoders - Converts regular latitude-longitude grid data into icosahedral meshes for graph network processing.
  • Pretrained Model Snapshots - Loads and executes pretrained model snapshots to generate weather forecasts without training.
  • Pretrained Weather Forecast Models - Provides three pretrained model snapshots that generate global weather forecasts without training from scratch.
  • Grid-to-Mesh Converters - Transforms regular latitude-longitude grid data into triangular icosahedral meshes for graph network processing.
  • Grid-to-Mesh Spatial Modeling - Converts regular latitude-longitude grids into triangular icosahedral meshes for graph network processing.
7,732 stars·1,000 forks·Python·Apache-2.0·18 views

Star history

Star history chart for google-deepmind/graphcastStar history chart for google-deepmind/graphcast

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does google-deepmind/graphcast do?

GraphCast is a machine learning model that uses graph neural networks to produce global weather forecasts up to ten days ahead at high spatial resolution. The system represents the Earth's surface as an icosahedral mesh, enabling message passing between mesh nodes to capture atmospheric dynamics, and combines this with a learned multiscale processor that operates across coarse-to-fine mesh resolutions.

What are the main features of google-deepmind/graphcast?

The main features of google-deepmind/graphcast are: Weather Forecasting Graph Networks, Differentiable Forecast Trajectories, Weather Forecast Rollouts, Autoregressive Forecast Generators, Graph Neural Network Forecast Trainers, ERA5 Reanalysis Training Pipelines, Graph Neural Networks, Icosahedral Mesh Graph Networks.

Which projects share features with google-deepmind/graphcast?

Projects with overlapping indexed features include: rusty1s/pytorch_geometric — PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides… tkipf/pygcn — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for… alibaba/graph-learn — Graph-learn is a distributed graph processing engine and graph neural network framework designed for large-scale graph… mdeff/cnn_graph — Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning… google-deepmind/graph_nets — graph_nets is a graph-structured deep learning framework and library for building message-passing neural networks. It… magicleap/supergluepretrainednetwork — This project is a collection of neural network models and geometric tools designed for image feature matching, spatial…

Projects sharing features with Graphcast

These projects share indexed features with Graphcast. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    rusty1s/pytorch_geometric

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    PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides a framework for developing graph neural networks, including a specialized system for implementing node-to-node information exchange via customizable message passing, aggregation, and update functions. The library includes a sparse data processing toolkit that utilizes accelerated CPU and CUDA kernels to perform efficient reductions on large sparse datasets. It supports the creation of specialized architectures for structured data such as 3D meshes and point clouds. The proje

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  • tkipf/pygcntkipf avatar

    tkipf/pygcn

    5,404View on GitHub↗

    pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for semi-supervised node classification and the generation of node embeddings from graph-structured data. The system converts graph nodes into low-dimensional vectors based on neighborhood patterns and local similarities. It enables the prediction of node labels by leveraging both a small set of labeled examples and the overall graph topology. The library covers relational data analysis and semi-supervised graph learning. It includes computational primitives for message passing, adjacenc

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  • alibaba/graph-learnalibaba avatar

    alibaba/graph-learn

    1,341View on GitHub↗

    Graph-learn is a distributed graph processing engine and graph neural network framework designed for large-scale graph data. It provides specialized query interfaces to extract training subgraphs and node neighborhoods, enabling the construction and training of complex graph neural network models on massive datasets. The system integrates a real-time inference server to serve live predictions by sampling dynamic graphs with low latency while processing streaming graph updates. The project features a C++ core engine integration that executes graph sampling and tensor operations natively, coupl

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  • magicleap/supergluepretrainednetworkmagicleap avatar

    magicleap/SuperGluePretrainedNetwork

    4,035View on GitHub↗

    This project is a collection of neural network models and geometric tools designed for image feature matching, spatial alignment, and visual localization. It provides a pre-trained neural network model for identifying high-accuracy correspondences between sparse image features without requiring local training. The system utilizes a graph neural network matcher that employs attention mechanisms and message passing to learn spatial relationships between image feature points. It integrates a RANSAC camera pose estimator to filter feature matches and calculate the relative spatial transformation

    Pythondeep-learningfeature-matchinggraph-neural-networks
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