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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.
6,680 stars·871 forks·Python·Apache-2.0·9 vues

Historique des stars

Graphique de l'historique des stars pour google-deepmind/graphcastGraphique de l'historique des stars pour google-deepmind/graphcast

Questions fréquentes

Que fait google-deepmind/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.

Quelles sont les fonctionnalités principales de google-deepmind/graphcast ?

Les fonctionnalités principales de google-deepmind/graphcast sont : 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.

Quelles sont les alternatives open-source à google-deepmind/graphcast ?

Les alternatives open-source à google-deepmind/graphcast incluent : 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…

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Voir les 21 alternatives à Graphcast→