This is not an officially supported Google product.Ĭopyright 2023 DeepMind Technologies Limited.It’s the extensions where VSCode really starts to shine. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.Licence Statement: ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0).Copyright statement: Copyright '© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)'.The colab includes a fewĮxamples of ERA5 and HRES data that can be used as inputs to the models.ĮCMWF data product are subject to the following terms: The weights were trained on ECMWF's ERA5 and HRES data. The model weights are made available for use under the terms of the CreativeĬommons Attribution-NonCommercial-ShareAlike 4.0 International The Colab notebook and the associated code are licensed under the Apache xarray_tree.py: An implementation of tree.map_structure that works with.

xarray_jax.py: A wrapper to let JAX work with xarrays.

typed_graph_net.py: Implementation of simple graph neural networkīuilding blocks defined over TypedGraph's that can be combined to build.

typed_graph.py: Definition of TypedGraph's.Using a python loop to produce longer, but non-differentiable trajectories. rollout.py: Similar to autoregressive.py but used only at inference time.predictor_base.py: Defines the interface of the predictor, which GraphCast.Inputs according to historical values, and targets according to historical normalization.py: Wrapper for the one-step GraphCast used to normalize.model_utils.py: Utilities to produce flat node and edge vector featuresįrom input grid data, and to manipulate the node output vectors back.

losses.py: Loss computations, including latitude-weighting.icosahedral_mesh.py: Definition of an icosahedral multi-mesh.grid_mesh_connectivity.py: Tools for converting between regular grids on a.graphcast.py: The main GraphCast model architecture for one-step of.Three of these for the Grid2Mesh GNN, the Multi-mesh GNN and the Mesh2Grid Vectors of features for each of the nodes and edges. That operates on TypedGraph's where both inputs and outputs are flat deep_typed_graph_net.py: General purpose deep graph neural network (GNN).data_utils.py: Utils for data preprocessing.checkpoint.py: Utils to serialize and deserialize trees.casting.py: Wrapper used around GraphCast to make it work using.Outputs back as inputs at each step, in JAX a differentiable way. To produce a sequence of predictions by auto-regressively feeding the autoregressive.py: Wrapper used to run (and train) the one-step GraphCast.The one-step implementation of GraphCast architecture, is provided in Snapshot, generating predictions, computing the loss and computing gradients. The best starting point is to open graphcast_demo.ipynb in Colaboratory, which gives anĮxample of loading data, generating random weights or load a pre-trained The model weights, normalization statistics, and example inputs are available on Google Cloud Bucket.įull model training requires downloading theĭataset, available from ECMWF. This model can be initialized from HRES data (does Pressure levels) pre-trained on ERA5 data from 1979 to 2017 and fine-tuned on GraphCast_operational, a high-resolution model (0.25 degree resolution, 13 Resolution, 13 pressure levels, and a smaller mesh), trained on ERA5 data fromġ979 to 2015, useful to run a model with lower memory and compute constraints, GraphCast_small, a smaller, low-resolution version of GraphCast (1 degree Resolution, 37 pressure levels), trained on ERA5 data from 1979 to 2017, GraphCast, the high-resolution model used in the GraphCast paper (0.25 degree It also provides three pretrained models: This package contains example code to run and train GraphCast. GraphCast: Learning skillful medium-range global weather forecasting

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