GraphCast: Learning skillful medium-range global weather forecasting (2212.12794v2)

Abstract: Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical weather data to improve the underlying model. We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems.

• The paper introduces GraphCast, a graph neural network model that outperforms traditional methods in 90% of evaluation metrics.
• It leverages a refined multi-mesh approach and historical ERA5 data to predict hundreds of weather variables globally at 0.25° resolution.
• GraphCast produces forecasts in under a minute on a single TPU v4, offering rapid, skillful predictions for severe weather events and operational use.

Analysis of "GraphCast: Learning Skillful Medium-Range Global Weather Forecasting"

The paper presents an artificial intelligence approach named GraphCast, developed for medium-range global weather forecasting. This work is part of the evolving field of machine learning-based weather prediction (MLWP), which aims to enhance forecast accuracy by directly learning from historical weather data.

GraphCast utilizes a graph neural network (GNN) architecture, designed to predict weather outcomes by leveraging reanalysis data from the ERA5 dataset. The model can forecast hundreds of weather variables globally for up to 10 days at a 0.25-degree resolution. This is achieved efficiently, as GraphCast operates on a single Google Cloud TPU v4 device, generating predictions in under a minute.

Key Contributions

GraphCast introduces several methodological advances that distinguish it from traditional numerical weather prediction (NWP) systems and other MLWP models:

1. Integration with Historical Data: Unlike conventional NWPs that primarily depend on increased computational resources to enhance accuracy, GraphCast benefits from learning directly from historical data. This capability addresses one of the limitations of traditional systems, which cannot inherently improve from data abundance.
2. Neural Network Architecture: The architecture comprises GNNs configured in an "encode-process-decode" schema. This allows GraphCast to efficiently propagate information across the globe, overcoming challenges related to non-uniform spatial resolution inherent in traditional grids.
3. Multi-Mesh Representation: By employing a refined icosahedral mesh, GraphCast achieves homogeneous spatial representation, facilitating longer-range interactions and improved forecast accuracy.
4. Performance Benchmarks: GraphCast exhibited superior performance over the ECMWF's High RESolution forecast (HRES) in 90% of evaluated verification targets across various variables and pressure levels. Such performance is not only quantified in standard metrics like RMSE but also demonstrated in severe weather events forecasting, such as tropical cyclones and atmospheric rivers.

Implications and Future Directions

Practical Implications: GraphCast's ability to predict severe weather events more accurately can enhance strategic decision-making in sectors reliant on weather forecasts, such as agriculture, transportation, and disaster management. The integration of historical data without the need for tailored equations for every variable allows faster model adaptation to changing climate patterns, enhancing its utility in real-world scenarios.

Theoretical Implications: The work suggests a paradigm shift in weather prediction methodologies, integrating data-driven insights with model-based approaches. By continuously refining model architectures and datasets, MLWP models like GraphCast might eventually offer a comprehensive alternative to traditional NPWs.

Future Developments: The authors suggest the potential scalability of GraphCast. With advancements in computational resources and access to high-resolution data, future GraphCast iterations could operate at more granular resolutions, offering finer and more precise forecasts. Furthermore, tackling the challenge of uncertainty modeling explicitly within GraphCast could make it more robust, especially for long-range forecasts, where uncertainty amplification is a significant concern.

The paper paves the way for the broader application of machine learning in understanding and predicting complex geophysical phenomena. As computational tools evolve, integrating real-time data and improving model architectures will likely enhance the capability of MLWP systems like GraphCast, presenting new opportunities to complement and refine traditional weather forecasting methodologies.