DeepMind and ML Graphcast Forecasts Weather

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DeepMind & Google’s ML-Based GraphCast Outperforms the World’s Best Medium-Range Weather Forecasting System

Medium-range weather forecasts play a crucial role in agriculture, construction, travel and other industries. They also bring practical value to people’s daily lives, enabling us to plan outings and keeping us safe from extreme weather events. Traditional numerical weather prediction (NWP)-based forecasting models that run simulations on computing clusters however do not scale efficiently with today’s increasing weather data availability, and their accuracy relies on manual input from experts, which is time-consuming and cost inefficient.

In the new paper GraphCast: Learning Skillful Medium-Range Global Weather Forecasting, a research team from DeepMind and Google presents GraphCast, a machine-learning (ML)-based weather simulator that scales well with data and can generate a 10-day forecast in under 60 seconds. GraphCast outperforms the world’s most accurate deterministic operational medium-range weather forecasting system and all existing ML-based benchmarks.

The team summarizes their work’s key advances as follows:

A novel multi-mesh GNN architecture for learned weather simulation.

An autoregressive model that can be trained to generate forecasts on 0.25° latitude-longitude resolution and 37 levels of vertical resolution, for 40 or more steps.

An evaluation protocol with comprehensive coverage of medium-range forecast variables.

An ML-based forecasting model with greater skill than the best NWP-based deterministic model.

The most accurate ML-based weather forecasting model. ... '