DeepMind's GraphCast Outperforms Weather Models

Weather forecasting has relied on the same fundamental method for decades. Massive supercomputers crunch complex physics equations to simulate the atmosphere. This process is slow, expensive, and requires immense energy. Now, Google DeepMind has upended this standard with GraphCast. This new artificial intelligence model does not just match the world’s best weather simulation systems; it beats them.

The Shift from Physics to Data

For the last half-century, the gold standard in weather forecasting has been Numerical Weather Prediction (NWP). These systems use rigid physical laws of thermodynamics and fluid dynamics to predict what the atmosphere will do next. While effective, they are computationally heavy.

GraphCast takes a radically different approach. It is a machine learning model based on Graph Neural Networks (GNNs). Instead of trying to solve physics equations, it looks at patterns in historical data. DeepMind trained GraphCast on 40 years of historical weather data known as the ERA5 dataset. This dataset includes satellite images, radar data, and ground station measurements from 1979 to 2017.

By analyzing decades of cause and effect in the atmosphere, GraphCast learned how weather systems evolve without needing to be “taught” the laws of physics.

Breaking the Speed Limit

The most immediate advantage of this AI approach is speed.

  • Traditional Models: A high-resolution 10-day forecast using the standard HRES model from the European Centre for Medium-Range Weather Forecasts (ECMWF) takes hours to run on a supercomputer with thousands of processors.
  • GraphCast: The same 10-day forecast takes less than 60 seconds running on a single Google Cloud TPU v4 device.

This speed allows meteorologists to run thousands of simulations in the time it usually takes to run one. This capability is vital for probabilistic forecasting, where scientists try to determine the percentage chance of a specific event occurring.

Accuracy Metrics: Beating the Gold Standard

Speed is useless without accuracy. DeepMind published their findings in the journal Science, revealing that GraphCast outperformed the ECMWF’s HRES model on 90.3% of the tested targets.

The model evaluates weather variables at a resolution of 0.25 degrees longitude/latitude (approximately 28km x 28km at the equator). It tracks the following across 37 different altitude levels:

  1. Temperature
  2. Wind speed and direction
  3. Humidity
  4. Geopotential height
  5. Mean sea-level pressure

In the troposphere (the lowest part of the atmosphere where most weather happens), GraphCast was significantly more accurate than HRES in predicting temperature, wind, and pressure looking three to ten days into the future.

Case Study: Hurricane Lee

The theoretical metrics are impressive, but real-world performance is what matters for public safety. A prime example occurred in September 2023 with Hurricane Lee.

Forecasting where a hurricane will make landfall is notoriously difficult. About nine days before Hurricane Lee hit land, traditional models were offering vague and conflicting paths. They could not agree on where the storm would turn.

GraphCast, however, correctly predicted nine days in advance that Hurricane Lee would make landfall in Nova Scotia. The traditional forecasts did not converge on this location until about six days prior to landfall.

That three-day gap is massive in terms of disaster response. Three extra days allows cities to:

  • Position emergency supplies.
  • Issue evacuation orders earlier.
  • Secure power grids and infrastructure.

Predicting Extreme Temperatures

Beyond cyclones, GraphCast has shown a superior ability to predict extreme temperature events. Standard models often struggle to predict “outlier” events because they tend to regress toward the mean (average weather).

DeepMind’s model excels at identifying when temperatures will deviate significantly from the norm. This is particularly relevant as climate change makes heatwaves more frequent and severe. By accurately flagging a coming heatwave 5 or 10 days out, utility companies can prepare for high electricity loads, and health officials can open cooling centers.

How Graph Neural Networks Work

To understand why this works, you have to look at the architecture. GraphCast treats the Earth’s surface as a mesh grid (a graph). It uses a “multi-mesh” representation.

  1. Input: The model takes the state of the weather at the current time and the state from six hours ago.
  2. Processing: Information is passed between “nodes” on the grid. Long-range interactions are calculated instantly. For example, a pressure system in the Pacific is mathematically connected to conditions in California within the graph structure.
  3. Output: The model spits out the predicted state of the weather six hours into the future. This process is looped to create forecasts for 10 days out.

Because the model operates on a graph rather than a rigid 3D grid used in fluid dynamics, it can process spatial relationships much more efficiently.

The Future of Forecasting

Does this mean supercomputers and human meteorologists are obsolete? No.

GraphCast is currently viewed as a complement to traditional methods, not a total replacement. There are still limitations. For instance, AI models can sometimes hallucinate data or struggle with phenomena that were not present in their training data (like unprecedented climate change anomalies).

However, the industry is shifting. The ECMWF has already begun developing its own machine-learning model, the AIFS, after seeing the success of GraphCast and similar models from NVIDIA (FourCastNet) and Huawei (Pangu-Weather).

The future of weather forecasting is hybrid. We will likely see traditional physics models used to verify the incredibly fast, highly accurate predictions generated by AI.

Frequently Asked Questions

Is DeepMind’s GraphCast free to use? Yes, Google DeepMind has made the model code open-source. It is available on GitHub for researchers and meteorologists to download, experiment with, and integrate into their own systems.

Does GraphCast predict rain? Yes, it predicts precipitation, but this is one area where AI models generally struggle compared to temperature and wind. Predicting exact rainfall amounts in specific kilometers remains a challenge for both AI and traditional physics models.

How much energy does GraphCast save? Because the inference (prediction) phase runs on a single TPU chip for one minute rather than thousands of processors for an hour, the energy efficiency is roughly 1,000 times greater than traditional Numerical Weather Prediction methods.

Can it predict climate change? GraphCast is a weather forecasting tool (short-term), not a climate model (long-term). It predicts weather up to 10 days out. It is not designed to simulate decades of climate shifts, though the technology behind it could eventually be adapted for that purpose.