Google DeepMind’s GenCast model is ready to upend traditional weather forecasts
Key Takeaways
- Google DeepMind’s new ML model, GenCast, can accurately predict weather patterns up to 15 days in advance.
- GenCast has been found to surpasses traditional weather forecasting methods, including the European Center for Medium-Range Weather Forecasts (ECMWF).
- GenCast offers probabilistic forecasts, enhancing users’ understanding of multiple weather scenarios. The model has the potential to significantly improve weather forecasting and its applications in various sectors.
Traditional weather forecast models fail to reliably predict weather patterns beyond a week, but a new tool from Google’s AI subsidiary, DeepMind, seems to be breaking the limitation.
GenCast, as it has been titled, is currently outperforming the world’s leading weather forecasting systems, including the European Center for Medium-Range Weather Forecasts (ECMWF), with evidence pointing to its ability to generate accurate 15-day forecasts in minutes.
The findings were first published in the journal Nature (via The New York Times), and it highlights how Machine Learning (ML) based weather prediction has less forecast error than traditional numerical weather prediction (NWP), “which relies on physics-based simulations of the atmosphere.” GenCast, for reference, was trained on decades of reanalysis data, allowing it to generate 15-day global forecasts in just eight minutes. Supercomputers used for ECMWF, on the other hand, take hours.
Researchers pinned GenCast against the traditional ECMWF, which, for reference, is used by 35 nations for their official weather forecast needs. In the context of determining a designated set of 1,320 global wind speeds, temperatures and other atmospheric features, GenCast “outdid the center’s forecasts 97.2 percent of time.”
A leap beyond last year’s GraphCast
Google’s weather forecasting tool is already blowing away conventional models
GraphCast uses AI to forecast up to 10 days of weather in less than 1 minute
It’s worth noting that this isn’t DeepMind’s first foray into weather forecasting. Last year, the tech giant debuted GraphCast, which was able to accurately predict weather up to 10 days in advance. One of GenCast’s key advancements, however, lies in its probabilistic forecasting capabilities. This means that while GraphCast provides a single, definitive forecast, GenCast can offer a range of potential outcomes — which can better help users understand and prepare for the likelihood of different weather scenarios. For example, in GraphCast’s case, it might say “rain expected tomorrow,” while GenCast can offer a probabilistic forecast of “70 percent chance of rain tomorrow.”
On a more user-focused front, Google’s mobile weather app uses a related model called NowCast. Given the impressive capabilities of GenCast, it is highly plausible that the ML weather forecasting tool might one day be able to power the Pixel weather app, providing users with 15-day forecasts that they can actually rely on. However, Kerry Emanuel, a professor emeritus of atmospheric science at MIT suggests that GenCast won’t replace traditional forecasting methods, but it sure can complement them.
For now, GenCast’s weather predictions are expected to make their way to the Google Earth Engine and BigQuery, giving scientists and researchers worldwide access to analyze DeepMind’s forecasts.
