Google’s GenCast AI turns spotlight on powerful new weather forecasters Explained Premium

Google DeepMind's GenCast AI model revolutionizes weather forecasting with superior accuracy and longer-term predictions than traditional methods.

The story so far: On December 4, Google DeepMind unveiled GenCast, an artificial intelligence (AI) model the company said could forecast the weather better than most existing tools as well as more days in advance. Details of the model were published in a peer-reviewed paper in the journal Nature.

“Weather predictions ... are produced by running multiple numerical simulations of the atmosphere,” Vassili Kitsios, senior research scientist at the Commonwealth Scientific and Industrial Research Organisation of Australia, wrote earlier this month. “Each simulation starts from a slightly different estimate of the current weather. This is because we don’t know exactly what the weather is at this instant everywhere in the world. ... By solving equations describing the fundamental physical laws of nature, the simulations predict what will happen in the atmosphere.”

This process is called numerical weather prediction (NWP). The best NWP forecasts require the use of powerful supercomputers as well as high-quality data about the weather at a particular location. Even then NWPs can predict the weather only a week or so in advance.

Ensemble forecasts entered the picture in the 1990s. Here, scientists use an NWP model to produce multiple forecasts at a certain location in time, with different starting conditions. This collection of forecasts is called an ensemble and indicates the range of meteorological possibilities.

Google’s GenCast uses ensemble forecasting too but the options in the ensemble come from an AI model rather than an NWP. Engineers at Google trained this AI model on 40 years of reanalysis data, from 1979 to 2019. According to the European Centre for Medium-Range Weather Forecasts (ECMWF), “Reanalysis data provide the most complete picture currently possible of past weather and climate. They are a blend of observations with past short-range weather forecasts rerun with modern weather forecasting models.”

GenCast was trained in two steps: step I in 3.5 days and step II in 1.5 days, both with 32 TPU v5 instances. ‘TPU’ is short for ‘tensor processing unit’, an integrated circuit Google developed to run machine-learning models, sold via Google Cloud. In December 2023, Google Cloud launched a TPU called v5p: it contains 8,960 chips interconnected with a bandwidth of 4,800 Gbps/chip, and costs $4.2 per chip-hour on demand.

Just like ChatGPT is good at identifying what the next word in an unfinished sentence could be, GenCast is good at guessing what the weather will be in the next moment given the weather until some point. According to the Nature paper, GenCast had “greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production.” ENS refers to the ensemble forecasts generated by ECMWF, considered one of the best in NWP.