Google’s AI can accurately predict weather forecasts 15 days out
“Googling” today’s forecast could soon be even more reliable.
Just like your friendly TV meteorologist, current weather models could perhaps be a thing of the past. Google has unveiled an AI meteorology tech that is far faster and more accurate than traditional forecasts, per a study published in the journal “Nature.”
Devised by the search engine firm’s AI division, DeepMind, the “GenCast” model can tell if it’s going to rain 15 days ahead of time at a higher accuracy rate than the European Centre for Medium-Range Weather Forecasts’ ENS (ECMWF) — the world’s top operational forecasting system, per the Google Deepmind blog.
This discrepancy has to do with a completely new monsoon-divining methodology. Whereas current iterations are “deterministic, and provided a single, best estimate of future weather,” GenCast “comprises an ensemble of 50 or more predictions, each representing a possible weather trajectory,” the blog’s authors write.
A person walks by the Google DeepMind logo at the corporate offices in London, UK. REUTERS
This vast spread of data allows forecasts to predict the weather with far greater precision, specifically between 97.2% and 99.8% accuracies, depending on circumstances.
“Such ensemble forecasts are more useful than relying on a single forecast, as they provide decision-makers with a fuller picture of possible weather conditions in the coming days and weeks and how likely each scenario is,” reads the blog.
GenCast also employs a type of AI called a diffusion model that’s typically found in video, image and music generators.
This digital fine-toothed comb was 99.8% more accurate than the current most powerful system when the lead time was greater than 36 hours. Google
Unlike most versions, this cybernetic weatherperson has been adapted to the spherical geometry of the Earth and is trained on four decades of historical weather data (leading up to 2018) from the ECMWF ranging from temperature to pressure at various altitudes.
The only caveat was that the European Centre for Medium-Range Weather Forecasts’ (ECMWF) ENS had far higher resolution forecasts. AP
To evaluate the typhoon-detector’s efficacy, researchers compared GenCast’s predictions to the real weather data from 2019 and the ENS forecasts for that year.
They specifically looked at 1,320 combinations of different variables at different lead times.
The AI acid test revealed that the GenCast was more precise than ENS on 97.2% of these targets, and on 99.8% when the lead times were greater than 36 hours.
This veritable Deep Blue of weather detection was also far more efficient.
It reportedly takes a single Google Cloud processor just eight minutes to create one 15-day forecast in GenCast’s ensemble, compared to the hours required to generate physics-based ensemble forecasts — such as those produced by ENS — on a supercomputer with tens of thousands of processors.
In addition, GenCast could better forecast extreme weather events — extreme heat and cold, and high wind speeds — which could help meteorologists keep better tabs on hurricanes and typhoons.
The only downside is that the current ENS system can generate significantly higher-resolution forecasts than its AI counterpart, the Daily Mail reported.
DeepMind reps also admitted that the current meteorology machines are irreplaceable for now because, for one, they provide the data used to train models like GenCast.
