Tech

Google builds AI model that can predict future weather disasters

Google has released an artificial intelligence (AI) model that it claims can generate accurate weather forecasts at scale while being cheaper than traditional physics-based predictions.

The “Scalable Ensemble Envelope Diffusion Sampler” (SEEDS) model is designed similarly to popular large-scale language models (LLMs) such as ChatGPT and generative AI tools such as Sora, which generates videos from text prompts.

SEEDS generates many ensembles, or multiple weather scenarios, much faster and cheaper than traditional forecasting models. The research team described their findings in a paper published March 29 in the journal Science Advances.

Weather is difficult to predict, and there are many variables that can lead to potentially devastating weather events, from hurricanes to heat waves. As climate change worsens and extreme weather events become more common, accurately predicting weather can save lives by giving people time to prepare for the worst effects of natural disasters.

The physically-based forecasts currently used by weather services collect a variety of measurements and provide a final forecast that averages over many different modeled forecasts, or ensembles, based on all variables. . Weather forecasts are not based on a single forecast, but on a series of forecasts per forecast cycle that provide a range of expected future conditions.

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That is, most weather forecasts are accurate enough for more common situations, such as mild weather or warm summer days, but do not have predictive models that are good enough to find the expected outcomes of extreme weather events. That is not possible with most services.

Current forecasting also uses deterministic or probabilistic forecasting models in which random variables are introduced in the initial conditions. However, this leads to a rapid increase in error rate. This means that it is difficult to accurately predict extreme weather or even future weather.

Unexpected errors in initial conditions also cause variables to grow exponentially over time, and it is costly to model predictions sufficient to account for variables in such detail. This can have a significant impact on the prediction results. A Google scientist estimates that he needs 10,000 predictions within his model to predict an event that has just a 1% chance of occurring.

SEEDS creates predictive models from physical measurements collected by meteorological agencies. In particular, we focus on the relationship between units of potential energy per mass of the Earth's gravitational field in the central troposphere and sea level pressure, two common measures used for prediction.

Traditional methods can only generate ensembles of approximately 10 to 50 predictions. However, by using AI, the current version of SEEDS can estimate up to 31 prediction ensembles based on one or two “seed predictions” used as input data.

The researchers tested the system by modeling the 2022 European heatwave using historical weather data recorded at the time. Just seven days before the heat wave, US operational ensemble forecast data did not indicate such an event was imminent, a Google representative said in a blog post on the research portal. They added that ensembles with less than 100 predictions (more than traditionally) may also have missed predictions.

The scientists described the computational costs associated with performing calculations using SEEDS as “negligible” compared to today's methods. According to Google, the AI system also achieved a throughput of 256 ensembles for every three minutes of processing time on a sample Google Cloud architecture. This can be easily scaled by employing more accelerators.

Sources

1/ https://Google.com/

2/ https://www.livescience.com/technology/artificial-intelligence/google-builds-ai-model-seeds-that-can-predict-future-weather-catastrophes

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