What you need to know about Google’s breakthrough weather forecasting model

The sun will rise tomorrow, and you won’t have to bet your bottom dollar to make sure of it, Google’s DeepMind team has been released Its latest weather forecast model this week outperforms the leading traditional weather forecast model in the vast majority of tests it puts to it.

The generative AI model is called GenCast, and it’s a diffusion model like popular AI tools including Midjourney, DALL·E 3, and Stable Diffusion. Based on the team’s tests, GenCast predicts better extreme weatherthe movement of tropical storms and wind strength over Earth’s mighty land was the team’s discussion of the GenCast talk published this week Nature.

Where GenCast departs from other diffusion models is that it is (obviously) weather-focused and “adapted to the spherical geometry of the Earth,” as described by several of the paper’s co-authors. DeepMind blog post.

Instead of a written prompt like “draw a picture of a dachshund in the style of Salvador Dali,” GenCast’s input is the most recent weather conditions, which the model then uses to generate a probability distribution of future weather scenarios.

Traditional weather forecasting models such as HECThe leading model of the European Center for Medium-Range Weather Forecasts makes its predictions by solving the equations of physics.

“One of the limitations of these traditional models is that the equations they solve are only approximations of atmospheric dynamics,” said Ilan Price, senior researcher at Google DeepMind and lead author of the team’s latest findings, in an email to Gizmodo.

The first seeds for GenCast were planted in 2022, but the model released this week includes architectural changes and an improved diffusion setup that have made the model better equipped to predict Earth’s weather, including extreme weather events, up to 15 days out.

“GenCast is not limited to learning dynamics/patterns that are precisely known and can be written into an equation,” Price added. traditional models”.

Google has been doing weather forecasting for a while and in recent years has made some significant strides towards more accurate predictions using AI methods.

Last year, DeepMind scientists, some of whom co-authored the new paper,released GraphCasta machine learning-based method that outperformed current medium-range weather forecasting models on 90% of the targets used in the tests. Just five months ago, the group consisted mostly of DeepMind researchers published NeuralGCM, a hybrid weather forecasting model that combines traditional physics-based weather forecasting with machine learning components The team found that “end-to-end deep learning is compatible with tasks performed by conventional (models) and can enhance large-scale physical simulations , which are important for understanding and predicting the Earth system.”

The resolution achieved by GenCast is about six times that of NeuralGCM, but this was expected. “NeuralGCM is designed as a general-purpose atmospheric model, primarily to support climate simulations, while the higher resolution of GenCast is often expected. for medium-range forecasting models, which is GenCast’s specific target use case,” Price added. “This is why we emphasized a wide range of estimates framework, which are the defining use cases for medium-range operational forecasting, such as extreme weather forecasting.

In recent work, the team trained GenCast on historical weather data up to 2018 and then tested the model’s ability to predict weather patterns in 2019. GenCast outperformed ENS on 97.2% of targets using a variety of weather variables: with different dates before the event; With occurrence times greater than 36 h, GenCast was more accurate than ENS on 99.8% of targets.

The team also tested GenCast’s ability to predict the track of a tropical cyclone, particularly Typhoon Hagibis, which hit Japan in October weather produces wetter, heavier precipitationand: hurricanes are setting records because of how quickly they intensify and how early in the season they form, accurate forecasting of storm tracks will be critical to mitigating their financial and human costs.

But that’s not all: In a proof-of-principle experiment described in the study, the DeepMind team found that GenCast was more accurate than ENS in predicting the total wind power generated by clusters of more than 5,000 wind farms in the Global Power Plant Database. : GenCast forecasts were about 20% better than ENS with lead times of two days or less, and maintained statistically significant improvements up to one week. In other words, the model doesn’t just have disaster mitigation value, it can inform where and how we deploy energy infrastructure.

“The development of GenCast, a machine learning weather forecast (MLWP) model, is a milestone in the evolution of weather forecasting, as highlighted in a recent Google DeepMind paper,” an ECMWF spokesperson told Gizmodo in an emailed statement is one of the latest machine learning models to be reviewed in a series of high-profile scientific articles on MLWP from around the world highlighting weather the continuous (r)evolution of prediction’.

The ECMWF paper also compared the model’s performance to ENS 11-mile (18-kilometer) resolution. Now five years later, ENS is operating at 5.6-mile (9-km) resolution innovative science from a machine learning perspective, but these improvements need to be tested on how well they perform in extreme weather events to fully appreciate their value.” concludes the statement.

What does this all mean to you, you casual climate estimator? Well, the DeepMind team has made the GenCast code and models available for non-commercial use, so you can tool it up if you’re curious and on the current weather forecast archive release.

“This will enable the wider research and meteorological community to engage, test, operate and build on our work, accelerating further progress in the field,” Price said the model can begin to be incorporated into the operational environment.”

There’s still no timeline for when GenCast and the other models will go live, though the DeepMind blog notes that the models are “beginning to enhance user experiences on Google Search and Maps.”

Whether you’re here for weather or AI applications, there’s a lot to like about GenCast and DeepMind’s broader suite of predictive models.The accuracy of such tools will be paramount forecasting extreme weather events in sufficient time to protect victims from harm, be it floods in Appalachia or Tornadoes in Florida.

12/6 15:00. This story has been updated to include comments from ECMWF.