Battaglia: Logistics and supply chains have barely tapped AI weather forecasting
Peter Battaglia · How AI is changing weather forecasting · Jan 2, 2026 · at 43:42
Google DeepMind research director Peter Battaglia discusses future industrial use cases enabled by next-generation AI weather models.
0:00 / 0:18exact quote · 18.7s
720p mp4 · rendered on demand · StarZero watermark
“I think that supply chains and logistics and, you know, even just like lots of choices that, you know, driving and these type of things, I think, really could be better informed by better weather forecasts, and I don't think we've even begun to kind of get into this, and we need to see the quality of the forecast get better and more customized to these use cases to start unlocking that.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Peter Battaglia
Battaglia: Data pipelines matter more than neural network architectures today
“I don't feel that neural network architectures these days tend to be the sort of exciting part. It's usually more of like the training and the sort of data how you handle the data and that kind of thing.”
DeepMind uses transformers to model spatial interactions in weather, not time
“We actually use transformers not to model the spatial, the interactions in weather over time, like the sequence of text, but in space.”
Battaglia: AI weather models leverage macroscopic spatial ranges unlike traditional simulations
“In a traditional model, the way it simulates that is it, in very fine detail, it kind of figures out, like, what's the pressure, and the temperature, and the wind, and the moisture, and what are those things? What's going to happen next is determined strictly …”
DeepMind primarily uses traditional supervised learning for AI weather models
“What we use in our weather forecasting models, and a lot of folks out in the community are using it as, you know, as this field is advancing and this AI-based weather forecasting is developing, we're still mostly using fairly traditional machine learning, supe…”
Battaglia: Early ML in weather improved downstream calibration, not base models
“So actually, the first advent of like, AI and machine learning in weather forecasting, or at least some of the earliest, was not, like, trying to overhaul the whole weather forecast process itself, but making, you know, using more and more, like, statistical m…”
Battaglia: Precipitation and wind are harder to forecast due to fine-scale dynamics
“So that type of very high resolution, complex, you know, patterns of precipitation, for example, Wind as well. Those are much harder to predict because you're effectively predicting a lot more information. You can't just sort of summarize it by saying, oh, eve…”