What is it worth to know the weather forecast 1 day earlier? That’s not a hypothetical question, traditional forecasting systems have been improving their skill at a rate of 1 day per decade. In other words, today’s 6-day forecast is as accurate as the 5-day forecast ten years ago. No one expects this rate of improvement to hold steady, it has to slow down eventually, right? Well in the last couple years GPUs and modern deep learning have actually sped it up.

Since 2022 there has been a flurry of weather deep learning systems research at companies like NVIDIA, Google DeepMind, Huawei and Microsoft (some of them built by yours truly). These models have little to no built-in physics and learn to forecast purely from data. Astonishingly, this approach, done correctly, produces better forecasts than traditional simulations of the physics of our atmosphere.

Jayesh and Cris came face-to-face with this technology’s potential while they were respectively leading the [ClimaX](https://arxiv.org/abs/2301.10343) and [Aurora](https://arxiv.org/abs/2405.13063) projects at Microsoft. The foundation models they built improved on the ECMWF’s forecasts, considered the gold standard in weather prediction, while only using a fraction of the available training data. Our mission at Silurian is to scale these models to their full potential and push them to the limits of physical predictability. Ultimately, we aim to model all infrastructure that is impacted by weather including the energy grid, agriculture, logistics, and defense. Hence: simulate the Earth.

Before we do all that, this summer we’ve built our own foundation model, GFT (Generative Forecasting Transformer), a 1.5B parameter frontier model that simulates global weather up to 14 days ahead at approximately 11km resolution (https://www.ycombinator.com/launches/Lcz-silurian-simulate-t...). Despite the scarce amount of extreme weather data in historical records, we have seen that GFT is performing extremely well on predicting 2024 hurricane tracks (https://silurian.ai/posts/001/hurricane_tracks). You can play around with our hurricane forecasts at https://hurricanes2024.silurian.ai. We visualize these using [cambecc/earth] (https://github.com/cambecc/earth), one of our favorite open source weather visualization tools.

We’re excited to be launching here on HN and would love to hear what you think!