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Comment by tfehring | Hacker News Reader

I’m by no means an expert in weather forecasting, but I have some familiarity with the methods. My understanding is that non-“AI” weather models basically subdivide the atmosphere into a 3d grid of cells that are on the order of hundreds to thousands of meters in each dimension, treat each cell as atomic/homogeneous at a given point in time, and then advance the relevant differential equations deterministically to forecast changes across the grid over time. This approach, again based on my limited understanding, is primarily held back by the sparse resolution and the computational resources needed to improve it, not by limitations of our understanding of the underlying physics. (Relatedly, I believe these models can be very sensitive to small changes in the initial conditions.) It’s not hard to imagine a neural net learning a more efficient way to encode and forecast the underlying physical patterns.

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FWIW, the UK Met Office's models are described here: https://www.metoffice.gov.uk/research/approach/modelling-sys...

This is true for many but not all weather models.

GFS and IFS are both medium-range global models in the class Google is targeting. These models are spectral models, meaning they pivot the input spatial grid into the frequency domain, carry out weather computations in the frequency domain, and pivot back to provide output grids.

The intuition here is that, at global scale over many days, the primary dynamics are waves doing what waves do. Representing state in terms of waves reduces the accumulation of numerical errors. On the other hand, this only works on spheroids and it comes at the expense of greatly complicating local interactions, so the use of spectral methods for NWP is far from universal.

> It’s not hard to imagine a neural net learning a more efficient way to encode and forecast the underlying physical patterns.

And that is where your understanding breaks down.

What makes weather prediction difficult is the same thing that make fluid-dynamics difficult: the non-linearity of the equations involved.

With experience and understanding of the problem at hand, you can make some pretty good non-linear predictions on the response of your system. Until you cannot. And the beauty of the non-linear response is that your botched prediction will be way, way off.

It's the same for AI. It will see some nicely hidden pattern based on the data it is fed, and will generate some prediction based on it. Until it hits one of those critical moments when there is no substitute to solving the actual equations, and it will produce absolute rubbish.

And that problem will only get compounded by the increasing turbulence level in the atmosphere due to global warming, which is breaking down the long-term, fairly stable, seasonal trends.

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