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Comment by ppaattrriicckk | Hacker News Reader

It might not come as much of a shocker, but a couple of researchers last year claimed a strong correlation between raw compute and predictive power in various fields/domains, including weather forecasting: https://arxiv.org/pdf/2206.14007.pdf#page=10 (page 10 specifically has graphs on Weather Forecasting vs. Compute).

In their study they claimed a strong correlation in these fields (vs. compute):

* Weather Forecasting

* Protein Folding

* Oil Exploration (at BP)

* Chess

* Go

... The latter 2 being games, which I personally do not find surprising. But I do find it inspiring that we can "just" calculate our way out of some important issues. That hopefully translates well to other fields.

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That's an interesting paper (if a little lightweight) but it's begged the question: why is temperature measurement variance the rubric for evaluating weather models? (from footnote on page 9): "Consistent with the norms in this field, only the error in the prediction of maximum and minimum temperature is shown, but this result holds when we use other temperature indicators such as average temperature."

Any meteorologists on HN able to weigh in?

Thank you for that link, Ive been subconsciously holding off on assuming there was a compute / predictive power correlation even though it seems natural. But it would probably be dangerously naïve to have assumed that connection. Anyway good for us! Go humans! (computers)

Glad you found it useful.

A small caveat, though: The correlation is linear with the logarithm of compute. So here's hoping Moore's law & friends live on a tad longer!

And a somewhat unrelated fun fact: The authors surprisingly found the lowest correlation between compute and the performance in the domain of Go (and not the real world). Although the data is very sparse, I suspect that it's due to algorithmic advances.

>I do find it inspiring that we can "just" calculate our way out of some important issues.

In the case of oil exploration, we can calculate our way into some!

Calculate, suffocate, annihilate. The fact that we now need computers to find oil says a lot about how much we've already used up, and the precarious position we find ourselves in as a result.

Computers have been used in the industry since at least the 1970s. These days, every major oil company has $XXX million in supercomputers cranking through seismic data in a number of extremely computationally intensive ways. Reservoir simulation is also computationally intensive, but can be done at the individual workstation level.

Here's a paper from May 1985 titled "Applications of supercomputers in the petroleum industry" - https://journals.sagepub.com/doi/abs/10.1177/003754978504400.... Found that without even looking for oldest example.

I mean...it's surprising this is a paper isn't it? Atoms, mass, charges, energy, forces etc are largely understood. The problem with weather, protein folding, oil exploration (and many other things simulations are run to predict) has always been that you can't do enough calculations in any reasonable amount of time (/money) so you have to figure short cuts which are approximately right. It's the same as graphics.

It's self evident that the answer to a lot of these things is just "more compute" and "better shortcuts". Like, GPUs and deep neural nets.

Correct me if I'm wrong but given that weather patterns are fundamentally chaotic, at some point throwing more compute at the wall probably won't produce anything better?

It's true - the Lyapunov exponent shows that even arbitrarily close points in the system's phase space become separated by exponentially larger distances in time. So even with a computer the size of the universe, you can't really go further than 14 days. I'd highly recommend this Omega Tau podcast episode if you're interested in hearing more about chaos and predictability:

http://omegataupodcast.net/119-chaos/

This is literally one of the defining points of a chaotic system.

In that case there's probably still room for improvement. Hell, having properly reliable predictions for at least one day ahead in all cases would be stellar.

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