Two new AI-based weather-forecasting systems challenging the status quo
phys.org
phys.org
[1]:https://rafmetman.wordpress.com/meteorology/ww2-meteorology/etc etc
At least these days the LLMs have gotten us a little closer to the original promise of AI.
That's awesome -- years of analysis paying off in a very cool and interesting way. It says that this particular model doesn't predict precipitation -- just atmospheric conditions -- but there's a lot of really interesting potential, here.
https://en.wikipedia.org/wiki/The_American_Boy%27s_Handy_Boo...
had a chapter about using simple heuristics that, in principle, are similar to what a machine learning algorithm can learn.
Not as accurate as systems from previous decades, not more accurate then the existing systems. What is so special about the now?
This entire class of global numerical weather forecast models has had more-or-less a monotonic increase in forecast accuracy over the past five decades. E.g., a 72 hour forecast from the current generation of these models has similar error statistics to a 24 hour forecast from its predecessors in the early 2000's.
What's special about these AI forecast models is that they are significantly cheaper to run than the existing global numerical weather forecast models. Modern meteorology involves a great deal of statistical analysis to overcome chaotic uncertainty. One way we build these statistical analyses is to run dozens of forecasts with the same model, using slightly different initial conditions, to see how the forecasts diverge. But these ensembles are very under-disperse - a few dozen members just doesn't fully sample the uncertainty. Now, if you can run 1000x the number of ensemble members, a whole new world of possibility opens up.
And that's before you consider just training the AI system to directly output a posterior distribution representing this uncertainty in the first place...
In the world of meteorology, the way you build an "accurate" (e.g. "user-perceived accuracy") forecast is to consume the entire previous class of forecasts and apply statistics/ML to post-process them. The greater your ability to probe uncertain in the forecasts from these models - e.g. by running larger ensembles or tailoring the ones you run to try to quantify the uncertainty more explicitly - the better your opportunity to improve those 'accuracy" metrics. So yes - the opportunity here is running larger sets of tailored forecast simulations as a way to statistically optimize forecast accuracy.
>> and did its work in just a fraction of the time.
Incidentally, this post, along with the rest of the Dark Sky blog, was deleted after they were acquired by Apple.
Implementing cToF is left as an exercise for the reader
... in the console, and it's almost entirely F. (except for the text summary at the top, which can include other numbers, so isn't safe to mess with this way). You could turn this into a bookmarklet and do this one-click. https://caiorss.github.io/bookmarklet-maker/
I know, totally impractical, no use on a phone, etc. I just wondered how easy it would be to fix.
(edited to add: lol, so mcluck had the same idea. the hour-temp and regex stuff in mine is because replacing just temp__value leaves the hour-by-hour temps in the top panel, which include the degree symbol as part of the text, so I want to leave that alone)
Also that these new AI systems are performing at the same level of the old non-AI approaches I feel is a real testament to the developers of those systems. This is not something we see in many other cases.
Why would they use AI where weather models exist, and work? What exactly would AI bring into space?
- weather is not climate
- we are not in a situation where weather models don't work
I mean keeping in mind the number of variables involved and, more recently, rapid climate change, el niño and forest fires that are much more difficult for an AI to keep in mind (I think).
It's a competitor to existing models and supercomputers, but it has to prove its reliability first.
This aint a retail product recommendation. If the AI makes a bad choice it will cost millions/billions of dollars and we could lose many MANY lives.
The problem is that they offer little to no advantage over the highly optimized ML-based forecast post-processing systems widely in use in the industry. You see an awful lot of hype from start-ups proclaiming their AI forecasts are "the most accurate ever"... when in reality they barely improve at all over the status quo that can be achieved with rather simple statistical modeling.
> One would think this would be one of the first areas that would adopt AI given the data available.
Actually, the challenge has been that there _isn't_ enough data available. Sure, we have lots of satellite observations and many other sources, but none of these paint a holistic picture of the atmosphere of the sort you'd need to actually forecast the weather with any precision. ERA-5 - a model-based "re-analysis" that assimilates many observations and tries to create a coherent picture - is only a few years old, and has been the keystone that unlocked all of this development over the past few years.