301–310 of 310 posts
I've been really impressed at how much better weather forecasting has become already. I remember weather forecasts feeling like a total crapshoot as recently as 15 years ago or so.
If you live in an area with a lot of microclimates within one city, weather forecasting is honestly no better than astrology.
Earlier quoted context omitted.
The point was that weather, unlike a sandheap, is a chaotic hydrodynamic system with turbulent flows, that means it's computationally intractable to do exactly, which is why weather forecasts are only good for a few days anyway. The example you gave does not really explain anything.
The sandheap is chaotic too - just one sand grain tumbling can be enough to start a landslip. But the end result tends not to depend on the minute details - if sand grain A didn't cause the landslip, then a few seconds later sand grain B would have.
Chaos is when the outcome differs greatly with small changes in input.
the resolution, while seemingly impressive, is very imprecise compared to the SOTA in the theoretical modelling side.
this discredits the computational claims made by the paper for me. i understand that the current simulations can go down to meter scale, but i wonder what the compuational requirements are when you calculate for this resolution.
I find this quite surprising actually. You'd think predicting the weather is mostly a matter of fast computation. The physical rules are well understood, so to get a better estimate use a finer mesh in your finite element computation and use a smaller time scale in estimating your differential equations. Neural networks are notoriously bad at exact approximation. I mean you can never beat a calculator when the issue…
I did my doctorate in the Met Office. Weather forecasting is two separate problems. The first of these is physics - given the state of the atmosphere right now, what will it do. And this is hard, because there are so many different effects, combined with the fact that our computational models have a limited resolution. There's a huge amount of work that goes into making the simulation behave like a real atmosphere do…
What's your take on GraphCast - do you see it as a step forward?
Earlier quoted context omitted.
I did my doctorate in the Met Office. Weather forecasting is two separate problems. The first of these is physics - given the state of the atmosphere right now, what will it do. And this is hard, because there are so many different effects, combined with the fact that our computational models have a limited resolution. There's a huge amount of work that goes into making the simulation behave like a real atmosphere do…
Seems like you know what you are talking about! What's your take on GraphCast - do you see it as a step forward?
The system does have some problems. As mentioned in the article, it is a black box, so we can't look at what it has worked out and see why it differs from our physics models. It doesn't build an internal physical model of the atmosphere, so it may not be able to forecast as far into the future as a physics based model. It also seems limited in scope - it makes one particular type of forecast quite well, but not others such as local forecasts (limited area higher resolution).
What might be very interesting is to see if this system can be integrated into a physics-model-based forecasting system. At the moment, the local models get extra data from the global models, which helps them know what weather is going to blow in through the boundaries of the local model. If this system can improve the global model, then that might be able to help the local models, even if the system isn't good at doing local forecasts itself.
Weather forecasting has for a long time been a mixture of methods, usually depending on the range of the forecast. If you want to know if it's going to rain in the next five minutes, looking out the window is more accurate than going to the forecast. Within the next few hours, a very simple model that just looks at the weather radar and the wind direction to predict where the rain will fall is more accurate than a physics model (but less accurate for the next five minutes than looking out the window) - that's called "nowcasting". So it may be that this new system can slot in somewhere in-between nowcasting and physics-based forecasting.
I think it'll be a very interesting development over the next few years. I think it's particularly interesting that the system uses so little compute time, which implies to me that maybe it could be made even better with more resources dedicated to it. I'm not in this field of study any more, but I'll be watching the news.
Is this really an "AI" story? Aren't existing weather forecasting models , already a form of "AI"? I'm no AI/ML expert, but isn't the real story here is that a new model (like GPT-4.0) is better than the previous/existing model (GPT-3.5). It's just grabs way more attention calling the new model "AI" (vs not referring to the old as such).
No, existing models use more numerical methods. This is using a completely different approach. > GraphCast utilizes what researchers call a "graph neural network" machine-learning architecture, trained on over four decades of ECMWF's historical weather data. It processes the current and six-hour-old global atmospheric states, generating a 10-day forecast in about a minute on a Google TPU v4 cloud computer. Google's m…
Beyond the difficulty of running calculations (or even accurately measuring the current state), is there a reason to believe weather is unpredictable? I would imagine we probably have a solid mathematical model of how weather behaves, so given enough resources to measure and calculate, could you, in theory, predict the daily weather going 10 years into the future? Or is there something inherently “random” there?
AFAIK there's nothing random anywhere except near atomic/subatomic scale. Everything else is just highly chaotic/hard-to-forecast deterministic causal chains.
But the real problem is chaos - which says that even with perfect data, unless you also have computations with infinite precision and time/spatial/temperature/pressure/etc resolution, eventually you wind up far from reality.
The use of ensembles reduces the effect of chaos a bit, although they tend to smooth it out - so your broad pattern 12 days out might be more accurately forecast than without them, but the weather at your house may not be.
Iterative DL models tend to smooth it faster, according to a recent paper.
How could an ai, programmed with the bias of people that already suck at predicting the weather, even get close to being accurate?
It's the problem that's hard.