> For example, deep learning weather models have proven to be excellent at forecasting the tracks of hurricanes. But while these models are better at predicting where hurricanes will go, they tend to be lower-performing on the intensity changes of such storms relative to physics-based models.
For context, intensity changes are the current Big Problem. Otis [1] had its track predicted almost exactly, but its explosive intensification from a tropical storm to a Cat 5 was totally unpredicted. Possibly some of the ~$12bn damage could have been avoided if Mexico had known that in advance.
I've said this before in another comment some months back, but I'll repeat: my worry is that these models aren't learning some comprehensive new climate dynamics model with parameterisations [2] , but only fitting what the Earth has historically done. And if AI weather prediction is only learning what climate dynamics do 95% of the time, it's almost by definition not useful for predicting extreme weather and it will get less accurate the more the climate changes. You're just going to get more Otises.
[2] much as I would welcome, with open arms, some accurate AI-generated black-box parameterisations for e.g. subgrid precipitation - might be more explainable than the FORTRAN black-box parameterisations we have now :)*
My naive understanding is that the majority of temperature data comes from where humans are: the surface. Hurricanes are 3d, extending up for miles. The models go almost entirely off the surface temperatures, with very very sparse balloon data (which is a poor sample, since a balloon will follow the air it's put in). Wouldn't the whole volume, or at least a little of it, need to be observed, since the energy in that volume is what's powering the hurricane, not the energy on the surface? I would assume this is why the models have trouble.
Satellites also measure the temperature/height of clouds, and there's also some data from aircraft. A lot of commercial aircraft automatically report the temperature/pressure as they fly. The only problem is that a lot of their flight is in the stratosphere, but they give good data in their climb/descent.
Compared to weather balloons, it's quite a bit more data. In the U.S. there are only 91 weather balloon launching sites, so that's 182 observations per day. AMDAR has 700 aircraft, and each one probably makes about 4 flights per day, and they get a temperature profile going up and going down, so that's 5600 profiles per day. There are about 450 airports in the U.S. with regular commercial service, and the majority of these are covered.
One thing these models gave going for them is they won’t come at the problem with preconceived notions.
A sensor that’s off by some constant factor is feeding bad data to a physics model and resulting are deemed incorrect if they don’t match the future value of that sensor. AI on the other hand could self correct for such issues because the data doesn’t mean anything only the patterns.
I can only assume current models include everything even tangentially relevant from albedo to topology and ocean currents. But that doesn’t mean they include everything relevant just everything people consider relevant.
Eh, atmospheric CO2 (and other GHGs) is just another input parameter. I don't see any reason that the model wouldn't be able to incorporate all of the data. If the climate system is just getting more chaotic, well, you're still running multiple projections, and you'll see that in increased variance.
Because we have well-measured inputs for CO2 between basically 250 PPM and $current_day PPM. Do you see how this statement
> Eh, atmospheric CO2 (and other GHGs) is just another input parameter
only works for CO2 inputs outside our measurements, if the climate response to those inputs is linear (and thus predictable from the responses we have already seen)?
I claim that the climate response to CO2 forcing is, in fact, strongly nonlinear, and further that it's nonlinear for other "unusual inputs" - not just CO2 - things like sea surface temperature or unusually low pressure troughs. So-called extreme weather. I can't bring up good citations at the moment, sorry, but here's a somewhat exaggerated thought experiment:
Take an AI model trained on weather measurements ~1970-2024. Also take a model of the primitive fluid equations on a rotating sphere. What predictions might you expect from each one for an asteroid hitting an empty patch of the West Pacific?
For context, intensity changes are the current Big Problem. Otis [1] had its track predicted almost exactly, but its explosive intensification from a tropical storm to a Cat 5 was totally unpredicted. Possibly some of the ~$12bn damage could have been avoided if Mexico had known that in advance.
I've said this before in another comment some months back, but I'll repeat: my worry is that these models aren't learning some comprehensive new climate dynamics model with parameterisations [2] , but only fitting what the Earth has historically done. And if AI weather prediction is only learning what climate dynamics do 95% of the time, it's almost by definition not useful for predicting extreme weather and it will get less accurate the more the climate changes. You're just going to get more Otises.
[1] https://en.wikipedia.org/wiki/Hurricane_Otis
[2] much as I would welcome, with open arms, some accurate AI-generated black-box parameterisations for e.g. subgrid precipitation - might be more explainable than the FORTRAN black-box parameterisations we have now :)*