Pretty much the only thing I think AI could be useful for - forecasting the weather based off tracking massive amounts of data. I look forward to seeing how this particular field of study is improved.
Bonus points, AI weather modeling, for once, saves energy relative to physics models. Pair it with some sort of light weight physical model to keep the hallucinations at bay, and you’ve got a good combo.
Yeah, I’ve long thought that weather forecasts are a perfect use case for AI. AI is great with complicated systems that are hard to model accurately but have lots of available data.
Current weather forecasts kinda suck. I try to schedule jobs around when it’s going to rain, and have to frequently reschedule because rain forecasts aren’t very accurate. I really hope we can see improvements.
It would be amazing if it could have a significant impact on spatial and temporal accuracy of things like rain. I feel like for me the existing weather report is good enough for “it will probably rain tomorrow” but it’s really hit-or-miss when you get to hourly resolution. A good model may be able to go so far as to say “it will probably rain between 3-4pm on the east side of town tomorrow, and 2-3pm on the west side”
That’s the dream at least. With enough data and a sophisticated enough model it feels like it could be possible.
I’m not convinced you can ever get that resolution. There’s a big difference between modeling the broad trends and trying to remove the uncertainty from a process that’s inherently probabilistic.
Theoretically with enough data it could predict exactly what is going to happen do we have enough data currently to do that probably not but weather isn’t just completely random we just don’t understand it enough yet
It’s an insanely complex, coupled system full of turbulence, so that “theoretically” is doing some heavy lifting. The best models now need to be run on supercomputers, despite scores of scientists and software people constantly trying to find further optimizations for the algorithms. AI isn’t going to better discriminate signal from noise when the biggest constraint on the existing S/N ratio is the lack of suffiicient compute resource.
Furthermore, unless the AI does explainability, which it almost certainly doesn’t, nobody’s going to use its output in life-and-limb-critical applications like first responders, defense, even road gritting.
My argument is that that is not the case.
There are many systems in nature that have randomness fundamentally built in. You can model the broad strokes, but the low level details are inherently unpredictable because random processes are involved at the low level. You can predict the general pattern of airflow over a jet wing, but it’s not a lack of input resolution that makes it impossible to project the path of a specific molecule.
That has to do with the forecast’s grid size, along with some irreducibly complex and ill-conditioned physics that AI won’t help with. The best general-forecast global models now have a 10km grid. Some specialist models go down to about 2km. So, depending on the size of your town, probably not fine-grained enough, though there are also point forecasts that take into account terrain, albedo and other fine-grained features and can be pretty accurate, especially when there are some good observation stations nearby so the forecast models can be continually trained based on actual data.
You’re better off looking at QPFs than regular forecasts.
But, if you’re wanting something like “will there be rain at this GPS coordinate at this time”, then under some conditions that is just impossible to predict. It’s not a problem with the how clever the models are or a lack of data, the physics makes it legitimately random.
Ensemble forecasts are better yet, but you need to learn what they mean before using them. Not many people understand how to interpret probabilistic forecast data.
I thought QPFs are generated from ensemble forecasts?
Rain forecasts are mostly spot on for me. Keep in mind, %chance of rain is covering a wide area. If we want better rain forecasts we have to dial in the resolution.
You’ll also need more accurate remote sensor data (precipitation happens in a very narrow range of temperature, pressure and humidity), better observation data, better terrain models (microclimate is influenced by the interaction of terrain and the atmosphere). The forecast grid sizes used now are based on choosing the smallest grid size we can afford to compute that yields meaningful forecast data. Computational cost more than quadruples each time grid size halves. “More than” because altitude levels matter too, though for terrestrial forecasts, the ones near the ground matter a whole lot more than what’s happening at 6km.
I had one time a couple weeks ago where I was scheduling jobs on Monday, we were supposed to be rained out Tuesday, light/scattered showers Wednesday, and heavy rain Thursday.
Actual results was no rain Tuesday, absolute downpour on Wednesday, and sunny Thursday and Friday.