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WeatherNext 3

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Re: WeatherNext 3

#111
post #31

It’s crazy we’ll never have forecasts as good as dark sky again. What kind of magic were they doing?

> It’s crazy we’ll never have forecasts as good as dark sky again.

'Nowcasting' is an area of active research, both with machine learning and with physics-informed or visual flow approaches.

Part of the problem from the machine learning side is that these are _huge_ problems. NVidia's StormCast (https://research.nvidia.com/publication/2024-08_kilometer-sc...) works globally at kilometer scales, and you can imagine how big those grids are. Even with patch training, you're dealing with very large datasets.

At the same time, this is not exactly a high-profile area of research. National weather centres focus on actionable medium-range weather predictions, and meteorologists can look at radar themselves and perform mark-one-eyeball predictions for very short-range watches and warnings. Some private-sector actors will pay for short range predictions, but they're often looking for something hyperlocalized (e.g. weather at this particular construction site, for crane safety) or specialized (near-real-time cloud and wind predictions for renewable energy).

Most public-accessible weather predictions are downstream of either a public-sector effort (which doesn't internalize benefits, leading to under-resourcing) or a byproduct of another private-sector offering.

Re: WeatherNext 3

#112
post #52

Reading through the paper and... This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of…

> The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take")

In a high-level view, it's the result of specialized decoding heads.

Traditionally one would take gridded forecast outputs, then process those with comprehensible actions like "find all local pressure minima in the ocean, then filter to ones which correspond to warm cores, etc." to infer (diagnose) the presence of a cyclone.

One problem with this is that gridded forecasts suffer from known biases and tradeoffs. For example, a forecast on a ~25km grid is just on the edge of being able to represent the eye of a hurricane (50km scales), and it certainly can't accurately represent the sharp transition of wind in the eyewall. That means that the forecast winds are almost certainly a smoothed (and therefore less intense) version of what observers would see.

The WN2 approach (paper: https://www.nature.com/articles/s41586-026-10953-2) adds a direct readout head to the model: given latent-space access to the full forecast, it tries to predict the bona-fide cyclone observations (https://www.ncei.noaa.gov/products/international-best-track-...).

It's kind of like a post-processing or bias correction (see for example https://www.ecmwf.int/en/about/media-centre/aifs-blog/2026/a..., which applies in physical space), but by having access to the model latent space and by being included in model training it is (probably!) higher-quality than a pure, after-the-fact approach.

Re: WeatherNext 3

#114
post #63

Earlier quoted context omitted.

>and every other app I check shows it. Other apps have a different government?

That person is probably misremembering or looking later after the update. I pull weather data from multiple apps all the time because I’m a weather nerd and they all agree equally poorly.

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