6 ms·

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". Th

by rtpg 9d ago

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 predictive sciences are ultimately about mixing together a bunch of inputs to attempt to decipher some output. Do we end up being able to take stuff from here and figure out some new ideas about modelling the climate as a whole?

desterothx 9d ago
Sure we do, you can follow the weights so to say, to see which data turns out to be more important vs less important for predictions of a higher quality
Majromax 9d ago
> 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 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-archive 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/ai-tropical-cyclone-forecasts 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.

by rtpg 9d ago

desterothx 9d ago

Sure we do, you can follow the weights so to say, to see which data turns out to be more important vs less important for predictions of a higher quality

Majromax 9d ago

> 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 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-archive 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/ai-tropical-cyclone-forecasts 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.