Deep Learning for Day Forecasts from Sparse Observations · HackerPapers discussions

Deep Learning for Day Forecasts from Sparse Observations — mentioned by blueelephanttea

Tue, 29 Oct 2024 17:20:28 GMT

> When it comes to medium range predictions. If you look at nowcasting, I believe the top dog is metnet 3 (https://arxiv.org/abs/2306.06079). I don't consider 24 hour forecasts "medium range". And I don't really view MetNet-3 a competitor to GFS or ECMWF at all (currently).

Paper and discussion history. A mention is not an endorsement.

Deep Learning for Day Forecasts from Sparse Observations — mentioned by marcyb5st

Tue, 29 Oct 2024 16:33:27 GMT

When it comes to medium range predictions. If you look at nowcasting, I believe the top dog is metnet 3 (https://arxiv.org/abs/2306.06079). On a tangential note, I worked on weather forecasting a bit before, and talking with some people still in the field it seems that these organizations (NOAA, ECMWF) are stuck with NWP and refuse to embrace AI models. Not sure if they can't attract talent and so are somewhat stuck or if their higher ups are "old school" and can't really see the potential that this approach has to offer. It is just sad that the private sector is outclassing institutions and…

Paper and discussion history. A mention is not an endorsement.

Deep Learning for Day Forecasts from Sparse Observations — mentioned by spdustin

Fri, 03 Nov 2023 20:47:30 GMT

Here's the paper about MetNet-3: https://arxiv.org/abs/2306.06079

Paper and discussion history. A mention is not an endorsement.

Deep Learning for Day Forecasts from Sparse Observations — mentioned by counters

Fri, 03 Nov 2023 19:09:22 GMT

It doesn't seem like MetNet outputs a complete 3D atmospheric state, just specific (and mostly surface-level) forecast predictands [1]. The analysis you're describing could be done with a traditional numerical weather prediction and data assimilation system (especially if that system implements 4DVar, since you'll already have the model adjoint available - well, technically the tangent linear, but still applicable here). [1]: https://arxiv.org/pdf/2306.06079.pdf

Paper and discussion history. A mention is not an endorsement.