``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ```
As an end user, all I care is that there's one accurate forecasted scenario.
Again, you, as an end user, don't need to know any of that. The CRPS scorecard is a very specific measure of error. I don't expect them to reveal the technical details of the model, but an industry expert instantly knows what WeatherBench[1] is, the code it runs, the data it uses, and how that CRPS scorecard was generated.
By having better dispersed ensemble forecasts, we can more quickly address observation gaps that may be needed to better solidify certain patterns or outcomes, which will lead to more accurate deterministic forecasts (aka the ones you get on your phone). These are a piece of the puzzle, though, and not one that you will ever actually encounter as a layperson.
Sorry - not sure this is a reasonable take-away. The models here are all still initialized from analysis performed by ECMWF; Google is not running an in-house data assimilation product for this. So there's no feedback mechanism between ensemble spread/uncertainty and the observation itself in this stack. The output of this system could be interrogated using something like Ensemble Sensitivity Analysis, but there's nothing novel about that and we can do that with existing ensemble forecast systems.
I am personally not interested in predicting the weather as end users expect it, rather I am interested in representative evolutions of wind patterns. I.e. specify some location (say somewhere in the North Sea, or perhaps on mainland Western Europe), and a date (say Nov 12) without specifying a year, and would like to have the wind patterns at different heights for that location say for half an hour. Basically running with different seeds, I want to have representative evolutions of the wind vector field (without specifying starting conditions, other than location and date, i.e. NO prior weather).
Are there any ML models capable of delivering realistic and representative wind gust models?
(The context is structural stability analysis of hypothetical megastructures)
Calculating the stability and structural requirements for a super-chimney to the tropopause, would require representative higher temporal frequency wind fields
Do you know if I can extract such a high time resolution from LENS since a cursory look at ERA5 showed a time resolution of just 1 hour?
The advantage of an ML model is that its usually possible to calculate the joint probability for a wind field, or to selectively generate a dataset with N-th percentile wind fields etc.
If its differentiable, and the structural stress assumptions are known, then one can "optimize" towards wind profiles that are simultaneously more dangerous and more probable, to identify what needs adressing. Thats why an ML model of local wind patterns would be desirable. ML is more than just LLM's. What one typically complains of in the context of LLM's: that there's no error bars on the output, is not entirely correct: just like differentiable ML models for physical and other phenomena they too allow to calculate the joint probability of sentences, except instead of modeling natural phenomena it is modelling what humans uttered in the corpus (or implicit corpus after RLHF etc). A base model LLM can quite accurately predict the likelihood of a human expressing a certain phrase, but thats modeling human expressions, not their validity. An ML model trained on actual weather data, or fine grained simulated weather data results in comparatively more accurate probability distributions, because physics isn't much of an opinion.