``` 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.
Quite a lot of weather sites offer this data in an easily eatable visual format.
Sure, those big physics-based models are very computationally intensive (national weather bureaus run them on sizeable HPC clusters), but you only need to run them every few hours in a central location and then distribute the outputs online. It's not like every forecaster in a country needs to run a model, they just need online access to the outputs. Even if they could run the models themselves, they would still need the mountains of raw observation data that feeds the models (weather stations, satellite imagery, radars, wind profilers...). And these are usually distributed by... the national weather bureau of that country. So the weather bureau might as well do the number crunching as well and distribute that.
Developing an ensemble of possible scenarios has been the central insight of weather forecasting since the 1960s when Edward Lorenz discovered that tiny differences in initial conditions can grow exponentially (the "butterfly effect"). Since they could really do it in the 90s, all competitive forecasts are based on these ensemble models.
When you hear "a 70% chance of rain," it more or less means "there was rain in 70 of the 100 scenarios we ran."[0] There is no "single accurate forecast scenario."
[0] Acknowledging this dramatically oversimplifies the models and the location where the rain could occur.
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.
As a layperson, what _is_ useful is to look at the difference between models. My long range favourite is to compare ECMWF and GFS27 and if the deviation is high (windy app has this) then you can bet that at least one of them is likely wrong
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)
At least for the US NWS: if 30 of 100 scenarios result in 50% shower coverage, and 70 out of 100 result in 0%, this is reported as 15% chance of rain. Which is exactly the same as 15 with 100% coverage and 85 with 0% coverage, or 100 with 15% coverage.
Understanding this, and digging further into the forecast, gives a better sense of whether you're likely to encounter widespread rainfall or spotty rainfall in your local area.
Definitely. Training on the historical data creates compelling forecasts but it comes off as a magic box. Where are the missing physics for the high performance cluster?
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.