WeatherNext 2: Our most advanced weather forecasting model

If the US does less data gathering (balloon starts, buoy maintenance, setting up weather huts in super remote sites, etc.) it will affect all forecasts.

Models all use a "current world state" of all sensors available to bootstrap their runs.

Similar thing happened during the beginning of Covid-19: they are using modified cargo/passenger planes to gather weather data during their routine trips. Suddenly this huge data source was gone (but was partially replaced by the experimental ADM-Aeolus satellite - which turned out to be a huge global gamer changer due to its unexpected high quality data)

15 years later and still no word from Google if they will use the barometers in Android devices to assimilate surface pressure data. It has been shown that this can improve forecast accuracy. I think IBM may be doing it with their weather apps, but Google/Apple would have dramatically more data available.

Apple even bought Dark Sky, which purported to do this but never released any information - so I doubt they really did do it. And if they did, I doubt Apple continued the practice.

Been waiting a long time to hear Google announce they'll use your barometer to give you a better forecast. Still waiting I guess.

> 15 years later and still no word from Google if they will use the barometers in Android devices to assimilate surface pressure data.

For WeatherNext, the answer is 'no'. The paper (https://arxiv.org/abs/2506.10772) describes in detail what data the model uses, and direct assimilation of user barometric data is not on the list.

This is really important: You're not the end user of this product. These types of models are not built for laypeople to access them. You're an end user of a product that may use and process this data, but the CRPS scorecard, for example, should mean nothing to you. This is specifically addressing an under-dispersion problem in traditional ensemble models, due to a limited number (~50) and limited set of perturbed initial conditions (and the fact that those perturbations do very poorly at capturing true uncertainty).

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.

[1]: https://sites.research.google/gr/weatherbench/

Im pretty deep into this topic and what might be interesting to an outsider is that the leading models like neuralgcm/weathernext 1 before as well as this model now are all trained with a "crps" objective which I haven't seen at all outside of ml weather prediction.

Essentially you add random noise to the inputs and train by minimizing the regular loss (like l1) and at the same time maximizing the difference between 2 members with different random noise initialisations. I wonder if this will be applied to more traditional genai at some point.