The evaluation of the air pollution / chemistry forecasts in the arxiv paper [1] is really threadbare and entirely disconnected from the actual literature of at

The evaluation of the air pollution / chemistry forecasts in the arxiv paper [1] is really threadbare and entirely disconnected from the actual literature of atmospheric chemical transport modeling and air quality forecasting. The highlighted success case - a sandstorm, where PM10 is the key parameter being analyzed - is probably entirely explained by adequate performance of the underlying "weather" portion of the model (e.g. there is obviously a very strong correlation between intense winds and sandstorms in a desert), but the authors don't provide a more nuanced discussion of the forecast meteorology and transport dynamics of that scenario. It's cool that their model has learned about this relationship, but a greater editorial point is that _we already have global air pollution models_ - particularly, the one this model was trained (fine-tuned) against! That's not to discount all the really novel and interesting new developments with this paper, but the editorialization around AI applications in the weather and climate space is getting way out of hand, especially when most of these applications are incremental improvements at best over the existing technology in the field (with the key exception of cost/efficiency of generating forecasts - that's a true revolution). The Aurora paper is really cool, but issues like these greatly temper my enthusiasm for it. I hope that the research team seeks input and collaboration with domain experts in meteorology and atmospheric chemistry in the future to find ways to leverage this new technology in impactful and useful ways that actually will benefit society. [1]: https://arxiv.org/html/2405.13063v2 https://arxiv.org/html/2405.13063v2