> No. But I'd suspect a tabula rasa approach to weather–particularly given it hasn't been rolled out globally in one go–incorporates satellite data, local measurements, et cetera.

There most likely won't ever be such an effort - even in companies that are targeting verticalization of the "weather supply chain" (proprietary observations + models + decision support tools) - if only because it would be utterly foolish to exclude the vast amounts of data collected by government agencies and the wide variety of players in the weather enterprise. At best, verticalized weather companies can produce niche value over baseline from the single modality of proprietary data they collect.

The infrastructure for observing and forecasting the weather is incredibly sophisticated, and has been evolving for about 150 years at this point. The quality of contemporary numerical weather prediction likely doesn't leave much headroom towards the threshold of fundamental physical limitations on predictability. This is why there are groans and eye rolls from the weather community each time a new player steps forward with yet-another-AI-model-trained-on-ERA5-reanalysis and boasts some comically small improvement in average forecast skill.

With all that being said, there's likely an exciting frontier opening up as the AI models push towards encompassing data assimilation. But the applications that start to become extremely interesting there won't have any noticeable impact on average forecast quality for your typical weather app.