> "it's more efficient if you ignore the part where it's not"

Even when you include training, the payoff period is not that long. Operational NWP is enormously expensive because high-resolution models run under soft real-time deadlines; having today's forecast tomorrow won't do you any good.

The bigger problem is that traditional models have decades of legacy behind them, and getting them to work on GPUs is nontrivial. That means that in a real way, AI model training and inference comes at the expense of traditional-NWP systems, and weather centres globally are having to strike new balances without a lot of certainty.

It's more efficient anyway because the inference is what everyone will use for forecasting. Researchers will be using huge amounts of compute to develop better models, but that's also currently the case, and it isn't the majority of weather simulation use.

There's an interesting parallel to Formula One, where there are limits on the computational resources teams can use to design their cars, and where they can use an aerodynamic model that was previously trained to get pretty good outcomes with less compute use in the actual design phase.

I suggest reading up on fixed costs vs variable costs and why it is generally preferable to push costs to fixed.

Assuming you’re not throwing the whole thing out after one forecast, it is probably better to reduce runtime energy usage even if it means using more for one-time training.

I mean that’s cute, but surely you can add up the two parts (single training plus globally distributed inference) and understand that the net efficiency would be an improvement?