Technical milestone reached: global earth system simulations with 1.2 km resoln

Which is *exactly* why ultra-high resolution global weather simulation has dubious prospects for improving forecasts. When you're at spatial scales where you need to parameterize convection, there's an inherent "smoothness" to model solutions that suppresses noisy errors. If you go to cloud-resolving scales - which is needed for simulations like the ones here - you don't get the benefit of that smoothness anymore, because you need to actually resolve scales of motion that are incredibly fine. It's a losing proposition; you'll never get it "perfect", so you're much more likely to spin up an error cascade with significant impacts on forecast down the line, through things like the structure of organized convection.

But dynamically uninteresting, quasi-balanced setups and modes? There's far less to worry about in terms of the butterfly effect, and any errors you might worry about will be dwarfed by the fact that we don't have good data to assimilate in places like the remote oceans anyways.

It's also worth pointing out that the mathematics and understanding of error / perturbation growth in the atmosphere are well-understood. In fact, this fundamentally underpins how we've developed data assimilation approaches over the past two or three decades that allow us to effectively leverage new datasets such as satellite data to increase forecast quality and reliability at longer lead times. So it's somewhat trivial to actually directly quantify these "butterflies."

Why do you think that we need "femtometer resolution" for "very precise" 100 day weather forecasting? What even is "very precise" 100 day weather forecasting? I think it's very amusing to do the math on how much memory would be required to run a crude primitive equation dycore over even the tiniest of domains at femtometer resolution :)

> It's not as though this is part of a growing trend to abandon conventional weather and climate modeling.

The thing is, there *absolutely is* a trend towards private investment in weather modeling going towards faux-moonshot ideas like cubesat constellations without demonstrated ROI and that would require evolutionary leaps forward in data assimilation, or for deep learning to replace weather models. A miniature version of this already played out with precipitation nowcasting - probably the easiest weather forecasting problem that you could approach with an AI system, yet the approaches that have been developed so far barely improve over optical flow or other simple approaches, let alone advance our capability to forecast, say, convective initiation.

The future of weather forecasting is larger ensembles (O(100-500) ensemble members, across 2-5 different models) of near-convective-resolving global models at meso-gamma (2-10 km resolution) fed into slightly more sophisticated statistical post-processing systems - almost certainly trained using simple AI/ML techniques on large-scale reforecasts of these parent model systems, or brute-forcing purely Bayesian statistical approaches.

I'm not sure the added precision is helpful if it's just 2.5 days ahead. An extra week of accurate forecasts is a bit pointless if it takes 8 days.

> Why do you think that we need "femtometer resolution" for "very precise" 100 day weather forecasting?

Due to sensitive dependence on initial conditons. Even using measurements at meter resolution will cause the accuracy of a forecast to begin to break down after only a few days.

> What even is "very precise" 100 day weather forecasting?

Anywhere from accurate to exact.

> I think it's very amusing to do the math on how much memory would be required to run a crude primitive equation dycore over even the tiniest of domains at femtometer resolution

And Bill Gates thought 64K should be enough for anybody. Do you really think computers will only have a few GB of memory 50 years from now?

> there absolutely is a trend towards private investment in weather modeling going towards faux-moonshot ideas like cubesat constellations without demonstrated ROI and that would require evolutionary leaps forward in data assimilation, or for deep learning to replace weather models

This straw man does not exactly demonstrate that conventional weather and climate modeling is being abandoned anytime soon. If the unconventional private investments aren't profitable, the market will deal with them.

> The future of weather forecasting is

much like the local weather, impossible to predict with any accuracy years into the future, and yet the tools used to measure it are consistently getting more accurate, cheaper and smaller. Maybe like bottle-openers, weather sensors may superfluously start appear on everything. The more widespread the measurements, the more data descibing initial conditions, the better the forecast will be at any interval.

No, but I wouldn't mind weather measuments every cubic femtometer of the lower atmosphere and a fast enough computer with enough memory to cruch the data and accurately report what the weather will be like on 29 February.