I'm someone working in the field with weather modelers/forecasters but not a modeler myself. Reading this discussion has been an absolute treat ...

The most fascinating thing about the concept of error in models, including in ensembles, is you can only calculate and propagate error for contributors that you can quantify. There are many unquantifiable sources of error. Imagine a physical process that you are unaware of that propagates as a bias, for example ice nucleation via aerosols. Perhaps you don't even model aerosols. How do you account for error here? What does error even mean?

Ensembles only show you intramodel variability. Which is like error, sort of, but only really represents a combination of "real" variability in initial conditions and how that propagates through your physics/parameterizations.

"models" the HN commentators make for their businesses surely have parallel concepts, but I don't see anyone talking about them. Only discussion about the errors you know when the ugliest errors are the ones that no one knows.

Even with all the hype around deep learning and GPUs, the weather services are building a lot of new, CPU-only supercomputers [1]. There are plans to use more GPUs and ML in the coming years, but the weather models are real, tangible, economically impactful and strategically essential products. It's pretty inspiring to see governments dumping a huge amount of computing power into the working models, and also funding research ideas.

[1]: https://www.mdpi.com/2073-431X/11/7/114

Anybody happen to know if there's anything more detailed from Environment Canada than their forecast pages?

https://weather.gc.ca/city/pages/on-143_metric_e.html

I really like that discussion type forecast.

It seems unlikely that the modelling and forecasting has become worse, so I guess there is some sort of change happening to the climate making it more unstable and less predictable?

I think that is a change in definition. 15 years ago it was only rain if you were sure to get drenched. Now rain means 1mm of water hit the ground in your general vicinity. I blame an abundance of data combined people who refuse to get damp and need an umbrella if there is any chance at all.

The other thing is that many forecasts have a large range. So it might be that the left third of the forecast area has like a 99.9% chance of zero precipitation, the right third has a 99.9% chance of some precipitation, and there is some uncertainly about where the border will be, (and near the border might have less precipitation than the edge).

The result is that I will be told ~50% chance of precipitation, but the places I care about might well be in the essentially 0% or essentially 100% parts.

The single forecast for a decent size city problem impacts other parts like forecasted highs/low. Even without a front, in some cities that have incorporated much of their suburbs, it is not uncommon for the eastmost part and westmost part to differ by 5-6 degrees, and that is ignoring the inherent temperature differences found in the downtown areas.