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A lot of weather agencies across the world run ensembles including US, Canada, and the UK. Ensembles are the future of weather forecasting but weather models ar

A lot of weather agencies across the world run ensembles including US, Canada, and the UK. Ensembles are the future of weather forecasting but weather models are so computationally heavy models have a resolution/forecast length tradeoff which is even bigger when trying to run 20-50 ensemble members. You can have a high resolution model that runs to 2 days or so or have a longer range model at much coarser resolution. ECMWF recently upgraded their ensemble to run at the same resolution as the HRES. The HRES is basically the ensemble control member at this point [1] [1] https://www.ecmwf.int/en/about/media-centre/news/2023/model-upgrade-increases-skill-and-unifies-medium-range-resolutions https://www.ecmwf.int/en/about/media-centre/news/2023/model-...

earthscienceman 3y ago
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.
dguest 3y ago
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 https://www.mdpi.com/2073-431X/11/7/114

earthscienceman 3y ago

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.

dguest 3y ago

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