Comment by DoctorOetker
21 hours ago
Sorry to hijack you: I have some questions regarding current weather models:
I am personally not interested in predicting the weather as end users expect it, rather I am interested in representative evolutions of wind patterns. I.e. specify some location (say somewhere in the North Sea, or perhaps on mainland Western Europe), and a date (say Nov 12) without specifying a year, and would like to have the wind patterns at different heights for that location say for half an hour. Basically running with different seeds, I want to have representative evolutions of the wind vector field (without specifying starting conditions, other than location and date, i.e. NO prior weather).
Are there any ML models capable of delivering realistic and representative wind gust models?
(The context is structural stability analysis of hypothetical megastructures)
I mean - you don't need any ML for that. Just go grab random samples from a ~30 day window centered on your day of interest over the region of interest from a reanalysis product like ERA5. If the duration of ERA5 isn't sufficient (e.g. you wouldn't expect on average to see events with a >100 year return period given the limited temporal extent of the dataset) then you could take one step further and pull from an equilibrium climate model simulation - some of these are published as part of the CMIP inter-comparison, or you could go to special-built ensembles like the CESM LENS [1]. You could also use a generative climate downscaling model like NVIDIA's Climate-in-a-bottle, but that's almost certainly overkill for your application.
[1]: https://www.cesm.ucar.edu/community-projects/lens