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| ▲ | sunshinesnacks 2 hours ago | parent | next [-] | | Pretty much all of the AI weather prediction models are trained on ECMWF ERA5, which is kinda like a numerical weather prediction model run to forecast at t=0. ERA5 is historical weather data, but it’s a “reanalysis” of it. | | |
| ▲ | tcumulus 2 hours ago | parent [-] | | Indeed, you can imagine this as some sort of advanced physics-based interpolation of various measurements (land stations, satellite data, ...) to fill in every cell in a latitude-longitude grid. This is not only used for ERA5 (training data for the models), but also to determine the initial conditions for every grid cell which are used to roll out the forecast. So AI weather models depend greatly on the NWP/physics used in for reanalysis and initial conditions. That being said, there is also research being conducted in training models straight from the raw data (weather stations, satellite, ...), thus bypassing the "interpolation" step. | | |
| ▲ | sunshinesnacks an hour ago | parent [-] | | Yeah. I can’t remember names off the top of my head, but there are a few companies, and I think many researchers, working on AI “data assimilation” for this. |
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| ▲ | plantain 3 hours ago | parent | prev | next [-] | | Historical weather data is discrete. You need continuous state for weather modelling which is currently achieved through conventional reforecasts using those historical observations. | |
| ▲ | RandomLensman 2 hours ago | parent | prev [-] | | From a quick read: ECMWF and IBTrACS data - the former is model based (with measurement data crunched), the latter purely observational. |
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