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Problem with these models coild be that they are trained with historical weather and patterns, but when new weather effects come up they will get worse over tim
by m3kw9 3y ago
Problem with these models coild be that they are trained with historical weather and patterns, but when new weather effects come up they will get worse over time.
- NavinF 3y agoThese models are not as simplistic as you imagine.
- dekhn 3y agothey might not be simplistic, but we've already got experience with google making a model and then external conditions change, invalidating the model- see Google Flu Trends. After the team launched it and got their promos and moved on, the zombie jobs training the model failed to make useful predictions the next year. The model was not simplistic; it's just that it didn't generalize and needed constant attention from humans.
- m3kw9 3y agoMaybe not as magical as you imagine
- potatoman22 3y agoYou're completely right. This model, as with any other predictive model, is subject to degredation in performance when the data-generating process changes. But given MetNet-2 came out in 2021, they'll probably release an updated version before the performance degrades due to changes in weather patterns.
- bitshiftfaced 3y agoOpposed to training them on future weather and patterns?
- waveBidder 3y agoopposed to first principles modeling. the problem isn't that we don't have good weather models, the problem is they're chaotic, so you need exponentially more data for linear increases in forecast window. and I do literally mean exponential there.