I don’t know of anywhere you could get such information, short of analyzing the ensemble of simulation outputs yourself. With typical ensemble sizes of “a few” to “a few dozen”, you probably can get useful confidence intervals but probably can’t get useful conditional probabilities.
(specifically: you’d want to search for your national weather service’s “Thredds data service”, then get the “opendap” link, and use xarray.open_dataset() on that URL… with lazy loading, the data are usually TB+)
The traditional weather forecast consists of summary statistics over saved timesteps of detailed simulations, which run forward from a best-possible reconstruction of the current state of the atmosphere. Data-assimilation, or “hindcasting”/”nowcasting”, is itself a neat trick, and the dual of forecasting—you have past and present observations; you have a model of the system dynamics; you can sample from plausible system states which are compatible with observations or even solve for the most-likely state given observations (including subsequent observations). I don’t think enough people realize that we can be so much more confident about the details of the weather last week than today, even in remote places where nobody was watching!
I don’t know of anywhere you could get such information, short of analyzing the ensemble of simulation outputs yourself. With typical ensemble sizes of “a few” to “a few dozen”, you probably can get useful confidence intervals but probably can’t get useful conditional probabilities.
(specifically: you’d want to search for your national weather service’s “Thredds data service”, then get the “opendap” link, and use
xarray.open_dataset()on that URL… with lazy loading, the data are usually TB+)The traditional weather forecast consists of summary statistics over saved timesteps of detailed simulations, which run forward from a best-possible reconstruction of the current state of the atmosphere. Data-assimilation, or “hindcasting”/”nowcasting”, is itself a neat trick, and the dual of forecasting—you have past and present observations; you have a model of the system dynamics; you can sample from plausible system states which are compatible with observations or even solve for the most-likely state given observations (including subsequent observations). I don’t think enough people realize that we can be so much more confident about the details of the weather last week than today, even in remote places where nobody was watching!