151–160 of 310 posts
Are there any experts around that can chime in on the possible impacts of this technology if widely adopted?
From what I can tell from reading & based off https://colab.research.google.com/github/deepmind/graphcast/... , one needs access to ECMWF Era5 or HRES data-sets or something similar to be able to run and use this model. Unknown what licensing options ECMWF offers for Era5, but to use this model in any live fashion, I think one is probably going to need a small fortune. Maybe some other dataset can be adapted (likely…
To use the data in live fashion I think you would need to get license from ECMWF...
In case someone is looking for historical weather data for ML training and prediction, I created an open-source weather API which continuously archives weather data. Using past and forecast data from multiple numerical weather models can be combined using ML to achieve better forecast skill than any individual model. Because each model is physically bound, the resulting ML model should be stable. See: https://open-me…
In case someone is looking for historical weather data for ML training and prediction, I created an open-source weather API which continuously archives weather data. Using past and forecast data from multiple numerical weather models can be combined using ML to achieve better forecast skill than any individual model. Because each model is physically bound, the resulting ML model should be stable. See: https://open-me…
Is there somewhere to see historical forecasts? So not "the weather on 25 December 2022 was such and such" but rather "on 20 December 2022 the forecast for 25 December 2022 was such and such"
Earlier quoted context omitted.
Is there somewhere to see historical forecasts? So not "the weather on 25 December 2022 was such and such" but rather "on 20 December 2022 the forecast for 25 December 2022 was such and such"
I’ve always wanted to see something like that. I always wonder if forecasts are a coin flip beyond a window of a few hours.
https://www.nhc.noaa.gov/verification/verify5.shtml
Our 96 hour projections are as accurate today as the 24 hour projections were in 1990.
This is fascinating: > For inputs, GraphCast requires just two sets of data: the state of the weather 6 hours ago, and the current state of the weather. The model then predicts the weather 6 hours in the future. This process can then be rolled forward in 6-hour increments to provide state-of-the-art forecasts up to 10 days in advance.
Weather is markovian
Furthermore, a predictive model is not working with a complete picture of the weather, but rather some limited-resolution measurements. So, even ignoring non-weather, there may be local weather phenomena detected at time t0, escaping detection at time t1, but still affecting weather at time t2.
In case someone is looking for historical weather data for ML training and prediction, I created an open-source weather API which continuously archives weather data. Using past and forecast data from multiple numerical weather models can be combined using ML to achieve better forecast skill than any individual model. Because each model is physically bound, the resulting ML model should be stable. See: https://open-me…
Is it inevitable that all market alpha gets mined by AI?