I'm a novice software developer who's about to write some Python scripts for getting weather data from several U.S. stations, as well as a few in the Pacific (for checking Southern Oscillation Index & the like).

I've been oriented toward NOAA's site mostly, but having seen some mentions about 'European models' of weather forecasting in various articles over the past year, I wonder if it would be better to look toward mining the ECMWF.

Basically, I'm looking at current surface temperature and wind speeds, then trying to get a daily forecast out to 15-20 days forward. Haven't dug into ECMWF yet, but on NOAA's site, they do provide probabilistic outlooks, albeit on a monthly frequency (example). I could always check weather.com's 10-Day or Monthly, but again, I'm wondering about the accuracy.

Anyone with experience comparing forecasted vs actual results on a thorough basis, and can share some perspective?


1 Answer 1


You can compare verification scores using this application from ECMWF made from guidelines by the WMO. The example below fits nicely with what is widely known, namely that IFS (the ECMFWF global model) scores generally best. In recent year the UK Met Office global model have also scored better than GFS. The figure shows the root mean square error of MSLP. The example below of course only covers verification for one month, so you should examine longer periods and focus on the fields you are most interested in.

Verification for MSLP

  • $\begingroup$ Thank you, Whir! Exactly what I was looking for. $\endgroup$
    – CB001
    Commented Dec 18, 2019 at 16:56

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