GRIB Model

GFS Ensemble

Provider:National Centers for Environmental Prediction, NOAA (USA)
Model scope:Global
Update frequency:every 6 hours
Resolution:0.5°, 30.0nm, 55.6km
Model duration:49 forecasts starting at 0 hr, ending at 16 days
Parameters:pressure, wind, rain, temperature, 250 mb, 500 mb, 850 mb, ensemble standard deviation
GRIB model date:Sun May 26 06:00:00 2024 UTC
Download date:Sun May 26 12:52:03 2024 UTC
Download delay:6hr 52min

Note: the Download delay is the amount of time required for the GRIB model to compute its forecast and then for the LuckGrib cluster to download the data and make it available. The LuckGrib delay is generally less than 10 minutes, the remainder of the delay is the model compute time.


This is an ensemble model, and in this case, it is the result of averaging 21 slightly different weather models together. The idea in an ensemble model is that there is instability in many weather systems and that capturing the initial state of the atmosphere for the weather models can not be done perfectly. The ensemble process slightly alters the initial inputs and/or the numerical model in order to generate a range of different results. This suite of results can be analyzed to generate the average and standard deviation.

You can download the ensemble mean (average), standard deviation and the control member, which allows you estimate the uncertainty in the forecasts being generated.

There is a tutorial which provides more information on this topic.

The following description has been taken directly from the official documentation

The Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models. The proverbial butterfly flapping her wings can have a cascading effect leading to wind gusts thousands of miles away. This extreme example illustrates that tiny, unnoticeable differences between reality and what is actually measured can, over time, lead to noticeable differences between what a weather model forecast predicts and reality itself. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations.

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