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The North American Mesoscale Forecast System (NAM) is one of the National Centers For Environmental Prediction's (NCEP) major models for producing weather forecasts. NAM generates multiple grids (or domains) of weather forecasts over the North American continent at various horizontal resolutions.

The Global Forecast System (GFS) is a National Centers for Environmental Prediction (NCEP) weather forecast model that generates data for dozens of atmospheric and land-soil variables, including temperatures, winds, precipitation, soil moisture, and atmospheric ozone concentration.

NOAA is upgrading its Global Forecast System (GFS) weather model to boost weather forecasting capabilities across the U.S. These advancements will improve hurricane genesis forecasting, modeling for snowfall location, heavy rainfall forecasts, and overall model performance.

The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance.

In turn, the two most common and well-known global models are GFS (Global Forecast System) and ECMWF (European Centre for Medium-Range Weather Forecasts), a little less common and well-known is ICON (Icosahedral Nonhydrostatic). GFS is an American model, ECMWF is a model from England, and ICON is from Germany.

GFS (Global Forecast System) is the main US global weather model from the National Centers for Environmental Prediction (NCEP) of the National Weather Service (NWS) of the National Oceanic and Atmospheric Administration (NOAA), the main US weather service, with headquarters in Washington, D.C.

Today, the NWS runs an extensive suite of weather models, ranging from local, high-resolution, short-term models that help pinpoint severe weather (the High-Resolution Rapid Refresh model (HRRR)), medium resolution models that produce quality short-range forecasts for the North American continent (the North American ...

Model Output Statistics (MOS) is a type of statistical post-processing, a class of techniques used to improve numerical weather models' ability to forecast by relating model outputs to observational or additional model data.