Global Ensemble Forecast System

Global Ensemble Forecast System
Name	Global Ensemble Forecast System
Developer	National Centers for Environmental Prediction / Environmental Modeling Center
Released	1990s (ensemble operations expanded 2000s)
Programming language	Fortran, C, Python (programming language)
Operating system	Unix, Linux
License	Public domain (U.S. government)
Website	NCEP EMC pages

Global Ensemble Forecast System

The Global Ensemble Forecast System provides probabilistic numerical weather prediction through multiple model realizations to support decision-making for National Weather Service, Federal Aviation Administration, United States Department of Commerce, European Centre for Medium-Range Weather Forecasts, and international partners such as World Meteorological Organization and United Nations. It combines dynamical modeling, data assimilation, and statistical post-processing to generate ensemble forecasts used by agencies including National Oceanic and Atmospheric Administration, Met Office (United Kingdom), Canadian Meteorological Centre, and research institutions like National Center for Atmospheric Research.

Overview

The system runs a set of perturbed initial conditions and model physics configurations to produce probabilistic fields for variables such as 500 hPa height, surface temperature, and precipitation, supporting stakeholders like Federal Emergency Management Agency, Air Force Weather Agency, U.S. Geological Survey, and commercial providers including AccuWeather and The Weather Company. Ensemble output feeds into impact models used by U.S. Army Corps of Engineers, NOAA National Hurricane Center, Energy Information Administration, and transportation authorities in cities such as New York City and Los Angeles. The GFS ensemble complements other ensemble systems including the ECMWF Ensemble, Canadian Ensemble Prediction System, and the Met Office Unified Model ensembles.

History and Development

Development traces to early stochastic forecasting experiments at National Meteorological Center and collaborations with universities like Massachusetts Institute of Technology, University of Washington, Princeton University, and Colorado State University. Operational ensemble expansions occurred following cooperative efforts with European Organisation for the Exploitation of Meteorological Satellites and advances in observing systems from NOAA satellites and GOES series. Key milestones involved integration of advanced data assimilation techniques from Data Assimilation Research Testbed and algorithmic contributions by researchers affiliated with Scripps Institution of Oceanography and Lamont–Doherty Earth Observatory. Funding and oversight were provided through programs administered by National Science Foundation and Department of Energy research initiatives.

Model Architecture and Methodology

The architecture uses a spectral/dynamical core coupled to physical parameterizations developed by Environmental Modeling Center scientists with perturbation strategies informed by studies at University Corporation for Atmospheric Research and European Centre for Medium-Range Weather Forecasts. Ensemble members differ via stochastic physics, perturbed sea surface temperatures from NOAA Fisheries, and initial condition perturbations generated by bred vector and ensemble transform techniques pioneered at Met Office and Geophysical Fluid Dynamics Laboratory. Post-processing employs Bayesian model averaging, quantile mapping, and machine learning methods developed at institutions such as Stanford University and Massachusetts Institute of Technology.

Data Sources and Initialization

Initialization ingests observations from global networks including Global Telecommunication System, radiosonde launches coordinated by World Meteorological Organization, satellite radiances from NOAA-20, Suomi NPP, and polar-orbiting platforms, as well as remote sensing from Radar networks maintained by National Weather Service. Ocean analyses use data from Argo (oceanography), drifting buoys managed by National Data Buoy Center, and sea surface temperature products from NOAA Coral Reef Watch. Assimilation systems ingest aircraft reports from Airlines and surface synoptic observations from international services coordinated through International Civil Aviation Organization.

Forecast Products and Applications

Operational products include probabilistic guidance for 1–16 day ranges, ensemble mean and spread fields, time-lagged ensemble outputs used by National Hurricane Center for tropical cyclone risk, and tailored feeds for sectors like aviation routing for Federal Aviation Administration and renewable energy forecasting for companies such as NextEra Energy. Hydrological services at U.S. Geological Survey utilize ensemble precipitation for flood forecasting, while agricultural advisories from United States Department of Agriculture and commodity traders use probabilistic temperature and precipitation projections. Emergency managers in agencies like FEMA rely on ensemble-driven scenario planning for extreme events similar to analyses done after Hurricane Katrina.

Verification and Performance

Verification employs metrics such as continuous ranked probability score, Brier score, and rank histograms used by research groups at National Center for Atmospheric Research and European Centre for Medium-Range Weather Forecasts. Retrospective studies compare performance with operational products from ECMWF and Met Office ensembles and academic evaluations published by authors from Columbia University and University of Reading. Improvements in medium-range skill trace to denser satellite observations from COSMIC and assimilation upgrades inspired by projects at Jet Propulsion Laboratory and NASA.

Operational Implementation and Distribution

Runs are executed on high-performance computing clusters operated by National Centers for Environmental Prediction and hosted on supercomputers procured through programs like U.S. Office of Science and Technology Policy initiatives. Dissemination channels include the NOAA Central Library data feeds, National Weather Service dissemination services, real-time access via Open Geospatial Consortium-compliant servers, and commercial redistribution by vendors such as IBM and The Weather Company. International exchange supports cooperative forecasting through World Meteorological Organization Regional Associations and bilateral data sharing with agencies including Japan Meteorological Agency and Australian Bureau of Meteorology.

Category:Meteorological models