GFS: Global Forecast System 384-Hour Predicted Atmosphere Data

NOAA/GFS0P25
Dataset Availability
2015-07-01T00:00:00Z - 2022-05-24T00:00:00
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("NOAA/GFS0P25")
Tags
climate cloud emc flux forecast geophysical gfs humidity ncep noaa precipitation radiation temperature vapor weather wind

Description

The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). The GFS dataset consists of selected model outputs (described below) as gridded forecast variables. The 384-hour forecasts, with 3-hour forecast interval, are made at 6-hour temporal resolution (i.e. updated four times daily). Use the 'creation_time' and 'forecast_time' properties to select data of interest.

The GFS is a coupled model, composed of an atmosphere model, an ocean model, a land/soil model, and a sea ice model which work together to provide an accurate picture of weather conditions. See history of recent modifications to the global forecast/analysis system, the model performance statistical web page, and the documentation homepage for more information.

Bands

Resolution
27830 meters

Bands

Name Units Min Max Description
temperature_2m_above_ground °C -69.18* 52.25*

Temperature 2m above ground
specific_humidity_2m_above_ground kg/kg 0* 0.03*

Specific humidity 2m above ground
relative_humidity_2m_above_ground % 1* 100.05*

Relative humidity 2m above ground
u_component_of_wind_10m_above_ground m/s -60.73* 59.28*

U component of wind 10m above ground
v_component_of_wind_10m_above_ground m/s -63.78* 59.39*

V component of wind 10m above ground
total_precipitation_surface kg/m^2 0* 626.75*

Until 2019-11-07 06:00:00, this field represents the precipitation at surface at the forecasted time. After that date, this field holds the cumulative precipitation at surface added together from all forecasts starting from hour 0 (only for assets with forecast_hours > 0)
precipitable_water_entire_atmosphere kg/m^2 0* 100*

Precipitable water for entire atmosphere
total_cloud_cover_entire_atmosphere % 0* 100*

Total cloud cover for entire atmosphere (only for assets with forecast_hours > 0)
downward_shortwave_radiation_flux W/m^2 0* 1230*

Downward shortwave radiation flux (only for assets with forecast_hours > 0)
* estimated min or max value

Image Properties

Image Properties

Name Type Description
creation_time DOUBLE

Time of creation
forecast_hours DOUBLE

Forecast hours
forecast_time DOUBLE

Forecast time

Terms of Use

Terms of Use

NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution.

Citations

Citations:

  • Alpert, J., 2006 Sub-Grid Scale Mountain Blocking at NCEP, 20th Conf. WAF/16 Conf. NWP P2.4.

  • Alpert, J. C., S-Y. Hong and Y-J. Kim: 1996, Sensitivity of cyclogenesis to lower troposphere enhancement of gravity wave drag using the EMC MRF”, Proc. 11 Conf. On NWP, Norfolk, 322-323.

  • Alpert,J,, M. Kanamitsu, P. M. Caplan, J. G. Sela, G. H. White, and E. Kalnay, 1988: Mountain induced gravity wave drag parameterization in the NMC medium-range forecast model. Pre-prints, Eighth Conf. on Numerical Weather Prediction, Baltimore, MD, Amer. Meteor. Soc., 726-733.

  • Buehner, M., J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes Geophys., 20, 669–682.

  • Chun, H.-Y., and J.-J. Baik, 1998: Momentum Flux by Thermally Induced Internal Gravity Waves and Its Approximation for Large-Scale Models. J. Atmos. Sci., 55, 3299–3310.

  • Chun, H.-Y., Song, I.-S., Baik, J.-J. and Y.-J. Kim. 2004: Impact of a Convectively Forced Gravity Wave Drag Parameterization in NCAR CCM3. J. Climate, 17, 3530–3547.

  • Chun, H.-Y., Song, M.-D., Kim, J.-W., and J.-J. Baik, 2001: Effects of Gravity Wave Drag Induced by Cumulus Convection on the Atmospheric General Circulation. J. Atmos. Sci., 58, 302–319.

  • Clough, S.A., M.W. Shephard, E.J. Mlawer, J.S. Delamere, M.J. Iacono, K.Cady-Pereira, S. Boukabara, and P.D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes, J. Quant. Spectrosc. Radiat. Transfer, 91, 233-244. doi:10.1016/j.jqsrt.2004.05.058

  • Ebert, E.E., and J.A. Curry, 1992: A parameterization of ice cloud optical properties for climate models. J. Geophys. Res., 97, 3831-3836.

  • Fu, Q., 1996: An Accurate Parameterization of the Solar Radiative Properties of Cirrus Clouds for Climate Models. J. Climate, 9, 2058-2082.

  • Han, J., and H.-L. Pan, 2006: Sensitivity of hurricane intensity forecast to convective momentum transport parameterization. Mon. Wea. Rev., 134, 664-674.

  • Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather and Forecasting, 26, 520-533.

  • Han, J., M. Witek, J. Teixeira, R. Sun, H.-L. Pan, J. K. Fletcher, and C. S. Bretherton, 2016: Implementation in the NCEP GFS of a hybrid eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Weather and Forecasting, 31, 341-352.

  • Hou, Y., S. Moorthi and K. Campana, 2002: Parameterization of Solar Radiation Transfer in the NCEP Models, NCEP Office Note #441, pp46. Available here

  • Hu, Y.X., and K. Stamnes, 1993: An accurate parameterization of the radiative properties of water clouds suitable for use in climate models. J. Climate, 6, 728-74.

  • Iacono, M.J., E.J. Mlawer, S.A. Clough, and J.-J. Morcrette, 2000: Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3, J. Geophys. Res., 105(D11), 14,873–14,890.2.

  • Johansson, Ake, 2008: Convectively Forced Gravity Wave Drag in the NCEP Global Weather and Climate Forecast Systems, SAIC/Environmental Modelling Center internal report.

  • Juang, H-M, et al. 2014:Regional Spectral Model workshop in memory of John Roads and Masao Kanamitsu, BAMS, A. Met. Soc, ES61-ES65.

  • Kim, Y.-J., and A. Arakawa (1995), Improvement of orographic gravity wave parameterization using a mesoscale gravity-wave model, J. Atmos. Sci.,52, 875–1902.

  • Kleist, D. T., 2012: An evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS , Ph.D. Thesis, Dept. of Atmospheric and Oceanic Science, University of Maryland-College Park, 149 pp.

  • Lott, F and M. J. Miller: 1997, “A new subgrid-scale orographic drag parameterization: Its formulation and testing”, QJRMS, 123, pp101-127.

  • Mlawer, E.J., S.J. Taubman, P.D. Brown, M.J. Iacono, and S.A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16663-16682.

  • Sela, J., 2009: The implementation of the sigma-pressure hybrid coordinate into the GFS. NCEP Office Note #461, pp25.

  • Sela, J., 2010: The derivation of sigmapressure hybrid coordinate semi-Lagrangian model equations for the GFS. NCEP Office Note #462 pp31.

  • Yang, F., 2009: On the Negative Water Vapor in the NCEP GFS: Sources and Solution. 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction, 1-5 June 2009, Omaha, NE.

  • Yang, F., K. Mitchell, Y. Hou, Y. Dai, X. Zeng, Z. Wang, and X. Liang, 2008: Dependence of land surface albedo on solar zenith angle: observations and model parameterizations. Journal of Applied Meteorology and Climatology.No.11, Vol 47, 2963-2982.

Explore in Earth Engine

var dataset = ee.ImageCollection('NOAA/GFS0P25')
                  .filter(ee.Filter.date('2018-03-01', '2018-03-02'));
var temperatureAboveGround = dataset.select('temperature_2m_above_ground');
var visParams = {
  min: -40.0,
  max: 35.0,
  palette: ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
};
Map.setCenter(71.72, 52.48, 3.0);
Map.addLayer(temperatureAboveGround, visParams, 'Temperature Above Ground');
The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). The GFS dataset consists of selected model outputs (described below) as gridded forecast variables. The 384-hour forecasts, with 3-hour forecast interval, are made at 6-hour temporal resolution (i.e. updated four times …
NOAA/GFS0P25, climate,cloud,emc,flux,forecast,geophysical,gfs,humidity,ncep,noaa,precipitation,radiation,temperature,vapor,weather,wind
2015-07-01T00:00:00Z/2022-05-24T00:00:00
Google Earth Engine