![NOAA/GFS0P25](https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GFS0P25_sample.png) Dataset Availability : 2015-07-01T00:00:00Z--2026-11-11T06:00:00Z Dataset Producer : [NOAA/NCEP/EMC](https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs) Cadence : 6 Hours Tags : [climate](https://developers.google.com/earth-engine/datasets/tags/climate.md.txt) [cloud](https://developers.google.com/earth-engine/datasets/tags/cloud.md.txt) [flux](https://developers.google.com/earth-engine/datasets/tags/flux.md.txt) [forecast](https://developers.google.com/earth-engine/datasets/tags/forecast.md.txt) [geophysical](https://developers.google.com/earth-engine/datasets/tags/geophysical.md.txt) [humidity](https://developers.google.com/earth-engine/datasets/tags/humidity.md.txt) [ncep](https://developers.google.com/earth-engine/datasets/tags/ncep.md.txt) [noaa](https://developers.google.com/earth-engine/datasets/tags/noaa.md.txt) [precipitation](https://developers.google.com/earth-engine/datasets/tags/precipitation.md.txt) [radiation](https://developers.google.com/earth-engine/datasets/tags/radiation.md.txt) [temperature](https://developers.google.com/earth-engine/datasets/tags/temperature.md.txt) [vapor](https://developers.google.com/earth-engine/datasets/tags/vapor.md.txt) [weather](https://developers.google.com/earth-engine/datasets/tags/weather.md.txt) [weather-and-climate](https://developers.google.com/earth-engine/datasets/tags/weather-and-climate.md.txt) [wind](https://developers.google.com/earth-engine/datasets/tags/wind.md.txt) emc gfs #### 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 1-hour (up to 120 hours) and 3-hour (after 120 hours) forecast intervals, 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. Note that this model may change; see [history of recent modifications to the global forecast/analysis system](https://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html) and the [documentation](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php) for more information. There may be significant hour-to-hour and day-to-day fluctuations that require noise-reduction techniques to be applied to bands before analysis. Note that the available forecast hours and intervals have changed over time: - From 2015/04/01-2017/07/09: 36-hour forecasts, excluding hour 0, at 3-hour intervals. - From 2017/07/09-2021/06/11: 384-hour forecasts, at 1-hour intervals from hours 0-120, at 3-hour intervals from hours 120-240, and 12-hour intervals from hours 240-384. - From 2021/06/12: 384-hour forecasts, at 1-hour intervals from hours 0-120 and 3-hour intervals from hours 120-384. Some bands are only available starting on 2025/01/15 as noted in the band descriptions. ### Bands **Bands** Pixel size: 27830 meters (all bands) | Name | Units | Min | Max | Pixel Size | Description | |---|---|---|---|---|---| | `temperature_2m_above_ground` | °C | -69.18\* | 52.25\* | 27830 meters | Temperature 2m above ground | | `specific_humidity_2m_above_ground` | Mass fraction | 0\* | 0.03\* | 27830 meters | Specific humidity 2m above ground | | `dew_point_temperature_2m_above_ground` | °C | -81.05\* | 29.05\* | 27830 meters | Dew point temperature 2m above ground (available starting from 2025/01/15) | | `relative_humidity_2m_above_ground` | % | 1\* | 100.05\* | 27830 meters | Relative humidity 2m above ground | | `maximum_temperature_2m_above_ground` | °C | -60.73\* | 59.28\* | 27830 meters | Maximum temperature 2m above ground (available starting from 2025/01/15, but only for assets with forecast_hours \> 0) | | `minimum_temperature_2m_above_ground` | °C | -63.78\* | 59.39\* | 27830 meters | Minimum temperature 2m above ground (available starting from 2025/01/15, but only for assets with forecast_hours \> 0) | | `u_component_of_wind_10m_above_ground` | m/s | -60.73\* | 59.28\* | 27830 meters | U component of wind 10m above ground | | `v_component_of_wind_10m_above_ground` | m/s | -63.78\* | 59.39\* | 27830 meters | V component of wind 10m above ground | | `total_precipitation_surface` | kg/m\^2 | 0\* | 626.75\* | 27830 meters | Cumulative precipitation at surface for the previous 1-6 hours, depending on the value of the "forecast_hours" property according to the formula ((F - 1) % 6) + 1 (and only for assets with forecast_hours \> 0). As a consequence, to calculate the total precipitation by hour X, double-counting should be avoided by only summing the values for forecast_hours that are multiples of 6 plus any remainder to reach X. It also means that to determine the precipitation for just hour X, one must subtract the value for the preceding hour unless X is the first hour in a 6-hour window. | | `precipitable_water_entire_atmosphere` | kg/m\^2 | 0\* | 100\* | 27830 meters | Precipitable water for entire atmosphere | | `u_component_of_wind_planetary_boundary_layer` | m/s | -66.8\* | 62.18\* | 27830 meters | U component of wind planetary boundary layer (available starting from 2025/01/15) | | `v_component_of_wind_planetary_boundary_layer` | m/s | -63.08\* | 57.6\* | 27830 meters | V component of wind planetary boundary layer (available starting from 2025/01/15) | | `gust` | m/s | 0\* | 57.41\* | 27830 meters | Wind Speed (Gust) (available starting from 2025/01/15) | | `precipitation_rate` | kg/m\^2/s | 0\* | 0.032\* | 27830 meters | Precipitation Rate (available starting from 2025/01/15) | | `haines_index` | | 2\* | 6\* | 27830 meters | Haines Index (available starting from 2025/01/15) | | `ventilation_rate` | m\^2/s | 0\* | 234000\* | 27830 meters | Ventilation Rate (available starting from 2025/01/15) | | `total_cloud_cover_entire_atmosphere` | % | 0\* | 100\* | 27830 meters | Total cloud cover for entire atmosphere (previously only for assets with forecast_hours \> 0, but available for those with forecast_hours == 0 starting from 2025/01/15) | | `downward_shortwave_radiation_flux` | W/m\^2 | 0\* | 1230\* | 27830 meters | Downward shortwave radiation flux (only for assets with forecast_hours \> 0) | | `downward_longwave_radiation_flux` | W/m\^2 | 0\* | 100\* | 27830 meters | Downward longwave radiation flux (available starting from 2025/01/15, but only for assets with forecast_hours \> 0) | | `upward_shortwave_radiation_flux` | W/m\^2 | 0\* | 1230\* | 27830 meters | Upward shortwave radiation flux (available starting from 2025/01/15, but only for assets with forecast_hours \> 0) | | `upward_longwave_radiation_flux` | W/m\^2 | 0\* | 100\* | 27830 meters | Upward longwave radiation flux (available starting from 2025/01/15, but only for assets with forecast_hours \> 0) | | `planetary_boundary_layer_height` | m | 7.77\* | 6312.67\* | 27830 meters | Planetary boundary layer height (available starting from 2025/01/15) | \* 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](https://doi.org/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](https://www.emc.ncep.noaa.gov/officenotes/newernotes/on441.pdf) - 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. ### DOIs - ### Explore with Earth Engine > [!NOTE] > **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine) ### Code Editor (JavaScript) ```javascript 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'); ``` Python setup See the [Python Environment](https://developers.google.com/earth-engine/guides/python_install.md.txt) page for information on the Python API and using `geemap` for interactive development. ```python import ee import geemap.core as geemap ``` ### Colab (Python) ```python dataset = ee.ImageCollection('NOAA/GFS0P25').filter( ee.Filter.date('2018-03-01', '2018-03-02') ) temperature_above_ground = dataset.select('temperature_2m_above_ground') vis_params = { 'min': -40.0, 'max': 35.0, 'palette': ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'], } m = geemap.Map() m.set_center(71.72, 52.48, 3.0) m.add_layer(temperature_above_ground, vis_params, 'Temperature Above Ground') m ``` [Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GFS0P25) [GFS: Global Forecast System 384-Hour Predicted Atmosphere Data](https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25.md.txt) 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 1-hour (up to 120 hours) and 3-hour (after 120 hours) forecast intervals, are ... NOAA/GFS0P25, climate,cloud,flux,forecast,geophysical,humidity,ncep,noaa,precipitation,radiation,temperature,vapor,weather,weather-and-climate,wind 2015-07-01T00:00:00Z/2026-11-11T06:00:00Z -90 -180 90 180 Google Earth Engine https://developers.google.com/earth-engine/datasets - [https://doi.org/10.1016/j.jqsrt.2004.05.058](https://doi.org/https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs) - [https://doi.org/10.1016/j.jqsrt.2004.05.058](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25)