CISL RDA: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive

Name: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive
Creator: National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce
Published: 2015-01-26

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	NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive ds084.1 \| DOI: 10.5065/D65D8PWK
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Data Citations:

This dataset has been cited 57 times.

Published works that cited this dataset

Published works that cited this dataset:

2022

ARDIANTO, R., A. ISMANTO, J. SAMPURNO, and S. WIDADA, 2022: TIDAL FLOOD MODEL PROJECTION USING LAND SUBSIDENCE PARAMETER IN PONTIANAK, INDONESIA. Geographia Technica, 17, 135-147, https://doi.org/10.21163/GT_2022.172.12

Alerskans, E., J. Nyborg, M. Birk, and E. Kaas, 2022: A transformer neural network for predicting near-surface temperature. Meteor. Applications, 29, https://doi.org/10.1002/met.2098

Buzin, I., S. Klyachkin, S. Frolov, K. Smirnov, S. Mikhaltceva, Y. Sokolova, Y. Gudoshnikov, G. Voinov, M. Grigoryev, and , 2022: COMPRESSION OF THE ICE COVER IN THE PECHORA SEA: A NATURAL PHENOMENON AND ITS IMPACT ON MARINE OPERATIONS. Arktika: Ekologia i Ekonomika, 12, 500-512, https://doi.org/10.25283/2223-4594-2022-4-500-512

Fujiwara, K., and R. Kawamura, 2022: Intensification of a distant hurricane by warm‐core eddies in the Gulf Stream in boreal fall. Atmospheric Science Letters, https://doi.org/10.1002/asl.1141

Gebremichael, M., H. Yue, V. Nourani, and R. Damoah, 2022: The Skills of Medium-Range Precipitation Forecasts in the Senegal River Basin. Sustainability, 14, 3349, https://doi.org/10.3390/su14063349

Gebremichael, M., H. Yue, and V. Nourani, 2022: The Accuracy of Precipitation Forecasts at Timescales of 1–15 Days in the Volta River Basin. Remote Sensing, 14, 937, https://doi.org/10.3390/rs14040937

Köcher, G., T. Zinner, C. Knote, E. Tetoni, F. Ewald, and M. Hagen, 2022: Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples. Atmos. Meas. Tech., 15, 1033-1054, https://doi.org/10.5194/amt-15-1033-2022

Lachatre, M., S. Mailler, L. Menut, A. Cholakian, P. Sellitto, G. Siour, H. Guermazi, G. Salerno, and S. Giammanco, 2022: Modelling SO₂ conversion into sulfates in the mid-troposphere with a 3D chemistry transport model: the case of Mount Etna's eruption on 12 April 2012. Atmos. Chem. Phys., 22, 13861-13879, https://doi.org/10.5194/acp-22-13861-2022

Liu, Z., C. Snyder, J. J. Guerrette, B. Jung, J. Ban, S. Vahl, Y. Wu, Y. Trémolet, T. Auligné, B. Ménétrier, A. Shlyaeva, S. Herbener, E. Liu, D. Holdaway, and B. T. Johnson, 2022: Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation. Geosci. Model Dev., 15, 7859-7878, https://doi.org/10.5194/gmd-15-7859-2022

Maynard, I., and A. Abdulla, 2022: Assessing benefits and costs of expanded green hydrogen production to facilitate fossil fuel exit in a net-zero transition. Renewable Energy Focus, https://doi.org/10.1016/j.ref.2022.12.002

Parker, D. J., A. M. Blyth, S. J. Woolnough, A. J. Dougill, C. L. Bain, E. de Coning, M. Diop-Kane, A. Kamga Foamouhoue, B. Lamptey, O. Ndiaye, P. Ruti, E. A. Adefisan, L. K. Amekudzi, P. Antwi-Agyei, C. E. Birch, C. Cafaro, H. Carr, B. Chanzu, S. J. Clarke, H. Coskeran, S. K. Danuor, F. M. de Andrade, K. Diakaria, C. Dione, C. A. Diop, J. K. Fletcher, A. T. Gaye, J. L. Groves, M. Gudoshava, A. J. Hartley, L. C. Hirons, I. Ibrahim, T. D. James, K. A. Lawal, J. H. Marsham, J. N. Mutemi, E. C. Okogbue, E. Olaniyan, J. B. Omotosho, J. Portuphy, A. J. Roberts, J. Schwendike, Z. T. Segele, T. M. Stein, A. L. Taylor, C. M. Taylor, T. A. Warnaars, S. Webster, B. J. Woodhams, and L. Youds, 2022: The African SWIFT Project: Growing Science Capability to Bring about a Revolution in Weather Prediction. , 103, E349-E369, https://doi.org/10.1175/BAMS-D-20-0047.1

Prince, K. C., and C. Evans, 2022: Convectively Generated Negative Potential Vorticity Enhancing the Jet Stream through an Inverse Energy Cascade during the Extratropical Transition of Hurricane Irma. , 79, 2901-2918, https://doi.org/10.1175/jas-d-22-0094.1

San Jose, R., and J. L. Perez-Camanyo, 2022: High-resolution impacts of green areas on air quality in Madrid. Air Qual Atmos Health, https://doi.org/10.1007/s11869-022-01263-3

Shirai, T., Y. Enomoto, M. Watanabe, and T. Arikawa, 2022: Sensitivity analysis of the physics options in the Weather Research and Forecasting model for typhoon forecasting in Japan and its impacts on storm surge simulations. Coastal Engineering Journal, 1-27, https://doi.org/10.1080/21664250.2022.2124040

Sun, D., W. Huang, Y. Luo, J. Luo, J. S. Wright, H. Fu, and B. Wang, 2022: A Deep Learning‐Based Bias Correction Method for Predicting Ocean Surface Waves in the Northwest Pacific Ocean. Geophysical Research Letters, 49, https://doi.org/10.1029/2022gl100916

Tositti, L., E. Brattich, C. Cassardo, P. Morozzi, A. Bracci, A. Marinoni, S. Di Sabatino, F. Porcù, and A. Zappi, 2022: Development and evolution of an anomalous Asian dust event across Europe in March 2020. Atmos. Chem. Phys., 22, 4047-4073, https://doi.org/10.5194/acp-22-4047-2022

Villalba-Pradas, A., and F. J. Tapiador, 2022: Empirical values and assumptions in the convection schemes of numerical models. Geosci. Model Dev., 15, 3447-3518, https://doi.org/10.5194/gmd-15-3447-2022

West, T. K., and W. J. Steenburgh, 2022: Formation, Thermodynamic Structure, and Airflow of a Japan Sea Polar Airmass Convergence Zone. Mon. Wea. Rev., 150, 157-174, https://doi.org/10.1175/MWR-D-21-0095.1

Weston, M. J., S. J. Piketh, F. Burnet, S. Broccardo, C. Denjean, T. Bourrianne, and P. Formenti, 2022: Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia. Atmos. Chem. Phys., 22, 10221-10245, https://doi.org/10.5194/acp-22-10221-2022

Wise, A. S., J. T. Neher, R. S. Arthur, J. D. Mirocha, J. K. Lundquist, and F. K. Chow, 2022: Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain. Wind Energy Sci., 7, 367-386, https://doi.org/10.5194/wes-7-367-2022

Wu, L., D. D. Morabito, J. P. Teixeira, L. Huang, H. M. Nguyen, H. Su, M. A. Soriano, L. Pan, D. S. Kahan, and R. Bhawar, 2022: Prediction of Atmospheric Noise Temperature at the Deep Space Network With Machine Learning. Radio Sci., 57, https://doi.org/10.1029/2022RS007483

Yang, L., W. Duan, Z. Wang, and W. Yang, 2022: Toward targeted observations of the meteorological initial state for improving the PM_2.5 forecast of a heavy haze event that occurred in the Beijing–Tianjin–Hebei region. Atmos. Chem. Phys., 22, 11429-11453, https://doi.org/10.5194/acp-22-11429-2022

Yue, H., M. Gebremichael, and V. Nourani, 2022: Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products. Hydrol. Earth Syst. Sci., 26, 167-181, https://doi.org/10.5194/hess-26-167-2022

Zacharias, D. C., and A. Fornaro, 2022: Spill, Transport and Fate Model (STFM): Development and Validation. Revista Ambiente e Agua, 17, https://doi.org/10.4136/ambi-agua.2789

2021

Alerskans, E., and E. Kaas, 2021: Local temperature forecasts based on statistical post-processing of numerical weather prediction data. Meteor. Applications, 28, https://doi.org/10.1002/met.2006

Alessi, M. J., and A. T. DeGaetano, 2021: A comparison of statistical and dynamical downscaling methods for short-term weather forecasts in the US Northeast. Meteor. Applications, 28, https://doi.org/10.1002/met.1976

Anande, D. M., and M. Park, 2021: Impacts of projected urban expansion on rainfall and temperature during rainy season in the middle-eastern region in tanzania. Atmosphere, 12, https://doi.org/10.3390/atmos12101234

Fujiwara, K., and R. Kawamura, 2021: Active Role of Sea Surface Temperature Changes Over the Kuroshio in the Development of Distant Tropical Cyclones in Boreal Fall. JGR Atmospheres, 126, https://doi.org/10.1029/2021jd035056

Gonzalez, J. P., F. C. Zambrano, J. R. Valencia, and A. G. Betancourt, 2021: Numerical model for atmospheric temperature prediction in the Pamplonita River Basin, Norte de Santander, Colombia. Periodicals Eng. Nat. Sci., 9, 428-438, https://doi.org/10.21533/pen.v9i3.1510

Hewage, P., M. Trovati, E. Pereira, and A. Behera, 2021: Deep learning-based effective fine-grained weather forecasting model. Pattern Anal. Applications, 24, 343-366, https://doi.org/10.1007/s10044-020-00898-1

Kilicarslan, B. M., I. Yucel, H. Pilatin, E. Duzenli, and M. T. Yilmaz, 2021: Improving WRF‐Hydro runoff simulations of heavy floods through the sea surface temperature fields with higher spatio‐temporal resolution. Hydrological Processes, 35, https://doi.org/10.1002/hyp.14338

Kirthiga, S. M., B. Narasimhan, and C. Balaji, 2021: A multi-physics ensemble approach for short-term precipitation forecasts at convective permitting scales based on sensitivity experiments over southern parts of peninsular India. J. Earth Sys. Sci., 130, https://doi.org/10.1007/s12040-021-01556-8

Lam, M., and J. C. Fung, 2021: Model sensitivity evaluation for 3dvar data assimilation applied on wrf with a nested domain configuration. Atmosphere, 12, https://doi.org/10.3390/atmos12060682

Myslenkov, S., A. Zelenko, Y. Resnyanskii, V. Arkhipkin, and K. Silvestrova, 2021: Quality of the Wind Wave Forecast in the Black Sea Including Storm Wave Analysis. Sustainability, 13, 13099, https://doi.org/10.3390/su132313099

Narasimha Rao, N., S. Paul, M. S. Skekhar, G. P. Singh, A. K. Mitra, and S. C. Bhan, 2021: Unprecedented heavy rainfall event over Yamunanagar, India during 14 July 2016: An observational and modelling study. Meteorological Applications, 28, https://doi.org/10.1002/met.2039

Phillipson, L., Y. Li, and R. Toumi, 2021: Strongly coupled assimilation of a hypothetical ocean current observing network within a regional ocean-atmosphere coupled model: An OSSE case study of typhoon hato. Mon. Wea. Rev., 149, 1317-1336, https://doi.org/10.1175/MWR-D-20-0108.1

Prata, A. T., L. Mingari, A. Folch, G. Macedonio, and A. Costa, 2021: FALL3D-8.0: A computational model for atmospheric transport and deposition of particles, aerosols and radionuclides - Part 2: Model validation. Geosci. Model Dev., 14, 409-436, https://doi.org/10.5194/gmd-14-409-2021

Rabbani, K. G., S. Das, S. K. Panda, A. Kabir, and M. K. Mallik, 2021: Physical and Dynamical Characteristics of Thunderstorms Over Bangladesh Based on Radar, Satellite, Upper-Air Observations, and WRF Model Simulations. Pure Appl. Geophysics, 178, 3747-3767, https://doi.org/10.1007/s00024-021-02847-3

Williamson, C. J., A. Kupc, A. Rollins, J. Kazil, K. D. Froyd, E. A. Ray, D. M. Murphy, G. P. Schill, J. Peischl, C. Thompson, I. Bourgeois, T. B. Ryerson, G. S. Diskin, J. P. DiGangi, D. R. Blake, T. V. Bui, M. Dollner, B. Weinzierl, and C. A. Brock, 2021: Large hemispheric difference in nucleation mode aerosol concentrations in the lowermost stratosphere at mid-and high latitudes. Atmos. Chem. Phys., 21, 9065-9088, https://doi.org/10.5194/acp-21-9065-2021

2020

Brands, S., G. Fernández-García, M. García Vivanco, M. Tesouro Montecelo, N. Gallego Fernández, A. D. Saunders Estévez, P. E. Carracedo García, A. Neto Venâncio, P. Melo Da Costa, P. Costa Tomé, C. Otero, M. L. Macho, and J. Taboada, 2020: An exploratory performance assessment of the CHIMERE model (version 2017r4) for the northwestern Iberian Peninsula and the summer season. Geosci. Model Dev., 13, 3947-3973, https://doi.org/10.5194/gmd-13-3947-2020

Hewage, P., A. Behera, M. Trovati, E. Pereira, M. Ghahremani, F. Palmieri, and Y. Liu, 2020: Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station. Soft Computing, 24, 16453-16482, https://doi.org/10.1007/s00500-020-04954-0

Jyoteeshkumar, P., P. V. Kiran, and C. Balaji, 2020: Chennai extreme rainfall event of 2015 under future climate projections using the pseudo global warming dynamic downscaling method. Current Sci., 118, 1968-1979, https://doi.org/10.18520/cs/v118/i12/1968-1979

Kupc, A., C. J. Williamson, A. L. Hodshire, J. Kazil, E. Ray, T. P. Bui, M. Dollner, K. D. Froyd, K. McKain, A. Rollins, G. P. Schill, A. Thames, B. B. Weinzierl, J. R. Pierce, and C. A. Brock, 2020: The potential role of organics in new particle formation and initial growth in the remote tropical upper troposphere. Atmos. Chem. Phys., 20, 15037-15060, https://doi.org/10.5194/acp-20-15037-2020

Lachatre, M., S. Mailler, L. Menut, S. Turquety, P. Sellitto, H. Guermazi, G. Salerno, T. Caltabiano, and E. Carboni, 2020: New strategies for vertical transport in chemistry transport models: application to the case of the Mount Etna eruption on 18 March 2012 with CHIMERE v2017r4. Geosci. Model Dev., 13, 5707-5723, https://doi.org/10.5194/gmd-13-5707-2020

Lin, J., K. Emanuel, and J. L. Vigh, 2020: Forecasts of hurricanes using large-ensemble outputs. Wea. Forecasting, 35, 1713-1731, https://doi.org/10.1175/WAF-D-19-0255.1

Lindner, M., J. Rosenow, and H. Fricke, 2020: Aircraft trajectory optimization with dynamic input variables. CEAS Aeronautical J., 11, 321-331, https://doi.org/10.1007/s13272-019-00430-0

Mendez Turrubiates, R. F., M. Gross, and V. Magar, 2020: Local quantitative precipitation forecast with minimal data requirement—an ensemble approach. Wea. Forecasting, 35, 821-839, https://doi.org/10.1175/WAF-D-19-0077.1

Ruppert, J. H., A. A. Wing, X. Tang, and E. L. Duran, 2020: The critical role of cloud–infrared radiation feedback in tropical cyclone development. Proc Natl Acad Sci USA, 117, 27884-27892, https://doi.org/10.1073/pnas.2013584117

Valmassoi, A., J. Dudhia, S. Di Sabatino, and F. Pilla, 2020: Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3.8.1 model: The Po Valley (Italy) case study. Geosci. Model Dev., 13, 3179-3201, https://doi.org/10.5194/gmd-13-3179-2020

2019

Herman, A., K. Wojtysiak, and M. Moskalik, 2019: Wind wave variability in Hornsund fjord, west Spitsbergen. Estuar. Coast. Shelf Sci., 217, 96-109, https://doi.org/10.1016/j.ecss.2018.11.001

Pilguj, N., M. Taszarek, �. Pajurek, and M. Kryza, 2019: High-resolution simulation of an isolated tornadic supercell in Poland on 20 June 2016. Atmos. Res., 218, 145-159, https://doi.org/10.1016/j.atmosres.2018.11.017

Taszarek, M., N. Pilguj, J. Orlikowski, A. Surowiecki, S. Walczakiewicz, W. Pilorz, K. Piasecki, L. Pajurek, and M. Półrolniczak, 2019: Derecho evolving from a Mesocyclone-A Study of 11 August 2017 severe weather outbreak in Poland: Event analysis and high-resolution simulation. Mon. Wea. Rev., 147, 2283-2306, https://doi.org/10.1175/MWR-D-18-0330.1

Uzun, M., M. U. Demirezen, E. Koyuncu, G. Inalhan, J. Lopez, and M. Vilaplana, 2019: Deep Learning Techniques for Improving Estimations of Key Parameters for Efficient Flight Planning. AIAA/IEEE Digital Avionics Systems Conference - Proceedings, Institute of Electrical and Electronics Engineers Inc., https://doi.org/10.1109/DASC43569.2019.9081804

Williamson, C. J., A. Kupc, D. Axisa, K. R. Bilsback, T. Bui, P. Campuzano-Jost, M. Dollner, K. D. Froyd, A. L. Hodshire, J. L. Jimenez, J. K. Kodros, G. Luo, D. M. Murphy, B. A. Nault, E. A. Ray, B. Weinzierl, J. C. Wilson, F. Yu, P. Yu, J. R. Pierce, and C. A. Brock, 2019: A large source of cloud condensation nuclei from new particle formation in the tropics. Nature, 574, 399-403, https://doi.org/10.1038/s41586-019-1638-9

Xylogiannopoulos, K., P. Karampelas, and R. Alhajj, 2019: Multivariate motif detection in local weather big data. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, Association for Computing Machinery, Inc, 749-756, https://doi.org/10.1145/3341161.3343518

Zhang, S., and Z. Pu, 2019: Numerical simulation of rapid weakening of Hurricane Joaquin with assimilation of high-definition sounding system dropsondes during the tropical cyclone intensity experiment: Comparison of three- and four-dimensional ensemble-variational data assimilation. Wea. Forecasting, 34, 521-538, https://doi.org/10.1175/WAF-D-18-0151.1

2018

Mccorkle, T. A., J. D. Horel, A. A. Jacques, and T. Alcott, 2018: Evaluating the experimental High-Resolution Rapid Refresh-Alaska modeling system using US array pressure observations. Wea. Forecasting, 33, 933-953, https://doi.org/10.1175/WAF-D-17-0155.1

Abstract:

The NCEP operational Global Forecast System analysis and forecast grids are on a 0.25 by 0.25 global latitude longitude grid. Grids include analysis and forecast time steps at a 3 hourly interval from 0 to 240, and a 12 hourly interval from 240 to 384. Model forecast runs occur at 00, 06, 12, and 18 UTC daily. For real-time data access please use the NCEP data server.

Temporal Range:

2015-01-15 00:00 +0000 to 2023-01-16 18:00 +0000 (Entire dataset)

Period details by dataset product

Period details by dataset product:

2015-01-15 00:00 +0000 to 2016-01-16 18:00 +0000 (NCEP GFS 0.25 Degree Analysis and Forecast Grids for 2015)

2016-01-01 00:00 +0000 to 2017-01-16 18:00 +0000 (NCEP GFS 0.25 Degree Analysis and Forecast Grids for 2016)

2017-01-01 00:00 +0000 to 2018-01-16 18:00 +0000 (NCEP GFS 0.25 Degree Analysis and Forecast Grids for 2017)

2018-01-01 00:00 +0000 to 2023-01-16 18:00 +0000 (NCEP GFS 0.25 Degree Analysis and Forecast Grids for 2018)

2019-01-01 00:00 +0000 to 2020-01-16 18:00 +0000 (NCEP GFS 0.25 Degree Analysis and Forecast Grids for 2019)

2020-01-01 00:00 +0000 to 2021-01-16 18:00 +0000 (NCEP GFS 0.25 Degree Analysis and Forecast Grids for 2020)

Updates:

Daily

Variables:

Air Temperature	Albedo	Cloud Frequency	Cloud Liquid Water/Ice
Dew Point Temperature	Evaporation	Geopotential Height	Gravity Wave
Heat Flux	Humidity	Hydrostatic Pressure	Ice Depth/Thickness
Ice Extent	Land Use/Land Cover Classification	Longwave Radiation	Maximum/Minimum Temperature
Planetary Boundary Layer Height	Potential Temperature	Precipitation Amount	Precipitation Rate
Runoff	Sea Level Pressure	Sea Surface Temperature	Shortwave Radiation
Skin Temperature	Snow Depth	Snow Water Equivalent	Soil Moisture/Water Content
Soil Temperature	Surface Winds	Total Precipitable Water	Upper Air Temperature
Upper Level Winds	Vertical Wind Velocity/Speed	Visibility	Vorticity
Wind Shear

GRIB2 parameter table: HTML | XML

Variables by dataset product

Variables by dataset product:

Variable list for NCEP GFS 0.25 Degree Analysis and Forecast Grids

Variable list for NCEP GFS 0.25 Degree Analysis and Forecast Grids:

5-wave geopotential height
Absolute vorticity
Albedo
Apparent temperature
Best (4 layer) lifted index
Categorical freezing rain (yes=1; no=0)
Categorical ice pellets (yes=1; no=0)
Categorical rain (yes=1; no=0)
Categorical snow (yes=1; no=0)
Clear sky UV-B downward solar flux
Cloud water
Cloud water mixing ratio
Cloud workfunction
Convective available potential energy
Convective inhibition
Convective precipitation
Convective precipitation rate
Derived radar reflectivity
Dewpoint temperature
Downward longwave radiation flux
Downward shortwave radiation flux
Field capacity
Frictional velocity
Geopotential height
Graupel
Ground heat flux
Haines index
High cloud cover
ICAO standard atmosphere reference height
Ice cover
Ice growth rate
Ice temperature
Ice thickness
Ice water mixing ratio
Icing severity
Land cover (0=sea, 1=land)
Land-sea coverage (nearest neighbor) [land=1, sea=0]
Latent heat flux
Liquid volumetric soil moisture (non-frozen)
Low cloud cover
Maximum temperature
Maximum/Composite radar reflectivity
Medium cloud cover
Meridional flux of gravity wave stress
Minimum temperature
Momentum flux, u-component
Momentum flux, v-component
MSLP (Eta model reduction)
Ozone mixing ratio
Percent frozen precipitation
Planetary boundary layer height
Plant canopy surface water
Potential evaporation rate
Potential temperature
Precipitable water
Precipitation rate
Pressure
Pressure of level from which parcel was lifted
Pressure reduced to MSL
Rain water mixing ratio
Relative humidity
Sensible heat flux
Snow depth
Snow water mixing ratio
Soil temperature
Soil type
Specific humidity
Storm relative helicity
Sunshine duration
Surface lifted index
Surface roughness
Temperature
Total cloud cover
Total ozone
Total precipitation
u-component of storm motion
u-component of wind
Upward longwave radiation flux
Upward shortwave radiation flux
UV-B downward solar flux
v-component of storm motion
v-component of wind
Vegetation
Ventilation rate
Vertical speed shear
Vertical velocity (geometric)
Vertical velocity (pressure)
Visibility
Volumetric soil moisture content
Water equivalent of accumulated snow depth
Water runoff
Wilting point
Wind speed (gust)
Zonal flux of gravity wave stress

Vertical Levels:

See the detailed metadata for level information
GRIB2 level table

Data Types:

Grid

Spatial Coverage:

Longitude Range: Westernmost=180W Easternmost=180E
Latitude Range: Southernmost=90S Northernmost=90N

Detailed coverage information

Detailed coverage information:

0.25° x 0.25° from 0E to 359.75E and 90N to 90S (1440 x 721 Longitude/Latitude)

Data Contributors:

DOC/NOAA/NWS/NCEP

National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce

Related Resources:

WRF Preprocessing System (WPS):

The GRIB-formatted data in this dataset can be used to initialize the Weather Research and Forecasting (WRF) Model.
WRF Vtables

How to Cite This Dataset:

RIS

BibTeX

National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2015, updated daily. NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D65D8PWK. Accessed† dd mmm yyyy.
†Please fill in the "Accessed" date with the day, month, and year (e.g. - 5 Aug 2011) you last accessed the data from the RDA.
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Total Volume:

351.79 TB

Data Formats:

WMO GRIB2

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Data Access:

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Metadata Record:

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Data License:

This work is licensed under a Creative Commons Attribution 4.0 International License.

Citation counts are compiled through information provided by publicly-accessible APIs according to the guidelines developed through the https://makedatacount.org/ project. If journals do not provide citation information to these publicly-accessible services, then this citation information will not be included in RDA citation counts. Additionally citations that include dataset DOIs are the only types included in these counts, so legacy citations without DOIs, references found in publication acknowledgements, or references to a related publication that describes a dataset will not be included in these counts.

The Research Data Archive is managed by the Data Engineering and Curation Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research in Boulder, Colorado. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the National Science Foundation.

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