Global Ensemble Forecast System

To illustrate the use of ensemble based probabilistic forecast, the relative measure of predictability (RMOP, Toth et al. 2003), is shown in the above animation from the GEFS run from 0000 UTC 4 November 2020, out to 15 days (360 hours). Considering the 120-h forecast valid at 0000 UTC 9 November 2020, the shading indicates the RMOP of the ensemble mean 500-hPa height at each grid point, compared to ensemble forecasts of 500-hPa height over the previous 30 days. These are in 10% increments as indicated by the color bar at the bottom of the graphic. Shading at 90% indicates that at least 9 of 10 ensemble forecasts in the past 30 days had fewer ensemble members in the same "bin" as the ensemble mean than the present forecast. The blue numbers over each box represents the percentage of time that a forecast with the given degree of predictability has verified over the past 30 days. Here, over the 90% predictability box we see that only 61-84% of the forecasts with 90% relative predictability at 120 hours have verified in the same climatological bin as the observed 500-hPa height at 120 hours over the past 30 days. Note that in general, the values are generally lower than the RMOP numbers below the bar. This is because the underlying forecast model is imperfect, the initial conditions are imprecise, and the atmosphere behaves chaotically.

1. Introduction / History

The Global Ensemble Forecast System (GEFS) has been operational at NCEP since December 1992, with the initial version using the NCEP Global Spectral Model (GSM) at T62L18 resolution (about 200km in horizontal and 18 vertical sigma levels) and the initial condition perturbations (2 pairs perturbed and 1 control members) were generated by breeding vector (BV) method (Toth and Kalnay 1993; Toth and Kalnay 1997; Toth et al. 1997; Toth et al. 2001; Zhu et al. 2002; Buizza et al. 2005; Zhu 2005). The GEFS ran once per day, out to 12 days in the early 90s. During the early 2000s, the 1st generation of GEFS reforecast (1979 - 2006) was produced off-line from using NCEP GFS/GEFS 1998 model version by NOAA PSL (Hamill et al. 2006) to demonstrate the improved ensemble reliability through bias correction and calibration.

In August 2005, the GEFS was upgraded to run four times per day (0000, 0600, 1200 and 1800 UTC) and produced forecasts out to 16 days. Each of these cycles have 10 (5 pairs) perturbed members that were initialized using BV method and cycled every 6 hours. If tropical cyclones were present in the initial times, a tropical storm relocation (TSR) technique was applied to each ensemble member to adjust the initial central location to the observed location (Liu et al. 2006). An extended BV method with Ensemble Transform and Rescaling (BV-ETR) (Wei et al. 2006; 2008) and 6-hour cycling was implemented operationally in 2006.

In early 2010, the GEFS was upgraded with enhanced representation of model uncertainty using the Stochastic Total Tendency Perturbation (STTP) algorithm (Hou et al., 2008). The stochastic tendency perturbations were updated every 6 hours. Meanwhile, the 2nd generation of NOAA GEFS reforecasts were produced off-line for 29 years (1985 - 2013) by NOAA PSL (Hamill et al. 2013; NOAA/PSL reforecast website) using GEFS v10 configurations and CFS reanalysis.

Through another major upgrade in December 2015, the GEFS initial perturbations were chosen from the operational hybrid Global Data Assimilation System (GDAS) 80-member Ensemble Kalman Filter (EnKF; Whitaker et al., 2008) 6-h forecasts along with tropical storm relocation and centralization of the initial perturbations (Zhou et al. 2016; 2017).

Details on each GEFS upgrade can be found in the GEFS implementation log. Evolution of the GEFS configurations to date are summarized in the table below:

Version

Implementation

Initial uncertainty

TS relocation

Model Uncertainty

Resolution

FCST length

Ens. size (members)

Daily frequency

V1.0

1992.12

Bred vector

None

None

T62L18 ~200km

12

2+1

00UTC

V2.0

1994.03

T62L18 ~200km

16

10+1 (00UTC)

4+1 (12UTC)

00UTC

12UTC

V3.0

2000.06

V4.0

2001.01

T126L28(0-2.5) ~100km

T62L28(2.5-16) ~200km

10+1

V5.0

2004.03

T126(0-3.5) ~100km

T62L28(3.5-16) ~200km

V6.0

2005.08

T126L28(0-7.5) ~100km

T62L28(7.5-16) ~200km

 

V7.0

2006.05

TSR

T126L28 ~100km

14+1

00UTC

06UTC

12UTC

18UTC

(16 days)

 

 

 

 

 

00UTC (35 days)

V8.0

2007.03

(BV- ETR)

20+1

V9.0

2010.02

STTP

T190L28 ~70k

V10.0

2012.02

T254L42 (0-8) ~50km

T190L42 (8-16) ~70km

V11.0

2015.12

 

 

EnKF (f06)

TL574L64 (0-8) ~33km

TL382L64 (8-16) ~50km

V12.0*

2020.10

None

SPPT+SKEB

C384L64 (0-35) ~25km

16(35)

30+1+1

2. Current Status

On September 23, 2020, GEFS version 12 was implemented into NCEP operations with several significant upgrades. It is the first global-scale coupled system at NCEP, as it includes the integration of the Global Wave Ensemble System (GWES) via one-way coupling to the atmospheric model via the NEMS mediator. Additionally, a special 32th unperturbed GEFS forecast (GEFS-Aerosol) is run to 5 days with the GSDCHEM chemistry component via one-way coupling to the atmospheric model. These two will replace the existing standalone Global Wave Ensemble and the NEMS GFS Aerosol Component (NGAC) systems. GEFS v12 runs 4 times per day (00; 06; 12; 18UTC) with 31 members at each lead-time at C384L64 (about 25 km horizontal resolution and 64 vertical hybrid levels for atmosphere component) and out to 16 days at each cycle, except for 35 days at 0000 UTC.

3. Configuration of GEFS Version 12

  • Replace the Global Spectral Model with the global FV3 dynamical core (GFSv15.1 version);
  • Upgrade the physical parameterization schemes to those implemented with GFSv15.1, including new Geophysical Fluid Dynamics Laboratory (GFDL) microphysics;
  • Replace the Stochastic Total Tendency Perturbation (STTP) scheme with the new model perturbation techniques including 5-scale Stochastic Perturbation of Physical Tendencies (SPPT) scheme and Stochastic Kinetic Energy Backscatter (SKEB) scheme (Buizza et al. 1999; Berner et al. 2009; Zhu et al. 2019; Zhou et al. 2019);
  • Introduce “2-tiered” SST approach for lower boundary conditions over the ocean by considering an evolving ocean SST state with gradual changes with lead time. The 2-tiered SST approach relaxes the SST analysis to a bias-corrected SST prediction from the operational Climate Forecast System v2 (Saha et al., 2010)
  • Expand the number of ensemble members from 21 (20 perturbed and 1 unperturbed/control) to 31 (30 perturbed and 1 unperturbed/control) members;
  • Increase model horizontal resolution to 0.25 degree (~25 km) and maintain the same resolution throughout the forecast period for the atmosphere;
  • Improved interpolation of grib2 files from the model’s native Gaussian grid;
  • Removal of lower resolution output, and inclusion of new 0.25deg output onto NCEP web services;
  • Add forecast guidance for weeks 3-4 for the atmospheric model only. For the 00Z cycle, the forecast length will extend to 35 days with the same 31 ensemble members and uniform horizontal resolution;
  • Tropical cyclone relocation was removed;
  • A 20 year (2000-2019) reanalysis and 31 year (1989-2019) reforecast (Guan et al. 2020; Li et al. 2020) have been produced to support forecast calibration and other applications.

GWES changes in GEFS version 12 include:

  • Spherical spatial grid with increased resolution from 0.5 to 0.25 degree on average;
  • Increase in number of members from 21 to 31;
  • Extended the forecast range from 10 to 16 days;
  • Increased wind field intake stride from 3h to 1h due to coupling;
  • Improved physics from source-term coefficients that were tuned using an objective framework;

Details on the coupled Aerosol Model and upgrades over the NGAC system:

  • System renamed GEFSv12-Aerosol;
  • Increase in horizontal resolution from 1 degree to 0.25-degree (25 km) resolution grid;
  • Update to the latest version of NASA/ESRL GOCART aerosol model;
  • Implementation of the ARL Fengsha dust emissions model;
  • Use of Global Biomass Burning Emissions Product extended (GBBEPx) directly on the FV3 grid;
  • Update the sulfate anthropogenic emissions to the Community Emissions Data System (CEDS) 2014 base version;
  • Increase from 2 to 4 cycles per day;
  • Improved interpolation of grib2 files from the model’s native Gaussian grid;

For details on GEFS version 12 and earlier upgrades, please refer to NCEP Global Ensemble Forecast system implementation log for details. Please refer to GEFS-Aerosol web page for more details on that system.

4. Where to find GEFS data

  • The real-time GEFS data is available at:;
    • The NCEP FTP site
    • The NCEP NOMADS (National Operational Model Archive and Distribution System) site with the function of data filtering.
    • For a detailed list of GEFS output available on NCEP NOMADS site and the file inventories, see the NCO GEFS Product Inventory Page.
  • Archived GEFS data is available from the NOAA National Centers for Environmental Information (NCEI)
  • Selected GEFS data at 0.5 degree is also available through the WMO TIGGE data center to support THORPEX (The Observing System Research and Predictability Experiment) TIGGE (Interactive Grand Global Ensemble) project.
  • The 20 years GEFSv12 reforecast data is available through this AWS site.

5. Future Plans

GEFS v12 is the final upgrade of a global ensemble system separate from the deterministic GFS at NCEP. The next GEFS upgrade will be merged with the version 17 upgrade of the GFS upgrade planned for 2023-2024.

Other NCEP GEFS web pages:

- Official GEFS Graphics at the NCEP Model Analysis and Guidance Page : Mean/Spread and Spaghetti Plots

- Experimental GEFS plume diagram web page

- GEFS FAQ Page

- See the "Comprehensive List of Ensemble Web Sites" link above for a list of US (Government/Academic) and international ensemble information

- GEFS Verification Sites:

Please send any comments and suggestions about GEFS to Yuejian Zhu.

 

References for GEFS, NAEFS and post processing:

Link to a comprehensive list of publications since 1995

Berner, J., G.J. Shutts, M. Leutbecher, and T.N. Palmer. 2009. A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. Journal of the Atmospheric Sciences 66 (3): 603–626.

Buizza, R., M. Miller, and T. Palmer. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system.Quarterly Journal Royal Meteorological Society 125(560): 2887–2908

Buizza, R., P. L. Houtekamer, Z. Toth, G. Pellerin, M. Wei, Y. Zhu, 2005: A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems. Mon. Wea. Rev., 133, 1076-1097

Cui, B., Z. Toth, Y. Zhu and D. Hou, 2012: Bias Correction For Global Ensemble Forecast, Weather and Forecasting, 27, 396-410

Cui, B., Y. Zhu, Z. Toth and D. Hou, 2018: Development of Statistical Post-processor for NAEFS. To be submitted to Weather and Forecasting

Guan, H., B. Cui, Y. Zhu, 2015: Improvement of Statistical Postprocessing Using GEFS Reforecast Information, Weather and Forecasting, Vol. 30, 841-854

Guan, H. and Y. Zhu, 2017: Development of Verification Methodology for Extreme Weather Forecasts. Wea. Forecasting, 32, 470-491

Guan, H., Y. Zhu, E. Sinsky, W. Li, X. Zhou, D. Hou, C. Melhauser and R. Wobus, 2019: Systematic Error Analysis and Calibration of 2-m Temperature for the NCEP GEFS Reforecast of SubX Project. Wea. Forecasting, Vol. 34, 361-376.

Guan, H., Y. Zhu, E. Sinsky, B. Fu, X. Zhou, W. Li, X. Xue, D. Hou, B. Cui, and J. Peng, 2020: The NCEP GEFS-v12 Reforecasts to Support Subseasonal and Hydrometeorological Applications, STI Climate Bulletin, 79-82, https://doi.org/10.25923/t4qa-ae63

Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau, Jr., Y. Zhu, and W. Lapenta, 2013: NOAA's Second-generation Global Medium-range Ensemble Reforecast Data Set, Bull Amer. Meteor. Soc., 95, 1553-1565

Hamill, T., J. Whitaker and S. L. Mullen, 2006: Reforecasts: An important dataset for improving weather predictions. Bull. Amer. Meteor. Soc.,87, 33–46.

Hou, D., Z. Toth, Y. Zhu, W. Yang and R. Wobus, 2012: "A Stochastic Total Tendency Perturbation Scheme Representing Model- Related Uncertainties in the NCEP Global Ensemble Forecast System" Submitted to Tellus-A)

Han, J., Wang, W., Kwon, Y. C., Hong, S.-Y., Tallapragada, V., & Yang, F., 2017:. Updates in the NCEP GFS cumulus convection schemes with scale and aerosol awareness. Wea.Forecasting, 32(5), 2005–2017. https://doi.org/10.1175/WAF-D-17-0046.1

Hou, D., Z. Toth, Y. Zhu, and W. Yang, 2008: Evaluation of the impact of the stochastic perturbation schemes on global ensemble forecast. Proc. 19th Conf. on Probability and Statistics, New Orleans, LA, Amer. Meteor. Soc. (Available online at https://ams.confex.com/ams/88Annual/webprogram/Paper134165.html.)

Hou, D., M. Charles, Y. Luo, Z. Toth, Y. Zhu, R. Krzysztofowicz, Y. Lin, P. Xie, D-J. Seo, M. Pena and B. Cui, 2012: Climatology-Calibrated Precipitation Analysis at Fine Scales: Statistical Adjustment of STAGE IV towards CPC Gauge-Based Analysis, Journal of Hydrometeorology Vol. 15 2542-2557.

Li, W., Y. Zhu, X. Zhou, D. Hou, E. Sinsky, C. Melhauser, M. Pena, H. Guan and R. Wobus, 2018: Evaluating the MJO Forecast Skill from Different Configurations of NCEP GEFS Extended Forecast. Climate Dynamics, 52, 4923–4936. https://doi.org/10.1007/s00382-018-4423-9

Li, W., H. Guan, Y. Zhu, X. Zhou, B. Fu, D. Hou, E. Sinsky, and X. Xue, 2020: Prediction Skill of the MJO, NAO and PNA in the NCEP FV3-GEFS 35-day Experiments, STI Climate Bulletin, 124-127, https://doi.org/10.25923/t4qa-ae63

Liu, Q., S. J. Lord, N. Surgi, Y. Zhu, R. Wobus, Z. Toth and T. Marchok, 2006: Hurricane Relocation in Global Ensemble Forecast System, Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., P5.13.

Ma, J., Y. Zhu, D. Wobus and P. Wang, 2012: An Effective Configuration of Ensemble Size and Horizontal Resolution for the NCEP GEFS, Advance in Atmospheric Sciences, Vol. 29, No. 4, 782-794

Ma, J., Y. Zhu, D. Hou, X. Zhou and M. Pena, 2014: Ensemble Transform with 3D Rescaling Initialization Method, Monthly Weather Review, Vol. 142, 4053-4073

Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. Shutts, M. Steinheimer, and A. Weisheimer, 2009: Stochastic Parametrization and Model Uncertainty. ECMWF Tech. Memo. 598, 44.

Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057.

Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102.

Toth, Z. and E. Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330.

Toth, Z., E. Kalnay, S. Tracton, R. Wobus, and J. Irwin, 1997: A synoptic evaluation of the NCEP ensemble. Wea. Forecasting, 12, 140–153.

Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297-3319. Wea. Forecasting, 12, 140–153.

Toth. Z., Y. Zhu and T. Marchok, 2001: The Use of Ensembles to Identify Forecasts with Small and Large Uncertainty. Wea. Forecasting, Vol. 16, 436-477.

Wei, M., Z. Toth, R. Wobus, and Y. Zhu, C. H. Bishop, X. Wang, 2006: Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus 58A, 28-44

Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial Perturbations Based on the Ensemble Transform (ET) Technique in the NCEP Global Operational Forecast System, Tellus 59A, 62-79

Whitaker, Jeffrey S., Thomas M. Hamill, Xue Wei, Yucheng Song, Zoltan Toth, 2008: Ensemble Data Assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463-482.

Zhou, X., Y. Zhu, D. Hou, and D. Kleist 2016: Comparison of the Ensemble Transform and the Ensemble Kalman Filter in the NCEP Global Ensemble Forecast System. Wea. Forecasting, Vol. 31, 2058-2074.

Zhou, X., Y. Zhu, D. Hou, Y. Luo, J. Peng and R. Wobus, 2017: The NCEP Global Ensemble Forecast System with the EnKF Initialization. Wea. Forecasting, 32, 1989-2004. 

Zhou, X., Y. Zhu, B. Fu, D. Hou, J. Peng, Y. Luo and W. Li, 2019: The Development of Next NCEP Global Ensemble Forecast System. STI Climate Bulletin, 159-163. 

Zhou, X., Y. Zhu, D. Hou, and D. Kleist, 2016: Comparison of the Ensemble Transform and the Ensemble Kalman Filter in the NCEP Global Ensemble Forecast System. Wea. Forecasting, Vol. 31, 2058-2074.

Zhu, Y., Z. Toth, R. Wobus, D. Richardson, and K. Mylne, 2002: On the Economic Value of Ensemble Based Weather Forecasts. Bulletin of American Meteorological Society, Vol. 83, 73-83.

Zhu, Y. 2005: Ensemble Forecast: A New Approach to Uncertainty and Predictability. Advance in Atmospheric Sciences, Vol. 22, No. 6, 781-788.

Zhu, Y., and Y. Luo, 2014: Precipitation Calibration Based on Frequency Matching Method (FMM), Weather and Forecasting, Vol. 30, 1109-1124

Zhu, Y., X. Zhou, M. Pena, W. Li, C. Melhauser, and D. Hou, 2017: Impact of Sea Surface Temperature Forcing on Weeks 3 & 4 Forecast Skill in the NCEP Global Ensemble Forecasting System. Wea. Forecasting, Vol. 32, 2159-2173 DOI: 10.1175/WAF-D-17-0093.1

Zhu, Y., W. Li, X. Zhou, and D. Hou, 2019: Stochastic Representation of NCEP GEFS to Improve Subseasonal Forecasts. Current trends in the Representation of Physical Processes in Weather and Climate Models, Editors: Randall, D.A., Srinivasan, J., Nanjundiah, R.A., Mukhopadhyay, P. Springer Atmospheric Sciences, 317-328

Zhu, Y., X. Zhou, W. Li, D. Hou, C. Melhauser, E. Sinsky, M. Pena, B. Fu, H. Guan, W. Kolczynski, R. Wobus and V. Tallapragada, 2018: Towards the Improvement of Sub-Seasonal Prediction in the NCEP Global Ensemble Forecast System (GEFS). Journal of Geophysical Research, 6732-6745, https://doi.org/10.1029/2018JD028506

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