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The NCEP operational Climate Forecast System : configuration, products, and plan for the future

Learn about the NCEP operational Climate Forecast System, its configuration, products, and upcoming developments. Explore atmospheric and oceanic components, coupled model details, ensemble strategy, products, and future goals.

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The NCEP operational Climate Forecast System : configuration, products, and plan for the future

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  1. The NCEP operational Climate Forecast System : configuration, products, and plan for the future Hua-Lu Pan Environmental Modeling Center NCEP

  2. The NCEP coupled Climate Forecast System (CFS) model • Global Forecast System 2003 • T62 in horizontal; 64 layers in vertical • Recent upgrades in model physics • Solar radiation (Hou, 1996) • cumulus convection (Hong and Pan, 1998) • gravity wave drag (Kim and Arakawa, 1995) • cloud water/ice (Zhao and Carr,1997) 1.Atmospheric component 2. Oceanic component • GFDL MOM3 (Pacanowski and Griffies, 1998) • 1/3°´1° in tropics; 1°´1° in extratropics; 40 layers • Quasi-global domain (74°S to 64°N) • Free surface 3. Coupled model • Once-a-day coupling • Sea ice extent taken as observed climatology

  3. The other components of CFS • Atmospheric initial condition : NCEP reanalysis-2 • The reanalysis-2 is now an operational product and part of the CFS with a 4-day lag • The pentad global precipitation analysis will become operational in the near future • Ocean initial condition : NCEP GODAS driven by the NCEP reanalysis-2 fluxes • GODAS runs in two modes, a 14-day lag final cycle and a 7-day lag CFS analysis

  4. Ensemble strategy • One 10-month* run per day • This strategy makes the most sense for the operational center computer usage • CPC can obtain a 20-30 member ensemble at any time • 7-day lag GODAS analysis • Daily update from the 14-day lag GODAS analysis • 14-day lag GODAS is cycled but the 7-day lag one is not cycled • PLEASE USE ONLY THE ENSEMBLE AND NOT ANY SINGLE RUN!!!!!

  5. CFS Products • Monthly mean fields: • Atmospheric fields in GRIB form : 2.5 degree global grid of height, temperature, winds (U and V), relative humidity, etc at 17 levels • Ocean fields in GRIB form : (2degx1deg) temperature, wind, etc at 40 levels • Single level fields in GRIB form : global Gaussian grid of precipitation, 2-meter temperature, 10-meter winds, surface fluxes of heat and momentum, etc

  6. CFS product (II) • ftp://tgftp.nws.noaa.gov/SL.us008001/SL.opnl/MT.cfs_MR.fcst for real data • ftp://tgftp.nws.noaa.gov/SL.us008001/SL.opnl/MT.cfs_MR.clim for corresponding climatology • All data will stay on the site for 7 days to allow time to download (rotating 7-day archive)

  7. Seasonal retrospective forecasts by CFS to provide calibration and a priori skill assessment • 15-member ensemble over 23 years from 1981-2003 • Runs are complete • 10 month runs • Initial atmospheric states: Five 00Z analyses centered on the 1st, the 11th and the 21th day of each month from the Reanalysis-2 archive • Initial ocean states: the 1st, the 11th and the 21th of each month from NCEP GODAS (Global Ocean Data Assimilation System)

  8. Retrospective forecast productsmonthly and seasonal mean • CFS uncorrected forecast climatology • Reanalysis-2 and GODAS climatology • CFS forecast standard deviations • Reanalysis-2 and GODAS standard deviations • CFS uncorrected forecast root-mean-square error

  9. 3-month lead forecast

  10. Three-month mean Nino34 SST correlation skill Three-month mean Nino34 SST RMSE (K)

  11. Conclusions • The CFS system has predictive skill in the the equatorial tropical SST comparable to the statistical methods. • With a complete retrospective forecast for 23 years, the prediction skill for North America temperature and precipitation can be assessed. • The CFS is run without flux correction and can be used as a 1-tier system.

  12. Caveats • North America seasonal prediction skills are still low but the skill masked regions seem complementary to the statistical tool results. • We performed the kind of rigorous tests van den Dool and Livezey have been advocating for years. This is the kind of evaluation that CPC forecasters have long demanded. • Statistical tools used by CPC are the benchmark for models. CFS is beginning to be competitive. Longer term goal is to beat statistical tools consistently just like NWP in the early days against human forecasters.

  13. Why? • Weather model for climate? • Model tropics improved a lot over the past 15 years • 28 layer versus 64 layer? • Stratosphere influence • Surface stress and model drift? • AMIP and CFS surface stress

  14. Lessons learned • Retrospective forecast requires huge human and computer resources and must be planned. • Consistent re-analysis of the atmosphere and the ocean should be done using the same model and analysis system for the real time forecasts. • Consistent retrospective forecast configuration and real-time configuration should be used.

  15. User participation • Some of the results so far seem encouraging and we need more people to look at the products • We will provide all re-forecasts on a NOMADS server as soon as we can • We encourage MOS development for seasonal predictions

  16. Plan for the future • Twice daily run in 2005 • T126L64 system in 2007 with re-analysis and re-forecast with goal for 2008 implementation • Ocean model may be MOM4 or HYCOM (resolution?) • Physics in a well-tested GFS will be used

  17. Other topics • Regional Climate Models • Disk space requirement for CFS re-forecast • Fair re-forecast evaluation • Added value versus cost • Multi-model ensemble • Added value versus cost • Maintenance issues

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