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

for the future

Hua-Lu Pan

Environmental Modeling Center

NCEP

The NCEP coupled Climate Forecast System (CFS) model

1. Atmospheric component 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)

2. Oceanic component GFDL MOM3 (Pacanowski and Griffies, 1998) 1/3 in tropics; 1 in extratropics; 40 layers Quasi-global domain (74S to 64N) Free surface

3. Coupled model Once-a-day coupling Sea ice extent taken as observed climatology

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

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!!!!!

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

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)

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)

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

3-month lead forecast

Initial month

0-month lead 3-month lead 6-month lead

CLIPER CFS03 CLIPER CFS03 CLIPER CFS03

Apr 0.85 0.94 0.57 0.89 0.68 0.87

Jul 0.82 0.94 0.87 0.93 0.84 0.89

Oct 0.96 0.98 0.86 0.94 0.69 0.68

Jan 0.95 0.96 0.69 0.67 0.60 0.49

Three-month mean Nino34 SST correlation skill

Three-month mean Nino34 SST RMSE (K)

Initial month

0-month lead 3-month lead 6-month lead

CLIPER CFS03 CLIPER CFS03 CLIPER CFS03

Apr 0.34 0.23 0.66 0.47 0.84 0.57

Jul 0.46 0.36 0.62 0.49 0.53 0.55

Oct 0.34 0.20 0.49 0.63 0.47 0.72

Jan 0.29 0.33 0.47 0.68 0.63 0.90

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.

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.

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

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.

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

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

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