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1. Ensemble Version



Version Identifier Code47r140r133r231r1
Date of first implementation of this version

 

 

 

 

Please provide a short description of the Ensemble Prediction System
Global ensemble system that simulates initial unceratinties using singular vectors and perturbations from an ensemble of data assimiliations.  Model uncertainties represented with SPPT and SKEB. Based on 51 members, run twice-a-day up to day 15,  (extended to 32 days twice weekly, on Mondays and Thursdays).
Global ensemble system that simulates initial
unceratinties
uncertainties using singular vectors and model uncertainties due to physical parameterisations using a stochastic scheme. Based on 51 members, run twice-a-day up to day 15, with at 00UTC a coupled ocean system from day 10 to day 15 (extended to 32 days once a week, on Thursdays).
Research or Operational? If not operational, are there any plans to become so?Operational

Operational
Global or Regional EPS? (See section 7 for items specific to regional EPS)
Global
Global


Data time of first forecast run 



Date of last forecast with this version (if applicable)



Data time of last forecast run (if applicable)



Is there a higher-resolution control forecast available? (If yes, this should be described in a separate sheet of this spreadsheet.)
NoNo





Brief summary of main changes from previous version (keywords).
Coupling to NEMO ocean model, 91 levels with top a 1 Pa, higher horizontal resolution, EDA initial perturbations, revised model uncertainty representationN/A - First version listed










2. Configuration of the EPS



Horizontal resolution of the model. (Where variable resolution is used, please describe in full.)
TL639TL399
Horizontal configuration and resolution of the output grid
TL639 L91 for day 1 to day 10 (leg 1) and TL319 L91 after day 10 (leg 2)
The resolution archived is N320 reduced gaussian grid for leg1 and N160 reduced gaussian grid for leg2.		T399 L62 for day 1 to day 10 (leg 1) and T255 L62 for T+246 to day 15 (leg 2)
The resolution archived is N200 reduced gaussian grid for leg1 and N128 reduced gaussian grid for leg2.
Number of model levels
9162
Type of model levels (eg sigma)
sigmasigma
Forecast length and forecast step interval
T+0h to T+360h at 6hT+0h to T+360h at 6h
Runs per day (Times in UTC)
2 (00, 12)2 (00, 12)
Is there an unperturbed control forecast included? (Y/N)
YY
Number of perturbed ensemble members (excluding control)
5050
Integration time step
20 min for leg 1 and 45 min for leg 230 min
Top of model - model section
~0.01hPa~5hPa
Is model coupled to an ocean model?
YesNo
If yes, please describe ocean model briefly including any ensemble perturbations applied
NEMO 1deg, 5 different ocean analyses

Additional comments








3. Initial conditions and Perturbations



Data assimilation method for control analysis
4D-Var 12h window4D-Var 12h window
Resolution of model used to generate control analysis
TL1279L137TL799L91
Control variables used in data assimilation



Ensemble initial perturbation strategy
Singular Vectors (Total energy norm) and ensemble of data assimilations(EDA)Singular Vectors (Total energy norm)
Optimisation time in forecast (if applicable)
T+48T+48
Horizontal resolution of perturbations (if different from model resolution)
singular vectors T42L91 and TL399L137 for EDAT42L62
Initial perturbed area
globalExtra tropical (<30S, >30N) + up to 6 tropical areas
Are perturbations to observations employed? (Y/N)
YNo
Perturbations added to control analysis or derived directly from ensemble analysis
AddedAdded
Perturbations in +/- pairs? (Y/N)
YY
Additional comments








4. Model Uncertainty Perturbations



Is model physics perturbed?  If yes, briefly describe method(s).
Y. Uses Stochastically Perturbed Parameterization Tendencies (SPPT) and Stochastic Kinetic Energy Backscatter (SKEB)Y. Stochastic perturbation of physics tendency by factor in range [0.5,1.5]
Do all ensemble members use exactly the same model version, or are, for example, different parameterization schemes used? Please describe any differences.
SameSame
Is model dynamics perturbed? If yes, briefly describe method(s).
NN
Are the above model uncertainty perturbations applied to the control forecast? 
NN
Additional comments








5. Surface Boundary Perturbations



Perturbations to sea-surface temperature?  If yes, briefly describe method(s).
Y, 5 different ocean analysesN
Perturbations to soil moisture?  If yes, briefly describe method(s).
Y, from EDAN
Perturbations to surface wind stress or roughness?  If yes, briefly describe method(s).
NN
Any other surface perturbations?  If yes, briefly describe method(s).
NN
Are the above surface perturbations applied to the control forecast?
NN/A
Additional comments








6. Other details of model



Description of model grids.
Linear gridLinear grid
List of model levels in appropriate coordinates
http://old.ecmwf.int/products/data/technical/model_levels/model_def_91.html

Operational configurations of the ECMWF Integrated Forecasting SystemOperational configurations of the ECMWF Integrated Forecasting System
http://www.ecmwf.int/products/data/technical/model_levels/model_def_62.html

What kind of Large scale dynamics is in use (e.g. gridpoint semi-Lagrangian)? 
Spectral semi-lagrangianSpectral semi-lagrangian
What kind of boundary layer parametrization is in use?
See https://software.ecmwf.int/wiki/display/IFS/CY40R1+Official+IFS+Documentation#CY40R1OfficialIFSDocumentation-IV.Physicalprocesses

See Moist EDMF with Klein/Hartmann stratus/shallow convection criteria
What kind of convection parametrization is in use?
Tiedtke 89, Bechtold et al 2004 (QJ), see IFS documentation link aboveTiedtke 89, Bechtold et al 2004 (QJ) which improved the triggering
What kind of large-scale precipitation scheme is in use?
see IFS documentation link above

What Cloud scheme is in use?
see IFS documentation link aboveTiedtke 93 prognostic cloud fraction
What kind of land-surface scheme is in use?
HTESSEL, see IFS documentation link aboveHTESSEL
How is radiation parametrized?
See IFS documentation link above
See http://www.ecmwf.int/research/ifsdocs_old/PHYSICS/Chap2_Radiation2.html#959602 
See 
Other relevant details?


















7. Regional Ensemble specifics



Regional domain descriptor (lat/long of boundaries)



Normal source of boundary conditions



Are boundary conditions perturbed?



Specification of boundary conditions required.



Are boundary condition requirements compatible with any other global models or standards? If so, please describe



Are initial conditions downscaled from a global analysis or is a regional analysis used?



Is regional ensemble a downscaling of global ensemble perturbations, or are specific regional perturbations calculated?



Additional comments













8. Further Information



Scientific contact



URLs for Scientific documentation



Technical contact point



URLs for Technical documentation



Other contact points



List key reference papers for
modelList key reference papers for EPS(a) Buizza, R., & Palmer, T. N., 1995: The singular-vector structure of the atmospheric general circulation. J. Atmos. Sci., 52, 9, 1434-1456.  (b) Molteni,F., Buizza,R., Palmer,T.N. and Petroliagis,T., 1996: The ECMWF Ensemble Prediction System: Methodology and Validation Q.J.R Meteorol.Soc. (1996) Vol 122, pp 73-119.  (c) Buizza, R., Miller, M., & Palmer, T. N., 1999a: Stochastic representation of
model
uncertainties in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc., 125, 2887-2908. (d) Buizza, R., Bidlot, J.-R., Wedi, N., Fuentes, M., Hamrud, M., Holt, G., & Vitart, F., 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Q. J. Roy. Meteorol. Soc., 133, 681-695.
(a) Buizza, R., & Palmer, T. N., 1995: The singular-vector structure of the atmospheric general circulation. J. Atmos. Sci., 52, 9, 1434-1456.  (b) Molteni,F., Buizza,R., Palmer,T.N. and Petroliagis,T., 1996: The ECMWF Ensemble Prediction System: Methodology and Validation Q.J.R Meteorol.Soc. (1996) Vol 122, pp 73-119.  (c) Buizza, R., Miller, M., & Palmer, T. N., 1999a: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc., 125, 2887-2908. (d) Buizza, R., Bidlot, J.-R., Wedi, N., Fuentes, M., Hamrud, M., Holt, G., & Vitart, F., 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Q. J. Roy. Meteorol. Soc., 133, 681-695.




URLs for system documentation
http://www.ecmwf.int/products/forecasts/guide/index.html 

User guide to ECMWF forecast productsUser guide to ECMWF forecast products
http://www.ecmwf.int/products/forecasts/guide/index.html 

Data policy of originating centre for usage of data in TIGGE
Users of the ECMWF data sets are requested to reference the source of the data in any publication, e.g. "ECMWF ERA-40 data used in this study/project have been provided by ECMWF/have been obtained from the ECMWF Data Server".Users of the ECMWF data sets are requested to reference the source of the data in any publication, e.g. "ECMWF ERA-40 data used in this study/project have been provided by ECMWF/have been obtained from the ECMWF Data Server".










9. TIGGE Specific Information



Version Identifier Code



Date of first forecast in TIGGE
1st October 20061st October 2006
Data time of first forecast run in TIGGE
00Z00Z
Date of last forecast in TIGGE
N/AN/A
Data time of last forecast run in TIGGE
N/AN/A
Is there a higher-resolution control forecast included in TIGGE? If so give tab name where it is described.
Yes, there is a control forecast run at TL639 and a high resolution forecast run at TL1279Yes, there is a control forecast run at T399 and a high resolution forecast run at T799





Brief summary of main changes from previous version (keywords).
Resolution, coupling to ocean, representation of initial and model uncertaintiesN/A - First version in TIGGE

Key reference papers for EPS

(a) Buizza, R., & Palmer, T. N., 1995: The singular-vector structure of the atmospheric general circulation. J. Atmos. Sci., 52, 9, 1434-1456.  (b) Molteni,F., Buizza,R., Palmer,T.N. and Petroliagis,T., 1996: The ECMWF Ensemble Prediction System: Methodology and Validation Q.J.R Meteorol.Soc. (1996) Vol 122, pp 73-119.  (c) Buizza, R., Miller, M., & Palmer, T. N., 1999a: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc., 125, 2887-2908. (d) Buizza, R., Bidlot, J.-R., Wedi, N., Fuentes, M., Hamrud, M., Holt, G., & Vitart, F., 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Q. J. Roy. Meteorol. Soc., 133, 681-695.