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1. Ensemble version
Ensemble identifier code	RUMS
Short Description	Global ensemble system that simulates initial uncertainties using breeding method. It is based on 20 members, run weekly (Wednesday at 00Z) up to day 61.
Research or operational	Operational
Data time of first forecast run	07/01/2015
2. Configuration of the EPS
Is the model coupled to an ocean model ?	No
If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied
Is the model coupled to a sea Ice model?	No - Sea ice initial conditions are persisted up to day 15 and then relaxed to climatology up to day 45.
If yes, please describe sea-ice model briefly including any ensemble perturbation applied
Is the model coupled to a wave model?	No
If yes, please describe wave model briefly including any ensemble perturbation applied
Ocean model
Horizontal resolution of the atmospheric model	1.125 x 1.40625 degrees lat-lon
Number of model levels	28
Top of model	5 hPa
Type of model levels	sigma
Forecast length	61 days (1464 hours)
Run Frequency	weekly (Wednesday 00Z up to May 2017, Thursdays 00Z since June 2017)
Is there an unperturbed control forecast included?	Yes
Number of perturbed ensemble members	19
Integration time step	36 minutes
3. Initial conditions and perturbations
Data assimilation method for control analysis	3D Var
Resolution of model used to generate Control Analysis	0.5 degrees
Ensemble initial perturbation strategy	Breeding perturbations added to control analysis
Horizontal and vertical resolution of perturbations	1.125 x 1.40625 degrees lat-lon.
Perturbations in +/- pairs	No
4. Model uncertainties perturbations
Is model physics perturbed?	No
Do all ensemble members use exactly the same model version?	Yes
Is model dynamics perturbed?	No
Are the above model perturbations applied to the control forecast?	No
5. Surface boundary perturbations
Perturbations to sea surface temperature?	No
Perturbation to soil moisture?	No
Perturbation to surface stress or roughness?	No
Any other surface perturbation?	No
Are the above surface perturbations applied to the Control forecast?	No
Additional comments -
6. Other details of the models
Description of model grid	Regular lat-lon grid, sigma-coordinate in vertical.
List of model levels in appropriate coordinates	.0001, .0092, .01935, .03234, .04904, .06975, .09376, .12045, .15003, .1837, .2231, .2692, .3204, .3751, .4321, .4905, .5503, .6101, .6692, .72532, .77773, .82527, .86642, .90135, .93054, .95459, .97418, .99, 1.0
What kind of large scale dynamics is used?	Finite-difference semi-implicit semi-Lagrangian, vorticity-divergence formulation (Tolstykh, JCP 2002; section 2 in Shashkin, Tolstykh, GMD 2014)
What kind of boundary layer parameterization is used?	pTKE scheme (Geleyn, J.-F., et al 2006) with shallow convection included
What kind of convective parameterization is used?	Bougeault (MWR 85), Ducrocq and Bougeault (95), Gerard and Geleyn (QJ 2005)
What kind of large-scale precipitation scheme is used?	Geleyn et al 1994
What cloud scheme is used?	Xu-Randall (JAS 96), diagnostic
What kind of land-surface scheme is used?	ISBA
How is radiation parametrized?	Ritter, Geleyn (1992), Geleyn et al (2005)
Other relevant details?
7. Re-forecast Configuration
Number of years covered	26
Produced on the fly or fix re-forecasts?	On the fly
Frequency	Produced on the fly once a week to calibrate the Wednesday 00Z real-time forecasts. The re-forecasts consist of a 10-member ensemble starting the same day and month as the Wednesday real-time forecasts for the years 1985-2010.
Ensemble size	10 members
Initial conditions	quasiassimilation with ERA Interim data
Is the model physics and resolution the same as for the real-time forecasts	Yes
If not, what are the differences	N/A
Is the ensemble generation the same as for real-time forecasts?	Yes
If not, what are the differences	N/A
Other relevant information	HMCR re-forecasts are produced on the fly. Every week a new set of re-forecasts is produced to calibrate the real-time ensemble forecast of the given day. The ensemble re-forecasts consist of a 10-member ensemble starting the same day and month as a Wednesday real-time forecast, but covering 1985-2010 years. The re-forecast dataset is therefore updated every week in the S2S archive.

8. References

Description of the model and its parameterizations

Tolstykh M. A. Global semi-Lagrangian numerical weather prediction model, FOP, Obninsk, Moscow, Russia, pp. 111, 2010 [Russian]
Dynamics is presented in sections 2.1 and 2.2 in V. V. Shashkin and M. A. Tolstykh, Inherently mass-conservative version of the semi-Lagrangian absolute vorticity (SL-AV) atmospheric model dynamical core, Geosci. Mod. Dev. 2014 V 7 P 407-417.

Parameterizations

L.Gerard, and Geleyn J.-F., 2005: Evolution of a subgrid deep convection parametrization in a limited area model with increasing resolution. Quart. J. Roy. Meteor. Soc., 131, 2293–2312.
Catry, B., Geleyn J.-F., F. Bouyssel, J. Cedilnik, R. Brožková, M. Derková and R. Mladek, 2008: A new subgrid scale lift formulation in a mountain drag parameterisation. Meteorologische Zeitschrift, 17, pp. 193-208.
Noilhan, J. and Planton S., 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, pp. 536-549.
Ritter B. and Geleyn J.-F., 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, pp. 303-325.
Geleyn J.-F., Fournier R., Hello G. and Pristov N., 2005: A new bracketing technique for flexible and economical computation of thermal radiative fluxes, scattering effects included on the basis the Net Exchanged Rate (NER) formalism. WGNE ‘Blue Book’ 2005, pp. 4/7-8.
Brožková R., Derkova M., Bellus M., Farda A., 2006: Atmospheric forcing by ALADIN/MFSTEP and MFSTEP-oriented atmospheric tunings. Ocean Sci., 2, pp. 113–121.
Geleyn J.-F., Váňa F., Cedilnik J., Tudor M. and Catry B., 2006: An intermediate solution between diagnostic exchange coefficients and prognostic TKE methods for vertical turbulent transport. WGNE ‘Blue Book’ 2006, pp. 4/11-12.

Some results of the extended range forecasts

Tolstykh M.A., Diansky N.A., Gusev A.V., Kiktev D.B., 2014: Simulation of seasonal anomalies of atmospheric circulation using coupled atmosphere–ocean model. Izvestiya, Atmospheric and Oceanic Physics, Vol. 50, No. 2, pp. 111–121.

1. Ensemble version

2. Configuration of the EPS

3. Initial conditions and perturbations

4. Model uncertainties perturbations

5. Surface boundary perturbations

6. Other details of the models

7. Re-forecast Configuration

8. References