1. Ensemble version



Ensemble identifier codeRUMSRUMS
Short DescriptionGlobal ensemble system that simulates initial uncertainties using breeding method. It is based on 41 members, run weekly (Wednesday at 00Z) up to day 46.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 operationalOperationalOperational
Data time of first forecast run 15/09/2022 07/01/2015

2. Configuration of the EPS



Is the model coupled to an ocean model ?NoNo
If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied

If no, please describe the sea surface temperature boundary conditions (climatology, reanalysis ...)

Is the model coupled to a sea Ice model?No - Sea ice initial conditions are relaxed to climatology using individual coefficients for 0.75x0.75 degree cells.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?NoNo
If yes, please describe wave model briefly including any ensemble perturbation applied

Ocean model

Horizontal resolution of the atmospheric model0.9 x 0.72 degrees lat-lon1.125 x 1.40625 degrees lat-lon
Number of model levels9628
Top of model0.04 hPa5 hPa
Type of model levelshybridsigma
Forecast length46 days (1104 hours)61 days (1464 hours)
Run Frequencyweekly (Thursdays)weekly (Wednesday 00Z up to May 2017, Thursdays 00Z since June 2017)
Is there an unperturbed control forecast included?YesYes
Number of perturbed ensemble members4019
Integration time step24 minutes36 minutes

3. Initial conditions and perturbations



Data assimilation method for control analysis

3D-Var analysis for upper-air fields; OI analysis for screen-level temperature and humidity; simplified extended Kalman filter for soil moisture

 3D Var
Resolution of model used to generate Control Analysis0.5 degrees for upper air; 0.72x0.9 degrees lat-lon for screen-level and soil variables 0.5 degrees
Ensemble initial perturbation strategyBreeding perturbations added to control analysisBreeding perturbations added to control analysis
Horizontal and vertical resolution of perturbations0.72 x 0.9 degrees lat-lon1.125 x 1.40625 degrees lat-lon.
Perturbations in +/- pairsNoNo

4. Model uncertainties perturbations



Is model physics perturbed?YesNo
Do all ensemble members use exactly the same model version?YesYes
Is model dynamics perturbed?NoNo
Are the above model perturbations applied to the control forecast?NoNo

5. Surface boundary perturbations



Perturbations to sea surface temperature?NoNo
Perturbation to soil moisture?NoNo
Perturbation to surface stress or roughness?NoNo
Any other surface perturbation?NoNo
Are the above surface perturbations applied to the Control forecast?NoNo
Additional comments -

6. Other details of the models



Description of model gridRegular lat-lon grid, hybrid pressure based coordinate in vertical.Regular lat-lon  grid, sigma-coordinate in vertical.
List of model levels in appropriate coordinatessee the section 9 below.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 et al, GMD 2017)

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?

Bastak-Duran et al (JAS 2014)

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) with our modification of momentum transport

Bougeault (MWR 85), Ducrocq and Bougeault (95), Gerard and Geleyn (QJ 2005)
What kind of large-scale precipitation scheme is used?

Gerard et al 2009

Geleyn et al 1994
What cloud scheme is used?Xu-Randall (JAS 96), diagnosticXu-Randall (JAS 96), diagnostic
What kind of land-surface scheme is used?

INM RAS – MSU

ISBA
How is radiation parametrized?

CLIRAD SW (Tarasova, Fomin 2005), RRTMG LW (Mlawer et al 1997)

Ritter, Geleyn (1992), Geleyn et al (2005)
Other relevant details?

7. Re-forecast Configuration



Number of years covered2526
Produced on the fly or fix re-forecasts?On the flyOn the fly
Frequency

Produced on the fly once a week to calibrate the Thursday 00Z real-time forecasts. The re-forecasts  consist of a 11-member ensemble starting the same day and month as Thursday real-time forecasts for the years 1991-2015.

Produced on the fly once a week to calibrate the Thursday 00Z real-time forecasts. The re-forecasts  consist of a 11-member ensemble starting the same day and month as Thursday real-time forecasts for the years 1991-2015.

Ensemble size11 members10 members
Initial conditions

quasiassimilation with ERA5 data for upper air, SEKF for soil moisture, OI for soil temperature

quasiassimilation with ERA Interim data
Is the model physics and resolution  the same as for the real-time forecastsYesYes
If not, what are the differences N/A N/A
Is the ensemble generation the same as for real-time forecasts?YesYes
If not, what are the differencesN/AN/A
Other relevant informationHMCR 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 11-member ensemble starting the same day and month as a Thursday real-time forecast, but covering 1991-2015 years. The re-forecast dataset is therefore updated every week in the S2S archive.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

Dynamics

Tolstykh M., Shashkin V., Fadeev R., Goyman G. Vorticity-divergence semi-Lagrangian global atmospheric model SL-AV20: dynamical core, Geosci. Model Dev., 2017, V. 10, P. 1961-1983.

Turbulence

Bašták Ďurán, I., Geleyn J.-F., and Váňa F. A Compact Model for the Stability Dependency of TKE Production–Destruction–Conversion Terms Valid for the Whole Range of Richardson Numbers, J. Atmos. Sci., 2014, V. 71, P. 3004–3026.

SW radiation

Chou, M.-D., Suarez M. J. A solar radiation parameterization (CLIRAD-SW) for atmospheric studies – 1999. NASA Tech. Memo. 10460, V. 15, NASA Goddard Space Flight Center, Greenbelt, MD, 48 pp.

Tarasova T., Fomin B. The Use of New Parameterizations for Gaseous Absorption in the CLIRAD-SW Solar Radiation Code for Models, J. Atmos. and Oceanic Technology. 2007. V. 24, I. 6, P. 1157–1162.

LW radiation

Mlawer E.J., Taubman S.J., Brown P.D., Iacono M.J. and Clough S.A.: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res. 1997, V. 102, P. 16663-16682.

Deep convection

Gerard L., Geleyn J.-F. Evolution of a subgrid deep convection parametrization in a limitedarea model with increasing resolution. Quart. J. Roy. Meteor. Soc. 2006, V. 131. P. 2293 - 2312. (and references herein)

Large scale precipitation and microphysics

Gerard L., Piriou J.-M., Brozkova R., et al. Cloud and Precipitation Parameterization in a Meso-Gamma-Scale Operational Weather Prediction Model. — Mon. Wea. Rev., 2009, V. 137, P. 3960—3977.

Orographic gravity wave drag

Catry B., Geleyn J.-F., Bouyssel F., Cedilnik J., Brožková R.,Derková M., and Mladek R.: A new sub-grid scale lift formulation in a mountain drag parameterisation scheme, Meteorol. Z., 2008, V. 17, P. 193–208.

Non-orographic gravity wave drag

Hines C.O. Doppler-spread parameterization of gravity-wave momentum deposition in the middle atmosphere. Part 1: Basic formulation, J. Atm. & Solar-Terrestrial Phys. 1997. V. 59, I. 4, P. 371-386.

Multilayer soil

Volodin E. M. and Lykosov V. N., Parameterization of Heat and Moisture Processes in Soil–Vegetation System. Part 1. Description and Calculations Using Local Observational Data, Izv., Atmos. Oceanic Phys., No. 4, V. 34 (1998).

9. List of model levels in appropriate coordinates

(RUMS 15/09/2022)

N

A

B

1

2.6858925E-5

0

2

6.521674E-5

0

3

9.5737065E-5

0

4

1.3942415E-4

0

5

2.011347E-4

0

6

2.870224 E-4

0

7

4.046345E-4

0

8

5.6289845E-4

0

9

7.7195615E-4

0

10

1.0428361E-3

0

11

1.387315E-3

0

12

1.818298E-3

0

13

2.3497605E-3

0

14

2.993734E-3

0

15

3.7552525E-3

0

16

4.62815E-3

0

17

5.5946875E-3

0

18

6.633841E-3

0

19

7.7301835E-3

0

20

8.8752315E-3

0

21

1.0066637E-2

0

22

1.1306625E-2

0

23

1.2599275E-2

0

24

1.3949105E-2

0

25

1.536087E-2

0

26

1.6839435E-2

0

27

1.838968E-2

0

28

2.001639E-2

0

29

2.1724365E-2

0

30

2.351879E-2

0

31

2.351879E-2

0

32

2.739097E-2

0

33

2.948251E-2

0

34

3.168817E-2

0

35

3.401692E-2

0

36

3.647871E-2

0

37

3.908456E-2

0

38

4.1846685E-2

0

39

4.4778645E-2

0

40

4.789549E-2

0

41

5.1213915E-2

0

42

5.475246E-2

0

43

5.8531705E-2

0

44

6.257452E-2

0

45

6.690632E-2

0

46

7.155365E-2

1.711604E-6

47

7.6541645E-2

1.1460423E-5

48

8.1897783E-2

3.6816742E-5

49

8.7654268E-2

8.4632497E-5

50

9.3845508E-2

1.6405206E-4

51

0.10050788

2.8738153E-4

52

0.10768070

4.7119814E-4

53

0.11540868

7.3797217E-4

54

0.12374385

1.1184485E-3

55

0.13274668

1.6551708E-3

56

0.14248693

2.4078173E-3

57

0.15304148

3.4611242E-3

58

0.16447771

4.9349952E-3

59

0.17682732

6.9968291E-3

60

0.19005918

0.20404697

61

0.20404697

1.3880277E-2

62

0.21853412

1.9403728E-2

63

0.23311013

2.6931367E-2

64

0.24723154

3.7048655E-2

65

0.26025510

5.0472252E-2

66

0.27143429

6.8094264E-2

67

0.27988784

9.1050159E-2

68

0.28453942

0.12079088

69

0.28413173

0.15857732

70

0.27779435

0.20402365

71

0.26598128

0.25427937

72

0.25031986

0.30581109

73

0.23247062

0.35649843

74

0.21349702

0.40570793

75

0.19400596

0.45341879

76

0.17442633

0.49961317

77

0.15510822

0.54420098

78

0.13635191

0.58704339

79

0.11841143

0.62799050

80

0.10149555

0.66690305

81

8.5771185E-2

0.70365821

82

7.1364710E-2

0.73815564

83

5.8362444E-2

0.77032265

84

4.6811675E-2

0.80011772

85

3.6722423E-2

0.82753238

86

2.8070594E-2

0.85259051

87

2.0801944E-2

0.87534661

88

1.4836649E-2

0.89588350

89

1.0074879E-2

0.91430797

90

6.40262E-3

0.93074583

91

3.697367E-3

0.94533689

92

1.83359E-3

0.95822921

93

6.8736347E-4

0.969574337

94

1.40194E-4

0.9795227

95

1.003E-6

0.988301

96

0.

0.9962179