1. Ensemble version | |||
|---|---|---|---|
| Ensemble identifier code | EK40 | EB40 | RUMS |
| Short Description | Global ensemble system that simulates initial incertainties using local ensemble transform Kalman filter. It is based on 41 members, runs weekly (Wednesday at 00Z) up to day 46. | Global 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 operational | Operational | Operational | Operational |
| Data time of first forecast run | 16/10/2024 | 15/09/2022 | 07/01/2015 |
2. Configuration of the EPS | |||
| Is the model coupled to an ocean model ? | No | No | No |
| 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 ...) | Sea surface temperature is gradually relaxed from real-time initial data towards climatology using individual coefficients for 0.75x0.75 degree cells. | ||
| 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 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? | No | No | No |
| If yes, please describe wave model briefly including any ensemble perturbation applied | |||
| Ocean model | |||
| Horizontal resolution of the atmospheric model | 0.9 x 0.72 degrees lat-lon | 0.9 x 0.72 degrees lat-lon | 1.125 x 1.40625 degrees lat-lon |
| Number of model levels | 96 | 96 | 28 |
| Top of model | 0.04 hPa | 0.04 hPa | 5 hPa |
| Type of model levels | hybrid | hybrid | sigma |
| Forecast length | 46 days (1104 hours) | 46 days (1104 hours) | 61 days (1464 hours) |
| Run Frequency | weekly (Thursdays) | weekly (Thursdays) | weekly (Wednesday 00Z up to May 2017, Thursdays 00Z since June 2017) |
| Is there an unperturbed control forecast included? | Yes | Yes | Yes |
| Number of perturbed ensemble members | 40 | 40 | 19 |
| Integration time step | 24 minutes | 24 minutes | 36 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 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 Analysis | 0.5 degrees for upper air; 0.72x0.9 degrees lat-lon for screen-level and soil variables | 0.5 degrees for upper air; 0.72x0.9 degrees lat-lon for screen-level and soil variables | 0.5 degrees |
| Ensemble initial perturbation strategy | Initial conditions are generated using local ensemble transform Kalman filter, in situ observations and atmospheric motion vector observations are utilized. The ensemble mean is then replaced with the HMCR operational analysis. | Breeding perturbations added to control analysis | Breeding perturbations added to control analysis |
| Horizontal and vertical resolution of perturbations | 0.72 x 0.9 degrees lat-lon | 0.72 x 0.9 degrees lat-lon | 1.125 x 1.40625 degrees lat-lon. |
| Perturbations in +/- pairs | No | No | No |
4. Model uncertainties perturbations | |||
| Is model physics perturbed? | Yes | Yes | No |
| Do all ensemble members use exactly the same model version? | Yes | Yes | Yes |
| Is model dynamics perturbed? | No | No | No |
| Are the above model perturbations applied to the control forecast? | Yes | No | No |
5. Surface boundary perturbations | |||
| Perturbations to sea surface temperature? | No | No | No |
| Perturbation to soil moisture? | No | No | No |
| Perturbation to surface stress or roughness? | No | No | No |
| Any other surface perturbation? | No | No | No |
| Are the above surface perturbations applied to the Control forecast? | No | No | No |
| Additional comments - | |||
6. Other details of the models | |||
| Description of model grid | Regular lat-lon grid, hybrid pressure based coordinate in vertical. | Regular lat-lon grid, hybrid pressure based coordinate in vertical. | Regular lat-lon grid, sigma-coordinate in vertical. |
| List of model levels in appropriate coordinates | see the section 9 below | see 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 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) | 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) 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 | Gerard et al 2009 | Geleyn et al 1994 |
| What cloud scheme is used? | Xu-Randall (JAS 96), diagnostic | Xu-Randall (JAS 96), diagnostic | Xu-Randall (JAS 96), diagnostic |
| What kind of land-surface scheme is used? | INM RAS – MSU | INM RAS – MSU | ISBA |
| How is radiation parametrized? | CLIRAD SW (Tarasova, Fomin 2005), RRTMG LW (Mlawer et al 1997) | 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 covered | 30 | 25 | 26 |
| Produced on the fly or fix re-forecasts? | On the fly | On the fly | On 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 1991-2020. | 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 size | 11 members | 11 members | 10 members |
| Initial conditions | quasiassimilation with ERA5 data for upper air, SEKF for soil moisture, OI for soil temperature | 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 forecasts | Yes | Yes | Yes |
| If not, what are the differences | N/A | N/A | N/A |
| Is the ensemble generation the same as for real-time forecasts? | No | Yes | Yes |
| If not, what are the differences | Global ensemble system uses breeding method to generate initial re-forecast ensembles’ initial conditions. | N/A | 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 11-member ensemble starting the same day and month as a Thursday real-time forecast, but covering 1991-2020 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 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 limited‐area 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 |