Name: ECMWF Ensemble
The S2S database contains real-time forecasts from ECMWF from 1st January 2015, and the associated re-forecasts.
Ensemble identifier code: CY43R1
Short Description: Global ensemble system that simulates initial uncertainties using singular vectors and ensemble of data assimilation and model uncertainties due to physical parameterizations using a stochastic scheme. based on 51 members, runs twice a week (Monday and Thursday at 00Z) up to day 46.
Research or operational: Operational
Data time of first forecast run: 24 November 2016
Is the model coupled to an ocean model ? Yes from day 0
If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied: Ocean model is NEMO3.4.1 with a 0,25 degree horizontal resolution, 75 vertical levels, initialized from ECMWF Ocean Analysis + 4 perturbed analyses produced by perturbing the wind field during the ocean analysis. Frequency of coupling is hourly.
Is the model coupled to a sea Ice model? Yes
If yes, please describe sea-ice model briefly including any ensemble perturbation applied: Interactive sea-ice model (the Louvain-la-Neuve Sea Ice Model - LIM2). Initial perturbations of sea-ice from the 5 ensemble ocean/sea-ice analysis/re-analysis. No stochastic perturbations.
Is the model coupled to a wave model? Yes
If yes, please describe wave model briefly including any ensemble perturbation applied: ECMWF wave model. No perturbation. Resolution is 0.25 degrees up to day 15 and 0.5 degrees after day 15.
Ocean model: NEMO 0.25 degree resolution
Horizontal resolution of the atmospheric model: Tco639 (about 16 km) up to day 15 and Tco319 (about 32 km) after day 15
Number of model levels: 91
Top of model: 0.01 hPa
Type of model levels: sigma
Forecast length: 46 days (1104 hours)
Run Frequency: twice a week (Monday 00Z and Thursday 00Z)
Is there an unperturbed control forecast included?: Yes
Number of perturbed ensemble members: 50
Integration time step: 12 minutes for day 0-15 and 20 minutes for day 15-46
Data assimilation method for control analysis: 4D Var (atmosphere) and 3DVAR (ocean/sea-ice)
Resolution of model used to generate Control Analysis: TL1279L137
Ensemble initial perturbation strategy: Singular vectors + Ensemble Data Assimilation perturbations added to control analysis
Horizontal and vertical resolution of perturbations: T42L91 SVs+ T399L137 EDA perturbations
Perturbations in +/- pairs: Yes
Initialization of land surface:
What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? IFS Documentation, Physical Processes, Chapter 8 Surface parameterisation, 2016 http://www.ecmwf.int/en/elibrary/16648-part-iv-physical-processes
How is soil moisture initialized in the forecasts? (climatology / realistic / other) realistic
If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization.
LDAS-based (simplified EKF) as used in all IFS forecast and described here: https://confluence.ecmwf.int/display/LDAS/LDAS+Home
Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). Yes horizontal interpolations. For soil moisture the interpolation is standardized on soil moisture index (to account for different soil texture in input and target resolution grid).
Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? Yes in a diagnostic wave using temperature and a latent heat barrier (described in Viterbo et al. 1999, see IFS documentation)
If all model soil layers are not initialized in the same way or from the same source, please describe. The LDAS is active on the top 1m of soil moisture (the first 3 layers) and the forth layer (1 to 2,89 m deep) is not initialised.
How is snow initialized in the forecasts? (climatology / realistic / other) realistic
If “realistic”, does the snow come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. LDAS-based (Optimal Interpolation) as used in all IFS forecast and described here: https://confluence.ecmwf.int/display/LDAS/LDAS+Home
Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) horizontal interpolation
Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties? Snow mass and snow temperature are initialized by the LDAS, snow albedo and snow density are cycled from the model forecast (open loop).
How is soil temperature initialized in the forecasts? (climatology / realistic / other) realistic
If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. LDAS-based (Optimal Interpolation) as used in all IFS forecast and described here: https://confluence.ecmwf.int/display/LDAS/LDAS+Home
Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? Both the top soil temperature and the snow temperature (if present) are initialized.
Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) horizontal interpolation
If all model soil layers are not initialized in the same way or from the same source, please describe. Only the first soil layer temperature is initialized, the other layers are cycled from the model forecast (open loop).
How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM? If so, how is it initialized? No, a monthly climatology of vegetation is used
If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?) Leaf Area Index and Albedo monthly climatology both based on MODIS collection 5
What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? FAO dominant soil texture class (as in Van Genuchten, 1980)
If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences. The re-forecasts initialization is based on ERA-Interim and ERA-Interim/Land datasets, while the real-time forecasts are based on the IFS operational initial conditions of the ENS/EDA systems.
Is model physics perturbed? If yes, briefly describe methods: Stochastic physics in the atmosphere (SPPT and SKEB schemes).
Do all ensemble members use exactly the same model version? Same
Is model dynamics perturbed? No
Are the above model perturbations applied to the control forecast? No
Perturbations to sea surface temperature? Yes (5-member ensemble of ocean analyses/re-analyses)
Perturbation to soil moisture? Yes (EDA)
Perturbation to surface stress or roughness? No (generated by wave model)
Any other surface perturbation? No
Are the above surface perturbations applied to the Control forecast? NA
Additional comments
Description of model grid: Cubic octohedral grid
List of model levels in appropriate coordinates: http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels
What kind of large scale dynamics is used? Spectral semi-lagrangian
What kind of boundary layer parameterization is used? Moist EDMF with Klein/Hartmann stratus/shallow convection criteria
What kind of convective parameterization is used? Tiedtke 89, Bechtold et al 2004 (QJ)
What kind of large-scale precipitation scheme is used?
What cloud scheme is used? Tiedtke 91 prognostic cloud fraction
What kind of land-surface scheme is used? HTESSEL
How is radiation parametrized? CY43R1Official IFS Documentation
Other relevant details?
Number of years covered: 20 past years
Produced on the fly or fix re-forecasts? On the fly
Frequency: Produced on the fly twice a week to calibrate the Monday and Thursday 00Z real-time forecasts. The re-forecasts consists of a 11-member ensemble starting the same day and month as the Thursday real-time forecasts for the past 20 years.
Ensemble size: 11 members
Initial conditions: ERA interim (T255L60) + Soil reanalysis (Tco639) + ORAS5 ocean initial conditions (0.25 degree)
Is the model physics and resolution the same as for the real-time forecasts: Yes
If not, what are the differences: NA
Is the ensemble generation the same as for real-time forecasts? Yes. Except for EDA perturbations which are taken from the most recent year.
If not, what are the differences: NA
Other relevant informations:
ECMWF re-forecasts are produced on the fly. This means that every week a 2 new set of re-forecasts are produce to calibrate the Monday and Thursday real-time ensemble forecasts of the following week using the latest version of IFS. The ensemble re-forecasts consist of a 11-member ensemble starting the same day and month as a real-time forecast (Monday and Thursday), but covering the past 20 years. For instance the first re-forecast set archived in the S2S database with this new version of the ECMWF model was the re-forecast used to calibrate the real-time forecast of 14 May 2015 (a Thursday). This set consisted of a 11-member ensemble starting on 1st January 1995, 1st January 1996, ... 1st January 2014 (20 years, 11 member ensemble = 220-member climate ensemble). The re-forecast dataset is therefore updated every week in the S2S archive.
The ECMWF re-forecasts are archived in the S2S database using two dates: "date" and "hdate" (see examples below): hdate is the actual date of the re-forecast (e.g. 19950101) while date is the date of the real-time forecast (=ModelversionDate in grib2) associated to the re-forecast (20150101). The reason we need 2 dates is because the ECMWF re-forecasts are produced on the fly and we need to avoid the re-forecasts produced in the future years to overwrite the re-forecasts currently produced. Therefore ModelversionDate allows us to distinguish the re-forecasts produced in 2015 from those produced in 2016, 2017...
Comprehensive description of the model physics: CY43R1 Official IFS Documentation
Description of the extended range forecasts: http://www.ecmwf.int/en/forecasts/documentation-and-support/extended-range-forecasts