1. Ensemble version |
|
|
|
|
|
|---|
| Ensemble identifier code | CY46R1 |
|---|
CY45R1 | CY41R3 | CY43R1 | CY41R2 | CY41R1 | CY40R1 |
| 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. | 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. | 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, |
|---|
run | runs twice a week (Monday and Thursday at 00Z) up to day |
32.| Research or operational | Operational | Operational | Operational | Operational | Operational | Operational | Operational |
|---|
Data time of first forecast run | 24/11/2016 | 14/05/2015 | 21/11/2013 | 2. Configuration of the EPS | Is the model coupled to an ocean model? | Yes from day 0 | Yes from day 0 | 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. | Ocean model is NEMO3.4.1 with a 1 degree horizontal resolution, 42 vertical levels, initialized from ECMWF Ocean Analysis + 4 perturbed analyses produced by perturbing the wind field during the ocean analysis. Frequency of coupling is 3-hourly. | | 46. | 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. | 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, run twice a week (Monday and Thursday at 00Z) up to day 32. |
| Research or operational | Operational | Operational | Operational | Operational | Operational |
|---|
| Data time of first forecast run | 11/06/2019 | 24/11/2016 | 8/03/2016 | 14/05/2015 | 21/11/2013 |
|---|
2. Configuration of the EPS |
|
|
|
|
|
|---|
| Is the model coupled to an ocean model? | Yes from day 0 | Yes from day 0 | Yes from day 0 | Yes from day 0 | 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 |
|---|
Ocean model is NEMO3.4.1 with a 1 degree horizontal resolution, 42 | vertical levels, initialized from ECMWF Ocean Analysis + 4 perturbed analyses produced by perturbing the wind field during the ocean analysis. Frequency of coupling is |
3-Is the model coupled to a sea Ice model? | Yes | No - Sea ice initial conditions are persisted up to day 15 and then relaxed to climatology up to day 45. | 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 | 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. | N/A | | 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. | Ocean model is NEMO3.4.1 with a 1 degree horizontal resolution, 42 vertical levels, initialized from ECMWF Ocean Analysis + 4 perturbed analyses produced by perturbing the wind field during the ocean analysis. Frequency of coupling is 3-hourly. | Ocean model is NEMO3.4.1 with a 1 degree horizontal resolution, 42 vertical levels, initialized from ECMWF Ocean Analysis + 4 perturbed analyses produced by perturbing the wind field during the ocean analysis. Frequency of coupling is 3-hourly. | Ocean model is NEMO3.4.1 with a 1 degree horizontal resolution, 42 vertical levels, initialized from ECMWF Ocean Analysis + 4 perturbed analyses produced by perturbing the wind field during the ocean analysis. Frequency of coupling is 3-hourly. |
N/A |
| Is the model coupled to a |
|---|
wave Yes | If yes, please describe wave model briefly including any ensemble perturbation applied | | No - Sea ice initial conditions are persisted up to day 15 and then relaxed to climatology up to day 45. | No - Sea ice initial conditions are persisted |
ECMWF wave model. No perturbation. Resolution is 0.25 degrees 0.5 degrees after day 15.ECMWF wave model. No perturbation. Resolution is 0.5 degrees. | ECMWF wave model. No perturbation. Resolution is 0.5 degrees. | Ocean model | NEMO 0.25 degree resolution | NEMO 1 degree resolution | NEMO 1 degree resolution | Horizontal resolution of the atmospheric model | Tco639 (about 16 km) up to day 15 and Tco319 (about 32 km) after day 15 | TL639 (about 32 km) up to day 10 and TL319 (about 64 km) after day 10 | TL639 (about 32 km) up to day 10 and TL319 (about 64 km) after day 10 | Number of model levels | 91 | 91 | 91 | Top of model | 0.01 hPa | 0.01 hPa | 0.01 hPa | Type of model levels | sigma | sigma | sigma | Forecast length | 46 days (1104 hours) | 46 days (1104 hours) | 32 days (768 hours) | Run Frequency | twice a week (Monday 00Z and Thursday 00Z) | twice a week (Monday 00Z and Thursday 00Z) | twice a week (Monday 00Z and Thursday 00Z) | Is there an unperturbed control forecast included? | Yes | Yes | Yes | Number of perturbed ensemble members | 50 | 50 | 50 | Integration time step | 12 minutes for day 0-15 and 20 minutes for day 15-46 | 20 minutes for day 0-10 and 45 minutes for day 10-46 | 20 minutes for day 0-10 and 45 minutes for day 10-32 | 3. Initial conditions and perturbations | Data assimilation method for control analysis | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | | then relaxed to climatology up to day 45. | 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 | 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. | 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. | N/A | N/A | N/A |
|---|
| Is the model coupled to a wave model? | Yes | Yes | Yes | Yes | 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. | ECMWF wave model. No perturbation. Resolution is 0.25 degrees up to day 15 and 0.5 degrees after day 15. | ECMWF wave model. No perturbation. Resolution is 0.5 degrees. | ECMWF wave model. No perturbation. Resolution is 0.5 degrees. | ECMWF wave model. No perturbation. Resolution is 0.5 degrees. |
|---|
| Ocean model | NEMO 0.25 degree resolution | NEMO 0.25 degree resolution | NEMO 1 degree resolution | NEMO 1 degree resolution | NEMO 1 degree resolution |
|---|
| Horizontal resolution of the atmospheric model | Tco639 (about 16 km) up to day 15 and Tco319 (about 32 km) after day 15 | Tco639 (about 16 km) up to day 15 and Tco319 (about 32 km) after day 15 | Tco639 (about 16 km) up to day 15 and Tco319 (about 32 km) after day 15 | TL639 (about 32 km) up to day 10 and TL319 (about 64 km) after day 10 | TL639 (about 32 km) up to day 10 and TL319 (about 64 km) after day 10 |
|---|
| Number of model levels | 91 | 91 | 91 | 91 | 91 |
|---|
| Top of model | 0.01 hPa | 0.01 hPa | 0.01 hPa | 0.01 hPa | 0.01 hPa |
|---|
| Type of model levels | sigma | sigma | sigma | sigma | sigma |
|---|
| Forecast length | 46 days (1104 hours) | 46 days (1104 hours) | 46 days (1104 hours) | 46 days (1104 hours) | 32 days (768 hours) |
|---|
| Run Frequency | twice a week (Monday 00Z and Thursday 00Z) | twice a week (Monday 00Z and Thursday 00Z) | twice a week (Monday 00Z and Thursday 00Z) | twice a week (Monday 00Z and Thursday 00Z) | twice a week (Monday 00Z and Thursday 00Z) |
|---|
| Is there an unperturbed control forecast included? | Yes | Yes | Yes | Yes | Yes |
|---|
| Number of perturbed ensemble members | 50 | 50 | 50 | 50 | 50 |
|---|
| Integration time step | 12 minutes for day 0-15 and 20 minutes for day 15-46 | 12 minutes for day 0-15 and 20 minutes for day 15-46 | 12 minutes for day 0-15 and 20 minutes for day 15-46 | 20 minutes for day 0-10 and 45 minutes for day 10-46 | 20 minutes for day 0-10 and 45 minutes for day 10-32 |
|---|
3. Initial conditions and perturbations |
|
|
|
|
|
|---|
| Data assimilation method for control analysis | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) | 4D Var (atmosphere) and 3DVAR (ocean/sea-ice) |
|---|
| Resolution of model used to generate Control Analysis | TL1279L137 | TL1279L137 | TL1279L137 | TL1279L137 | TL1279L137 |
|---|
| Ensemble initial perturbation strategy | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis |
|---|
| Horizontal and vertical resolution of perturbations | T42L91 SVs+ T399L137 EDA perturbations | T42L91 SVs+ T399L137 EDA perturbations | T42L91 SVs+ T399L137 EDA perturbations | T42L91 SVs+ T399L137 EDA perturbations | T42L91 SVs+ T399L137 EDA perturbations |
|---|
| Perturbations in +/- pairs | Yes | Yes | Yes | Yes | Yes |
|---|
| Initialization of land surface |
|
|
|
|
|
|---|
| 3.1 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 | IFS Documentation, Physical Processes, Chapter 8 Surface parameterisation, 2016 http//www.ecmwf.int/en/elibrary/16648-part-iv-physical-processes | IFS Documentation, Physical Processes, Chapter 8 Surface parameterisation, 2016 http//www.ecmwf.int/en/elibrary/16648-part-iv-physical-processes |
|
|
|---|
| 3.2 How is soil moisture initialized in the forecasts? (climatology / realistic / other) | realistic | realistic | realistic |
|---|
Resolution of model used to generate Control Analysis | TL1279L137 | TL1279L137 | TL1279L137 | Ensemble initial perturbation strategy | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Singular vectors + Ensemble Data Assimilation perturbations added to control analysis | Horizontal and vertical resolution of perturbations | T42L91 SVs+ T399L137 EDA perturbations | T42L91 SVs+ T399L137 EDA perturbations | T42L91 SVs+ T399L137 EDA perturbations | Perturbations in +/- pairs | Yes | Yes | Yes | Initialization of land surface | 3.1 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 | 3.2 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//software.ecmwf.int/wiki/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. | 3.3 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//software.ecmwf.int/wiki/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). | 3.4 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 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 initialization data onto the forecast model grid? If so, please give original data resolution(s) | Yes 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). | | interpolations. For soil moisture the interpolation is standardized on soil moisture index (to account for different soil texture in input and target resolution grid). | 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). | 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 |
|---|
3.5 How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM is it No, a monthly climatology of vegetation is used| Yes in a diagnostic wave using temperature and a latent heat barrier (described in Viterbo et al. 1999, see IFS documentation) | Yes in a diagnostic wave using temperature and a latent heat barrier (described in Viterbo et al. 1999, see IFS documentation) | Yes in a diagnostic wave using temperature and a latent heat barrier (described in Viterbo et al. 1999, see IFS documentation) |
|
|
| 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 | 3.6 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) | 3.7 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. | 4. Model uncertainties perturbations | Is model physics perturbed? If yes, briefly describe methods | Stochastic physics in the atmosphere (SPPT and SKEB schemes). | Stochastic physics in the atmosphere (SPPT and SKEB schemes). | Stochastic physics in the atmosphere (SPPT and SKEB schemes). | Do all ensemble members use exactly the same model version? | Same | Same | Same | Is model dynamics perturbed? | No | No | No | Are the above model perturbations applied to the control forecast? | No | No | No | 5. Surface boundary perturbations | Perturbations to sea surface temperature? | Yes (5-member ensemble of ocean analyses/re-analyses) | No | No | Perturbation to soil moisture? | Yes (EDA) | No | No | Perturbation to surface stress or roughness? | No (generated by wave model) | No | No | Any other surface perturbation? | No | No | No | Are the above surface perturbations applied to the Control forecast? | N/A | N/A | N/A | Additional comments | N/A | N/A | N/A | 6. Other details of the models | Description of model grid | Cubic octohedral grid | Linear grid | Linear grid | List of model levels in appropriate coordinates | http//www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http//www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http//www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | What kind of large scale dynamics is used? | Spectral semi-lagrangian | Spectral semi-lagrangian | Spectral semi-lagrangian | What kind of boundary layer parameterization is used? | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | | 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. | 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. | 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. |
|
|
|---|
| 3.3 How is snow initialized in the forecasts? (climatology / realistic / other) | realistic | realistic | 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//software.ecmwf.int/wiki/display/LDAS/LDAS+Home | LDAS-based (Optimal Interpolation) as used in all IFS forecast and described here https//software.ecmwf.int/wiki/display/LDAS/LDAS+Home | LDAS-based (Optimal Interpolation) as used in all IFS forecast and described here https//software.ecmwf.int/wiki/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 | If so, please give original data resolution(s) horizontal interpolation | 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). | Snow mass and snow temperature are initialized by the LDAS, snow albedo and snow density are cycled from the model forecast (open loop). | Snow mass and snow temperature are initialized by the LDAS, snow albedo and snow density are cycled from the model forecast (open loop). |
|
|
|---|
| 3.4 How is soil temperature initialized in the forecasts? (climatology / realistic / other) | realistic | realistic | 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//software.ecmwf.int/wiki/display/LDAS/LDAS+Home | LDAS-based (Optimal Interpolation) as used in all IFS forecast and described here https//software.ecmwf.int/wiki/display/LDAS/LDAS+Home | LDAS-based (Optimal Interpolation) as used in all IFS forecast and described here https//software.ecmwf.int/wiki/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. | Both the top soil temperature and the snow temperature (if present) are initialized. | 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 | horizontal interpolation | 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). | Only the first soil layer temperature is initialized, the other layers are cycled from the model forecast (open loop). | Only the first soil layer temperature is initialized, the other layers are cycled from the model forecast (open loop). |
|
|
|---|
| 3.5 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 | No, a monthly climatology of vegetation is used | 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 | Leaf Area Index and Albedo monthly climatology both based on MODIS collection 5 | Leaf Area Index and Albedo monthly climatology both based on MODIS collection 5 |
|
|
|---|
| 3.6 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) | FAO dominant soil texture class (as in Van Genuchten, 1980) | FAO dominant soil texture class (as in Van Genuchten, 1980) |
|
|
|---|
| 3.7 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 ERA5, while the real-time forecasts are based on the IFS operational initial conditions of the ENS/EDA systems. | 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. | 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. |
|
|
|---|
4. Model uncertainties perturbations |
|
|
|
|
|
|---|
| Is model physics perturbed? If yes, briefly describe methods | Stochastic physics in the atmosphere (SPPT and SKEB schemes). | Stochastic physics in the atmosphere (SPPT and SKEB schemes). |
| Stochastic physics in the atmosphere (SPPT and SKEB schemes). | Stochastic physics in the atmosphere (SPPT and SKEB schemes). |
|---|
| Do all ensemble members use exactly the same model version? | Same | Same |
| Same | Same |
|---|
| Is model dynamics perturbed? | No | No |
| No | No |
|---|
| Are the above model perturbations applied to the control forecast? | No | No |
| No | No |
|---|
5. Surface boundary perturbations |
|
|
|
|
|
|---|
| Perturbations to sea surface temperature? | Yes (5-member ensemble of ocean analyses/re-analyses) | Yes (5-member ensemble of ocean analyses/re-analyses) |
| No | No |
|---|
| Perturbation to soil moisture? | Yes (EDA) | Yes (EDA) |
| No | No |
|---|
| Perturbation to surface stress or roughness? | No (generated by wave model) | No (generated by wave model) |
| No | No |
|---|
| Any other surface perturbation? | No | No |
| No | No |
|---|
| Are the above surface perturbations applied to the Control forecast? | N/A | N/A |
| N/A | N/A |
|---|
| Additional comments | N/A | N/A |
| N/A | N/A |
|---|
6. Other details of the models |
|
|
|
|
|
|---|
| Description of model grid | Cubic octohedral grid | Cubic octohedral grid | Cubic octohedral grid | Linear grid | Linear grid |
|---|
| List of model levels in appropriate coordinates | http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http//www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels |
|---|
| What kind of large scale dynamics is used? | Spectral semi-lagrangian | Spectral semi-lagrangian | Spectral semi-lagrangian | Spectral semi-lagrangian | Spectral semi-lagrangian |
|---|
| What kind of boundary layer parameterization is used? | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria | Moist EDMF with Klein/Hartmann stratus/shallow convection criteria |
|---|
| What kind of convective parameterization is used? | Tiedtke 89, Bechtold et al 2004 (QJ) | Tiedtke 89, Bechtold et al 2004 (QJ) | Tiedtke 89, Bechtold et al 2004 (QJ) | Tiedtke 89, Bechtold et al 2004 (QJ) | 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 | Tiedtke 91 prognostic cloud fraction | Tiedtke 91 prognostic cloud fraction | Tiedtke 91 prognostic cloud fraction | Tiedtke 91 prognostic cloud fraction |
|---|
| What kind of land-surface scheme is used? | HTESSEL | HTESSEL | HTESSEL | HTESSEL | HTESSEL |
|---|
| How is radiation parametrized? | Official IFS documentation | CY43R1 official IFS documentation | CY41R2 official IFS documentation | CY41R1 official IFS documentation | CY40R1 official IFS documentation |
|---|
| Sea-ice thickness | Average sea-ice thickness (computed only where there is sea-ice) |
|
|
|
|
|---|
| Other relevant details? |
|
|
|
|
|
|---|
7. Re-forecast Configuration |
|
|
|
|
|
|---|
| Number of years covered | 20 past years | 20 past years | 20 past years | 20 past years | 20 past years |
|---|
| Produced on the fly or fix re-forecasts? | On the fly | On the fly | On the fly | On the fly | 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. | 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. | 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. | 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. | 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 | 11 members | 11 members | 11 members | 5 members |
|---|
| Initial conditions | ERA5 + ORAS5 ocean initial conditions (0.25 degree) | ERA interim (T255L60) + Soil reanalysis (Tco639) + ORAS5 ocean initial conditions (0.25 degree) | ERA interim (T255L60) + Soil reanalysis (T255) + ORAS4 ocean initial conditions (1 degree) | ERA interim (T255L60) + Soil reanalysis (T255) + ORAS4 ocean initial conditions (1 degree) | ERA interim (T255L60) + Soil reanalysis (T255) + ORAS4 ocean initial conditions (1 degree) |
|---|
| Is the model physics and resolution the same as for the real-time forecasts | Yes | Yes | Yes | Yes | Yes |
|---|
| If not, what are the differences | N/A | N/A | N/A | N/A | N/A |
|---|
| Is the ensemble generation the same as for real-time forecasts? | Yes. Except for EDA perturbations which are taken from the most recent year. | Yes. Except for EDA perturbations which are taken from the most recent year. | Yes. Except for EDA perturbations which are taken from the most recent year. | Yes. Except for EDA perturbations which are taken from the most recent year. | Yes. Except for EDA perturbations which are taken from the most recent year. |
|---|
| If not, what are the differences | N/A | N/A | N/A | N/A | N/A |
|---|
| Other relevant information | 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... | 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 |
|---|
What kind of convective parameterization is used? | Tiedtke 89, Bechtold et al 2004 (QJ) | Tiedtke 89, Bechtold et al 2004 (QJ) | 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 | Tiedtke 91 prognostic cloud fraction | Tiedtke 91 prognostic cloud fraction | What kind of land-surface scheme is used? | HTESSEL | HTESSEL | HTESSEL | How is radiation parametrized? | CY43R1 official IFS documentation | CY41R1 official IFS documentation | CY40R1 official IFS documentation | Other relevant details? | N/A | 7. Re-forecast Configuration | Number of years covered | 20 past years | 20 past years | 20 past years | Produced on the fly or fix re-forecasts? | On the fly | On the fly | 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. | 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. | 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 forecasts for forecast (Monday and Thursday), but covering the past 20 years. |
Ensemble size | 11 members | 11 members | 5 members | Initial conditions | ERA interim (T255L60) + Soil reanalysis (Tco639) + ORAS5 ocean initial conditions (0.25 degree) | ERA interim (T255L60) + Soil reanalysis (T255) + ORAS4 ocean initial conditions (1 degree) | ERA interim (T255L60) + Soil reanalysis (T255) + ORAS4 ocean initial conditions (1 degree) | 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... |
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? | Yes. Except for EDA perturbations which are taken from the most recent year. | Yes. Except for EDA perturbations which are taken from the most recent year. | Yes. Except for EDA perturbations which are taken from the most recent year. | If not, what are the differences | N/A | N/A | N/A | Other relevant information | 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, and re-forecasts covering all the 4 seasons will only be available at the end of 2015. The ECMWF re-forecasts are archived in the S2S database using two |
dates dates: "date" and "hdate" (see examples below) |
hdate : 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... | 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, and re-forecasts covering all the 4 seasons will only be available at the end of 2015. 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... | ECMWF re-forecasts are produced on the fly. This means that every week a new set of re-forecasts is produce to calibrate the real-time ensemble forecast of the following week using the latest version of IFS. The ensemble re-forecasts consist of a 5-member ensemble starting the same day and month as a Thursday real-time forecast, but covering the past 20 years. For instance the first re-forecast set archived in the S2S database was the re-forecast used to calibrate the real-time forecast of 1st January 2015 (a Thursday). This set consisted of a 5-member ensemble starting on 1st January 1995, 1st January 1996, ... 1st January 2014 (20 years, 5 member ensemble = 100-member climate ensemble). The re-forecast dataset is therefore updated every week in the S2S archive, and re-forecasts covering all the 4 seasons will only be available at the end of 2015. 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... |
...