1. Ensemble version | |
|---|---|
| Ensemble identifier code | CPTEC-BAM 1.2 |
| Short Description | CPTEC-BAM 1.2 (Guimarães et al. 2019) global atmospheric model ensemble predictions generated with perturbed atmospheric initial conditions. |
| Research or operational | Operational |
| Data time of first forecast run | 04/01/2023 |
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. | N/A |
| If no, please describe the sea surface temperature boundary conditions (climatology, reanalysis ...) | Forecasts are produced every Wednesday and Thursday with persisted total (not anomaly) sea surface temperature (SST) from the National Oceanic and Atmospheric Administration (NOAA) of the day prior to the start (initialization) date for a total of 35 days of integration. |
| Is the model coupled to a sea Ice model? | No |
| If yes, please describe sea-ice model briefly including any ensemble perturbation applied. | N/A |
| Is the model coupled to a wave model? | No |
| If yes, please describe wave model briefly including any ensemble perturbation applied | N/A |
| Ocean model | N/A |
| Horizontal resolution of the atmospheric model | TQ126 (~ 100 km) |
| Number of model levels | 42 |
| Top of model | 2 hPa |
| Type of model levels | Sigma |
| Forecast length | 35 days |
| Run Frequency | Twice a week |
| Is there an unperturbed control forecast included? | Yes |
| Number of perturbed ensemble members | 10 |
| Integration time step | 5 minutes |
3. Initial conditions and perturbations | |
| Data assimilation method for control analysis | The used analysis is obtained from the Global Data Assimilation System (GDAS) of the National Centers for Environmental Prediction (NCEP) weather forecast model in the USA |
| Resolution of model used to generate Control Analysis | TQ126L42 |
| Ensemble initial perturbation strategy | EOF-based method (see Mendonça and Bonatti, 2009 and Guimarães et al., 2019) |
| Horizontal and vertical resolution of perturbations | TQ126L42 |
| Perturbations in +/- pairs | 5 positive (+) and 5 negative (-) perturbations |
| 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? | IBIS-CPTEC surface model (see Kubota 2012 and Guimarães et al., 2019) |
| Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? | No |
| 3.2 How is soil moisture initialized in the forecasts? (climatology / realistic / other) | Climatology |
| 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. | N/A |
| Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). | Yes. Original horizontal resolution is 13 km and original vertical resolution is 137 levels, final horizontal resolution is TQ126 (~100 km) and final vertical resolution is 42 levels. |
| Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? | Yes, the LSM differentiate between liquid and ice content of the soil. Both are initialized using climatology. |
| If all model soil layers are not initialized in the same way or from the same source, please describe. | N/A |
| 3.3 How is snow initialized in the forecasts? (climatology / realistic / other) | Climatology |
| 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. | N/A |
| Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) | Yes. Original horizontal resolution is 13 km and original vertical resolution is 137 levels, final horizontal resolution is TQ126 (~100 km) and final vertical resolution is 42 levels. |
| Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties? | Only snow depth is initialized as climatology. |
| 3.4. How is soil temperature initialized in the forecasts? (climatology / realistic / other) | Climatology |
| 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. | N/A |
| 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)? | N/A |
| Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) | Yes. Original horizontal resolution is 13 km and original vertical resolution is 137 levels, final horizontal resolution is TQ126 (~100 km) and final vertical resolution is 42 levels. |
| If all model soil layers are not initialized in the same way or from the same source, please describe. | N/A |
| 3.5. How are time-varying vegetation properties represented in the LSM? | Vegetation properties vary in time according to LSM hydrological and carbon cycles. |
| 3.6. What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? | United States Geological Survey (USGS) |
| 3.7 If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences. | N/A |
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 |
| Additional Comments | N/A |
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 | N/A |
6. Other details of the models | |
| Description of model grid | Spectral (Triangular truncation) |
| List of model levels in appropriate coordinates | 42 vertical layers at 1008.93, 1000.19, 990.15, 978.66, 965.55, 950.63, 933.74, 914.72, 893.41, 869.67, 843.41, 814.59, 783.21, 749.34, 713.15, 674.87, 634.83, 593.41, 551.08, 508.33, 465.68, 423.65, 382.71, 343.31, 305.81, 270.49, 237.57, 207.17, 179.35, 154.08, 131.29, 110.88, 92.70, 76.60, 62.40, 49.93, 39.02, 29.50, 21.21, 14.01, 7.75, and 2.07 hPa |
| What kind of large scale dynamics is used? | Spectral Eulerian/semi-lagrangian |
| What kind of boundary layer parameterization is used? | Bretherton and Park (2009) |
| What kind of convective parameterization is used? | Revised version of the simplified Arakawa–Schubert deep convection scheme (Han and Pan, 2011) |
| What kind of large-scale precipitation scheme is used? | Morison et al., (2009) |
| What cloud scheme is used? | Slingo (1989) |
| What kind of land-surface scheme is used? | IBIS-CPTEC surface model (Kubota, 2012) |
| How is radiation parametrized? | |
| Long Wave Radiation | CLIRAD-LW (Chou et al., 2001) |
| Short Wave radiation | CLIRAD-SW [Chou and Suarez (1999) modified by Tarasova and Fomin (2000)] |
| Sea-ice thickness | N/A |
| Other relevant details? | See References |
7. Re-forecast Configuration | |
| Number of years covered | 20 years (1999-2018) |
| Produced on the fly or fix re-forecasts? | Fix re-forecasts |
| Frequency | The re-forecast consists of a 11-member ensemble starting every Wednesday from 6 Jan 1999 till 26 Dec 2018 |
| Ensemble size | 11 members |
| Initial conditions | ERA-Interim (Dee et al., 2011) |
| 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 |
8. References
Bretherton, C.S. and Park, S., 2009: A new moist turbulence parameterization in the Community Atmosphere Model. Journal of Climate, 22, 3422–3448
Chou, M.D. and Suarez, M.J., 1999: A solar radiation parameterization (CLIRAD-SW) for atmospheric studies. Suarez, M.J. (Ed.). Series on Global Modeling and Data Assimilation. Report number: NASA/TM-1999-104606/VOL15, pp 40.
Chou, M.-D., Suarez, M.J., Liang, X.-Z., Yan, M.M.-H. and Cote, C., 2001: A thermal infrared radiation parameterization for atmospheric studies. Report number: NASA/TM-2001-104606/VOL19
Coelho, C. A. S., de Souza, D. C., Kubota, P. Y., Costa, S. M. S., Menezes, L., Guimarães, B. S., Figueroa, S. N., Bonatti, J. P., Cavalcanti, I. F. A., Sampaio, G, Klingaman, N. P., Baker, J. C. A., 2021: Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2. Climate Dynamics. 56, 873- 898. https://doi.org/10.1007/s00382-020-05508-8
Coelho, C. A. S., Baker, J. C. A., Spracklen, D. V., Kubota, P. Y. Souza, D. C., Guimarães, B. S., Figueroa, S. N., Bonatti, J. P., Sampaio, G., Klingaman, N. P., Chevuturi, A., Woolnough, S. J., Hart, N., Zilli, M., Jones, C. D., 2022: A perspective for advancing climate prediction services in Brazil. Climate Resilience and Sustainability. https://doi.org/10.1002/cli2.29
Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, D.P., Bechtold, P., ACM, B., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., AP, M.N., Monge-Sanz, B.M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553–597
Guimaraes, B.S., Coelho, C.A.S., Woolnough, S.J., Kubota, P.Y., Bastarz, C.F., Figueroa, S.N., Bonatti, J.P., Souza, D.C., 2019: Configuration and hindcast quality assessment of a Brazilian global sub-seasonal prediction system. Quarterly Journal of the Royal Meteorological Society. Vol 146 (728). Pages 1067-1084. https://doi.org/10.1002/qj.3725
Guimarães, B. S., Coelho, C. A. S., Woolnough, S. J., Kubota, P. Y., Bastarz, C. F., Figueroa, S. N., Bonatti, J. P., de Souza, D. C., 2021: An inter-comparison performance assessment of a Brazilian global sub-seasonal prediction model against four sub-seasonal to seasonal (S2S) prediction project models. Climate Dynamics. 56. Pages 2359-2375. https://doi.org/10.1007/s00382-020-05589-5
Han, J. and Pan, H.L., 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather and Forecasting, 26, 520–533.
Kubota, P.Y., 2012: Variability of storage energy in the soil-canopy system and its impact on the definition of precipitation standard in South America (in Portuguese with abstract in English). PhD thesis, São José dos Campos, Brazil, Instituto Nacional de Pesquisas Espaciais (INPE).
Mendonça AM, Bonatti J (2009) Experiments with EOF-based perturbation methods and their impact on the CPTEC/INPE ensemble prediction system. Mon Weather Rev 137:1438–1459
Morrison, H., Thompson, G. and Tatarskii, V., 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one-and two-moment schemes. Monthly Weather Review, 137, 991–1007.
- Slingo A., 1989: A GCM Parameterization for the Shortwave Radiative Properties of Water Clouds. Journal of the Atmospheric Sciences. 46 (10), 1419-1427. DOI: https://doi.org/10.1175/1520-0469(1989)046<1419:AGPFTS>2.0.CO;2
- Tarasova, T.A. and Fomin, B.A., 2000: Solar radiation absorption due to water vapor: advanced broadband parameterizations. Journal of Applied Meteorology, 39, 1947–1951.