Abstract
To represent model uncertainties at the physical process level in the China Meteorological Administration global ensemble prediction system (CMA-GEPS), a stochastically perturbed parameterization (SPP) scheme is developed by perturbing 16 parameters or variables selected from three physical parameterization schemes for the planetary boundary layer, cumulus convection, and cloud microphysics. Each chosen quantity is perturbed independently with temporally and spatially correlated perturbations sampled from log-normal distributions. Impacts of the SPP scheme on CMA-GEPS are investigated comprehensively by using the stochastically perturbed parametrization tendencies (SPPT) scheme as a benchmark. In the absence of initial-condition perturbations, perturbation structures introduced by the two schemes are investigated by analyzing the ensemble spread of three forecast variables’ physical tendencies and perturbation energy in ensembles generated by the separate use of SPP and SPPT. It is revealed that both schemes yield different perturbation structures and can simulate different sources of model uncertainty. When initial-condition perturbations are activated, the influences of the two schemes on the performance of CMA-GEPS are assessed by calculating verification scores for both upper-air and surface variables. The improvements in ensemble reliability and probabilistic skill introduced by SPP and SPPT are mainly located in the tropics. Besides, the vast majority of the reliability improvements (including increases in ensemble spread and reductions in outliers) are statistically significant, and a smaller proportion of the improvements in probabilistic skill (i.e., decreases in continuously ranked probability score) reach statistical significance. Compared with SPPT, SPP generally has more beneficial impacts on 200-hPa and 2-m temperature, along with 925-hPa and 2-m specific humidity, during the whole 15-day forecast range. For other examined variables, such as 850-hPa zonal wind, 850-hPa temperature, and 700-hPa humidity, SPP tends to yield more reliable ensembles at lead times beyond day 7, and to display comparable probabilistic skills with SPPT. Both SPP and SPPT have small impacts in the extratropics, primarily due to the dominant role of the singular vectors-based initial perturbations.
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References
Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674–701, doi: https://doi.org/10.1175/1520-0469(1974)031<0674:ioacce>2.0.co;2.
Berner, J., G. J. Shutts, M. Leutbecher, et al., 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603–626, doi: https://doi.org/10.1175/2008jas2677.1.
Berner, J., U. Achatz, L. Batté, et al., 2017: Stochastic parameterization: Toward a new view of weather and climate models. Bull. Amer. Meteor. Soc., 98, 565–588, doi: https://doi.org/10.1175/bams-d-15-00268.1.
Bouttier, F., B. Vié, O. Nuissier, et al., 2012: Impact of stochastic physics in a convection-permitting ensemble. Mon. Wea. Rev., 140, 3706–3721, doi: https://doi.org/10.1175/mwr-d-12-00031.1.
Bowler, N. E., A. Arribas, K. R. Mylne, et al., 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703–722, doi: https://doi.org/10.1002/qj.234.
Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 1–3, doi: https://doi.org/10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2.
Buizza, R., M. Milleer, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 2887–2908, doi: https://doi.org/10.1002/qj.49712556006.
Buizza, R., P. L. Houtekamer, G. Pellerin, et al., 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 1076–1097, doi: https://doi.org/10.1175/mwr2905.1.
Chen, J., and X. L. Li, 2020: The review of 10 years development of the GRAPES global/regional ensemble prediction. Adv. Meteor. Sci. Technol., 10, 9–18, 29, doi: https://doi.org/10.3969/j.issn.2095-1973.2020.02.003. (in Chinese)
Chen, J., Z. S. Ma, and Y. Su, 2017: Boundary layer coupling to Charney-Phillips vertical grid in GRAPES model. J. Appl. Meteor. Sci., 28, 52–61, doi: https://doi.org/10.11898/1001-7313.20170105. (in Chinese)
Chen, J., Z. S. Ma, Z. Li, et al., 2020: Vertical diffusion and cloud scheme coupling to the Charney-Phillips vertical grid in GRAPES global forecast system. Quart. J. Roy. Meteor. Soc., 146, 2191–2204, doi: https://doi.org/10.1002/qj.3787.
Chen, X. M., Q. J. Liu, and J. C. Zhang, 2007: A numerical simulation study on microphysical structure and cloud seeding in cloud system of QiLian Mountain region. Meteor. Mon., 33, 33–43, doi: https://doi.org/10.3969/j.issn.1000-0526.2007.07.004. (in Chinese)
Christensen, H. M., 2020: Constraining stochastic parametrisation schemes using high-resolution simulations. Quart. J. Roy. Meteor. Soc., 146, 938–962, doi: https://doi.org/10.1002/qj.3717.
Duda, J. D., X. G. Wang, F. Y. Kong, et al., 2016: Impact of a stochastic kinetic energy backscatter scheme on warm season convection-allowing ensemble forecasts. Mon. Wea. Rev., 144, 1887–1908, doi: https://doi.org/10.1175/mwr-d-15-0092.1.
Feng, J., Z. Toth, M. Peña, et al., 2020: Partition of analysis and forecast error variance into growing and decaying components. Quart. J. Roy. Meteor. Soc., 146, 1302–1321, doi: https://doi.org/10.1002/qj.3738.
Fleury, A., F. Bouttier, and F. Couvreux, 2022: Process-oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction. Quart. J. Roy. Meteor. Soc., 148, 981–1000, doi: https://doi.org/10.1002/qj.4242.
Hacker, J. P., S. Y. Ha, C. Snyder, et al., 2011: The U.S. air force weather agency’s mesoscale ensemble: Scientific description and performance results. Tellus A, 63, 625–641, doi: https://doi.org/10.1111/j.1600-0870.2010.00497.x.
Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559–570, doi: https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2.
Hong, S. Y., and H. L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339, doi: https://doi.org/10.1175/1520-0493(1996)124<2322:nblvdi>2.0.co;2.
Huo, Z. H., Y. Z. Liu, J. Chen, et al., 2020: The preliminary appliation of tropical cyclone targeted singular vectors in the GRAPES global ensemble forecasts. Acta Meteor. Sinica, 78, 48–59, doi: https://doi.org/10.11676/qxxb2020.006. (in Chinese)
Jankov, I., J. Berner, J. Beck, et al., 2017: A performance comparison between multiphysics and stochastic approaches within a North American RAP ensemble. Mon. Wea. Rev., 145, 1161–1179, doi: https://doi.org/10.1175/mwr-d-16-0160.1.
Jankov, I., J. Beck, J. Wolff, et al., 2019: Stochastically perturbed parameterizations in an HRRR-based ensemble. Mon. Wea. Rev., 147, 153–173, doi: https://doi.org/10.1175/mwr-d-18-0092.1.
Lang, S. T. K., S. J. Lock, M. Leutbecher, et al., 2021: Revision of the stochastically perturbed parametrisations model uncertainty scheme in the integrated forecasting system. Quart. J. Roy. Meteor. Soc., 147, 1364–1381, doi: https://doi.org/10.1002/qj.3978.
Leutbecher, M., S. J. Lock, P. Ollinaho, et al., 2017: Stochastic representations of model uncertainties at ECMWF: State of the art and future vision. Quart. J. Roy. Meteor. Soc., 143, 2315–2339, doi: https://doi.org/10.1002/qj.3094.
Li, X. L., and Y. Z. Liu, 2019: The improvement of GRAPES global extratropical singular vectors and experimental study. Acta Meteor. Sinica, 77, 552–562, doi: https://doi.org/10.11676/qxxb2019.020. (in Chinese)
Li, X. L., M. Charron, L. Spacek, et al., 2008: A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations. Mon. Wea. Rev., 136, 443–462, doi: https://doi.org/10.1175/2007mwr2109.1.
Li, X. L., J. Chen, Y. Z. Liu, et al., 2019: Representations of initial uncertainty and model uncertainty of GRAPES global ensemble forecasting. Trans. Atmos. Sci., 42, 348–359, doi: https://doi.org/10.13878/j.cnki.dqkxxb.20190318001. (in Chinese)
Liu, K., Q. Y. Chen, and J. Sun, 2015: Modification of cumulus convection and planetary boundary layer schemes in the GRAPES global model. J. Meteor. Res., 29, 806–822, doi: https://doi.org/10.1007/s13351-015-5043-5.
Lock, S. J., S. T. K. Lang, M. Leutbecher, et al., 2019: Treatment of model uncertainty from radiation by the stochastically perturbed parametrization tendencies (SPPT) scheme and associated revisions in the ECMWF ensembles. Quart. J. Roy. Meteor. Soc., 145, 75–89, doi: https://doi.org/10.1002/qj.3570.
Ma, Z. S., Q. J. Liu, C. F. Zhao, et al., 2018: Application and evaluation of an explicit prognostic cloud-cover scheme in GRAPES global forecast system. J. Adv. Model. Earth Syst., 10, 652–667, doi: https://doi.org/10.1002/2017ms001234.
McCabe, A., R. Swinbank, W. Tennant, et al., 2016: Representing model uncertainty in the Met Office convection-permitting ensemble prediction system and its impact on fog forecasting. Quart. J. Roy. Meteor. Soc., 142, 2897–2910, doi: https://doi.org/10.1002/qj.2876.
Ollinaho, P., S. J. Lock, M. Leutbecher, et al., 2017: Towards process-level representation of model uncertainties: Stochastically perturbed parametrizations in the ECMWF ensemble. Quart. J. Roy. Meteor. Soc., 143, 408–422, doi: https://doi.org/10.1002/qj.2931.
Palmer, T. N., R. Buizza, F. Doblas-Reyes, et al., 2009: Stochastic Parametrization and Model Uncertainty. European Centre for Medium-Range Weather Forecasts Technical Memorandum No. 598, ECMWF, Reading, 42 pp.
Pan, H. L., and W. S. Wu, 1995: Implementing a Mass Flux Convection Parameterization Package for the NMC Medium-Range Forecast Model. NMC Office Note 409, NOAA, Washington, 40 pp.
Peng, F., X. L. Li, J. Chen, et al., 2019: A stochastic kinetic energy backscatter scheme for model perturbations in the GRAPES global ensemble prediction system. Acta Meteor. Sinica, 77, 180–195, doi: https://doi.org/10.11676/qxxb2019.009. (in Chinese)
Peng, F., X. L. Li, and J. Chen, 2020: Impacts of different stochastic physics perturbation schemes on the GRAPES global ensemble prediction system. Acta Meteor. Sinica, 78, 972–987, doi: https://doi.org/10.11676/qxxb2020.074. (in Chinese)
Pincus, R., H. W. Barker, and J. J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res. Atmos., 108, 4376, doi: https://doi.org/10.1029/2002JD003322.
Romine, G. S., C. S. Schwartz, J. Berner, et al., 2014: Representing forecast error in a convection-permitting ensemble system. Mon. Wea. Rev., 142, 4519–4541, doi: https://doi.org/10.1175/mwr-d-14-00100.1.
Sanchez, C., K. D. Williams, and M. Collins, 2016: Improved stochastic physics schemes for global weather and climate models. Quart. J. Roy. Meteor. Soc., 142, 147–159, doi: https://doi.org/10.1002/qj.2640.
Shen, X. S., J. J. Wang, Z. C. Li, et al., 2020: Research and operational development of numerical weather prediction in China. J. Meteor. Res., 34, 675–698, doi: https://doi.org/10.1007/s13351-020-9847-6.
Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079–3102, doi: https://doi.org/10.1256/qj.04.106.
Tennant, W. J., G. J. Shutts, A. Arribas, et al., 2011: Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill. Mon. Wea. Rev., 139, 1190–1206, doi: https://doi.org/10.1175/2010mwr3430.1.
Tiedtke, M., W. A. Heckley, and J. Slingo, 1988: Tropical forecasting at ECMWF: The influence of physical parametrization on the mean structure of forecasts and analyses. Quart. J. Roy. Meteor. Soc., 114, 639–664, doi: https://doi.org/10.1002/qj.49711448106.
Wang, S. Z., X. S. Qiao, J. Z. Min, et al., 2019: The impact of stochastically perturbed parameterizations on tornadic super-cell cases in East China. Mon. Wea. Rev., 147, 199–220, doi: https://doi.org/10.1175/mwr-d-18-0182.1.
Wang, Y., M. Bellus, J. F. Geleyn, et al., 2014: A new method for generating initial condition perturbations in a regional ensemble prediction system: Blending. Mon. Wea. Rev., 142, 2043–2059, doi: https://doi.org/10.1175/mwr-d-12-00354.1.
Wastl, C., Y. Wang, A. Atencia, et al., 2019: A hybrid stochastically perturbed parametrization scheme in a convection-permitting ensemble. Mon. Wea. Rev., 147, 2217–2230, doi: https://doi.org/10.1175/mwr-d-18-0415.1.
Wilks, D. S., 2005: Effects of stochastic parametrizations in the Lorenz’ 96 system. Quart. J. Roy. Meteor. Soc., 131, 389–407, doi: https://doi.org/10.1256/qj.04.03.
Xu, Z. Z., J. Chen, Z. Jin, et al., 2020: Representing model uncertainty by multi-stochastic physics approaches in the GRAPES ensemble. Adv. Atmos. Sci., 37, 328–346, doi: https://doi.org/10.1007/s00376-020-9171-1.
Xue, J. S., and D. H. Chen, 2008: Scientific Design and Application of GRAPES Numerical Prediction System. Science Press, Beijing, 383 pp. (in Chinese)
Yuan, Y., X. L. Li, J. Chen, et al., 2016: Stochastic parameterization toward model uncertainty for the GRAPES mesoscale ensemble prediction system. Meteor. Mon., 42, 1161–1175, doi: https://doi.org/10.7519/j.issn.1000-0526.2016.10.001. (in Chinese)
Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173–1185, doi: https://doi.org/10.1175/1520-0469(2003)060<1173:eomcom>2.0.co;2.
Zheng, M. H., E. K. M. Chang, and B. A. Colle, 2019: Evaluating U.S. east coast winter storms in a multimodel ensemble using EOF and clustering approaches. Mon. Wea. Rev., 147, 1967–1987, doi: https://doi.org/10.1175/mwr-d-18-0052.1.
Zhou, X. Q., Y. J. Zhu, D. C. Hou, et al., 2017: Performance of the new NCEP global ensemble forecast system in a parallel experiment. Wea. Forecasting, 32, 1989–2004, doi: https://doi.org/10.1175/waf-d-17-0023.1.
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Supported by the National Natural Science Foundation of China (41905090).
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Peng, F., Li, X. & Chen, J. Stochastically Perturbed Parameterizations for the Process-Level Representation of Model Uncertainties in the CMA Global Ensemble Prediction System. J Meteorol Res 36, 733–749 (2022). https://doi.org/10.1007/s13351-022-2011-8
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DOI: https://doi.org/10.1007/s13351-022-2011-8