数值天气预报和气候预测的集合预报方法：思考与展望

段晚锁; 汪叶; 霍振华; 周菲凡

doi:10.3878/j.issn.1006-9585.2018.18133

数值天气预报和气候预测的集合预报方法：思考与展望

doi: 10.3878/j.issn.1006-9585.2018.18133

1.
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029;中国科学院大学,北京100049
2.
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029;中国科学院大学,北京100049;河南大学数学与统计学院,河南开封475004
3.
中国气象局国家气象中心,北京 100081
4.
中国科学院大气物理研究所云降水物理与强风暴重点实验室(LACS),北京100029

基金项目: 国家重点研发计划2018YFC1506402，国家自然科学基金项目41525017

计量
- 文章访问数: 1027
- HTML全文浏览量: 49
- PDF下载量: 807
- 被引次数: 0
出版历程
- 收稿日期: 2018-10-18

Ensemble Forecast Methods for Numerical Weather Forecast and Climate Prediction: Thinking and Prospect

1.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;University of Chinese Academy of Sciences, Beijing 100049
2.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;University of Chinese Academy of Sciences, Beijing 100049;School of Mathematics and Statistics, Henan University, Kaifeng, Henan Province 475004
3.
National Meteorological Center, China Meteorological Administration, Beijing 100081
4.
Key Laboratory of Cloud-Precipitation Physics and Severe Storm (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Funds: ational Key Research and Development Program of China (Grant 2018YFC1506402)，National Natural Science Foundation of China Grant 41525017ational Key Research and Development Program of China (Grant 2018YFC1506402)，National Natural Science Foundation of China (Grant 41525017)

摘要

摘要: 从初始误差、模式误差以及两者综合影响的角度，综述了天气、气候集合预报方法的研究进展，指出了传统方法的优势，同时也评论了这些方法的局限性，提出了对未来先进集合预报方法的一些思考，以及需要解决的挑战性问题和可能的应用。
- 初始误差 /
- 模式误差 /
- 集合预报
Abstract: This article reviews the research progress of ensemble forecast methods for weather and climate from the perspective of initial errors, model errors, and the combined effects of the above two. The authors clarify the advantages of traditional methods; meanwhile, they comment on the limitations of these methods. Finally, the authors propose some ideas on developing more advanced ensemble forecast methods, raise some challenging problems that need to be resolved, and address possible applications of the ensemble methods.
- Initial error /
- Model error /
- Ensemble forecast

HTML全文

参考文献(87)

[1]	Anderson J L . 1997. The impact of dynamical constraints on the selection of initial conditions for ensemble predictions: Low-order perfect model results[J]. Mon. Wea. Rev., 125(11): 2969-2983. doi:10.1175/1520-0493(1997)125 <2969:TIODCO> 2.0.CO;2
[2]	Barkmeijer J , Iversen T , Palmer T N . 2003. Forcing singular vectors and other sensitive model structures[J]. Quart.J. Roy. Meteor.Soc., 129(592): 2401-2423. doi:10.1256/qj.02.126
[3]	Bishop C H , Toth Z . 1999. Ensemble transformation and adaptive observations[J]. J. Atmos. Sci., 56(11): 1748-1765. doi:10.1175/1520-0469(1999)056 <1748:ETAAO> 2.0.CO;2
[4]	Bowler N E . 2006.Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model[J]. Tellus A: Dynamic Meteorology and Oceanography, 58(5): 538-548. doi:10.1111/j.1600-0870.2006.00197.x
[5]	? Brankovi? , Palmer T N , Molteni F , et al . 1990. Extended-range predictions with ECMWF models: Time‐lagged ensemble forecasting [J]. Quart. J. Roy. Meteor. Soc.,116(494): 867-912. doi:10.1002/qj.49711649405
[6]	Buckingham C , Marchok T , Ginis I , et al . 2010. Short- and medium-range prediction of tropical and transitioning cyclone tracks within the NCEP global ensemble forecasting system [J]. Wea. Forecasting, 25(6): 1736-1754. doi:10.1175/2010WAF2222398.1
[7]	Buizza R , Houtekamer P L , Pellerin G , et al . 2005. A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems [J]. Mon. Wea. Rev., 133(5): 1076-1097. doi:10.1175/MWR2905.1
[8]	Buizza R , Miller M , Palmer T N . 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system[J]. Quart. J. Roy. Meteor. Soc., 125(560): 2887-2908. doi:10.1002/qj.49712556006
[9]	Chen D K , Zebiak S E , Busalacchi A J , et al . 1995. An improved procedure for El Ni?o forecasting: Implications for predictability [J]. Science, 269(523): 1699-1702. doi:10.1126/science.269.5231.1699
[10]	Chen D K , Cane M A , Kaplan A , et al . 2004. Predictability of El Ni?o over the past 148 years [J]. Nature, 428(6984): 733-736. doi:10.1038/nature02439
[11]	Chen G H , Wu C C , Huang Y H . 2018a. The role of near-core convective and stratiform heating/cooling in tropical cyclone structure and intensity[J]. J. Atmos. Sci., 75(1): 297-326. doi:10.1175/JAS-D-17-0122.1
[12]	Chen Y J , Zhang F Q , Green B W , et al . 2018b.Impacts of ocean cooling and reduced wind drag on Hurricane Katrina (2005) based on numerical simulations [J]. Mon. Wea. Rev., 146(1): 287-306. doi:10.1175/MWR-D-17-0170.1
[13]	DeMaria M , Sampson C R , Knaff J A , et al . 2014. Is tropical cyclone intensity guidance improving? [J]. Bull. Amer. Meteor. Soc., 95(3): 387-398. doi:10.1175/BAMS-D-12-00240.1
[14]	Dong L , Zhang F Q . 2016.OBEST: An observation-based ensemble subsetting technique for tropical cyclone track prediction[J]. Wea. Forecasting, 31(1): 57-70. doi:10.1175/WAF-D-15-0056.1
[15]	Duan W S , Huo Z H . 2016. An approach to generating mutually independent initial perturbations for ensemble forecasts: Orthogonal conditional nonlinear optimal perturbations[J]. J. Atmos. Sci., 73(3): 997-1014. doi:10.1175/JAS-D-15-0138.1
[16]	Duan W S , Li X Q , Tian B . 2018. Towards optimal observational array for dealing with challenges of El Ni?o-Southern Oscillation predictions due to diversities of El Ni?o[J]. Climate Dyn., 51(9-10): 3351-3368. doi:10.1007/s00382-018-4082-x
[17]	Duan W S , Liu X C , Zhu K Y , et al . 2009. Exploring the initial errors that cause a significant “spring predictability barrier” for El Ni?o events [J]. J. Geophys. Res., 114(C4): C04022. doi:10.1029/2008JC004925
[18]	Duan W S , Tian B , Xu H . 2014. Simulations of two types of El Ni?o events by an optimal forcing vector approach[J]. Climate Dyn., 43(5-6): 1677-1692. doi:10.1007/s00382-013-1993-4
[19]	Duan W S , Zhao P . 2015. Revealing the most disturbing tendency error of Zebiak-Cane model associated with El Ni?o predictions by nonlinear forcing singular vector approach[J]. Climate Dyn., 44(9-10): 2351-2367. doi:10.1007/s00382-014-2369-0
[20]	Duan W S , Zhou F F . 2013. Non-linear forcing singular vector of a two-dimensional quasi-geostrophic model[J]. Tellus A: Dynamic Meteorology and Oceanography, 65(1): 18452. doi:10.3402/tellusa.v65i0.18452
[21]	Duan W S , Mu M . 2018. Predictability of El Ni?o-Southern oscillation events[J]. Oxford Research Encyclopedia of Climate Science. doi:10.1093/acrefore/9780190228620.013.80
[22]	Eckel F A , Mass C F . 2005. Aspects of effective mesoscale, short-range ensemble forecasting[J]. Wea. Forecasting, 20(3): 328-350. doi:10.1175/WAF843.1
[23]	Emanuel K , Zhang F Q . 2016. On the predictability and error sources of tropical cyclone intensity forecasts[J]. J. Atmos. Sci., 73(9): 3739-3747. doi:10.1175/JAS-D-16-0100.1
[24]	Epstein E S . 1969.Stochastic dynamic prediction[J]. Tellus, 21(6): 739-759. doi:10.1111/j.2153-3490.1969.tb00483.x
[25]	Evans R E , Harrison M S J , Graham R J , et al . 2000. Joint medium-range ensembles from the Met. Office and ECMWF systems [J]. Mon. Wea. Rev., 128(9): 3104-3127. doi:10.1175/1520-0493(2000)128 <3104:JMREFT>2.0.CO;2
[26]	Evensen G . 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. J. Geophys. Res., 99(C5): 10143-10162. doi:10.1029/94JC00572
[27]	Feng J , DingR Q , Liu D Q , et al . 2014. The application of nonlinear local Lyapunov vectors to ensemble predictions in Lorenz systems [J]. J. Atmos. Sci., 71(9): 3554-3567. doi:10.1175/JAS-D-13-0270.1
[28]	Goerss J S . 2000. Tropical cyclone track forecasts using an ensemble of dynamical models[J]. Mon. Wea. Rev., 128(4): 1187-1193. doi:10.1175/1520-0493(2000)128 <1187:TCTFUA> 2.0.CO;2
[29]	Green B W , Zhang F Q . 2013. Impacts of air-sea flux parameterizations on the intensity and structure of tropical cyclones[J]. Mon. Wea. Rev., 141(7): 2308-2324. doi:10.1175/MWR-D-12-00274.1
[30]	Hamill T M , Snyder C , Morss R E . 2000. A comparison of probabilistic forecasts from bred, singular-vector, and perturbed observation ensembles[J]. Mon. Wea. Rev., 128(6): 1835-1851. doi:10.1175/1520-0493(2000)128 <1835:ACOPFF> 2.0.CO;2
[31]	Harrison M S , Palmer T N , Richardson D S , et al . 1999. Analysis and model dependencies in medium-range ensembles: Two transplant case-studies [J]. Quart. J. Roy. Meteor. Soc., 125(559): 2487-2515. doi:10.1002/qj.49712555908
[32]	Hendon H H , Lim E , Wang G M , et al . 2009. Prospects for predicting two flavors of El Ni?o [J]. Geophys. Res. Lett., 36(19). doi:10.1029/2009GL040100
[33]	Hou D C , Kalnay E , Droegemeler K K . 2001. Objective verification of the SAMEX’98 ensemble forecasts[J]. Mon. Wea. Rev., 129(1): 73-91. doi:10.1175/1520-0493(2001)129 <0073:OVOTSE> 2.0.CO;2
[34]	Hou D C , Toth Z , Zhu Y J . 2006. A stochastic parameterization scheme within NCEP global ensemble forecast system [C]// Extended abstract, the 18th AMS Conference on Probability and Statistics. Atlanta, Georgia: Amer. Meteor. Soc.
[35]	Hou D , Toth Z , Zhu Y J , et al . 2008. Impact of a stochastic perturbation scheme on NCEP global ensemble forecast system [C]// Extended abstract, the 19th AMS Conference on Probability and Statistics. New Orleans, Louisiana: Amer. Meteor. Soc.
[36]	Houtekamer P L , Derome J . 1994. Prediction experiments with two-member ensembles[J]. Mon. Wea. Rev., 122(9): 2179-2191. doi:10.1175/1520-0493(1994)122 <2179:PEWTME> 2.0.CO;2
[37]	Houtekamer P L , Lefaivre L , Derome J , et al . 1996. A system simulation approach to ensemble prediction [J]. Mon. Wea. Rev., 124(6): 1225-1242. doi:10.1175/1520-0493(1996)124 <1225:ASSATE>2.0.CO;2
[38]	Houtekarner P L , Charron M , Mitchell H L , et al . 2007. Status of the global EPS at environment Canada [C]// Proceedings of ECMWF Workshop on Ensemble Prediction.United Kingdom: ECMWF, 57-68.
[39]	Huo Z H , Duan W S . 2019. The application of the orthogonal conditional nonlinear optimal perturbations method to typhoon track ensemble forecasts[J]. Science China Earth Sciences, 62 (2): 376-388. doi:10.1007/s11430-018-9248-9
[40]	Huo Z , Duan W , Zhou F . 2019. Ensemble forecasts of tropical cyclone track with orthogonal conditional nonlinear optimal perturbations[J]. Adv. Atmos. Sci., 36(2): 231-247.
[41]	Jankov I , W A Jr Gallus , Segal M , et al . 2005. The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall [J]. Wea. Forecasting, 20(6): 1048-1060. doi:10.1175/WAF888.1
[42]	Jeong H I , Lee D Y , Ashok K , et al . 2012. Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter [J]. Climate Dyn., 39(1-2): 475-493. doi:10.1007/s00382-012-1359-3
[43]	Jiang Z N , Mu M . 2009. A comparison study of the methods of conditional nonlinear optimal perturbations and singular vectors in ensemble prediction[J]. Advances in Atmospheric Sciences, 26(3): 465-470. doi:10.1007/s00376-009-0465-6
[44]	Kalnay E . 2003. Atmospheric Modeling, Data Assimilation and Predictability [M]. Cambridge University Press, Cambridge, 341pp.
[45]	Kirtman B P , Min D H . 2009. Multimodel ensemble ENSO prediction with CCSM and CFS[J]. Mon. Wea. Rev., 137(9): 2908-2930. doi:10.1175/2009MWR2672.1
[46]	Kirtman B , Power S B , Adedoyin J A , et al . 2013. Near-term climate change: Projections and predictability [C]// Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
[47]	Leith C E . 1974. Theoretical skill of Monte Carlo forecasts[J]. Mon. Wea. Rev., 102(6): 409-418. doi:10.1175/1520-0493(1974)102 <0409:TSOMCF> 2.0.CO;2
[48]	Leutbecher M , Palmer T N . 2008. Ensemble forecasting[J]. J. Comput. Phys., 227(7): 3515-3539. doi:10.1016/j.jcp.2007.02.014
[49]	Li Q Q , Wang Y Q , Duan Y H . 2014. Effects of diabatic heating and cooling in the rapid filamentation zone on structure and intensity of a simulated tropical cyclone[J]. J. Atmos. Sci., 71(9): 3144-3163. doi:10.1175/JAS-D-13-0312.1
[50]	Li X L , Pu Z X . 2008. Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations[J]. Mon. Wea. Rev., 136(12): 4819-4838. doi:10.1175/2008MWR2366.1
[51]	Lopez H , Kirtman B P . 2014. WWBs, ENSO predictability, the spring barrier and extreme events[J]. J. Geophys. Res.: Atmos., 119(17): 10114-10138. doi:10.1002/2014JD021908
[52]	Lorenz E N . 1965. A study of the predictability of a 28-variable atmospheric model[J]. Tellus, 17(3): 321-333. doi:10.1111/j.2153-3490.1965.tb01424.x
[53]	Lorenz E N . 1969. The predictability of a flow which possesses many scales of motion[J]. Tellus, 21(3): 289-307. doi:10.1111/j.2153-3490.1969.tb00444.x
[54]	Magnusson L , K?llén E , Nycander J . 2008. Initial state perturbations in ensemble forecasting[J]. Nonlinear Processes in Geophysics, 15(5): 751-759, doi:10.5194/npg-15-751-2008
[55]	McCabe A , Swinbank R , Tennant W , et al . 2016. Representing model uncertainty in the Met Office convection‐Permitting ensemble prediction system and its impact on fog forecasting [J]. Quart. J. Roy. Meteor. Soc., 142(700): 2897-2910. doi:10.1002/qj.2876
[56]	Moore A M , Kleeman R . 1996. The dynamics of error growth and predictability in a coupled model of ENSO[J]. Quart. J. Roy. Meteor. Soc., 122(534): 1405-1446, doi:10.1002/qj.49712253409
[57]	Moore A , Zavala-Garay J , Tang Y M , et al . 2006. Optimal forcing patterns for coupled models of ENSO [J]. J. Climate, 19(18): 4683-4699. doi:10.1175/JCLI3870.1
[58]	Mu M , Duan W S , Chen D K , et al . 2015. Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting [J]. National Science Review, 2(2): 226-236. doi:10.1093/nsr/nwv021
[59]	Mu M , Duan W S , Chou J . 2004. Recent advances in predictability studies in China (1999-2002)[J]. Advances in Atmospheric Sciences, 21(3): 437-443. doi:10.1007/BF02915570
[60]	Mu M , Duan W S , Wang B . 2003. Conditional nonlinear optimal perturbation and its applications[J]. Nonlinear Processes in Geophysics, 10(6): 493-501. doi:10.5194/npg-10-493-2003
[61]	Mu M , Duan W S , Wang Q , et al . 2010. An extension of conditional nonlinear optimal perturbation approach and its applications [J]. Nonlinear Processes in Geophysics, 17(2): 211-220. doi:10.5194/npg-17-211-2010
[62]	Mu M , Jiang Z N . 2008. A new approach to the generation of initial perturbations for ensemble prediction: Conditional nonlinear optimal perturbation[J]. Chinese Science Bulletin, 53(13): 2062-2068. doi:10.1007/s11434-008-0272-y
[63]	Munsell E B , Sippel J A , Braun S A , et al . 2015. Dynamics and predictability of hurricane Nadine (2012) evaluated through convection-permitting ensemble analysis and forecasts [J]. Mon. Wea. Rev., 143(11): 4514-4532. doi:10.1175/MWR-D-14-00358.1
[64]	Mureau R , Molteni F , Palmer T N . 1993. Ensemble prediction using dynamically conditioned perturbations[J]. Quart. J. Roy. Meteor. Soc., 119(510): 299-323. doi:10.1002/qj.49711951005
[65]	Palmer T N , Buizza R , Molteni F , et al . 1994. Singular vectors and the predictability of weather and climate [J]. Philosophical Transactions: Physical Sciences and Engineering, 348(1688): 459-475. doi:10.1098/rsta.1994.0105
[66]	Palmer T N , D?ring A , Seregin G . 2014. The real butterfly effect[J]. Nonlinearity, 27(9): R123-R141. doi:10.1088/0951-7715/27/9/R123
[67]	Qi Q Q , Duan W S , Zheng F , et al . 2017. On the “spring predictability barrier” for strong El Ni?o events as derived from an intermediate coupled model ensemble prediction system [J]. Science China Earth Sciences, 60(9): 1614-1631. doi: 10.1007/s11430-017-9087-2
[68]	Rios-Berrios R , Vukicevic T , Tang B . 2014. Adopting model uncertainties for tropical cyclone intensity prediction[J]. Mon. Wea. Rev., 142(1): 72-78. doi:10.1175/MWR-D-13-00186.1
[69]	Shutts G , Palmer T . 2004. The use of high resolution numerical simulations of tropical circulation to calibrate stochastic physics schemes [C]// Proceeding of ECMWF/CLIVAR Workshop on Simulation and Prediction of Intra-Seasonal Variability with Emphasis on the MJO. Reading, United Kingdom: ECMWF, 83-102.
[70]	Stensrud D J , Bao J W , Warner T T . 2000. Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems[J]. Mon. Wea. Rev., 128(7): 2077-2107. doi:10.1175/1520-0493(2000)128 <2077:UICAMP> 2.0.CO;2
[71]	Tennekes H . 1991. Karl Popper and the accountability of numerical weather forecasting [C]// ECMWF Workshop Proceedings. New Developments in Predictability. London: ECMWF.
[72]	Thompson P D . 1957. Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns[J]. Tellus, 9(3): 275-295. doi:10.3402/tellusa.v9i3.9111
[73]	Torn R D . 2016. Evaluation of atmosphere and ocean initial condition uncertainty and stochastic exchange coefficients on ensemble tropical cyclone intensity forecasts[J]. Mon. Wea. Rev., 144(9): 3487-3506. doi:10.1175/MWR-D-16-0108.1
[74]	Toth Z , Kalnay E . 1997. Ensemble forecasting at NCEP and the breeding method[J]. Mon. Wea. Rev., 125(12): 3297-3319. doi:10.1175/1520-0493(1997)125 <3297:EFANAT> 2.0.CO;2
[75]	Von Leeuwen P J . 2009. Particle filtering in geophysical systems[J]. Mon. Wea. Rev., 137(12): 4089-4114. doi:10.1175/2009MWR2835.1
[76]	Vukicevic T , Posselt D . 2008. Analysis of the impact of model nonlinearities in inverse problem solving[J]. J. Atmos. Sci., 65(9): 2803-2823. doi:10.1175/2008JAS2534.1
[77]	Wang J Z , Chen J , Du J , et al . 2018. Sensitivity of ensemble forecast verification to model bias [J]. Mon. Wea. Rev., 146(3): 781-796. doi:10.1175/MWR-D-17-0223.1
[78]	Weber H C . 2003. Hurricane track prediction using a statistical ensemble of numerical models[J]. Mon. Wea. Rev., 131(5): 749-770. doi:10.1175/1520-0493(2003)131 <0749:HTPUAS> 2.0.CO;2
[79]	Xue M , Schleif J , Kong F Y , et al . 2013. Track and intensity forecasting of hurricanes: Impact of convection-permitting resolution and global ensemble Kalman filter analysis on 2010 Atlantic season forecasts [J]. Wea. Forecasting, 28(6): 1366-1384. doi:10.1175/WAF-D-12-00063.1
[80]	Yamaguchi M , Majumdar S J . 2010. Using TIGGE data to diagnose initial perturbations and their growth for tropical cyclone ensemble forecasts[J]. Mon. Wea. Rev., 138(9): 3634-3655. doi:10.1175/2010MWR3176.1
[81]	Yang S C , Kalnay E , Enomoto T . 2015. Ensemble singular vectors and their use as additive inflation in EnKF[J]. Tellus A: Dynamic Meteorology and Oceanography, 67(1): 26536. doi:10.3402/tellusa.v67.26536
[82]	Zhang J A , Marks F D . 2015. Effects of horizontal diffusion on tropical cyclone intensity change and structure in idealized three-dimensional numerical simulations[J]. Mon. Wea. Rev., 143(10): 3981-3995. doi:10.1175/MWR-D-14-00341.1
[83]	Zhang Z , Krishnamurti T N . 1997. Ensemble forecasting of hurricane tracks[J]. Bull. Amer. Meteor. Soc., 78(12): 2785-2795. doi:10.1175/1520-0477(1997)078 <2785:EFOHT> 2.0.CO;2
[84]	Zhang Z , Krishnamurti T N . 1999. A perturbation method for hurricane ensemble predictions[J]. Mon. Wea. Rev., 127(4): 447-469. doi:10.1175/1520-0493(1999)127 <0447:APMFHE> 2.0.CO;2
[85]	郑飞, 朱江, 张荣华, 等 . 2016. 2015年超级厄尔尼诺事件的成功预报[J]. 中国科学院院刊 Zheng F, Zhu J, Zhang R H, et al . 2016. Successful prediction for the super El Ni?o Event in 2015[J]. Bulletin of Chinese Academy of Sciences, doi:10.16418/j.issn.1000-3045.2016.02.012
[86]	Zheng F , Zhu J . 2016. Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model[J]. Climate Dyn., 47(12): 3901-3915. doi:10.1007/s00382-016-3048-0
[87]	智协飞, 朱寿鹏, 孙晶, 等 . 2015. 基于BGM与ETKF的台风“苏拉”(1209)集合预报的对比试验I: 路径预报[J]. 大气科学学报, 38(6): 776-784. Zhi Xiefei, Zhu Shoupeng, Sun Jing, et al . 2015. Comparative experiments of ensemble forecasting of typhoon Saola (1209) based on BGM and ETKF, Part I: Track forecast[J]. Transactions of Atmospheric Sciences (in Chinese), 38(6): 776-784. doi:10.13878/j.cnki.dqkxxb.20150420002