A MULTIMODEL ENSEMBLE METHOD FOR NINO3.4 INDEX FORECAST
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摘要: 基于贝叶斯模式平均方法(Bayesian Model Averaging)，发展了一个NINO3.4指数的多模式客观权重集合预报方法(简称OBJ)。该方法基于训练期内单个模式的预报结果，用线性回归订正单个预报的偏差，依据模式的预报效果估计单个模式的权重。利用2002年2月—2015年10月美国哥伦比亚大学国际气候与社会研究所(IRI)提供的7个单一模式对NINO3.4指数的预报结果进行OBJ试验，并采用均方根误差对多模式集合平均预报(简称ENS)和OBJ的预报结果进行检验和评估。结果表明，ENS的预报效果优于7个单一模式的预报效果，而OBJ预报效果优于ENS预报效果，其NINO3.4指数的均方根误差比ENS方法降低了4%。将单一模式预报结果按时间划分为训练期和预报期，利用独立样本估计OBJ的参数并进行预报试验，这些试验也表明，OBJ能进一步提高预报精度。Abstract: Based on Bayesian Model Averaging method, we developed an Objective Weighting Ensemble Method (OBJ) for NINO3.4, which is based on the prediction skill of individual models over a training period. In the OBJ method, the weights for individual models are estimated based on their past performances. Potential biases of individual models are corrected by linear regression of the individual model predictions with corresponding observations. In the conventional multimodel ensemble mean method (ENS), equal weights were used for individual models. Using predictions of NINO3.4 index of seven models from February 2002 to October 2015 for a total of 165 months, the weights of each model used in OBJ were estimated, then the skills of ENS and OBJ were compared using root mean square error. The results show that OBJ is better than ENS. The root mean square error of NINO3.4 index forecast decreases by 4% when OBJ is used, compared with that when ENS is used. Lastly, dividing the 165 months into two groups, the weights of OBJ were estimated using data in one group and hindcasts were verified using data in the other group. These results also confirm that the OBJ method is slightly more accurate than the ENS method.
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表 1 本文所用到的NINO3.4预报模式的名称和简单介绍
动力-统计混合模式 简介 Scripps Hybrid Coupled Model (HCM) 热带太平洋环流模式与统计大气模式所建立的混合型海气耦合模式 统计模式 简介 NOAA/NCEP/CPC Markov 利用随季节变化的海表温度异常、海平面高度、风应力异常和经验正交函数构造的统计预报模式 NOAA/ESRL Linear Inverse Model (LIM) 利用多元场同时和滞后的协方差构造的统计模式 NOAA/NCEP/CPC Constructed Analogue (CA) 利用观测资料初始条件的相似程度构造的统计模式 NOAA/NCEP/CPC Canonical Correlation Analysis (CCA) 利用变量和预报量之间的多元回归关系的预报模式，使用的变量有海平面气压、20 ℃等温线深度、海表面温度异常等 NOAA/AOML CLIPER 热带太平洋表层海温的逐步多元回归 UBC Neural Network (NN) 利用神经元方法构造的海平面气压和热带太平洋表层海温的统计模式 
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[1] 李天然, 张人禾, 温敏. ENSO对中国冬半年降水影响的不对称性及机制分析[J].热带气象学报, 2017, 33(1):1-10. [2] 汪子琪, 张文君, 耿新.两类ENSO对中国北方冬季平均气温和极端低温的不同影响[J].气象学报, 2017, 75(4): 564-580. [3] 李慧敏, 徐海明, 李智玉.厄尔尼诺年西北太平洋异常反气旋的年际变化特征及其影响[J].气象学报, 2017, 75(4): 581-595. [4] EPSTEIN E S. Stochastic dynamic prediction[J]. Tellus, 1969, 21(6):739-759. [5] LEITH C E. Theoretical skill of monte carlo forecasts[J]. Mon Wea Rev, 1974, 102(6):409-418. [6] FRAEDRICH K, SMITH N R. Combining predictive schemes in long-range forecasting[J]. J Clim, 1989, 2(3): 291-294. [7] KRISHNAMURTI T N, KISHTAWAL C M, LAROW T E, et al. Improved weather and seasonal climate forecasts from multimodel Superensemble[J]. Science, 1999, 258(5 433): 1 548-1 550. [8] 智协飞, 季晓东, 张璟, 等.基于TIGGE资料的地面气温和降水的多模式集成预报[J].大气科学学报, 2013, 36(3): 257-266. [9] ZHI X F, QI H X, BAI Y Q, et al. A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data[J]. Acta Meteorologica Sinica, 2012, 26(1):41-51. [10] 张涵斌, 智协飞, 王亚男, 等.基于TIGGE资料的西太平洋热带气旋多模式集成预报方法比较[J].气象, 2015, 41(9): 1 058-1 067. [11] 智协飞, 赵欢, 朱寿鹏, 等.基于CMIP5多模式回报资料的地面气温超级集合研究[J].大气科学学报, 2016, 39(1): 64-71. [12] SLOUGHTER J M, RAFTERY A E, GNEITING T, et al. Probabilistic quantitative precipitation forecasting using bayesian model averaging[J]. Mon Wea Rev, 2007, 135(9): 3 209-3 220. [13] WILSON L J, BEAUREGARD S, RAFTERY A E, et al. Calibrated surface temperature forecasts from the Canadian ensemble prediction system using bayesian model averaging[J]. Mon Wea Rev, 2007, 135(4): 4 226-4 230. [14] 刘建国, 谢正辉, 赵琳娜, 等.基于TIGGE多模式集合的24小时气温BMA概率预报[J].大气科学, 2013, 37(1): 43-53. [15] RAFTERY A E, GNEITING T, BALADAOI F, et al. Using bayesian model averaing to calibrate forecast ensembles[J]. Mon Wea Rev, 2005, 133(5): 1 155-1 174. [16] WANG X, CHAO Y, THOMPSON D R, et al. Multi-model ensemble forecasting and glider path planning in the Mid-Atlantic Bight[J]. Continental Shelf Res, 2013, 63(4): 223-234, DOI: http://dx.doi.org/10.1016/j.csr.2012.07.006. [17] 智协飞, 王晶, 林春泽, 等. CMIP5多模式资料中气温的BMA预测方法研究[J].气象科学, 2015, 35(4): 405-412. [18] GODDARD L, BAETHGEN W E, BHOJWANI H, et al. The international research institute for climate and society: why, what and how[J]. Earth Perspectives, 2014, 1(1): 10-23. [19] ZHENG Z, HU Z Z, L'HEUREUX M. Predictable components of ENSO evolution in real-time multi-model predictions[J]. Sci Rep, 2016, 6(1): 35909. DOI: 10.1038/srep35909. [20] BARNSTON A G, TIPPETT M K, L'HEUREUX M L, et al. Skill of real-time seasonal ENSO model predictions during 2002-2011 — Is our capability increasing?[J]. Bull Amer Meteor Soc, 2012, 93(5): 631-651. [21] HUANG B, BANZON V F, FREEMAN E, et al. Extended reconstructed sea surface temperature Version 4 (ERSST v4), Part Ⅰ: upgrade and intercomparisons[J]. J Clim, 2015, 28(3): 911-930. [22] RICHARD W R, RAYNER N A, SMITH T M, et al. An improved in situ and satellite SST analysis for climate[J]. J Clim, 2002, 15(13): 1 609-1 625. [23] 谷德军, 纪忠萍, 林爱兰.影响南海夏季风爆发年际变化的关键海区及机制初探[J].热带气象学报, 2018, 34(1):1-11. [24] 李忠贤, 范倩莹, 曾刚, 等.盛夏南海低空越赤道气流变化与东亚夏季风的联系[J].热带气象学报, 2018, 34(3): 339-346. [25] 李春晖, 吴志伟, 蒙伟光, 等.影响华南后汛期季风持续性暴雨和热带气旋持续性暴雨的大尺度环流背景分析[J].热带气象学报, 2017, 33(1):11-20. [26] 许琪, 管兆勇.海洋性大陆核心区域非绝热加热年代际变化及其与东亚夏季风变异的可能联系[J].热带气象学报, 2017, 33(1): 21-29. -
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