• Cao, H.-X., 1993: Self-memorization equation in atmospheric motion. Science China, 36B, 845855.

  • Central Meteorological Station Long-Term Forecasting Group, 1976: The technology experience of the long-term weather forecast (appendix) (in Chinese). Central Meteorological Observatory, Beijing, China, 233 pp.

  • Chang, C.-P., , Y. Zhang, , and T. Li, 2000a: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part I: Role of the subtropical ridge. J. Climate, 13, 43104325, doi:10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chang, C.-P., , Y. Zhang, , and T. Li, 2000b: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part II: Meridional structure of the monsoon. J. Climate, 13, 43264340, doi:10.1175/1520-0442(2000)013<4326:IAIVOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, X., , J. Xia, , and Q. Xu, 2009: Differential Hydrological Grey Model (DHGM) with self-memory function and its application to flood forecasting. Sci. China Ser. E: Technol. Sci., 52, 10391049, doi:10.1007/s11431-008-0320-5.

    • Search Google Scholar
    • Export Citation
  • David, H. A., , and J. L. Gunnink, 1997: The paired t test under artificial pairing. Amer. Stat., 51, 912, doi:10.1080/00031305.1997.10473578.

    • Search Google Scholar
    • Export Citation
  • Feng, G., , H. Cao, , X. Gao, , D. Wenjicet, , and J. Chou, 2001: Prediction of precipitation during summer monsoon with self-memorial model. Adv. Atmos. Sci., 18, 701709.

    • Search Google Scholar
    • Export Citation
  • Firdaus, E. U., , and H. F. von Bremen, 2002: Computation of Lyapunov characteristic exponents for continuous dynamical systems. Z. Angew. Math. Phys., 53, 123146, doi:10.1007/s00033-002-8146-7.

    • Search Google Scholar
    • Export Citation
  • Fu, J. L., 2012: The performance verification of the medium-range forecasting for T639, ECMWF and JAPAN models from September to November 2011 (in Chinese). Meteor. Mon., 38, 238243.

    • Search Google Scholar
    • Export Citation
  • Goldberg, D. E., , B. Korb, , and K. Deb, 1989: Messy genetic algorithms: Motivation, analysis, and first results. Complex Syst., 5, 493530.

    • Search Google Scholar
    • Export Citation
  • Grinsted, J., , J. C. Moore, , and S. Jevrajeva, 2004: Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes Geophys., 11, 561566, doi:10.5194/npg-11-561-2004.

    • Search Google Scholar
    • Export Citation
  • Gu, X., 1998: A spectral model based on atmospheric self-memorization principle. Chin. Sci. Bull., 43, 16921702, doi:10.1007/BF02883967.

    • Search Google Scholar
    • Export Citation
  • He, X.-Z., , and D.-Y. Gong, 2002: Interdecadal change in western Pacific subtropical high and climatic effects. J. Geogr. Sci., 12, 202209, doi:10.1007/BF02837475.

    • Search Google Scholar
    • Export Citation
  • Hong, M., , R. Zhang, , J. X. Li, , and K. F. Liu, 2013a: Inversion of the western Pacific subtropical high dynamic model and analysis of dynamic characteristics for its abnormality. Nonlinear Processes Geophys., 20, 131142, doi:10.5194/npg-20-131-2013.

    • Search Google Scholar
    • Export Citation
  • Hong, M., , R. Zhang, , and K. F. Liu, 2013b: Retrieving dynamic forecast model of the western Pacific subtropical high in abnormal years based on GA (in Chinese). Wuli Xuebao, 62, 113.

    • Search Google Scholar
    • Export Citation
  • Hong, M., , D. Wang, , R. Zhang, , X. Chen, , J.-J. Ge, , and D. Yu, 2015: Reconstruction and forecast experiments of a statistical–dynamical model of the western Pacific subtropical high and East Asian summer monsoon factors. Wea. Forecasting, 30, 206216, doi:10.1175/WAF-D-14-00048.1.

    • Search Google Scholar
    • Export Citation
  • Hu, T. S., , K. C. Lam, , and S. T. Ng, 2001: River flow time series prediction with a range-dependent neural network. Hydrol. Sci. J., 46, 729745, doi:10.1080/02626660109492867.

    • Search Google Scholar
    • Export Citation
  • Huang, J. P., , and Y. H. Yi, 1991: A nonlinear dynamic system reconstruction of actual data (in Chinese). Sci. China, 3, 331336.

  • Jiang, X., , and X. N. Cai, 2011: The performance verification of the medium-range forecasting for T639, ECMWF and JAPAN models from June to August 2011 (in Chinese). Meteor. Mon., 37, 14481452.

    • Search Google Scholar
    • Export Citation
  • Kurihara, K., 1989: A climatological study on the relationship between the Japanese summer weather and the subtropical high in the western northern Pacific. Geophys. Mag., 43, 45104.

    • Search Google Scholar
    • Export Citation
  • Liu, K. F., , R. Zhang, , P. Yu, , Y.-L. Wang, , and D.-D. Yu, 2007: Area exponent of western Pacific subtropical high forecast model based on wavelet decomposition support vector machine (in Chinese). J. Trop. Meteor., 23, 491496.

    • Search Google Scholar
    • Export Citation
  • Liu, K. F., , R. Zhang, , M. Hong, , D.-D. Yu, , and Y.-L. Wang, 2008: RBFNN based on hybrid hierarchy genetic algorithm and its application in subtropical high forecast (in Chinese). J. Trop. Meteor., 24, 507511.

    • Search Google Scholar
    • Export Citation
  • Liu, M., , R. Hang, , and B. Zhang, 2013: Influence of subtropical high’s variation period and structure on plum rain onset (in Chinese). J. Meteor. Sci., 33, 430435.

    • Search Google Scholar
    • Export Citation
  • Lu, R., 2001: Interannual variability of the summertime North Pacific subtropical high and its relation to atmospheric convection over the warm pool. J. Meteor. Soc. Japan, 79, 771783, doi:10.2151/jmsj.79.771.

    • Search Google Scholar
    • Export Citation
  • Lu, R., , and B. W. Dong, 2001: Westward extension of North Pacific subtropical high in summer. J. Meteor. Soc. Japan, 79, 12291241, doi:10.2151/jmsj.79.1229.

    • Search Google Scholar
    • Export Citation
  • Lu, R., , H. Ding, , C.-S. Ryu, , Z. Lin, , and H. Dong, 2007: Mid-latitude westward propagating disturbances preceding intraseasonal oscillations of convection over the subtropical western North Pacific during summer. Geophys. Res. Lett., 34, L21702, doi:10.1029/2007GL031277.

    • Search Google Scholar
    • Export Citation
  • Miyasaka, T., , and H. Nakamura, 2005: Structure and formation mechanisms of the Northern Hemisphere summertime subtropical highs. J. Climate, 18, 50465065, doi:10.1175/JCLI3599.1.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., and et al. , 2004: Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER). Bull. Amer. Meteor. Soc., 85, 853872, doi:10.1175/BAMS-85-6-853.

    • Search Google Scholar
    • Export Citation
  • Park, J.-Y., , J.-G. Jhun, , S.-Y. Yim, , and W.-M. Kim, 2010: Decadal changes in two types of the western North Pacific subtropical high in boreal summer associated with Asian summer monsoon/El Niño–Southern Oscillation connections. J. Geophys. Res., 115, D21129, doi:10.1029/2009JD013642.

    • Search Google Scholar
    • Export Citation
  • Qi, L., , Z.-Q. Zhang, , J.-H. He, , and H. Chen, 2008: A probe into the maintaining mechanism of one type of the double-ridges processes of West Pacific subtropical high (in Chinese). Chin. J. Geophys., 51, 495504, doi:10.1002/cjg2.1240.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., , P.-H. Chung, , and T. Li, 2007: Interannual and interdecadal variability of the summertime western North Pacific subtropical high. Geophys. Res. Lett., 34, L11701, doi:10.1029/2006GL029204.

    • Search Google Scholar
    • Export Citation
  • Takens, F., 1981: Detecting strange attractors in fluid turbulence. Detecting Strange Attractors in Fluid Turbulence: Proceedings of a Symposium Held at the University of Warwick 1979/80, D. Rand and L.-S. Yang, Eds., Lecture Notes in Mathematics, Vol. 898, Springer, 361–381.

  • Wang, H. Z., , R. Zhang, , Y. L. Wang, , and K.-F. Liu, 2006: Errors revised for numerical forecast products of subtropical high based on Kalman filtering (in Chinese). J. Trop. Meteor., 22, 661666.

    • Search Google Scholar
    • Export Citation
  • Wang, W., , J. Y. Su, , B. W. Hou, , J. Tian, , and D. H. Ma, 2012: Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model. Chin. Sci. Bull., 57, 34303435, doi:10.1007/s11434-012-5386-6.

    • Search Google Scholar
    • Export Citation
  • Wang, W.-C., , K.-W. Chau, , C.-T. Cheng, , and L. Qiu, 2009: A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol., 374, 294306, doi:10.1016/j.jhydrol.2009.06.019.

    • Search Google Scholar
    • Export Citation
  • Wang, X. R., , Y. Q. Yao, , Y. Shang, , X. P. Chen, , X. Q. Cheng, , and A. M. Shuai, 2002: The error analyze of medium-term numerical forecast for subtropical high in 1998. (in Chinese). J. Trop. Meteor., 18, 351358.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., , Q. Wang, , Y. Chen, , and M. Wu, 2007: Application of subtropical high index from ECMWF model (in Chinese). J. Meteor. Environ., 23, 2631.

    • Search Google Scholar
    • Export Citation
  • Wu, M. L., , H. Liang, , Y. Wang, , and Y.-M. Shen, 2009: Contrast tests of precipitation products between T213 and Germany numerical prediction in 2008 (in Chinese). J. Meteor. Environ., 25, 412.

    • Search Google Scholar
    • Export Citation
  • Xu, H. B., , R. Zhang, , and K. F. Liu, 2007: Numerical forecast products optimization of the West-Pacific subtropical high based on the wavelet decomposition and SOFM-BP artificial neural networks (in Chinese). J. Meteor. Environ., 23, 265270.

    • Search Google Scholar
    • Export Citation
  • Xue, F., , H. J. Wang, , and J. H. He, 2003: The influence of Mask Lin high and Australia high interannual variation to the East Asian summer monsoon precipitation (in Chinese). Chin. Sci. Bull., 3, 1927.

    • Search Google Scholar
    • Export Citation
  • Yang, J., , G. L. Feng, , J. H. Zhao, , and Z. H. A. Zhen, 2012: A study of objective and quantitative forecasting the western Pacific subtropical high and its indication for precipitation in summer over China (in Chinese). Acta Meteor. Sin., 70, 10321044.

    • Search Google Scholar
    • Export Citation
  • Yeh, S. W., , J.-S. Kug, , B. Dewitte, , M.-H. Kwon, , B. P. Kirtman, , and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511514, doi:10.1038/nature08316.

    • Search Google Scholar
    • Export Citation
  • Yu, D.-D., , R. Zhang, , M. Hong, , T.-Z. Min, , and P.-W. Guo, 2007: A characteristic correlation analysis between the Asia summer monsoon memberships and west Pacific subtropical high (in Chinese). J. Trop. Meteor., 1, 5867.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., , M. Hong, , Z.-B. Sun, , S.-J. Niu, , W.-J. Zhu, , J.-Z. Min, , and Q.-L. Wan, 2006: Non-linear dynamic model retrieval of subtropical high based on empirical orthogonal function and genetic algorithm. Appl. Math. Mech., 27, 16451654, doi:10.1007/s10483-006-1207-z.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y. N., , and J. Y. Zhang, 2011: The performance verification of the medium-range forecasting for T639, ECMWF and JAPAN models from December 2010 to February 2011 (in Chinese). Meteor. Mon., 37, 633638.

    • Search Google Scholar
    • Export Citation
  • Zhou, T. J., and et al. , 2009: Why the western Pacific subtropical high has extended westward since the late 1970s. J. Climate, 22, 21992214, doi:10.1175/2008JCLI2527.1.

    • Search Google Scholar
    • Export Citation
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A Dynamical–Statistical Forecasting Model of the Western Pacific Subtropical High Area Index Based on an Improved Self-Memorization Principle

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  • 1 Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China
  • | 2 Key Laboratory of Surficial Geochemistry, Ministry of Education, and Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China
  • | 3 Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China
  • | 4 Department of Biological and Agricultural Engineering, and Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas
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Abstract

A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.

Corresponding author address: Mei Hong, Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China. E-mail: flowerrainhm@126.com

Abstract

A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.

Corresponding author address: Mei Hong, Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China. E-mail: flowerrainhm@126.com
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