Skip to main content
SpringerLink
Log in

CNOP-based sensitive areas identification for tropical cyclone adaptive observations with PCAGA method

  • Published:
Asia-Pacific Journal of Atmospheric Sciences Aims and scope Submit manuscript

Abstract

In this paper, conditional nonlinear optimal perturbation (CNOP) was investigated to identify sensitive areas for tropical cyclone adaptive observations with principal component analysis based genetic algorithm (PCAGA) method and two tropical cyclones, Fitow (2013) and Matmo (2014), were studied with a 120 km resolution using the fifth-generation Mesoscale Model (MM5). To verify the effectiveness of PCAGA method, CNOPs were also calculated by an adjoint-based method as a benchmark for comparison on patterns, energies, and vertical distributions of temperatures. Comparing with the benchmark, the CNOPs obtained from PCAGA had similar patterns for Fitow and a little different for Matmo; the vertically integrated energies were located closer to the verification areas and the initial tropical cyclones. Experimental results also presented that the CNOPs of PCAGA had a more positive impact on the forecast improvement, which gained from the reductions of the CNOPs in the whole domain containing sensitive areas. Furthermore, the PCAGA program was executed 40 times for each case and all the averages of benefits were larger than the benchmark. This also proved the validity and stability of the PCAGA method. All results showed that the PCAGA method could approximately solve CNOP of complicated models without computing adjoint models, and obtain more benefits of reducing the CNOPs in the whole domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Buizza R, Cardinali C, Kelly G, et al., 2007: The value of observations. II: The value of observations located in singular-vector-based target areas. Quart. J. Roy. Meteor. Soc., 133, 1817–1832, doi: 10.1002/qj.149

    Google Scholar 

  • Cardinali, C., R. Buizza, G. Kelly, M. Shapiro, and J. N. Thépaut, 2007: The value of observations. III: Influence of weather regimes on targeting. Quart. J. Roy. Meteor. Soc., 133, 1833–1842, doi:10.1002/qj.148

    Google Scholar 

  • Chen, B.-Y., 2011: Observation system experiments for typhoon Nida (2004) using the CNOP method and DOTSTAR data. Atmos. Oceanic Sci. Lett., 4, 118–123, doi:10.1080/16742834.2011.11446914.

    Article  Google Scholar 

  • Chen, B.-Y., M. Mu, and X. Qin, 2013: The impact of assimilating dropwindsonde data deployed at different sites on typhoon track forecasts. Mon. Wea. Rev., 141, 2669–2682, doi:10.1175/MWR-D-12-00142.1.

    Article  Google Scholar 

  • Duan, W. S., M. Mu, and B. Wang, 2004: Conditional nonlinear optimal perturbations as the optimal precursors for El Nino-Southern Oscillation events. J. Geophys. Res., 109, D23105, doi:10.1029/2004JD004756.

    Article  Google Scholar 

  • Duan, W. S., M. Mu, 2006: Investigating decadal variability of El Nino-Southern Oscillation asymmetry by conditional nonlinear optimal perturbation. J. Geophys. Res., 111, Co7015, doi:10.1029/2005JC-003458

    Article  Google Scholar 

  • Duan, W. S., H. Xu, and M. Mu, 2008: Decisive role of nonlinear temperature advection in El Niño and La Niña amplitude asymmetry. J. Geophys. Res., 113, C1, doi:10.1029/2006JC003974.

    Google Scholar 

  • Duan, W. S., X. Feng, and M. Mu, 2009a: Investigating a nonlinear characteristic of El Niño events by conditional nonlinear optimal perturbation. Atmos. Res., 94, 10–18.

    Article  Google Scholar 

  • Duan, W. S., X. Liu, K. Zhu, and M. Mu, 2009b: Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events. J. Geophys. Res., 114, doi:10.1029/2008JC004925.

  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513.

    Article  Google Scholar 

  • Majumdar, S. J., S. D. Aberson, C. H. Bishop, R. Buizza, M. S. Peng, and C. A. Reynolds, 2006: A comparison of adaptive observing guidance for Atlantic tropical cyclones. Mon. Wea. Rev., 134, 2354–2372.

    Article  Google Scholar 

  • Mu, B., S. Wen, S. Yuan, and H. Li, 2015a: PPSO: PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation. Comput. Geosciences, 83, 65–71, doi:10.1016/j.cageo.2015.06.016.

    Article  Google Scholar 

  • Mu, B., L. Zhang, S. Yuan, and H. Li, 2015b: PCAGA: principal component analysis based genetic algorithm for solving conditional nonlinear optimal perturbation. 2015 International Joint Conference on Neural Networks (IJCNN), 1–8, doi:10.1109/IJCNN.2015.7280522.

    Google Scholar 

  • Mu, M., and W. S. Duan, 2003: A new approach to studying ENSO predictability: Conditional nonlinear optimal perturbation. Chinese Sci. Bull., 48, 747–749, doi:10.1007/BF03184224.

    Article  Google Scholar 

  • Mu, M., and Q. Zheng, 2005: Zigzag oscillations in variational data assimilation with physical “on-off” processes. Mon. Wea. Rev., 133, 2711–2720.

    Article  Google Scholar 

  • Mu, M., H. Xu, and W. S. Duan, 2007: A kind of initial errors related to “spring predictability barrier” for El Niño events in Zebiak-Cane model. Geophys. Res. Lett., 34, doi:10.1029/2006GL027412.

  • Mu, M., and W. S. Duan, 2013: Applications of conditional nonlinear optimal perturbation to the studies of predictability problems. Chinese J. Atmos. Sci., 37, 281–296 (in Chinese).

    Google Scholar 

  • Mu, M., and F. F. Zhou, 2015: The research progress of the typhoon targeted observations based on CNOP method. Adv. Meteor. Sci. Technol., 3, 6–17, doi:10.3969/j.issn.2095-1973.2015.03.001 (in Chinese with English abstract).

    Google Scholar 

  • Qin, X. H., 2010: A comparison study of the contributions of additional observations in the sensitive regions identified by CNOP and FSV to reducing forecast error variance for the typhoon morakot. Atmos. Oceanic Sci. Lett., 3, 258–262, doi:10.1080/16742834.2010.11446879

    Article  Google Scholar 

  • Qin, X. H., and M. Mu, 2012: Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts. Quart. J. Roy. Meteor. Soc., 138, 185–197, doi:10.1002/qj.902.

    Article  Google Scholar 

  • Qin, X. H., W. Duan, and M. Mu, 2013: Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations. Quart. J. Roy. Meteor. Soc., 139, 1544–1554, doi:10.1002/qj.2109

    Article  Google Scholar 

  • Qin, X. H., and M. Mu, 2014: Can adaptive observations improve tropical cyclone intensity forecasts? Adv. Atmos. Sci., 31, 252–262, doi:10.1007/s00376-013-3008-0.

    Article  Google Scholar 

  • Wen, S., S. Yuan, B. Mu, H. Li, and L. Chen, 2014: SAEP: Simulated annealing based ensemble projecting method for solving conditional nonlinear optimal perturbation. Proc. 14th International Conf., ICA3PP 2014, Vol. 8630, Dalian, China, 655–668

    Google Scholar 

  • Qin, X. H., S. Yuan, B. Mu, H. Li, and J. Ren, 2015a: PCGD: Principal components-based great deluge method for solving CNOP. 2015 IEEE Congress on Evolutionary Computation (CEC), 1513–1520, doi:10. 1109/CEC.2015.7257067

    Google Scholar 

  • Qin, X. H., S. Yuan, B. Mu, and H. Li, 2015b: Robust PCA-Based Genetic Algorithm for Solving CNOP. Proc. 11th International Conf., ICIC 2015, Vol. 9225, Fuzhou, China, 597–606, doi:10.1007/978-3-319-22180-9_59

    Google Scholar 

  • Wu, C. C., J. H. Chen, P. H. Lin, and K. H. Chou, 2007: Targeted observations of tropical cyclone movement based on the adjoint-derived sensitivity steering vector. J. Atmos. Sci., 64, 2611–2626, doi:10.1175/JAS3974.1.

    Article  Google Scholar 

  • Wu, C. C., and Coauthors, 2009: Intercomparison of targeted observation guidance for tropical cyclones in the northwestern Pacific. Mon. Wea. Rev., 137, 2471–2492, doi:10.1175/2009MWR2762.1.

    Article  Google Scholar 

  • Xu, H., 2006: Studies of Predictability Problems for Zebiak-Cane ENSO Model. Ph. D. dissertation, Institute of Atmospheric Physics, Chinese Academy of Sciences, 17–23 (in Chinese).

    Google Scholar 

  • Yamaguchi, M., T. Iriguchi, T. Nakazawa, and C. C. Wu, 2009: An observing system experiment for typhoon Conson (2004) using a singular vector method and DOTSTAR data. Mon. Wea. Rev., 137, 2801–2816.

    Article  Google Scholar 

  • Ye, F. H., L. Zhang, X. W. Gong, and Q. Zheng, 2011: Improved particle swarm optimization algorithms for conditional nonlinear optimal perturbation. J. Jiangnan Univ., 10, 468–472.

    Google Scholar 

  • Ying, M., W. Zhang, H. Yu, X. Lu, J. Feng, Y. Fan, Y. Zhu, and D. Chen, 2014: An overview of the China Meteorological Administration tropical cyclone database. J. Atmos. Oceanic Technol., 31, 287–301, doi: 10.1175/JTECH-D-12-00119.1.

    Article  Google Scholar 

  • Yuan, S., F. Ji, J. Yan, and B. Mu, 2015a: A Parallel Sensitive Area selection-based particle swarm optimization algorithm for fast solving CNOP. Proc. 22nd International Conf., ICONIP 2015, Vol. 9490, Istanbul, Turkey, 71–78, doi:10.1007/978-3-319-26535-3_9

    Google Scholar 

  • Yuan, S., J. Yan, B. Mu, and H. Li, 2015b: Parallel dynamic step size sphere-gap transferring algorithm for solving conditional nonlinear optimal perturbation. 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conference on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on, doi:10.1109/HPCC-CSS-ICESS. 2015.261.

    Google Scholar 

  • Yuan, S., Y. Qian, and B. Mu, 2015c: Paralleled continuous Tabu Search Algorithm with Sine Maps and staged strategy for solving CNOP. Proc. 2015 International Conference on Algorithms and Architectures for Parallel. Springer International Publishing, 281–294, doi:10.1007/978-3-319-27137-8_22.

    Google Scholar 

  • Yu, Y. S., W. S. Duan, H. Xu, and M. Mu, 2009: Dynamics of nonlinear error growth and season-dependent predictability of El Niño events in the Zebiak-Cane model. Quart. J. Roy. Meteor. Soc., 135, doi:10.1002/qj.526.

  • Zheng, Q., and M. Mu, 2006: The effects of the model errors generated by discretization of “on-off” processes on VDA. Nonlinear Processes in Geophysics, 13, 309–320.

    Article  Google Scholar 

  • Zheng, Q., L. Zhang, Y. Dai, and X. Lu, 2011: The feasibility of genetic algorithm for solving maximum prediction errors. Transactions Atmos. Sci., 34, 529–537 (in Chinese).

    Google Scholar 

  • Zheng, Q., J. Sha, H. Shu, and X. Lu, 2014: A variant constrained genetic algorithm for solving conditional nonlinear optimal perturbations. Adv. Atmos. Sci., 31, 219–229, doi:10.1007/s00376-013-2253-6.

    Article  Google Scholar 

  • Zhou, F., and M. Mu, 2011: The impact of verification area design on tropical cyclone targeted observations based on the CNOP method. Adv. Atmos. Sci., 28, 997–1010, doi:10.1007/s00376-011-0120-x.

    Article  Google Scholar 

  • Zhou, F., and M. Mu, 2012a: The impact of horizontal resolution on the CNOP and on its identified sensitive areas for tropical cyclone predictions. Adv. Atmos. Sci., 29, 36–46, doi:10.1007/s00376-011-1003-x.

    Article  Google Scholar 

  • Zhou, F., and M. Mu, 2012b: The time and regime dependencies of sensitive areas for tropical cyclone prediction using the CNOP method. Adv. Atmos. Sci., 29, 705–716, doi:10.1007/s00376-012-1174-0.

    Article  Google Scholar 

  • Zhou, F., and H. Zhang, 2014: Study of the schemes based on CNOP method to identify sensitive areas for typhoon targeted observations. Chinese J. Atmos. Sci., 38, 261–272, doi:10.3878/j.issn.1006-9895. 2013.13129 (in Chinese with English abstract).

    Google Scholar 

  • Zou, X., F. Vandenberghe, M. Pondeca, and Y.-H. Kuo, 1997: Introduction to adjoint techniques and the MM5 adjoint modeling system. NCAR Tech. Note, NCAR/TN-435-STR, 107 pp.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Software Engineering, Tongji Univeristy, Shanghai, China

    Lin-Lin Zhang, Shi-Jin Yuan & Bin Mu

  2. Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Fei-Fan Zhou

  3. School of Software Engineering, Tongji Univeristy, No.4800, CaoAn Road, Shanghai, China

    Shi-Jin Yuan

Authors
  1. Lin-Lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shi-Jin Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei-Fan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shi-Jin Yuan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, LL., Yuan, SJ., Mu, B. et al. CNOP-based sensitive areas identification for tropical cyclone adaptive observations with PCAGA method. Asia-Pacific J Atmos Sci 53, 63–73 (2017). https://doi.org/10.1007/s13143-017-0005-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13143-017-0005-8

Key words

Search

Navigation