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Identifying Typhoon Targeted Observations Sensitive Areas Using the Gradient Definition Based Method

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Abstract

Increasing additional observation in the typhoon targeted observation sensitive area can help providing more accurate initial field for numerical models, further to improve the typhoon forecast skill. The critical problem is how to identify the typhoon targeted observation sensitive area. Conditional nonlinear optimal perturbation (CNOP) has been proved to be an effective method. Generally, the CNOP is solved using adjoint-based method, which needs to utilize the adjoint models of the numerical models. However, the adjoint models for some numerical models have not been developed or only for some modules. The gradient definition based method is an adjoint-free method, which has been applied to solve the CNOP of Zebiak-Cane (ZC) model with 1080-dimensional solution space to study the optimal precursors of El Nino-Southern Oscillation (ENSO) event. It is very easy to realize, but the time efficiency will go down dramatically along with the rapidly increasing dimensions. In this paper, the gradient definition based method is applied to solve the CNOP of MM5 model with more than 105-dimensional solution space to identify the typhoon targeted observation sensitive area. Compared to the adjoint-based method, the identified sensitive area and the benefits of the CNOPs are very similar for typhoon Matmo (2014) and Fitow (2013), and higher time efficiency can be achieved. Furthermore, the OSSEs’ results show that the sensitive area identified can be used to improve the forecast skill of typhoon Matmo and Fitow to different extent.

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References

  • Aberson, S.D.: Targeted observations to improve operational tropical cyclone track forecast guidance. Mon. Weather Rev. 131, 1613–1628 (2003)

    Article  Google Scholar 

  • Ancell, B.C., Mass, C.F.: Structure, growth rates, and tangent linear accuracy of Adjoint sensitivities with respect to horizontal and vertical resolution. Mon. Weather Rev. 134, 2971–2988 (2006)

    Article  Google Scholar 

  • Bishop, C.H., Etherton, B.J., Majumdar, S.J.: Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Weather Rev. 129, 420–436 (2001)

    Article  Google Scholar 

  • Buizza, R., Cardinali, C., Kelly, G., Thépaut, J.N.: The value of observations. II: the value of observations located in singular-vector-based target areas. Q. J. R. Meteorol. Soc. 133, 1817–1832 (2007)

    Article  Google Scholar 

  • Hamill, T.M., Snyder, C.: Using improved background-error Covariances from an ensemble Kalman filter for adaptive observations. Mon. Weather Rev. 130(6), 1552–1572 (2002)

    Article  Google Scholar 

  • Langland, R.H.: Issues in targeted observing. Q. J. R. Meteorol. Soc. 131, 3409–3425 (2005)

    Article  Google Scholar 

  • Mu, M., Duan, W.: A new approach to studying ENSO predictability: conditional nonlinear optimal perturbation. Chin. Sci. Bull. 48, 1045–1047 (2003)

    Article  Google Scholar 

  • Mu, M., Wang, H.L., Zhou, F.-F.: A preliminary Application of conditional nonlinear optimal perturbation to adaptive observation. Chin. J. Atmos. Sci. 31, 1102–1112 (2007)

    Google Scholar 

  • Mu, B., Wen, S., Yuan, S., Li, H.: PPSO: PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation. Comput. Geosci. 83, 65–71 (2015a)

    Article  Google Scholar 

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

  • Mu, B., Ren, J., Yuan, S.J.: An efficient approach based on the gradient definition for solving conditional nonlinear optimal perturbation. Math. Probl. Eng. 2017, 1–10 (2017)

    Google Scholar 

  • Oosterwijk, A., Dijkstra, H.A., Leeuwen, T.V.: An Adjoint-free method to determine conditional nonlinear optimal perturbations. Comput. Geosci. 106, (2017)

  • Palmer, T.N., Gelaro, R., Barkmeijer, J., Buizza, R.: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci. 55, 633–653 (1998)

    Article  Google Scholar 

  • Qin, X.H.: The sensitive regions identified by CNOPs of three typhoon events. Atmos. Ocean. Sci. Lett. 03, 170–175 (2010)

    Article  Google Scholar 

  • Qin, X., Mu, M.: Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts. Q. J. R. Meteorol. Soc. 138, 185–197 (2012)

    Article  Google Scholar 

  • Qin, X., Duan, W., Mu, M.: Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations. Q. J. R. Meteorol. Soc. 139, 1544–1554 (2013)

    Article  Google Scholar 

  • Rabier, F., Gauthier, P., Cardinali, C., Langland, R., Tsyrulnikov, M., Lorenc, A., Steinle, P., Gelaro, R., Koizumi, K.: An update on THORPEX-related research in data assimilation and observing strategies. Nonlinear Process. Geophys. 15, 81–94 (2008)

    Article  Google Scholar 

  • Ren, J., Yuan, S., and Mu, B.: Parallel modified artificial bee colony algorithm for solving conditional nonlinear optimal perturbation. IEEE International Conference on High-Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems, 333–340 (2016)

  • Szunyogh, I., Toth, Z., Morss, R.E., Majumdar, S.J., Etherton, B.J., Bishop, C.H.: The effect of targeted Dropsonde observations during the 1999 winter storm reconnaissance program. Mon. Weather Rev. 128(10), 3520–3537 (2000)

    Article  Google Scholar 

  • Szunyogh, I., Toth, Z., Zimin, A.V., Majumdar, S.J., Persson, A.: Propagation of the effect of targeted observations: the 2000 winter storm reconnaissance program. Mon. Weather Rev. 130(5), 1144–1165 (2002)

    Article  Google Scholar 

  • Tan, X., Wang, B., Wang, D.: Impact of different Guidances on sensitive areas of targeting observations based on the CNOP method. J. Meteorol. Res. 24, 17–30 (2010)

    Google Scholar 

  • Wang, X., Zhou, F., Zhu, K.: The application of conditional nonlinear optimal perturbation to the binary typhoons interaction —FENGSHEN and FUNG-WONG. J. Trop. Meteorol. 20, 314–322 (2014)

    Google Scholar 

  • Wen, S., Yuan, S., Mu, B., and Li, H.: Robust PCA-based genetic algorithm for solving CNOP. International Conference on Intelligent Computing. Springer International Publishing, 597–606 (2015a)

  • Wen, S., Yuan, S., Mu, B., Li, H., and Ren, J.: PCGD: principal components-based great deluge method for solving CNOP. IEEE Congress on Evolutionary Computation, 1513–1520 (2015b)

  • Wu, C.C., Lin, P.H., Aberson, S., Yeh, T.C., Huang, W.P., Chou, K.H., Hong, J.S., Lu, G.C., Fong, C.T., Hsu, K.C.: Dropwindsonde observations for typhoon surveillance near the Taiwan region (DOTSTAR): an overview. Bull. Am. Meteorol. Soc. 86, 787–790 (2005)

    Article  Google Scholar 

  • Wu, C.C., Chen, J.H., Lin, P.H., Chou, K.H.: Targeted observations of tropical cyclone movement based on the Adjoint-derived sensitivity steering vector. J. Atmos. Sci. 64, 2611–2626 (2007)

    Article  Google Scholar 

  • Wu, C.C., Chen, J.H., Majumdar, S.J., Peng, M.S., Reynolds, C.A., Aberson, S.D., Buizza, R., Yamaguchi, M., Chen, S.G., Nakazawa, T.: Intercomparison of targeted observation guidance for tropical cyclones in the northwestern Pacific. Mon. Weather Rev. 137, 2471–2492 (2009)

    Article  Google Scholar 

  • Yuan, S., Qian, Y., and Mu, B.: Paralleled continuous Tabu search algorithm with sine maps and staged strategy for solving CNOP. International Conference on Algorithms and Architectures for Parallel Processing. Springer International Publishing, 281–294 (2015a)

  • Yuan, S., Yan, J., Mu, B., and Li, H.: Parallel dynamic step size sphere-gap transferring algorithm for solving conditional nonlinear optimal perturbation. IEEE International Conference on High Performance Computing and Communications, 559–565 (2015b)

  • Yuan, S., Li, M., Mu, B., and Wang, J.: PCAFP for solving CNOP in double-gyre variation and its parallelization on clusters. IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems, 284–291 (2016)

  • Zhang, L.L., Yuan, S.J., Mu, B., Zhou, F.F.: CNOP-based sensitive areas identification for tropical cyclone adaptive observations with PCAGA method. Asia-Pac. J. Atmos. Sci. 53, 63–73 (2017)

    Article  Google Scholar 

  • Zhou, F., Mu, M.: The impact of verification area design on tropical cyclone targeted observations based on the CNOP method. Adv. Atmos. Sci. 28, 997–1010 (2011)

    Article  Google Scholar 

  • Zhou, F., Zhang, H.: Study of the Schemes Based on CNOP Method to Identify Sensitive Areas for Typhoon Targeted Observations. Chin. J. Atmos. Sci. (in Chinese). 38, 261–272 (2014)

    Google Scholar 

  • Zhou, F., Qin, X., Chen, B., and Mu, M.: The advances in targeted observations for tropical cyclone prediction based on Conditional Nonlinear Optimal Perturbation (CNOP) method. Data Assimilation for Atmospheric Oceanic and Hydrologic Applications. II, 577–607 (2013).

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Acknowledgements

This work was supported by the National Natural Science Fund of China (grant number 41405097).

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Correspondence to Shijin Yuan.

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Responsible editor: Ben Jong-Dao Jou

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Mu, B., Ren, J., Yuan, S. et al. Identifying Typhoon Targeted Observations Sensitive Areas Using the Gradient Definition Based Method. Asia-Pacific J Atmos Sci 55, 195–207 (2019). https://doi.org/10.1007/s13143-018-0068-1

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