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Dynamic parameter sensitivity in numerical modelling of cyclone-induced waves: a multi-look approach using advanced meta-modelling techniques

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  • J. Rohmer

    (BRGM)

  • S. Lecacheux

    (BRGM)

  • R. Pedreros

    (BRGM)

  • H. Quetelard

    (Direction Régionale de Météo-France pour l’Océan Indien)

  • F. Bonnardot

    (Direction Régionale de Météo-France pour l’Océan Indien)

  • D. Idier

    (BRGM)

Abstract

The knowledge and prediction of cyclones as well as wave models experienced significant improvements in this last decade, opening the perspective of a better understanding of the wave sensitivity to the cyclone characteristics (e.g. track angle of approach θ, forward speed V f, radius of maximum wind R m, landfall position x o, etc.). Physically, waves are strongly linked to the time-varying evolution of the relative cyclone position. Thus, even assuming the main cyclone characteristics to be stationary, exploring the role played by each of them should necessarily be conducted in a dynamic manner. This problem is investigated using the advanced statistical tools of variance-based global sensitivity analysis (VBSA) in different ways to provide an overall view of wave height sensitivity to cyclone characteristics: (1) step-by-step: by computing the time series of sensitivity measures; (2) aggregated: by summarising the time-varying information into a single sensitivity indicator; (3). mode-based: by studying the sensitivity with respect to the occurrence of specific temporal patterns (e.g. up-down translation of the overall series). Yet, applying this multi-look dynamic sensitivity analysis faces two major difficulties: (1) VBSA requires a large number of simulations (typically > 10,000), which appears to be incompatible with the large computation time cost of numerical codes (>several hours for a single run); (2) integrating the time dimension imposes to process a large amount of information via vectors of large size (e.g. series of significant wave height H S discretised over several hundreds of time steps). In this study, we propose a joint procedure combining kriging meta-modelling (to overcome the 1st issue) and principal component analysis techniques (to overcome the 2nd issue by summarising the time information into a limited number of components). The applicability of this strategy is tested and demonstrated on a real case (Sainte-Suzanne city, located at Reunion Island) using a set of 100 cyclone-induced H S series, each of them being computed for different scenarios of cyclone characteristics, i.e. using only 100 long-running simulations. The key role of R m over the whole evolution of H S is shown by means of the aggregated option, with a more specific influence in the vicinity of Sainte-Suzanne (when the cyclone eye is located less than 200 km away from the site) as highlighted by the step-by-step option. The step-by-step option also highlights the influence of the landfall position on the H S peak reached in strong interaction with θ and R m. Finally, the role of V f in the occurrence of a turning point marking a shift near landfall between regimes of low-to-high H S values is also identified. The above results provide guidelines for future research efforts on cyclone characteristics prediction.

Suggested Citation

  • J. Rohmer & S. Lecacheux & R. Pedreros & H. Quetelard & F. Bonnardot & D. Idier, 2016. "Dynamic parameter sensitivity in numerical modelling of cyclone-induced waves: a multi-look approach using advanced meta-modelling techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 1765-1792, December.
  • Handle: RePEc:spr:nathaz:v:84:y:2016:i:3:d:10.1007_s11069-016-2513-8
    DOI: 10.1007/s11069-016-2513-8
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    References listed on IDEAS

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    Cited by:

    1. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. WoongHee Jung & Aikaterini P. Kyprioti & Ehsan Adeli & Alexandros A. Taflanidis, 2023. "Exploring the sensitivity of probabilistic surge estimates to forecast errors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1371-1409, January.
    3. Aikaterini P. Kyprioti & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Madison O. Campbell, 2021. "Incorporation of sea level rise in storm surge surrogate modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 531-563, January.
    4. Jize Zhang & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Jeffrey A. Melby & Fatimata Diop, 2018. "Advances in surrogate modeling for storm surge prediction: storm selection and addressing characteristics related to climate change," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1225-1253, December.
    5. S. Lecacheux & J. Rohmer & F. Paris & R. Pedreros & H. Quetelard & F. Bonnardot, 2021. "Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 227-251, January.
    6. López-Lopera, Andrés F. & Idier, Déborah & Rohmer, Jérémy & Bachoc, François, 2022. "Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).

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