Statistical evaluation of extreme ice loads

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientific

    Abstract

    The problem of how to estimate the probability of extreme icing events by historical data is crucial in the optimal design of structures in cold regions. For performing the extreme value analysis, numerous different methods are widely used, and several software packages available. There is no consensus on which method should be preferred. Furthermore, there are different criteria in use for the goodness of a method that estimates the cumulative distribution function and the extremes.By using the probabilistically correct goodness criterion, we present a method that provides estimates for the extremes that are considerably better than obtained by the conventional methods. This new method does not require subjective decisions by the user, and is particularly useful for small data sets, such as icing events observed over a short time-period.
    Original languageEnglish
    Title of host publicationProceedings of IWAIS 2019
    Number of pages5
    Publication statusPublished - 2019
    MoE publication typeB3 Non-refereed article in conference proceedings
    EventThe XVIII International Workshop on Atmospheric Icing of Structures, IWAIS 2019 - Reykjavik, Iceland
    Duration: 23 Jun 201928 Jun 2019
    Conference number: 18
    https://iwais2019.is/

    Workshop

    WorkshopThe XVIII International Workshop on Atmospheric Icing of Structures, IWAIS 2019
    Abbreviated titleIWAIS
    CountryIceland
    CityReykjavik
    Period23/06/1928/06/19
    Internet address

    Fingerprint

    ice
    cold region
    extreme event
    evaluation
    method
    software

    Keywords

    • extremes
    • extreme value analysis
    • return period
    • ice load
    • icing

    Cite this

    @inproceedings{10dd1ad96a2d46099e73a4ec2c69cba1,
    title = "Statistical evaluation of extreme ice loads",
    abstract = "The problem of how to estimate the probability of extreme icing events by historical data is crucial in the optimal design of structures in cold regions. For performing the extreme value analysis, numerous different methods are widely used, and several software packages available. There is no consensus on which method should be preferred. Furthermore, there are different criteria in use for the goodness of a method that estimates the cumulative distribution function and the extremes.By using the probabilistically correct goodness criterion, we present a method that provides estimates for the extremes that are considerably better than obtained by the conventional methods. This new method does not require subjective decisions by the user, and is particularly useful for small data sets, such as icing events observed over a short time-period.",
    keywords = "extremes, extreme value analysis, return period, ice load, icing",
    author = "Lasse Makkonen and Maria Tikanm{\"a}ki",
    year = "2019",
    language = "English",
    booktitle = "Proceedings of IWAIS 2019",

    }

    Makkonen, L & Tikanmäki, M 2019, Statistical evaluation of extreme ice loads. in Proceedings of IWAIS 2019 ., 5, The XVIII International Workshop on Atmospheric Icing of Structures, IWAIS 2019, Reykjavik, Iceland, 23/06/19.

    Statistical evaluation of extreme ice loads. / Makkonen, Lasse; Tikanmäki, Maria.

    Proceedings of IWAIS 2019 . 2019. 5.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientific

    TY - GEN

    T1 - Statistical evaluation of extreme ice loads

    AU - Makkonen, Lasse

    AU - Tikanmäki, Maria

    PY - 2019

    Y1 - 2019

    N2 - The problem of how to estimate the probability of extreme icing events by historical data is crucial in the optimal design of structures in cold regions. For performing the extreme value analysis, numerous different methods are widely used, and several software packages available. There is no consensus on which method should be preferred. Furthermore, there are different criteria in use for the goodness of a method that estimates the cumulative distribution function and the extremes.By using the probabilistically correct goodness criterion, we present a method that provides estimates for the extremes that are considerably better than obtained by the conventional methods. This new method does not require subjective decisions by the user, and is particularly useful for small data sets, such as icing events observed over a short time-period.

    AB - The problem of how to estimate the probability of extreme icing events by historical data is crucial in the optimal design of structures in cold regions. For performing the extreme value analysis, numerous different methods are widely used, and several software packages available. There is no consensus on which method should be preferred. Furthermore, there are different criteria in use for the goodness of a method that estimates the cumulative distribution function and the extremes.By using the probabilistically correct goodness criterion, we present a method that provides estimates for the extremes that are considerably better than obtained by the conventional methods. This new method does not require subjective decisions by the user, and is particularly useful for small data sets, such as icing events observed over a short time-period.

    KW - extremes

    KW - extreme value analysis

    KW - return period

    KW - ice load

    KW - icing

    M3 - Conference article in proceedings

    BT - Proceedings of IWAIS 2019

    ER -