TY - JOUR

T1 - Numerical Stochastic Model of Non-stationary Time Series of the Wind Chill Index

AU - Kargapolova, Nina

N1 - Funding Information: 65C05 65C20 86A10 Russian Foundation for Basic Research http://dx.doi.org/10.13039/501100002261 grant No 18-01-00149-a Russian Foundation for Basic Research and Goverment of the Novosibirsk region 19-41-543001-r_mol_a publisher-imprint-name Springer article-contains-esm No article-numbering-style ContentOnly article-registration-date-year 2020 article-registration-date-month 2 article-registration-date-day 13 article-toc-levels 0 journal-product ArchiveJournal numbering-style ContentOnly article-grants-type Regular metadata-grant OpenAccess abstract-grant OpenAccess bodypdf-grant Restricted bodyhtml-grant Restricted bibliography-grant Restricted esm-grant OpenAccess online-first true pdf-file-reference BodyRef/PDF/11009_2020_Article_9778.pdf pdf-type Typeset target-type OnlinePDF article-type OriginalPaper journal-subject-primary Statistics journal-subject-secondary Statistics, general journal-subject-secondary Life Sciences, general journal-subject-secondary Electrical Engineering journal-subject-secondary Economics, general journal-subject-secondary Business and Management, general journal-subject-collection Mathematics and Statistics open-access false Funding Information: This work was partly financially supported by the Russian Foundation for Basic Research (grant No 18-01-00149-a), by the Russian Foundation for Basic Research and the government of the Novosibirsk region according to the research project No 19-41-543001-r_mol_a. Publisher Copyright: © 2020, Springer Science+Business Media, LLC, part of Springer Nature. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/3

Y1 - 2021/3

N2 - A numerical stochastic model of the high-resolution time series of the wind chill index is considered. The model is constructed under the assumption that time series of the wind chill index are non-stationary non-Gaussian random processes with time-dependent one-dimensional distributions. This assumption makes possible to take into account both daily and seasonal variations of real meteorological processes. Data of the long-term real observations at weather stations were used for estimating the model parameters and for the verification of the model. Based on the simulated trajectories, some statistical properties of rare and adverse weather events, like long periods of time with a low wind chill index, are studied. The model is also used to study the dependence of the statistical properties of the wind chill index time series on a climate change.

AB - A numerical stochastic model of the high-resolution time series of the wind chill index is considered. The model is constructed under the assumption that time series of the wind chill index are non-stationary non-Gaussian random processes with time-dependent one-dimensional distributions. This assumption makes possible to take into account both daily and seasonal variations of real meteorological processes. Data of the long-term real observations at weather stations were used for estimating the model parameters and for the verification of the model. Based on the simulated trajectories, some statistical properties of rare and adverse weather events, like long periods of time with a low wind chill index, are studied. The model is also used to study the dependence of the statistical properties of the wind chill index time series on a climate change.

KW - 65C05

KW - 65C20

KW - 86A10

KW - Climate change

KW - Non-stationary random process

KW - Stochastic simulation

KW - Wind chill index

KW - MORTALITY

KW - PRECIPITATION

KW - TEMPERATURE

KW - CLIMATE

UR - http://www.scopus.com/inward/record.url?scp=85080948369&partnerID=8YFLogxK

UR - https://www.elibrary.ru/item.asp?id=43268689

U2 - 10.1007/s11009-020-09778-x

DO - 10.1007/s11009-020-09778-x

M3 - Article

AN - SCOPUS:85080948369

VL - 23

SP - 257

EP - 271

JO - Methodology and Computing in Applied Probability

JF - Methodology and Computing in Applied Probability

SN - 1387-5841

IS - 1

M1 - 2

ER -