Abstract
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.
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References
Balouktsis A, Tsanakas D, Vachtsevanos G (1986) Stochastic simulation of hourly and daily average wind speed sequences. Wind Eng 10(1):1–11
Bilmes J.A. (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical report TR-97-021. International computer science institute, Berkeley, CA. (avaliable at: http://ssli.ee.washington.edu/people/bulyko/papers/em.pdf)
Boustead BEM, Hilberg SD, Shulski MD, Hubbard KG (2015) The accumulated winter season severity index (AWSSI). J Appl Meteor Climatol 54:1693–1712
Buligina O. N., Veselov V. M., Razuvaev V. N., Aleksandrova T. M. (2014) The description of the data of the main meteorological parameters at weather stations situated in Russia. http://meteo.ru/data/163-basic-parameters
Cario M. C., Nelson B. L. (1997) Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix, Working paper, Department of Industrial Engineering and Management Sciences, Northwestern University, USA
Climate-Data.org, https://en.climate-data.org/
Environment and Climate Change Canada, http://www.ec.gc.ca/meteo-weather/default.asp?lang=n&n=5FBF816A-1#X-2015011511230218
Evstafieva AI, Khlebnikova EI, Ogorodnikov VA (2005) Numerical stochastic models for complexes of time series of weather elements. Russ. J. Num. Anal. Math. Modelling 20(6):535–548
Gabriel KR, Neumann J (1962) A Markov chain model for daily rainfall occurrences at Tel Aviv. Quart J Roy Meteor Soc 88:90–95
Gevorkyan M.N., Demidova A.V., Sobolewski R.A., Zaryadov I.S., Korolkova A.V., Kulyabov D.S., Sevastianov L.A. (2017) Approaches to Stochastic Modeling of Wind Turbines. https://arxiv.org/pdf/1711.03589.pdf
Gosling SN, McGregor GR, Lowe JA (2009) Climate change and heat-related mortality in six cities. Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change. Int J Biometeorol 53(1):31–51
Haugh M. (2016) An introduction to copulas. IEOR E4602: quantitative risk management. Lecture notes. Columbia University
Kargapolova N. (2018) Monte Carlo simulation of non-stationary air temperature time-series. Proc. of 8th Int. Conf. On simulation and modeling methodologies, technologies and applications: 323–329
Kargapolova N. (2019) Stochastic models of non-stationary time series of the average daily heat index. Proc. of 9th Int. Conf. On simulation and modeling methodologies, technologies and applications: 209–215
Kargapolova NA, Khlebnikova EI, Ogorodnikov VA (2019a) Numerical study of properties of air heat content indicators based on stochastic model of the joint meteorological series. Russ J Num Anal Math Modelling 34(2):1–10
Kargapolova NA, Khlebnikova EI, Ogorodnikov VA (2019b) Stochastic models of joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure. Commun Stat Simul Comput. https://doi.org/10.1080/03610918.2019.1635157
Katz RW (1977) Precipitation as a chain-dependent process. J Appl Meteorol 16:671–676
Kershaw SE, Millward AA (2012) A spatio-temporal index for heat vulnerability assessment. Environ Monit Assess 18:7329–7342
Kleiber W., Katz R.W., Rajagopalan B. (2012) Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes Water Resources Res 48, doi: https://doi.org/10.1029/2011WR011105
Kobisheva NV, Stadnik VV, Klueva MV, Pigoltsina GB, Akentieva EM, Galuk LP, Razova EN, Semenov UA (2008) Guidance on specialized climatological service of the economy. Asterion, St. Petersburg (in Russian)
Marple SL Jr (1987) Digital spectral analysis with applications. Prentice-Hall, Englewood Cliffs
Monbet V, Ailliot P (2017) Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature. Comp Stat Data Anal 108:40–51
Ogorodnikov VA, Prigarin SM (1996) Numerical Modelling of random processes and fields: algorithms and applications. VSP, Utrecht
Ohashi Y, Kikegawa Y, Ihara T, Sugiyama N (2014) Numerical simulations of outdoor heat stress index and heat disorder risk in the 23 wards of Tokyo. J Appl Meteor Climatol 53:583–597
Osczevski R, Bluestein M (2005) The new wind chill equivalent temperature chart. Bull Am Meteorol Soc 86:1453–1458
Parlange MB, Katz RW (2000) An extended version of the Richardson model for simulating daily weather variables. J Appl Meteorol 39:610–622
Piranashvili Z.A. (1966) Some problems of statistical probabilistic modelling of random processes. Probl. Of operations res.: 53-91 (in Russian)
Revich BA, Shaposhnikov DA, Anisimov OA, Belolutskaia MA (2018) Heat waves and cold spells in three arctic and subarctic cities as mortality risk factors. Hyg Sanit 97(9):791–798 (in Russian)
Richardson CW (1981) Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resour Res 17:182–190
Richardson C. W., Wright D.A. (1984) WGEN: a model for generating daily weather variables. U. S. Department of Agriculture, Agricultural Research Service, ARS-8
Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107
Shartova N, Shaposhnikov D, Konstantinov P, Revich B (2018) Cardiovascular mortality during heat waves in temperate climate: an association with bioclimatic indices. Int J Environmental Health Research 28(5):522–534
Siple PA, Passel CF (1945) Measurements of dry atmospheric cooling in sub-freezing temperatures. Proc Amer Philos Soc 89:177–199
Steadman RG (1979) The assessment of sultriness. Part II: effects of wind, extra radiation and barometric pressure on apparent temperature. J Appl Meteorol 18:874–885
Wilks DS (2002) Smoothing forecast ensembles with fitted probability distributions. Q J R Meteorol Soc 128:2821–2836
Zare S, Hasheminejad N, Shirvan HE, Hemmatjo R, Sarebanzadeh K, Ahmadi S (2018) Comparing universal thermal climate index (UTCI) with selected thermal indices/environmental parameters during 12 months of the year. Weather and Clim Extremes 19:49–57
Acknowledgements
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.
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Kargapolova, N. Numerical Stochastic Model of Non-stationary Time Series of the Wind Chill Index. Methodol Comput Appl Probab 23, 257–271 (2021). https://doi.org/10.1007/s11009-020-09778-x
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DOI: https://doi.org/10.1007/s11009-020-09778-x