Development of a numerical stochastic model of joint spatio-temporal fields of weather parameters for the south part of the Baikal natural territory
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Marina S. Akenteva
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Vasily A. Ogorodnikov
Abstract
The paper is focused on the construction of a numerical stochastic model of the joint spatio-temporal fields of air temperature, wind speed vector with three-hour resolution, and semidiurnal precipitation amounts according to observation data at a group of weather stations located in the south of the Baikal natural territory. The model also takes into account the dependence of one-dimensional distributions on temporal and spatial coordinates. The heterogeneity of the field in spatial correlations and the periodical correlation in time are also taken into account. The results of calculations for verification of the model are presented. An example of using the developed model to study the properties of time series of the wind chill index is given.
Funding statement: The work was supported by the Ministry of Science and Higher Education of the Russian Federation (project No. 075–15–2020–787 for implementation of large scientific project ‘Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory’).
References
[1] Ya. P. Dragan, V. A. Rozhkov, and I. N. Yavorskiy, Methods of Probabilistic Analysis of Oceanological Processes Rhythmic. Gidrometeoizdat, Leningrad, 1987 (in Russian).Suche in Google Scholar
[2] W. Kleiber, R. W. Katz, and B. Rajagopalan, Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes, Water Resour. Res. 48 (2012), W01523.10.1029/2011WR011105Suche in Google Scholar
[3] A. S. Marchenko and L. A. Minakova, Probabilistic model of air temperature time-series. Meteorology and Hydrology (1980), No. 9. 39–47 (in Russian).Suche in Google Scholar
[4] A. S. Marchenko and A. G. Siomochkin, FΦΦF-method for simulation of time series using real data. In: Numerical Method of Statistical Modelling. CC SB AS USSR, Novosibirsk, 1987, pp. 14–22 (in Russian).Suche in Google Scholar
[5] É. Mekis, L. A. Vincent, M. W. Shephard, and X. Zhang, Observed trends in severe weather conditions based on humidex, wind chill, and heavy rainfall events in Canada for 1953–2012. Atmos. Ocean 53 (2015), 383–397.10.1080/07055900.2015.1086970Suche in Google Scholar
[6] G. A. Mikhailov and A. V. Voytishek, Numerical Statistical Modelling. Monte Carlo Method. Academia, Moscow, 2006 (in Russian).Suche in Google Scholar
[7] National Standard of the Russian Federation 11079-2015. Ergonomics of the thermal environment. Specification of the cold stress and its interpretation on the basis of in the indicators of required thermal insulation of clothing and local cooling effect. Standartinform, Moscow, 2015 (in Russian).Suche in Google Scholar
[8] V. A. Ogorodnikov and S. M. Prigarin, Numerical Modelling of Random Processes and Fields: Algorithms and Applications. VSP, Utrecht, 1996.10.1515/9783110941999Suche in Google Scholar
[9] R. Osczevski and M. Bluestein, The new wind chill equivalent temperature chart. Bull. Amer. Meteorol. Soc. 86 (2005), 1453–1458.10.1175/BAMS-86-10-1453Suche in Google Scholar
[10] Z. A. Piranashvili, Some problems of statistical probabilistic modelling of random processes. In: Probl. Oper. Res. Tbilisi, 1966, pp. 53–91 (in Russian).Suche in Google Scholar
[11] S. M. Prigarin, Numerical Modeling of Random Processes and Fields. Inst. Comp. Math. Math. Geophys. Publ., Novosibirsk, 2005 (in Russian).Suche in Google Scholar
[12] M. A. Semenov and E. M. Barrow, Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35 (1997), 397–414.10.1023/A:1005342632279Suche in Google Scholar
[13] P. A. Siple and C. F. Passel, Measurements of dry atmospheric cooling in sub-freezing temperatures. Proc. Amer. Philos. Soc. 89 (1945), 177–199.Suche in Google Scholar
[14] G. G. Svanidze, Mathematical Modelling of Hydrologic Series. Gidrometeoizdat, Leningrad, 1977 (in Russian).Suche in Google Scholar
[15] Z. Zheng, H. Dai, Y. Wang, and W. Wang, A sample-based iterative scheme for simulating non-stationary non-Gaussian stochastic processes. Mechanical Systems and Signal Processing 151 (2021), 107420.10.1016/j.ymssp.2020.107420Suche in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Development of a numerical stochastic model of joint spatio-temporal fields of weather parameters for the south part of the Baikal natural territory
- Discrete curvatures for planar curves based on Archimedes’ duality principle
- Predictability of the low-frequency modes of the Arctic Ocean heat content variability: a perfect model approach
- Optimal stochastic forcings for sensitivity analysis of linear dynamical systems
- On the multi-annual potential predictability of the Arctic Ocean climate state in the INM RAS climate model
Artikel in diesem Heft
- Frontmatter
- Development of a numerical stochastic model of joint spatio-temporal fields of weather parameters for the south part of the Baikal natural territory
- Discrete curvatures for planar curves based on Archimedes’ duality principle
- Predictability of the low-frequency modes of the Arctic Ocean heat content variability: a perfect model approach
- Optimal stochastic forcings for sensitivity analysis of linear dynamical systems
- On the multi-annual potential predictability of the Arctic Ocean climate state in the INM RAS climate model
