Temperature trend analysis and extreme high temperature prediction based on weighted Markov Model in Lanzhou

Zhiqiang Pang () and Zhaoxu Wang ()
Additional contact information
Zhiqiang Pang: Lanzhou University of Finance and Economics
Zhaoxu Wang: Lanzhou University of Finance and Economics

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 1, No 38, 906 pages

Abstract: Abstract In this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high temperature prediction was constructed. Several non-parametric methods were used to analyse the trend of meteorological variables. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature and average temperature in Lanzhou all have a significant upward trend and show different performances in each season. In detail, the trend of maximum temperature in the summer is not significant, while the upward trend of minimum temperature in the winter is the most significant, which leads to more and more “warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperature and obtain the conclusion that the prediction results by the model are consistent with the actual situation.

Keywords: Meteorological variables; Trend analysis; Mann–Kendall test; Weighted Markov model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-04711-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04711-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-04711-y

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().