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.
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Thanks to Lanzhou Central Meteorological Observatory of Gansu Province for providing the original data for the research in this paper.
Conflict of interests
As far as we know, there are no competing economic interests or personal relationships that may affect the work described in this paper. We like to declare that the work was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. The data in this paper are the original observation data, and all the relevant codes are written by the author. If there is any need, you can contact the author via email.
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Pang, Z., Wang, Z. Temperature trend analysis and extreme high temperature prediction based on weighted Markov Model in Lanzhou. Nat Hazards 108, 891–906 (2021). https://doi.org/10.1007/s11069-021-04711-y
- Meteorological variables
- Trend analysis
- Mann–Kendall test
- Weighted Markov model