Trend Analyses of the Long Time Series of Monthly Mean Temperatures at Keszthely, Hungary

Authors

  • Tímea Kocsis Budapest Business University, Faculty of Commerce, Hospitality and Tourism, Department of Methodology for Business Analysis, Alkotmány utca 9-11., H-1054 Budapest https://orcid.org/0000-0003-3430-5569
  • Zsolt Törcsvári Budapest Business University, Faculty of Commerce, Hospitality and Tourism, Department of Methodology for Business Analysis, Alkotmány utca 9-11., H-1054 Budapest https://orcid.org/0000-0002-1210-3491
  • Lóránt Biró Budapest Business University, Faculty of Commerce, Hospitality and Tourism, Department of Methodology for Business Analysis, Alkotmány utca 9-11., H-1054 Budapest
  • Norbert Magyar Budapest Business University, Faculty of Commerce, Hospitality and Tourism, Department of Methodology for Business Analysis, Alkotmány utca 9-11., H-1054 Budapest

Keywords:

autocorrelation, Keszthely, moving average, temperature, trend analyses

Abstract

Keszthely has one of the longest meteorological measurements in Hungary. The first meteorological station was established in the framework of the Georgikon Academy of Agronomy. From 1871 till nowadays, Keszthely has unbroken records. The town itself has local importance for its tourism and the nearby wetland (natural reserve area of Kis-Balaton). The goal of this study is to examine the long time series of monthly mean temperature data of this meteorological station. The dataset composes 1776 data (from 1871 January to 2018 December), which were undergone to homogenisation method (MASH). Homogeneity was also checked by Pettitt’s homogeneity test, and no change-point could be identified. Monthly mean temperatures were not independent from each other, significant autocorrelation could be observed. Thus, linear approach for trend detection couldn’t be used, as its requirements for application were not fulfilled. The moving average (12MA, number of tags is 12) showed rising tendency. A modified Mann-Kendall trend test for autocorrelated data was applied to detect the tendency of the time series. Seasonality should be considered as well. The slope was calculated by Sen’s slope estimator. Using the autocorrelated (and seasonal) Mann-Kendall trend test, a significant increasing trend can be found (Kendall’s tau = 0.047, p-value = 0.013). Sen’s slope is estimated to 0.004°C (period=12).

Author Biography

  • Tímea Kocsis, Budapest Business University, Faculty of Commerce, Hospitality and Tourism, Department of Methodology for Business Analysis, Alkotmány utca 9-11., H-1054 Budapest

    correspondence
    jakuschnekocsis.timea@uni-bge.hu

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Published

2024-06-28

Issue

Vol. 28 No. Suppl. 2 (2024): 65th Georgikon Days Scientific Conference

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Articles

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Copyright (c) 2024 Tímea Kocsis, Zsolt Törcsvári, Lóránt Biró, Norbert Magyar

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