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Dynamic Analysis of Meteorological Parameters in Koice Climatic Station in Slovakia

Authors
  • ZELENAKOVA, M
  • JOTHIPRAKASH, V
  • ARJUN, S
  • KAPOSZTASOVA, D
  • HLAVATA, H
Publication Date
Dec 03, 2018
Source
DSpace at IIT Bombay
Keywords
License
Unknown
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Abstract

Evaporation and precipitation are often considered the most important processes in the water cycle. Recent studies have turned to chaotic analysis and short-term prediction for analyzing and forecasting the time series of such phenomena. However, even with chaos theory, the accurate forecasting of pan evaporation is not a straightforward business, as it involves a number of variables whose changes directly and/or indirectly affect the scale and amount of pan evaporation. In this study, the use of the false nearest neighbour method for the chaotic analysis of pan evaporation and related metrological parameters is discussed. A literature review is presented on chaos theory and its applications in modelling physical systems. Also, a review of the literature on multivariate analysis and the presence of chaos in meteorology are presented. A detailed procedure for finding the presence of chaos in a time series using false nearest neighbour (FNN) is discussed. The possible lag time to be considered in the FNN analysis is estimated using the autocorrelation function (ACF) and average mutual information (AMI) apart from the time-step of the measurement. Thus, FNN is studied with three different lag times of the time series. Six meteorological parameters: average temperature, relative humidity, wind speed, sunshine hours, dew point temperature, and pan evaporation are measured at the observation station Kosice in Slovakia for a period of 20 years. Thus, the available time series are analysed using ACF, AMI, and FNN methods, and the results obtained are analysed in the study. Nonlinear behaviour is seen in all of the observed parameters. Pan evaporation, average temperature, and dew point temperature are found to exhibit clear chaotic behaviour, while relative humidity, sunshine hours, and wind speed show stochastic behaviour.

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