Basic principles of the TEVY index for the quantification of temperature variability within a year

Abstract In the context of climate change, there is a need for the determination of appropriate indexes for the quantification of temperature variability. A new index (TEVY index) is proposed in this work. This index uses the deviation of the observed temperature values from those estimated from a Fourier harmonic analysis. For this purpose, a nearly 50-year time series data from 4 stations in Greece, with very different climatic conditions, are used. One station is located in the colder northern region of Greece, another one is in the warmest southern part, while the 2 other stations are representative of continental and Mediterranean climatic features. A Fourier harmonic analysis is carried out to obtain the Fourier series which simulates the observed data time series. Fourier harmonic analysis, which is relied on the Fourier transform, is a well-established method for time series analysis, particularly for modelling periodic data. Using this procedure, an index of temperature variability is proposed, as the sum of the divergence of the above-mentioned Fourier series from the observed data. The index results are analysed as a function of the different climatic features of each station.

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Examination of Temperature Variability over Lahore (Pakistan) and Dhaka (Bangladesh): A Comparative Study

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol10.iss4.2019.352 ◽

2020 ◽

Vol 10 (4) ◽

pp. 46-50

Author(s):

Khadija Shakrullah ◽

Safdar Ali Shirazi ◽

Sajjad Hussain Sajjad ◽

Zartab Jahan

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Temperature Variability ◽

Series Data ◽

Maximum Temperature ◽

Average Model ◽

Temperature Variations ◽

Moving Average Model ◽

Mega Cities

Lahore and Dhaka are rapid expanding and over populated cities of South Asia located in Pakistan andBangladesh respectively. The present study focuses on the evaluation of temperature variability in comparison of bothcities. This study primarily aims at the assessment and examination of temperature variations in both mega cities ofSouth Asia which are seasonal as well as the annual. The time series data were analysed by using statistical techniquesAutoregressive Moving Average Model (ARMA) and Autoregressive Integrated Average Model (ARIMA). The resultsreveal that the minimum temperature is increasing much faster than that of the maximum temperature of both cities.However, the temperature rise(in maximum and minimum) has been observed highest during the spring seasons in bothcities.

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Modeling and Forecasting of Tourism Time Series Data using ANN-Fourier Series Model and Monte Carlo Simulation

2021 9th International Symposium on Digital Forensics and Security (ISDFS) ◽

10.1109/isdfs52919.2021.9486325 ◽

2021 ◽

Author(s):

Salim Jibrin Danbatta ◽

Asaf Varol

Keyword(s):

Time Series ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Fourier Series ◽

Time Series Data ◽

Series Data ◽

Modeling And Forecasting

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Adaptive Fourier Series and the Analysis of Periodicities in Time Series Data

Journal of Time Series Analysis ◽

10.1111/1467-9892.00202 ◽

2000 ◽

Vol 21 (6) ◽

pp. 649-662 ◽

Cited By ~ 2

Author(s):

Robert V. Foutz ◽

Hoonja Lee

Keyword(s):

Time Series ◽

Fourier Series ◽

Time Series Data ◽

Series Data

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Identification of Variations in the Climatic Conditions of the Lerma-Chapala-Santiago Watershed by Comparative Analysis of Time Series

Advances in Meteorology ◽

10.1155/2018/1098942 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Luis Ricardo Manzano-Solís ◽

Miguel A. Gómez-Albores ◽

Carlos Díaz-Delgado ◽

Carlos Alberto Mastachi-Loza ◽

Raymundo Ordoñez-Sierra ◽

...

Keyword(s):

Time Series ◽

Comparative Analysis ◽

Time Series Data ◽

Summer Rainfall ◽

Kappa Statistic ◽

Climatic Conditions ◽

Series Data ◽

Summer Drought ◽

Climate Data ◽

Climate Maps

The current study presents a method for automating the Köppen–Garcia climate classification using a GIS module. This method was then applied in a case study of the Lerma-Chapala-Santiago watershed to compare time series data on climate from 1960 to 1989, 1981 to 2010, and 1960 to 2010. The kappa statistic indicated that the climate classifications of the generated model had a perfect degree of agreement with those of a prior nonautomated study. The climate data from the period 1960 to 2010 were used to create a climate map for the watershed. Overall, the dominant climates were dry, semiarid, temperate, and semiwarm temperate with a summer rainfall pattern. A comparative analysis of climate behavior between 1960 and 1989 and between 1981 and 2010 showed changes in temperature and extreme temperatures over 13.6% and 9.9%, respectively, of the watershed; the presence or absence of mid-summer drought also changed over 0.8% of the watershed. The module developed herein can be used to classify climates across all of Mexico, and data of varying spatial resolution and coverage can be inputted to the module. Finally, this module can be used to automate the creation of climate maps or to update climate maps at diverse spatial-temporal scales.

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Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data

Journal of Solar Energy Engineering ◽

10.1115/1.2189872 ◽

2005 ◽

Vol 128 (2) ◽

pp. 226-230 ◽

Cited By ~ 7

Author(s):

Juan-Carlos Baltazar ◽

David E. Claridge

Keyword(s):

Time Series ◽

Fourier Series ◽

Energy Use ◽

Time Series Data ◽

Linear Interpolation ◽

Cubic Splines ◽

Weather Data ◽

Series Data ◽

Data Sets ◽

Data Points

A study of cubic splines and Fourier series as interpolation techniques for filling in missing hourly data in energy and meteorological time series data sets is presented. The procedure developed in this paper is based on the local patterns of the data around the gaps. Artificial gaps, or “pseudogaps,” created by deleting consecutive data points from the measured data sets, were filled using four variants of the cubic spline technique and 12 variants of the Fourier series technique. The accuracy of these techniques was compared to the accuracy of results obtained using linear interpolation to fill the same pseudogaps. The pseudogaps filled were 1–6 data points in length created in 18 year-long sets of hourly energy use and weather data. More than 1000 pseudogaps of each gap length were created in each of the 18 data sets and filled using each of the 17 techniques evaluated. Use of mean bias error as the selection criterion found that linear interpolation is superior to the cubic spline and Fourier series methodologies for filling gaps of dry bulb and dew point temperature time series data. For hourly building cooling and heating use data, the Fourier series approach with 24 data points before and after each gap and six terms was found to be the most suitable; where there are insufficient data points to apply this approach, simple linear interpolation is recommended.

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