A Time Series Approach for Precipitation in Turkey

MakaleYalnız künyeScopus SJRQ3

Özet

The climate that has a dynamic structure varies continuously. Basic climatological features of any region depend on many meteorological variables, such as precipitation and temperature records. The changes in these two variables reveal important clues about climate variations. Turkey is one of the sensitive areas for climate variation around the world. In this study, statistical analysis is applied to historical data set consisted of monthly precipitation totals (mm.) which are provided by the Turkish State Meteorological Service(TSMS) for 1999-2010 period in Turkey. Furthermore, a sinusoidal model for each region obtained by using a time series method is proposed in order to obtain a sinusoidal form for the precipitation to each region. Besides a basic form of Frequency Domain Techniques, usual Box-Jenkins ARIMA approach is applied in this study. In conclusion, it is observed that the sinusoidal model predictions are more appropriate than ARIMA techniques and usual generalized linear model (GLM) techniques.

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AlanDeğerDil
dc.contributor.authorBalibey, Mesut
dc.contributor.authorTürkyilmaz, Serpil
dc.date.accessioned2026-10-28T15:03:45.584128290Z
dc.date.available2026-10-28T15:03:45.584128290Z
dc.date.issued2015
dc.description.abstractThe climate that has a dynamic structure varies continuously. Basic climatological features of any region depend on many meteorological variables, such as precipitation and temperature records. The changes in these two variables reveal important clues about climate variations. Turkey is one of the sensitive areas for climate variation around the world. In this study, statistical analysis is applied to historical data set consisted of monthly precipitation totals (mm.) which are provided by the Turkish State Meteorological Service(TSMS) for 1999-2010 period in Turkey. Furthermore, a sinusoidal model for each region obtained by using a time series method is proposed in order to obtain a sinusoidal form for the precipitation to each region. Besides a basic form of Frequency Domain Techniques, usual Box-Jenkins ARIMA approach is applied in this study. In conclusion, it is observed that the sinusoidal model predictions are more appropriate than ARIMA techniques and usual generalized linear model (GLM) techniques.
dc.identifier.endpage559
dc.identifier.issn2147-1762
dc.identifier.issue4
dc.identifier.startpage549
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/4790
dc.identifier.volume28
dc.identifier.wosWOS:000421194200004
dc.language.isoen
dc.publisherGAZI UNIV
dc.relation.ispartofGAZI UNIVERSITY JOURNAL OF SCIENCE
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectPrecipitation
dc.subjectPeriodicity
dc.subjectTime Series Model
dc.subjectPeriodogram
dc.subjectFrequency Domain Technique.
dc.subject.classificationMultidisciplinary Sciences
dc.subject.classificationScience & Technology - Other Topics
dc.titleA Time Series Approach for Precipitation in Turkey
dc.typeArticle
rdl.citation.wos6
rdl.oa.statusclosed
rdl.scopus.quartileQ3
rdl.source.indexWOS
rdl.wos.indexEmerging Sources Citation Index (ESCI)