Periodicity in precipitation and temperature for monthly data of Turkey

Akdi, Yılmaz; Ünlü, Kamil Demirberk

doi:10.1007/s00704-020-03459-y

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…However, such periodicities (15-17-year cycles) can come about for other regions of the planet [34]. Additionally, in Reference [35], it was revealed (periodogram-based time series methodology) that the monthly average precipitation has two different periodic structures of six months and twelve months that coincide with the seasonal pattern of the time series. However, the interannual periodicity is not explicit enough.…”

Section: Discussionmentioning

confidence: 99%

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

et al. 2022

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A comparative statistical analysis of the spatiotemporal variability of atmospheric precipitation characteristics (mean and extreme values) in Western Siberia was performed based on data acquired from meteorological stations, global precipitation datasets such as the project of Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) and from Global Precipitation Climatology Centre (GPCC), and reanalysis archives, including from National Centers of Environmental Prediction (NCEP-DOE) and the European Center for Medium Range Weather Forecasts (ERA5) for the period 1979–2018. The best agreement of the values from the observational data was observed with the values from GPCC. This archive also represented the periodicities in the time series of observational data from meteorological stations, especially in the short-period part of the spectrum. Underestimated values were revealed for the APHRODITE archive, while overestimated ones were found for the NCEP reanalysis data. In comparison with GPCC, the ERA5 dataset reproduced the general variability but with a smaller amplitude (the correlation coefficient was up to 0.9). In general, the median estimates of the precipitation amount derived from the meteorological stations’ data, as well from the reanalysis data, were in better agreement with each other rather than their extreme values. However, their temporal variability can be effectively described by other datasets.

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Section: Discussionmentioning

confidence: 99%

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

et al. 2022

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“…In this study, we use the methodology proposed by Akdi et al (2020a) which use periodogrambased time series to model monthly air pollution of Ankara, the capital city of Turkey. The recent literature shows that periodogram-based time series has many applications in economics Akdi et al (2020b); in weather pollutions Okkaoğlu et al (2020); in energy markets Akdi et al (2020c); and in meteorology Akdi and Ünlü (2020). The methodology has many advantages, but the most significant one can be shown that it does not require any assumption or parameter estimations except for the variance of the white noise series.…”

Section: Methodsmentioning

confidence: 99%

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Akdi

Gölveren

Ünlü

et al. 2021

Environ Monit Assess

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pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

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“…Several studies have assessed various methods such as periodogram, autoregressive integrated moving average, vector autoregression etc. to model and predict the temperature and precipitation climatology over many regions across the globe [4][5][6]. In a recent study, Akdi and Ünlü [4] identified the hidden cycles of precipitation and temperature by employing periodogram-based time series methodology.…”

Section: Introductionmentioning

confidence: 99%

“…to model and predict the temperature and precipitation climatology over many regions across the globe [4][5][6]. In a recent study, Akdi and Ünlü [4] identified the hidden cycles of precipitation and temperature by employing periodogram-based time series methodology. In numerous studies, the empirical orthogonal function (EOF) analysis is used to depict the variations of precipitation and their distribution with time [7,8].…”

Section: Introductionmentioning

confidence: 99%

Precipitation and Temperature Climatologies over India: A Study with AGCM Large Ensemble Climate Simulations

2022

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This study investigated the precipitation and temperature climatologies over India from large ensemble (100 members) historical climate simulations in two recent past climate periods (1951–1980 and 1981–2010). The main focus was to statistically examine the usefulness of such large historical climate simulations by discussing (1) the precipitation and temperature climatologies and their distribution patterns, (2) the annual cycle of the temperature and precipitation climatologies, and (3) the frequency distributions and potential spatial patterns of climate variability. We applied empirical orthogonal function to understand the characteristics and normal probability distribution function to investigate the frequency. Results indicated good agreements of these large ensembles simulated results with the observation over Indian region. The precipitation amount over many regions of India is decreased and temperature over entire India is increased in 1981–2010 compared to that in 1951–1980. The annual cycle of the precipitations over India indicated a decrease of the precipitation amounts from June through October, while an increase of precipitation for the months from November through January. The annual cycle of the temperature over India indicated an increase of temperature during July through March. The frequency distributions of monthly precipitations and temperatures indicate an overall decrease of precipitation and an overall increase of temperature in recent climate period. The reason of decreased precipitation in recent climate period is attributed to a decrease of relative humidity and cloud together with weaker vertical velocity over Indian region during 1981–2010. Overall study validates the usefulness of these large ensemble climate simulations for the assessment of climate over India and suggests that these datasets may be used for various purposes related to weather and climate over India.

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Periodicity in precipitation and temperature for monthly data of Turkey

Cited by 14 publications

References 39 publications

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Precipitation and Temperature Climatologies over India: A Study with AGCM Large Ensemble Climate Simulations

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