ARIMA representation for daily solar irradiance and surface air temperature time series

Kärner, Olavi

doi:10.1016/j.jastp.2009.03.018

Cited by 34 publications

(27 citation statements)

References 15 publications

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“…The latter is determined by means of the structure function. For this topic readers are referred to the detailed description by Monin et al (1976) and Kärner (2009). Therefore, the daily time series soil temperature data were first averaged to obtain the weekly data.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, various exponential smoothing models can be implemented by ARIMA models (McKenzie, 1984). ARIMA models have been widely used for air quality (Diaz-Robles et al, 2008), water quality (Ömer Faruk, 2010), runoff coefficients (Pektaş and Kerem Cigizoglu, 2013), solar irradiance and surface air temperature (Kärner, 2009), soil salt and water content (Aljoumani et al, 2012;Zou et al, 2010), soil dryness index (Li et al, 2003), soil heat flux (Kumar et al, 2009) and global near-surface mean temperature (Romilly, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Time series modelling of increased soil temperature anomalies during long period

Shirvani¹,

Moradi²,

Moosavi³

2015

International Agrophysics

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Soil temperature just beneath the soil surface is highly dynamic and has a direct impact on plant seed germination and is probably the most distinct and recognisable factor governing emergence. Autoregressive integrated moving average as a stochastic model was developed to predict the weekly soil temperature anomalies at 10 cm depth, one of the most important soil parameters. The weekly soil temperature anomalies for the periods of January1986-December 2011 and January 2012-December 2013 were taken into consideration to construct and test autoregressive integrated moving average models. The proposed model autoregressive integrated moving average (2,1,1) had a minimum value of Akaike information criterion and its estimated coefficients were different from zero at 5% significance level. The prediction of the weekly soil temperature anomalies during the test period using this proposed model indicated a high correlation coefficient between the observed and predicted data - that was 0.99 for lead time 1 week. Linear trend analysis indicated that the soil temperature anomalies warmed up significantly by 1.8°C during the period of 1986-2011.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time series modelling of increased soil temperature anomalies during long period

Shirvani¹,

Moradi²,

Moosavi³

2015

International Agrophysics

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“…Distinguishing between the scales helps to reach more precise presentation about the variability. Recent studies (Kärner, 2009;Kärner and de Freitas, 2011) have shown that the long range temporal variability in various daily surface air temperature anomaly increment x (t) = X (t) − X (t − k ) series can be represented as…”

Section: Introductionmentioning

confidence: 99%

“…E-mail: c.defreitas@auckland.ac.nz station-based surface air temperature series has shown that the increment interval k = 56 is sufficient to get an acceptable model (1) version (see Kärner (2009) for details). This means that the original daily record will be divided into 56 sub-series of increments over 56-d interval.…”

Section: Introductionmentioning

confidence: 99%

Detecting climate variability signals in long air temperature records

Kärner

Freitas

2013

Intl Journal of Climatology

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This work introduces a new climate description scheme based on a long range statistical model fitted to local daily temperature anomaly series. The appropriate model can be considered as a sum of two processes, namely, stationary white noise (WN) and nonstationary random walk (RW). In the case of local series, the variance of the WN component appears to be much larger than that for the generator of RW (also a WN, but independent on the first one). Such a situation enables us to approximate the range of variability for local temperature anomalies by means of the standard deviation of the stationary WN component of the model. This means that the climate description splits into two parts; one describes ordinary and the other extreme weather events. This paper presents a detailed description of that approach using the air temperature series from Stockholm (1756-2011) as an example. The scheme produced here enables us to see that large weather variability does not always mean climate variability. It separates climate and weather scale variability, which is important for precise determination of climate variability and does not confuse the weather and climate time scales in climate description.

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“…Silva et al (2008) descreveram o comportamento da série de temperatura média mensal da cidade de Uberlândia, MG, através de modelos da classe ARIMA. Kärner (2009) usou modelos ARIMA para comparação da variabilidade temporal de longo alcance entre a irradiação solar total e a temperatura do ar. Soebiyanto et al (2010) modelaram parâmetros climáticos utilizando modelos ARIMA com o objetivo de prever a transmissão de gripe em regiões frias.…”

Section: Introductionunclassified

Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS

Chechi¹,

Bayer

2012

Rev. bras. eng. agríc. ambient.

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R ESU M OEste trabalho apresenta uma análise de séries temporais dos dados de temperatura mínima e temperatura máxima mensal da cidade de Erechim, RS; apresenta-se uma comparação de duas classes de modelos tradicionais de previsão, nomeadamente: modelos da classe ARIMA e modelos de alisamento exponencial. Na classe de modelos ARIMA foram selecionados, utilizando-se critérios de informação, modelos do tipo SARIMA, que consideram a característica sazonal da temperatura do ar; já para os modelos de alisamento exponencial utilizaram-se os modelos Holt-Winters aditivo, em que as constantes de alisamento são determinadas de forma a minimizar o erro quadrático médio entre valores previstos e observados; esta análise permitiu a identificação de componentes como sazonalidade e períodos atípicos. Os modelos de previsão foram comparados para diferentes horizontes de previsão, sendo que os modelos da classe ARIMA se mostraram mais acurados. Os modelos ajustados se mostraram adequados para traçar previsões das variáveis de temperatura do ar, mostrando-se importantes ferramentas para a climatologia agrícola. Palavras-chave: ARIMA, alisamento exponencial, modelos de previsão, sazonalidadeUnivariate time series methods for forecasting the monthly mean air temperature in Erechim, RS A B ST R A C T This paper presents a time series analysis of the minimum and maximum air temperature of Erechim, RS. A comparison between two traditional classes of the forecasting models, namely: ARIMA models and exponential smoothing models is also presented. In the class of ARIMA models using criteria information, SARIMA type models that consider the seasonal characteristics of air temperature were selected, whereas for exponential smoothing models Holt-Winters additive algorithm were used. Smoothing constants are determined to minimize the mean square error between observed and predicted values. This analysis allowed the identification of components such as seasonality and atypical periods. The model predictions were compared for different forecast horizons. The ARIMA class models proved to be more accurate while the adjusted models were adequate for adjusting forecasts of variables of air temperature, being important tools for agricultural climatology.

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ARIMA representation for daily solar irradiance and surface air temperature time series

Cited by 34 publications

References 15 publications

Time series modelling of increased soil temperature anomalies during long period

Time series modelling of increased soil temperature anomalies during long period

Detecting climate variability signals in long air temperature records

Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS

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