Statistical modelling of agrometeorological time series by exponential smoothing
 
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1
Department of Mathematics, Lublin University of Technology, Nadbystrzycka 38a, 20-618 Lublin, Poland
2
INRES, University of Bonn, 53115 Bonn, Germany
3
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
 
Int. Agrophys. 2016, 30(1): 57–65
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ABSTRACT
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, long-term meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.
eISSN:2300-8725
ISSN:0236-8722