Statistical modelling of agrometeorological time series by exponential smoothing. Murat, M., Malinowska, I., Hoffmann, H., & Baranowski, P. International Agrophysics, 30:57–65, 2016. MACSUR or FACCE acknowledged.
doi  abstract   bibtex   
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm 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.
@Article {Murat2016a,
author = {Murat, M. and Malinowska, I. and Hoffmann, H. and Baranowski, P.}, 
title = {Statistical modelling of agrometeorological time series by exponential smoothing}, 
journal = {International Agrophysics}, 
volume = {30}, 
pages = {57--65}, 
year = {2016}, 
doi = {10.1515/intag-2015-0076}, 
abstract = {Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm 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.}, 
note = { MACSUR or FACCE acknowledged.}, 
keywords = {exponential smoothing; meteorological time series; statistical forecasting; daily temperature records; weighted moving averages; climate-change; prediction; forecasts; state; weather}, 
type = {CropM}}
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