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Author |
Baranowski, P.; Slawinski, C.; Krzyszczak, J. |
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Title |
Short presentation of partner’s P139 involvement in CropM |
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Conference Article |
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Year |
2013 |
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Keywords |
TradeM; CropM |
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MACSUR TradeM Workshop on Global Food Security Challenges – European Research approaches. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany, 2013-11-18 to 2013-11-20 |
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MA @ admin @ |
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2294 |
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Author |
Baranowski, P.; Mazurek, W.; Pastuszka-Wozniak, J. |
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Title |
Supervised classification of bruised apples on the base of hyperspectral imaging data |
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Conference Article |
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Year |
2013 |
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CropM |
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10th International Conference on Agrophysics, 2013-06-05 to 2013-06-07 |
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Call Number |
MA @ admin @ |
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2295 |
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Author |
Baranowski, P.; Krzyszczak, J.; Slawinski, C. |
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Title |
Multifractal analysis of chosen meteorological time series to assess climate impact in field level |
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Conference Article |
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Year |
2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2296 |
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Author |
Baranowski, P. |
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Title |
Multifractal analysis of meteorological time series to assess climate impact on chosen regions of Europe |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-4 |
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Over the last decades modelling of climate change through the analysis of empirical meteorological data has become of great interest. The standard approach gives satisfactory results only in the climatic zones with extreme dynamics of climate change, thus there is need to develop and apply more subtle methods such as fractal analysis and chaotic evolution analysis of the atmospheric system. The scaling analysis of meteorological time series is complicated because of the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the daily air temperature, wind velocity, relative air humidity, global radiation and precipitation through multifractal detrended fluctuation analysis on data from 31 years for stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response, indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent, by analysing the corresponding shuffled and surrogate time series. The results suggest that MFDFA is valuable for assessing the change of climate dynamics. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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Call Number |
MA @ admin @ |
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2119 |
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Author |
Baranowski, P.; Krzyszczak, J.; Slawinski, C.; Hoffmann, H.; Kozyra, J.; Nieróbca, A.; Siwek, K.; Gluza, A. |
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Title |
Multifractal analysis of meteorological time series to assess climate impacts |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
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39-52 |
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Keywords |
multifractal analysis; time series; agro-meteorological parameters; detrended fluctuation analysis; daily temperature records; catalonia ne spain; fractal analysis; river-basin; precipitation; variability; patterns; trends; china |
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Abstract |
Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performedwith 11 322 measured time series (31 yr) of daily air temperature, wind velocity, relative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. In log-log plots of the cumulative distributions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifractality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics. |
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English |
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0936-577x 1616-1572 |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4666 |
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