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Analysis of precipitation data with time series methods and investigation of causality relations

Symeonidis Petros

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Year 2023
Type of Item Diploma Work
Bibliographic Citation Petros Symeonidis, "Analysis of precipitation data with time series methods and investigation of causality relations", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2023
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The effects of climate change are already being felt around the world, with one of the main ones being the warming of the oceans, which may cause a change in the distribution and amount of rainfall. In particular, recent years have seen a change in the spatial and temporal distribution of rainfall in Europe, with extreme rainfall events that can lead to flooding with devastating environmental, financial and humanitarian consequences. However, the mechanisms associated with extreme rainfall events and their prediction are not fully understood. This paper investigates the influence of the North Atlantic Oscillation (NΑO) atmospheric index on precipitation in Greece, specifically in areas of Crete and Kilkis. In the research literature, the influence of the NAO on precipitation in Greece is based on correlation estimates. In contrast, this paper investigates the effect using causality analysis methodologies for the relationship between the NAO index and precipitation.The causality analysis was implemented through two approaches: Granger causality and causality by Liang, using time lags of 1-12 months between the precipitation and NAO time series. Both time series with reanalysis data and actual monthly precipitation measurements for Crete are used. The findings of the analysis support the effect of the NAO index on rainfall for all the study areas. Therefore, the results of the study document for the first time the NAO-precipitation dependence using causality analysis methods. At the same time, however, our analysis also identified an unexpected inverse effect of precipitation on the NAO index for regions of Crete. We investigated with the help of synthetic data whether this effect may be due to the interleaved nature and deviations of the rainfall time series from the normal distribution, but without obtaining categorical results. For future research, further investigation of this phenomenon is suggested, as well as extension of the analysis to different locations as well as the use of different methods of causality analysis.

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