Characterizing Temporal Changes and Inter-Site Correlations in Daily and Sub-Daily Precipitation Extremes

Abstract: information on weather extremes is essential for risk awareness in planning of infrastructure and agriculture, and it may also playa key role in our ability to adapt to recurrent or more or less unique extreme events. This thesis reports new statistical methodologies that can aid climate risk assessment under conditions of climate change. Thc increasing access to high temporal resolution of data is a central factor when developing novel techniques for this purpose. In particular, a procedure is introduced for analysis of long-term changes in daily and sub-daily records of observed or modeJled weather extremes. Extreme value theory is employed to enhance the power of the proposed statistical procedure, and intersite dependence is taken into account to enable regional analyses. Furthermore, new methods are prcsentcd to summarize and visualize spatial patterns in the temporal synchrony and dependence of weather events such as heavy precipitation at a network of meteorological stations. The work also demonstrates the significance of accounting for temporal synchrony in the diagnostics of inter-site asymptotic dependence.