Storms can be both destructive and valuable at the same time. They expose coastal areas to various risks but can also enhance the supply of wind energy and provide marine ecosystems with oxygen rich water. As the North Atlantic Oscillation (NAO) is known to have a significant impact on the wind climate in Europe, investigating its interconnection to storm frequency and intensity under global warming circumstances in the Northern Baltic Sea region was of interest in this study. Wind speed data series of annual storm counts were obtained from five meteorological stations along with PC-based NAO values over the period 1960-2017. The data series were analysed in Microsoft Excel and modelled using a Poisson regression or negative binomial regression model in SPSS Statistics. The results display an unsystematic spatial pattern both in the association to the NAO as well as in the overall storm frequency. However, storm (≥ 21 m s-1) frequency has generally been decreasing, whereas the proportion of severe storms (≥ 24 m s-1) has slightly been increasing, suggesting a tendency toward stronger but fewer storms. Even though only certain data series display statistically significant findings (p ≤ .05), a majority of the winter storms and severe winter storms display a positive association, indicating that a higher NAOI is related to a greater number of winter storms. The spatial and temporal variability in the obtained results can partially be explained by storm tracks and prevalent wind directions. Nevertheless, inhomogeneities do presumably affect the wind speed observations through internal and external influences and changes related to the meteorological stations. Future research should, therefore, also consider integrating other storm related parameters, such as direct air pressure measurements, wave heights and storm surges, as well as implement different data homogenization methods and techniques.

<p>Hourly archived rainfall records are separated into individual rainfall events with</p> <p>an Inter-Event Time Denition. Individual storms are characterized by their depth,</p> <p>duration, and peak intensity. Severe events are selected from among the events for</p> <p>a given station. A lower limit, or threshold depth is used to make this selection,</p> <p>and an upper duration limit is established. A small number of events per year are</p> <p>left, which have relatively high depth and average intensity appropriate to small</p> <p>to medium catchment responses. The Generalized Pareto Distributions are tted</p> <p>to the storm depth data, and a bounded probability distribution is tted to storm</p> <p>duration. Peak storm intensity is bounded by continuity imposed by storm depth</p> <p>and duration. These physical limits are used to develop an index measure of peak</p> <p>storm intensity, called intensity peak factor, bounded on (0; 1), and tted to the Beta</p> <p>distribution. The joint probability relationship among storm variables is established,</p> <p>combining increasing storm depth, increasing intensity peak factor, with decreasing</p> <p>storm duration as being the best description of increasing rainstorm severity. The</p> <p>joint probability of all three variables can be modelled with a bivariate copula of</p> <p>the marginal distributions of duration and intensity peak factor, combined simply</p> <p>with the marginal distribution of storm depth. The parameters of the marginal</p> <p>distributions of storm variables, and the frequency of occurrence of threshold-excess</p> <p>events are used to assess possible shifts in their values as a function of time and</p> <p>temperature, in order to evaluate potential climate change eects for several stations.</p> <p>Example applications of the joint probability of storm variables are provided that</p> <p>illustrate the need to apply the methods developed.</p> <p>The overall contributions of this research combine applications of existing probabilistic</p> <p>tools, with unique characterizations of rainstorm variables. Relationships</p> <p>between these variables are examined to produce a new description of storm severity,</p> <p>and to begin the assessment of the eects of climate change upon severe rainstorm</p> <p>events.</p> <p>i</p> / Doctor of Philosophy (PhD)