MASTERARBEIT / MASTER’S THESIS

The aim of the Mesoscale Verification Inter-Comparison over Complex Terrain (MesoVICT) project is to compare spatial verification methods with each other to gather information about proper ones. This is done for deterministic and ensembles forecasts. They are compared with a certain observation. However, the MesoVICT project provides two observation ensembles, which are computed from the University Vienna. Here, the Vienna Enhanced Resolution Analysis (VERA) is the basis for ensembles were station observation uncertainties and spatial patterns are represented. They are used for a so far little discussed topic, the verification from two ensembles against each other. Since this issue is complex, the spatial part is postponed and a point to point verification method inter-comparison, in the spirit of the MesoVICT project, is done. The forecast ensemble is provided by the weather service of Emilia-Romagna (COSMO-LEPS). As parameters the wind speed (V ), the potential temperature (Θ), precipitation (RR) and the pressure (p,ps f or pmd ) are chosen. Before starting with the evaluation, distribution tests are performed for the ensembles (Lilliefors-, Jarque-Beraand Anderson-Darling-Test). The conclusion is that non parametric statistics are useful, because the Gaussian distribution is not valid all the time. The verification is done with distance measurements from probability density functions (PDF), cumulative distribution functions (CDF) and via histograms. Six methods are selected. The integrated quadratic distance (IQD) uses two CDF’s and measures the sum of squared differences. The perfect receiver operating characteristic comparison (PROCC) needs the observation and forecast ensemble represented as PDF’s. This score is rather complex, but based on a comparison with a perfect result. Probability vectors are required for the Kullback-Leibler Divergence (KLD). It provides the difference of those. The Euclidean, Seorgel and Lorentzian distances are based on histograms and the related probabilities in each bin. The used methods can also be applied for a comparison of, for example, two forecast models. For satisfying the non parametric statistics restriction kernel density estimates are applied. This approach fits the PDF’s and CDF’s to the data without losing any previous properties. The ensemble verification with different parameters showed, that it is reasonable to use wind speed and potential temperature. The precipitation and the pressure have to be modified or evaluated with different methods. A table provides an overview of the used verification methods and defines a judgmental interval for each score. The kernel density estimate improves as well as deteriorates the results. Additionally, the score specific uncertainty and the uncertainty between different observations are calculated. Those estimates interact with each other. If the score specific uncertainty is small, the location difference between the observation ensembles are more important for the uncertainty between different observations. In the other hand, a big extent, due to the score specific uncertainty suppresses the location influence from the observations in the uncertainty between different observations and the score specific uncertainty is more prominent. Finally, different observation ensemble possibilities, a brief overview for a model comparison and extreme value approaches are shown.