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Outliers and Trend Detection Tests in Rainfall Extremes

Author(s): Panagiota Galiatsatou; Panayotis Prinos

Linked Author(s): Panagiotis Prinos

Keywords: Possible outlier; Ultimate event; Gumbel plot; Q-q plot; Posterior predictive distribution; Return level; Non-stationarity; Point process; Polynomial temporal trends; Deviance statistic

Abstract: In the present work daily rainfall time series is analyzed using Extreme Value methodologies. Four different tests are used for detection of a possible outlier: the estimation of xult, the conditional probability of the event estimated through a Bayesian formula, a Gumbel plot and a q-q plot for an appropriately selected model for extreme events. Return level plots are created using a point process model for extreme observations, both by including and excluding the possible outlier and discrepancies are discussed. Simple parametric models for extremal trends are then incorporated in the parameters of the point process model. The location and scale parameter of the model are assumed to vary as polynomial functions of time or as sinusoidal terms, treated as entirely distinct or closely inter-related. Alternatively, they are expressed in terms of two other parameters, the rate and the severity of extreme events. The deviance statistic is used to identify the significance of such trends. Daily rainfall from the city of Thessaloniki, Greece is used in the analysis.


Year: 2007

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