The statistical distribution of meteorological outliers

[1] A general expression for the statistical distribution of the probability of the highest event occurring in a record is presented. This result can be empirically applied to situations where records are available for multiple geographical locations. The empirical estimation of the probability of the highest events provides a means to assess whether the assumed (extreme value) distribution is appropriate for extrapolation or not. The approach allows for combining the highest events from different records and to validate estimated return periods in the order of the length of the combined records. The method is illustrated with an analysis of the annual extreme wind speeds over the North Atlantic area according to the ERA40 dataset, showing that the Gumbel distribution is in favor of the GEV distribution to estimate the (appropriately transformed) extreme wind speeds up to return periods of 104 years.

[1]  J. R. Wallis,et al.  Estimation of the generalized extreme-value distribution by the method of probability-weighted moments , 1985 .

[2]  G. P. Können,et al.  Statistics of Extreme Synoptic-Scale Wind Speeds in Ensemble Simulations of Current and Future Climate , 2004 .

[3]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[4]  J. D. Opsteegh,et al.  Improving 104‐year surge level estimates using data of the ECMWF seasonal prediction system , 2004 .

[5]  A. Jenkinson The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .

[6]  Clive Anderson,et al.  Estimating Changing Extremes Using Empirical Ranking Methods , 2002 .

[7]  L. Haan,et al.  Extreme value theory : an introduction , 2006 .

[8]  J. R. Wallis,et al.  Regional Frequency Analysis: An Approach Based on L-Moments , 1997 .

[9]  E. Stewart,et al.  The FORGEX method of rainfall growth estimation II: Description , 1999 .

[10]  Nicholas J. Cook,et al.  Towards better estimation of extreme winds , 1982 .

[11]  L. Haan,et al.  Extreme value theory , 2006 .

[12]  U. Lohmann,et al.  Aerosol radiative forcing and climate sensitivity deduced from the Last Glacial Maximum to Holocene transition , 2008 .

[13]  M. Rajeevan,et al.  Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data , 2008 .

[14]  Uwe Ulbrich,et al.  Three extreme storms over Europe in December 1999 , 2001 .

[15]  T. A. Buishand,et al.  Extreme rainfall estimation by combining data from several sites , 1991 .

[16]  F. Zwiers,et al.  Climatology and Changes of Extratropical Cyclone Activity: Comparison of ERA-40 with NCEP NCAR Reanalysis for 1958 2001 , 2006 .

[17]  F. Laio Cramer–von Mises and Anderson‐Darling goodness of fit tests for extreme value distributions with unknown parameters , 2004 .

[18]  A. Bernard,et al.  The plotting of observations on probability-paper , 1955 .

[19]  A. Benard,et al.  Het uitzetten van waarnemingen op waarschijnlijkheids‐papier1 , 1953 .

[20]  J. R. Wallis,et al.  Probability weighted moments compared with some traditional techniques in estimating Gumbel Parameters and quantiles , 1979 .

[21]  Jean Palutikof,et al.  Tests of the Generalized Pareto Distribution for Predicting Extreme Wind Speeds , 2000 .