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Modelling monthly near‐surface maximum daily gust speed distributions in Southwest Germany
Author(s) -
Jung Christopher,
Schindler Dirk
Publication year - 2016
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4617
Subject(s) - quantile , cumulative distribution function , wind speed , statistics , environmental science , mathematics , meteorology , probability distribution , probability density function , geography
This study deals with high spatial resolution modelling of near‐surface gust speed distributions in a complex region. The database on which gust speed distributions were modelled includes 69 daily maximum gust speed time series provided by the German Weather Service ( DWD ) for the period 1979–2013. Comparability of the time series was achieved by gap filling, homogenisation, detrending and measuring height correction. Monthly empirical cumulative distribution functions (CDF emp ) were fitted by 48 continuous theoretical cumulative distribution functions (CDF). The best‐fitting CDF was identified by evaluating the Kolmogorov–Smirnov statistic ( D ), probability–probability ( P–P ) plots and quantile–quantile ( Q–Q ) plots. The five‐parameter Wakeby distribution ( WK5 ) was found to be the most appropriate CDF for all months. Therefore, monthly WK5 ‐parameters were modelled on a 50 × 50 m resolution grid by an LSBoost approach based on sector‐ and distance‐limited predictors of surface roughness and terrain‐related variables (curvature, elevation, topographic exposure) as well as on ERA‐Interim reanalysis wind speed data available at the 850 hPa pressure level ( U 850hPa ). The final, modelled WK5 ‐distributions (WK5 mod ) yield monthly quantiles ( F distr ) of daily gust speed values and return periods (RP distr ) of near‐surface gust speed up to 10 years.