Statistical Downscaling of Daily Wind Speed Variations
Statistical Downscaling of Daily Wind Speed Variations
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DOI:
10.1175/jamc-d-13-0230.1
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发表时间:
2013-01
影响因子:
3
通讯作者:
Megan C. Kirchmeier;D. Lorenz;D. Vimont
中科院分区:
文献类型:
--
作者:
Megan C. Kirchmeier;D. Lorenz;D. Vimont
Thisstudypresentsthedevelopmentofamethodtostatistically downscale dailywindspeedvariationsin an extended Great Lakes region. A probabilistic approach is used, predicting a daily-varying probability density function (PDF) of local-scale daily wind speed conditioned on large-scale daily wind speed predictors. Advantages of a probabilistic method are that it provides realistic information on the variance and extremes in addition to information on the mean, it allows the autocorrelation of downscaled realizations to be tuned to matchtheautocorrelationoflocal-scaleobservations,anditallowsflexibilityintheuseofthefinaldownscaled product.MuchattentionisgiventofittingtheproperfunctionalformofthePDFbyinvestigatingtheobserved local-scale wind speed distribution (predictand) as a function of the decile of the large-scale wind (predictor). It is found that the local-scale standard deviation and the local-scale shape parameter (from a gamma distribution) are nonconstant functions of the large-scale predictor. As such, a vector generalized linear model is developed to relate the large-scale and local-scale wind speeds. Maximum likelihood and cross validation are used tofit local-scale gamma distribution shape and scale parameters to the large-scalewind speed.The result isadaily-varyingprobabilitydistributionoflocal-scalewindspeed,conditionedon thelarge-scalewindspeed.