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- W3043452039 abstract "Abstract. A new probabilistic post-processing method for wind vectors is presented in adistributional regression framework employing the bivariate Gaussiandistribution. In contrast to previous studies, all parameters of thedistribution are simultaneously modeled, namely the location and scaleparameters for both wind components and also the correlation coefficientbetween them employing flexible regression splines. To capture a possiblemismatch between the predicted and observed wind direction, ensemble forecastsof both wind components are included using flexible two-dimensional smoothfunctions. This encompasses a smooth rotation of the wind direction conditionalon the season and the forecasted ensemble wind direction. The performance of the new method is tested for stations located in plains,in mountain foreland, and within an alpine valley, employing ECMWF ensembleforecasts as explanatory variables for all distribution parameters. Therotation-allowing model shows distinct improvements in terms of predictiveskill for all sites compared to a baseline model that post-processes each windcomponent separately. Moreover, different correlation specifications are tested,and small improvements compared to the model setup with no estimatedcorrelation could be found for stations located in alpine valleys." @default.
- W3043452039 created "2020-07-23" @default.
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- W3043452039 date "2019-04-01" @default.
- W3043452039 modified "2023-09-26" @default.
- W3043452039 title "Bivariate Gaussian Models for Wind Vectors in a Distributional Regression Framework" @default.
- W3043452039 hasPublicationYear "2019" @default.
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