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1. Introduction
Forecasting surface wind speed has broad importance for aviation, wind energy, engineering, public safety, and other applications (Young and Kristensen 1992; Peterka and Shahid 1998; Ashley and Black 2008; Emeis 2014). Accurate sustained and gust wind speed forecasts at the surface at a variety of spatial and temporal resolutions are necessary for planning and warning decisions in these applications on both short and long time scales (Okumus and Dinler 2016). One challenge in predicting wind speed is that available numerical weather prediction (NWP) models and general circulation models (GCMs) do not have the necessary temporal resolution needed to inform or make decisions. For example, current operational NWP models have model time steps ranging from about 20 s to several minutes, depending on the resolution of the model, and usually only have output available at a much larger time step (e.g., 1 h). This challenge has led to the development of downscaling techniques to produce wind information at higher spatial and/or temporal resolution.
One possible method is dynamical downscaling. Dynamical downscaling involves running a high-resolution model over a limited domain using the initial and boundary conditions from a coarser NWP model or GCM (e.g., Horvath et al. 2012; Cao and Fovell 2016; Daines et al. 2016). The spatial and temporal resolution can be controlled to get tailored output for a particular application. However, dynamical downscaling can be computationally expensive, especially if there is a need to explicitly resolve the turbulent eddies within the boundary and surface layers (e.g., Talbot et al. 2012; Mirocha et al. 2014), resolve winds around complex terrain features (e.g., Horvath et al. 2012; Cao and Fovell 2016), or generate many years of wind data for climate change studies (e.g., Daines et al. 2016).
Another possible method is statistical downscaling. Statistical downscaling involves deriving transfer functions that relate NWP model or GCM fields to a more realistic representation of the local- to regional-scale surface wind speed or wind speed distribution. A number of methods have been developed, including varieties of regression (de Rooy and Kok 2004; Pryor et al. 2005; Cheng et al. 2012; Curry et al. 2012; Huang et al. 2015; Winstral et al. 2017), generalized linear models (Yan et al. 2002; Kirchmeier et al. 2014), cumulative distribution function transformations...