High-resolution wave climate analysis in the Helgoland area - GKSS

Chapter 3
Statistical-dynamical approach to the
wave simulation
It has been shown in the previous chapter that for an adequate assessment of the nearshore
wave statistics the large-scale wave data needs additional processing or downscaling.
The spectral wave model provides successful dynamical downscaling. However, faster methods
are sometimes required, especially in case of long-term hindcasts or scenario studies
and of limited computational resources. The strong dependency of the local (KMH) wave
parameters on the boundary conditions (HF) provides the opportunity to apply less timeconsuming
empirical downscaling models transforming medium-scale HF wave conditions
directly to the detailed high-resolution wave fields. The basis for the construction of the
statistical inter-scale relationships is provided by the medium-scale HF wave dataset and
by the corresponding local wave data obtained with the K-model. In case of reliable and
sufficiently homogeneous long-term measurements being available at the site of the construction,
they may be used instead of the K-model data. However, when such data is
not available, or information for some area surrounding the observation location is also
required, a very high-resolution wave model simulation (such as KMH), validated with at
least some existing data, is the best possible option. Thus, the combination of the statistical
methods and high-resolution wave data obtained with the dynamical method form the
statistical-dynamical approach investigated further.
In this chapter the general concept of downscaling is described together with a review
of the methods and recent applications. Several statistical techniques are applied to the
wave parameter downscaling with the main goal to obtain reliable small-scale extreme wave
statistics. The results of the method’s application to the Helgoland area are compared between
each other and with the independent dynamically obtained wave data. Comments
about the performance of the models, improvement options and potential limitations conclude
the chapter.