Mixed extreme wave climate model for reanalysis databases

TL;DR

This paper introduces a mixed extreme value model that combines reanalysis and instrumental wave data to improve the accuracy of extreme wave climate predictions for offshore and coastal engineering.

Contribution

It proposes a novel methodology that merges reanalysis and instrumental records for more reliable extreme wave analysis, applicable to various statistical distributions.

Findings

The mixed model reduces uncertainty in extreme wave predictions.

Application to real data demonstrates improved accuracy over traditional methods.

The model is versatile across different extreme value analysis distributions.

Abstract

Hindcast or wave reanalysis databases (WRDB) constitute a powerful source with respect to instrumental records in the design of offshore and coastal structures, since they offer important advantages for the statistical characterization of wave climate variables, such as continuous long time records of significant wave heights, mean and peak periods, etc. However, reanalysis data is less accurate than instrumental records, making extreme data analysis derived from WRDB prone to under predict design return period values. This paper proposes a mixed extreme value model to deal with maxima, which takes full advantage of both (i) hindcast or wave reanalysis and (ii) instrumental records, reducing the uncertainty in its predictions. The resulting mixed model consistently merges the information given by both kinds of data sets, and it can be applied to any extreme value analysis distribution, such as generalized extreme value, peaks over threshold or Pareto-Poisson. The methodology is illustrated using both synthetically generated and real data, the latter taken from a given location on the northern Spanish coast.