Nearshore wave forecasting and hindcasting by dynamical and statistical downscaling

TL;DR

This paper presents a high-resolution wave modeling system combining dynamical and statistical downscaling to provide rapid nearshore wave forecasts, hindcasts, and return value estimates, effectively addressing complex local wave conditions.

Contribution

It introduces a novel approach using a transfer function for statistical downscaling of wave hindcasts, enabling efficient and accurate nearshore wave predictions and return value estimations.

Findings

High correlation (0.96) with buoy data

Overestimation of wave height by 18%

Effective statistical downscaling method for rapid forecasts

Abstract

A high-resolution nested WAM/SWAN wave model suite aimed at rapidly establishing nearshore wave forecasts as well as a climatology and return values of the local wave conditions with Rapid Enviromental Assessment (REA) in mind is described. The system is targeted at regions where local wave growth and partial exposure to complex open-ocean wave conditions makes diagnostic wave modelling difficult. SWAN is set up on 500 m resolution and is nested in a 10 km version of WAM. A model integration of more than one year is carried out to map the spatial distribution of the wave field. The model correlates well with wave buoy observations (0.96) but overestimates the wave height somewhat (18%, bias 0.29 m). To estimate wave height return values a much longer time series is required and running SWAN for such a period is unrealistic in a REA setting. Instead we establish a direction-dependent transfer function between an already existing coarse open-ocean hindcast dataset and the high-resolution nested SWAN model. Return values are estimated using ensemble estimates of two different extreme-value distributions based on the full 52 years of statistically downscaled hindcast data. We find good agreement between downscaled wave height and wave buoy observations. The cost of generating the statistically downscaled hindcast time series is negligible and can be redone for arbitrary locations within the SWAN domain, although the sectors must be carefully chosen for each new location. The method is found to be well suited to rapidly providing detailed wave forecasts as well as hindcasts and return values estimates of partly sheltered coastal regions.