Bayesian trend analysis of extreme wind using observed and hindcast series off the Catalan coast, NW Mediterranean Sea

M I Ortego,R Tolosana-Delgado,J J Egozcue

doi:10.5194/nhess-14-2387-2014

Abstract

Abstract. It has been suggested that climate change might modify the occurrence rate and magnitude of large ocean-wave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere–ocean system. Forecasting models are commonly used to assess these effects, given that good-quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behaviour, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcast data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the peak-over-threshold (POT) approach and the generalized Pareto distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution. All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcast and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of average wind speed at a deep buoy location off the Catalan coast (NE Spain, western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time is addressed.

Highlights

Interest on natural hazard prevention, prediction and mitigation has increased along the last decades: strong wind storms, extreme wind gusts, hurricanes and tornados are not an exception
A wind speed time series (REMO) has been analysed, together with a wind speed data set registered in a deep buoy in front of the Tarragona coast
A non-stationary Poisson/generalized Pareto distribution (GPD) model accounting for linear time trends and differences between the hindcast and buoy series has been assessed

Summary

Introduction

Interest on natural hazard prevention, prediction and mitigation has increased along the last decades: strong wind storms, extreme wind gusts, hurricanes and tornados are not an exception. The dangers, that a climate change might induce, add an additional challenge to the statistical analysis of extreme wind data as possible trends in extremal winds might occur, increasing the inherent difficulties of extremal analysis. Performing extremal analysis requires long records spanning decades, even centuries, to characterise the rarest events. This need is exacerbated when the data series are potentially affected by a trend in time. Whichever model is fitted, the uncertainty of the estimates is large when only data series with few events are available

Methods

Results

Conclusion