Equivalent Storm Model for Long-Term Statistics of Sea Storms Off Norway

The paper deals with long-term analysis of ocean storms off Norway. Sixty years of wave model time series are considered for the analysis. The input data provide spectral characteristics of both wind and swell seas. The availability of global and partitioned significant wave heights enables the possibility of investigating how swell seas influence the storm shape in terms of growing and decay stages and on how this aspect affects the long-term estimates. The analysis is conducted by means of equivalent storm approach which consists of substituting the sequence of actual storms at a given site with a sequence of equivalent storms whose shape is fixed (such as triangular, power or exponential) and then calculating return periods of storm with given characteristics via analytical solutions derived on the basis of storm shape assumed. This is possible due to statistical equivalence between actual and equivalent storms which in turn leads to the equality of wave risk between actual and equivalent storm sequences at a given site. The equivalent storm associated with an actual one is defined by means of two parameters, related to the storm intensity and duration. The equivalent storm intensity is given by the maximum significant wave height in the actual storm history, while the duration is determined via an iterative procedure. In this paper the exponential shape is considered which is referred as equivalent exponential (EES) storm model. Some aspects related with the storm shape and its influence on return values estimate via EES model are investigated. Further, a sensitivity analysis of EES model to the storm threshold is proposed.

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Wave climate change in the Gulf of Bothnia

10.5194/egusphere-egu21-9808 ◽

2021 ◽

Author(s):

Jan-Victor Björkqvist ◽

Jani Särkkä ◽

Hedi Kanarik ◽

Laura Tuomi

Keyword(s):

Climate Change ◽

Wave Model ◽

Wave Climate ◽

Atmospheric Forcing ◽

Simulation Data ◽

Significant Wave ◽

Gulf Of Bothnia ◽

Wave Heights ◽

Significant Wave Heights ◽

Projected Changes

<p>Wave climate change in the Gulf of Bothnia in 2030&#8211;2059 was investigated using regional wave climate projections. For the simulations we used wave model WAM. As the atmospheric forcing for the wave model we had three global climate scenarios (HADGEM2-ES, MPI-ESM, EC-EARTH) downscaled with RCA4-NEMO regional model. The ice concentration for the wave model was obtained from NEMO ocean model simulations using the same atmospheric forcing. We used both RCP4.5 and RCP8.5 greenhouse gas scenarios. The spatial resolution of the simulation data was 1.8 km, enabling detailed analyses of the wave properties near the coast. From the simulation data we calculated statistics and return levels of significant wave heights using extreme value analysis, and assessed the projected changes in the wave climate in the Gulf of Bothnia. The projected increase in the significant wave heights is mainly due to the decreasing ice cover, especially in the Bothnian Bay. Projected changes in the most prevalent wind direction impacts the spatial pattern of the wave heights in the Bothnian Sea.</p>

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Assessing Climate Change in the North Atlantic Wave Regimes

Volume 2A: Structures, Safety, and Reliability ◽

10.1115/omae2020-18697 ◽

2020 ◽

Author(s):

M. Bernardino ◽

M. Gonçalves ◽

C. Guedes Soares

Keyword(s):

System Integration ◽

Wave Model ◽

Offshore Structures ◽

Future Climate ◽

Wave Direction ◽

Peak Period ◽

Significant Wave ◽

The North ◽

Wave Heights ◽

Mean Wave Period

Abstract An improved understanding of the present and future marine climatology is necessary for numerous activities, such as operation of offshore structures, optimization of ship routes and the evaluation of wave energy resources. To produce global wave information, the WW3 wave model was forced with wind and ice-cover data from an RCP8.5 EC-Earth system integration for two 30-year time slices. The first covering the periods from 1980 to 2009 represents the present climate and the second, covering the periods from 2070–2099, represents the climate in the end of the 21st century. Descriptive statistics of wind and wave parameters are obtained for different 30-year time slices. Regarding wind, magnitude and direction will be used. For wave, significant wave height (of total sea and swell), mean wave period, peak period, mean wave direction and energy will be investigated. Changes from present to future climate are evaluated, regarding both mean and extreme events. Maps of the theses statistics are presented. The long-term monthly joint distribution of significant wave heights and peak periods is generated. Changes from present to future climate are assessed, comparing the statistics between time slices.

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Wave climate in the Arkona Basin, the Baltic Sea

Ocean Science ◽

10.5194/os-8-287-2012 ◽

2012 ◽

Vol 8 (2) ◽

pp. 287-300 ◽

Cited By ~ 22

Author(s):

T. Soomere ◽

R. Weisse ◽

A. Behrens

Keyword(s):

Baltic Sea ◽

Wave Height ◽

Wave Model ◽

Wave Climate ◽

Wind Fields ◽

The Baltic Sea ◽

Wave Heights ◽

The Baltic ◽

Basic Features

Abstract. The basic features of the wave climate in the Southwestern Baltic Sea (such as the average and typical wave conditions, frequency of occurrence of different wave parameters, variations in wave heights from weekly to decadal scales) are established based on waverider measurements at the Darss Sill in 1991–2010. The measured climate is compared with two numerical simulations with the WAM wave model driven by downscaled reanalysis of wind fields for 1958–2002 and by adjusted geostrophic winds for 1970–2007. The wave climate in this region is typical for semi-enclosed basins of the Baltic Sea. The maximum wave heights are about half of those in the Baltic Proper. The maximum recorded significant wave height HS =4.46 m occurred on 3 November 1995. The wave height exhibits no long-term trend but reveals modest interannual (about 12 % of the long-term mean of 0.76 m) and substantial seasonal variation. The wave periods are mostly concentrated in a narrow range of 2.6–4 s. Their distribution is almost constant over decades. The role of remote swell is very small.

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Climatology, Variability and Extrema of Ocean Waves: The Web-Based KNMI/ERA-40 Wave Atlas

Volume 3: Safety and Reliability; Materials Technology; Douglas Faulkner Symposium on Reliability and Ultimate Strength of Marine Structures ◽

10.1115/omae2006-92568 ◽

2006 ◽

Author(s):

Andreas Sterl ◽

Sofia Caires

Keyword(s):

Wave Height ◽

Significant Wave Height ◽

Wave Model ◽

Ocean Waves ◽

Wave Period ◽

Web Based ◽

Significant Wave ◽

Wave Parameters ◽

Wave Heights ◽

The Web

The European Centre for Medium Range Weather Forecasts (ECMWF) has recently finished ERA-40, a reanalysis covering the period September 1957 to August 2002. One of the products of ERA-40 consists of 6-hourly global fields of wave parameters like significant wave height and wave period. These data have been generated with the Centre’s WAM wave model. From these results the authors have derived climatologies of important wave parameters, including significant wave height, mean wave period, and extreme significant wave heights. Particular emphasis is on the variability of these parameters, both in space and time. Besides for scientists studying climate change, these results are also important for engineers who have to design maritime constructions. This paper describes the ERA-40 data and gives an overview of the results derived. The results are available on a global 1.5° × 1.5° grid. They are accessible from the web-based KNMI/ERA-40 Wave Atlas at http://www.knmi.nl/waveatlas.

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Statistical trend analysis and extreme distribution of significant wave height from 1958 to 1999 – an application to the Italian Seas

Ocean Science ◽

10.5194/os-6-525-2010 ◽

2010 ◽

Vol 6 (2) ◽

pp. 525-538 ◽

Cited By ~ 29

Author(s):

G. Martucci ◽

S. Carniel ◽

J. Chiggiato ◽

M. Sclavo ◽

P. Lionello ◽

...

Keyword(s):

Extreme Values ◽

Wave Model ◽

Northern Adriatic Sea ◽

Sea State ◽

Northern Adriatic ◽

Significant Wave ◽

Peak Over Threshold ◽

Wave Heights ◽

Italian Coasts ◽

A Site

Abstract. The study is a statistical analysis of sea states timeseries derived using the wave model WAM forced by the ERA-40 dataset in selected areas near the Italian coasts. For the period 1 January 1958 to 31 December 1999 the analysis yields: (i) the existence of a negative trend in the annual- and winter-averaged sea state heights; (ii) the existence of a turning-point in late 80's in the annual-averaged trend of sea state heights at a site in the Northern Adriatic Sea; (iii) the overall absence of a significant trend in the annual-averaged mean durations of sea states over thresholds; (iv) the assessment of the extreme values on a time-scale of thousand years. The analysis uses two methods to obtain samples of extremes from the independent sea states: the r-largest annual maxima and the peak-over-threshold. The two methods show statistical differences in retrieving the return values and more generally in describing the significant wave field. The r-largest annual maxima method provides more reliable predictions of the extreme values especially for small return periods (<100 years). Finally, the study statistically proves the existence of decadal negative trends in the significant wave heights and by this it conveys useful information on the wave climatology of the Italian seas during the second half of the 20th century.

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Determination of Significant Wave Heights Using Damping Coefficients of Attenuated GNSS SNR Data from Static and Kinematic Observations

Remote Sensing ◽

10.3390/rs11040409 ◽

2019 ◽

Vol 11 (4) ◽

pp. 409 ◽

Cited By ~ 6

Author(s):

Ole Roggenbuck ◽

Jörg Reinking ◽

Tomke Lambertus

Keyword(s):

Signal To Noise Ratio ◽

Wave Model ◽

Significant Wave ◽

Sea Surface Roughness ◽

Wave Heights ◽

Gnss Reflectometry ◽

Significant Wave Heights ◽

Using Data ◽

Ocean Remote Sensing

Currently, GNSS reflectometry based on the signal-to-noise ratio (SNR) has become an established tool in ocean remote sensing. Here, the distance between an antenna and the water surface is measured by analyzing the oscillation of the SNR observation. Due to the antenna gain pattern, this oscillation is more pronounced for satellite signals coming from low elevation angles. Additionally, the sea surface roughness is related to the attenuation of the SNR oscillation. Hence, the significant wave height (SWH) can be estimated by analyzing the SNR signal. In this work, a method is presented with which the SWH can be calculated from the attenuation’s damping coefficient of the SNR observations measured with surface-based receivers. The method’s usability is demonstrated using data from a static antenna operated in the German Bight and with data from a ship-based antenna. The estimated SWH values were validated against numerical wave model data. For both experiments, a high correlation was found.

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Statistical trend analysis and extreme distribution of significant wave height from 1958 to 1999 – an application to the Italian Seas

Ocean Science Discussions ◽

10.5194/osd-6-2005-2009 ◽

2009 ◽

Vol 6 (3) ◽

pp. 2005-2036 ◽

Cited By ~ 5

Author(s):

G. Martucci ◽

S. Carniel ◽

J. Chiggiato ◽

M. Sclavo ◽

P. Lionello ◽

...

Keyword(s):

Extreme Values ◽

Wave Model ◽

Northern Adriatic Sea ◽

Sea State ◽

Northern Adriatic ◽

Significant Wave ◽

Peak Over Threshold ◽

Wave Heights ◽

Italian Coasts ◽

A Site

Abstract. The study is a statistical analysis of sea states timeseries derived using the wave model WAM forced by the ERA-40 dataset in selected areas near the Italian coasts. For the period 1 January 1958 to 31 December 1999 the analysis yields: (i) the existence of a negative trend in the annual- and winter-averaged sea state heights; (ii) the existence of a turning-point in late 70's in the annual-averaged trend of sea state heights at a site in the Northern Adriatic Sea; (iii) the overall absence of a significant trend in the annual-averaged mean durations of sea states over thresholds; (iv) the assessment of the extreme values on a time-scale of thousand years. The analysis uses two methods to obtain samples of extremes from the independent sea states: the r-largest annual maxima and the peak-over-threshold. The two methods show statistical differences in retrieving the return values and more generally in describing the significant wave field. The study shows the existence of decadal negative trends in the significant wave heights and by this it conveys useful information on the wave climatology of the Italian seas during the second half of the 20th century.

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Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights

Mathematics ◽

10.3390/math9212817 ◽

2021 ◽

Vol 9 (21) ◽

pp. 2817

Author(s):

Pushpa Dissanayake ◽

Teresa Flock ◽

Johanna Meier ◽

Philipp Sibbertsen

Keyword(s):

Time Series ◽

Long Range ◽

High Threshold ◽

Peaks Over Threshold ◽

Significant Wave ◽

Wave Heights ◽

Short And Long Term ◽

Significant Wave Heights ◽

Threshold Exceedances

The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data often exhibits a time series structure, this assumption is likely to be violated due to short- and long-term dependencies in practical settings, leading to clustering of high-threshold exceedances. In this paper, we first review popular approaches that either focus on modelling short- or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag–Leffler distribution modelling waiting times between exceedances. Further, we propose a new two-step approach capturing both short- and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average peaks-over-threshold (ARFIMA-POT) approach, which in a first step fits an ARFIMA model to the original series and then in a second step utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short- nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach, however, provides a significant improvement in terms of model fit, underlining the need for models that jointly incorporate short- and long-range dependence to address extremal clustering, and their theoretical justification.

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