LainePoiss®—A Lightweight and Ice-Resistant Wave Buoy

Alari, Victor; Björkqvist, Jan-Victor; Kaldvee, Valdur; Mölder, Kristjan; Rikka, Sander; Kask-Korb, Anne; Vahter, Kaimo; Pärt, Siim; Vidjajev, Nikon; Tõnisson, Hannes

doi:10.1175/jtech-d-21-0091.1

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…The devices recorded acceleration data with a 50 Hz sampling frequency, from which wave heights, mean wave period, and mean wave direction were derived in every 22 min. The exact procedures for converting the acceleration signal into surface waves are given by Alari et al [24]. The measurements were performed in autumn of 2021 and 2022.…”

Section: Methodsmentioning

confidence: 99%

Application of Shore Sediments Accumulated in Navigation Channel for Restoration of Sandy Beaches around Pärnu City, SW Estonia, Baltic Sea

Tõnisson,

Männikus,

Kont

et al. 2024

JMSE

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Sandy beaches high in recreation value make up 16% of the over 4000 km long shoreline of Estonia. The shore processes associated with climate change have remarkably accelerated over recent decades. Many sandy shores have suffered from strong erosion, including an excellent former beach at Valgeranna. The jetties, which were built in the 1860s to protect the navigation channel of Port Pärnu from clogging, have prevented natural sediment transport along the coast from south to north. At the same time, the sandy beach in Pärnu is expanding, and part of the sand accumulates with strong storms also in between the jetties, reducing the width of the shipping channel. The channel needs regular dredging, but, so far, the dredged sediment has been taken far away to the open sea and accumulated on the seabed. The current paper addresses the possibilities of using that sand for beach restoration in destructed and eroded areas. An overview of the applied methods and measurements during field studies is given. The results of modelling the processes of wave activity and sediment transport are discussed. The recycling of shore sediments is an important measure in sustainable coastal zone management. Different options and scenarios are analysed in order to find the most reasonable ways to bring sand back onto beaches and stabilize natural processes. Support from the state by working out respective laws and regulations would be motivating as well.

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Section: Methodsmentioning

confidence: 99%

Application of Shore Sediments Accumulated in Navigation Channel for Restoration of Sandy Beaches around Pärnu City, SW Estonia, Baltic Sea

Tõnisson,

Männikus,

Kont

et al. 2024

JMSE

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“…For the current article, surface gravity waves were measured and wave density spectra were calculated at four different locations in the Baltic Sea (Table I) using two different devices: Lainepoiss (LP [21]) and Datawell Waverider Mk-III (DWR [22]). The spectra were derived from 30 minutes of raw signal (50 Hz sampling rate for LP and 1.28 Hz for DWR).…”

Section: Data and Data Processing A Buoy Measurementsmentioning

confidence: 99%

Application of the LSTM Models for Baltic Sea Wave Spectra Estimation

Simon

Rikka

Nõmm

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The present paper proposes to apply long-shortterm memory (LSTM) deep learning models to transform Sentinel-1A/B interferometric wide (IW) swath image data into the wave density spectrum. Although spectral wave estimation methods for synthetic aperture radar data have been developed, similar approaches for coastal areas have not received enough attention. Partially, this is caused by the lack of high-resolution wave-mode data, as well as the nature of wind waves that have more complicated backscattering mechanisms compared to the swell waves for which the aforementioned methods were developed. The application of the LSTM model has allowed the transformation of the Sentinel-1A/B IW one-dimensional image spectrum into wave density spectra. The best results in the test data set led to the mean Pearson's correlation coefficient 0.85 for the comparison of spectra and spectra. The result was achieved with the LSTM model using V V and V H polarization spectra fed into the model independently. Experiments with LSTM neural networks that classify images into wave spectra with the Baltic Sea data set demonstrated promising results in cases where empirical methods were previously considered.

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