Meteotsunami research in the Strait of Georgia: Critical observational contributions from a student school network on Vancouver Island

2020 ◽  
Author(s):  
Alexander Rabinovich ◽  
Jadranka Šepić ◽  
Richard Thomson
Keyword(s):  
Sea Level ◽  
Vancouver Island ◽  
Small Scale ◽  
Tide Gauges ◽  
Strait Of Georgia ◽  
Level Data ◽  
Atmospheric Disturbances ◽  
Sea Level Oscillations ◽  
School Network ◽  
The Persian Gulf

<p>Meteorological tsunamis are frequently destructive tsunami-like waves generated by small-scale atmospheric disturbances. Several devastating events occurred recently in various regions of the world oceans, including the Balearic Islands, Sicily, the Adriatic and Black seas, the Great Lakes, the west coast of South Korea, the Netherlands and the Persian Gulf. Although this phenomenon has been actively studied for more than 25 years, the exact mechanism (or mechanisms) responsible for producing these extreme events remains a puzzle. One of the major problems making it difficult to determine the physical process generating meteotsunamis is the absence of a network of simultaneously working precise tide gauges and microbarographs in the affected region. A unique set of high-resolution atmospheric data from the meteorological “school network” of 132 school stations became available for 2008-2019 for the area of southern Vancouver Island and nearby Gulf Islands located in the Strait of Georgia. These data, combined with 1-min sea level data from Canadian Hydrographic Service (CHS) and USA National Oceanic and Atmospheric Administration (NOAA) tide gauges, has enabled us to examine both the spatial and temporal features of mesoscale atmospheric disturbances and coincident properties of the associated sea level oscillations. The data analyses, supported by a series of numerical experiments, has made it possible to reconstruct observed events and to determine the specific atmospheric parameters producing the strongest sea level response in the southern part of the Strait of Georgia. These experiments have helped us to recognize the most effective (and hence, most hazardous) directions and speeds of propagating atmospheric disturbances and to identify “hot spots” along the coast that are under the highest risk of large meteotsunamis.</p>

scholarly journals Testing an “IoT” Tide Gauge Network for Coastal Monitoring

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 17-32
Author(s):  
Philip Knight ◽  
Cai Bird ◽  
Alex Sinclair ◽  
Jonathan Higham ◽  
Andy Plater
Keyword(s):  
Sea Level ◽  
Morphological Changes ◽  
Tide Gauge ◽  
Low Cost ◽  
Pressure Sensors ◽  
Wave Activity ◽  
Tide Gauges ◽  
Coastal Monitoring ◽  
Level Data ◽  
Operational Constraints

A low-cost “Internet of Things” (IoT) tide gauge network was developed to provide real-time and “delayed mode” sea-level data to support monitoring of spatial and temporal coastal morphological changes. It is based on the Arduino Sigfox MKR 1200 micro-controller platform with a Measurement Specialties pressure sensor (MS5837). Experiments at two sites colocated with established tide gauges show that these inexpensive pressure sensors can make accurate sea-level measurements. While these pressure sensors are capable of ~1 cm accuracy, as with other comparable gauges, the effect of significant wave activity can distort the overall sea-level measurements. Various off-the-shelf hardware and software configurations were tested to provide complementary data as part of a localized network and to overcome operational constraints, such as lack of suitable infrastructure for mounting the tide gauges and for exposed beach locations.

Sea level in the Global Geodetic Observing System

2020 ◽  
Author(s):  
Elizabeth Bradshaw ◽  
Andy Matthews ◽  
Kathy Gordon ◽  
Angela Hibbert ◽  
Sveta Jevrejeva ◽  
...  
Keyword(s):  
Sea Level ◽  
Working Group ◽  
Sea Level Change ◽  
Tide Gauges ◽  
Controlled Vocabularies ◽  
Mean Sea Level ◽  
Level Change ◽  
Level Data ◽  
Metadata Standards

<p>The Permanent Service for Mean Sea Level (PSMSL) is the global databank for long-term mean sea level data and is a member of the Global Geodetic Observing System (GGOS) Bureau of Networks and Observations. As well as curating long-term sea level change information from tide gauges, PSMSL is also involved in developing other products and services including the automatic quality control of near real-time sea level data, distributing Global Navigation Satellite System (GNSS) sea level data and advising on sea level metadata development.<br>At the GGOS Days meeting in November 2019, the GGOS Focus Area 3 on Sea Level Change, Variability and Forecasting was wrapped up, but there is still a requirement in 2020 for GGOS to integrate and support tide gauges and we will discuss how we will interact in the future. A recent paper (Ponte et al., 2019) identified that only “29% of the GLOSS [Global Sea Level Observing System] GNSS-co-located tide gauges have a geodetic tie available at SONEL [Système d'Observation du Niveau des Eaux Littorales]” and we as a community still need to improve the ties between the GNSS sensor and tide gauges. This may progress as new GNSS Interferometric Reflectometry (GNSS-IR) sensors are installed to provide an alternative method to observe sea level. As well as recording the sea level, these sensors will also provide vertical land movement information from one location. PSMSL are currently developing an online portal of uplift/subsidence land data and GNSS-IR sea level observation data. To distribute the data, we are creating/populating controlled vocabularies and generating discovery metadata.<br>We are working towards FAIR data management principles (data are findable, accessible, interoperable and reusable) which will improve the flow of quality controlled sea level data and in 2020 we will issue the PSMSL dataset with a Digital Object Identifier. We have been working on improving our discovery and descriptive metadata including creating a use case for the Research Data Alliance Persistent (RDA) Identification of Instruments Working Group to help improve the description of a time series where the sensor and platform may change and move many times. Representatives from PSMSL will sit on the GGOS DOIs for Data Working Group and would like to contribute help with controlled vocabularies, identifying metadata standards etc. We will also contribute to the next GGOS implementation plan.<br>Ponte, Rui M., et al. (2019) "Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level." <em>Frontiers in Marine Science</em> 6(437).</p>

scholarly journals Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

2019 ◽  
Vol 11 (17) ◽  
pp. 4643
Author(s):  
Vivien Lai ◽  
Ali Najah Ahmed ◽  
M.A. Malek ◽  
Haitham Abdulmohsin Afan ◽  
Rusul Khaleel Ibrahim ◽  
...  
Keyword(s):  
Sea Level ◽  
Peninsular Malaysia ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mean Sea Level ◽  
Sea Levels ◽  
Tuning Parameters ◽  
Level Data ◽  
Input Dataset ◽  
Sea Level Oscillations

The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis.

Integrating Non-Tidal Sea Level data from altimetry and tide gauges for coastal sea level prediction

2012 ◽  
Vol 50 (8) ◽  
pp. 1099-1106 ◽  
Author(s):  
Yongcun Cheng ◽  
Ole Baltazar Andersen ◽  
Per Knudsen
Keyword(s):  
Sea Level ◽  
Tide Gauges ◽  
Level Data ◽  
Coastal Sea

Validation of sea level data in the east Asian marginal seas: Comparison between TOPEX/POSEIDON altimeter and in-situ tide gauges

2003 ◽  
Vol 20 (4) ◽  
pp. 650-660
Author(s):  
Yong-Hoon Youn ◽  
Im Sang Oh ◽  
Ki-Hyun Kim ◽  
Young-Hyang Park ◽  
Jong Woo Kim
Keyword(s):  
Sea Level ◽  
East Asian ◽  
Tide Gauges ◽  
Marginal Seas ◽  
East Asian Marginal Seas ◽  
Level Data

Sea-level Oscillations along the Australian Coast

1979 ◽  
Vol 30 (3) ◽  
pp. 295 ◽  
Author(s):  
DG Provis ◽  
R Radok
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Tide Gauges ◽  
Short Term ◽  
Sea Level Oscillations ◽  
Sea Level Variations ◽  
Long Term Variations

Sea level variations along Australia's coast were studied using records from tide gauges. The records were filtered to obtain two sets of time series, the short-term variations with periods between 1 and 20 days and the long-term variations with periods between 20 and 365 days. The coherence of the variations over long distances is noted and their magnitude is discussed with reference to possible causes.

scholarly journals Surface currents in the Porsanger fjord in northern Norway

2016 ◽  
Vol 37 (3) ◽  
pp. 337-360 ◽  
Author(s):  
Malgorzata Stramska ◽  
Andrzej Jankowski ◽  
Agata Cieszyńska
Keyword(s):  
Sea Level ◽  
Barents Sea ◽  
Surface Current ◽  
Current Data ◽  
Tidal Range ◽  
Surface Currents ◽  
Tidal Component ◽  
Level Data ◽  
Sea Level Oscillations ◽  
Tidal Constituents

Abstract We describe surface currents in the Porsanger fjord (Porsangerfjorden) located in the European Arctic in the vicinity of the Barents Sea. Our analysis is based on surface current data collected in the summer of 2014 using High Frequency (WERA, Helzel Messtechnik GmbH) radar system. One of our objectives was to separate out the tidal from the nontidal components of the currents and to determine the most important tidal constituents. Tides in the Porsanger fjord are substantial, with tidal range on the order of about 3 m. Tidal analysis attributes to tides about 99% of variance in sea level time series recorded in Honningsvaag. The most important tidal component in sea level data is the M2 component, with amplitude of ~90 cm. The S2 and N2 constituents (amplitude of ~20 cm) also play a significant role in the semidiurnal sea level oscillations. The most important diurnal component is K1 with amplitude of about 8 cm. The most important tidal component in analyzed surface currents records is the M2 component. The second most important component is the S2. Our results indicate that in contrast to sea level, only about 10-30% of variance in surface currents can be attributed to tidal currents. This means that about 70-90% of variance is due to wind-induced and geostrophic currents.

scholarly journals Spectral Analysis of Satellite Altimeter and Tide Gauge Data around the Northern Australian Coast

2020 ◽  
Vol 12 (1) ◽  
pp. 161 ◽  
Author(s):  
Zahra Gharineiat ◽  
Xiaoli Deng
Keyword(s):  
Time Series ◽  
Sea Level ◽  
High Frequency ◽  
Tide Gauge ◽  
Tidal Range ◽  
Tide Gauges ◽  
The North ◽  
Power Spectral ◽  
Level Data ◽  
Tidal Constituents

The north of Australia is known for its complex tidal system, where the highest astronomical tides (HATs) reach 12 m. This paper investigates the tidal behaviour in this region by developing spectral climatology for tide gauge and altimetry data. Power spectral density analysis is applied to detect the magnitude of ocean tides in 20 years of sea-level data from multimission satellite altimeters and tide gauges. The spectra of altimetry sea level anomaly (SLA) time series have their strongest peaks centred at approximately 2.11, 5.88, and 7.99 cycles per year (cpy), corresponding to the diurnal and semidiurnal tidal constituents K1, M2, and O1, respectively. Closer to the coastline, the spectra peak at high-frequency overtide and shallow-water constituents such as M4, MK4, and MK3. There have been many large, high-frequency spectral peaks near the coastline, indicating the difficulty of predicting tidal signals by coastal altimetry. Similar to altimetry observations, there are dominant semidiurnal and diurnal tidal peaks in tide gauge SLA time series accompanying a number of overtides. The semidiurnal and diurnal peaks are mostly higher on the northwest coast of Australia compared with the north and northeast coast. The results from both altimetry and tide gauges indicate that tidal range increases with increasing continental shelf.

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