A Comparison between Coastal Altimetry Data and Tidal Gauge Measurements in the Gulf of Genoa (NW Mediterranean Sea)

Picco, Paola; Vignudelli, Stefano; Repetti, Luca

doi:10.3390/jmse8110862

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…In other different region, [49] reported better correlation and RMSE (up to 0.92 and 4.5 cm, respectively) between coastal altimetry product and tide gauge measurement over the Gulf of Genoa, Italy. This different result could be directly linked to local topography and environmental conditions, which have a strong influence on local sea level variability.…”

Section: Evaluation Of Waveform Retracking Performance Against Tide Gaugementioning

confidence: 80%

Waveform Classification and Retracking of Jason-2 and Jason-3 in Halmahera Sea

et al. 2021

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The accuracy of sea surface heights (SSHs) estimation from satellite altimeters is strongly influenced by the microwave reflected signals (or waveforms). Waveforms in open oceans generally have ideal shapes following the Brown (1977) model. However, in coastal and shallow waters, the signals are disturbed by lands, thus resulting in complicated waveforms (non-Brown). Non-Brown waveforms produce inaccurate SSH estimations; therefore, specialized protocols such as waveform classification and retracking are crucial when attempting to produce accurate estimations. In this study, waveforms of Jason-2 and Jason-3 satellite altimeters in the Halmahera were classified and retracked using several algorithms, such as Offset Centre of Gravity (OCOG), Ice, Threshold, and Improved Threshold. The results showed that waveforms in the Halmahera Sea had ten generic classes with dominant class of the Browns. The validation results showed that all retrackers (except OCOG) had the value of correlations exceeding 0.75, and Root Mean Square Error (RMSE) smaller than 25 cm at a distance of 5-20 km from the land. The Threshold 10% was the most common retracker that appeared with the highest improvement percentage (IMP), meanwhile the Ice retracker consistently produced the best correlation (0.86) and the lowest RMSE (16cm). The retracking results showed that waveform retracking generally can improve SSH estimation accuracy from ocean (standard) retracker.

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Section: Evaluation Of Waveform Retracking Performance Against Tide Gaugementioning

confidence: 80%

Waveform Classification and Retracking of Jason-2 and Jason-3 in Halmahera Sea

et al. 2021

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“…However, airborne or satellite-based platforms display some limitations such as height and length sensitivity, and information acquisition by points instead of surface; this implies the need to extend the local punctual information to the whole adjacent area. Local measurement can be performed by specific instruments, notably floating accelerometer buoys [ 4 ] on water, altimeter gauges [ 5 ] connected to airplane operating with radio pulses, and other instruments working by using electrical and optical variations. The accelerometers allow the water displacement to be measured in function of time.…”

Section: Introductionmentioning

confidence: 99%

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm

Lay-Ekuakille

Okitadiowo

Luccio

et al. 2021

Sensors

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Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air–water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

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