scholarly journals Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models

2021 ◽  
Vol 13 (16) ◽  
pp. 3162
Author(s):  
Simon Benaïchouche ◽  
Clément Legoff ◽  
Yann Guichoux ◽  
François Rousseau ◽  
Ronan Fablet
Keyword(s):  
State Of The Art ◽  
Climate Modeling ◽  
Spatial Scales ◽  
Surface Current ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Surface Currents ◽  
Ship Routing ◽  
Variational Models ◽  
Art Methods

The estimation of ocean dynamics is a key challenge for applications ranging from climate modeling to ship routing. State-of-the-art methods relying on satellite-derived altimetry data can hardly resolve spatial scales below ∼100 km. In this work we investigate the relevance of AIS data streams as a new mean for the estimation of the surface current velocities. Using a physics-informed observation model, we propose to solve the associated the ill-posed inverse problem using a trainable variational formulation. The latter exploits variational auto-encoders coupled with neural ODE to represent sea surface dynamics. We report numerical experiments on a real AIS dataset off South Africa in a highly dynamical ocean region. They support the relevance of the proposed learning-based AIS-driven approach to significantly improve the reconstruction of sea surface currents compared with state-of-the-art methods, including altimetry-based ones.

scholarly journals Distinct Influence of Air–Sea Interactions Mediated by Mesoscale Sea Surface Temperature and Surface Current in the Arabian Sea

2017 ◽  
Vol 30 (20) ◽  
pp. 8061-8080 ◽  
Author(s):  
Hyodae Seo
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wind Stress ◽  
Arabian Sea ◽  
Spatial Scales ◽  
Surface Current ◽  
Sea Surface ◽  
Surface Currents ◽  
Ekman Pumping ◽  
Wind Stress Curl

Abstract During the southwest monsoons, the Arabian Sea (AS) develops highly energetic mesoscale variability associated with the Somali Current (SC), Great Whirl (GW), and cold filaments (CF). The resultant high-amplitude anomalies and gradients of sea surface temperature (SST) and surface currents modify the wind stress, triggering the so-called mesoscale coupled feedbacks. This study uses a high-resolution regional coupled model with a novel coupling procedure that separates spatial scales of the air–sea coupling to show that SST and surface currents are coupled to the atmosphere at distinct spatial scales, exerting distinct dynamic influences. The effect of mesoscale SST–wind interaction is manifested most strongly in wind work and Ekman pumping over the GW, primarily affecting the position of GW and the separation latitude of the SC. If this effect is suppressed, enhanced wind work and a weakened Ekman pumping dipole cause the GW to extend northeastward, delaying the SC separation by 1°. Current–wind interaction, in contrast, is related to the amount of wind energy input. When it is suppressed, especially as a result of background-scale currents, depth-integrated kinetic energy, both the mean and eddy, is significantly enhanced. Ekman pumping velocity over the GW is overly negative because of a lack of vorticity that offsets the wind stress curl, further invigorating the GW. Moreover, significant changes in time-mean SST and evaporation are generated in response to the current–wind interaction, accompanied by a noticeable southward shift in the Findlater Jet. The significant increase in moisture transport in the central AS implies that air–sea interaction mediated by the surface current is a potentially important process for simulation and prediction of the monsoon rainfall.

scholarly journals Improving the Altimeter-Derived Surface Currents Using High-Resolution Sea Surface Temperature Data: A Feasability Study Based on Model Outputs

2016 ◽  
Vol 33 (12) ◽  
pp. 2769-2784 ◽  
Author(s):  
M.-H. Rio ◽  
R. Santoleri ◽  
R. Bourdalle-Badie ◽  
A. Griffa ◽  
L. Piterbarg ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Circulation ◽  
Large Scale ◽  
Spatial Scales ◽  
Ocean Surface ◽  
Sea Surface ◽  
High Temporal Resolution ◽  
Surface Currents ◽  
Ocean Surface Currents

AbstractAccurate knowledge of ocean surface currents at high spatial and temporal resolutions is crucial for a gamut of applications. The altimeter observing system, by providing repeated global measurements of the sea surface height, has been by far the most exploited system to estimate ocean surface currents over the past 20 years. However, it neither permits the observation of currents moving away from the geostrophic balance nor is it capable of resolving the shortest spatial and temporal scales of the currents. Therefore, to overcome these limitations, in this study the ways in which the high-spatial-resolution and high-temporal-resolution information from sea surface temperature (SST) images can improve the altimeter current estimates are investigated. The method involves inverting the SST evolution equation for the velocity by prescribing the source and sink terms and employing the altimeter currents as the large-scale background flow. The method feasibility is tested using modeled data from the Mercator Ocean system. This study shows that the methodology may improve the altimeter velocities at spatial scales not resolved by the altimeter system (i.e., below 150 km) but also at larger scales, where the geostrophic equilibrium might not be the unique or dominant process of the ocean circulation. In particular, the major improvements (more than 30% on the meridional component) are obtained in the equatorial band, where the geostrophic assumption is not valid. Finally, the main issues anticipated when this method is applied using real datasets are investigated and discussed.

scholarly journals Improving the Altimeter-Derived Surface Currents Using Sea Surface Temperature (SST) Data: A Sensitivity Study to SST Products

2020 ◽  
Vol 12 (10) ◽  
pp. 1601 ◽  
Author(s):  
Daniele Ciani ◽  
Marie-Hélène Rio ◽  
Bruno Buongiorno Nardelli ◽  
Hélène Etienne ◽  
Rosalia Santoleri
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Current ◽  
Zonal Flow ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Global Network ◽  
Altimeter Data ◽  
Surface Currents

Measurements of ocean surface topography collected by satellite altimeters provide geostrophic estimates of the sea surface currents at relatively low resolution. The effective spatial and temporal resolution of these velocity estimates can be improved by optimally combining altimeter data with sequences of high resolution interpolated (Level 4) Sea Surface Temperature (SST) data, improving upon present-day values of approximately 100 km and 15 days at mid-latitudes. However, the combined altimeter/SST currents accuracy depends on the area and input SST data considered. Here, we present a comparative study based on three satellite-derived daily SST products: the Remote Sensing Systems (REMSS, 1/10 ∘ resolution), the UK Met Office OSTIA (1/20 ∘ resolution), and the Multiscale Ultra-High resolution SST (1/100 ∘ resolution). The accuracy of the marine currents computed with our synergistic approach is assessed by comparisons with in-situ estimated currents derived from a global network of drifting buoys. Using REMSS SST, the meridional currents improve up to more than 20% compared to simple altimeter estimates. The maximum global improvements for the zonal currents are obtained using OSTIA SST, and reach 6%. Using the OSTIA SST also results in slight improvements (≃1.3%) in the zonal flow estimated in the Southern Ocean (45 ∘ S to 70 ∘ S). The homogeneity of the input SST effective spatial resolution is identified as a crucial requirement for an accurate surface current reconstruction. In our analyses, this condition was best satisfied by the lower resolution SST products considered.

scholarly journals Satellite-Derived Surface Current Divergence in Relation to Tropical Atlantic SST and Wind

2007 ◽  
Vol 37 (5) ◽  
pp. 1357-1375 ◽  
Author(s):  
Robert W. Helber ◽  
Robert H. Weisberg ◽  
Fabrice Bonjean ◽  
Eric S. Johnson ◽  
Gary S. E. Lagerloef
Keyword(s):  
Atlantic Ocean ◽  
Velocity Component ◽  
Surface Current ◽  
Sea Surface ◽  
Surface Currents ◽  
Tropical Atlantic ◽  
Cold Tongue ◽  
Equatorial Atlantic ◽  
Zonal Velocity ◽  
Mass Divergence

Abstract The relationships between tropical Atlantic Ocean surface currents and horizontal (mass) divergence, sea surface temperature (SST), and winds on monthly-to-annual time scales are described for the time period from 1993 through 2003. Surface horizontal mass divergence (upwelling) is calculated using surface currents estimated from satellite sea surface height, surface vector wind, and SST data with a quasi-linear, steady-state model. Geostrophic and Ekman dynamical contributions are considered. The satellite-derived surface currents match climatological drifter and ship-drift currents well, and divergence patterns are consistent with the annual north–south movement of the intertropical convergence zone (ITCZ) and equatorial cold tongue evolution. While the zonal velocity component is strongest, the meridional velocity component controls divergence along the equator and to the north beneath the ITCZ. Zonal velocity divergence is weaker but nonnegligible. Along the equator, a strong divergence (upwelling) season in the central/eastern equatorial Atlantic peaks in May while equatorial SST is cooling within the cold tongue. In addition, a secondary weaker and shorter equatorial divergence occurs in November also coincident with a slight SST cooling. The vertical transport at 30-m depth, averaged across the equatorial Atlantic Ocean between 2°S and 2°N for the record length, is 15(±6) × 106 m3 s−1. Results are consistent with what is known about equatorial upwelling and cold tongue evolution and establish a new method for observing the tropical upper ocean relative to geostrophic and Ekman dynamics at spatial and temporal coverage characteristic of satellite-based observations.

scholarly journals Prediction of geostrophic currents using big weather data archive and neural networks for the Aegean Sea

2019 ◽  
Vol 9 (1) ◽  
pp. 10-20
Author(s):  
Timur İnan ◽  
Ahmet Fevzi BABA
Keyword(s):  
Neural Network ◽  
Surface Current ◽  
Aegean Sea ◽  
Current Data ◽  
Sea Surface ◽  
Weather Data ◽  
Time Of Arrival ◽  
Surface Currents ◽  
Data Archive ◽  
Wave Direction

Prediction of sea and weather environment variables like wind speed, wind direction, wave height, wave direction, sea surface current direction and magnitude has always been an important subject in marine engineering as they effect on ship speed and effect the time of arrival to destination point as well. In this study, we propose a neural network that can predict the latitudinal and longitudinal components of sea surface currents in the Aegean Sea. The system can predict the sea surface currents components using the wind components which are gathered from the INMARSAT weather report system. The neural network is trained using the historical data which is gathered from UCAR historical weather database and historical surface current data which is gathered from IFREMER database. Keywords: Sea surface current, weather report, prediction, neural network, big data archive.

scholarly journals Characteristic Analysis of Sea Surface Currents around Taiwan Island from CODAR Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 3025
Author(s):  
Yu-Hao Tseng ◽  
Ching-Yuan Lu ◽  
Quanan Zheng ◽  
Chung-Ru Ho
Keyword(s):  
Taiwan Strait ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Surface Currents ◽  
Spatiotemporal Resolution ◽  
The North ◽  
Taiwan Island ◽  
The Taiwan Strait ◽  
The Kuroshio ◽  
Kuroshio Region

Sea surface currents observed by high-frequency (HF) radars have been widely used in ocean circulation research. In this study, hourly sea surface currents observed by the Taiwan Coastal Ocean Dynamics Applications Radar (CODAR) system from 2015 to 2019 were analyzed by the empirical orthogonal function (EOF) analysis to reveal the characteristics of the sea surface currents around Taiwan Island. The study area is divided into two regions, the Kuroshio region east of Taiwan Island and the Taiwan Strait west of Taiwan Island. In the Kuroshio region, the first EOF mode shows that the Kuroshio is characterized by higher current speeds with greater variability in summer. The second and third EOF modes present a dipole eddy pair and single eddy impingement on the Kuroshio during different periods. The seasonal variation of the dipole eddy pair indicates that the cyclonic/anticyclonic eddy on the north/south side appears more frequently in summer. Single eddy impingement occurs at multiple periods, including daily, intraseasonal, interseasonal, and annual periods. For the Taiwan Strait, the first EOF mode displays the tide signals. The tides enter the Taiwan Strait from the north and south, forming strong sea surface currents around the northern tip of Taiwan Island and the Penghu Archipelago. The second EOF mode exhibits the seasonal changes of the sea surface currents driven by the monsoon winds. The sea surface currents in the northern Taiwan Strait are relatively strong, possibly due to the narrow and shallow terrain there. The high spatiotemporal resolution of sea surface currents derived from CODAR observations provide more detailed characteristics of sea surface circulation around Taiwan Island.

scholarly journals Characteristics of surface currents in Manado Bay, North Sulawesi, Indonesia

2021 ◽  
Vol 5 (1) ◽  
pp. 42-52
Author(s):  
Aulia Dyan Yohanlis ◽  
Mutiara Rachmat Putri
Keyword(s):  
Surface Current ◽  
Current Speed ◽  
Sea Surface ◽  
Marine Debris ◽  
Surface Currents ◽  
Hydrodynamic Simulation ◽  
Average Speed ◽  
Debris Accumulation ◽  
North Sulawesi ◽  
Current Flows

Manado Bay is a complex waterway located in Manado City, North Sulawesi, Indonesia. It is an entry point for the Indonesia Trough-Flow, and its circulation is affected by the seasonal winds. Manado City has no debris net on its river estuaries. Therefore, marine debris can easily be carried away by the ocean currents and accumulate in the tourism areas located along the coast of Manado Bay. Consequently, it is important to study the sea surface current circulation in Manado Bay to deal with marine debris accumulation. In the present study, we utilized the DELFT3D software to simulate the hydrodynamic circulation in Manado Bay from 2016-2017. We conducted a 2-dimension (2D) horizontal hydrodynamic simulation using tidal and wind forcing from European Centre for Medium-Range Weather (ECMWF). The simulation results indicate that the change in bathymetry and wind affect the sea surface currents. During the summer monsoon (June-August), the sea surface current flows from the northeast to the southwest with an average speed of 1.1 cm s-1. On the contrary, during the transitional monsoon 1 (September-November), the sea surface current flows from the southeast to the northwest with an average speed of 1.3 cm s-1. Meanwhile, in the winter monsoon (December-February), the sea surface current originated from the southwest flows to the east with an average velocity of 1.9 cm s-1. Then, it moves from west to east during transitional monsoon 2 (March-May) with an average speed of 1.5 cm s-1. The current speed increases whenthe water enters the strait between the Bunaken Islands due to refraction, diffraction, and shallowing effect. As current flows toward the shallower area, the current speed increases, compensating the water column reduction.

scholarly journals Observations of Surface Currents and Tidal Variability Off of Northeastern Taiwan from Shore-Based High Frequency Radar

2021 ◽  
Vol 13 (17) ◽  
pp. 3438
Author(s):  
Yu-Ru Chen ◽  
Jeffrey D. Paduan ◽  
Michael S. Cook ◽  
Laurence Zsu-Hsin Chuang ◽  
Yu-Jen Chung
Keyword(s):  
High Frequency ◽  
Tide Gauge ◽  
Surface Current ◽  
Sea Surface ◽  
Field Pattern ◽  
Surface Currents ◽  
High Frequency Radar ◽  
Synoptic Variability ◽  
Four Seasons ◽  
Frequency Components

A network of high-frequency radars (HFRs) has been deployed around Taiwan. The wide-area data coverage is dedicated to revealing near real-time sea-surface current information. This paper investigates three primary objectives: (1) describing the seasonal current synoptic variability; (2) determining the influence of wind forcing; (3) describing the tidal current field pattern and variability. Sea surface currents derived from HFR data include both geostrophic components and wind-driven components. This study explored vector complex correlations between the HFR time series and wind, which was sufficient to identify high-frequency components, including an Ekman balance among the surface currents and wind. Regarding the characteristics of mesoscale events and the tidal field, a year-long high-resolution surface dataset was utilized to observe the current–eddy–tide interactions over four seasons. The harmonic analysis results derived from surface currents off of northeastern Taiwan during 2013 are presented. The results agree well with the tidal parameters estimated from tide-gauge station observations. The analysis shows that this region features a strong, mixed, mainly semidiurnal tide. Continued monitoring by a variety of sensors (e.g., satellite and HFR) would improve the understanding of the circulation in the region.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

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