A Combination of Spatial Domain Filters to Detect Surface Ocean Current from Multi-Sensor Remote Sensing Data

This research investigates the applicability of combining spatial filter’s algorithm to extract surface ocean current. Accordingly, the raster filters were tested on 80–13,505 daily images to detect Kuroshio Current (KC) on weekly, seasonal, and climatological scales. The selected raster filters are convolution, Laplacian, north gradient, sharpening, min/max, histogram equalization, standard deviation, and natural break. In addition, conventional data set of sea surface currents, sea surface temperature (SST), sea surface height (SSH), and non-conventional data such as total heat flux, surface density (SSD), and salinity (SSS) were employed. Moreover, controversial data on ocean color are included because very few studies revealed that chlorophyll-α is a proxy to SST in the summer to extract KC. Interestingly, the performance of filters is uniform and thriving for seasonal and on a climatological scale only by combining the algorithms. In contrast, the typical scenario of identifying Kuroshio signatures using an individual filter and by designating a value spectrum is inapplicable for specific seasons and data set. Furthermore, the KC’s centerlines computed from SST, SSH, total heat flux, SSS, SSD, and chlorophyll-α correlate with sea surface currents. Deviations are observed in the various segments of Kuroshio’s centerline extracted from heat flux, chlorophyll-α, and SSS flowing across Tokara Strait from northeast Taiwan to the south of Japan.

Assimilating realistically simulated wide-swath altimeter observations in a high-resolution shelf-seas forecasting system

The impact of assimilating simulated wide-swath altimetry observations from the upcoming Surface Water and Ocean Topography (SWOT) mission is assessed using observing system simulation experiments (OSSEs). These experiments use the Met Office 1.5 km resolution North West European Shelf analysis and forecasting system. In an effort to understand the importance of future work to account for correlated errors in the data assimilation scheme, we simulate SWOT observations with and without realistic correlated errors. These are assimilated in OSSEs along with simulated observations of the standard observing network, also with realistic errors added. It was found that while the assimilation of SWOT observations without correlated errors reduced the RMSE (root mean squared error) in sea surface height (SSH) and surface current speeds by up to 20 %, the inclusion of correlated errors in the observations degraded both the SSH and surface currents, introduced an erroneous increase in the mean surface currents and degraded the subsurface temperature and salinity. While restricting the SWOT data to the inner half of the swath and applying observation averaging with a 5 km radius negated most of the negative impacts, it also severely limited the positive impacts. To realise the full benefits in the prediction of the ocean mesoscale offered by wide-swath altimetry missions, it is crucial that methods to ameliorate the effects of correlated errors in the processing of the SWOT observations and account for the correlated errors in the assimilation are implemented.

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

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.

Sensitivity of Surface Currents to Bathymetry in a Partially-Mixed Estuary with Applications to Inverse Modeling

An inversion technique was tested for estimating bathymetry from observations of surface currents in a partially-mixed estuary, Mouth of the Columbia River (MCR). The methodology uses an iterative ensemble-based assimilation scheme which is found to have good skill for recovering bathymetry from observations distributed in space and time. However, the inversion skill is highly dependent on the tidal phase, location of the observations, and flow-dependent estuary dynamics. Inversion skill was found to degrade during periods of higher river discharge (up to ~ 12,000m3), or low tidal amplitude, while inversion of depth-averaged velocities instead of surface velocities caused increased skill throughout the domain. These results point to dynamical limits on inversion skill, caused by changes in estuary dynamics that affect the sensitivity of surface velocities to bathymetry. An adjoint sensitivity analysis is used to visualize these effects and is combined with data-denial experiments to explore the flow-dependent inversion skill.

A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video

Mapping coastal bathymetry from remote sensing becomes increasingly more attractive for the coastal community. It is facilitated by a rising availability of drone and satellite data, advances in data science, and an open-source mindset. Coastal bathymetry, but also wave directions, celerity and near-surface currents can simultaneously be derived from aerial video of a wave field. However, the required video processing is usually extensive, requires skilled supervision, and is tailored to a fieldsite. This study proposes a video-processing algorithm that resolves these issues. It automatically adapts to the video data and continuously returns mapping updates and thereby aims to make wave-based remote sensing more inclusive to the coastal community. The code architecture for the first time includes the dynamic mode decomposition (DMD) to reduce the data complexity of wavefield video. The DMD is paired with loss-functions to handle spectral noise and a novel spectral storage system and Kalman filter to achieve fast converging measurements. The algorithm is showcased for fieldsites in the USA, the UK, the Netherlands, and Australia. The performance with respect to mapping bathymetry was validated using ground truth data. It was demonstrated that merely 32 s of video footage is needed for a first mapping update with average depth errors of 0.9–2.6 m. These further reduced to 0.5–1.4 m as the videos continued and more mapping updates were returned. Simultaneously, coherent maps for wave direction and celerity were achieved as well as maps of local near-surface currents. The algorithm is capable of mapping the coastal parameters on-the-fly and thereby offers analysis of video feeds, such as from drones or operational camera installations. Hence, the innovative application of analysis techniques like the DMD enables both accurate and unprecedentedly fast coastal reconnaissance. The source code and data of this article are openly available.

Derivation and Evaluation of Satellite-Based Surface Current

Observations of real-time ocean surface currents allow one to search and rescue at ocean disaster sites and investigate the surface transport and fate of ocean contaminants. Although real-time surface currents have been mapped by high-frequency (HF) radar, shipboard instruments, satellite altimetry, and surface drifters, geostationary satellites have proved their capability in satisfying both basin-scale coverage and high spatiotemporal resolutions not offered by other observational platforms. In this paper, we suggest a strategy for the production of operational surface currents using geostationary satellite data, the particle image velocimetry (PIV) method, and deep learning-based evaluation. We used the model scalar field and its gradient to calculate the corresponding surface current via PIV, and we estimated the error between the true velocity field and calculated velocity field by the combined magnitude and relevance index (CMRI) error. We used the model datasets to train a convolutional neural network, which can be used to filter out bad vectors in the surface current produced by arbitrary model scalar fields. We also applied the pretrained network to the surface current generated from real-time Himawari-8 skin sea surface temperature (SST) data. The results showed that the deep learning network successfully filtered out bad vectors in a surface current when it was applied to model SST and created stronger dynamic features when the network was applied to Himawari SST. This strategy can help to provide a quality flag in satellite data to inform data users about the reliability of PIV-derived surface currents.

An Eigenmode Study of Nanoantennas from Terahertz to Optical Frequencies

In this work, we present a rigorous full-wave eigenanalysis for the study of nanoantennas operating at both terahertz (THz) (0.1–10 THz), and infrared/optical (10–750 THz) frequency spectrums. The key idea behind this effort is to reveal the physical characteristics of nanoantennas such that we can transfer and apply the state-of-the-art antenna design methodologies from microwaves to terahertz and optics. Extensive attention is given to penetration depth in metals to reveal whether the surface currents are sufficient for the correct characterization of nanoantennas, or the involvement of volume currents is needed. As we show with our analysis, the penetration depth constantly reduces until the region of 200 THz; beyond this point, it shoots up, requiring volume currents for the exact characterization of the corresponding radiating structures. The cases of a terahertz rectangular patch antenna and a plasmonic nanoantenna are modeled, showing in each case the need of surface and volume currents, respectively, for the antenna’s efficient characterization.

The Generation of Switchable Polarized Currents in Nodal Ring Semimetals Using High-Frequency Periodic Driving

A nodal ring semimetal (NRSM) can be driven to a spin-polarized NRSM or a spin-polarized Weyl semimetal (SWSM) by a high-frequency electromagnetic field. We investigate the conditions in realizing these phases and propose a switchable spin-polarized currents generator based on periodically driven NRSMs. Both bulk and surface polarized currents are investigated. The polarization of bulk current is sensitive to the amplitude of the driving field and robust against the direction and polarization of the driving, the opaqueness of the lead-device interface and the misalignment between the nodal ring and the interface, which provides sufficient flexibility in manipulating the devices. Similar switchable polarized surface currents are also expected, which is contributed by the Fermi arc surface state associated with the Weyl semimetal (WSM) phases. The generation of polarized currents and the polarization switching effect offer opportunities to design periodic driving controlled topological spintronics devices based on NRSMs.