Buoy Network Research Articles

A Multi‐Sensor Approach for the Characterization of Tropical Cyclone Induced Swell—Application to TC Larry, 2021

AbstractSea states are likely highly complex and variable under tropical cyclone (TC) conditions. Outrunning the inner core TC vortex, swell systems can radiate in different directions with different wavelengths. Jointly using several sources of wave observations, it is demonstrated that directional properties of wave fields induced by a TC can now be very well recovered. Directional wave information extracted from Sentinel‐1 wave mode and CFOSAT SWIM are combined with Sofar Spotter drifters and NDBC buoy network. Specific filtering is applied to deal with respective limitations of these observation systems. High consistency between the different data sources is generally obtained, to provide evolving directional distributions of radiating swell systems during the whole TC life time. TC wave properties can then trace particular events associated to extended fetch effects, quantifying the asymmetrical directional wavelength distributions, as function of the TC main characteristics evolving with time (intensity, size, translation).

Advanced Moored Data Buoys for Catching Typhoons in the Western North Pacific

Abstract With the western North Pacific being one of the most active typhoon hotspots, a reliable buoy system is essential for in situ observations during typhoon passages. This study details the development and implementation of two improved buoys designed by National Taiwan University based on the Autonomous Temperature Line Acquisition System. It aims to establish a buoy network for near real-time, high-resolution meteorological and oceanic data collection during typhoon periods in the western North Pacific. The buoy system was distinguished by its reliable design, low power-consumption system, dual satellite and radio communication system, and inductive modem-based data acquisition system. Moreover, the system was proven robust to extreme weather conditions and fishing challenges. From 2015 to 2022, we conducted sea trials in typhoon hotspot regions every summer (normally June to October), demonstrating the buoys' durability and capability to withstand 21 typhoons while collecting valuable scientific data.

Footprint of the air-sea momentum transfer saturation observed by ocean wave buoy network in extreme tropical cyclones

Tropical cyclones are one of the most destructive natural phenomena, causing tremendous coastal disasters worldwide. The maximum intensity of tropical cyclones is determined by momentum and heat transfer at the air-sea interface. Momentum transfer corresponds to the momentum loss of tropical cyclones and, consequently, to the underlying ocean's momentum gain causing extreme ocean waves and storm surges. Air-side observations of wind profiles and ocean-side observations of ocean subsurface currents show a slowdown of momentum transfer under high tropical cyclone wind speeds in the previous studies. However, there is still disagreement regarding the slowdown owing to lack of data. Here, we show momentum transfer under high wind speed conditions by observing ocean waves. Although ocean wave observations are highly spatially limited, we deployed a fleet of drifting ocean wave buoys covering the active area of tropical cyclones in the Western North Pacific. The buoy fleet captured extreme waves near the eye of the strongest category 5 tropical cyclone, indicating an ocean wave footprint of the momentum transfer saturation above surface wind speeds of 25 m/s. Our approach from ocean surface wave observation is a unique contribution to determination on air-sea momentum transfer slowdown under extreme wind speeds, which can compensate for the knowledge from conventional air and ocean-side observations. This finding advances tropical cyclones, extreme ocean waves, and storm surge modeling.

Spatial diversity processing mechanism based on the distributed underwater acoustic communication system.

To address the problem of unreliable single-link underwater acoustic communication caused by large signal delays and strong multipath effects in shallow water environments, this paper proposes a distributed underwater acoustic diversity communication system (DUA-DCS). DUA-DCS employs a maneuverable distributed cross-medium buoy network to form multiple distributed, non-coherent, and parallel communication links. In the uplink, a receiving diversity processing mechanism of joint decision feedback equalizer embedded phase-locked loop and maximum signal-to-interference ratio combining (DFE-PLL-MSIRC) is proposed to achieve waveform-level diversity combining of underwater signals. A phase-locked loop module is embedded in each branch of the decision feedback equalizer to eliminate the residual frequency and phase errors after Doppler compensation. Meanwhile, the combining coefficients are determined based on the maximum signal-to-interference ratio criterion, taking into account the residual inter-symbol interference after equalization, resulting in efficient and accurate computation. Additionally, the combined decision values are fed back to the feedback filters in each branch to ensure more accurate feedback output. Simulation and lake experiment results demonstrate that, compared to the single-link communication system, DFE-PLL-MSIRC can achieve a diversity gain of more than 5.2 dB and obtain about 3 dB more diversity gain than the comparison algorithm. And the BER of DFE-PLL-MSIRC can be reduced by at least one order of magnitude, which is lower by at least 0.6 order of magnitude compared to the comparison algorithm. In the downlink, a transmitting diversity processing mechanism of complex orthogonal space-time block coding (COSTBC) is proposed. By utilizing a newly designed generalized complex orthogonal transmission matrix, complete transmission diversity can be achieved at the coding rate of 3/4. Compared to the single-link communication system, the system can achieve a diversity gain of more than 6 dB.

Bolstered by Buoys: Predicting El Ni�o

Scientists investigate the importance of a Pacific buoy network in monitoring and predicting the El Ni�o–Southern Oscillation.

GLOBAL WAVE CLIMATE TRENDS: WHAT DO THE SOUTHERN HEMISPHERE WAVE BUOYS TELL US?

Australia’s national wave data network presently consists of around 35 directional wave buoys distributed across the Australian coastline, with 11 of these buoys have been in operation since the mid-seventies. South Africa is home to 4 offshore wave buoys which have operating since the early eighties. New Zealand wave buoy network is relatively younger, with 3 wave buoys in operation since the early 2000s. All of these buoys have been providing invaluable longterm historical wave data which have allowed the offshore and coastal scientific community to better assess extreme wave climates and their impact on the coastline. Additionally, these data records of wave observations are key for model validation and to better understand the effects of climate change on the local and regional wave climate.

Design of buoy network in port water area for monitoring air pollution: A robust optimization approach

Buoy network is a promising technique to monitor air pollution in port water area, which is not limited by terrain and can provide continuous measurements at a low cost. Determining the number and location of buoys is a key issue in the design of the buoy network, however, it is affected by stochasticity of wind direction, wind speed, and emission intensity of ship exhaust. Therefore, a robust optimization approach is proposed to solve the problem of how to design buoy network in port water area for monitoring air pollution, i.e., to determine the number and location of buoys, with consideration of stochastic ship exhaust emissions and wind conditions. First, a bi-objective robust optimization model is developed based on the concept of p-robust to maximize monitoring capacity and minimize costs of buoy network. A method for constructing stochastic scenarios is subsequently proposed by combining Latin Hypercube Sampling, ship emission inventory, and atmospheric diffusion simulation. Then, a solution algorithm integrating Epsilon-constraint and Nash bargaining solution methods is presented to solve the model. Finally, a container port in eastern China is selected as a study case. Finally, the relative regret of the obtained robust layout is less than 13.04%. Results show that improving monitoring capacity and cross-scenario robustness is premised on increasing costs, decision-makers need to balance these aspects and design a buoy network with actual budgets. This study provides insights into how to design a buoy network to monitor air pollution in port water areas, and presents methods to determine the decision solution of a bi-objective robust optimization model, which helps maritime and environmental authorities manage ship emissions in port and coastal regions.

Impacts of Marine Surface Pressure Observations from a Spaceborne Differential Absorption Radar Investigated with an Observing System Simulation Experiment

Abstract A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

Deriving spatial wave data from a network of buoys and ships

The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship-as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state information.

Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency

Technological development in terms of the power requirement for data acquisition and processing opens new perspectives in the field of environmental monitoring. Near real-time data flow about the sea condition and a possible direct interface with applications and services devoted to marine weather networks would have a significant impact on several aspects, such as, for example, safety and efficiency. In this scenario, the needs of buoy networks have been analyzed, and the estimation of directional wave spectra from buoys’ data has been deeply investigated. Two methods have been implemented, namely the truncated Fourier series and the weighted truncated Fourier series, and they have been tested by both simulated and real experimental data, representative of typical Mediterranean Sea conditions. From simulation, the second method proved to be more efficient. From the application to real case studies, it emerged that it works effectively in real conditions, as confirmed by parallel meteorological observations. The estimation of the main propagation direction was possible with a small uncertainty of a few degrees, yet the method exhibits a limited directional resolution, which suggests the need for undertaking further studies, briefly addressed in the conclusions.

High‐Resolution Gridded Temperature and Salinity Fields From Argo Floats Based on a Spatiotemporal Four‐Dimensional Multigrid Analysis Method

AbstractThe shortage of in situ ocean observations greatly limits the development of gridded ocean subsurface environmental analysis products. Fully using the correlated information of the observations in time and space is an appropriate approach to this problem. Here we adopt a method of spatiotemporal four‐dimensional multigrid analysis. A weekly gridded Argo product is also constructed in the Northwest Pacific Ocean (0°–40°N, 120°–152°E) from 2005 to 2020, with a horizontal resolution of 0.5° × 0.5° and 65 layers in the upper 1,500 m. Then, the gridded Argo product is evaluated by using in situ Argo floats, satellite altimeter data, the Tropical Atmosphere Ocean/Triangle Trans‐Ocean Buoy Network (TAO/TRITON), high‐resolution shipboard observations and some global gridded Argo products. Compared with in situ Argo floats, the reconstructed fields retain the spatiotemporal information of Argo floats well. The maximum vertical root mean square errors of temperature and salinity between the gridded Argo product and in situ Argo floats are less than 0.47°C and 0.06 psu respectively. In addition, the product agrees well with independent observations of satellite altimeter data, TAO/TRITON. Compared with shipboard observations, the product can well reflect the structure of mesoscale eddies and detect some small‐scale water masses. Compared with global gridded Argo products, our product retains more mesoscale features and has spatiotemporal correlation.

Global atlas of extreme significant wave heights and relative risk ratios

Interest in offshore wind, wave, and tidal energy power generation to supply electricity to the grid nearshore or power off-grid applications far offshore has motivated numerous studies of the ocean wave climate, including extreme wave conditions that characterize project risk and wave loads for design. The present study uses the 30′ resolution 31-year Global WAVEWATCH III® model hindcast, the 4′ resolution 31-year coastal US model hindcast, and the US NOAA buoy network to estimate and map the global distribution of the mean, 50-, 5- and 1-year significant wave heights and the relative-risk-ratios computed by non-dimensionalizing these n-year extreme wave heights with their mean values. A two-step linear correction method is applied to correct systematic underbias of model-derived extreme wave heights relative to the observation-derived values, resulting in a 20% or more increase in model-derived values compared to uncorrected ones. The corrected 30’ resolution extreme wave height and relative risk ratio atlas generated herein provides important metrics that support resource characterization and international standards development for the marine energy industry, including resource and site assessment, and the establishment of upper design limits for device type classification and certification to streamline product line development.

Tropical Ocean Observations for Weather and Climate: A Decadal Overview of the Global Tropical Moored Buoy Array

This paper describes the evolution of the Global Tropical Moored Buoy Array (GTMBA) over the past decade since the last comprehensive and coordinated overview of the Pacific-Atlantic-Indian Ocean system in 2010. GTMBA provides sustained and systematic observations in real time for weather and climate research, forecasting, and assessments. It is maintained through multi-national consortia that support the Tropical Atmosphere Ocean (TAO) Array and the Triangle Trans-Ocean Buoy Network (TRITON) in the Pacific, the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), and the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean. Phenomena of interest span a wide range of weather and climate timescales, including tropical cyclones, the Madden-Julian Oscillation, the seasonal cycle, monsoon circulations, El Niño-Southern Oscillation, climate variations on decadal timescales, and trends related to climate change. Recent scientific advances enabled by GTMBA are reviewed along with array design changes that respond to new scientific imperatives and operational exigencies, and future directions are discussed.

Remapping of Temperature Profile Measurements in OMNI Buoy Systems

Abstract The OMNI (Ocean Moored Buoy Network for northern Indian Ocean) buoy network comprises 12 buoy systems that measure surface meteorological parameters along with temperature and salinity profile measurements at discrete levels up to 500 m. All the OMNI buoy systems are deployed with slack-line moorings, which respond more to wind, wave, and current forcing compared to taut-line mooring. Subsurface temperature measurements are subject to change depending on both environmental condition and mooring design. The standard sensor fit of the OMNI buoy systems has only one pressure sensor fixed at 500 m, which shows significant depth variability. In order to see the spatial and seasonal variability in the vertical movement of the mooring line and the associated temperature variability, four deployments with additional pressure measurements at 200 m are analyzed. It is observed that the depth/temperature variability exhibits significant seasonality with maximum variability during pre-monsoon season. Also, the effect of this movement in the shallower depths is analyzed with four more pressure sensors in the mooring line for a 1-year period in the central Bay of Bengal. The analysis shows that the maximum value of average and root mean square (RMS) temperature deviations is 0.38 °C and 0.48 °C in the deepest interpolated depth at 400 m where the mooring line experiences a greater range of motion and the actual temperature variability in shallower depths is negligible particularly up to 75 m (<0.01°C). The study reveals the necessity of additional pressure measurements for better remapping of temperature profile measurements.

A high‐resolution AUV navigation framework with integrated communication and tracking for under‐ice deployments

AbstractWe developed an environmentally adaptive under‐ice navigation framework that was deployed in the Arctic Beaufort Sea during the United States Navy Ice Exercise in March 2020 (ICEX20). This navigation framework contained two subsystems developed from the ground up: (1) an on‐board hydrodynamic model‐aided navigation (HydroMAN) engine, and (2) an environmentally and acoustically adaptive integrated communication and navigation network (ICNN) that provided acoustic navigation aiding to the former. The HydroMAN synthesized measurements from an inertial navigation system (INS), ice‐tracking Doppler velocity log (DVL), ICNN and pressure sensor into its self‐calibrating vehicle flight dynamic model to compute the navigation solution. The ICNN system, which consisted of four ice buoys outfitted with acoustic modems, trilaterated the vehicle position using the one‐way‐travel‐times (OWTT) of acoustic datagrams transmitted by the autonomous underwater vehicle (AUV) and received by the ice buoy network. The ICNN digested salinity and temperature information to provide model‐assisted real‐time OWTT range conversion to deliver accurate acoustic navigation updates to the HydroMAN. To decouple the contributions from the HydroMAN and ICNN subsystems towards a stable navigation solution, this article evaluates them separately: (1) HydroMAN was compared against DVL bottom‐track aided INS during pre‐ICEX20 engineering trials where both systems provided similar accuracy; (2) ICNN was evaluated by conducting a static experiment in the Arctic where the ICNN navigation updates were compared against GPS with ICNN error within low tens of meters. The joint HydroMAN‐ICNN framework was tested during ICEX20, which provided a nondiverging high‐resolution navigation solution—with the majority of error below 15 m—that facilitated a successful AUV recovery through a small ice hole after an 11 km untethered run in the upper and mid‐water column.

Environmentally adaptive acoustic communication and navigation for underice autonomous operation

The Arctic acoustic environment has changed dramatically, with multi-year ice being less abundant and the influx of warmer Pacific water, the so-called “Beaufort Lens,” creating a local maximum in the sound speed at a depth of 50–80 m. In contrast to the classic Arctic sound speed profile which had a monotonic increase in sound speed yielding a surface duct with strong ice interaction, the warm water lens creates a lower duct supporting extremely efficient propagation to long ranges. On the other hand, this double duct environment is decremental to short range communication and navigation of underwater vehicles. Thus, for a transmitter in one channel, the lens creates distinct shadow zones in the other channel at ranges between 1 and 4 km, which are the typical operational ranges of small AUVs, severely affecting navigation and communication performance. In addition, the temporal variability of is significant due to internal waves. During ICEX20 MIT and WHOI demonstrated an integrated communication and navigation concept where a network of ice-moored modem buoys with GPS tracking were used to track an under-ice AUV using regular CTD updates of optimal ranging, which in combination with a novel dynamic model constrained navigation fusion engine demonstrating GPS-grade navigation accuracy over several hours of operation. Using an EOF framework for updating the onboard environmental awareness on the AUV, the concept supports autonomous depth selection for optimal communication connectivity. [Work supported by ONR and UWDC/ASL.]

Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys

Due to recent technological advancements in the field of cloud-based satellite remote sensing, the barriers to global analysis ready dataset access and processing have lowered drastically increasing the opportunity for large-scale long-term satellite-derived shoreline monitoring solutions. However, validation studies employing synchronous and independent in-situ instantaneous shorelines at scale are limited. Thanks to a unique dataset of 47 synchronous UAV photogrammetric surveys in 15 coastal locations in Victoria, Australia, over 3 years, we validated Sentinel-2 instantaneous subpixel shorelines extracted using the most common water indices, CoastSat and a tidal-balanced convolutional neural network (Unet+++). We assessed the accuracy of the water level estimation using 9 wave run-up models and wave data sourced from a nearshore wave buoy network, a regional hindcast model and a global altimeter wave dataset. This paper also presents the first validation of Deep Learning-derived shorelines using synchronous independent in-situ data. We found the best overall performance and least threshold-sensitive shoreline extraction approach to be the WI index, whilst NDWI had the poorest performances despite being the most utilized across the literature. We found that the thresholdless Unet+++ solution coupled with an ensemble wave run-up model and the altimeter wave data is an adequate trade-off in terms of accuracy, adaptability and wave data access for global scale shoreline studies.Moreover, we highlighted the very high spatial variability of beach-scale shoreline extraction performances and found that generally, the best SDS extractor only performs best along 30–40% of a coastline. Our results suggest that future satellite-derived shoreline approaches should investigate the relationships between scene-dependent variables and shoreline accuracies in order to better understand the causes of beach-scale performance variations and increase the accuracy of satellite-derived shorelines.

Irish Wave Data—Rogues, Analysis and Continuity

The Marine Institute of Ireland operates a network of weather buoys around Ireland. A wave of 32.3 m height (crest–trough) was recorded by one of these buoys, the M6 buoy, off the coast of Ireland in October 2020. In this paper, the technological evolution of this network is explored, with a particular emphasis on this extremely high wave. Raw data and bulk parameters collected during the event are presented, and the wider met-ocean context is outlined. In addition, wave data across the buoy deployment period from dual wave sensors installed on the buoy are analysed. Differences in calculation methods are discussed, rogue incidence rates are calculated, and the sensors are found to be generally in good agreement for key sea state parameters. Considerations specific to this network of buoys are described, including recent advances in technology that may affect continuity of historic records. Wave data from the buoys are found to be robust; the importance of keeping technological changes in mind and using the full raw dataset for analysis purposes are highlighted.

Understanding CYGNSS wind performance against in-situ and satellite winds during low-to-moderate and cyclonic conditions

An assessment of the performance of CYGNSS operational wind products is done against a multitude of reference datasets which include in-situ wind speed from Indian National Centre for Ocean Information Services (INCOIS) network of buoys (hereafter, INCOIS-buoys) in the Indian Ocean, SCATSAT-1 operational wind products and cyclone best track data from India Meteorological Department (IMD). The validation study is done for CYGNSS data version 2.1 for the period 17th March 2017 till 31st December 2017. The performance of the operational wind products is examined through a two-tier approach- first one covering low-to-moderate wind regimes (less than or equal to 15 m/s) where in-situ data from INCOIS-buoys and rain-free SCATSAT-1 products are used. While the former gives an idea of CYGNSS winds in the Indian Ocean basin, the latter can be treated as global reference dataset. In the second approach, high wind (greater than15 m/s) performance of CYGNSS winds are evaluated on a case study basis during some of the tropical cyclones (TC) of 2017. The first approach gives good accuracy of minimum variance estimator (MVE) wind speed products in terms of standard deviations of 1.85 m/s (INCOIS-buoys) and 2.07 m/s (SCATSAT-1). MVE is the best product amongst all the five different operational wind products viz. MVE, FDS-NBRCS, FDS-LES, YSLF-NBRCS, YSLF-LES of CYGNSS. Time series analyses of high wind performance during the TC- Mora and Ockhi show better sensitivity of young sea wind products especially YSLF-NBRCS compared to YSLF-LES. However, the fully developed wind product, FDS-NBRCS shows equally good sensitivity of wind speed.

Data assimilation for the two-dimensional shallow water equations: Optimal initial conditions for tsunami modelling

Accurate modelling of tsunami waves requires complete boundary and initial data, coupled with the appropriate mathematical model. However, necessary data is often missing or inaccurate, and may not have sufficient resolution to capture the dynamics of such nonlinear waves accurately. We demonstrate that variational data assimilation for the continuous shallow water equations (SWE) is a feasible approach for recovering initial conditions. We showed that the necessary conditions for reconstructing one-dimensional initial conditions in Kevlahan et al. (2019) can be extended to the maximum Euclidean distance between pairwise observations to two-dimensions. We use Sadourny finite-difference finite volume simulations to verify convergence to the true initial conditions can be achieved for observations arranged in multiple configurations, for both isotropic and anisotropic initial conditions, and with realistic bathymetry data in two dimensions. We compare observations arranged in straight lines, in a grid, and along concentric circles, and assess the optimal number and configuration of observation points such that convergence to the true initial conditions is achieved. These idealised results with simplified two-dimensional geometry are a first step towards more physically realistic settings. Recent advances in altimetry observation data now permit much denser measurements of sea surface height than is possible with a fixed buoy network. This provides the opportunity to use the method developed here for more accurate tsunami forecasts in realistic settings.