Ocean Observations Research Articles

Assimilation of Scattered Ocean Observations Using the Localized Equivalent-Weights Particle Filter with Statistical Observations

Abstract Marine physical data are currently derived mainly from satellite remote sensing, sparsely distributed buoys, and mobile observation platforms. The buoys and mobile observation platforms are more suitable for acquiring deep ocean data. Traditional methods for dealing with such observations rely on linear and Gaussian assumptions in the assimilation of nonlinear marine characteristics, which may introduce bias in analysis and forecasting. The particle-filter assimilation method is of increasing interest because it has advantages in dealing with nonlinear assimilation problems, although it has limitations with sparsely distributed observations. To address this problem, we introduced inverse distance-weighted interpolation into the localization scheme of the localized equivalent-weights particle filter with time-distributed statistical observations method based on data from sparsely distributed buoys and mobile platforms. This improved the utilization rate of observations and increased forecast accuracy. Theoretical experiments were undertaken to highlight the characteristics of the improved method, using reanalysis data from the European Centre for Medium-Range Weather Forecasts as evaluation criteria in comparing the method with the localized weighted ensemble Kalman filter method—a hybrid particle-filter method. Results indicate that the method effectively improves assimilation and the accuracy of state estimation and forecasts of ocean temperature and salinity. Significance Statement This paper addresses the problem of assimilating sparsely distributed observations in processing ocean elements. We improved the localization scheme to deal with the sparse-observation problem based on the localized equivalent-weights particle filter with time distribution statistical observation (LEWPF–T–Sobs). This method can effectively handle the assimilation problem with sparse observations while retaining the advantages of traditional particle filtering assimilation, thus improving the accuracy of estimates of physical ocean data.

Early-twentieth-century cold bias in ocean surface temperature observations

The observed temperature record, which combines sea surface temperatures with near-surface air temperatures over land, is crucial for understanding climate variability and change1–4. However, early records of global mean surface temperature are uncertain owing to changes in measurement technology and practice, partial documentation5–8, and incomplete spatial coverage9. Here we show that existing estimates of ocean temperatures in the early twentieth century (1900–1930) are too cold, based on independent statistical reconstructions of the global mean surface temperature from either ocean or land data. The ocean-based reconstruction is on average about 0.26 °C colder than the land-based one, despite very high agreement in all other periods. The ocean cold anomaly is unforced, and internal variability in climate models cannot explain the observed land–ocean discrepancy. Several lines of evidence based on attribution, timescale analysis, coastal grid cells and palaeoclimate data support the argument of a substantial cold bias in the observed global sea-surface-temperature record in the early twentieth century. Although estimates of global warming since the mid-nineteenth century are not affected, correcting the ocean cold bias would result in a more modest early-twentieth-century warming trend10, a lower estimate of decadal-scale variability inferred from the instrumental record3, and better agreement between simulated and observed warming than existing datasets suggest2.

A Bowen ratio-informed method for coordinating the estimates of air-sea turbulent heat fluxes

Abstract Accurate quantification of turbulent heat fluxes [THF, comprising sensible heat flux (SHF) and latent heat flux (LHF)] and Bowen ratio (β, ratio of SHF and LHF) is essential for understanding the air-sea interaction. However, the biased estimates of SHF and LHF by widely applied bulk aerodynamic models, and the separate estimates of SHF and LHF by data-driven models, both may result in unrealistic β estimates. This study for the first time innovatively proposes a Bowen ratio-informed data-driven model (BrTHF) for coordinating the estimations of THF and β using the multivariate random forest technique and a combination of eddy covariance flux observations and meteorological and oceanic observations collected from 53 historical cruises. The result shows that the BrTHF model could not only achieve high-accuracy SHF and LHF estimates, but also avoid the outliers of β estimates that were commonly found in un-synergistic random forest and well-known COARE3.5 models.

Evaluation of sea surface temperature from ocean reanalysis products over the North Indian Ocean

Ocean and sea ice reanalyses (ORAs or ocean syntheses) are reconstructions of the ocean and sea ice states using an ocean model integration constrained by atmospheric surface forcing and ocean observations via a data assimilation method. Ocean reanalyses are a valuable tool for monitoring and understanding long-term ocean variability at depth, mainly because this part of the ocean is still largely unobserved. Sea surface temperature (SST) is the key variable that drives the air–sea interaction process on different time scales. Despite improvements in model and reanalysis schemes, ocean reanalyses show errors when evaluated with independent observations. The independent evaluation studies of SST from ocean reanalysis over the Indian Ocean are limited. In this study, we evaluated the SST from 10 reanalysis products (ECCO, BRAN, SODA, NCEP-GODAS, GODAS-MOM4p1, ORAS5, CGLORS, GLORYS2V4, GLOSEA, and GREP) and five synthetic observation products (COBE, ERSST, OISST, OSTIA, and HadISST) and from the pure observation-based product AMSR2 for 2012–2017 with 12 in-situ buoy observations (OMNI) over the Arabian Sea and Bay of Bengal. Even though the reanalysis and observational products perform very well in the open ocean, the performance is poorer near the coast and islands. The reanalysis products perform comparatively better than most of the observational products. COBE and OISST perform better among the synthetic observational products in the northern Indian Ocean. GODAS-MOM4p1 and GREP performs best among the reanalysis products, often surpassing the observational products. ECCO shows poorer performance and higher bias in the Bay of Bengal. Comparing the BRAN daily and monthly SST, the monthly SST performance of reanalysis is better than the daily time scale.

Three-Dimensional Path Following Control for Underactuated AUV Based on Ocean Current Observer

In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based on a current observer and nonlinear disturbance observer (NDO) has been developed, addressing the 3D path-following issue for AUVs operating in the ocean environment. Accounting for uncertainties like variable ocean currents, this research establishes the AUV’s kinematics and dynamics models and formulates the tracking error within the Frenet–Serret coordinate system. The kinematic controller is designed through the line-of-sight method and the backstepping method, and the dynamic controller is developed using the nonlinear disturbance observer and the integral sliding mode control method. Furthermore, an ocean current observer is developed for the real-time estimation of current velocities, thereby mitigating the effects of ocean currents on navigational performance. Theoretical analysis confirms the system’s asymptotic stability, while numerical simulation attests to the proposed method’s efficacy and robustness in 3D path following.

Performance Characteristics of Newly Developed Real-Time Wave Measurement Buoy Using the Variometric Approach

Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument.

Relative Impact of Assimilation of Multi-Source Observations using 3D-Var on Simulation of Extreme Rainfall Events over Karnataka, India

This study explores the impact of assimilating diverse observational data on forecasting extreme rainfall events (EREs) using a three dimensional variational (3D-Var) assimilation approach. It focuses on 38 EREs across three meteorological divisions in Karnataka, India, using a high-resolution (03-km) Weather Research and Forecasting (WRF) model with three nested domains. Five distinct experiments were conducted, including a Control experiment without assimilation, and subsequent experiments integrating observations from various sources like atmospheric profiles from Atmospheric InfraRed Sounder (AIRS) and Moderate resolution Imaging Spectroradiometer (MODIS) satellites and radiosondes, ocean surface wind observations from Advanced Scatterometer (ASCAT), Special Sensor Microwave Imager (SSMI), and WindSAT satellites and buoys, ground observations from Karnataka State Natural Disaster Monitoring Centre (KSNDMC), as well as a combined assimilation experiment with all available observations. The accuracy of rainfall forecasts is evaluated by comparing model outputs with high-resolution telemetric rain-gauge (TRG; 6480 stations) data and other meteorological parameters against telemetric weather station (TWS; 860 stations) data from KSNDMC. Assimilation experiments show positive improvements over control experiment in predicting rainfall. Results consistently indicate underprediction of rainfall in the intricate topographical region of the Western Ghats (WG) across all experiments, contrasting with overprediction along the coastal areas of Karnataka. The experiment involving Ocean Winds showcased a substantial 40 % reduction in rainfall overprediction (above 2 mm threshold). Both Ocean Winds and Station Data assimilation notably enhanced rainfall prediction accuracy over most of the regions in Karnataka, with Ocean Winds exhibiting the highest improvement (53 %), closely followed by Station Data (50 %). Importantly, assimilating Ocean Winds and Station Data aided in reducing overprediction, while assimilating Satellite Profiles reduced underprediction in the interior part of Karnataka but increased overprediction over the coastal region compared to the control experiment. Frequency of occurrence of rainfall is considerably enhanced along the coastline in all 3D-Var experiments. Bias score indicates maximum improvement in assimilation using Ocean Winds and Station Data. Simulation of basic meteorological parameters also improved with assimilation particularly during the day hours. The results underscore the crucial role of assimilation of satellite and in-situ observations in improving forecast accuracy of EREs during the monsoon season.

Multidecadal decline in sea ice meltwater volume and Pacific Winter Water salinity in the Bering Sea revealed by ocean observations

Large amounts of freshwater and nutrients pass through the Bering Strait to the Arctic Ocean, making the Bering Sea a crucial marginal sea of the North Pacific Ocean. The hydrography and biological production of the Bering Sea are strongly influenced by the amount of sea ice produced and melted. The sea ice extent and production exhibited large interannual variability but no visible trend until 2016 when a strong decrease began. However, records of sea ice before 1979 and the beginning of satellite-based estimates do not exist. In this paper, we devised a methodology using historical temperature and salinity data, supplemented by historical oxygen isotope (δ18O) data, to estimate sea ice melt and its temporal variability in the Bering Sea from 1950 onward. Our results, consistent with estimates of sea ice thickness, indicate that the sea ice melt volume has declined significantly —following lower sea ice extent and production— with a decrease between 35 and 50 km3 (from 442 km3) between pre-1980 and post-1980 climatologies. In particular, our meltwater time series reveals a decline of 160 km3 between 2012 and 2018, which also reflects the strong decrease in sea ice volume between 2016 and 2018 that numerous previous studies have highlighted. We also evaluated the change in the salinity of the Pacific Winter Water (PWW), whose formation is also related to sea ice production. The time series of PWW salinity exhibits a strong decreasing trend, with a freshening of about 0.3 between the mid-1950s and the mid-2010s, that we attribute to a combination of a reduced sea ice production and the freshening of the Alaskan Coastal Current water. The decline in meltwater volume and PWW salinity that we observed strongly influences the stratification over the Bering shelf, with a significant weakening of the stratification in coastal polynya regions, and a stronger and increasingly temperature-controlled stratification in the rest of the shelf. These changes could have adverse consequences on the biological productivity of the northern Bering Sea.

OceanCrowd: Vessel Trajectory Data-Based Participant Selection for Mobile Crowd Sensing in Ocean Observation

OceanCrowd: Vessel Trajectory Data-Based Participant Selection for Mobile Crowd Sensing in Ocean Observation

Measurement of ocean currents by seafloor distributed optical-fiber acoustic sensing.

Ocean current measurements play a crucial role in aiding our understanding of ocean dynamics and circulation systems. Traditional methods, such as drifters and ocean buoys, are sparsely distributed and of limited effectiveness due to the nature of the marine environment and high operating expenses. Distributed acoustic sensing (DAS) is an emerging technology using submarine optical-fiber (OF) cables as dense seismo-acoustic arrays, offering a new perspective for ocean observations. Here, in situ observations of ocean surface gravity waves (OSGWs) and ocean currents by DAS were made along a pre-existing 33.6 km seafloor OF cable. The average current velocity and water depth along the cable were determined from observed OSGW-induced seafloor noise (0.05-0.2 Hz) using ambient-noise interferometry and frequency-domain beamforming. Variations in current velocity were derived at high spatiotemporal resolution using the frequency-domain waveform-stretching method. The inverted current velocity was verified by nearby ocean buoy observations and forecasting results. The observations demonstrate the effectiveness of DAS-instrumented OF cables in monitoring ocean currents.

The international multi-system OSEs/OSSEs by the UN Ocean Decade Project SynObs and its early results

“Synergistic Observing Network for Ocean Prediction (SynObs)” was launched in 2022 as a project of the United Nations Decade of Ocean Science for Sustainable Development to evaluate the importance of ocean observation systems and co-design the future evolution of the ocean observing network. SynObs is currently leading the flagship OSEs/OSSEs, an internationally coordinated activity in which observing system experiments (OSEs) and observing system simulation experiments (OSSEs) are conducted using a variety of ocean and coupled atmosphere–ocean prediction systems to evaluate ocean observation impacts consistent across most prediction systems. The flagship OSEs/OSSEs comprises the ocean prediction (OP) OSEs for high-resolution ocean predictions, the subseasonal-to-seasonal (S2S) OSEs for long-term lead-time coupled ocean–atmosphere predictions, and the OP OSSEs for evaluating new and future observing systems. SynObs plans to use the results of the flagship OSEs to contribute to the reports on the ocean observing network design made by international organizations and projects. Here, we introduce this initiative, and we report on some initial results. Some observation impacts consistent across four ocean prediction systems are found by a preliminary analysis of the analysis runs for the OP OSEs. For example, impacts of the altimetry data on the assimilated sea surface height (SSH) field are generally large in the westerly boundary current regions and around Antarctic Circumpolar Currents where SSH has large variability but are small in the tropical regions, despite the relatively large SSH variability there. The analysis also indicates the possibility that there are some characteristic differences in the observation impacts between low-resolution and eddy-resolving ocean prediction systems. Although OSE outputs of only four ocean prediction systems are available now, we will make further investigation, adding OSE outputs of other prediction systems that will be submitted in the near future.

Carbon-centric dynamics of Earth’s marine phytoplankton

Marine phytoplankton are fundamental to Earth's ecology and biogeochemistry. Our understanding of the large-scale dynamics of phytoplankton biomass has greatly benefited from, and is largely based on, satellite ocean color observations from which chlorophyll-a (Chla), a commonly used proxy for carbon biomass, can be estimated. However, ocean color satellites only measure a small portion of the surface ocean, meaning that subsurface phytoplankton biomass is not directly monitored. Chla is also an imperfect proxy for carbon biomass because cellular physiology drives large variations in their ratio. The global network of Biogeochemical (BGC)-Argo floats now makes it possible to complement satellite observations by addressing both these issues at once. In our study, we use ~100,000 water-column profiles from BGC-Argo to describe Earth's phytoplankton carbon biomass and its spatiotemporal variability. We estimate the global stock of open ocean phytoplankton biomass at ~314 Tg C, half of which is present at depths not accessible through satellite detection. We also compare the seasonal cycles of carbon biomass stocks and surface Chla visible from space and find that surface Chla does not accurately identify the timing of the peak annual biomass in two-thirds of the ocean. Our study is a demonstration of global-scale, depth-resolved monitoring of Earth's phytoplankton, which will be crucial for understanding future climate-related changes and the effects of geoengineering interventions if implemented.

Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.

Seasonality of pCO2 and air-sea CO2 fluxes in the Central Labrador Sea

The Labrador Sea in the subpolar North Atlantic is known for its large air-to-sea CO2 fluxes, which can be around 40% higher than in other regions of intense ocean uptake like the Eastern Pacific and within the Northwest Atlantic. This region is also a hot-spot for storage of anthropogenic CO2. Deep water is formed here, so that dissolved gas uptake by the surface ocean directly connects to deeper waters, helping to determine how much atmospheric CO2 may be sequestered (or released) by the deep ocean. Currently, the Central Labrador Sea acts as a year-round sink of atmospheric CO2, with intensification of uptake driven by biological production in spring and lasting through summer and fall. Observational estimates of air-sea CO2 fluxes in the region rely upon very limited, scattered data with a distinct lack of wintertime observations. Here, we compile surface ocean observations of pCO2 from moorings and underway measurements, including previously unreported data, between 2000 and 2020, to create a baseline seasonal climatology for the Central Labrador Sea. This is used as a reference to compare against other observational-based and statistical estimates of regional surface pCO2 and air-sea fluxes from a collection of global products. The comparison reveals systematic differences in the representation of the seasonal cycle of pCO2 and uncertainties in the magnitude of air-sea CO2 fluxes. The analysis reveals the paramount importance of long-term, seasonally-resolved data coverage in this region in order to accurately quantify the size of the present ocean sink for atmospheric CO2 and its sensitivity to climate perturbations.

Buoys for marine weather data monitoring and LoRaWAN communication

Climate change not only affects the environment directly but also challenges the existence of human civilization. Changes observed in the marine environment indicate the planet's health. Continuous ocean observations (ambient conditions, water quality, aquatic life, and disaster management) are crucial for checking and balancing climate change. This research aims to design three prototype smart moored buoys suitable for environmental monitoring. During the project, biconical buoys with a diameter of 30 cm and a height of 80 cm were fabricated. The buoys were fitted with temperature, salinity, and pH sensors to monitor and gather meteorological data. The collected data is transmitted to the cloud environment through a wireless communication device which is installed in all buoys through a LoRaWAN gateway, where it is stored and retrieved for analysis. The energy requirement of these buoys for driving the sensor network and data transmission are taken care by harvesting the solar energy through photovoltaic panels and the secondary energy storage device housed in the buoy. These buoys are installed along the coast in the municipality of Villasimius, Sardinia, Italy. These buoys have been placed within the marine protected area of Capo Carbonara, Sardinia, Italy, at a suitable distance from the communication gateway.

Towards a sustained and fit-for-purpose European ocean observing and forecasting system

The EU funded project EuroSea brought together key actors of the European ocean observing and forecasting communities with key users of the ocean observing products and services in order to better integrate existing ocean observation systems and tools, and to improve the delivery of ocean information to users. EuroSea was constructed around the ocean observing value chain that connects observations to users of ocean information, and, just as intended, the value chain concept was a useful prism to improve the system. In this article, we summarize some of the main take-home messages from EuroSea on the needs for developing the European Ocean Observing System and its links with modeling and forecasting systems. During the project, the challenges and gaps in the design and coordination of the European ocean observing and forecasting system were identified and mapped. Many gaps and challenges related to the observations of physical, chemical and biological Essential Ocean Variables were identified. Some of these gaps are related to technological developments, while others are caused by insufficient and short-term funding leading to a not sustainable system, management, and cooperation between different entities, as well as limitations in foresight activities, policies and decisions. This article represents a compilation of the broader needs for advancing the observing and forecasting system, and is meant as a guide for the community, and to funders and investors to advance ocean observing and the delivery of ocean information in Europe. To enhance the sustainability of ocean observations, which is paramount for a reliable provision of quality oceanographic data and services, several recommendations were compiled for ocean observing networks, frameworks, initiatives, as well as the ocean observing funders within the European nations, and the European Commission.

Eastern Tropical Pacific atmospheric and oceanic projected changes based on CMIP6 models

Atmosphere and ocean dynamics and their projections for the 21st century are assessed in the Eastern Tropical Pacific, using an ensemble of 17 models from the Coupled Model Intercomparison Project – CMIP6, under two radiative scenarios. Projections in the Panama Bight (PB) and Equatorial Pacific cold tongue (CT) are studied in more detail. In the 2071–2100 period and SSP5-8.5 scenario, referenced to the 1985–2014 period, air temperature (sea surface temperature) is expected to rise ∼3.5 °C (∼3 °C). Precipitation is projected to increase > 3 mm day−1 in the mean position of the Intertropical Convergence Zone, and decrease toward the north. A similar meridional pattern is projected in sea level atmospheric pressure and sea surface salinity (SSS) with negative anomalies toward the south. Large seasonal variations, which dominate the region, are projected to remain similar for the rest of the century. However, in January-April a weakening in the Panama wind jet and intensification of surface wind in the CT is expected, while in the June-November season, a weakening of the Choco wind jet will affect both sub-regions. Mean sea surface height (SSH) is expected to decrease, probably dominated by barotropic wind effects over SSS reduction effect on SSH. However, sterodynamic sea level (SDSL) is projected to rise (∼21 cm) driven by the global mean thermosteric contribution. For the end of the century, a mean sea level rise of ∼69 cm is estimated in the ETP, with SDSL being about half the barystatic contribution. These projections should be used with caution, as climate models have shown limitation reproducing atmospheric and ocean observations in the tropical Pacific Ocean during the last decades, due to large internal variability and systematic biases.

Experimental Investigation of Meter-Level Resolution Radar Measurement at Ka Band in Yellow Sea

The backscatter characteristics of ocean surfaces are of great importance in active marine remote-sensing fields. This paper presents the high spatial and temporal resolution dual co-polarized (VV and HH) and cross-polarized (HV) Ka-band sea-surface backscattering measurements taken from the Yellow Sea research platform at incidence angles ranging from 30° to 50° and in the wind speed range from 5.8 to 8.6 m/s. The experimental results show that the backscattering coefficient in HH polarization is close to (or even surpassing) that in VV polarization within a wind speed range of 7.1 to 8.6 m/s for Ka band under high resolution at medium incidence angles (30°–50°). Further analysis of the 10-ms short-time observation samples found that the sea surface echoes in VV polarization are more sensitive to wave motions, exhibiting more complex scattering characteristics such as multi-peaks and reducing scattering energy, especially at high wind speeds and large incident angles. The Doppler velocity analysis also confirms that rapid ocean wave changes can be detected within a short observation period, especially in VV polarization. The research in this article not only demonstrates the high spatial and temporal resolution capabilities of Ka-band radar for ocean surface observation but also reveals its great potential in interpreting and inversing rapidly evolving marine phenomena.

High-resolution observations of the ocean upper layer south of Cape St. Vincent, the western northern margin of the Gulf of Cádiz

Abstract. This article presents an Eulerian physical and biogeochemical dataset from the Iberian Margin Cape St. Vincent Ocean observatory (IbMa-CSV), a facility of the European Multidisciplinary Seafloor and water column Observatory European Research Infrastructure Consortium (EMSO-ERIC), located 10 nautical miles south of Cape St. Vincent (Portugal), the southwest tip of the Iberian Peninsula and western limit of the northern margin of the Gulf of Cádiz (GoC). The observatory was installed on the shelf break, and the data time series spans 4 months for most of the variables. The upper 150 m were sampled intensively with a wave-powered vertical profiler at an average rate of 4.5 profiles per hour recording at 2 Hz when ascending at an approximate velocity of 0.2 m s−1 and 10 Hz when descending at a variable velocity. The vertical resolution was always higher than 0.2 m. Measured channels were conductivity, temperature, pressure, chlorophyll a, dissolved O2 concentration, and turbidity. Derived channels are sea pressure, depth, salinity, speed of sound, specific conductivity, dissolved O2 saturation, density anomaly, spiciness, and Brunt–Väisälä frequency. The acquired dataset includes the flow velocity and direction along the water column, taken from an upward-looking 300 kHz acoustic Doppler current profiler (ADCP) recorded every hour for 3 m depth bins extending the same depth range of the vertical profiler. A standard quality-control scheme was applied to the dataset. The dataset is preserved for multiple use and is accessible in the Sea Open Scientific Data Publication (SEANOE) repository via the following address: https://doi.org/10.17882/94769 (Rautenbach et al., 2022).

Propulsion and energy extraction performance of wave-powered mechanism considering wave and current

Wave-powered mechanism can extract wave energy as propulsion, enabling long-term ocean observation. The kinematic properties of wave propulsion mechanisms are susceptible to ocean current loads in addition to wave effects, presenting complex dynamic behaviors. In this paper, to understand the effects of ocean currents on the kinematic performances of wave propulsion mechanism, the dynamic performance of a wave propulsion mechanism in the combined condition of wave and current is analyzed based on numerical simulation method for fluid-rigid body coupling and experimental methods. The performance of the propulsion mechanism against the current under counter-current conditions and the contribution of flow to the propulsion performance under co-current conditions are discussed. The intrinsic relationship between wave height, current velocity and current direction is investigated, and the energy extraction performance of wave propulsion mechanism is analyzed. According to the results, the ability of propulsion mechanism to resist counter-current is obtained, and it is found that as the counter-current velocity increases, the hydrofoil will stall and the propulsion force and efficiency decreases significantly. Whilst, the co-current has a promoting effect on the forward of the mechanism, but within a certain range of co-current speed, the mechanism will be negatively affected by the shedding vortex. And the discrimination mechanisms for wave-dominated propulsion and current-dominated propulsion are obtained. This work reveals the combined effect of waves and currents on the motion performance of wave propulsion mechanism, providing a reference for the development of surface vehicles that utilize multiple ocean energies for synergistic propulsion.