Buoy Data Research Articles

Long-gap Filling Method for the Coastal Monitoring Data

Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model’s RMS errors are 0.93 and 0.35~1.95, respectively.

Wind-Stress Variations from Deep to Shallow Water during Hurricanes for Air-Sea-Land Interaction Applications

In September 2020 Hurricane Sally impacted two National Data Buoy Center (www.ndbc.noaa.gov) buoys near its track: 42040 in the deep water and 42012 in the shallow. Using pertinent air-sea interaction formulas from the literature, analyses of these buoy data indicate that, under fully rough airflow and wind sea conditions, U* = a Hs2 /Tp3 + b, here U* is the friction velocity, Hs is the significant wave height, and Tp is the peak wave period. It is found that a = 28 and b = 0.12 for the deep water environment, a = 30 and b = 0.26 for the shoaling wave environment, and a = 31 and b = 0.14 for the transitional water-depth environment, respectively. All units are in SI. Verifications and applications of these proposed formulas to estimate storm surge, wind speed, surface currents and seabed scours are presented. Because an extensive network for monitoring waves along the coastlines of the United States by the Coastal Data Information Program (CDIP) (CDIP About (ucsd.edu)), these proposed formulas are particularly useful. In addition, it is demonstrated that the parameter of wave steepness can be used to explain why there are large variations in the drag coefficient and wave-age formulations in the literature.

A Fine Grid Tide-Wave-Ocean Circulation Coupled Model for the Yellow Sea: Comparison of Turbulence Closure Schemes in Reproducing Temperature Distributions

The performance of three turbulence closure schemes (TCSs), the generic length scale scheme (GLS), the Mellor–Yamada 2.5 scheme (MY2.5) and the K-profile parameterization scheme (KPP), embedded in the ocean model ROMS, was compared with attention to the reproduction of summertime temperature distribution in the Yellow Sea. The ROMS model has a horizontal resolution of 1/30° and 30 vertical sigma layers. For model validation, root mean square errors were checked, comparing model results with wave and temperature buoy data as well as tidal station data supplied by various organizations within the Republic of Korea. Computed temperature and vertical temperature diffusion coefficients were mainly compared along Lines A (36° N) and B (125° E) crossing the central Yellow Sea, Lines C (32° N) and E (34° N) passing over the Yangtze Bank and Line D off the Taean Peninsula. Calculations showed that GLS and MY2.5 produced vertical mixing stronger than KPP in both the surface and bottom layers, but the overall results were reasonably close to each other. The lack of observational data was a hindrance in comparing the detailed performance between the TCSs. However, it was noted that the simulation capability of cold patches in the tidal mixing front can be useful in identifying the better performing turbulence closure scheme. GLS and MY2.5 clearly produced the cold patch located near the western end of Line E (122° E–122.3° E), while KPP hardly produced its presence. Similar results were obtained along Line D but with a less pronounced tidal mixing front. Along Line C, GLS and MY2.5 produced a cold patch on the western slope of the Yellow Sea, the presence of which had never been reported. Additional measurements near 125° E–126° E of Line C and along the channel off the Taean Peninsula (Line D) are recommended to ensure the relative performance superiority between the TCSs.

A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements

Snowpack on sea ice can adjust changes in sea ice conditions and plays a vital role in the Earth's climate system. Snow depth, an important parameter of snowpack, is a necessary variable for retrieving sea ice thicknesses based on satellite altimeter data. Here, regression analysis (RA) is used to determine the best gradient ratio (GR) combination of brightness temperatures for estimating snow depths, and the RA model is proposed. Based on the RA model, one additional deep learning model is built, namely, the 5-variable long short-term memory (5VLSTM) model (or the RA-5VLSTM model). Meanwhile, an additional neural network model is built for comparisons, namely, the 5-variable genetic wavelet neural network (5VGWNN) model (or the RA-5VGWNN model). Using Operation IceBridge (OIB) and ice mass balance buoy (IMB) data, these three models, plus three existing algorithms, are compared to assess their performances in estimating snow depth. The results show that the RA-5VLSTM model performed pretty well among the six algorithms, with an RMSE of 7.16 cm. The RA-5VLSTM model, a robust approach, was less influenced by the uncertainty in the input data. From January to April during 2012–2020, the average monthly snow depth in the Beaufort Sea and the Chukchi Sea mainly showed a downward trend, while an upward trend was observed in the Central Arctic in most months. Variations in snow depth in the Central Arctic were mainly affected by the autumn 2-m air temperature (T2m) and the sea surface temperature (SST). Variations in snow depth in the Chukchi Sea were mainly affected by the autumn sea ice velocity.

SHIP OPERABILITY PREDICTED FROM LONG TERM DIRECTIONAL WAVE RECORDS

Ship operability assessments have traditionally been made using wind and wave data derived from wave atlases, however there are several drawbacks, including the fact that they are usually based on observation rather than measurement, and that spreading or directional effects are lost – such as the separation of sea and swell directions. An alternative approach is demonstrated here, instead of the data summarised in the wave atlas scatter diagram, long term hourly historical wave buoy data may be used. Detailed data sets, including directional wave spectra, are available for a number of specific locations. Direct use of many years’ hourly wave data involves significant computational effort, but results may be achieved within a reasonable time. The technique is demonstrated with the examples of four naval ships and two sites. Analysis considered two main themes, the differences in the ship performance calculated when (a) using wave buoy data rather than wave atlas data for the same sea area and (b) using the most complex available model of the ocean waves compared with the simplified wave descriptions in common use. For (a) the wave buoy data both looked rather different than the wave buoy data for the same nominal area, and produced rather different ship performance results. For (b) it was shown that there were also significant differences between the operability calculated for the four different ships at one of the sites. The implications for operability assessment in the ship procurement process are briefly discussed.

SET-UP DUE TO RANDOM WAVES: INFLUENCE OF THE DIRECTIONAL SPECTRUM


 
 
 The correct estimation of set-up is very important to evaluate coastal hazard and to design coastal structures. In this paper, we derived a mathematical expression for wave set-up in the context of random waves. The solution to this expression assumes straight, parallel depth contours and constant average flow parameters in the longshore direction. We then investigated the effect of different types of sea state taking account of different frequency spectrum and spreading function assumed in the expression on estimates of wave set-up. We found the set-up was highly influenced by the frequency spectrum used. Finally, we applied this expression to estimate set-up values at locations in Italy and in the United States using buoy data provided by ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale) and NDBC (National Data Buoy Centre).
 
 

The Importance of Spring Mixing in Evaluating Carbon Dioxide and Methane Flux From a Small North‐Temperate Lake in Wisconsin, United States

AbstractIn limnological studies of temperate lakes, most studies of carbon dioxide (CO2) and methane (CH4) emissions have focused on summer measurements of gas fluxes despite the importance of shoulder seasons to annual emissions. This is especially pertinent to dimictic, small lakes that maintain anoxic conditions and turnover quickly in the spring and fall. We examined CO2 and CH4 dynamics from January to October 2020 in a small humic lake in northern Wisconsin, United States through a combination of discrete sampling and high frequency buoy and eddy covariance data collection. Eddy covariance flux towers were installed on buoys at the center of the lake while it was still frozen to continually measure CO2 and CH4 across seasons. Despite evidence for only partial turnover during the spring, there was still a notable 19‐day pulse of CH4 emissions after lake ice melted with an average daytime flux rate of 8–30 nmol CH4 m−2 s−1. Our estimate of CH4 emissions during the spring pulse was 16 mmol CH4 m−2 compared to 22 mmol CH4 m−2 during the stratified period from June to August. We did not observe a linear accumulation of gases under‐ice in our sampling period during the late winter, suggesting the complexity of this dynamic period and the emphasis for direct measurements throughout the ice‐covered period. The results of our study help to better understand the magnitude and timing of greenhouse gas emissions in a region expected to experience warmer winters with decreased ice duration.

Evaluation of the ERA5 Significant Wave Height against NDBC Buoy Data from 1979 to 2019

Significant wave height (SWH) is a vital parameter in marine science research and engineering application. The up-to-date reanalysis product ERA5 SWH brings new possibilities to the long-term analysis. However, a systematic assessment of ERA5 SWH on a large scale is still lacking. This paper presents an evaluation of ERA5 SWH against observations from 103 buoys in the North American Atlantic and Pacific sourced by the National Data Buoy Center over the period 1979 to 2019. Overall, the ERA5 SWH has a good agreement with the in-situ observations, with a bias of −0.058 m, root mean squared error of 0.325 m, correlation coefficient of 0.961 and scatter index of 18.54%. The accuracy of ERA5 SWH is satisfactory under the most typical sea states (0.5 m < SWH < 4 m). The monthly analysis shows the performance of ERA5 SWH in summer is the best. The water depth and offshore distance have also been identified to impact the reliability of ERA5 SWH. Although the statistics vary at different locations, the performances of ERA5 SWH at most stations are reasonable. In addition, an evident improvement in the validity over time is observed, which can be attributed to the assimilation of the altimeter wave height.

Impact of Different Wind Representations on Resonant Ocean Near-inertial Motions in the Gulf of Mexico

This study investigates the resonated diurnal impact of the surface wind forcing from several atmospheric wind products, including CCMP (Cross-Calibrated Multi-Platform), ERA Interim, NCEP-2 (NCEP-DOE AMIP-II Reanalysis), MERRA (Modern-Era Retrospective analysis for Research and Application), NARR (Northern American Regional Reanalysis) and NAM (North American Mesoscale Forecast System), on the wind-driven near-inertial ocean motions in the northern Gulf of Mexico by numerical simulation, and in comparison, with NDBC buoy surface observed winds. Our analyses show that the near-inertial wind power input, which can affect vertical mixing in the water column, is closely associated with the variability of land-sea breeze by various wind products. In comparison with buoy observations in the northern Gulf of Mexico, the MERRA has the worst land-sea breeze representation, while the NCEP2 and CCMP, which are chosen to represent the coarsest and medium–high horizontal resolution, have the best performance. We further show that by comparison of the buoy data, that the near-inertial wind power input derived from all the wind products is underestimated by 30–50% approximately.

Sustenance of Indian Moored Buoy Network During COVID-19 Pandemic – A Saga of Perseverance

The moored buoy network in the Indian Ocean revolutionized the observational programs with systematic time-series measurement of in situ data sets from remote marine locations. The real-time meteorological and oceanographic data sets significantly improved the weather forecast and warning services particularly during extreme events since its inception in 1997. The sustenance of the network requires persistent efforts to overcome the multitude of challenges such as vandalism, biofouling, rough weather, corrosion, ship time availability, and telemetry issues, among others. Besides these, the COVID-19 pandemic constrained the normal functioning of activities, mainly by delaying the maintenance of the network that resulted in losing a few expensive buoy system components and precious data sets. However, the improvements in the buoy system, in-house developed data acquisition system, and efforts in ensuring the quality of measurements together with “best practice methods” enabled 73% of the buoy network to be functional even when the cruises were reduced to 33% during the COVID-19 lockdown in 2020. The moored buoys equipped with an Indian buoy data acquisition system triggered high-frequency transmission during the Super cyclone Amphan in May 2020, which greatly helped the cyclone early warning services during the COVID-19 pandemic. The COVID-19 lockdown points toward the reliability and enhanced utility of moored buoy observations particularly when other modes of measurements are limited and necessitates more such platforms to better predict the weather systems. The present study analyzed the enhancement of the buoy program and improvisation of the buoy system that extended the life beyond the stipulated duration and enabled the high-frequency data transmission during cyclones amid the COVID-19 lockdown. The recommendations to better manage the remote platforms specifically in the event of a pandemic based on the operational experience of more than two decades were also presented.

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

Observations of surface wave fields in the Arabian Sea under tropical cyclone Tauktae

The characteristics of surface wave fields in the eastern Arabian Sea under tropical cyclone Tauktae were investigated using buoy data. On May 16, 2021, the cyclone passed within 96 km of the buoy location, and a maximum wave height of 9.7 m with a significant wave height of 5.6 m was recorded by the buoy, moored at 23.5 m water depth off Goa. As the cyclone approached the buoy location, a shift in the frequency caused waves to turn into unimodal spectra with high wave energy and spectral peak parameter. Further, the evolution of cyclone-induced waves and their spatial variation have been investigated using the ERA5 reanalysis data. The results suggest that for distances of ∼400 km spatial extend from the cyclone's eyewall, the cyclone induced waves to have a high impact with significant wave height above 3 m for a cyclone sustained wind speed of around 100–120 knots. The dominance of wind-seas over swells and the directional shift are noticeable, specifically along the west coast of India during the tropical cyclone passage.

Estimating ocean wave directional spreading using wave following buoys: a comparison of experimental buoy and gauge data

Directional spreading of ocean waves plays an important role in various aspects of ocean engineering, such as wave-induced loads, nonlinear wave evolution, and wave breaking. Wave following buoys, which are widely deployed across the oceans, offer the potential to measure directional wave properties. To assess the accuracy of directional spreading estimates made using buoy measurements, we compared estimates based on experimentally obtained buoy and wave gauge measurements, first presented in McAllister and van den Bremer (J Phys Oceanogr 50:399–414, 2020) Buoy and gauge measurements were recorded at the same locations and in identical sea states, allowing for a like-for-like comparison. We examine experiments with both following (unimodal in direction) and crossing (bimodal in direction) sea states. In addition to this, we use synthetic wave data to investigate the effects of wave generation and nonlinearity on spreading estimates. Our results show that while directional estimates produced using buoy measurements are reasonably accurate in following sea states, they struggle to identify distinct directional peaks in crossing sea states. We find that spreading estimates made using buoy measurements tend to underestimate the degree of directional spreading by approximately 9-14% in following sea states, which is most apparent in narrowly spread conditions.

Expanding freshwater biologger studies to view fish as environmental sensing platforms

While recording fish habitat use by electronic sensors, biologgers can also be viewed as autonomous environmental monitoring systems with the organism as a vehicle. This dual perspective has provided novel results from marine ecosystems, but has not been applied to freshwater ecosystems. To understand limitations in fresh water, we evaluated miniature depth and temperature recorders as aquatic monitoring systems in a Laurentian Great Lake: Erie. As part of an acoustic telemetry study, biologgers were opportunistically implanted in a subsample of walleye Sander vitreus. Biologgers recorded temperature and depth at half-hour intervals for up to 1 year. Recaptures provided six biologgers for analysis of seasonal temperature patterns and lake stratification, key variables for understanding dimictic lakes. Depth-resolved temperature patterns showed close correspondence with independent weather buoy measurements. Because the buoy was deployed late in the season, biologger data provided improved estimates of the start of stratification, which had important implications for understanding development of hypoxia in the hypolimnion. Drawbacks to biologger data included imprecise knowledge of fish location and reliance on tag recoveries from the fishery. Optimistically, our results show how biologgers could be part of a monitoring approach that integrates limnological surveys with fisheries science.

Participatory process in environmental monitoring design: examples from the Port of Limassol

Sea transport and seaborne trade have increased significantly in the past few decades. As sea traffic hubs, ports have high risks because of the limitation in manoeuvrability, number of vessels, and land-based port activities. In the coastal city of Limassol, water and air pollution has been anecdotally attributed to port activities. The STEAM project (Sea Traffic Management in the Eastern Mediterranean, INTEGRATED/0916/0063, [1]) aims to set up a monitoring plan to aid in the identification and mitigation of pollution sources. The project followed a participatory process, where port stakeholders and scientists were consulted and included in the ideation, design and implementation process. This participatory process developed a greater sense of stakeholder ownership in the environmental monitoring programs and facilitated their adoption. According to the consultation process, air and water quality are the most important factors to monitor. Five static and one mobile multi-sensor monitoring stations make up the air quality monitoring design for the Port of Limassol. Three air quality stations were installed within the port area along with two stations near the anchorage area. Two environmental data buoys and two oil detectors make up the water quality monitoring stations. The oil detectors will be placed within the port. One environmental data buoy will be placed downstream of the port, while the second buoy will be placed between the port entrance, the Limassol Marina and the anchorage area.

Application of Data Assimilation for Spectral Wave Model in Coastal Regions of South Korea

Kim, H.; Kim, Y.H.; Oh, S.M.; Kang, K., and Kim, K.O., 2021. Application of data assimilation for spectral wave model in coastal regions of South Korea. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 76–80. Coconut Creek (Florida), ISSN 0749-0208. The primary factor determining the accuracy of wave simulation is the accuracy of the wind data, which represents the main force of wave generation. Its accuracy also depends on the optimality of parameters related to the growth, dissipation of wave energy and nonlinear energy transfer between wave components. Wave models have limitations due to errors of wind fields and a lack of understanding of wave dynamics such as white capping dissipation, wave-bottom interaction processes and long-term wave propagation. This paper investigates the performance of a wave model based on WAVEWATCH III (WW3) version 6.07 equipped with spectral wave data assimilation using the optimal interpolation (OI) method, which updates the spectral wave energy of the model by assimilating the observed significant wave height. Data assimilation was applied for the coastal region of Korea using near real-time global satellite wave observation data provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and the buoy data of the Korea Meteorological Administration (KMA). The verification of the model was done by comparing simulated results with observations by KMA buoys, which suggests the successful application of the data assimilation to improve the model accuracy.

Quality evaluation and calibration of the SWIM significant wave height product with buoy data

Quality evaluation and calibration of the SWIM significant wave height product with buoy data

Evaluation of AIRS and CrIS SST Measurements Relative to Three Globally Gridded SST Products Between 2013 and 2019

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work.

3D Ocean Water Wave Surface Analysis on Airborne LiDAR Bathymetric Point Clouds

Water wave monitoring is a vital issue for coastal research and plays a key role in geomorphological changes, erosion and sediment transportation, coastal hazards, risk assessment, and decision making. However, despite missing data and the difficulty of capturing the data of nearshore fieldwork, the analysis of water wave surface parameters is still able to be discussed. In this paper, we propose a novel approach for accurate detection and analysis of water wave surface from Airborne LiDAR Bathymetry (ALB) large-scale point clouds data. In our proposed method we combined the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method with a connectivity constraint and a multi-level analysis of ocean water surface. We adapted for most types of wave shape anatomies in shallow waters, nearshore, and onshore of the coastal zone. We used a wavelet analysis filter to detect the water wave surface. Then, through the Fourier Transformation Approach, we estimated the parameters of wave height, wavelength, and wave orientation. The comparison between the LiDAR measure estimation technique and available buoy data was then presented. We quantified the performance of the algorithm by measuring the precision and recall for the waves identification without evaluating the degree of over-segmentation. The proposed method achieves 87% accuracy of wave identification in the shallow water of coastal zones.

Wave energy flux in the Caribbean Sea: Trends and variability

Wave Energy Flux (WEF) in the Colombian Caribbean Sea is assessed using a 60 years wave reanalysis from 1958 to 2017. Significant wave height (Hs) and mean wave energy period (Tm_1,0) are validated against in situ buoy data from the National Oceanic and Atmospheric Administration and from the Dirección General Marítima in the Central Caribbean Sea and in front of the Colombian shelf. WEF is calculated at virtual buoys allocated in the central Caribbean, at the continental shelf and around insular territories revealing a long-term decrease in magnitude with an annual rate between 0.01 and 0.2% with different regional behavior. In addition, WEF variations at interannual scale are found to be associated with ENSO phases. Wave energy power decreased from 1989 to 2017 in all analyzed locations.