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Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.
Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.
In December 1991, the National Data Buoy Center (NDBC) deployed two meteorological buoys in the Southern California Bight on a transect between San Diego and San Clemente Island. Each buoy consisted of a 10-m discus hull instrumented to measure a suite of meteorological parameters, and, for the first time in the NDBC buoy program, acoustic Doppler current profilers (ADCPs) were included to gather hourly current profiles beneath the two buoys. Moorings instrumented with seven vector-measuring current meters (VMCMs) were deployed adjacent to the NDBC buoys for several months and provided current observations for comparison with the ADCP measurements. When wave-induced buoy motion is not overly large, the observations of horizontal current made by the ADCP and the VMCM are highly correlated. Time series of differences between ADCP and VMCM measurements are characterized by a mean difference (bias error) of about 0.01 m/s and standard deviation of about 0.035 m/s for 1-h observations. Estimates of current spectra from ADCP and VMCM records suggest that the ADCP system can be characterized by a white noise level of 2 x 10(exp -3) sq m/sq s/cph. However, when the in situ environment is such that large surface waves are present (including breaking waves and whitecaps), erroneous current values are usually reported by the ADCP. Mean values of vertical velocities reported by the ADCP appear to be much larger than what could be physically expected and are therefore deemed unreliable.
This NASA grant was funded as a result of an unsolicited proposal submission to Kennedy Space Center. The proposal proposed the development and testing of a shallow water optical water quality buoy. The buoy is meant to work in shallow aquatic systems (ponds, rivers, lagoons, and semi-enclosed water areas where strong wind wave action is not a major environmental During the project period of three years, a demonstration of the buoy was conducted. The last demonstration during the project period was held in November, 1996 when the buoy was demonstrated as being totally operational with no tethered communications line. During the last year of the project the buoy was made to be solar operated by large gel cell batteries. Fund limitations did not permit the batteries in metal enclosures as hoped for higher wind conditions, however the system used to date has worked continuously for in- situ operation of over 18 months continuous deployment. The system needs to have maintenance and somewhat continuous operational attention since various components have limited lifetime ages. For example, within the last six months the onboard computer has had to be repaired as it did approximately 6 months after deployment. The spectrograph had to be repaired and costs for repairs was covered by KB Science since no ftmds were available for this purpose after the grant expired. Most recently the computer web page server failed and it is currently being repaired by KB Science. In addition, the cell phone operation is currently being ftmded by Dr. Bostater in order to maintain the system's operation. The above points need to be made to allow NASA to understand that like any sophisticated measuring system in a lab or in the field, necessary funding and maintenance is needed to insure the system's operational state and to obtain quality factor. The proposal stated that the project was based upon the integration of a proprietary and confidential sensor and probe design that was developed by KB Science and Engineering and is currently patented by KB Science. The buoy's purpose was to collected hyperspectral optical signatures for analysis and resulting estimation of water quality parameters such as chlorophyll-a, seston and dissolved organic matter (DOC). The ultimate goal of the project was to develop a buoy that would integrate a probe to measure upwelling light from a source and thus relate this backscattered light to water quality parameters.
The Arctic Ocean remains one of the most poorly sampled regions on Earth, where improved in situ environmental monitoring is vital for advancing oceanographic and atmospheric studies. However, data collection efforts are constrained by the short operational lifespans and high costs of conventional systems. Drifting buoys powered by pendulum-driven wave energy harvesters offer a cost-effective alternative, yet earlier designs have neither been optimized for real-world wave conditions nor validated in the Arctic. In this study, we develop a self-powered drifting buoy that integrates a pendulum-driven triboelectric nanogenerator (TENG) system with a mechanical motion rectifier, a high-gear-ratio transmission, and power management circuits. Through coupled buoy–pendulum dynamic simulations and laboratory testing using a motion simulator, we identify an optimal pendulum mass of 1.6 kg (12.7% of total buoy weight) that maximizes energy output while maintaining buoy stability. Laboratory experiments achieved average power outputs of 12.7 mW under Arctic-like wave and temperature conditions. The system was successfully deployed in the Bering Sea, where it generated 11 J of energy in 3.1 m waves, marking the first Arctic deployment of a TENG-based drifting buoy for sea surface temperature monitoring. This work establishes a cost-effective framework for designing self-powered Arctic monitoring platforms and advances the feasibility of long-term environmental observations in real Arctic waters.
The accurate measurement of waves underpins marine energy resource characterization, device design, and project development. Datawell wave buoys are widely deployed and have long served as a trusted standard for wave measurements. We quantify the measurement performance, including wave elevation and energy flux estimation, of a Datawell DWR-MkIII buoy using prescribed monochromatic heave motions on a large-amplitude six-degree-of-freedom motion platform at the National Laboratory of the Rockies, assuming the buoy behaves as an ideal wave follower. Commanded motions were validated with an optical motion tracking system while buoy elevation and raw acceleration were recorded. Wave elevations were propagated to wave energy flux estimation using four methods, including one frequency-domain method and three time-domain methods. The Bayesian optimization was applied for design of experiments, and records from three test sites were also applied and evaluated in the present study. Results show two error regions within the nominal period range of 1.6 s to 30 s. For wave periods between 5 s and 25 s, the buoy provides accurate wave height measurements. For short periods less than 5 s, the 1.28 Hz sampling frequency induces sub-Nyquist artifacts that bias elevation and can drive maximum energy flux estimation errors above 100%. For long periods exceeding 25 s, the buoy reported elevation is underpredicted with error depending on period but relatively independent of wave height, with maximum wave height and wave energy flux errors reaching 64% and 87%, respectively. Furthermore, analysis of three field-derived cases shows that frequency-domain estimates at 1.28 Hz agree within 2% of the corresponding 100 Hz estimates, while larger method-dependent differences are observed for the Hilbert method.
Ocean observation buoys are currently powered-constrained by battery storage capacity or available solar power. Power constraints limit the number of measurements that can be made and the lifetime of the buoys. Ocean surface wave energy could be used for power production, but wave energy converters are not yet commercially available for ocean observation buoys. Here we present the design and testing of a drifting wave energy converter buoy using a pendulum transmission system (PTS). The wave energy converter buoy was designed to operate in Arctic temperatures and wave conditions, but it could be used in areas with warmer temperatures and larger waves. Field tests measured a maximum power production of 5 watts in waves with a significant wave height of 0.42 meters and a ten minute average power production of 37 milliwatts. Power production increased with the energy period and significant wave height. The energy harvesting capabilities of the PTS showed the utility of adding a wave energy conversion device to a drifting ocean observation buoy.
Two analyses are performed to evaluate the effect of drift buoys and the FGGE's special observing system (SOS) on forecasting. The FGGE analysis utilizes all level II-b conventional and special data, and the Nosat analysis employs only surface and conventional upper air data. Twelve five-day forecasts are produced from these data. An additional experiment utilizing the FGGE data base minus buoys data, and the Nosat data base including buoys data is being conducted. The forecasts are compared and synoptic evaluation of the effect of buoys data is described. The results reveal that the FGGE data base with the SOS significantly improves forecasting in the Southern Hemisphere and the loss of buoys data does not have a great effect on forecasting. The Nosat data has less impact on forecasting; however, the addition of buoys data provides an improvement in forecast skills.
A network of autonomous, ice-tethered buoys was deployed around the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) experiment in late September 2019 for a year-long drift in the Arctic Transpolar Drift Stream. The buoys were deployed as part of the MOSAiC distributed network (DN) which included 12 multi-instrumented ice stations and an additional 116 GPS buoys distributed primarily within a 40 km radius of the MOSAiC Central Observatory. Buoy coverage within the DN was maintained with additional deployments throughout the year-long drift allowing for collection of data over a full sea ice growth and melt cycle. All GPS position data from buoys deployed within the DN have been assembled and processed into the collection of 216 quality-controlled buoy drift tracks presented in this dataset covering the period 26 September 2019 – 23 May 2021. The drift tracks in this collection are ideal for studies of dynamic sea ice motion around the MOSAiC experiment at cascading spatial scales ranging from 100s of meters to 100s of km.
The data quality, impact, and application of various buoy systems deployed during the FGGE are discussed. Three general categories of buoy systems provided critical coverage of environmental phenomena in the Arctic Ocean basin, the tropical oceans, and the Southern Hemisphere. Results of the added pressure information in the Arctic are presented. The impacts of the Southern Hemisphere buoy systems on pressure fields, SST fields, and ocean circulation are provided. Those findings from the tropical drifters, relevant to the FGGE time scales, are indicated. All three types of buoy systems, each uniquely engineered for application in different regions of our planet, contributed to the broad objective of advancing weather and climate predictions in several special ways. Comments are provided about the role of buoys in a future observing system, and a consortium of interested countries is strongly advocated in order to accelerate this role.
The fine spatial resolution of the ICESat-2 (IS2) satellite altimeter allows monitoring the evolution of sea ice thickness with detailed dynamic information (e.g. ridges and leads). In this study, we first assess the ability of IS2 to estimate thermodynamic ice growth and dynamic thickening during the ice-growing season in the central Arctic Ocean. As an indicator of the thermodynamic ice growth, we use 10 thermistor string-based sea ice mass balance array (SIMBA) buoys deployed at a scale of ~ 50 km from the Icebreaker Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. We collect IS2 data within 20 km buffer distance from the individual buoys, and calculate the mode, median, and mean of the IS2-derived ice thickness. The IS2 modal thickness shows the least bias (−0.169 m) with the buoy ice thickness, representing level ice thickness. In addition, the increasing rate of the IS2 modal thickness is close to the thermodynamic ice growth with a small bias of −0.054 cm/day. However, the increasing rates of the IS2 median and mean thickness are greater than the thermodynamic ice growth by about 0.114 cm/day and 0.198 cm/day, respectively, because they also include ice growth caused by thickness redistribution during dynamic deformation. The dynamic contributions may account for 26.1 ± 10.3% and 34.4 ± 10.1% of the total increase of the IS2 median and mean thickness, respectively. Within a ~ 50 km radius area from the MOSAiC Central Observatory, IS2 measurements exhibit that the ridge fraction increased from <2% in November to ~ 4% in March (~0.029%/day of average increasing rate) and ridge height increased about 0.047 cm/day during the same period. However, lead formation does not show significant contributions to the dynamic ice thickening because leads are temporary features lasting only 2–3 days. Although there are considerable uncertainties in IS2 ice thickness estimation and IS2-buoy thickness comparison, this study emphasizes the importance of combining measurements by IS2 and SIMBA buoys to explain the regional sea ice mass balance with separating the thermodynamic and dynamic contributions.
The Mobile Data Buoy System was conceived to serve the users requirement for obtaining water quality parameters from two separate watershed systems. In view of the cost constraints of the ERTS program it was obvious that the network of 10 sampling stations required could not be of the fixed installation type; therefore, it was decided to go to a system of battery powered buoys of a size that could be used in one watershed system for a period of time and then moved to another by use of a relatively small 6.7 m (22 foot) boat. The basic idea of the water quality measurement program was to establish the water quality pattern of change from the headwaters of the watersheds to and through the Mobile Bay. This would allow the investigator to develop a good picture of the state's major water resources and the pressures from pollution that are being imposed. At this point in deployment of this mobile system of buoys, it is too early to put a quantitative value on the system, however it appears less expensive than known fixed installations as to first cost. It has a basic advantage in that it can be moved, at very little expense, to alternate sites where it is desired to obtain water quality data. It is to be noted this buoy system which covers a 80 Km (50 mile) stretch of the Black Warrior River and then skips down 483 Km (300 miles) to Mobile Bay for the next measurements would not be feasible unless there is a satellite to collect and relay the data.
Information was exchanged between people directly involved with the development, use, and/or potential use of free drifting buoys. Tracking systems and techniques, where methods and accuracy of optical, radio, radar, satellite, and sonic tracking of free-drifting buoys were discussed. Deployment and retrieval covering methods currently used or planned in the deployment and retrieval of free-drifting buoys from boats, ships, helicopters, fixed platforms, and fixed-wing aircraft were reported. Simulation, sensors, and data emphasizing the status of water circulation modeling, and sensors useful on free-drifting buoys, and data display and analysis were described.
Data from four buoys tracked by Nimbus 6 and concurrent ice concentrations retrieved from Nimbus 7 scanning multichannel microwave radiometer data are used to investigate the progress and behavior of an area of sea ice as it drifts from the southwestern Weddell Sea. The overall drift characteristics and their relationship to ice edge displacement are examined within the framework of four zones. Three phases are identified in the large-scale behavior of the Weddell Sea ice cover, namely, a rapid equatorward and eastward advance, a quasi-equilibrium phase, and a period of rapid recession. Outbreaks of cold continental air alternate with incursions of relatively warm air from the north; warm conditions are recorded as far as 1200 km in from the ice edge in winter. Closed loops in the buoy trajectories, which are clockwise to the south of 63 deg S, reverse to become anticlockwise to the north. A coherence is observed in the response of the buoys to the passage of storms, even though the buoys separated by a distance of over 100 km.
During the period 20-23 September 1990, the remnants of Supertyphoon Flo moved into the central North Pacific Ocean with sustained wind speeds of 28 m/s. The strong wind and large fetch area associated with this storm generated long-period swell that propagated to the west coast of North America. National Data Buoy Center moored-buoy stations, located in a network that ranged from the Gulf of Alaska to the California Bight, provided wave spectral estimates of the swell from this storm. The greatest dominant wave periods measured were approximately 20-25 s, and significant wave heights measured ranged from 3 to 8 m. Wave spectra from an array of three nondirectional buoys are used to find the source of the long-period swell. Directional wave spectra from a heave-pitch-roll buoy are also used to make an independent estimate of the source of the swell. The ridge-line method, using time-frequency contour plots of wave spectral energy density, is used to determine the time of swell generation, which is used with the appropriate surface pressure analysis to infer the swell generation area. The diagnosed sources of the swell are also compared with nowcasts from the Global Spectral Ocean Wave Model of the Fleet Numerical Oceanography Center. A simple method of predicting the propagation of ocean swell, by applying a simple kinematic model of wave propagation to the estimated point and time source, is demonstrated.
QuikSCAT backscatter and DMSP SSM/I radiance data are used to derive sea ice motion for both the Arctic and Antarctic region using wavelet analysis method. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing satellite data from Synthetic Aperture Radar (SAR). Sea ice motion of the Arctic for the period from October 1999 to March 2000 derived from QuikSCAT and SSM/I data agrees well with that derived from ocean buoys quantitatively. Thus the ice tracking results from QuikSCAT and SSM/I are complement to each other, Then, three sea-ice drift daily results from QuikSCAT, SSM/I, and buoy data can be merged to generate composite maps with more complete coverage of sea ice motion than those from single data source. A series of composite sea ice motion maps for December 1999 show that the major circulation patterns of sea ice motion are changing and shifting significantly within every four days and they are dominated by wind forcing. Sea-ice drift in the summer can not be derived from NSCAT and SSM/I data. In later summer of 1999 (in September), however, QuikSCAT data can provide good sea ice motion information in the Arctic. QuiksCAT can also provide at least partial sea ice motion information until June 15 in early summer 1999. For the Antarctic, case study shows that sea ice motion derived from QuikSCAT data is predominantly forced by and is consistent with wind field derived from QuikSCAT around the polar region. These calibrated/validated results indicate that QuikSCAT, SSM/I, and buoy merged daily ice motion are suitably accurate to identify and closely locate sea ice processes, and to improve our current knowledge of sea ice drift and related processes through the data assimilation of ocean-ice numerical model.
The verification phase of the Jason-1 satellite altimeter mission presents a unique opportunity for comparing near-simultaneous, independent satellite measurements. We here examine simultaneous significant wave height measurements by the Jason-1 and Topex/Poseidon altimeters. These data are also compared with in-situ measurements from deep-ocean buoys and with predicted wave heights from the Wave Watch 111 operational model. The rms difference between Jason and Topex wave heights is 21 cm, and this can be further lowered by application of median filters to reduce high-frequency noise. This noise is slightly larger in the Jason dataset, amounting to about 7 cm rms for frequencies above 0.05 Hz, which is the frequency at which the coherence between Topex and Jason measurements drops to zero. The probability density function for Jason shows a dearth of small waves relative to Topex. Buoy comparisons confirm that this problem lies with the Jason measurements. The buoy comparisons confirm previous reports that Topex wave heights are roughly 5% smaller than buoy measurements for waves between 2 and 5m; Jason heights in general are 2.7% smaller than Topex. Spurious dips in the Topex density function for 3- and 6-meter waves, a problem that has existed since the beginning of the mission, can be solved by waveform retracking..
Significant sudden wind gusts can pose a threat to aviation near the coastline, as well as small (sailing) boats and commercial ships approaching the ports. Such cases can result in wind speed changes of more than an order of magnitude within 5 minutes, which can then last up to 20 minutes or more. Although the constellation of scatterometers is a good means of studying maritime convection, those sudden gusts are not easily captured because of the low time resolution. The National Data Buoy Center (NDBC) provides continuous measurements of wind speed and direction along the US coastal regions every 6 minutes. Buoys are platforms placed at specific places on the seas, especially along coastlines, providing data for atmospheric and oceanic studies. Next Generation Radars (NEXRADs), after the recent upgrade of the network to dual-pol systems, offer enhanced capabilities to study atmospheric phenomena. NEXRADs provide continuous full-volume scans approximately every 5 minutes and therefore are close to the time resolution of the buoy measurements. Use of single- Doppler retrievals might also provide a means of further validation.