WorldWideScience

Sample records for buoys

  1. National Data Buoy Center Buoy Locations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Buoy table consists of location information, ownership, and general geographic descriptions of buoys and weather stations. In addition to buoys operated by the...

  2. Controllable Buoys and Networked Buoy Systems

    Science.gov (United States)

    Davoodi, Faranak (Inventor); Davoudi, Farhooman (Inventor)

    2017-01-01

    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.

  3. Development of drifting buoys

    Digital Repository Service at National Institute of Oceanography (India)

    Nayak, M.R.; Peshwe, V.B.; Tengali, S.

    Polar orbiting satellites equipped with random access data collection and position fixing systems have made long-term remote oceanographic/meteorological observations possible by means of instrumented drifting buoys fitted with ARGOS telementry...

  4. NDBC Standard Meteorological Buoy Data, 1970-present

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. Moored buoys are the weather sentinels of the...

  5. Buoy Dynamics in Subsurface Zones

    Directory of Open Access Journals (Sweden)

    Randy Guillen

    2009-01-01

    Full Text Available The objective of this paper is to find the tension acting on a line that anchors a buoy submerged just beneath the surface of the ocean. Since the problem statement only gives the geometric shapes and dimensions of the buoy, we must use calculus to find its volume and surface area through integration of the volumes and surfaces of revolution formed by the specific parts of the buoy along an axis. The volume and surface area determine the buoyancy force and force of gravity, the two forces acting on the buoy that affect the tension in the line. After calculating this data, we were able to conclude that the tension affecting the line would be approximately 78 kN if the buoy was made of 1% carbon steel with a thickness of 6.35 mm. This problem is useful in several engineering disciplines.

  6. Mooring Line for an Oceanographic Buoy System

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A mooring line for an oceanographic buoy system includes four sections. The first section is a protected cable that is connectable to the buoy. The second section is...

  7. Buoy-Rope-Drum Wave Power System

    Directory of Open Access Journals (Sweden)

    Linsen Zhu

    2013-01-01

    Full Text Available A buoy-rope-drum wave power system is a new type of floating oscillating buoy wave power device, which absorbs energy from waves by buoy-rope-drum device. Based on the linear deep water wave theory and pure resistive load, with cylinder buoy as an example, the research sets up the theoretical model of direct-drive buoy-rope-drum wave power efficiency and analyzes the influence of the mass and load of the system on its generating efficiency. It points out the two main categories of the efficient buoy-rope-drum wave power system: light thin type and resonance type, and optimal designs of their major parameters are carried out on the basis of the above theoretical model of generating efficiency.

  8. Advanced Approach of Multiagent Based Buoy Communication

    Directory of Open Access Journals (Sweden)

    Gediminas Gricius

    2015-01-01

    Full Text Available Usually, a hydrometeorological information system is faced with great data flows, but the data levels are often excessive, depending on the observed region of the water. The paper presents advanced buoy communication technologies based on multiagent interaction and data exchange between several monitoring system nodes. The proposed management of buoy communication is based on a clustering algorithm, which enables the performance of the hydrometeorological information system to be enhanced. The experiment is based on the design and analysis of the inexpensive but reliable Baltic Sea autonomous monitoring network (buoys, which would be able to continuously monitor and collect temperature, waviness, and other required data. The proposed approach of multiagent based buoy communication enables all the data from the costal-based station to be monitored with limited transition speed by setting different tasks for the agent-based buoy system according to the clustering information.

  9. On the Optimization of Point Absorber Buoys

    Directory of Open Access Journals (Sweden)

    Linnea Sjökvist

    2014-05-01

    Full Text Available A point absorbing wave energy converter (WEC is a complicated dynamical system. A semi-submerged buoy drives a power take-off device (PTO, which acts as a linear or non-linear damper of the WEC system. The buoy motion depends on the buoy geometry and dimensions, the mass of the moving parts of the system and on the damping force from the generator. The electromagnetic damping in the generator depends on both the generator specifications, the connected load and the buoy velocity. In this paper a velocity ratio has been used to study how the geometric parameters buoy draft and radius, assuming constant generator damping coefficient, affects the motion and the energy absorption of a WEC. It have been concluded that an optimal buoy geometry can be identified for a specific generator damping. The simulated WEC performance have been compared with experimental values from two WECs with similar generators but different buoys. Conclusions have been drawn about their behaviour.

  10. Fluid Structure Interaction Analysis of Planar Buoy

    OpenAIRE

    Patel, Rakeshbhai; Hanif, Muhammad Adnan; Oad, Rajev Kumar

    2011-01-01

    Different types of wave energy convertors are being studied using the heave motion of floating bodies to generate electricity. In this thesis, we investigate the interaction of floating buoys from hydrodynamic point of view. The dynamic heave response of buoy under two different load cases and represented by single degree of freedom model is studied. The fluid-structure interactions based on 2-dimensional linear potential flow theory were modeled and simulated using finite element method. The...

  11. Worldwide Buoy Technology Survey. Volume 1. Report

    Science.gov (United States)

    1991-02-01

    WB Weiseler Bojen (Germany - Manufacturer) WGDB Wine Glass Discrepancy Buoy WHOI Woods Hole Oceanographic Institution ZLBC Zeni Lite Buoy Co. (Japan...de Nantes - St, Nazaire, Port Autonome de Bordeaux , and Port Autonome de Marseille. Overseas locations also include Antigua and St. Frias in the...navigation. The navigation aids used along the Norwegian coast are tailored to the needs of the specific geographic and climatic conditions prevailing

  12. Buoy Technology Survey Recommendations for Development

    Science.gov (United States)

    1991-07-01

    Maritime Buoyage System MTS Marine Technology Society NAVFAC Naval Facilities Engineering Command NBS New Buoy Systems NKK Nippon Kogi Kogyo (Japan...8.7 LED LIGHTS RATIONALE The Japanese (Nippon Kogi Kogyo Co., Ltd.) are developing an LED (Light Emitting Diode) light for use on floating aids that...buoys. APPROACH Study the Paint Spraying and Radio Transmission Systems developed by Japan’s "Nippon Kogi Kogyo Co." and "Ryokuseisha Corp

  13. The O-Buoy Chemical Network

    Science.gov (United States)

    Matrai, P. A.; Williams, C. R.; Rauschenberg, C. D.

    2012-12-01

    Autonomous, sea ice-tethered buoys ("O-Buoys") are being deployed across the Arctic sea ice for long-term atmospheric measurements, with several O-Buoys having been deployed within the Hudson Bay, Beaufort Sea, and the North Pole. These buoys provide in-situ measurements of ozone, CO_{2} and BrO, as well as meteorological parameters, over the frozen ocean. O-Buoys were designed to transmit daily data over a period of 2 years while deployed in sea ice, as part of automated ice-drifting stations. Due to the logistical challenges of measurements over the Arctic Ocean region, most long term,in-situ observations of atmospheric chemistry have been made at coastal sites or islands near the coast, leaving large spatial and temporal gaps that O-Buoys can overcome. The significant uncertainty that remains in our understanding of the temporal and spatial variability in these parameters as well as the magnitude and/or frequency of long (CO_{2}) and short (ozone depletion) patterns can be overcome. Advances in floatation, communications, power management, and sensor hardware have been made to the original design to overcome the challenges of diminished Arctic sea ice which have resulted in our longest deployments into the summer so far.

  14. Oceanographic measurements from the Texas Automated Buoy System (TABS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Texas Automated Buoy System contains daily oceanographic measurements from seven buoys off the Texas coast from Brownsville to Sabine. The Texas General Land...

  15. IABP Drifting Buoy Pressure, Temperature, Position, and Interpolated Ice Velocity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The International Arctic Buoy Programme (IABP) maintains a network of drifting buoys to provide meteorological and oceanographic data for real-time operational...

  16. 33 CFR 401.14 - Anchor marking buoys.

    Science.gov (United States)

    2010-07-01

    ... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false Anchor marking buoys. 401.14... OF TRANSPORTATION SEAWAY REGULATIONS AND RULES Regulations Condition of Vessels § 401.14 Anchor marking buoys. A highly visible anchor marking buoy of a type approved by the Manager and the Corporation...

  17. Pacific Ocean buoy temperature date - TAO/TRITON database & National Buoy Data Center database

    Data.gov (United States)

    U.S. Environmental Protection Agency — Pacific Ocean buoy temperature data. This dataset is associated with the following publication: Carbone, F., M. Landis, C.N. Gencarelli, A. Naccarato, F. Sprovieri,...

  18. An array effect of wave energy farm buoys

    Directory of Open Access Journals (Sweden)

    Hyuck-Min Kweon

    2012-12-01

    Full Text Available An ocean buoy energy farm is considered for Green energy generation and delivery to small towns along the Korean coast. The present studypresents that the floating buoy-type energy farm appears to be sufficiently feasible fortrapping more energy compared to afixed cylinder duck array. It is also seen from the numerical resultsthat the resonated waves between spaced buoys are further trapped by floating buoy motion. Our numerical study is analyzed by a plane-wave approximation, in which evanescent mode effects are included in a modified mild-slope equation based on the scattering characteristics for a single buoy.

  19. An array effect of wave energy farm buoys

    Science.gov (United States)

    Kweon, Hyuck-Min; Lee, Jung-Lyul

    2012-12-01

    An ocean buoy energy farm is considered for Green energy generation and delivery to small towns along the Korean coast. The present studypresents that the floating buoy-type energy farm appears to be sufficiently feasible fortrapping more energy compared to afixed cylinder duck array. It is also seen from the numerical resultsthat the resonated waves between spaced buoys are further trapped by floating buoy motion.Our numerical study is analyzed by a plane-wave approximation, in which evanescent mode effects are included in a modified mild-slope equation based on the scattering characteristics for a single buoy.

  20. Shallow Water Optical Water Quality Buoy

    Science.gov (United States)

    Bostater, Charles

    1998-01-01

    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

  1. Drifting buoy data collected by the National Data Buoy Center (NDBC) in oceans world-wide from 1984-05-01 to 1998-10-27

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This collection contains drifting buoy data collected from May 1984 through October 1998 from buoys deployed by the National Data Buoy Center, Stennis Space Center,...

  2. 47 CFR 90.248 - Wildlife and ocean buoy tracking.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 5 2010-10-01 2010-10-01 false Wildlife and ocean buoy tracking. 90.248... SERVICES PRIVATE LAND MOBILE RADIO SERVICES Non-Voice and Other Specialized Operations § 90.248 Wildlife... tracking of, and the telemetry of scientific data from, ocean buoys and animal wildlife. (b) Transmitters...

  3. 33 CFR 149.320 - What are the requirements for ring life buoys?

    Science.gov (United States)

    2010-07-01

    ... length. The light must be mounted on a bracket near the ring life buoy so that, when the ring life buoy is cast loose, the light will be pulled free of the bracket. (c) To each ring life buoy, there must... equipment. (b) Each ring life buoy must have a floating electric water light approved under approval series...

  4. Model Predictive Control of Buoy Type Wave Energy Converter

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Sichani, Mahdi Teimouri; Mirzaei, Mahmood

    2014-01-01

    The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator...... is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible...

  5. Directional waverider buoy in Indian waters - Experiences of NIO

    Digital Repository Service at National Institute of Oceanography (India)

    AshokKumar, K.; Diwan, S.G.

    Information on directional waves is extremely important in the design of harbour structures, such as breakwaters and jetties and to study the sediment transport pattern. Till recent days our country has been using waverider buoys which give all wave...

  6. An overview of a moored ocean data buoy programme

    Digital Repository Service at National Institute of Oceanography (India)

    Nayak, M.R.

    This paper addresses the rationale. history, strategy and management techniques used in the developmcnt of NIO oceanographic data buoy programme. The system is used for short term as well as long term oceanographic observations. The technical...

  7. Response of surface buoy moorings in steady and wave flows

    Digital Repository Service at National Institute of Oceanography (India)

    Anand, N.M.; Nayak, B.U.; SanilKumar, V.

    A numerical model has been developed to evaluate the dynamics of surface buoy mooring systems under wave and current loading. System tension response and variation of tension in the mooring line at various depths have been evaluated for deep water...

  8. 33 CFR 62.23 - Beacons and buoys.

    Science.gov (United States)

    2010-07-01

    ... are aids to navigation structures which are permanently fixed to the earth's surface. They range from... have a round shape. (2) Mariners attempting to pass a buoy close aboard risk collision with a yawing...

  9. System Identification and Control of a Joint-Actuated Buoy

    Science.gov (United States)

    2014-05-09

    14 Figure 9: Green and Orange Colored Stripes of Craft Foam on the Buoy Payload ........... 15 Figure 10: Buoy Step...circuit board. The copper and blue paper were then heated in a press-n- peel press for just over one minute to allow the printer toner to bond with the...markers. Their matte finish kept variations due to brightness at a minimum, and the colors of orange and green were chosen because there were no

  10. Wireless Sensor Networks Buoy For Coastal Waters Observation

    OpenAIRE

    Hidayat, Rizqi Rizaldi; Jaya, Indra; Hestirianoto, Totok

    2016-01-01

    The availability of data in real time and continuous is important to monitor in environmental change as early as possible. Wireless sensor networks (WSN) offer a new paradigm in the field of oceanography that can measure the parameters of complex marine environment using a moored buoy. This paper described design of a data transmission system with a moored buoy and tested the performance of WSN instrument based on ZigBee protocol radio module for monitoring coastal water environment in real t...

  11. Sea-Change in Ocean Observations on Moored Buoys from the National Data Buoy Center (NDBC)

    Science.gov (United States)

    Bouchard, R. H.; Elliott, J.; Pounder, D.; Kern, K.

    2014-12-01

    The presentation will provide the technical specifications, the systems engineering processes, and preliminary results from laboratory and field tests, as the National Data Buoy Center (NDBC) undertakes a fundamental and broad transformation (sea-change) of its ocean observing systems on moored buoys. This transformation is necessary to gain efficiencies in maintaining operational ocean observation networks and to increase their reliability, which will reduce maintenance costs. The presentation will also compare and contrast existing and planned systems. The Self-Contained Ocean Observations Payload (SCOOP) takes advantage of the advances in communications and small, efficient, multi-purpose sensors to reduce the size and costs of systems and expand the suite of available real-time ocean observations. The communications will allow NDBC to increase the precision and decrease the latency of the observations. The hallmark of SCOOP is the modularity of the payloads that allow NDBC to host specialized systems, for the oceanographic research community, which may include observing ocean acidification and algal blooms, and tracking marine life, alongside its standard suite of meteorological, oceanographic, and wave systems. SCOOP will include cameras, primarily to document vandalism incidents, but they can also serve to corroborate many of the automatic observations. The two-year integration project - focused on recapitalization of NDBC's network of Hurricane Weather buoys - is aided by NDBC's 40 years of experience with marine observations and its continually improving approach to testing. Testimony to the rigor of NDBC's development and test procedures is that the World Meteorological Organization and the Intergovernmental Ocean Commission have designated NDBC as the first Regional Marine Instrumentation Center (RMIC). Integral to the fielding of these new systems is a Mission Control Center (MCC) performing the real-time, specialized monitoring and analyses and

  12. Drifting buoy data from buoy casts in a world wide distribution as part of the Tropical Ocean Global Atmosphere (TOGA) from 1989-07-01 to 1989-07-31 (NODC Accession 8900227)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data were collected using buoy casts in a world wide distribution from July 1, 1989, to July 31, 1989. Data were submitted by National Data Buoy Center...

  13. Development of an Autonomous Aerosol Sampler for Ocean Buoys and Land Sites

    National Research Council Canada - National Science Library

    Scholkovitz, Edward

    1998-01-01

    ... (aerosol embedded filters) from moored ocean buoys and remote areas on land. Research on aerosols, in particular, and atmospheric chemistry, in general, has not been previously attempted from buoys...

  14. Model Predictive Control of Buoy Type Wave Energy Converter

    DEFF Research Database (Denmark)

    Soltani, Mohsen N.; Sichani, Mahdi T.; Mirzaei, Mahmood

    2014-01-01

    The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator...... is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible....... This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....

  15. Meteorological buoy measurements in the Iceland Sea, 2007-2009

    Science.gov (United States)

    Nína Petersen, Guðrún

    2017-10-01

    The Icelandic Meteorological Office (IMO) conducted meteorological buoy measurements in the central Iceland Sea in the time period 2007-2009, specifically in the northern Dreki area on the southern segment of the Jan Mayen Ridge. Due to difficulties in deployment and operations, in situ measurements in this region are sparse. Here the buoy, deployment and measurements are described with the aim of giving a future user of the data set information that is as comprehensive as possible. The data set has been quality-checked, suspect data removed and the data set made publicly available from PANGAEA Data Publisher (https://doi.org/10.1594/PANGAEA.876206).

  16. UpTempO Buoys for Understanding and Prediction

    Science.gov (United States)

    2015-09-30

    program was initiated by Professor Peter Niiler at Scripps (UCSD) to drop 200 m long thermistor string buoys ahead of hurricanes in the Gulf of Mexico...via Air Force C130 planes, the so- called “ hurricane hunters.” In recent years, a surplus of buoys developed which coincided with a lack of... displacement over the retreat seasons of 2007-2013 in the Laptev Sea. The ice edge advances southward only when the wind is strong and the open water to

  17. The November 2011 irruption of buoy barnacles Dosima fascicularis ...

    African Journals Online (AJOL)

    They were accompanied by large numbers of bluebottles Physalia physalis and other neustonic organisms. Of 100 buoy barnacle colonies examined, only three were attached to obvious floating items (two Janthina shells and one piece of plastic). Dissection failed to reveal foreign attachment sites in 40 floats, but digesting ...

  18. Oceansat-2 and RAMA buoy winds: A comparison

    Indian Academy of Sciences (India)

    Goswami and Rajagopal. (2003) showed the impact of scatterometer data assimilation in numerical models to improve the weather forecast over India. Satheesan et al. (2007) evaluated the performance of QuikSCAT wind vec- tors against in-situ buoy observations over the. Indian Ocean and reported the mean difference.

  19. Determination of wave direction using an orbital following buoy

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Almeida, A.M.; Vaithiyanathan, R.; Vethamony, P.

    as computed simulated data. This is a preliminary report on program development. In the case of observed data, wave directions obtained with BUOY-D-P agreed within within plus or minus 5 degrees with the single available visual estimate of swell direction...

  20. Small Flux Buoy for Characterizing Marine Surface Layers

    Science.gov (United States)

    2013-06-01

    34 7. VectorNav VN-100 Rugged Accelerometer :......................... 34 C. MASFLUX FIELD DEPLOYMENT AND DATA QUALITY...oceanographic and GPS sensor specifications. ........ 31 Table 3. Accelerometer and compass specifications. ....................................... 32...superior over other flux sampling buoys in its wave measurements. Owing to its unique configurations of capacitance wires, it can sample much smaller

  1. Precise-Orientation-Beamforming Scheme for Wireless Communications between Buoys

    Directory of Open Access Journals (Sweden)

    Zhihui Wu

    2016-01-01

    Full Text Available Utilizing wireless sensor network (WSN to monitor the marine environment is one of the major techniques in oceanographic monitoring, and how to increase the limited communication distance between the buoys in WSN has become a hot research issue. In this paper, a new technique called precise-orientation-beamforming (POB which uses the beamforming algorithm to increase the communication distance between buoys is presented. As was widely applied in the radar and sonar, the beamforming method was not used to extend the communication distance between buoys so far. The POB method overcomes the unstable position of buoys caused by waves by implementing the orientation filter. The whole process includes two steps: First, the real-time attitude of the antenna array is calculated by the orientation filter. With the known relative direction of the destination node to the antenna array, the second step is to control phased array antenna beamforming parameters, directing the beam at the destination node. The POB scheme has been simulated under the condition of regular waves. The results reveal that POB provides significant power gains and improves the distance between two communicating nodes effectively.

  2. DEVELOPMENT OF BUOY-MOUNTED OCEANOGRAPHIC SENSORS (BMOS).

    Science.gov (United States)

    transducers. (c) analysis of the advantages and disadvantages of digital versus frequency transmission modes for sensor-to-buoy data link; (d) development ... of and potential use of an inductively coupled clamp-on sensor technique; (e) techniques for abatement of biological growth (fouling) on sensitive

  3. Optimization of wave energy capture of wave-powered navigational lighting buoys of seadromes

    Directory of Open Access Journals (Sweden)

    WANG Guangda

    2017-12-01

    Full Text Available [Objectives] This paper proposes an optimized design for wave-power navigational lighting buoys of seadromes.[Methods] Based on the theory of three-dimensional potential flow, the buoyant motion response of a buoy is calculated. A type of array of wave-power navigational lighting buoys located in an offshore seadrome is proposed,and a procedure for the design optimization of its component buoys is presented. Matching the best Power Take-Off(PTO damping, the diameter to draft ratio and array distance with the best energy capture width ratio are acquired, and the energy capture for the short-term forecast of the buoy array is accomplished. On this basis, combined with the actual sea conditions, energy capture for the long-term forecast of an individual buoy is accomplished. The influence of the buoy diameter, buoy draft and array distance on the energy capture width ratio is discussed.[Results] The results show that the energy capture width ratio is at its greatest when the diameter to draft ratio is between 2.4-2.6; the smaller the distance between array buoys, the greater the energy capture width of each buoy.[Conclusions] The results can provide a reference and suggestions for the optimization of the design of wave energy generation for arrays buoy.

  4. Texas Automated Buoy System 1995-2005 and Beyond

    Science.gov (United States)

    Guinasso, N. L.; Bender, L. C.; Walpert, J. N.; Lee, L. L.; Campbell, L.; Hetland, R. D.; Howard, M. K.; Martin, R. D.

    2005-05-01

    TABS was established in l995 to provide data to assess oil spill movement along Texas coast for the Texas General Land Office Oil Spill Prevention and Response Program. A system of nine automated buoys provide wind and current data in near real time. Two of these buoys are supported by the Flower Garden Banks Joint Industry Program. A TABS web site provides a public interface to view and download the data. A real time data analysis web page presents a wide variety of useful data products derived from the field measurements. Integration efforts now underway include transfer of buoy data to the National Data Buoy Center for quality control and incorporation into the Global Telecommunications Stream. The TGLO ocean circulation nowcast/forecast modeling system has been in continuous operation since 1998. Two models, POM and ROMS, are used to produce forecasts of near-surface wind driven currents up to 48 hours into the future. Both models are driven using wind fields obtained from the NAM (formerly Eta) forecast models operated by NOAA NCEP. Wind and current fields are displayed on websites in both static and animated forms and are updated four times per day. Under funding from the SURA/SCOOP program we are; 1) revamping the system to conform with the evolving Data Management and Communications (DMAC) framework adopted by the NSF Orion and OCEAN.US IOOS programs, 2) producing model-data comparisons, and 3) integrating the wind and current fields into the GNOME oil trajectory model used by NOAA/Hazmat. Academic research is planned to assimilate near real-time observations from TABS buoys and some 30-40 ADCP instruments scheduled to be mounted on offshore oil platforms in early 2005. Texas Automated Buoy System (TABS) and its associated modeling efforts provide a reliable source of accurate, up-to-date information on currents along the Texas coast. As the nation embarks on the development of an Integrated Ocean Observing System (IOOS), TABS will be an active participant

  5. The wave buoy analogy - estimating high-frequency wave excitations

    DEFF Research Database (Denmark)

    Nielsen, Ulrik Dam

    2008-01-01

    The paper deals with the wave buoy analogy where a ship is considered as a wave buoy, so that measured ship responses are used as a basis to estimate wave spectra and associated sea state parameters. The study presented follows up on a previous paper, Nielsen [Nielsen UD. Response-based estimation...... of sea state parameters — influence of filtering. Ocean Engineering 2007;34:1797–810.], where time series of ship responses were generated from a known wave spectrum for the purpose of the inverse process — the estimation of the underlying wave excitations. Similar response generations and vice versa...... be estimated reasonably well, even considering high-frequency wave components of a wind sea wave spectrum....

  6. Numerical modelling of the HAB Energy Buoy: Stage 1

    DEFF Research Database (Denmark)

    Kurniawan, Adi

    2017-01-01

    . The model is further able to give an estimate of the power production of the device in a given wave climate as well as other statistical estimates of the device motions and loads. The performance of different device shapes and dimensions has been evaluated, where displacement limits appropriate for each......This report presents the results of the first stage of the project "Numerical modelling of the HAB Energy Buoy". The objectives of this stage are to develop a numerical model of the HAB Energy Buoy, a self-reacting wave energy device consisting of two heaving bodies, and to investigate a number...... of variations of the device geometry in order to arrive at a design optimized for the target deployment site. The findings will be used as a basis to inform planned small-scale wave tank tests. A review of literature on self-reacting wave energy devices consisting of two heaving bodies has been conducted...

  7. Snow depth evolution on sea ice from Snow Buoy measurement

    Science.gov (United States)

    Nicolaus, M.; Arndt, S.; Hendricks, S.; Hoppmann, M.; Katlein, C.; König-Langlo, G.; Nicolaus, A.; Rossmann, H. L.; Schiller, M.; Schwegmann, S.; Langevin, D.

    2016-12-01

    Snow cover is an Essential Climate Variable. On sea ice, snow dominates the energy and momentum exchanges across the atmosphere-ice-ocean interfaces, and actively contributes to sea ice mass balance. Yet, snow depth on sea ice is one of the least known and most difficult to observe parameters of the Arctic and Antarctic; mainly due to its exceptionally high spatial and temporal variability. In this study; we present a unique time series dataset of snow depth and air temperature evolution on Arctic and Antarctic sea ice recorded by autonomous instruments. Snow Buoys record snow depth with four independent ultrasonic sensors, increasing the reliability of the measurements and allowing for additional analyses. Auxiliary measurements include surface and air temperature, barometric pressure and GPS position. 39 deployments of such Snow Buoys were achieved over the last three years either on drifting pack ice, on landfast sea ice or on an ice shelf. Here we highlight results from two pairs of Snow Buoys installed on drifting pack ice in the Weddell Sea. The data reveals large regional differences in the annual cycle of snow depth. Almost no reduction in snow depth (snow melt) was observed in the inner and southern part of the Weddell Sea, allowing a net snow accumulation of 0.2 to 0.9 m per year. In contrast, summer snow melt close to the ice edge resulted in a decrease of about 0.5 m during the summer 2015/16. Another array of eight Snow Buoys was installed on central Arctic sea ice in September 2015. Their air temperature record revealed exceptionally high air temperatures in the subsequent winter, even exceeding the melting point but with almost no impact on snow depth at that time. Future applications of Snow Buoys on Arctic and Antarctic sea ice will allow additional inter-annual studies of snow depth and snow processes, e.g. to support the development of snow depth data products from airborne and satellite data or though assimilation in numerical models.

  8. A study of the optimum draft of multiple resonance power buoys for maximizing electric power production

    Directory of Open Access Journals (Sweden)

    Hyuck-Min Kweon

    2014-12-01

    Full Text Available To maximize electric power production using wave energy extractions from resonance power buoys, the maximum motion displacement spectra of the buoys can primarily be obtained under a given wave condition. In this study, wave spectra observed in shoaling water were formulated. Target resonance frequencies were established from the arithmetic means of modal frequency bands and the peak frequencies. The motion characteristics of the circular cylindrical power buoys with corresponding drafts were then calculated using numerical models without considering PTO damping force. Results showed that the heave motions of the power buoys in shoaling waters with insufficient drafts produced greater amplification effects than those in deep seas with sufficient drafts.

  9. A Floating Ocean Energy Conversion Device and Numerical Study on Buoy Shape and Performance

    Directory of Open Access Journals (Sweden)

    Ruiyin Song

    2016-05-01

    Full Text Available Wave and current energy can be harnessed in the East China Sea and South China Sea; however, both areas are subject to high frequencies of typhoon events. To improve the safety of the ocean energy conversion device, a Floating Ocean Energy Conversion Device (FOECD with a single mooring system is proposed, which can be towed to avoid severe ocean conditions or for regular maintenance. In this paper, the structure of the FOECD is introduced, and it includes a catamaran platform, an oscillating buoy part, a current turbine blade, hydraulic energy storage and an electrical generation part. The numerical study models the large catamaran platform as a single, large buoy, while the four floating buoys were modeled simply as small buoys. Theoretical models on wave energy power capture and efficiency were established. To improve the suitability of the buoy for use in the FOECD and its power harvesting capability, a numerical simulation of the four buoy geometries was undertaken. The shape profiles examined in this paper are cylindrical, turbinate (V-shaped and U-shaped cone with cylinder, and combined cylinder-hemisphere buoys. Simulation results reveal that the suitability of a turbinate buoy is the best of the four types. Further simulation models were carried out by adjusting the tip radius of the turbinate buoy. Three performance criteria including suitability, power harvesting capability and energy capture efficiency were analyzed. It reveals that the turbinate buoy has almost the same power harvesting capabilities and energy capture efficiency, while its suitability is far better than that of a cylindrical buoy.

  10. System for Monitoring, Determining, and Reporting Directional Spectra of Ocean Surface Waves in Near Realtime from a Moored Buoy

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A moored buoy floating at the ocean surface and anchored to the seafloor precisely measures acceleration, pitch, roll, and Earth's magnetic flux field of the buoy...

  11. UpTempO Buoys for Understanding and Prediction

    Science.gov (United States)

    2014-09-30

    GPS, alkaline batteries for 1.5 years of operation, Iridium antenna , sea level pressure sensor, and sea surface temperature sensor at 0.12 m depth... NOAA product, (ii) the high resolution MUR product available from JPL’s PODAAC = Physical Oceanography Distributed Active Archive Center, and (iii...observations and the widely used NOAA OIv2 (a.k.a. “Reynolds”) SST field. A major goal of the overall UpTempO buoy program is to improve these fields by

  12. Power Production Analysis of the OE Buoy WEC for the CORES Project

    DEFF Research Database (Denmark)

    Lavelle, John; Kofoed, Jens Peter

    , however, suffers from the occurrence of sudden stops (2). Another type of self-rectifying turbine, an impulse turbine, was used in place of a Wells turbine to, in refit of the OE Buoy, in order to compare the two types.OE Buoy was deployed in Galway Bay, Ireland during March, April and May of 2011, during...

  13. Development of a GNSS Buoy for Monitoring Water Surface Elevations in Estuaries and Coastal Areas

    Directory of Open Access Journals (Sweden)

    Yen-Pin Lin

    2017-01-01

    Full Text Available In this work, a Global Navigation Satellite System (GNSS buoy that utilizes a Virtual Base Station (VBS combined with the Real-Time Kinematic (RTK positioning technology was developed to monitor water surface elevations in estuaries and coastal areas. The GNSS buoy includes a buoy hull, a RTK GNSS receiver, data-transmission devices, a data logger, and General Purpose Radio Service (GPRS modems for transmitting data to the desired land locations. Laboratory and field tests were conducted to test the capability of the buoy and verify the accuracy of the monitored water surface elevations. For the field tests, the GNSS buoy was deployed in the waters of Suao (northeastern part of Taiwan. Tide data obtained from the GNSS buoy were consistent with those obtained from the neighboring tide station. Significant wave heights, zero-crossing periods, and peak wave directions obtained from the GNSS buoy were generally consistent with those obtained from an accelerometer-tilt-compass (ATC sensor. The field tests demonstrate that the developed GNSS buoy can be used to obtain accurate real-time tide and wave data in estuaries and coastal areas.

  14. Initial Construction and Deployments of the Long-Term Ambient-Noise Buoy.

    Science.gov (United States)

    acoustic ambient noise in the deep ocean. With a nucleus of surplus equipment from two buoy projects of the 1960’s plus additional equipment designed...and built at NRL, seven Ambient Noise Buoys were fabricated. One was designed for investigation in the frequency range of 1 to 10 Hz with four

  15. Development of a GNSS Buoy for Monitoring Water Surface Elevations in Estuaries and Coastal Areas.

    Science.gov (United States)

    Lin, Yen-Pin; Huang, Ching-Jer; Chen, Sheng-Hsueh; Doong, Dong-Jiing; Kao, Chia Chuen

    2017-01-18

    In this work, a Global Navigation Satellite System (GNSS) buoy that utilizes a Virtual Base Station (VBS) combined with the Real-Time Kinematic (RTK) positioning technology was developed to monitor water surface elevations in estuaries and coastal areas. The GNSS buoy includes a buoy hull, a RTK GNSS receiver, data-transmission devices, a data logger, and General Purpose Radio Service (GPRS) modems for transmitting data to the desired land locations. Laboratory and field tests were conducted to test the capability of the buoy and verify the accuracy of the monitored water surface elevations. For the field tests, the GNSS buoy was deployed in the waters of Suao (northeastern part of Taiwan). Tide data obtained from the GNSS buoy were consistent with those obtained from the neighboring tide station. Significant wave heights, zero-crossing periods, and peak wave directions obtained from the GNSS buoy were generally consistent with those obtained from an accelerometer-tilt-compass (ATC) sensor. The field tests demonstrate that the developed GNSS buoy can be used to obtain accurate real-time tide and wave data in estuaries and coastal areas.

  16. Drifting buoy data observed during 1985 through 1989 and assembled by the Responsible National Oceanographic Data Center (RNODC) for Drifting Buoy Data (NODC Accession 9100057)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical and meteorological data were collected from drifting buoys from a World-Wide distribution from 2 January 1985 to 31 December 1989. Data were processed by...

  17. Drifting buoy data observed during 1992 and assembled by the Responsible National Oceanographic Data Center (RNODC) for Drifting Buoy Data (NODC Accession 9300091)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical and meteorological data were collected from drifting buoys from a World-Wide distribution from 01 January 1992 to 31 December 1992. Data were processed by...

  18. Oceanographic profile Temperature and Salinity measurements collected during the Arctic Buoy Program using drifting buoy in the Arctic from 1985-1994 (NODC Accession 0001497)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Between 1985 and 1994, the Polar Science Center at the University of Washington deployed 24 ARGOS data buoys in ice floes on the Arctic Ocean, from which six...

  19. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  20. A Wave Power Device with Pendulum Based on Ocean Monitoring Buoy

    Science.gov (United States)

    Chai, Hui; Guan, Wanchun; Wan, Xiaozheng; Li, Xuanqun; Zhao, Qiang; Liu, Shixuan

    2018-01-01

    The ocean monitoring buoy usually exploits solar energy for power supply. In order to improve power supply capacity, this paper proposes a wave power device according to the structure and moving character of buoy. The wave power device composes of pendulum mechanism that converts wave energy into mechanical energy and energy storage mechanism where the mechanical energy is transferred quantitatively to generator. The hydrodynamic equation for the motion of buoy system with generator devise is established based on the potential flow theory, and then the characteristics of pendulum motion and energy conversion properties are analysed. The results of this research show that the proposed wave power devise is able to efficiently and periodically convert wave energy into power, and increasing the stiffness of energy storage spring is benefit for enhancing the power supply capacity of the buoy. This study provides a theory reference for the development of technology on wave power generator for ocean monitoring buoy.

  1. Evidence that grey seals (Halichoerus grypus use above-water vision to locate baited buoys

    Directory of Open Access Journals (Sweden)

    Arne Fjälling

    2007-01-01

    Full Text Available Fishing gear in the Baltic is often raided by grey seals (Halichoerus grypus. The seals remove the fish and damage the nets, or entangle themselves and drown. In order to develop ways of mitigating the seals-fisheries conflict, it is important to know exactly how the seals locate the fishing gear. A field experiment was conducted in order to clarify whether seals use their vision above water to do this. Bait (herring; Clupea harengus was attached to the anchor lines of buoys of the type that is commonly used to mark the position of fishing gear. In all, 643 buoys were set. Some of the buoys (210 were also fitted with camera traps. Weather data were collected from official weather stations nearby. Bait loss (mean 18% was significantly correlated with buoy size (P = 0.002 and wind speed (P = 0.04. There was a significant association between bait loss and seal observations near the buoys (P = 0.05. Five photos of grey seals were obtained from the camera traps. No fish-eating birds, such as cormorants or mergansers, were ever observed near the buoys or caught on camera. It was concluded that a main cause of missing bait was scavenging by grey seals, and that they did use above-water vision to locate the buoys. It was also concluded that wind strength (i.e. wave action contributed tothe bait loss. The camera trap buoys had a somewhat lower bait loss than the other buoys (P = 0.054, which was attributed to a scaring effect. Neither the number of seal observations nor the bait loss differed significantly between the 2 study areas in the experiment (P = 0.43 and P = 0.83, respectively. Bait loss was not affected by the buoy colour (red, white, or grey; P = 0.87. We suggest that the findings of this experiment could be put into practice in a seal-disturbed area by deploying a number of decoy buoys, or by hiding live buoys below the surface of the water. This would increase the cost of foraging for the seals, and hence discourage them from exploiting

  2. Aquarius: transitioning to an unmanned life support buoy.

    Science.gov (United States)

    Smith, S; Cooper, C

    1998-01-01

    The Aquarius underwater laboratory (or habitat) is the world's only operational saturation facility currently supporting scientific research and is operated by the University of North Carolina at Wilmington. The underwater laboratory accommodates and supports six aquanauts (scientists and habitat technicians) at habitat depth for 10-30-day missions. In the past, life support systems were provided by a manned support barge or Mobile Support Base (MSB) moored directly above Aquarius. The MSB was manned 24 h a day during saturation missions, which required 12 support staff in three separate shifts. A new unmanned Life Support Buoy (LSB) replaces the MSB and provides life support systems and is the voice, video, and data communications bridge from the habitat to the shore base. The LSB transmits status of all life support systems to the habitat and the shore base, thus minimizing the need for support staff to be present overtop of Aquarius during missions.

  3. Improving coastal wave hindcasts by combining offshore buoy observations with global wave models.

    Science.gov (United States)

    Crosby, S. C.; O'Reilly, W. C.; Guza, R. T.

    2014-12-01

    Waves conditions in southern California are sensitive to offshore wave directions. Due to blocking by coastal islands and refraction across complex bathymetry, a transform incident offshore swell-spectra to shallow water buoy locations. A nearly continuous 10 yr data set of approximately 14 buoys is used. Comparisons include standard bulk parameters (e.g. significant wave height, peak period), the frequency-dependent energy spectrum (needed for run-up estimation) and radiation stress component Sxy (needed for alongshore current and sediment transport estimation). Global wave model uncertainties are unknown, complicating the formulation of optimum assimilation constraints. Several plausible models for estimating offshore waves are tested. Future work includes assimilating nearshore buoy observations, with the long-term objective of accurate regional wave hindcasts using an efficient mix of global wave models and buoys. This work is supported by the California Department of Parks and Recreation, Division of Boating and Waterways Oceanography Program.

  4. Hardware design of a submerged buoy system based on electromagnetic inductive coupling

    Directory of Open Access Journals (Sweden)

    Song Dalei

    2016-01-01

    Full Text Available This paper mainly introduces the hardware design of a new type of ocean buoy for multi-scale marine dynamic process. The buoy system can collect a number of real-time marine environment data and then transmit all the data back to the landing site through wireless module. The authors mainly designed the hardware circuit of the buoy system, including data collection system, data communication system, data storage system. Due to the buoy system will complete the marine observation work continuously for at least a month, so we add the low power consumption function which can realize the intermittent work for the data collection system. This paper also introduces the electromagnetic induction coupling technology of underwater sensors, the sea surface communication network technology, etc. The system can also extends to the ecological regional anomaly monitoring and the early warning of disaster weather.

  5. NODC Standard Format Drifting Buoy (F156) Data (1975-1994) (NODC Accession 0014200)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data type contains time series ocean circulation data determined by tracking the movement of drifting buoys, drogues or other instrumented devices. Movement is...

  6. Physical and optical data collected from drifting buoys between May 1993 - December 1996 (NODC Accession 0000586)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Upwelling and downwelling irradiances were collected from surface optical drifter buoys off the California coast (NE Pacific limit-180) from 05 May 1993 to 06...

  7. PacIOOS Water Quality Buoy KN (WQB-KN): Kilo Nalu, Oahu, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points....

  8. PacIOOS Water Quality Buoy 03 (WQB-03): Kiholo Bay, Big Island, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points....

  9. PacIOOS Water Quality Buoy 04 (WQB-04): Hilo, Big Island, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points....

  10. NODC Standard Product: NOAA Marine environmental buoy database Webdisc (7 disc set) (NODC Accession 0090141)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This CD-ROM set contains the historic archive of meteorological and oceanographic data collected by moored buoys and C-MAN stations operated by the NOAA National...

  11. PacIOOS Water Quality Buoy AW (WQB-AW): Ala Wai, Oahu, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points....

  12. Analysis of Floating Buoy of a Wave Power Generating Jack-Up Platform Haiyuan 1

    Directory of Open Access Journals (Sweden)

    Date Li

    2013-01-01

    Full Text Available The paper focuses on the performance of floating buoys of a wave power generating jack-up platform called Haiyuan 1, in order to work out the optimum designed draft and hydraulic pressure. The performance of the buoy, especially its delivered power, is an important issue in designing oscillating buoy wave energy converter. In this case, major factors affect the performance including incident wave, designed draft, and hydraulic pressure on the buoy. To find out the relationship among design draft, hydraulic pressure, and delivered power, the key point is to precisely estimate wave induced motion of the buoy. Three-dimensional theory and time domain method based on potential theory were adopted in the paper. Unlike ship and other floating structures, motion of wave energy converter (WEC buoy in wave will be weakened because of energy take-off, which will cause significant draft changing with time. Thus, draft changing should be taken into consideration as well. In addition, green water problem occurs more frequently than that in ship and other floating structures and also might the reduce delivered power. Therefore, green water problem will also be taken into account when choosing the optimum designed draft and hydraulic pressure. The calculation indicates that the optimum designed draft is 0.935 m, while the optimum designed hydraulic pressure is 30 kN.

  13. Surface wind-drifted currents observed by drifting buoys in the East China Sea

    Science.gov (United States)

    Komatsu, K.

    Surface and upper layer currents were observed by drifting GPS buoys in the East China Sea from February to March in 2001 and 2003. Both observations showed that two buoys deployed at the same position 120 nautical miles northwestward from the Kuroshio made different trajectories each other. The buoy drogued at 15m depth drifted northward, indicating the Kuroshio Branch Current extending to the Japan Sea, whose trajectory was properly reproduced by a high resolution 3-D model assimilated to satellite sea level. On the other hand, the buoy without drogue was drawn in eastward to the Kuroshio and its trajectory was not reproduced by the numerical model. In the region where currents were comparatively weak, the no-drogue buoy drifted to the direction which gave good agreement in synoptic time scale with the surface current direction inferred from the Ekman drift using wind data based on QuikSCAT. However the drifting speed of the buoy was over twice faster than 3.5% of the wind speed, indicating the contamination of drifting effects due to wind waves. These results suggested that a small difference of the vertical position of organic/inorganic matters in the surface layer let their future routes change drastically under the multiple drifting effects.

  14. Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data

    Directory of Open Access Journals (Sweden)

    Xifeng Wang

    2016-09-01

    Full Text Available Wave periods estimated from satellite altimetry data behave differently from those calculated from buoy data, especially in low-wind conditions. In this paper, the geometric mean wave period T a is calculated from buoy data, rather than the commonly used zero-crossing wave period T z . The geometric mean wave period uses the fourth moment of the wave frequency spectrum and is related to the mean-square slope of the sea surface measured using altimeters. The values of T a obtained from buoys and altimeters agree well (root mean square difference: 0.2 s only when the contribution of high-frequency sea waves is estimated by a wavenumber spectral model to complement the buoy data, because a buoy cannot obtain data from waves having wavelengths that are shorter than the characteristic dimension of the buoy.

  15. United States Naval Academy Polar Science Program's Visual Arctic Observing Buoys; The IceGoat

    Science.gov (United States)

    Woods, J. E.; Clemente-Colon, P.; Nghiem, S. V.; Rigor, I.; Valentic, T. A.

    2012-12-01

    The U.S. Naval Academy Oceanography Department currently has a curriculum based Polar Science Program (USNA PSP). Within the PSP there is an Arctic Buoy Program (ABP) student research component that will include the design, build, testing and deployment of Arctic Buoys. Establishing an active, field-research program in Polar Science will greatly enhance Midshipman education and research, as well as introduce future Naval Officers to the Arctic environment. The Oceanography Department has engaged the USNA Ocean Engineering, Systems Engineering, Aerospace Engineering, and Computer Science Departments and developed a USNA Visual Arctic Observing Buoy, IceGoat1, which was designed, built, and deployed by midshipmen. The experience gained through Polar field studies and data derived from these buoys will be used to enhance course materials and laboratories and will also be used directly in Midshipman independent research projects. The USNA PSP successfully deployed IceGoat1 during the BROMEX 2012 field campaign out of Barrow, AK in March 2012. This buoy reports near real-time observation of Air Temperature, Sea Temperature, Atmospheric Pressure, Position and Images from 2 mounted webcams. The importance of this unique type of buoy being inserted into the U.S. Interagency Arctic Buoy Program and the International Arctic Buoy Programme (USIABP/IABP) array is cross validating satellite observations of sea ice cover in the Arctic with the buoys webcams. We also propose to develop multiple sensor packages for the IceGoat to include a more robust weather suite, and a passive acoustic hydrophone. Remote cameras on buoys have provided crucial qualitative information that complements the quantitative measurements of geophysical parameters. For example, the mechanical anemometers on the IABP Polar Arctic Weather Station at the North Pole Environmental Observatory (NPEO) have at times reported zero winds speeds, and inspection of the images from the NPEO cameras have showed

  16. Implementation of PLUTO Buoy for Monitoring Water Quality in Indonesia, Reflection and Future Plans

    Science.gov (United States)

    Chandra, H.; Krismono, K.; Kusumaningrum, P. D.; Sianturi, D.; Firdaus, Y.; Taukhid, I.; Borneo, B. B.

    2016-02-01

    Research and development of PLUTO (Perairan Selalu Termonitor/Waters Always Monitored) buoy has reached its fourth year in 2015. Try out has been done in coastal waters, fishponds, fishing port ponds, and reservoirs. In the first year (2010) try out has been performed on coastal waters with off line measurement system. The buoy used temperature, salinity, DO and pH sensors. In the second year (2013) try out was carried out on fishponds and fishing port ponds using telemetry measurement system. In the third year (2014) try out was carried out on water reservoir with telemetry measurement system. In the fourth year (2015) android application is developed to monitor 4 water reservoirs and 1 lake. Beside that, observation point is added to 3 point depth for one buoy. Parameters used are temperature, DO, and turbidity. Three PLUTO buoys are placed in each reservoir, at inlet, outlet, and at center of fish cultivation. Through Ocean Science Meeting in New Orleans it is hoped that there will be input and suggestion from the experts for future development of the monitoring system for public inland waters (especially reservoir and lake) in Indonesia. Keywords: buoy PLUTO, salinity, temperature, Dissolved Oxygen (DO), pH, turbidity, telemetry

  17. Heaving displacement amplification characteristics of a power buoy in shoaling water with insufficient draft

    Directory of Open Access Journals (Sweden)

    Hyuck-Min Kweon

    2013-12-01

    Full Text Available The resonance power buoy is a convincing tool that can increase the extraction efficiency of wave energy. The buoy needs a corresponding draft, to move in resonance with waves within the peak frequency band where wave energy is concentrated. However, it must still be clarified if the buoy acts as an effective displacement amplifier, when there is insufficient water depth. In this study, the vertical displacement of a circular cylinder-type buoy was calculated, with the spectrum data observed in a real shallow sea as the external wave force, and with the corresponding draft, according to the mode frequency of normal waves. Such numerical investigation result, without considering Power Take-Off (PTO damping, confirmed that the area of the heave responses spectrum can be amplified by up to about tenfold, compared with the wave energy spectrum, if the draft corresponds to the peak frequency, even with insufficient water depth. Moreover, the amplification factor of the buoy varied, according to the seasonal changes in the wave spectra.

  18. Wave observations from an array of directional buoys over the southern Brazilian coast

    Science.gov (United States)

    Pereira, Henrique Patricio Prado; Violante-Carvalho, Nelson; Nogueira, Izabel Christina Martins; Babanin, Alexander; Liu, Qingxiang; de Pinho, Uggo Ferreira; Nascimento, Fabio; Parente, Carlos Eduardo

    2017-10-01

    It is well known that the majority of buoy measurements are located around the US coast and along some Europeans countries. The lack of long-term and densely spaced in situ measurements in the Southern Hemisphere in general, and the South Atlantic in particular, hinders several investigations due to the lack of detailed metocean information. Here, we present an effort to overcome this limitation, with a dense network of buoys along the Brazilian coast, equipped with several meteorological and oceanographic sensors. Out of ten currently operational buoys, three are employed to present the main characteristics of waves in the Southern part of the network. For the first time, sensor characteristics and settings are described, as well as the methods applied to the raw wave data. Statistics and distributions of wave parameters, swell propagating events, comparison with a numerical model and altimeters and a discussion about the occurrence of freak waves are presented.

  19. Storm wave buoy equipped with micromechanical inertial unit: Results of development and testing

    Science.gov (United States)

    Gryazin, D. G.; Staroselcev, L. P.; Belova, O. O.; Gleb, K. A.

    2017-07-01

    The article describes the results of developing a wave buoy to measure the statistical characteristics of waves and the characteristics of directional spectra of three-dimensional waves. The device is designed for long-term measurements lasting up to a season, which can help solve problems in forecasting waves and preventing emergencies from wave impact on offshore platforms, hydraulic structures, and other marine facilities. The measuring unit involves triads of micromechanical gyroscopes, accelerometers, and a three-component magnetometer. A description of the device, results of laboratory research of its characteristics, and bench and full-scale tests are offered. It is noted that to assess the performance characteristics, comparative tests of the Storm wave buoy were conducted with a standard string wave probe installed on an offshore platform. It is shown that the characteristics and capabilities of the wave buoy make it possible to oust foreign devices from the domestic market.

  20. Wave observations from an array of directional buoys over the southern Brazilian coast

    Science.gov (United States)

    Pereira, Henrique Patricio Prado; Violante-Carvalho, Nelson; Nogueira, Izabel Christina Martins; Babanin, Alexander; Liu, Qingxiang; de Pinho, Uggo Ferreira; Nascimento, Fabio; Parente, Carlos Eduardo

    2017-12-01

    It is well known that the majority of buoy measurements are located around the US coast and along some Europeans countries. The lack of long-term and densely spaced in situ measurements in the Southern Hemisphere in general, and the South Atlantic in particular, hinders several investigations due to the lack of detailed metocean information. Here, we present an effort to overcome this limitation, with a dense network of buoys along the Brazilian coast, equipped with several meteorological and oceanographic sensors. Out of ten currently operational buoys, three are employed to present the main characteristics of waves in the Southern part of the network. For the first time, sensor characteristics and settings are described, as well as the methods applied to the raw wave data. Statistics and distributions of wave parameters, swell propagating events, comparison with a numerical model and altimeters and a discussion about the occurrence of freak waves are presented.

  1. 77 FR 29254 - Safety Zones, Large Cruise Ships; Lower Mississippi River, Southwest Pass Sea Buoy to Mile Marker...

    Science.gov (United States)

    2012-05-17

    ... River, Southwest Pass Sea Buoy to Mile Marker 96.0; New Orleans, LA AGENCY: Coast Guard, DHS. ACTION... transit the Lower Mississippi River between mile marker 96.0, New Orleans, LA and the Southwest Pass Sea...; Large Cruise Ships; Lower Mississippi River, Southwest Pass Sea Buoy to Mile Marker 96.0, New Orleans...

  2. Application of Buoy Observations in Determining Characteristics of Several Typhoons Passing the East China Sea in August 2012

    Directory of Open Access Journals (Sweden)

    Ningli Huang

    2013-01-01

    Full Text Available The buoy observation network in the East China Sea is used to assist the determination of the characteristics of tropical cyclone structure in August 2012. When super typhoon “Haikui” made landfall in northern Zhejiang province, it passed over three buoys, the East China Sea Buoy, the Sea Reef Buoy, and the Channel Buoy, which were located within the radii of the 13.9 m/s winds, 24.5 m/s winds, and 24.5 m/s winds, respectively. These buoy observations verified the accuracy of typhoon intensity determined by China Meteorological Administration (CMA. The East China Sea Buoy had closely observed typhoons “Bolaven” and “Tembin,” which provided real-time guidance for forecasters to better understand the typhoon structure and were also used to quantify the air-sea interface heat exchange during the passage of the storm. The buoy-measured wind and pressure time series were also used to correct the intensity of “Damrey” initially determined by CMA.

  3. An autonomous drifting buoy system for long term pCO2 observation

    Science.gov (United States)

    Nakano, Y.; Fujiki, T.; Wakita, M.; Azetsu-Scott, K.; Watanabe, S.

    2009-04-01

    Many studies have been carried out around the world to understand what happens to carbon dioxide (CO2) once it is emitted into the atmosphere, and how it relates to long-term climate change. However, the sea surface pCO2 observations on volunteer observation ships and research vessels concentrated in the North Atlantic and North Pacific. To assess the spatial and temporal variations of surface pCO2 in the global ocean, new automated pCO2 sensor which can be used in platform systems such as buoys or moorings is strongly desired. We have been developing the small drifting buoy system (diameter 250-340 mm, length 470 mm, weight 15 kg) for pCO2 measurement, with the support of the Japan EOS Promotion Program (JEPP), the Ministry of Education, Culture, Sports, Science and Technology (MEXT). The objective is to provide simplified, automated measurements of pCO2 over all the world's oceans, an essential factor in understanding how the ocean responds to climate change. The measurement principle for the pCO2 sensor is based on spectrophotometry (e.g. Lefèvre et al., 1993; Degrandpre et al., 1995). The CO2 in the surrounding seawater equilibrates with the indicator solution across the gas permeable membranes. The equilibration process causes a change of pH in the indicator solution, which results in the change of optical absorbance. The pCO2 is calculated from the optical absorbance of the pH indicator solution equilibrated with CO2 in seawater through a gas permeable membrane. In our analytical system, we used an amorphous fluoropolymer tubing form of AF-2400 by DuPontTM for the gas permeable membrane due to its high gas permeability coefficients. The measurement system of the sensor consisted mainly of a LED light source, optical fibers, a CCD detector, and a downsized PC. The measured data were transmitted to the laboratory by satellite communication (Argos system). In the laboratory experiment, we obtained a high response time (less than 2 minutes) and a precision

  4. Satellite-tracked drifting buoy observations in the south equatorial current in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Shetye, S.R.; Michael, G.S.

    Three satellite-tracked drifting buoys released in the south equatorial current in the Indian Ocean followed the path of the current moving westward approximately zonally in the vicinity of 10 degrees S latitude. On nearing the east coast of Africa...

  5. HAB Buoy: a new instrument for in situ monitoring and early warning ...

    African Journals Online (AJOL)

    A new microplankton imaging and analysis instrument, HAB Buoy, is described. It integrates a high-speed camera for in-flow image acquisition with automatic specimen labelling software, known as DiCANN (Dinoflagellate Categorisation by Artificial Neural Network). Some preliminary results are presented together with a ...

  6. CytoBuoy: a step forward towards using flow cytometry in operational oceanography

    Directory of Open Access Journals (Sweden)

    G. B.J. Dubelaar

    2000-06-01

    Full Text Available While the performance of biological sensors in real time monitoring networks is limited to bulk values like chlorophyll fluorescence, in practice the implementation of automated phytoplankton taxonomy remains a remote option. Aiming to reduce this gap we developed a flow cytometer called CytoBuoy for autonomous in situ operation, for instance in a moored buoy with wireless data transfer. Although not comparable to microscopy, flow cytometers detect and count particles allowing a limited level of particle characterization based on the light scatter and fluorescence properties of the individual particles. CytoBuoy analyses a large size range of particles, typical for marine coastal zones and fresh waters. The `field´ design implies a tradeoff between the accuracy and versatility of laboratory flow cytometers and the qualities needed for trouble free autonomous operation in situ. The optics and electronics however were designed for maximal reflection of the particle morphology in the measured signals. Whereas standard cytometers reduce these to single peak or area `listmode´ numbers, the signal courses are preserved fully by CytoBuoy and transferred to the computer as raw data, which allows more extended morphological analysis. Extended field tests will have to show how the system holds in various environments and weather conditions.

  7. 33 CFR 149.321 - How many ring life buoys must be on each deepwater port?

    Science.gov (United States)

    2010-07-01

    ... on each deepwater port? 149.321 Section 149.321 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) DEEPWATER PORTS DEEPWATER PORTS: DESIGN, CONSTRUCTION, AND EQUIPMENT Lifesaving Equipment Manned Deepwater Port Requirements § 149.321 How many ring life buoys must be...

  8. An improvement of the GPS buoy system for detecting tsunami at far offshore

    Science.gov (United States)

    Kato, T.; Terada, Y.; Nagai, T.; Kawaguchi, K.; Koshimura, S.; Matsushita, Y.

    2012-12-01

    We have developed a GPS buoy system for detecting a tsunami before its arrival at coasts and thereby mitigating tsunami disaster. The system was first deployed in 1997 for a short period in the Sagami bay, south of Tokyo, for basic experiments, and then deployed off Ofunato city, northeastern part of Japan, for the period 2001-2004. The system was then established at about 13km south of Cape Muroto, southwestern part of Japan, since 2004. Five tsunamis of about 10cm have been observed in these systems, including 2001 Peru earthquake (Mw8.3), 2003 Tokachi-oki earthquake (Mw8.3), 2004 Off Kii Peninsula earthquake (Mw7.4), 2010 Chile earthquake (Mw8.8), and 2011 Tohoku-Oki earthquake (Mw9.0). These experiments clearly showed that GPS buoy is capable of detecting tsunami with a few centimeter accuracy and can be monitored in near real time by applying an appropriate filter, real-time data transmission using radio and dissemination of obtained records of sea surface height changes through internet. Considering that the system is a powerful tool to monitor sea surface variations due to wind as well as tsunami, the Ministry of Land, Infrastructure, Transport and Tourism implemented the system in a part of the Nationwide Ocean Wave information network for Ports and HArbourS (NOWPHAS) system and deployed the system at 15 sites along the coasts around the Japanese Islands. The system detected the tsunami due to the 11th March 2011 Tohoku-Oki earthquake with higher than 6m of tsunami height at the site Off South Iwate (Kamaishi). The Japan Meteorological Agency that was monitoring the record updated the level of the tsunami warning to the greatest value due to the result. Currently, the GPS buoy system uses a RTK-GPS which requires a land base for obtaining precise location of the buoy by a baseline analysis. This algorithm limits the distance of the buoy to, at most, 20km from the coast as the accuracy of positioning gets much worse as the baseline distance becomes longer

  9. Dynamic analysis of propulsion mechanism directly driven by wave energy for marine mobile buoy

    Science.gov (United States)

    Yu, Zhenjiang; Zheng, Zhongqiang; Yang, Xiaoguang; Chang, Zongyu

    2016-07-01

    Marine mobile buoy(MMB) have many potential applications in the maritime industry and ocean science. Great progress has been made, however the technology in this area is far from maturity in theory and faced with many difficulties in application. A dynamic model of the propulsion mechanism is very necessary for optimizing the parameters of the MMB, especially with consideration of hydrodynamic force. The principle of wave-driven propulsion mechanism is briefly introduced. To set a theory foundation for study on the MMB, a dynamic model of the propulsion mechanism of the MMB is obtained. The responses of the motion of the platform and the hydrofoil are obtained by using a numerical integration method to solve the ordinary differential equations. A simplified form of the motion equations is reached by omitting terms with high order small values. The relationship among the heave motion of the buoy, stiffness of the elastic components, and the forward speed can be obtained by using these simplified equations. The dynamic analysis show the following: The angle of displacement of foil is fairly small with the biggest value around 0.3 rad; The speed of mobile buoy and the angle of hydrofoil increased gradually with the increase of heave motion of buoy; The relationship among heaven motion, stiffness and attack angle is that heave motion leads to the angle change of foil whereas the item of speed or push function is determined by vertical velocity and angle, therefore, the heave motion and stiffness can affect the motion of buoy significantly if the size of hydrofoil is kept constant. The proposed model is provided to optimize the parameters of the MMB and a foundation is laid for improving the performance of the MMB.

  10. Wave Observations from Central California: SeaSonde Systems and In Situ Wave Buoys

    Directory of Open Access Journals (Sweden)

    Regan M. Long

    2011-01-01

    Full Text Available Wave data from five 12-13 MHz SeaSondes radars along the central California coast were analyzed to evaluate the utility of operational wave parameters, including significant wave height, period, and direction. Data from four in situ wave buoys served to verify SeaSonde data and independently corroborate wave variability. Hourly averaged measurements spanned distance is 150 km alongshore × 45 km offshore. Individual SeaSondes showed statistically insignificant variation over 27 km in range. Wave height inter-comparisons between regional buoys exhibit strong correlations, approximately 0.93, and RMS differences less than 50 cm over the region. SeaSonde-derived wave data were compared to nearby buoys over timescales from 15 to 26 months, and revealed wave height correlations =0.85−0.91 and mean RMS difference of 53 cm. Results showed that height RMS differences are a percentage of significant wave height, rather than being constant independent of sea state. Period and directions compared favorably among radars, buoys, and the CDIP model. Results presented here suggest that SeaSondes are a reliable source of wave information. Supported by buoy data, they also reveal minimal spatial variation in significant wave height, period, and direction in coastal waters from ~45 km × ~150 km in this region of the central California coast. Small differences are explained by sheltering from coastal promontories, and cutoff boundaries in the case of the radars.

  11. Meteorological, oceanographic, and buoy data from JAMSTEC from five drifting buoys, named J-CAD (JAMSTEC Compact Arctic Drifter) in the Arctic Ocean from 2000 to 2003 (NODC Accession 0002201)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — In 1999, JAMSTEC and MetOcean Data System Ltd. developed a new drifting buoy, named J-CAD (JAMSTEC Compact Arctic Drifter), to conduct long-term observations in the...

  12. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-01 (NCEI Accession 0070959)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  13. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-12 (NODC Accession 0101426)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  14. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during July 2016 (NCEI Accession 0156326)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  15. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-07 (NODC Accession 0095565)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  16. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-08 (NODC Accession 0122005)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  17. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-08 (NODC Accession 0112958)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  18. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during December 2014 (NODC Accession 0125264)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  19. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-04 (NODC Accession 0106521)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  20. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during April 2015 (NCEI Accession 0128073)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  1. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-01 (NODC Accession 0085139)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  2. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-05 (NODC Accession 0108385)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  3. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-10 (NODC Accession 0114407)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  4. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2005-10 (NODC Accession 0002436)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  5. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2005-06 (NODC Accession 0002309)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  6. Drifting and moored buoy data observed during 2015 and assembled by the Global Data Assembly Center for Drifting Buoy Data (formerly Responsible National Oceanographic Data Center (RNODC)), Canada (NCEI Accession 0156004)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Buoy data is available in real time to platform operators via telecommunications providers and distributed on the Global Telecommunications System (GTS) of the World...

  7. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-06 (NCEI Accession 0074384)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  8. Drifting buoy and other data as part of the Outer Continental Drifting buoy and other data as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 04 June 1976 to 01 October 1976 (NODC Accession 7700020)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy and other data was collected by the University of Washington (UW) as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP)....

  9. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2005-04 (NODC Accession 0002176)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  10. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-02 (NODC Accession 0104259)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  11. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2005-09 (NODC Accession 0002415)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  12. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-02 (NODC Accession 0086627)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  13. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during February 2016 (NCEI Accession 0145373)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  14. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during September 2014 (NCEI Accession 0122592)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  15. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-01 (NODC Accession 0116427)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  16. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-06 (NODC Accession 0092557)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  17. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during November 2014 (NODC Accession 0122594)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  18. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-11 (NODC Accession 0099948)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  19. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-09 (NODC Accession 0098547)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  20. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-01 (NODC Accession 0103632)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  1. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-05 (NCEI Accession 0073426)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  2. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during September 2015 (NCEI Accession 0136935)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  3. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-10 (NODC Accession 0099428)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  4. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during August 2016 (NCEI Accession 0156603)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  5. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during July 2015 (NCEI Accession 0130916)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  6. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-09 (NODC Accession 0113792)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  7. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-06 (NODC Accession 0120329)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  8. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-03 (NODC Accession 0088199)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  9. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during January 2015 (NODC Accession 0125752)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  10. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during August 2015 (NCEI Accession 0131704)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  11. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-04 (NODC Accession 0090312)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  12. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-05 (NODC Accession 0119474)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  13. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2005-05 (NODC Accession 0002226)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  14. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2005-11 (NODC Accession 0002469)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  15. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-08 (NCEI Accession 0077456)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  16. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-12 (NODC Accession 0115760)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  17. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-02 (NODC Accession 0117491)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  18. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during November 2015 (NCEI Accession 0139254)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  19. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during May 2015 (NCEI Accession 0129415)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  20. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during March 2015 (NODC Accession 0127371)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  1. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-12 (NODC Accession 0083918)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  2. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-04 (NODC Accession 0118539)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  3. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during April 2016 (NCEI Accession 0150816)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  4. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-08 (NODC Accession 0095593)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  5. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-07 (NODC Accession 0121505)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  6. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during January 2016 (NCEI Accession 0142963)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  7. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-11 (NODC Accession 0082371)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  8. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-03 (NCEI Accession 0072077)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  9. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-10 (NODC Accession 0079513)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  10. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2012-05 (NODC Accession 0090313)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  11. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during October 2015 (NCEI Accession 0137949)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  12. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-11 (NODC Accession 0115123)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  13. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-07 (NODC Accession 0111971)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  14. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during December 2015 (NCEI Accession 0140790)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  15. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during June 2015 (NCEI Accession 0129884)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  16. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-04 (NCEI Accession 0072886)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  17. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-09 (NODC Accession 0078579)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  18. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-07 (NCEI Accession 0074922)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  19. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-03 (NODC Accession 0104424)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  20. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during September 2014 (NODC Accession 0122593)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  1. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2013-06 (NODC Accession 0110477)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  2. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during February 2015 (NODC Accession 0126669)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  3. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during March 2016 (NCEI Accession 0146738)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  4. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during May 2016 (NCEI Accession 0153542)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  5. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2014-03 (NODC Accession 0117682)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  6. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during October 2014 (NODC Accession 0122591)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  7. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during June 2016 (NCEI Accession 0155886)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  8. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys during 2011-02 (NCEI Accession 0071368)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Data Buoy Center (NDBC) established the Coastal-Marine Automated Network (C-MAN) for the National Weather Service in the early 1980's. NDBC has...

  9. Improvement of the accuracy of continuous GPS/Acoustic measurement using a slackly moored buoy

    Science.gov (United States)

    Imano, M.; Kido, M.; Ohta, Y.; Takahashi, N.; Fukuda, T.; Ochi, H.; Honsho, C.; Hino, R.

    2016-12-01

    For the real-time detection of seafloor crustal movement and tsunami associated with large earthquakes, it is necessary to monitor them continuously in their source regions. For this purpose, Tohoku University, JAMSTEC, and JAXA have co-developed a continuous GPS/Acoustic (GPS/A) measurement system using a moored buoy, and the third sea-trial is ongoing for a year in Kumano-nada, Nankai Trough. In this presentation, we report of the positioning accuracy of the continuous GPS/Acoustic measurement in the buoy system. We have adopted the array positioning technique developed by researchers at the Scripps Institute of Oceanography with some improvements. The advantage of this method is that errors in assumed sound velocity and array geometry (relative positions of individual seafloor transponders) little affect positioning results when measurements are conducted in the vicinity of the array center. However, the GPS/A measurement using a moored buoy is generally conducted under much worse condition than the conventional one using a research vessel. In our system, the mooring cable length was determined to be 1.5 times the water depth for safety reasons against strong current. Therefore, the buoy is drifting within a relatively wide area by the wind and the current, and measurements are randomly performed at various points within the area. These features can lead to significant systematic errors in the array positioning, because the effect of errors in pre-defined array geometry increases as the observation point goes farther from the array center. At the moments, the positioning accuracy of GPS/A measurement using a moored buoy is estimated as 0.6/0.7 m, for the EW/NS components, respectively, from the data obtained during the third sea-trial. It is considered that errors in the assumed array geometry result in considerable errors in the array positioning. Therefore, it is necessary to determine the array geometry more precisely in order to improve the accuracy of GPS

  10. Fatigue Life Prediction of the Keel Structure of a Tsunami Buoy Using Spectral Fatigue Analysis Method

    Directory of Open Access Journals (Sweden)

    Angga Yustiawan

    2013-09-01

    Full Text Available One  of  the  components  of  the  Indonesia  Tsunami  Early  Warning  System  (InaTEWS  is  a  surface  buoy.  The  surface buoy  is  exposed  to  dynamic  and  random  loadings  while  operating  at  sea,  particularly  due  to  waves.  Because  of  the cyclic  nature  of  the  wave  load,  this  may  result  in  a fatigue  damage  of  the  keel  structure,  which  connects  the  mooring line  with  the  buoy  hull.  The  operating  location  of  the buoy  is  off  the  Java  South  Coast  at  the  coordinate (10.3998  S, 108.3417  E. To  determine  the  stress  transfer  function, model  tests  were  performed,  measuring  the  buoy  motions  and the stress at the mooring line. A spectral fatigue analysis method is applied for the purpose of estimating the fatigue life of the keel structure. Utilizing the  model-test results, the S-N curve obtained in a previous study and the  wave data at the buoy location, it is found that the fatigue life of the keel structure is approximately 11 years.

  11. WATER TEMPERATURE and Other Data from DRIFTING BUOY From World-Wide Distribution from 19910101 to 19910331 (NODC Accession 9100101)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting Buoy Data from the Canadian Data Center, submitted by Mr. Gerald P Lesblam, Marine Environmental Data Service (MEDS) Ottawa, Ontario, Canada in GF-3 format...

  12. NODC Standard Product: NOAA Marine environmental buoy database 1993 with Updates (19 disc set) (NCEI Accession 0095199)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This set of CD-ROMs holds marine meteorological, oceanographic, and wave spectra data collected by moored buoys and C-MAN (Coastal-Marine Automated Network) stations...

  13. Data from a Directional Waverider Buoy off Kailua Bay, Windward Oahu, Hawaii during August 2000 - July 2004 (NODC Accession 0001660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Through various funding channels, the Department of Oceanography at the University of Hawaii (UH) has maintained a Datawell Mark 2 Directional Waverider Buoy roughly...

  14. WATER TEMPERATURE and Other Data from DRIFTING BUOY From World-Wide Distribution from 19781122 to 19810113 (NODC Accession 8600071)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 359 Drifting Surface Buoys were deployed in the Southern Hemisphere oceans from November 22, 1978 to January 13, 1981 as part of the First Global Atmospheric...

  15. Data from a Directional Waverider Buoy off Waimea Bay, North Shore, Oahu during December 2001 - July 2004 (NODC Accession 0001626)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Through various funding channels, the Department of Oceanography at the University of Hawaii (UH) has maintained a Datawell Directional Waverider Buoy roughly 5 km...

  16. Analytical Study on an Oscillating Buoy Wave Energy Converter Integrated into a Fixed Box-Type Breakwater

    Directory of Open Access Journals (Sweden)

    Xuanlie Zhao

    2017-01-01

    Full Text Available An oscillating buoy wave energy converter (WEC integrated to an existing box-type breakwater is introduced in this study. The buoy is installed on the existing breakwater and designed to be much smaller than the breakwater in scale, aiming to reduce the construction cost of the WEC. The oscillating buoy works as a heave-type WEC in front of the breakwater towards the incident waves. A power take-off (PTO system is installed on the topside of the breakwater to harvest the kinetic energy (in heave mode of the floating buoy. The hydrodynamic performance of this system is studied analytically based on linear potential-flow theory. Effects of the geometrical parameters on the reflection and transmission coefficients and the capture width ratio (CWR of the system are investigated. Results show that the maximum efficiency of the energy extraction can reach 80% or even higher. Compared with the isolated box-type breakwater, the reflection coefficient can be effectively decreased by using this oscillating buoy WEC, with unchanged transmission coefficient. Thus, the possibility of capturing the wave energy with the oscillating buoy WEC integrated into breakwaters is shown.

  17. Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys

    Science.gov (United States)

    Blockley, E. W.; Martin, M. J.; Hyder, P.

    2012-07-01

    In this study, the quality of near-surface current forecasts from the FOAM ocean forecasting system is assessed using the trajectories of Lagrangian drifting buoys. A method is presented for deriving pseudo-Eulerian estimates of ocean currents from the positions of Surface Velocity Program drifters and the resulting data are compared to velocities observed by the global tropical moored buoy array. A quantitative analysis of the global FOAM velocities is performed for the period 2007 and 2008 using currents derived from over 3000 unique drifters (providing an average of 650 velocity observations per day). A potential bias is identified in the Southern Ocean which appears to be caused by wind-slip in the drifter dataset as a result of drogue loss. The drifter-derived currents are also used to show how the data assimilation scheme and a recent system upgrade impact upon the quality of FOAM current forecasts.

  18. Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys

    Directory of Open Access Journals (Sweden)

    E. W. Blockley

    2012-07-01

    Full Text Available In this study, the quality of near-surface current forecasts from the FOAM ocean forecasting system is assessed using the trajectories of Lagrangian drifting buoys. A method is presented for deriving pseudo-Eulerian estimates of ocean currents from the positions of Surface Velocity Program drifters and the resulting data are compared to velocities observed by the global tropical moored buoy array. A quantitative analysis of the global FOAM velocities is performed for the period 2007 and 2008 using currents derived from over 3000 unique drifters (providing an average of 650 velocity observations per day. A potential bias is identified in the Southern Ocean which appears to be caused by wind-slip in the drifter dataset as a result of drogue loss. The drifter-derived currents are also used to show how the data assimilation scheme and a recent system upgrade impact upon the quality of FOAM current forecasts.

  19. OPTIMAL LOCATION OF TSUNAMI WARNING BUOYS AND SEA LEVEL MONITORING STATIONS IN THE MEDITERRANEAN SEA

    Directory of Open Access Journals (Sweden)

    Mathew Gabor

    2010-01-01

    Full Text Available The present study determines the optimal location of detection components of a tsunami warning system in the Mediterranean region given the existing and planned infrastructure. Specifically, we examine the locations of existing tsunameters DART buoys and coastal sea-level monitoring stations to see if additional buoys and stations will improve the proportion of the coastal population that may receive a warning ensuring a timely response. A spreadsheet model is used to examine this issue. Based on the historical record of tsunamis and assuming international cooperation in tsunami detection, it is demonstrated that the existing network of sea level stations and tsunameters enable around ninety percent of the coastal population of the Mediterranean Sea to receive a 15 minute warning. Improvement in this result can be achieved through investment in additional real-time, coastal, sea level monitoring stations. This work was undertaken as a final year undergraduate research project.

  20. Fatigue Life Prediction of the Keel Structure of a Tsunami Buoy Using Spectral Fatigue Analysis Method

    OpenAIRE

    Angga Yustiawan; Ketut Suastika; Wibowo Nugroho

    2013-01-01

    One  of  the  components  of  the  Indonesia  Tsunami  Early  Warning  System  (InaTEWS)  is  a  surface  buoy.  The  surface buoy  is  exposed  to  dynamic  and  random  loadings  while  operating  at  sea,  particularly  due  to  waves.  Because  of  the cyclic  nature  of  the  wave  load...

  1. Monitoring High-Frequency Ocean Signals Using Low-Cost Gnss/imu Buoys

    Science.gov (United States)

    Huang, Yu-Lun; Kuo, Chung-Yen; Shih, Chiao-Hui; Lin, Li-Ching; Chiang, Kai-wei; Cheng, Kai-Chien

    2016-06-01

    In oceans there are different ocean signals covering the multi-frequencies including tsunami, meteotsunami, storm surge, as sea level change, and currents. These signals have the direct and significant impact on the economy and life of human-beings. Therefore, measuring ocean signals accurately becomes more and more important and necessary. Nowadays, there are many techniques and methods commonly used for monitoring oceans, but each has its limitation. For example, tide gauges only measure sea level relative to benchmarks and are disturbed unevenly, and satellite altimeter measurements are not continuous and inaccurate near coastal oceans. In addition, high-frequency ocean signals such as tsunami and meteotsunami cannot be sufficiently detected by 6-minutes tide gauge measurements or 10-day sampled altimetry data. Moreover, traditional accelerometer buoy is heavy, expensive and the low-frequency noise caused by the instrument is unavoidable. In this study, a small, low-cost and self-assembly autonomous Inertial Measurement Unit (IMU) that independently collects continuous acceleration and angular velocity data is mounted on a GNSS buoy to provide the positions and tilts of the moving buoy. The main idea is to integrate the Differential GNSS (DGNSS) or Precise Point Positioning (PPP) solutions with IMU data, and then evaluate the performance by comparing with in situ tide gauges. The validation experiments conducted in the NCKU Tainan Hydraulics Laboratory showed that GNSS and IMU both can detect the simulated regular wave frequency and height, and the field experiments in the Anping Harbor, Tainan, Taiwan showed that the low-cost GNSS buoy has an excellent ability to observe significant wave heights in amplitude and frequency.

  2. A climatology of air-sea interactions at the Mediterranean LION and AZUR buoys

    Science.gov (United States)

    Caniaux, Guy; Prieur, Louis; Bouin, Marie-Noëlle; Giordani, Hervé

    2014-05-01

    The LION and AZUR buoys (respectively at 42.1°N 4.7°E and 43.4°N 7.8°E) provide an extended data set since respectively 1999 and 2001 to present for studying air-sea interactions in the northwestern Mediterranean basin. The two buoys are located where high wind events occur (resp. north western and north easterly gale winds), that force and condition deep oceanic winter convection in that region. A short-term climatology (resp. 13 and 11 years) of air-sea interactions has been developed, which includes classical meteo-oceanic parameters, but also waves period and significant wave heights and radiative fluxes. Moreover turbulent surface fluxes have been estimated from various bulk parameterizations, in order to estimate uncertainties on fluxes. An important dispersion of turbulent fluxes is found at high wind speeds according to the parameterization used, larger than taking into account the second order effects of cool skin, warm layer and waves. An important annual cycle affects air temperatures (ATs), SSTs and turbulent fluxes at the two buoys. The annual cycle of ATs and SSTs can be well reconstructed from the first two annual harmonics, while for the turbulent heat fluxes the erratic occurrence of high and low flux events, well correlated with high/dry and low windy periods, strongly affect their annual and interannual cycles. The frequency of high surface heat fluxes and high wind stress is found highest during the autumn and winter months, despite the fact that north-westerly gale winds occur all year long at LION buoy. During calm weather period, ATs and SSTs experience an important diurnal cycle (on average 1 and 0.5°C respectively), that affect latent and sensible heat fluxes. Finally, an estimate of the interannual variability of the turbulent fluxes in Autumn and Winter is discussed, in order to characterize their potential role on deep ocean convection.

  3. Wave and Current Observations in a Tidal Inlet Using GPS Drifter Buoys

    Science.gov (United States)

    2013-03-01

    sensitive, and reliable. MEMS accelerometers use capacitance measurements between two surfaces to determine the acceleration of an object. A movable...was refined by adding an accelerometer and utilizing horizontal Doppler velocity measurements to better resolve the wave surface motions. The WRD...particularly in the wind-wave band. Vertical measurements were significantly improved through the addition of the accelerometer . A large array of WRD buoys

  4. MONITORING HIGH-FREQUENCY OCEAN SIGNALS USING LOW-COST GNSS/IMU BUOYS

    Directory of Open Access Journals (Sweden)

    Y.-L. Huang

    2016-06-01

    Full Text Available In oceans there are different ocean signals covering the multi-frequencies including tsunami, meteotsunami, storm surge, as sea level change, and currents. These signals have the direct and significant impact on the economy and life of human-beings. Therefore, measuring ocean signals accurately becomes more and more important and necessary. Nowadays, there are many techniques and methods commonly used for monitoring oceans, but each has its limitation. For example, tide gauges only measure sea level relative to benchmarks and are disturbed unevenly, and satellite altimeter measurements are not continuous and inaccurate near coastal oceans. In addition, high-frequency ocean signals such as tsunami and meteotsunami cannot be sufficiently detected by 6-minutes tide gauge measurements or 10-day sampled altimetry data. Moreover, traditional accelerometer buoy is heavy, expensive and the low-frequency noise caused by the instrument is unavoidable. In this study, a small, low-cost and self-assembly autonomous Inertial Measurement Unit (IMU that independently collects continuous acceleration and angular velocity data is mounted on a GNSS buoy to provide the positions and tilts of the moving buoy. The main idea is to integrate the Differential GNSS (DGNSS or Precise Point Positioning (PPP solutions with IMU data, and then evaluate the performance by comparing with in situ tide gauges. The validation experiments conducted in the NCKU Tainan Hydraulics Laboratory showed that GNSS and IMU both can detect the simulated regular wave frequency and height, and the field experiments in the Anping Harbor, Tainan, Taiwan showed that the low-cost GNSS buoy has an excellent ability to observe significant wave heights in amplitude and frequency.

  5. Review of 5kW wave energy LOPF buoy design study and test

    DEFF Research Database (Denmark)

    Margheritini, Lucia

    stage, in line with the TRL and four phases development (proof of concept, design and feasibility study, field trials and half or full‐scale trials) promoted by AAU and supported by the marine renewable energy sector. To complement this, the IEC 114 standards define 3 stages of testing (1=small scale......The purpose of this project was to document the mechanical power production against a target power curve of a 5kW grid connected wave energy buoy in Nissum Bredning at Helligsø. This test site is typically used for open sea testing of scale 1:10 devices in irregular waves. In order to better adapt...... to the moderate wave height, the buoy was down sized by a factor of 3 and a new lower target power curve for the buoy was agreed to. Downsizing the project also had the advantage that it is more cost effective and fast to experiment with small wave energy devices than with big devices, at an early development...

  6. Oceanographic Multisensor Buoy Based on Low Cost Sensors for Posidonia Meadows Monitoring in Mediterranean Sea

    Directory of Open Access Journals (Sweden)

    Sandra Sendra

    2015-01-01

    Full Text Available There are some underwater areas with high ecological interest that should be monitored. Posidonia and seagrasses exert considerable work in protecting the coastline from erosion. In these areas, many animals and organisms live and find the grassland food and the protection against predators. It is considered a bioindicator of the quality of coastal marine waters. It is important to monitor them and maintain these ecological communities as clean as possible. In this paper, we present an oceanographic buoy for Posidonia meadows monitoring. It is based on a set of low cost sensors which are able to collect data from water such as salinity, temperature, and turbidity and from the weather as temperature, relative humidity, and rainfall, among others. The system is mounted in a buoy which keeps it isolated to possible oxidation problems. Data gathered are processed using a microcontroller. Finally the buoy is connected with a base station placed on the mainland through a wireless connection using a FlyPort module. The network performance is checked in order to ensure that no delays will be generated on the data transmission. This proposal could be used to monitor other areas with special ecological interest and for monitoring and supervising aquaculture activities.

  7. Using Buoy and Radar Data to Study Sudden Wind Gusts Over Coastal Regions

    Science.gov (United States)

    Priftis, Georgios; Chronis, Themis; Lang, Timothy J.

    2017-01-01

    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.

  8. Layout of buoys and seafloor transponders for next-generation measurement system for ocean floor crustal deformation

    Science.gov (United States)

    Sakata, T.; Nagai, S.; Tadokoro, K.; Ikuta, R.

    2012-12-01

    We are developing a geodetic method for monitoring crustal deformation under the ocean. We deployed benchmarks on the ocean floor and determine their positions through acoustic ranging from a vessel whose position is determined by kinematic GPS technique. Both sound speed structure and the benchmark (transponder) positions are determined simultaneously from the two-way travel time of ultrasonic signals. To monitor the crustal deformation at the focal area of anticipated plate boundary earthquakes, a lower margin of error is desirable. The most effective factor is a temporal-spatial variation of sound speed structure. In our measurement system, we can average temporal variations of sound speed structure, although they also include spatial variations. We are planning to install a moored buoy-based next generation measurement system using the tomographic technique as a method of distinguishing temporal and spatial variations of sound speed structure completely. We need to consider that the positions of the buoys are controlled by water current. We can control only the area of drifting by adjusting the length of the mooring cables and the buoyancy of the buoys. If we want to make the buoy stable around one point, we can make the cable short but we must make the buoyancy larger to avoid sinking by the current, which requires more cost. An appropriate designing of length of the cable and buoyancy is very important. We theoretically investigated the relationship between buoy-transponder geometry and the accuracy of transponder position. We assumed a system composed of three transponders installed at a depth of 1000 m and three buoys. The configurations of both buoy and transponder were equilateral triangles. The length of a side of them was 2000m.We assumed that the sound speed structure consisted of two layers. We defined 'initial sound speed structure (ISSS)' on which the value of sound speed in the first layer (0-100 m in depth) was 1523 m/s and it in the second layer

  9. O-buoy measurements over the Arctic sea ice: Temporal and spatial extents of ozone depletion events

    Science.gov (United States)

    Halfacre, J. W.; Shepson, P. B.; Simpson, W. R.; Knepp, T. N.; Pratt, K. A.; Matrai, P. A.; Bottenheim, J. W.; Perovich, D. K.; Baldwin, M. E.; Fuentes, J. D.

    2011-12-01

    During springtime in the Arctic, interactions between sea ice and the atmosphere lead to unique halogen chemistry, resulting in significant losses of tropospheric ozone and mercury. However, significant uncertainty remains in our understanding of the temporal and spatial extents of Arctic ozone depletion events. Due to the logistical challenges of measurements over the Arctic Ocean region, most in-situ observations of ozone depletion events have been made at coastal sites or islands near the coast, leaving a large spatial gap. In addition, many supporting measurements are made in campaign-mode, with no long term observations. Therefore, autonomous sea ice tethered buoys ("O-buoys") are being deployed across the Arctic sea ice for long-term atmospheric measurements. These buoys provide in-situ measurements of ozone, CO2 and BrO, as well as meteorological parameters, over the frozen ocean, where most ozone depletion events are thought to occur. To date, several O-buoys have been deployed within the Hudson Bay and Beaufort Sea. From data from the first set of O-buoy deployments, we will discuss the observed temporal and spatial extents of ozone depletion events, as well as the calculated halogen atom concentrations and measured BrO concentrations associated with these events.

  10. Rancang Bangun Instrumen Sistem Buoy Menggunakan A-Wsn Protokol Zigbee Untuk Pengamatan Ekosistem Pesisir (Development of Buoy System Instrument using A-WSN ZigBee Protocol for Coastal Ecosystem Monitoring

    Directory of Open Access Journals (Sweden)

    Acta Withamana

    2013-12-01

    Full Text Available Luasnya perairan dan lingkungan laut yang tidak bersahabat menimbulkan tantangan tersendiri untuk diobservasi. Aktivitas observasi secara konvensional di laut, yang menggunakan kapal sebagai wahana bergerak, membutuhkan biaya yang tinggi dan tidak efisien untuk memperoleh resolusi spasial dan temporal yang diinginkan. Buoy tertambat telah lama digunakan sebagai salah satu pilihan untuk aktivitas observasi laut. Namun ukuran yang besar dari rancangan buoy yang ada pada umumnya tidak cocok untuk pengamatan ekosistem pesisir. Perkembangan teknologi semikonduktor yang pesat melahirkan konsep wireless sensor network (WSN. Komunikasi protokol ZigBee memiliki kelebihan penggunaan energi yang efisien dan kemudahan pemasangan. Riset ini dilakukan untuk mengembangkan instrumen buoy tertambat dan menguji apakah WSN dapat diaplikasikan di wilayah pesisir. Buoy tertambat yang dikembangkan memiliki kinerja yang baik dan stabil sebagai wahana instrumen. Kinerja jaringan ZigBee menunjukan tingkat keberhasilan pengiriman data sebesar 100% pada uji coba statis. Menggunakan empat buah baterai NiMH, instrumen ini dapat bekerja selama kurang lebih 39 jam untuk coordinator dan router, serta 89 jam untuk end device. Pengujian di lapangan menunjukan hasil terburuk sebesar 84.94% keberhasilan pengiriman data pada E1, dan hasil terbaik sebesar 100% keberhasilan pengiriman data pada R1 dan E3. Data suhu permukaan laut yang diterima juga dapat menggambarkan sebaran suhu permukaan di Pulau Panggang. Hasil penelitian memberikan gambaran bahwa Instrumen Sistem Buoy Menggunakan A-Wsn Protokol Zigbee sangat berpotensi untuk digunakan dalam pengamatan ekosistem pesisir. Kata kunci: instrumen, buoy tertambat, ZigBee, suhu permukaan laut, observasi pesisir   Ocean observation has become a challenge due to its vast and rough condition. The conventional observation, for example using ship as a mobile platform, is very expensive and inefficient to obtain desired spatial and temporal

  11. Novel two-stage piezoelectric-based ocean wave energy harvesters for moored or unmoored buoys

    Science.gov (United States)

    Murray, R.; Rastegar, J.

    2009-03-01

    Harvesting mechanical energy from ocean wave oscillations for conversion to electrical energy has long been pursued as an alternative or self-contained power source. The attraction to harvesting energy from ocean waves stems from the sheer power of the wave motion, which can easily exceed 50 kW per meter of wave front. The principal barrier to harvesting this power is the very low and varying frequency of ocean waves, which generally vary from 0.1Hz to 0.5Hz. In this paper the application of a novel class of two-stage electrical energy generators to buoyant structures is presented. The generators use the buoy's interaction with the ocean waves as a low-speed input to a primary system, which, in turn, successively excites an array of vibratory elements (secondary system) into resonance - like a musician strumming a guitar. The key advantage of the present system is that by having two decoupled systems, the low frequency and highly varying buoy motion is converted into constant and much higher frequency mechanical vibrations. Electrical energy may then be harvested from the vibrating elements of the secondary system with high efficiency using piezoelectric elements. The operating principles of the novel two-stage technique are presented, including analytical formulations describing the transfer of energy between the two systems. Also, prototypical design examples are offered, as well as an in-depth computer simulation of a prototypical heaving-based wave energy harvester which generates electrical energy from the up-and-down motion of a buoy riding on the ocean's surface.

  12. Characterization of sea-ice kinematic in the Arctic outflow region using buoy data

    Directory of Open Access Journals (Sweden)

    Ruibo Lei

    2016-01-01

    Full Text Available Data from four ice-tethered buoys deployed in 2010 were used to investigate sea-ice motion and deformation from the Central Arctic to Fram Strait. Seasonal and long-term changes in ice kinematics of the Arctic outflow region were further quantified using 42 ice-tethered buoys deployed between 1979 and 2011. Our results confirmed that the dynamic setting of the transpolar drift stream (TDS and Fram Strait shaped the motion of the sea ice. Ice drift was closely aligned with surface winds, except during quiescent conditions, or during short-term reversal of the wind direction opposing the TDS. Meridional ice velocity south of 85°N showed a distinct seasonal cycle, peaking between late autumn and early spring in agreement with the seasonality of surface winds. Inertia-induced ice motion was strengthened as ice concentration decreased in summer. As ice drifted southward into the Fram Strait, the meridional ice speed increased dramatically, while associated zonal ice convergence dominated the ice-field deformation. The Arctic atmospheric Dipole Anomaly (DA influenced ice drift by accelerating the meridional ice velocity. Ice trajectories exhibited less meandering during the positive phase of DA and vice versa. From 2005 onwards, the buoy data exhibit high Arctic sea-ice outflow rates, closely related to persistent positive DA anomaly. However, the long-term data from 1979 to 2011 do not show any statistically significant trend for sea-ice outflow, but exhibit high year-to-year variability, associated with the change in the polarity of DA.

  13. Comparison of ECMWF surface meteorology and buoy observations in the Ligurian Sea

    Directory of Open Access Journals (Sweden)

    R. Bozzano

    2004-01-01

    Full Text Available Since numerical weather prediction (NWP models are usually used to force ocean circulation models, it is important to investigate their skill in reproducing surface meteorological parameters in open sea conditions. Near-surface meteorological data (air temperature, relative humidity, barometric pressure, wind speed and direction have been acquired from several sensors deployed on an offshore large spar buoy in the Ligurian Sea (Northern Mediterranean Sea from February to December 2000. The buoy collected 7857 valid records out of 8040 during 335 days at sea.

    These observations have been compared with data from NWP models and specifically, the outputs of the ECMWF analysis in the two grid points closest to the buoy position. Hourly data acquired by the buoy have been undersampled to fit the data set of the model composed by values computed at the four synoptic hours. For each mentioned meteorological parameter an analysis has been performed by evaluating instantaneous synoptic differences, distributions, daily and annual variations and related statistics. The comparison shows that the model reproduces correctly the baric field while significant differences result for the other variables, which are more affected by local conditions. This suggests that the observed discrepancies may be due to the poor resolution of the model that probably is not sufficient to appropriately discriminate between land and ocean surfaces in a small basin such as the Ligurian Sea and to take into account local peculiarities.

    The use of time- and space-averaged model data reduces the differences with respect to the in situ observations, thus making the model data usable for analysis with minor requirements about time and space resolution.

    Although this comparison is strongly limited and we cannot exclude measurement errors, its results suggest a great caution in the

  14. Comparison of ECMWF surface meteorology and buoy observations in the Ligurian Sea

    Directory of Open Access Journals (Sweden)

    R. Bozzano

    2004-01-01

    Full Text Available Since numerical weather prediction (NWP models are usually used to force ocean circulation models, it is important to investigate their skill in reproducing surface meteorological parameters in open sea conditions. Near-surface meteorological data (air temperature, relative humidity, barometric pressure, wind speed and direction have been acquired from several sensors deployed on an offshore large spar buoy in the Ligurian Sea (Northern Mediterranean Sea from February to December 2000. The buoy collected 7857 valid records out of 8040 during 335 days at sea. These observations have been compared with data from NWP models and specifically, the outputs of the ECMWF analysis in the two grid points closest to the buoy position. Hourly data acquired by the buoy have been undersampled to fit the data set of the model composed by values computed at the four synoptic hours. For each mentioned meteorological parameter an analysis has been performed by evaluating instantaneous synoptic differences, distributions, daily and annual variations and related statistics. The comparison shows that the model reproduces correctly the baric field while significant differences result for the other variables, which are more affected by local conditions. This suggests that the observed discrepancies may be due to the poor resolution of the model that probably is not sufficient to appropriately discriminate between land and ocean surfaces in a small basin such as the Ligurian Sea and to take into account local peculiarities. The use of time- and space-averaged model data reduces the differences with respect to the in situ observations, thus making the model data usable for analysis with minor requirements about time and space resolution. Although this comparison is strongly limited and we cannot exclude measurement errors, its results suggest a great caution in the use of the model data, especially at high frequency resolution. They may lead to incorrect estimates of

  15. Current components, physical, ocean circulation, wind circulation, and other data from moored buoys, CTD casts, drifting buoys, and in situ wind recorders from AIRCRAFT and other platforms from the North Atlantic Ocean and other locations as part of the Seasonal Response of the Equatorial Atlantic Experiment/Français Océan et Climat dans l'Atlantique Equatorial (SEQUAL/FOCAL) project from 1980-01-25 to 1985-12-18 (NODC Accession 8700111)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Current components, physical, ocean circulation, wind circulation, and other data were collected from moored buoys, CTD casts, drifting buoys, and in situ wind...

  16. 33 CFR 165.812 - Security Zones; Lower Mississippi River, Southwest Pass Sea Buoy to Mile Marker 96.0, New Orleans...

    Science.gov (United States)

    2010-07-01

    ... River, Southwest Pass Sea Buoy to Mile Marker 96.0, New Orleans, LA. 165.812 Section 165.812 Navigation..., Southwest Pass Sea Buoy to Mile Marker 96.0, New Orleans, LA. (a) Location. Within the Lower Mississippi... Mississippi River mile marker 96.0 in New Orleans, Louisiana. These moving security zones encompass all waters...

  17. Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate

    KAUST Repository

    Giraldi, Loic

    2017-04-07

    This work addresses the estimation of the parameters of an earthquake model by the consequent tsunami, with an application to the Chile 2010 event. We are particularly interested in the Bayesian inference of the location, the orientation, and the slip of an Okada-based model of the earthquake ocean floor displacement. The tsunami numerical model is based on the GeoClaw software while the observational data is provided by a single DARTⓇ buoy. We propose in this paper a methodology based on polynomial chaos expansion to construct a surrogate model of the wave height at the buoy location. A correlated noise model is first proposed in order to represent the discrepancy between the computational model and the data. This step is necessary, as a classical independent Gaussian noise is shown to be unsuitable for modeling the error, and to prevent convergence of the Markov Chain Monte Carlo sampler. Second, the polynomial chaos model is subsequently improved to handle the variability of the arrival time of the wave, using a preconditioned non-intrusive spectral method. Finally, the construction of a reduced model dedicated to Bayesian inference is proposed. Numerical results are presented and discussed.

  18. Thin ice and storms: Sea ice deformation from buoy arrays deployed during N-ICE2015

    Science.gov (United States)

    Itkin, Polona; Spreen, Gunnar; Cheng, Bin; Doble, Martin; Girard-Ardhuin, Fanny; Haapala, Jari; Hughes, Nick; Kaleschke, Lars; Nicolaus, Marcel; Wilkinson, Jeremy

    2017-06-01

    Arctic sea ice has displayed significant thinning as well as an increase in drift speed in recent years. Taken together this suggests an associated rise in sea ice deformation rate. A winter and spring expedition to the sea ice covered region north of Svalbard-the Norwegian young sea ICE2015 expedition (N-ICE2015)—gave an opportunity to deploy extensive buoy arrays and to monitor the deformation of the first-year and second-year ice now common in the majority of the Arctic Basin. During the 5 month long expedition, the ice cover underwent several strong deformation events, including a powerful storm in early February that damaged the ice cover irreversibly. The values of total deformation measured during N-ICE2015 exceed previously measured values in the Arctic Basin at similar scales: At 100 km scale, N-ICE2015 values averaged above 0.1 d-1, compared to rates of 0.08 d-1 or less for previous buoy arrays. The exponent of the power law between the deformation length scale and total deformation developed over the season from 0.37 to 0.54 with an abrupt increase immediately after the early February storm, indicating a weakened ice cover with more free drift of the sea ice floes. Our results point to a general increase in deformation associated with the younger and thinner Arctic sea ice and to a potentially destructive role of winter storms.

  19. 77 FR 65816 - Safety Zone; Large Cruise Ships; Lower Mississippi River, Southwest Pass Sea Buoy to Mile Marker...

    Science.gov (United States)

    2012-10-31

    ..., Southwest Pass Sea Buoy to Mile Marker 96.0; New Orleans, LA AGENCY: Coast Guard, DHS. ACTION: Final rule... the Lower Mississippi River between the Port of New Orleans Cruise Ship Terminal, mile marker 96.0... intending to transit the Lower Mississippi River between mile marker 96.0, New Orleans, LA and the Southwest...

  20. Comparison of ERA-Interim waves with buoy data in the eastern Arabian Sea during high waves

    Digital Repository Service at National Institute of Oceanography (India)

    Shanas, P.R.; SanilKumar, V.

    at two locations in eastern Arabian Sea One location is a deep water location and another one is a shallow water location The comparison of significant wave height (SWH) between ERA dataset and buoy data at both the locations shows good correlation...

  1. Verification of model wave heights with long-term moored buoy data: Application to wave field over the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Samiksha, S.V.; Polnikov, V.G.; Vethamony, P.; Rashmi, R.; Pogarskii, F.; Sudheesh, K.

    research work through Women Scientist Scheme (WOS-A). We are grateful to ECMWF for providing the required ERA wind data and INCOIS, Hyderabad for providing the buoy data for model validation. We are also thankful to CERSAT, IFREMER, France for providing...

  2. A comprehensive comparison of SST of satellite, ship, buoy and model data in the sea around Korean Peninsula

    Science.gov (United States)

    Kwak, M.; Cho, Y.; Kwak, H.; Seo, G.

    2012-12-01

    Sea surface temperature (SST) affects atmospheric temperature through air-sea interaction proces. Therefore a sufficient number of SST data with high accuracy is essential for improving weather forecasting precisely. A comparison of SST data provided by several oceanic and atmospheric organization is necessary because methods in observation and calculation have different properties and processes respectively. In situ data observed routinely by National Fisheries Research and Development Institute, Korea is compared with the satellite observed SSTs (AVHRR+AMSR, OSTIA). Buoy data operated by Korea Meteorological Administration is compared with the satellite observed SSTs and model SST calculated by ocean circulation model (Regional Ocean Modeling system). with harmonic analysis. These comparative studies clearly reveal that satellite observed SST is about 2°C higher than that of in situ SST in coastal area. The difference of SST between in situ SST and satellite SST in summer is higher than that in winter. The correlation coefficient of in situ data with the AVHRR+AMSR SST (r2=0.65) is lower than that with OSTIA SST (r2=0.80). Annual amplitude of SST observed by buoy, satellite and calculated by model in coastal area is commonly larger than that of SST of those in open ocean. Phase difference of SST between satellite and buoy data is about 10° at 365-day cycle. Phase difference of SST between model and buoy data is about 20° at 365-day cycle.

  3. Development of a GPS buoy system for monitoring tsunami, sea waves, ocean bottom crustal deformation and atmospheric water vapor

    Science.gov (United States)

    Kato, Teruyuki; Terada, Yukihiro; Nagai, Toshihiko; Koshimura, Shun'ichi

    2010-05-01

    We have developed a GPS buoy system for monitoring tsunami for over 12 years. The idea was that a buoy equipped with a GPS antenna and placed offshore may be an effective way of monitoring tsunami before its arrival to the coast and to give warning to the coastal residents. The key technology for the system is real-time kinematic (RTK) GPS technology. We have successfully developed the system; we have detected tsunamis of about 10cm in height for three large earthquakes, namely, the 23 June 2001 Peru earthquake (Mw8.4), the 26 September 2003 Tokachi earthquake (Mw8.3) and the 5 September 2004 earthquake (Mw7.4). The developed GPS buoy system is also capable of monitoring sea waves that are mainly caused by winds. Only the difference between tsunami and sea waves is their frequency range and can be segregated each other by a simple filtering technique. Given the success of GPS buoy experiments, the system has been adopted as a part of the Nationwide Ocean Wave information system for Port and HArborS (NOWPHAS) by the Ministry of Land, Infrastructure, Transport and Tourism of Japan. They have established more than eight GPS buoys along the Japanese coasts and the system has been operated by the Port and Airport Research Institute. As a future scope, we are now planning to implement some other additional facilities for the GPS buoy system. The first application is a so-called GPS/Acoustic system for monitoring ocean bottom crustal deformation. The system requires acoustic waves to detect ocean bottom reference position, which is the geometrical center of an array of transponders, by measuring distances between a position at the sea surface (vessel) and ocean bottom equipments to return the received sonic wave. The position of the vessel is measured using GPS. The system was first proposed by a research group at the Scripps Institution of Oceanography in early 1980's. The system was extensively developed by Japanese researchers and is now capable of detecting ocean

  4. Theory and application of calibration techniques for an NDBC directional wave measurements buoy

    Science.gov (United States)

    Steele, K. E.; Lau, J. C.-K.; Hsu, Y.-H. L.

    1985-01-01

    The National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA) deployed a 10-m-diameter discus-type hull in the Pacific Ocean some 185 km southwest of Los Angeles, CA, in April 1984. Aboard this hull was an electronic system capable of acquiring, processing, and transmitting to shore directional wave measurements. For this system to produce accurate data, a number of factors had to be taken into account. These factors included noise, amplitude and phase alterations due to mechanical and electrical components, and magnetic fields arising from the hull. Comprehensive calibration and verification techniques were developed and applied to ensure data quality. The system configuration is described with emphasis on the methods used in the data processing to correct for the various factors. Examples of the resulting corrected data are given.

  5. Software and Support Development for an Environmental Data Buoy System for Predicting Surf-Zone Characteristics

    Science.gov (United States)

    1981-02-28

    34I.oaoY. .ic . of lb. "IN.o List Ciey. 01010. ocd ZIP Code Prep. -ae 1o. P~rolort. Tool, Ar.$. .d lot U.11 Mombers Kale, be.. lb .. be,iw code Ire.t lb...C-1-114-8o Offi,.c Ne.. and Address gt.lo lb, .1. tO tt, olf t no.1 -e 0.44,,..reoo tocldoth Oh otf.*0, of thecoleollbog 0 01cC1. yolo . t. I.loalrd...written, and tested with actual data for a microcomputer system, a similar version of which could be ultimately implemented in the buoy for complete

  6. Perancangan Sistem Prediktor Daya Pada Panel Photovoltaic di Buoy Weather Station

    Directory of Open Access Journals (Sweden)

    Aini Prisilia Susanti

    2013-09-01

    Full Text Available Buoy weather station merupakan stasiun informasi cuaca yang banyak dijumpai di pelabuhan, khususnya di Surabaya. Untuk mengoperasikannya diperlukan sumber daya listrik berupa panel photovoltaic. Efek fotolistrik pada PV mampu merubah energi cahaya menjadi energi listrik. Besarnya daya yang dihasilkan tergantung dari intensitas matahari, temperatur permukaan, dan keadaan geografis setempat. Untuk memprediksi daya keluaran per setengah jam yang dihasilkan oleh panel PV maka digunakan metode jaringan syaraf tiruan dengan algoritma backpropagation pada software Matlab. Variabel yang digunakan berupa data daya yang diperoleh dari tegangan dan arus yang dihasilkan oleh panel PV di daerah Surabaya. Data daya selama 3 hari per setengah jam tersebut dijadikan data input dan target pada Matlab. Hasil terbaik perancangan sistem prediksi daya keluaran panel PV menggunakan JST pada Matlab yaitu Mean Square Error (MSE sebesar 0,0113 dan akurasi ketepatan prediksi sebesar 99,81%.

  7. Presentation of the optical sensor OPTISENS designed for Eulerian measurements of phytoplankton on a moored buoy

    Science.gov (United States)

    Volent, Zsolt; Johnsen, Geir

    1993-12-01

    Since 1988 a newly designed transmissometer (OPTISENS) has been used along the southern Norwegian coast to monitor phytoplankton blooms. The instrument has been suspended on a buoy equipped with an ARGOS PTT for satellite transmission of data. The OPTISENS is a light beam transmissometer with 3 different colors of the light, i.e. red, green and blue with peak wavelengths at 650 nm, 555 nm and 470 nm, respectively. Long-term measurements in the sea and laboratory experiments of in vivo absorption- and fluorescence excitation spectra, have demonstrated that it not only can distinguish between different particles but also identify of different groups of bloom-forming phytoplankton, some of which are toxic. The attenuation coefficient ratios between the colors blue, green, yellow and red will be discussed for different phytoplankton groups.

  8. Accurate Linking of Lake Erie Water Level with Shoreline Datum Using GPS Buoy and Satellite Altimetry

    Directory of Open Access Journals (Sweden)

    Kai-Chien Cheng

    2008-01-01

    Full Text Available There is a need to accurately link the water level to the shoreline vertical datum for various applications including coastal management, lake/river/estuary/wetland hydrological or storm surge modeling/forecasting. Coastal topography is historically surveyed and referenced to the predetermined vertical datum in terms of orthometric heights, or the heights above the geoid, which is poorly known in terms of accuracy and lack of adequate spatial resolution for coastal applications such as estuary or storm surge modeling. We demonstrate an accurate linking of the lake surface to a shoreline datum using satellite techniques, including GPS buoy and satellite altimetry, water level gauges, and local geoid and lake circulation models. The possible error sources are analyzed and an error budget is reported in this study. An innovated method to estimate geoid height near the water level gauge using a GPS buoy is proposed. It is found that at a 95% confidence interval, the method is consistent with the National Geodetic Survey GEOID03 geoid model. The lake surface represented using a lake circulation model provided by the Great Lakes Forecasting Systems is also verified with kriging based on the data (1999 - 2001 from the water level gauge, and TOPEX/POSEIDON altimeter. Mean discrepancies of 2.7 and 7.2 cm are found with the data from the gauges around Lake Erie, and from the combination of the gauges and the altimeter, respectively. It reveals that the current dominant limitation of more accurate linking of water surface to shoreline is the insufficient knowledge of geoid in the current models. Further improvement is feasible through more accurate and higher resolution modeling of the lake geoid.

  9. The M3A multi-sensor buoy network of the Mediterranean Sea

    Directory of Open Access Journals (Sweden)

    F. Zanon

    2007-05-01

    Full Text Available A network of three multi-sensor timeseries stations able to deliver real time physical and biochemical observations of the upper thermocline has been developed for the needs of the Mediterranean Forecasting System during the MFSTEP project. They follow the experience of the prototype M3A system that was developed during the MFSPP project and has been tested during a pilot pre-operational period of 22 months (2000–2001. The systems integrate sensors for physical (temperature, salinity, turbidity, current speed and direction as well as optical and chemical observations (dissolved oxygen, chlorophyll-a, PAR, nitrate. The south Aegean system (E1-M3A follows a modular design using independent mooring lines and collects biochemical data in the upper 100 m and physical data in the upper 500 m of the water column. The south Adriatic buoy system (E2-M3A uses similar instrumentation but on a single mooring line and also tests a new method of pumping water samples from relatively deep layers, performing analysis in the protected "dry" environment of the buoy interior. The Ligurian Sea system (W1-M3A is an ideal platform for air-sea interaction processes since it hosts a large number of meteorological sensors while its ocean instrumentation, with real time transmission capabilities, is confined in the upper 50 m layer. Despite their different architecture, the three systems have common sampling strategy, quality control and data management procedures. The network operates in the Mediterranean Sea since autumn 2004 collecting timeseries data for calibration and validation of the forecasting system as well for process studies of regional dynamics.

  10. Temperature and salinity profile data collected by drifting buoy and XBT in the Worldwide Oceans from 18 October 1999 to 28 February 2000 (NODC Accession 0000115)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile data were collected using moored buoy, profiling floats, and XBT casts in a world wide distribution from 18 October 1999 to 28 February 2000....

  11. Temperature profile and wind speed data collected from buoy casts in the Gulf of Mexico from NOAA Ship RESEARCHER for 1977-07-26 (NCEI Accession 7800034)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile and wind speed data were collected using buoy casts in the Gulf of Mexico from NOAA Ship RESEARCHER from 26 July 1977 to 26 July 1977. Data were...

  12. Temperature, salinity, and other data from buoy casts in the Arctic Ocean, Barents Sea and Beaufort Sea from 1948 to 1993 (NODC Accession 9800040)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature, salinity, and other data were collected using buoy casts in the Arctic Ocean, Barents Sea and Beaufort Sea from 1948 to 1993. Data were collected by the...

  13. Oceanographic profile temperature, salinity and pressure measurements collected using moored buoy in the Indian Ocean from 2001-2006 (NODC Accession 0002733)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature and salinity measurements in the Equatorial Indian from 2001 to 2006 from the TRITON (TRIANGLE TRANS-OCEAN BUOY NETWORK); JAPAN AGENCY FOR MARINE-EARTH...

  14. Temperature data from buoy casts in the North Atlantic Ocean from the COLUMBUS and HMAS SWAN from 01 August 1928 to 04 September 1932 (NODC Accession 0000242)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature data were collected using buoy casts from the COLUMBUS and HMAS SWAN from August 1, 1928 to September 4, 1932 in the North Atlantic Ocean. Data were...

  15. Drifting buoy and other data as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 17 May 1976 to 23 December 1976 (NODC Accession 7800105)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected by the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Outer Continental Shelf Environmental Assessment...

  16. WATER TEMPERATURE and Other Data from DRIFTING BUOY From TOGA Area - Pacific (30 N to 30 S) from 19921208 to 19930719 (NODC Accession 9500059)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The drifting buoy data set in this accession was collected from TOGA Area - Pacific (30 N to 30 S) in Equatorial Pacific, North of Australia as part of Tropical...

  17. Fall Freeze-up of Sea Ice in the Beaufort-Chukchi Seas Using ERS-1 SAR and Buoy Data

    Science.gov (United States)

    Holt, B.; Winebrenner, B.; D., Nelson E.

    1993-01-01

    The lowering of air temperatures below freezing in the fall indicates the end of summer melt and the onset of steady sea ice growth. The thickness and condition of ice that remains at the end of summer has ramifications for the thickness that that ice will attain at the end of the following winter. This period also designates a shifting of key fluxes from upper ocean freshening from ice melt to increased salinity from brine extraction during ice growth. This transitional period has been examined in the Beaufort and Chukchi Seas using ERS-1 SAR imagery and air temperatures from drifting buoys during 1991 and 1992. The SAR imagery is used to examine the condition and types of ice present in this period. Much of the surface melt water has drained off at this time. Air temperatures from drifting buoys coincident in time and within 100 km radius of the SAR imagery have been obtained...

  18. ASSIMILATION OF REAL-TIME DEEP SEA BUOY DATA FOR TSUNAMI FORECASTING ALONG THAILAND’S ANDAMAN COASTLINE

    Directory of Open Access Journals (Sweden)

    Seree Supharatid

    2008-01-01

    Full Text Available The occurrence of 2004 Indian Ocean tsunami enhanced the necessity for a tsunami early warning system for countries bordering the Indian Ocean, including Thailand. This paper describes the assimilation of real-time deep sea buoy data for tsunami forecasting along Thailand’s Andaman coastline. Firstly, the numerical simulation (by the linear and non-linear shallow water equations was carried out for hypothetical cases of tsunamigenic earthquakes with epicenters located in the Andaman micro plate. Outputs of the numerical model are tsunami arrival times and the maximum wave height that can be expected at 58 selected communities along Thailand Andaman coastline and two locations of DART buoys in the Indian Ocean. Secondly, a “neural” network model (GRNN was developed to access the data from the numerical computations for subsequent construction of a tsunami database that can be displayed on a web-based system. This database can be updated with the integration from two DART buoys and from several GRNN models.

  19. Applications to marine disaster prevention spilled oil and gas tracking buoy system

    CERN Document Server

    2017-01-01

    This book focuses on the recent results of the research project funded by a Grant-in-Aid for Scientific Research (S) of the Japan Society for the Promotion of Science (No. 23226017) from FY 2011 to FY 2015 on an autonomous spilled oil and gas tracking buoy system and its applications to marine disaster prevention systems from a scientific point of view. This book spotlights research on marine disaster prevention systems related to incidents involving oil tankers and offshore platforms, approaching these problems from new scientific and technological perspectives. The most essential aspect of this book is the development of a deep-sea underwater robot for real-time monitoring of blowout behavior of oil and gas from the seabed and of a new type of autonomous surface vehicle for real-time tracking and monitoring of oil spill spread and drift on the sea surface using an oil sensor. The mission of these robots is to provide the simulation models for gas and oil blowouts or spilled oil drifting on the sea surface w...

  20. Virtual radar ice buoys - a method for measuring fine-scale sea ice drift

    Science.gov (United States)

    Karvonen, J.

    2016-01-01

    Here we present an algorithm for continuous ice drift estimation based on coastal and ship radar data. The ice drift is estimated for automatically selected ice targets in the images. These targets are here called virtual buoys (VBs) and are tracked based on an optical flow method. To maintain continuous ice drift tracking new VBs are added after a given number of VBs have been lost; i.e. they can not be tracked reliably any more. Here we also apply the algorithm to data of three test cases to demonstrate its capabilities and properties. Two of these cases use coastal radar data and one ship radar data. Ice drift velocity and direction information derived from the VB motion are computed and compared to the prevailing ice and weather conditions. Also a quantity measuring the local divergence or convergence is computed for some VBs to demonstrate the capability to estimate derived kinematic sea ice parameters from VB location time series. The results produced by the algorithm can be used as an input for estimation of the dynamic properties of sea the ice field, such as ice divergence or convergence, shear, vorticity, and total deformation.

  1. Rancang Bangun Maximum Power Point Tracking pada Panel Photovoltaic Berbasis Logika Fuzzy di Buoy Weather Station

    Directory of Open Access Journals (Sweden)

    Bayu Prima Juliansyah Putra

    2013-09-01

    Full Text Available Salah satu aplikasi yang sering digunakan dalam bidang energi terbarukan adalah panel photovoltaic. Panel ini memiliki prinsip kerja berdasarkan efek photovoltaic dimana lempengan logam akan menghasilkan energi listrik apabila diberi intensitas cahaya. Untuk menghasilkan daya keluaran panel yang maksimal, maka diperlukan suatu algoritma yang biasa disebut Maximum Power Point Tracking (MPPT.MPPT yang diterapkan pada sistem photovoltaic berfungsi untuk mengatur nilai tegangan keluaran panel sehingga titik ker-janya beroperasi pada kondisi maksimal. Algoritma MPPT pada panel ini telah dilakukan dengan menggunakan logika fuzzy melalui mikrokontroler Arduino Uno sebagai pem-bangkit sinyal Pulse Width Modulation (PWM yang akan dikirimkan menuju DC-DC Buck Boost Converter. Keluaran dari buck boost converterakan dihubungkan secara langsung dengan buoy weather station untuk menyuplai energi listrik tiap komponen yang berada di dalamnya. Untuk menguji performansi dari algoritma MPPT yang telah dirancang, maka sistem akan diuji menggunakan variasi beban antara metode direct-coupled dengan MPPT menggunakan logika fuzzy. Hasil pengujian menunjukkan bahwa MPPT dengan logika fuzzy dapat menghasilkan daya maksimum daripada direct-coupled. Pada sistem panel photovoltaic ini memiliki range efisiensi 33.07589 % hingga 74.25743 %. Daya mak-simal dapat dicapai oleh sistem untuk tiap variasi beban dan efisiensi maksimal dapat dicapai pada beban 20 Ohm dari hasil pengujian sistem MPPT.

  2. Temperature profile and current meter data collected using drifting buoy and profiling buoy casts from the Atlantic Ocean as part of the International Decade of Ocean Exploration / Mid-Ocean Dynamics Experiment (IDOE/MODE) project from 01 September 1972 to 01 April 1974 (NODC Accession 7400622)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile east-west current component, north-south current component, and other data were collected using drifting buoy and profiling float casts from the...

  3. A year-long journey across the Arctic Ocean: the story of the chemical composition of the air as recorded by O-Buoy # 4

    Science.gov (United States)

    Netcheva, S.; Bottenheim, J. W.; Carlsen, M. S.; Chavez, F.; Matrai, P. A.; Perovich, D. K.; Shepson, P.; Simpson, W. R.; Valentic, T. A.

    2012-12-01

    A number of autonomous, ice-tethered buoys have been deployed in different parts of the Arctic and Sub-Arctic Ocean as part of the USA-Canada collaborative project O-Buoy since 2009. The main feature of these buoys is their capability to simultaneously measure the concentrations of atmospheric constituents important for climate change and air quality, such as ozone, carbon dioxide, bromine monoxide, and meteorological parameters directly over the sea ice. O-Buoy # 4 was deployed from the CCGS Louis S. St-Laurent icebreaker along a survey trip undertaken by the Canadian Extended Continental Shelf Mapping Program at latitude 88.15°N and longitude 157.49°W on September 5, 2011. O-Buoy # 4 provided input into various fields of the Arctic contemporary measurement and observation technology that include equipment design, instrumentation control, power management and analytical instrumentation performance through approximately a year long journey, guided by the Arctic transpolar drift system and moving close to the North Pole. The relevant meteorological observations have been integrated into the marine weather observation network of WMO and the wind speed and direction data records were utilized for weather forecast model validation purposes. Indisputably, the highest achievement of O-buoy #4 is the continuous data set presenting the seasonal levels and the variations of the chemical composition of the atmosphere in the High Arctic. The comparison of the ozone concentrations record with the only existing year-long, ice-based record of ozone data collected by the French schooner TARA and other coastal observatories such as Alert (82.45°N, 62.508°W) supports the hypothesis made by Hopper et all. back in 1994 that the air over the Arctic Ocean surface contains ozone at very low concentrations through the spring season. Unfortunately, no other long term observations over the ice exists to compare O-buoy recorded data with to advance our understanding of the path, the

  4. Temperature profile and other data collected using moored buoy in the Pacific Ocean (30-N to 30-S) as part of the International Decade of Ocean Exploration / North Pacific Experiment (IDOE/NORPAX) project from 06 November 1977 to 24 March 1978 (NODC Accession 8200053)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Air pressure, current, wind and temperature time series data were collected from moored buoys from TOGA Area in Pacific (30 N to 30 S). Buoy data from the equatorial...

  5. Field evaluation of remote wind sensing technologies: Shore-based and buoy mounted LIDAR systems

    Energy Technology Data Exchange (ETDEWEB)

    Herrington, Thomas [Stevens Inst. of Technology, Hoboken, NJ (United States)

    2017-11-03

    In developing a national energy strategy, the United States has a number of objectives, including increasing economic growth, improving environmental quality, and enhancing national energy security. Wind power contributes to these objectives through the deployment of clean, affordable and reliable domestic energy. To achieve U.S. wind generation objectives, the Wind and Water Power Program within the Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) instituted the U.S. Offshore Wind: Removing Market Barriers Program in FY 2011. Accurate and comprehensive information on offshore wind resource characteristics across a range of spatial and temporal scales is one market barrier that needs to be addressed through advanced research in remote sensing technologies. There is a pressing need for reliable offshore wind-speed measurements to assess the availability of the potential wind energy resource in terms of power production and to identify any frequently occurring spatial variability in the offshore wind resource that may impact the operational reliability and lifetime of wind turbines and their components and to provide a verification program to validate the “bankability” of the output of these alternative technologies for use by finance institutions for the financing of offshore wind farm construction. The application of emerging remote sensing technologies is viewed as a means to cost-effectively meet the data needs of the offshore wind industry. In particular, scanning and buoy mounted LIDAR have been proposed as a means to obtain accurate offshore wind data at multiple locations without the high cost and regulatory hurdles associated with the construction of offshore meteorological towers. However; before these remote sensing technologies can be accepted the validity of the measured data must be evaluated to ensure their accuracy. The proposed research will establish a unique coastal ocean test-bed in the Mid-Atlantic for

  6. The SailBuoy remotely-controlled unmanned vessel: Measurements of near surface temperature, salinity and oxygen concentration in the Northern Gulf of Mexico

    OpenAIRE

    Ghani, Mahmud Hasan; Hole, Lars R.; Fer, Ilker; Kourafalou, Vassiliki H.; Wienders, Nicolas; Kang, HeeSook; Drushka, Kyla; Peddie, David

    2014-01-01

    An experimental deployment of a new type of unmanned vessel is presented. The Christian Michelsen Research SailBuoy, a remotely-controlled surface vehicle, sampled near-surface properties during a two-month mission in the northern Gulf of Mexico in March–May, 2013. Averaged over the entire deployment, the vessel speed over ground was View the MathML source42±30cm s⎻¹ (± one standard deviation) with a maximum of View the MathML source180cm s⎻¹. During the 62 days of the mission, the SailBuoy c...

  7. Long-Term Observations of Atmospheric CO2, O3 and BrO over the Transitioning Arctic Ocean Pack-ice: The O-Buoy Chemical Network

    Science.gov (United States)

    Matrai, P.

    2016-02-01

    Autonomous, sea ice-tethered O-Buoys have been deployed (2009-2016) across the Arctic sea ice for long-term atmospheric measurements (http://www.o-buoy.org). O-Buoys (15) provide in-situ concentrations of three sentinel atmospheric chemicals, ozone, CO2 and BrO, as well as meteorological parameters and imagery, over the frozen ocean. O-Buoys were designed to transmit daily data over a period of 2 years while deployed in sea ice, as part of automated ice-drifting stations that include snow/ice measurement systems (e.g. Ice Mass Balance buoys) and oceanographic measurements (e.g. Ice Tethered Profilers). Seasonal changes in Arctic atmospheric chemistry are influenced by changes in the characteristics and presence of the sea ice vs. open water as well as air mass trajectories, especially during the winter-spring and summer-fall transitions when sea ice is melting and freezing, respectively. The O-Buoy Chemical Network provides the unique opportunity to observe these transition periods in real-time with high temporal resolution, and to compare them with those collected on land-based monitoring stations located. Due to the logistical challenges of measurements over the Arctic Ocean region, most long term, in-situ observations of atmospheric chemistry have been made at coastal or island sites around the periphery of the Arctic Ocean, leaving large spatial and temporal gaps that O-Buoys overcome. Advances in floatation, communications, power management, and sensor hardware have been made to overcome the challenges of diminished Arctic sea ice. O-Buoy data provide insights into enhanced seasonal, interannual and spatial variability in atmospheric composition, atmospheric boundary layer control on the amount of halogen activation, enhancement of the atmospheric CO2 signal over the more variable and porous pack ice, and to develop an integrated picture of the coupled ocean/ice/atmosphere system. As part of the Arctic Observing Network, we provide data to the community (www.aoncadis.org).

  8. SeaBuoySoft – an On-line Automated Windows based Ocean Wave height Data Acquisition and Analysis System for Coastal Field’s Data Collection

    Directory of Open Access Journals (Sweden)

    P.H.Tarudkar

    2014-12-01

    Full Text Available Measurement of various hydraulic parameters such as wave heights for the research and the practical purpose in the coastal fields is one of the critical and challenging but equally important criteria in the field of ocean engineering for the design and the development of hydraulic structures such as construction of sea walls, break waters, oil jetties, fisheries harbors, all other structures, and the ships maneuvering, embankments, berthing on jetties. This paper elucidates the development of “SeaBuoySoft online software system for coastal field‟s wave height data collection” for the coastal application work. The system could be installed along with the associated hardware such as a Digital Waverider Receiver unit and a Waverider Buoy at the shore. The ocean wave height data, transmitted by wave rider buoy installed in the shallow/offshore waters of sea is received by the digital waverider receiver unit and it is interfaced to the SeaBuoySoft software. The design and development of the software system has been worked out in-house at Central Water and Power Research Station, Pune, India. The software has been developed as a Windows based standalone version and is unique of its kind for the reception of real time ocean wave height data, it takes care of its local storage of wave height data for its further analysis work as and when required. The system acquires real time ocean wave height data round the clock requiring no operator intervention during data acquisition process on site.

  9. Moball-Buoy Network: A Near-Real-Time Ground-Truth Distributed Monitoring System to Map Ice, Weather, Chemical Species, and Radiations, in the Arctic

    Science.gov (United States)

    Davoodi, F.; Shahabi, C.; Burdick, J.; Rais-Zadeh, M.; Menemenlis, D.

    2014-12-01

    The work had been funded by NASA HQ's office of Cryospheric Sciences Program. Recent observations of the Arctic have shown that sea ice has diminished drastically, consequently impacting the environment in the Arctic and beyond. Certain factors such as atmospheric anomalies, wind forces, temperature increase, and change in the distribution of cold and warm waters contribute to the sea ice reduction. However current measurement capabilities lack the accuracy, temporal sampling, and spatial coverage required to effectively quantify each contributing factor and to identify other missing factors. Addressing the need for new measurement capabilities for the new Arctic regime, we propose a game-changing in-situ Arctic-wide Distributed Mobile Monitoring system called Moball-buoy Network. Moball-buoy Network consists of a number of wind-propelled self-powered inflatable spheres referred to as Moball-buoys. The Moball-buoys are self-powered. They use their novel mechanical control and energy harvesting system to use the abundance of wind in the Arctic for their controlled mobility and energy harvesting. They are equipped with an array of low-power low-mass sensors and micro devices able to measure a wide range of environmental factors such as the ice conditions, chemical species wind vector patterns, cloud coverage, air temperature and pressure, electromagnetic fields, surface and subsurface water conditions, short- and long-wave radiations, bathymetry, and anthropogenic factors such as pollutions. The stop-and-go motion capability, using their novel mechanics, and the heads up cooperation control strategy at the core of the proposed distributed system enable the sensor network to be reconfigured dynamically according to the priority of the parameters to be monitored. The large number of Moball-buoys with their ground-based, sea-based, satellite and peer-to-peer communication capabilities would constitute a wireless mesh network that provides an interface for a global

  10. Long term Measurements of ozone, bromine monoxide and carbon dioxide over the Frozen Arctic Ocean Surface: first data from O-Buoy Deployments

    Science.gov (United States)

    Bottenheim, J. W.; Matrai, P. A.; Netcheva, S.; Perovich, D. K.; Shepson, P. B.; Simpson, W. R.; O-Buoy Team

    2010-12-01

    We report results from the first deployments of O-Buoys in the ice of the Arctic Ocean and the Hudson Bay in 2009-2010. O-Buoys were developed to obtain long term, autonomous, atmospheric chemistry measurements in the marine boundary layer (MBL) over polar ice surfaces. It is well known that in Arctic spring the abundance of ozone (O3) in the MBL often episodically declines to very low levels, and it is assumed that halogens from sea-salt activation are responsible, especially bromine atoms which will lead to increases in bromine monoxide (BrO). Such chemical processing is hypothesized to be triggered over the frozen Arctic Ocean, and be especially effective over briny, first year ice. Even less is known about the variability of carbon dioxide (CO2) over frozen surfaces; global models largely treat the Arctic Ocean ice as an impenetrable lid between the ocean and overlying atmosphere. Clearly long term in-situ measurements are required to shed light on actually occurring processes, and start exploring their climatic implications in a changing, warming Arctic. Such measurements are impeded by serious logistical difficulties under the extreme environmental conditions of the open frozen Arctic Ocean, hence O-Buoys. After successful proof of concept in the spring of 2009 near Barrow, Alaska, we deployed the first O-Buoy in the Beaufort Sea in the fall of 2009. This O-Buoy operated successfully until contact was lost in July 2010 after 9 months of continuous operation, probably due loss of the power source after breakup of the ice. A second O-Buoy was deployed for about 40 days in the frozen Hudson Bay, while a third O-Buoy made observations in the Arctic Ocean near Borden Island for about 25 days. The results permit a unique long term look at the variations of the target chemical species at locations inaccessible by other means. Ozone depletion events (ODEs) in the spring, and concurrent BrO levels will be discussed. The data allow us to better explore the spatial and

  11. Parametric study of the vertical rigid production riser of Spar Buoy Platform with fatigue emphasis; Estudo parametrico do riser rigido vertical de producao da plataforma Spar Buoy com enfase na fadiga

    Energy Technology Data Exchange (ETDEWEB)

    Ribeiro, Elton J.B. [PETROBRAS, Rio de Janeiro, RJ (Brazil). Centro de Pesquisas; Ellwanger, Gilberto B. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia; Queija, Marcos S. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Fundacao COPPETEC - Coordenacao de Projetos, Pesquisas e Estudos Tecnologicos

    2000-07-01

    The objective of this work is present the result of the parametric studies carried out to assess fatigue life in vertical rigid production riser of Spar buoy platform. The following parameters were studied in relation of the fatigue life effects: drag coefficient amplification due to Vortex Induced Vibrations; influence of high and low frequency platform dynamic response; hydrodynamic effect of the trapped water inside platform hull; structural damping effect; horizontal guides along the platform hull. An in house developed computer program called Anflex and its pos processor to fatigue life analyses called Posfal were employed to perform the parametric studies. The environmental loads considered in the analyses were wind, current and wave with more expected probability of occurrence in Campos Basin. A random dynamic approach was employed. (author)

  12. Circulation and hydrological characteristics of the North Aegean Sea: a contribution from real-time buoy measurements

    Directory of Open Access Journals (Sweden)

    K. NITTIS

    2002-06-01

    Full Text Available In the framework of the POSEIDON Project, a network of open sea oceanographic buoys equipped with meteorological and oceanographic sensors has been operational in the Aegean Sea since 1998. The analysis of upper-ocean physical data (currents at 3m, temperature and salinity at 3-40m depths collected during the last 2 years from the stations of the North Aegean basin indicates a strong temporal variability of flow field and hydrological characteristics in both synoptic and seasonal time scales. The northern part of the basin is mainly influenced by the Black Sea Water outflow and the mesoscale variability of the corresponding thermohaline fronts, while the southern stations are influenced by the general circulation of the Aegean Sea with strong modulations caused by the seasonally varying atmospheric forcing.

  13. Uncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunami

    KAUST Repository

    Sraj, Ihab

    2014-11-01

    Tsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning\\'s n friction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning\\'s n coefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the G. eoC. law model and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.

  14. The Adopt-A-Buoy Project: A Firsthand Experience for Students in Collecting, Processing and Analyzing Environmental Data

    Science.gov (United States)

    Richter-Menge, J.; Stott, G.; Harriman, C.; Perovich, D. K.; Elder, B. C.; Polashenski, C.

    2013-12-01

    Over the past 4 school years, our team of Arctic sea ice researchers and middle school teachers has collaborated in an educational outreach activity to develop a series of earth science classes aimed at 8th grade science students. Central to the effort is an environmental observation site installed at the school, designed to closely mimic sea ice mass balance buoys deployed as part of an NSF-sponsored Arctic Observing Network (AON) project. The site located at the school collects data on air temperature, barometric pressure, snow depth, and snow and ground temperatures. Working directly with the research team over the course of the school year, students learn to collect, process, and analyze the local environmental data. Key to the experience is the students' opportunity to pose and address open-ended questions about a set of scientific data that is inherently familiar to them, since it reflects the seasonal conditions they are witnessing (e.g. the 2011-12 New England winter with no snow). During the series of classes, students are also exposed to the similar set of environmental data collected in the Arctic, via a sea ice mass balance buoy they ';adopt.' The arctic data set opens the door to discussions about climate change and its particularly dramatic affect on the arctic environment. Efforts are underway to transform this outreach project into an expanded earth science classroom module for use at other schools. Portability will require an approach that makes connections to the Arctic without a reliance on the multiple visits to the classroom by the research team (e.g. forming and facilitating partnerships with Arctic schools and field researchers via the internet). We are also evaluating the possibility of constructing low cost, portable weather stations to be used with the module.

  15. Evaluation of wind vectors measured by QuikSCAT using ocean buoy data along the Galician coast (NW Iberian Peninsula)

    Science.gov (United States)

    Alvarez, I.; Gomez-Gesteira, M.; deCastro, M.; Sousa, M. C.; Dias, J. M.; Santos, F.

    2012-04-01

    Wind above sea surface constitutes a key parameter to analyze coastal phenomena, like upwelling in the NW part of the Iberian Peninsula. The present work analyzes the accuracy of QuikSCAT for the period 2000-2009 by comparing satellite data with in situ data from three buoys placed along the NW coast of the Iberian Peninsula. The use of these long data series has two main objectives, on the one hand, the margin of error diminishes with the extent of the series and, on the other hand, it allows capturing the high inter-annual variability of the area. According to previous studies, the wet season is characterized by a high variability, in such a way that wind patterns change from year to year. The comparison confirms a low skill of QuikSCAT for low speed winds (<3 m/s) as previously pointed out by other authors. Once these winds were discarded, QuikSCAT revealed a higher accuracy for winds within the range 6-12 m/s. In direction, winds blowing from coast seem to be less accurately calculated by the satellite. Statistical results were similar for the three buoys in spite of the different coastal orientations. In average, the RMSE and bias for wind speed were 1.5 m/s and 0.2 m/s, respectively. Statistical data were also similar for wind direction, with the mean RMSE on the order of 34° and the mean bias on the order of 4° in absolute value. These statistical parameters are at least as accurate as those calculated in other near-shore areas all over the world. In particular, the bias was observed to be lower (in absolute value) than measured by most of the authors, which is possibly due to the high percentage of sampled winds lying within the interval [6-12] m/s, where satellite measurements are more accurate.

  16. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); AMSM, ROS; Long: -168.16025, Lat: -14.55134 (WGS84); Sensor Depth: 1.00m; Data Range: 20060307-20070902.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  17. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20040622-20040809.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  18. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); CNMI, SAI; Long: 145.72285, Lat: 15.23750 (WGS84); Sensor Depth: 0.00m; Data Range: 20030819-20050921.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  19. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 0.00m; Data Range: 20050411-20060904.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  20. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 0.00m; Data Range: 20020911-20030718.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  1. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); AMSM, ROS; Long: -168.16018, Lat: -14.55140 (WGS84); Sensor Depth: 0.00m; Data Range: 20020224-20040208.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  2. Physical profile and meteorological data from CTD casts during cruises to service the TAO/TRITON buoys in the equatorial Pacific from 02 March 2002 to 22 November 2002 (NODC Accession 0000945)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical profile data and meteorological data were collected from CTD casts in the equatorial Pacific Ocean during cruises to to service the TAO/TRITON buoy array....

  3. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20040622-20040808.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  4. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); PRIA, PAL; Long: -162.10280, Lat: 05.88468 (WGS84); Sensor Depth: 1.00m; Data Range: 20060326-20071017.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  5. CRED Sea Surface Temperature (SST) Buoy; NWHI, MID; Long: -177.34402, Lat: 28.21788 (WGS84); Sensor Depth: 0.33m; Data Range: 20020920-20030727.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  6. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); PRIA, PAL; Long: -162.10280, Lat: 05.88468 (WGS84); Sensor Depth: 0.00m; Data Range: 20060325-20080401.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  7. Upper ocean currents and sea surface temperatures (SST) from Satellite-tracked drifting buoys (drifters) as part of the Global Drifter Program for Hawaii region 1980/02/01 - 2009/03/31 (NODC Accession 0063296)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Satellite-tracked drifting buoys ("drifters") collect measurements of upper ocean currents and sea surface temperatures (SST) around the world as part of the Global...

  8. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, KUR; Long: -178.34455, Lat: 28.41863 (WGS84); Sensor Depth: 1.00m; Data Range: 20040629-20041005.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  9. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, KUR; Long: -178.34453, Lat: 28.41852 (WGS84); Sensor Depth: 1.00m; Data Range: 20060917-20080929.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  10. Drifting buoy and other data from the Arctic Ocean in support of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 04 June 1976 to 27 November 1976 (NODC Accession 7700205)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data were collected from the Arctic Ocean by the University of Washington in support of the Outer Continental Shelf Environmental Assessment Program...

  11. Temperature, current meter, and other data from moored buoy as part of the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) project, 30 July 1974 - 14 August 1974 (NODC Accession 7601675)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature, current meter, and other data were collected using moored buoy from the CAPRICORNE from July 30, 1974 to August 14, 1974. Data were collected as part of...

  12. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.72200, Lat: -14.28428 (WGS84); Sensor Depth: 0.33m; Data Range: 20020729-20031227.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  13. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MID; Long: -177.34402, Lat: 28.21788 (WGS84); Sensor Depth: 1.00m; Data Range: 20020724-20020920.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  14. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, KUR; Long: -178.34455, Lat: 28.41863 (WGS84); Sensor Depth: 0.00m; Data Range: 20030806-20041005.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  15. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 0.00m; Data Range: 20040919-20050411.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  16. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); PRIA, PAL; Long: -162.10283, Lat: 05.88468 (WGS84); Sensor Depth: 0.00m; Data Range: 20020315-20021024.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  17. CRED Sea Surface Temperature (SST) Buoy; NWHI, NEC; Long: -164.69775, Lat: 23.57152 (WGS84); Sensor Depth: 0.33m; Data Range: 20030714-20030825.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  18. CRED Sea Surface Temperature (SST) Buoy; NWHI, LIS; Long: -173.91610, Lat: 25.96767 (WGS84); Sensor Depth: 0.33m; Data Range: 20030724-20041009.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  19. CRED Sea Surface Temperature (SST) Buoy; NWHI, LAY; Long: -171.74252, Lat: 25.77290 (WGS84); Sensor Depth: 0.33m; Data Range: 20020915-20030722.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  20. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.72245, Lat: -14.28436 (WGS84); Sensor Depth: 0.33m; Data Range: 20040223-20060219.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  1. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.83355, Lat: -14.32838 (WGS84); Sensor Depth: 0.33m; Data Range: 20060221-20080221.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  2. CRED Sea Surface Temperature (SST) Buoy; NWHI, KUR; Long: -178.34319, Lat: 28.41816 (WGS84); Sensor Depth: 0.33m; Data Range: 20040706-20060917.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  3. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.56228, Lat: -14.28372 (WGS84); Sensor Depth: 0.33m; Data Range: 20060218-20080223.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  4. CRED Sea Surface Temperature (SST) Buoy; CNMI, SAI; Long: 145.69478, Lat: 15.17021 (WGS84); Sensor Depth: 0.33m; Data Range: 20070521-20090413.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  5. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.56225, Lat: -14.28368 (WGS84); Sensor Depth: 0.33m; Data Range: 20080223-20090420.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  6. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.56228, Lat: -14.28372 (WGS84); Sensor Depth: 0.33m; Data Range: 20020212-20020522.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  7. CRED Sea Surface Temperature (SST) Buoy; CNMI, GUA; Long: 144.79778, Lat: 13.51902 (WGS84); Sensor Depth: 0.33m; Data Range: 20030924-20040531.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  8. CRED Sea Surface Temperature (SST) Buoy; CNMI, PAG; Long: 145.75743, Lat: 18.12728 (WGS84); Sensor Depth: 0.33m; Data Range: 20050906-20070604.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  9. CRED Sea Surface Temperature (SST) Buoy; PRIA, JAR; Long: -159.97426, Lat: -00.37555 (WGS84); Sensor Depth: 0.33m; Data Range: 20080327-20090223.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  10. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.72266, Lat: -14.28457 (WGS84); Sensor Depth: 0.33m; Data Range: 20080220-20100222.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  11. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.72247, Lat: -14.28437 (WGS84); Sensor Depth: 0.33m; Data Range: 20060220-20080219.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  12. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.72200, Lat: -14.28428 (WGS84); Sensor Depth: 0.33m; Data Range: 20020306-20020523.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  13. CRED Sea Surface Temperature (SST) Buoy; NWHI, LAY; Long: -171.74250, Lat: 25.77240 (WGS84); Sensor Depth: 0.33m; Data Range: 20040924-20060910.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  14. CRED Sea Surface Temperature (SST) Buoy; NWHI, NEC; Long: -164.69775, Lat: 23.57152 (WGS84); Sensor Depth: 0.33m; Data Range: 20050410-20060108.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  15. Drifting buoy and other data from the Bering Sea as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 27 May 1977 to 07 January 1978 (NODC Accession 7800692)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Bering Sea by the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Outer Continental Shelf...

  16. Drifting buoy and other data from the Gulf of Alaska as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 21 October 1976 to 11 November 1976 (NODC Accession 7700740)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Gulf of Alaska by the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Outer Continental Shelf...

  17. Drifting buoy and other data from the Beaufort Sea as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 05 November 1975 to 01 October 1976 (NODC Accession 7700019)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy and other data was collected from the Beaufort Sea by the University of Washington (UW) as part of the Outer Continental Shelf Environmental Assessment...

  18. Drifting buoy and other data from drifting platforms in the Bering Sea as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 17 January 1981 to 20 June 1981 (NODC Accession 8200120)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from drifting platforms in the Bering Sea by the Flow Research Company as part of the Outer Continental Shelf Environmental...

  19. Drifting buoy and other data from the Bering Sea as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 13 September 1975 to 25 September 1975 (NODC Accession 7600632)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Bering Sea by the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Outer Continental Shelf...

  20. Drifting buoy and other data from the Beaufort Sea and other locations as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 04 April 1977 to 03 July 1977 (NODC Accession 7700780)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Beaufort Sea by the University of Washington (UW) as part of the Outer Continental Shelf Environmental Assessment Program...

  1. Drifting buoy and other data from the Arctic Ocean and Beaufort Sea as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 04 November 1975 to 01 October 1976 (NODC Accession 7700114)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Arctic Ocean and Beaufort Sea by the University of Washington (UW) as part of the Outer Continental Shelf Environmental...

  2. Drifting buoy and other data from the Arctic Ocean and other locations as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 02 November 1975 to 03 June 1976 (NODC Accession 7601626)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected by the University of Washington - Seattle (UW) as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP)....

  3. Drifting buoy and other data from the Chukchi Sea as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 27 June 1977 to 07 November 1977 (NODC Accession 7800005)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Chukchi Sea by the University of Washington (UW) as part of the Outer Continental Shelf Environmental Assessment Program...

  4. Drifting buoy and other data from the Gulf of Alaska as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 26 October 1980 to 27 March 1981 (NODC Accession 8200115)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Gulf of Alaska by the Science Application INC (SAI) as part of the Outer Continental Shelf Environmental Assessment Program...

  5. Drifting buoy and other data from the Beaufort Sea and other locations as part of the Outer Continental Shelf Environmental Assessment Program (OCSEAP) from 03 March 1977 to 05 April 1977 (NODC Accession 7700543)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Drifting buoy data was collected from the Beaufort Sea and other locations by the University of Washington (UW) as part of the Outer Continental Shelf Environmental...

  6. Current meter and temperature profile data from current meter and buoy casts in the TOGA area of Pacific Ocean from 27 April 1993 to 09 June 1994 (NODC Accession 9700042)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Current meter and temperature profile data were collected using current meter and buoy casts in the TOGA area of Pacific Ocean from 27 April 1993 to 09 June 1994....

  7. Physical and chemical data collected using CTD and buoy casts from NOAA Ship RESEARCHER and another platform from 1974-06-27 to 1974-07-18 (NCEI Accession 7601653)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical and chemical data were collected using CTD and buoy casts from NOAA Ship RESEARCHER and NOAA Ship OCEANOGRAPHER from 27 June 1974 to 18 July 1974. Data were...

  8. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, KUR; Long: -178.34453, Lat: 28.41852 (WGS84); Sensor Depth: 0.00m; Data Range: 20060917-20080929.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  9. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); PRIA, PAL; Long: -162.10282, Lat: 05.88467 (WGS84); Sensor Depth: 0.00m; Data Range: 20040330-20060325.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  10. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, LIS; Long: -173.91608, Lat: 25.96767 (WGS84); Sensor Depth: 0.00m; Data Range: 20010920-20020612.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  11. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, PHR; Long: -175.81612, Lat: 27.85325 (WGS84); Sensor Depth: 0.00m; Data Range: 20020918-20030811.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  12. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); AMSM, ROS; Long: -168.16025, Lat: -14.55134 (WGS84); Sensor Depth: 0.00m; Data Range: 20060307-20080312.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  13. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, KUR; Long: -178.34455, Lat: 28.41863 (WGS84); Sensor Depth: 0.00m; Data Range: 20020922-20030806.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  14. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 0.00m; Data Range: 20030718-20030826.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  15. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); AMSM, ROS; Long: -168.16018, Lat: -14.55140 (WGS84); Sensor Depth: 0.00m; Data Range: 20040208-20060307.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  16. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Environmental Data Logger (EDL); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 0.00m; Data Range: 20030826-20040809.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to sea surface measure water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  17. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, PHR; Long: -175.81612, Lat: 27.85325 (WGS84); Sensor Depth: 0.00m; Data Range: 20030812-20040926.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  18. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, KUR; Long: -178.34457, Lat: 28.41858 (WGS84); Sensor Depth: 0.00m; Data Range: 20041005-20060917.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  19. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, PHR; Long: -175.81590, Lat: 27.85408 (WGS84); Sensor Depth: 0.00m; Data Range: 20011026-20020917.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  20. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, MAR; Long: -170.63382, Lat: 25.44652 (WGS84); Sensor Depth: 0.00m; Data Range: 20030824-20040922.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  1. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, PHR; Long: -175.81593, Lat: 27.85397 (WGS84); Sensor Depth: 0.00m; Data Range: 20040927-20060912.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  2. CRED Sea Surface Temperature (SST) Buoy; NWHI, LIS; Long: -173.91588, Lat: 25.96764 (WGS84); Sensor Depth: 0.19m; Data Range: 20090910-20100922.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  3. CRED Sea Surface Temperature (SST) Buoy; NWHI, KUR; Long: -178.34322, Lat: 28.41813 (WGS84); Sensor Depth: 0.19m; Data Range: 20090916-20100918.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  4. CRED Sea Surface Temperature (SST) Buoy; CNMI, GUA; Long: 144.80048, Lat: 13.52900 (WGS84); Sensor Depth: 0.19m; Data Range: 20070513-20090405.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  5. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.76310, Lat: -14.36667 (WGS84); Sensor Depth: 0.19m; Data Range: 20070616-20080115.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  6. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.83339, Lat: -14.32838 (WGS84); Sensor Depth: 0.19m; Data Range: 20050806-20060221.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  7. CRED Sea Surface Temperature (SST) Buoy; NWHI, LIS; Long: -173.91583, Lat: 25.96762 (WGS84); Sensor Depth: 0.19m; Data Range: 20081004-20090910.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  8. CRED Sea Surface Temperature (SST) Buoy; Maug, Commonwealth of the Northern Mariana Islands; Long: 145.23196, Lat: 20.02909 (WGS84); Sensor Depth: 0.19m; Data Date Range: 20090428-20110418.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  9. CRED Sea Surface Temperature (SST) Buoy; NWHI, KUR; Long: -178.34327, Lat: 28.41817 (WGS84); Sensor Depth: 0.19m; Data Range: 20080929-20090916.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  10. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.76332, Lat: -14.36675 (WGS84); Sensor Depth: 0.19m; Data Range: 20060226-20070406.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  11. CRED Sea Surface Temperature (SST) Buoy; PRIA, WAK; Long: 166.62210, Lat: 19.30740 (WGS84); Sensor Depth: 0.19m; Data Range: 20070501-20090118.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  12. CRED Sea Surface Temperature (SST) Buoy; PRIA, PAL; Long: -162.04044, Lat: 05.87450 (WGS84); Sensor Depth: 0.19m; Data Range: 20080402-20091124.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  13. CRED Sea Surface Temperature (SST) Buoy; NWHI, LAY; Long: -171.74242, Lat: 25.77248 (WGS84); Sensor Depth: 0.19m; Data Range: 20060910-20071203.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  14. CRED Sea Surface Temperature (SST) Buoy; PRIA, PAL; Long: -162.06183, Lat: 05.88278 (WGS84); Sensor Depth: 0.19m; Data Range: 20060324-20070514.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  15. CRED Sea Surface Temperature (SST) Buoy; NWHI, PHR; Long: -175.81592, Lat: 27.85393 (WGS84); Sensor Depth: 0.19m; Data Range: 20080922-20090923.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  16. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MAR; Long: -170.63382, Lat: 25.44652 (WGS84); Sensor Depth: 1.00m; Data Range: 20030824-20040421.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  17. CRED Sea Surface Temperature (SST) Buoy; CNMI, GUA; Long: 144.80047, Lat: 13.52900 (WGS84); Sensor Depth: 0.33m; Data Range: 20051005-20070513.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  18. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.83335, Lat: -14.32823 (WGS84); Sensor Depth: 0.33m; Data Range: 20020228-20020531.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  19. Bacteriology data from moored buoy casts and other instruments in the Delaware Bay and North Atlantic Ocean during the Ocean Continental Shelf (OCS-Mid Atlantic Ocean) project, 05 November 1976 - 16 August 1977 (NODC Accession 7800207)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Bacteriology data were collected using moored buoy casts and other instruments in the Delaware Bay and North Atlantic Ocean from November 5, 1976 to August 16, 1977....

  20. Physical, meteorological, and other data from SIO BUOY and FIXED PLATFORMS in the North Pacific in support of the North Pacific Study Program from 30 June 1969 to 14 September 1971 (NODC Accession 7400658)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical, meteorological , and other data were collected from SIO BUOY and FIXED PLATFORMS from 30 June 1969 to 14 September 1971. Data were collected by Scripps...

  1. CRED Sea Surface Temperature (SST) Buoy; PRIA, KIN; Long: -162.34213, Lat: 06.39241 (WGS84); Sensor Depth: 0.33m; Data Range: 20040402-20060329.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  2. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20050411-20060904.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  3. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MID; Long: -177.34402, Lat: 28.21788 (WGS84); Sensor Depth: 1.00m; Data Range: 20011022-20020325.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  4. CRED Sea Surface Temperature (SST) Buoy; AMSM, TUT; Long: -170.56228, Lat: -14.28372 (WGS84); Sensor Depth: 0.33m; Data Range: 20020729-20040217.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  5. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, MAR; Long: -170.63382, Lat: 25.44652 (WGS84); Sensor Depth: 1.00m; Data Range: 20030721-20030823.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  6. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, MAR; Long: -170.63378, Lat: 25.44642 (WGS84); Sensor Depth: 1.00m; Data Range: 20060915-20080918.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  7. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); PRIA, PAL; Long: -162.10289, Lat: 05.88463 (WGS84); Sensor Depth: 1.00m; Data Range: 20080401-20090515.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  8. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20020911-20030305.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  9. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); PRIA, PAL; Long: -162.10280, Lat: 05.88468 (WGS84); Sensor Depth: 1.00m; Data Range: 20060326-20080401.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  10. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20020423-20020910.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  11. CRED Sea Surface Temperature (SST) Buoy; NWHI, NEC; Long: -164.69775, Lat: 23.57152 (WGS84); Sensor Depth: 0.33m; Data Range: 20020911-20030713.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  12. CRED Sea Surface Temperature (SST) Buoy; NWHI, LIS; Long: -173.91608, Lat: 25.96768 (WGS84); Sensor Depth: 0.33m; Data Range: 20020917-20030723.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  13. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MAR; Long: -170.63382, Lat: 25.44652 (WGS84); Sensor Depth: 1.00m; Data Range: 20021001-20030321.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  14. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, MAR; Long: -170.63382, Lat: 25.44643 (WGS84); Sensor Depth: 0.00m; Data Range: 20051016-20060907.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  15. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, MAR; Long: -170.63382, Lat: 25.44643 (WGS84); Sensor Depth: 1.00m; Data Range: 20051020-20060907.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  16. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, MAR; Long: -170.63382, Lat: 25.44643 (WGS84); Sensor Depth: 0.00m; Data Range: 20040924-20051015.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  17. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Environmental Data Logger (EDL); NWHI, MAR; Long: -170.63378, Lat: 25.44642 (WGS84); Sensor Depth: 0.00m; Data Range: 20060907-20080918.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  18. Temperature and other data collected using moored buoy as part of the International Decade of Ocean Exploration / North Pacific Experiment (IDOE/NORPAX) project from 05 July 1976 to 31 January 1977 (NODC Accession 8200026)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Fluorescence, temperature, depth, east-west current component, north-south current component, pressure and other data were collected using moored buoy from July 5,...

  19. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MAR; Long: -170.63382, Lat: 25.44643 (WGS84); Sensor Depth: 1.00m; Data Range: 20051016-20060907.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  20. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MAR; Long: -170.63382, Lat: 25.44643 (WGS84); Sensor Depth: 1.00m; Data Range: 20040924-20051014.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  1. Bacteriology, wind wave spectra, and benthic organism data from moored buoy casts and other instruments in the Gulf of Mexico during the Brine Disposal project, 01 February 1978 - 03 May 1979 (NODC Accession 7900247)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Bacteriology, wind wave spectra, and benthic organism data were collected using moored buoy casts and other instruments in the Gulf of Mexico from February 1, 1978...

  2. CTD, current meter, meteorological buoy, and bottle data from the Gulf of Mexico from the ALPHA HELIX and other platforms in support of LATEX A from 18 March 1993 to 23 September 1993 (NODC Accession 9400149)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CTD, current meter, meteorological buoy, and bottle data were collected from the Gulf of Mexico from the ALPHA HELIX and other platforms. Data were collected by...

  3. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, KUR; Long: -178.34455, Lat: 28.41863 (WGS84); Sensor Depth: 1.00m; Data Range: 20030807-20040415.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  4. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, PHR; Long: -175.81595, Lat: 27.85396 (WGS84); Sensor Depth: 0.91m; Data Range: 20060915-20080828.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  5. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); PRIA, PAL; Long: -162.10282, Lat: 05.88467 (WGS84); Sensor Depth: 1.00m; Data Range: 20040330-20060325.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  6. Wind wave spectra and other data from several locations around U.S. using moored buoy and other instruments from 01 August 2003 to 31 August 2003 (NODC Accession 0001177)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Wind wave spectra and other data were collected using moored buoy and other instruments from August 1, 2003 to August 31, 2003. Data were collected from the Great...

  7. Current meter and temperature profile data from current meter and buoy casts in the TOGA area of Pacific Ocean from 29 March 1991 to 24 December 1993 (NODC Accession 9900057)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Current meter and temperature profile data were collected using current meter and buoy casts in the TOGA area of Pacific Ocean from 29 March 1991 to 24 December...

  8. CRED Sea Surface Temperature (SST) Buoy; AMSM, TAU; Long: -169.50891, Lat: -14.24402 (WGS84); Sensor Depth: 0.33m; Data Range: 20080301-20100312.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  9. CRED Sea Surface Temperature (SST) Buoy; AMSM, TAU; Long: -169.41890, Lat: -14.23567 (WGS84); Sensor Depth: 0.33m; Data Range: 20020214-20020317.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  10. Physical profile data collected in the Equatorial Pacific during cruises to service the TAO array, a network of deep ocean moored buoys, from 2007-04-07 to the present

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — As part of the Tropical Atmosphere Ocean (TAO) Program, the National Data Buoy Center (NDBC) was responsible for the at-sea collection, quality control and...

  11. CRED Sea Surface Temperature (SST) Buoy; CNMI, MAU; Long: 145.23217, Lat: 20.02920 (WGS84); Sensor Depth: 0.33m; Data Range: 20030903-20050911.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  12. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); CNMI, SAI; Long: 145.72285, Lat: 15.23750 (WGS84); Sensor Depth: 1.00m; Data Range: 20040622-20050227.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  13. CRED Sea Surface Temperature (SST) Buoy; NWHI, MID; Long: -177.34402, Lat: 28.21788 (WGS84); Sensor Depth: 0.33m; Data Range: 20030729-20041001.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  14. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, MAR; Long: -170.63378, Lat: 25.44642 (WGS84); Sensor Depth: 1.00m; Data Range: 20060915-20080918.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  15. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20011017-20020120.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  16. Physical and meteorological data from buoys from the NW Pacific (limit-180) by the Japan Meteorological Agency and other institutions from 01 January 1978 to 31 December 1991 (NODC Accession 9400143)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Buoy data was collected in the NW Pacific (limit-180) from 01 January 1978 to 31 December 1991. Data were collected by the Japan Meteorological Agency and other...

  17. Significant Wave Heights, Periods, and Directions, and Air and Sea Temperature Data from a Directional Waverider Buoy off Diamond Head, Oahu during March-April 2000 (NODC Accession 0000475)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A directional waverider buoy located about one nautical mile south of Diamond Head, Oahu, provided an approximately 10-day time series of wave characteristics and...

  18. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); AMSM, ROS; Long: -168.16025, Lat: -14.55134 (WGS84); Sensor Depth: 1.00m; Data Range: 20060307-20080312.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  19. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, KUR; Long: -178.34457, Lat: 28.41858 (WGS84); Sensor Depth: 1.00m; Data Range: 20041006-20060916.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  20. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); CNMI, SAI; Long: 145.72288, Lat: 15.23746 (WGS84); Sensor Depth: 1.00m; Data Range: 20050921-20060525.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  1. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20040919-20050411.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  2. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, LIS; Long: -173.91608, Lat: 25.96767 (WGS84); Sensor Depth: 1.00m; Data Range: 20011020-20011225.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  3. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, MAR; Long: -170.63382, Lat: 25.44652 (WGS84); Sensor Depth: 1.00m; Data Range: 20020424-20020802.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  4. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); PRIA, PAL; Long: -162.10289, Lat: 05.88463 (WGS84); Sensor Depth: 1.00m; Data Range: 20080401-20100410.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  5. CRED Coral Reef Early Warning System (CREWS) Standard Buoy, Supplemental Sea Surface Temperature Recorder (SBE39); NWHI, KUR; Long: -178.34453, Lat: 28.41852 (WGS84); Sensor Depth: 1.00m; Data Range: 20060918-20080929.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Standard (CREWS-STD) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  6. CRED Sea Surface Temperature (SST) Buoy; NWHI, LAY; Long: -171.74252, Lat: 25.77290 (WGS84); Sensor Depth: 0.33m; Data Range: 20030724-20040923.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  7. CRED Sea Surface Temperature (SST) Buoy; NWHI, LIS; Long: -173.91600, Lat: 25.96770 (WGS84); Sensor Depth: 0.33m; Data Range: 20050614-20060925.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data from Coral Reef Ecosystem Division (CRED), NOAA Pacific Islands Fisheries Science Center (PIFSC) Sea Surface Temperature (SST) Buoys provide a time series of...

  8. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); PRIA, PAL; Long: -162.10283, Lat: 05.88468 (WGS84); Sensor Depth: 1.00m; Data Range: 20020315-20021023.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  9. CRED Coral Reef Early Warning System (CREWS) Enhanced Buoy, Sea Surface Temperature and Conductivity Recorder (SBE37); NWHI, FFS; Long: -166.27183, Lat: 23.85678 (WGS84); Sensor Depth: 1.00m; Data Range: 20050413-20060904.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CREWS Enhanced (CREWS-ENH) buoys are equipped to measure sea surface water temperature and conductivity (Sea-Bird Model SBE37-SM, Sea-Bird Electronics, Inc.,...

  10. An experimental study of the effect of mooring systems on the dynamics of a SPAR buoy-type floating offshore wind turbine

    Science.gov (United States)

    Hong, Sinpyo; Lee, Inwon; Park, Seong Hyeon; Lee, Cheolmin; Chun, Ho-Hwan; Lim, Hee Chang

    2015-09-01

    An experimental study of the effect of mooring systems on the dynamics of a SPAR buoy-type floating offshore wind turbine is presented. The effects of the Center of Gravity (COG), mooring line spring constant, and fair-lead location on the turbine's motion in response to regular waves are investigated. Experimental results show that for a typical mooring system of a SPAR buoy-type Floating Offshore Wind Turbine (FOWT), the effect of mooring systems on the dynamics of the turbine can be considered negligible. However, the pitch decreases notably as the COG increases. The COG and spring constant of the mooring line have a negligible effect on the fairlead displacement. Numerical simulation and sensitivity analysis show that the wind turbine motion and its sensitivity to changes in the mooring system and COG are very large near resonant frequencies. The test results can be used to validate numerical simulation tools for FOWTs.

  11. An experimental study of the effect of mooring systems on the dynamics of a SPAR buoy-type floating offshore wind turbine

    Directory of Open Access Journals (Sweden)

    Sinpyo Hong

    2015-05-01

    Full Text Available An experimental study of the effect of mooring systems on the dynamics of a SPAR buoy-type floating offshore wind turbine is presented. The effects of the Center of Gravity (COG, mooring line spring constant, and fair-lead location on the turbine’s motion in response to regular waves are investigated. Experimental results show that for a typical mooring system of a SPAR buoy-type Floating Offshore Wind Turbine (FOWT, the effect of mooring systems on the dynamics of the turbine can be considered negligible. However, the pitch decreases notably as the COG increases. The COG and spring constant of the mooring line have a negligible effect on the fairlead displacement. Numerical simulation and sensitivity analysis show that the wind turbine motion and its sensitivity to changes in the mooring system and COG are very large near resonant frequencies. The test results can be used to validate numerical simulation tools for FOWTs.

  12. A study of principal hull parameters for a Spar buoy foundation for a vertical axis wind turbine in the MW-class

    OpenAIRE

    Nilsen, Sølve

    2016-01-01

    Master's thesis in Offshore technology: Marine and subsea technology This thesis analyses a vertical axis wind turbine (VAWT) in the MW-class used in relation with a floating Spar buoy. The objective was to study the effect of varying principal hull parameters including diameter and draft on the overall system’s hydrostatic and hydrodynamic performance. First, a spreadsheet was constructed, containing certain engineering simplifications to evaluate a number of floating geometries ...

  13. Optimization and Annual Average Power Predictions of a Backward Bent Duct Buoy Oscillating Water Column Device Using the Wells Turbine.

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Christopher S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bull, Diana L [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Willits, Steven M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Fontaine, Arnold A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-08-01

    This Technical Report presents work completed by The Applied Research Laboratory at The Pennsylvania State University, in conjunction with Sandia National Labs, on the optimization of the power conversion chain (PCC) design to maximize the Average Annual Electric Power (AAEP) output of an Oscillating Water Column (OWC) device. The design consists of two independent stages. First, the design of a floating OWC, a Backward Bent Duct Buoy (BBDB), and second the design of the PCC. The pneumatic power output of the BBDB in random waves is optimized through the use of a hydrodynamically coupled, linear, frequency-domain, performance model that links the oscillating structure to internal air-pressure fluctuations. The PCC optimization is centered on the selection and sizing of a Wells Turbine and electric power generation equipment. The optimization of the PCC involves the following variables: the type of Wells Turbine (fixed or variable pitched, with and without guide vanes), the radius of the turbine, the optimal vent pressure, the sizing of the power electronics, and number of turbines. Also included in this Technical Report are further details on how rotor thrust and torque are estimated, along with further details on the type of variable frequency drive selected.

  14. Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA.

    Science.gov (United States)

    Heddam, Salim

    2016-09-01

    This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

  15. EML, VEGA, ODM, LTER, GLEON - considerations and technologies for building a buoy information system at an LTER site

    Science.gov (United States)

    Gries, C.; Winslow, L.; Shin, P.; Hanson, P. C.; Barseghian, D.

    2010-12-01

    At the North Temperate Lakes Long Term Ecological Research (NTL LTER) site six buoys and one met station are maintained, each equipped with up to 20 sensors producing up to 45 separate data streams at a 1 or 10 minute frequency. Traditionally, this data volume has been managed in many matrix type tables, each described in the Ecological Metadata Language (EML) and accessed online by a query system based on the provided metadata. To develop a more flexible information system, several technologies are currently being experimented with. We will review, compare and evaluate these technologies and discuss constraints and advantages of network memberships and implementation of standards. A Data Turbine server is employed to stream data from data logger files into a database with the Real-time Data Viewer being used for monitoring sensor health. The Kepler work flow processor is being explored to introduce quality control routines into this data stream taking advantage of the Data Turbine actor. Kepler could replace traditional database triggers while adding visualization and advanced data access functionality for downstream modeling or other analytical applications. The data are currently streamed into the traditional matrix type tables and into an Observation Data Model (ODM) following the CUAHSI ODM 1.1 specifications. In parallel these sensor data are managed within the Global Lake Ecological Observatory Network (GLEON) where the software package Ziggy streams the data into a database of the VEGA data model. Contributing data to a network implies compliance with established standards for data delivery and data documentation. ODM or VEGA type data models are not easily described in EML, the metadata exchange standard for LTER sites, but are providing many advantages from an archival standpoint. Both GLEON and CUAHSI have developed advanced data access capabilities based on their respective data models and data exchange standards while LTER is currently in a phase of

  16. Validation of the NASA GEWEX-SRB Radiation Data over the Tropical Oceans: Comparisons with PIRATA, RAMA, TAO and WHOI Buoy Observations

    Science.gov (United States)

    Zhang, T.; Stackhouse, P. W.; Gupta, S. K.; Cox, S. J.; Mikovitz, J. C.

    2013-12-01

    The NASA/GEWEX SRB (Global Energy and Water Exchanges, Surface Radiation Budget) project produces and archives shortwave and longwave radiation budget flux estimates at the top of the atmosphere and at the Earth's surface. The latest version in the archive, Release 3.0, is available as 3-hourly, 3-hourly-monthly, daily and monthly means continuously over the period from July 1983 to December 2007 on a quasi-equal-area grid system of 44016 grid boxes. SRB Release 4 with further improvements in data quality and higher spatial resolution is being developed. The SRB shortwave/longwave fluxes at the Earth's surface from the algorithm GSW(V3.0)/GLW(V3.1) have been extensively validated against high-quality ground-based observations, in particular observed data from the Baseline Surface Radiation Network (BSRN). Comparisons with nearly 6000 site-months of both shortwave and longwave data from 52 BSRN sites show generally good agreement. In addition, the GEWEX SRB data have also been found to compare favorably with the World Radiation Data Centre (WRDC) data and the Global Energy Balance Archive (GEBA) data. In spite of the fact that the BSRN sites are scattered on all seven continents, the validation of the SRB data over the vast oceans had not been done until recently. In this paper, we present comparisons of the GEWEX-SRB data shortwave/longwave data with observations made on arrays moored in tropical oceans. Specifically, we have data from 21 buoys, or moorings, from Prediction and Research Moored Array in the Atlantic (PIRATA), 14 buoys from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean, 20 buoys from the Tropical Atmosphere Ocean (TAO) array in the Pacific, and 3 buoys from the Woods Hole Oceanographic Institute (WHOI) (2 in the Pacific and 1 in the Atlantic). The data from these buoys span 12 years from 2000 to 2011, though not necessarily continuously. It is found that except for occasional

  17. Assessing the role of solar radiation in heating, photosynthesis, and photo-oxidation in upper Arctic Ocean waters via autonomous buoys

    Science.gov (United States)

    Hill, V. J.; Steele, M.; Light, B.

    2016-02-01

    As part of the Arctic Observing Network, a new ice-tethered buoy has been developed for monitoring the role of sunlight in regulating ocean temperature, phytoplankton growth, and carbon cycling. A 20 or 50 m string (depending on local bathymetry) supports sensors both within and below the ice for the hourly measurement of downwelling irradiance, temperature, Chlorophyll a, light backscattering, and dissolved organic material (DOM). Two buoys were deployed in March 2014 and two in March 2015. Because the buoys are engineered to survive melting out of first year ice, they have successfully provided complete seasonal records of water column warming, phytoplankton abundance and photo-oxidation patterns in the Pacific Arctic Region. The data collected will be used to determine whether reduced ice extent and thinner ice are driving increases in under ice warming, accelerating bottom ice ablation, increasing available photosynthetic radiation to support large under ice blooms, and to quantify photo-oxidation of the DOM pool. Observations so far have revealed strong under ice daily warming as high as ±0.5 °C driven by local solar radiation. Water column absorption was dominated by colored dissolved organic material which served to trap solar radiation in the upper water column. Chlorophyll concentrations observed in June and July indicated high phytoplankton abundance beneath the ice. Light intensity at this time was not sufficient to support growth rates high enough to produce the 8 to 10 mg m-3 of chlorophyll observed. We hypothesize that phytoplankton were advected under the ice from the ice edge. However, once there phytoplankton were able to sustain low growth rates leading to nutrient limitation before open water status was reached. Strong daily cycles of photo-oxidation have also been observed in the late summer that indicate the fast cycling of highly labile DOM in the open waters of the Pacific Arctic Region.

  18. Measurement of Near-Surface Salinity, Temperature and Directional Wave Spectra using a Novel Wave-Following, Lagrangian Surface Contact Buoy

    Science.gov (United States)

    Boyle, J. P.

    2016-02-01

    Results from a surface contact drifter buoy which measures near-surface conductivity ( 10 cm depth), sea state characteristics and near-surface water temperature ( 2 cm depth) are described. This light (measurement, a nine-degrees-of-freedom motion package for derivation of directional wave spectra and a thermocouple for water temperature measurement. Data retrieval for expendable, ocean-going operation uses an onboard Argos transmitter. Scientific results as well as data processing algorithms are presented from laboratory and field experiments which support qualification of buoy platform measurements. These include sensor calibration experiments, longer-term dock-side biofouling experiments during 2013-2014 and a series of short-duration ocean deployments in the Gulf Stream in 2014. In addition, a treatment method will be described which appears to minimize the effects of biofouling on the inductive conductivity probe when in coastal surface waters. Due to its low cost and ease of deployment, scores, perhaps hundreds of these novel instruments could be deployed from ships or aircraft during process studies or to provide surface validation for satellite-based measurements, particularly in high precipitation regions.

  19. Response of Land-Sea Interface in Xiamen Bay to Extreme Weather Events Observed with the Ecological Dynamic Buoy Array, a Multifunctional Sensors System

    Science.gov (United States)

    Wu, J.; Hong, H.; Pan, W.; Zhang, C.

    2016-12-01

    Recent climate observations suggest that global climate change may result in an increase of extreme weather events (such as tropical cyclones, intense precipitation i.e. heavy rains) in frequency and/or intensity in certain world regions. Subtropical coastal regions are often densely populated areas experiencing rapid development and widespread changes to the aquatic environment. The biogeochemical and ecological responses of coastal systems to extreme weather events are of increasing concern. Enhanced river nutrients input following rain storms has been linked to the ecological responses at land-sea interface. These land-sea interactions can be studied using multifunctional sensors systems. In our study, the Ecological Dynamic Buoy Array, a monitoring system with multiple sensors, was deployed in Xiamen Bay for near real time measurements of different parameters. The Ecological Dynamic Buoy Array is a deep water net cage which functions in long-term synchronous observation of dynamic ecological characteristics with the support of an aerograph, water-watch, LOBO (Land/Ocean Biogeochemical Observatory), ADCP, CTD chain system, YSI vertical profiler, flow cytometer, sea surface camera, and "communication box". The study showed that rain storms during multiple typhoons resulted in greater fluctuations of salinity, N concentration, and other water environmental conditions, which might have been connected with algal blooms (so-called red tide) in Xiamen Bay.

  20. Wind waves spectra and other data collected using moored buoy in the Great Lakes, Gulf of Mexico, South Pacific and East/West coast of US from 01 June 2000 to 30 June 2000 (NODC Accession 0000217)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Wind wave spectra and other data were collected using moored buoys in the Great lakes, Gulf of Mexico, South Pacific and East/West coast of US from June 01, 2000 to...

  1. Variations in return value estimate of ocean surface waves - a study based on measured buoy data and ERA-Interim reanalysis data

    Science.gov (United States)

    Muhammed Naseef, T.; Sanil Kumar, V.

    2017-10-01

    An assessment of extreme wave characteristics during the design of marine facilities not only helps to ensure their safety but also assess the economic aspects. In this study, return levels of significant wave height (Hs) for different periods are estimated using the generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) based on the Waverider buoy data spanning 8 years and the ERA-Interim reanalysis data spanning 38 years. The analysis is carried out for wind-sea, swell and total Hs separately for buoy data. Seasonality of the prevailing wave climate is also considered in the analysis to provide return levels for short-term activities in the location. The study shows that the initial distribution method (IDM) underestimates return levels compared to GPD. The maximum return levels estimated by the GPD corresponding to 100 years are 5.10 m for the monsoon season (JJAS), 2.66 m for the pre-monsoon season (FMAM) and 4.28 m for the post-monsoon season (ONDJ). The intercomparison of return levels by block maxima (annual, seasonal and monthly maxima) and the r-largest method for GEV theory shows that the maximum return level for 100 years is 7.20 m in the r-largest series followed by monthly maxima (6.02 m) and annual maxima (AM) (5.66 m) series. The analysis is also carried out to understand the sensitivity of the number of observations for the GEV annual maxima estimates. It indicates that the variations in the standard deviation of the series caused by changes in the number of observations are positively correlated with the return level estimates. The 100-year return level results of Hs using the GEV method are comparable for short-term (2008 to 2016) buoy data (4.18 m) and long-term (1979 to 2016) ERA-Interim shallow data (4.39 m). The 6 h interval data tend to miss high values of Hs, and hence there is a significant difference in the 100-year return level Hs obtained using 6 h interval data compared to data at 0.5 h interval. The

  2. Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

    Directory of Open Access Journals (Sweden)

    Jianyong Xing

    2016-05-01

    Full Text Available The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A, launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC and the Tropical Atmospheric Ocean project (TAO, the European Centre for Medium Range Weather Forecasting (ECMWF reanalysis data (ERA-Interim, and the advanced scatterometer (ASCAT to calculate the error statistics including mean bias, root mean square error (RMSE, and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m·s−1, respectively. The corresponding RMSEs of the wind direction were 46.63°, 43.11°, 39.93°, and 47.47°, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m·s−1 and 39.68° for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

  3. Assessing the performance of the Cretan Sea ecosystem model with the use of high frequency M3A buoy data set

    Directory of Open Access Journals (Sweden)

    G. Triantafyllou

    2003-01-01

    Full Text Available During the Mediterranean Forecasting System Pilot Project a buoy was deployed in the Cretan Sea and for the first time high-frequency physical and biogeochemical data were collected over an extended period, providing a unique opportunity for the evaluation of an ecosystem model. The model both tuned and validated in the Cretan Sea in the past, is explored and quantified. In addition, the optimal parameter set is determined while the effects of high-frequency forcing are explored. The model results are satisfactory, especially at the upper part of the water column, while the inability of 1-D modelling in fully exploring the hydrodynamics of the particular area is depicted and further developments are suggested. Key words. Oceanography; general (numerical modeling – Oceanography; biological and chemical (ecosystems and ecology

  4. Promoting climate, ocean and data literacy by hosting a CO2 buoy from NOAA's Pacific Marine Environmental Lab at the Exploratorium

    Science.gov (United States)

    Sabine, C. L.; Miller, M. K.; Maenner, S.; Sutton, A.

    2016-02-01

    The Exploratorium's new museum site on the San Francisco waterfront is a unique location for place-based learning about climate impacts on the ocean. With access to the Bay and surrounding environment, and strong partnerships with a national network of NOAA scientists and local researchers, the museum can serve as an educational node for a variety of atmosphere and ocean observing networks. The most visible and iconic instrument at the museum's Pier 15 location is a CO2 buoy from NOAA's Pacific Marine Environmental Lab in Seattle. Part of an international network of real-time ocean acidification sensors, the NOAA buoy streams temperature, salinity, atmospheric and surface water CO2 data from the Exploratorium location to NOAA. Near real-time and archived ocean and atmosphere carbon data are then shared with and displayed in the museum's Bay Observatory along with other water quality, weather, and air quality conditions. Displaying both the instruments and the data they provide gives the public a better understanding of where climate data comes from, how scientists make meaning from time series data, and the value of long-term observation in understanding climate change and the ways that humans impact the environment. However, creating interactive exhibits from environmental data presents many challenges, including interpreting complex earth systems and biological and human interactions. What is the impact of the adjacent urban center and the estuary on the Bay's carbon content? How do we tease out long-term trends from the local variability? How do we connect the place-based learning to global processes and impacts? We'll address some of these challenges in the presentation and include the importance of collaborative partnerships between informal education institutions and researchers in place-based education about climate and environmental change.

  5. Place-based Learning Collaboration: Promoting climate, ocean and data literacy by hosting a CO2 buoy from NOAA's Pacific Marine Environmental Lab at the Exploratorium

    Science.gov (United States)

    Miller, M. K.; Sabine, C. L.; Maenner, S.; Sutton, A.; Raleigh, C.

    2015-12-01

    The Exploratorium's new museum site on the San Francisco waterfront is a unique location for place-based learning about climate impacts on the ocean. With access to the Bay and surrounding environment, and strong partnerships with a national network of NOAA scientists and local researchers, the museum can serve as an educational node for a variety of atmosphere and ocean observing networks. The most visible and iconic instrument at the museum's Pier 15 location is a CO2 buoy from NOAA's Pacific Marine Environmental Lab in Seattle. Part of an international network of real-time ocean acidification sensors, the NOAA buoy streams temperature, salinity, atmospheric and surface water CO2 data from the Exploratorium location to NOAA. Near real-time and archived ocean and atmosphere carbon data is displayed in the museum's Bay Observatory along with other water quality, weather, and air quality conditions. Displaying both the instruments and the data they provide gives the public a better understanding of where climate data comes from, how scientists make meaning from time series data, and the value of long-term observation in understanding climate change and the ways that humans impact the environment. However, creating interactive exhibits from environmental data presents many challenges, including interpreting complex earth systems and biological and human interactions. What is the impact of the adjacent urban center and the estuary on the Bay's carbon content? How do we tease out long-term trends from the local variability? How do we connect the place-based learning to global processes and impacts? We'll address some of these challenges in the presentation and include the importance of collaborative partnerships between informal education institutions and researchers in place-based education about climate and environmental change.

  6. Wave data from buoy deployments from the R/V KEXUE #1 as part of the Coupled Ocean-Atmosphere Response Experiment (COARE) and Tropical Ocean Global Atmosphere (TOGA) projects from 1992-11-01 to 1993-02-20 (NODC Accession 9600021)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Wave data were collected from buoy deployments from the R/V KEXUE #1 as part of the Coupled Ocean-Atmosphere Response Experiment (COARE) and Tropical Ocean Global...

  7. Dissolved inorganic carbon, total alkalinity, pH, dissolved oxygen, and nutrients collected from profile, discrete sampling, and time series observations using CTD, Niskin bottle, and other instruments from R/V Gulf Challenger near a buoy off the coast of New Hampshire, U.S. in the Gulf of Maine from 2011-01-11 to 2015-11-18 (NCEI Accession 0142327)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This archival package contains discrete measurements of dissolved inorganic carbon, total alkalinity, pH, dissolved oxygen, and nutrients collected at the buoy off...

  8. Temperature profile and current meter data collected using moored buoy and profiling floats in the North Atlantic Ocean as part of the International Decade of Ocean Exploration / Mid-Ocean Dynamics Experiment (IDOE/MODE) project from 03 October 1972 to 13 July 1973 (NODC Accession 7500548)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature, pressure, east-west current component, north-south current component, fluorescence, and other data were collected using moored buoy and profiling floats...

  9. Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from Surface underway observations using Barometric pressure sensor, Carbon dioxide (CO2) gas analyzer and other instruments from the Drifting Buoy in the Indian Ocean, South Atlantic Ocean and others from 2001-11-20 to 2007-05-08 (NODC Accession 0117495)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NODC Accession 0117495 includes Surface underway, biological, chemical, meteorological and physical data collected from Drifting Buoy in the Indian Ocean, South...

  10. Temperature and pressure data collected using drifting buoy and profiling floats from the North Atlantic Ocean in part of the IDOE/POLYMODE (International Decade of Ocean Exploration / combination of USSR POLYGON project and US MODE) from 10 January 1975 to 31 May 1981 (NODC Accession 8700121)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature and pressure data were collected using drifting buoy and profiling floats from CHAIN, GILLISS, OCEANUS, and ENDEAVOR from the North Atlantic Ocean from...

  11. Physical and chemical data from CTD, current meter, and buoy casts from the XIANG YANG HONG 14 and NOAA Ship OCEANOGRAPHER in the TOGA area of Pacific Ocean (30 N to 30 S) from 25 January 1987 to 24 October 1987 (NODC Accession 8700356)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical and chemical data were collected using current meter, CTD, and buoy casts in the TOGA area of the Pacific Ocean from NOAA Ship OCEANOGRAPHER and R/V...

  12. Temperature and upwelling / downwelling irradiance data from drifting buoy in the Southern Oceans as part of the Joint Global Ocean Flux Study/Southern Ocean (JGOFS/Southern Ocean) project, from 1994-12-25 to 1998-06-28 (NODC Accession 9900183)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature and upwelling / downwelling irradiance data were collected using drifting buoy in the Southern Oceans from December 25, 1994 to June 28, 1998. Data were...

  13. Temperature profile and pressure data collected using moored buoy from the Atlantic Ocean with support from the IDOE/POLYMODE (International Decade of Ocean Exploration / combination of USSR POLYGON project and US MODE) from 04 May to 18 December 1975 (NODC Accession 7601247)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile and pressure data were collected using moored buoy from the Atlantic Ocean from May 4, 1975 to December 18, 1975. Data were submitted by...

  14. Current speed data collected using drifting buoy in the TOGA area as part of the International Decade of Ocean Exploration / North Pacific Experiment / Hawaii-Tahiti Shuttle (IDOE/NORPAX/HITIS) project from 07 February 1979 to 17 December 1980 (NODC Accession 8200021)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This accession contains data from Drifting Buoys deployed during the Hawaii/Tahiti Shuttle Experiment between February 7, 1979 and December 17, 1980. Data consists...

  15. Dissolved inorganic carbon, total alkalinity, pH, and other variables collected from time series and profile observations using CTD, Niskin bottle,and other instruments near CenGOOS buoy off the coast of Mississippi in the Gulf of Mexico from 2012-10-15 to 2014-04-22 (NCEI Accession 0131199)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This archival package contains time series profile (discrete bottle) data that were collected at the GenGOOS buoy off the coast of Mississippi. The CenGOOS 3-m...

  16. Measuring bioavailable metals using diffusive gradients in thin films (DGT) and transplanted seaweed (Fucus vesiculosus), blue mussels (Mytilus edulis) and sea snails (Littorina saxatilis) suspended from monitoring buoys near a former lead-zinc mine in West Greenland.

    Science.gov (United States)

    Søndergaard, Jens; Bach, Lis; Gustavson, Kim

    2014-01-15

    Measuring loads of bioavailable metals is important for environmental assessment near mines and other industrial sources. In this study, a setup of monitoring buoys was tested to assess loads of bioavailable metals near a former Pb-Zn mine in West Greenland using transplanted seaweed, mussels and sea snails. In addition, passive DGT samplers were installed. After a 9-day deployment period, concentrations of especially Pb, Zn and Fe in the species were all markedly elevated at the monitoring sites closest to the mine. Lead concentrations in all three species and the DGT-Pb results showed a significant linear correlation. Zinc and Fe concentrations were less correlated indicating that the mechanisms for Zn and Fe accumulation in the three species are more complex. The results show that there is still a significant load of metals from the mine and that such buoys can be an adequate method to assess present loads of bioavailable metals. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Environmental preferences of tuna and non-tuna species associated with drifting fish aggregating devices (DFADs) in the Atlantic Ocean, ascertained through fishers' echo-sounder buoys

    Science.gov (United States)

    Lopez, Jon; Moreno, Gala; Lennert-Cody, Cleridy; Maunder, Mark; Sancristobal, Igor; Caballero, Ainhoa; Dagorn, Laurent

    2017-06-01

    Understanding the relationship between environmental variables and pelagic species concentrations and dynamics is helpful to improve fishery management, especially in a changing environment. Drifting fish aggregating device (DFAD)-associated tuna and non-tuna biomass data from the fishers' echo-sounder buoys operating in the Atlantic Ocean have been modelled as functions of oceanographic (Sea Surface Temperature, Chlorophyll-a, Salinity, Sea Level Anomaly, Thermocline depth and gradient, Geostrophic current, Total Current, Depth) and DFAD variables (DFAD speed, bearing and soak time) using Generalized Additive Mixed Models (GAMMs). Biological interaction (presence of non-tuna species at DFADs) was also included in the tuna model, and found to be significant at this time scale. All variables were included in the analyses but only some of them were highly significant, and variable significance differed among fish groups. In general, most of the fish biomass distribution was explained by the ocean productivity and DFAD-variables. Indeed, this study revealed different environmental preferences for tunas and non-tuna species and suggested the existence of active habitat selection. This improved assessment of environmental and DFAD effects on tuna and non-tuna catchability in the purse seine tuna fishery will contribute to transfer of better scientific advice to regional tuna commissions for the management and conservation of exploited resources.

  18. Experimental Comparison Between Mahoney and Complementary Sensor Fusion Algorithm for Attitude Determination by Raw Sensor Data of Xsens Imu on Buoy

    Science.gov (United States)

    Jouybari, A.; Ardalan, A. A.; Rezvani, M.-H.

    2017-09-01

    The accurate measurement of platform orientation plays a critical role in a range of applications including marine, aerospace, robotics, navigation, human motion analysis, and machine interaction. We used Mahoney filter, Complementary filter and Xsens Kalman filter for achieving Euler angle of a dynamic platform by integration of gyroscope, accelerometer, and magnetometer measurements. The field test has been performed in Kish Island using an IMU sensor (Xsens MTi-G-700) that installed onboard a buoy so as to provide raw data of gyroscopes, accelerometers, magnetometer measurements about 25 minutes. These raw data were used to calculate the Euler angles by Mahoney filter and Complementary filter, while the Euler angles collected by XSense IMU sensor become the reference of the Euler angle estimations. We then compared Euler angles which calculated by Mahoney Filter and Complementary Filter with reference to the Euler angles recorded by the XSense IMU sensor. The standard deviations of the differences between the Mahoney Filter, Complementary Filter Euler angles and XSense IMU sensor Euler angles were about 0.5644, 0.3872, 0.4990 degrees and 0.6349, 0.2621, 2.3778 degrees for roll, pitch, and heading, respectively, so the numerical result assert that Mahoney filter is precise for roll and heading angles determination and Complementary filter is precise only for pitch determination, it should be noted that heading angle determination by Complementary filter has more error than Mahoney filter.

  19. Tracing high time-resolution fluctuations in dissolved organic carbon using satellite and buoy observations: Case study in Lake Taihu, China

    Science.gov (United States)

    Huang, Changchun; Yunmei, Li; Liu, Ge; Guo, Yulong; Yang, Hao; Zhu, A.-xing; Song, Ting; Huang, Tao; Zhang, Mingli; Shi, Kun

    2017-10-01

    Field measurements of dissolved organic carbon (DOC) concentration and remote-sensing reflectance were conducted to develop a regional, empirical red-blue algorithm to retrieve surface DOC from Geostationary Ocean Color Imager (GOCI) data for Lake Taihu, China. The auxiliary data (in-situ observations of the optical properties and water quality, buoy measurements of hydrodynamic data and water chemical parameters) were used to investigate the spatial and temporal variations in DOC. GOCI was shown to be capable of successfully obtaining hourly variations in DOC, with a root mean square error percentage (RMSP) of 17.29% (RMSE = 0.69 mg/L) for the match-up data. The GOCI-derived DOC in Lake Taihu confirms that the highest DOC concentration is in northwest Lake Taihu, followed by Meiliang Bay, Gonghu Bay and northeast Lake Taihu. Hourly DOC variation is significant and presents a different trend for each lake segment due to the variety of influencing factors. Discharge of DOC from surrounding rivers is an important factor to the variation of DOC in northeast Lake Taihu. However, organic products of algae will be the primary contributor to DOC when algal bloom occurred. During the period of algal bloom, high DOC levels in Lake Taihu can lead to hypoxia when coupled with high temperatures and low disturbance.

  20. Fully nonlinear time-domain simulation of a backward bent duct buoy floating wave energy converter using an acceleration potential method

    Directory of Open Access Journals (Sweden)

    Kyoung-Rok Lee

    2013-12-01

    Full Text Available A floating Oscillating Water Column (OWC wave energy converter, a Backward Bent Duct Buoy (BBDB, was simulated using a state-of-the-art, two-dimensional, fully-nonlinear Numerical Wave Tank (NWT technique. The hydrodynamic performance of the floating OWC device was evaluated in the time domain. The acceleration potential method, with a full-updated kernel matrix calculation associated with a mode decomposition scheme, was implemented to obtain accurate estimates of the hydrodynamic force and displacement of a freely floating BBDB. The developed NWT was based on the potential theory and the boundary element method with constant panels on the boundaries. The mixed Eulerian-Lagrangian (MEL approach was employed to capture the nonlinear free surfaces inside the chamber that interacted with a pneumatic pressure, induced by the time-varying airflow velocity at the air duct. A special viscous damping was applied to the chamber free surface to represent the viscous energy loss due to the BBDB's shape and motions. The viscous damping coefficient was properly selected using a comparison of the experimental data. The calculated surface elevation, inside and outside the chamber, with a tuned viscous damping correlated reasonably well with the experimental data for various incident wave conditions. The conservation of the total wave energy in the computational domain was confirmed over the entire range of wave frequencies.

  1. Variability of CO2 fugacity at the western edge of the tropical Atlantic Ocean from the 8°N to 38°W PIRATA buoy

    Science.gov (United States)

    Bruto, Leonardo; Araujo, Moacyr; Noriega, Carlos; Veleda, Dóris; Lefèvre, Nathalie

    2017-06-01

    Hourly data of CO2 fugacity (fCO2) at 8°N-38°W were analyzed from 2008 to 2011. Analyses of wind, rainfall, temperature and salinity data from the buoy indicated two distinct seasonal periods. The first period (January to July) had a mean fCO2 of 378.9 μatm (n = 7512). During this period, in which the study area was characterized by small salinity variations, the fCO2 is mainly controlled by sea surface temperature (SST) variations (fCO2 = 24.4*SST-281.1, r2 = 0.8). During the second period (August-December), the mean fCO2 was 421.9 μatm (n = 11571). During these months, the region is subjected to the simultaneous action of (a) rainfall induced by the presence of the Intertropical Convergence Zone (ITCZ); (b) arrival of fresh water from the Amazon River plume that is transported to the east by the North Equatorial Countercurrent (NECC) after the retroflection of the North Brazil Current (NBC); and (c) vertical input of CO2-rich water due to Ekman pumping. The data indicated the existence of high-frequency fCO2 variability (periods less than 24 h). This high variability is related to two different mechanisms. In the first mechanism, fCO2 increases are associated to rapid increases in SST and are attributed to the diurnal cycle of solar radiation. In addition, low wind speed contributes to SST rising by inhibiting vertical mixing. In the second mechanism, fCO2 decreases are associated to SSS decreases caused by heavy rainfall.

  2. MOBY, A Radiometric Buoy for Performance Monitoring and Vicarious Calibration of Satellite Ocean Color Sensors: Measurement and Data Analysis Protocols. Chapter 2

    Science.gov (United States)

    Clark, Dennis K.; Yarbrough, Mark A.; Feinholz, Mike; Flora, Stephanie; Broenkow, William; Kim, Yong Sung; Johnson, B. Carol; Brown, Steven W.; Yuen, Marilyn; Mueller, James L.

    2003-01-01

    The Marine Optical Buoy (MOBY) is the centerpiece of the primary ocean measurement site for calibration of satellite ocean color sensors based on independent in situ measurements. Since late 1996, the time series of normalized water-leaving radiances L(sub WN)(lambda) determined from the array of radiometric sensors attached to MOBY are the primary basis for the on-orbit calibrations of the USA Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Japanese Ocean Color and Temperature Sensor (OCTS), the French Polarization Detection Environmental Radiometer (POLDER), the German Modular Optoelectronic Scanner on the Indian Research Satellite (IRS1-MOS), and the USA Moderate Resolution Imaging Spectrometer (MODIS). The MOBY vicarious calibration L(sub WN)(lambda) reference is an essential element in the international effort to develop a global, multi-year time series of consistently calibrated ocean color products using data from a wide variety of independent satellite sensors. A longstanding goal of the SeaWiFS and MODIS (Ocean) Science Teams is to determine satellite-derived L(sub WN)(labda) with a relative combined standard uncertainty of 5 %. Other satellite ocean color projects and the Sensor Intercomparison for Marine Biology and Interdisciplinary Oceanic Studies (SIMBIOS) project have also adopted this goal, at least implicitly. Because water-leaving radiance contributes at most 10 % of the total radiance measured by a satellite sensor above the atmosphere, a 5 % uncertainty in L(sub WN)(lambda) implies a 0.5 % uncertainty in the above-atmosphere radiance measurements. This level of uncertainty can only be approached using vicarious-calibration approaches as described below. In practice, this means that the satellite radiance responsivity is adjusted to achieve the best agreement, in a least-squares sense, for the L(sub WN)(lambda) results determined using the satellite and the independent optical sensors (e.g. MOBY). The end result of this approach is to

  3. Special Purpose Buoys - USACE IENC

    Data.gov (United States)

    Department of Homeland Security — These inland electronic Navigational charts (IENCs) were developed from available data used in maintenance of Navigation channels. Users of these IENCs should be...

  4. Direct Drive Wave Energy Buoy

    Energy Technology Data Exchange (ETDEWEB)

    Rhinefrank, Kenneth E. [Columbia Power Technologies, Inc.; Lenee-Bluhm, Pukha [Columbia Power Technologies, Inc.; Prudell, Joseph H. [Columbia Power Technologies, Inc.; Schacher, Alphonse A. [Columbia Power Technologies, Inc.; Hammagren, Erik J. [Columbia Power Technologies, Inc.; Zhang, Zhe [Columbia Power Technologies, Inc.

    2013-07-29

    The most prudent path to a full-scale design, build and deployment of a wave energy conversion (WEC) system involves establishment of validated numerical models using physical experiments in a methodical scaling program. This Project provides essential additional rounds of wave tank testing at 1:33 scale and ocean/bay testing at a 1:7 scale, necessary to validate numerical modeling that is essential to a utility-scale WEC design and associated certification.

  5. Direct Drive Wave Energy Buoy

    Energy Technology Data Exchange (ETDEWEB)

    Rhinefrank, Kenneth [Columbia Power Technologies, Inc., Charlottesville, VA (United States); Lamb, Bradford [Columbia Power Technologies, Inc., Charlottesville, VA (United States); Prudell, Joseph [Columbia Power Technologies, Inc., Charlottesville, VA (United States); Hammagren, Erik [Columbia Power Technologies, Inc., Charlottesville, VA (United States); Lenee-Bluhm, Pukha [Columbia Power Technologies, Inc., Charlottesville, VA (United States)

    2016-08-22

    This Project aims to satisfy objectives of the DOE’s Water Power Program by completing a system detailed design (SDD) and other important activities in the first phase of a utility-scale grid-connected ocean wave energy demonstration. In early 2012, Columbia Power (CPwr) had determined that further cost and performance optimization was necessary in order to commercialize its StingRAY wave energy converter (WEC). CPwr’s progress toward commercialization, and the requisite technology development path, were focused on transitioning toward a commercial-scale demonstration. This path required significant investment to be successful, and the justification for this investment required improved annual energy production (AEP) and lower capital costs. Engineering solutions were developed to address these technical and cost challenges, incorporated into a proposal to the US Department of Energy (DOE), and then adapted to form the technical content and statement of project objectives of the resulting Project (DE-EE0005930). Through Project cost-sharing and technical collaboration between DOE and CPwr, and technical collaboration with Oregon State University (OSU), National Renewable Energy Lab (NREL) and other Project partners, we have demonstrated experimentally that these conceptual improvements have merit and made significant progress towards a certified WEC system design at a selected and contracted deployment site at the Wave Energy Test Site (WETS) at the Marine Corps Base in Oahu, HI (MCBH).

  6. The Catalog of Event Data of the Operational Deep-ocean Assessment and Reporting of Tsunamis (DART) Stations at the National Data Buoy Center

    Science.gov (United States)

    Bouchard, R.; Locke, L.; Hansen, W.; Collins, S.; McArthur, S.

    2007-12-01

    DART systems are a critical component of the tsunami warning system as they provide the only real-time, in situ, tsunami detection before landfall. DART systems consist of a surface buoy that serves as a position locater and communications transceiver and a Bottom Pressure Recorder (BPR) on the seafloor. The BPR records temperature and pressure at 15-second intervals to a memory card for later retrieval for analysis and use by tsunami researchers, but the BPRs are normally recovered only once every two years. The DART systems also transmit subsets of the data, converted to an estimation of the sea surface height, in near real-time for use by the tsunami warning community. These data are available on NDBC's webpages, http://www.ndbc.noaa.gov/dart.shtml. Although not of the resolution of the data recorded to the BPR memory card, the near real-time data have proven to be of value in research applications [1]. Of particular interest are the DART data associated with geophysical events. The DART BPR continuously compares the measured sea height with a predicted sea-height and when the difference exceeds a threshold value, the BPR goes into Event Mode. Event Mode provides an extended, more frequent near real-time reporting of the sea surface heights for tsunami detection. The BPR can go into Event Mode because of geophysical triggers, such as tsunamis or seismic activity, which may or may not be tsunamigenic. The BPR can also go into Event Mode during recovery of the BPR as it leaves the seafloor, or when manually triggered by the Tsunami Warning Centers in advance of an expected tsunami. On occasion, the BPR will go into Event Mode without any associated tsunami or seismic activity or human intervention and these are considered "False'' Events. Approximately one- third of all Events can be classified as "False". NDBC is responsible for the operations, maintenance, and data management of the DART stations. Each DART station has a webpage with a drop-down list of all

  7. Levels of Cd, Cu, Pb and V in marine sediments in the vicinity of the Single Buoy Moorings (SBM3) at Mina Al Fahal in the Sultanate of Oman.

    Science.gov (United States)

    Al-Husaini, Issa; Abdul-Wahab, Sabah; Ahamad, Rahmalan; Chan, Keziah

    2014-06-15

    Recently in the Sultanate of Oman, there has been a rapid surge of coastal developments. These developments cause metal contamination, which may affect the habitats and communities at and near the coastal region. As a result, a study was conducted to assess the level of metal contamination and its impact on the marine sediments in the vicinity of the Single Buoy Moorings 3 (SBM3) at Mina Al Fahal in the Sultanate of Oman. Marine subtidal sediment samples were collected from six different stations of the SBM3 for the period ranging from June 2009 to April 2010. These samples were then analyzed for their level and distribution of the heavy metals of cadmium (Cd), copper (Cu), lead (Pb) and vanadium (V). Overall, low concentrations of all four heavy metals were measured from the marine sediments, indicating that the marine at SBM3 is of good quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Member states buoy up beleagured EMBL

    CERN Multimedia

    Balter, M

    1999-01-01

    EMBL's governing council, made up of delegates from the lab's 16 member countries, agreed in principle to meet the costs of a multimillion-dollar pay claim, the result of a recent ruling by the ILO in Geneva (1 page).

  9. Stockyard equipment buoyed by commodity prices

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2011-03-15

    Recent contract signings by heavy plant manufacturers show that the market for stockyard equipment is holding its own, despite continued economic uncertainty worldwide. Development by major manufacturers of stacker/reclaimers and bucket wheel excavators are reported. 1 photo.

  10. Satellite-Respondent Buoys Identify Ocean Debris

    Science.gov (United States)

    2009-01-01

    NASA operates a series of Earth-observing satellites, which help scientists learn more about our home planet. Through partnerships with universities and other government agencies, like the National Oceanic and Atmospheric Administration (NOAA), the Space Agency helps scientists around the world capture precise movements of the Earth s crust to learn more about the underground processes related to earthquakes and volcanic eruptions, create accurate assessments of wind resources for future energy use, and preserve endangered species by generating much-needed data about their environments. This work, done primarily from space with satellites using a variety of complex instruments to take readings of the surface below, generates leagues of valuable data that aid scientists on the ground - or in some cases on the water. As much of the Earth is covered in water liquid, frozen, saltwater, or fresh much of NASA s remote sensing work focuses on the oceans and their health. This valuable, mammoth (yet fragile) resource provides insight into the overall health of our planet, as water, in addition to being abundant, is a key ingredient to all known life on Earth. As part of its ocean-observing work, NASA partnered with NOAA and private industry to develop remote sensing technologies for protecting the seas of the North Pacific from a nefarious and pervasive problem: derelict fishing gear.

  11. The Effects of Buoy Density and Buoy Flashing Pattern on Steering Through a Channel Bend,

    Science.gov (United States)

    1981-08-01

    Given our ecological of a seemingly difficult to maneuver vessel such as a VLCC nitche , which we occupy, our visual systems have evolved with an enormous...However, when the avoided. hua stransplanted from his nitche to either air or sea mnofthe stimulus arrays which we normally deal with In conclusion, the

  12. TAO/TRITON, RAMA, and PIRATA Buoys, Daily, Relative Humidity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily Relative Humidity data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  13. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Relative Humidity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Relative Humidity data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  14. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Relative Humidity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Relative Humidity data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  15. Direct Drive Wave Energy Buoy – 33rd scale experiment

    Energy Technology Data Exchange (ETDEWEB)

    Rhinefrank, Kenneth E. [Columbia Power Technologies, Inc.; Lenee-Bluhm, Pukha [Columbia Power Technologies, Inc.; Prudell, Joseph H. [Columbia Power Technologies, Inc.; Schacher, Alphonse A.; Hammagren, Erik J.; Zhang, Zhe [Columbia Power Technologies, Inc.

    2013-07-29

    Columbia Power Technologies (ColPwr) and Oregon State University (OSU) jointly conducted a series of tests in the Tsunami Wave Basin (TWB) at the O.H. Hinsdale Wave Research Laboratory (HWRL). These tests were run between November 2010 and February 2011. Models at 33rd scale representing Columbia Power’s Manta series Wave Energy Converter (WEC) were moored in configurations of one, three and five WEC arrays, with both regular waves and irregular seas generated. The primary research interest of ColPwr is the characterization of WEC response. The WEC response will be investigated with respect to power performance, range of motion and generator torque/speed statistics. The experimental results will be used to validate a numerical model. The primary research interests of OSU include an investigation into the effects of the WEC arrays on the near- and far-field wave propagation. This report focuses on the characterization of the response of a single WEC in isolation. To facilitate understanding of the commercial scale WEC, results will be presented as full scale equivalents.

  16. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Buoyancy Flux

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Buoyancy Flux data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  17. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Salinity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Salinity data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean, http://www.pmel.noaa.gov/tao/rama/),...

  18. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Wind

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Wind data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean, http://www.pmel.noaa.gov/tao/rama/), and...

  19. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Dynamic Height

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Dynamic Height data (a measure of the elevation of the sea level, calculated by integrating the specific volume anomaly of the sea water...

  20. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Position

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Position data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean, http://www.pmel.noaa.gov/tao/rama/),...

  1. Stationary Tether Device for Buoy Apparatus and System for Using

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A rigid, neutrally buoyant hydrodynamicaly-faired tether and associated fastening hardware that loosely holds a bathymetric float at a predetermined distance from a...

  2. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Longwave Radiation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Incoming Longwave Radiation data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  3. Development of the Sensor for Environmental Assessment (SEA Buoy)

    Science.gov (United States)

    2014-01-01

    current profiler. The phased array current profiler was an Edo Corporation development program intended to provide an ocean current profiler with a...of designs, the final implementation incorporates a Navmar designed variable gain amplifier for hydrophone outputs to the signal conditioning and

  4. SpaceBuoy: A University Nanosat Space Weather Mission

    Science.gov (United States)

    2012-03-26

    United Launch Alliance, SpaceX , Sypes Canyon Communications, and Bridger Photonics, while others have chosen to continue their education in graduate...data are available. As can be seen, the Space Science and Engineering Laboratory places high value on public outreach and will continue to do so

  5. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Temperature

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Temperature data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean, http://www.pmel.noaa.gov/tao/rama/),...

  6. TAO/TRITON, RAMA, and PIRATA Buoys, Daily, Currents

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily Currents data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean, http://www.pmel.noaa.gov/tao/rama/), and...

  7. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Dynamic Height

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Dynamic Height data (a measure of the elevation of the sea level, calculated by integrating the specific volume anomaly of the sea water...

  8. Buoyed by Divestment Victories, Activists Protest CIA Recruiting.

    Science.gov (United States)

    Greene, Elizabeth

    1987-01-01

    Inspired by victories in the South African divestment movement and fueled by the Iran-Contra affair, student activists are intensifying the perennial crusade to ban Central Intelligence Agency (CIA) recruiters from campuses, but other students dislike the interference and the CIA says that job-seekers are increasing. (MSE)

  9. Instrumented Full Scale Tests of a Drifting Buoy and Drogue

    Science.gov (United States)

    1975-12-01

    of very high accuracy (better than 200 feet) owing to the phase-tracking system employed. Both an automatic Epsco and a Simrad/Internav Loran C system...from.the output of a Simrad/Internav LORAN C navi- gator. A similar Epsco system was also employed while coupled to a separate antenna. The Epsco unit gave

  10. UpTempO Buoys for Understanding and Predictions

    Science.gov (United States)

    2017-04-01

    that the most popular global gridded SST data set in use today (NOAA’s dOISST a.k.a. “Reynolds SST”) is overly warm at higher ice concentrations...understand the evolution of heat content in the upper Arctic Ocean within the Seasonal Ice Zone (SIZ), both seasonally during summer warming and...fall cooling, and interannually as sea ice retreats and the warming season lengthens. The effort was a contribution to the multi-investigator ONR

  11. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Wind Stress

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Wind Stress data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  12. Moored surface buoy observations of the diurnal warm layer

    KAUST Repository

    Prytherch, J.

    2013-09-01

    An extensive data set is used to examine the dynamics of diurnal warming in the upper ocean. The data set comprises more than 4700 days of measurements at five sites in the tropics and subtropics, obtained from surface moorings equipped to make comprehensive meteorological, incoming solar and infrared radiation, and high-resolution subsurface temperature (and, in some cases, velocity) measurements. The observations, which include surface warmings of up to 3.4°C, are compared with a selection of existing models of the diurnal warm layer (DWL). A simple one-layer physical model is shown to give a reasonable estimate of both the magnitude of diurnal surface warming (model-observation correlation 0.88) and the structure and temporal evolution of the DWL. Novel observations of velocity shear obtained during 346 days at one site, incorporating high-resolution (1 m) upper ocean (5-15 m) acoustic Doppler current profile measurements, are also shown to be in reasonable agreement with estimates from the physical model (daily maximum shear model-observation correlation 0.77). Physics-based improvements to the one-layer model (incorporation of rotation and freshwater terms) are discussed, though they do not provide significant improvements against the observations reported here. The simplicity and limitations of the physical model are used to discuss DWL dynamics. The physical model is shown to give better model performance under the range of forcing conditions experienced across the five sites than the more empirical models. ©2013. American Geophysical Union. All Rights Reserved.

  13. 33 CFR 144.01-25 - Ring life buoys.

    Science.gov (United States)

    2010-07-01

    ... water light will pull free of the bracket. ... have a water light of an approved automatic electric type constructed in accordance with 46 CFR Subpart 161.010. A water light constructed in accordance with former 46 CFR Subpart 161.001 that was installed...

  14. NOAA marine environmental buoy data from the National Data Buoy Center for 2001-09 (NODC Accession 0000595)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Wind wave spectra and meteorological data were collected from the US East/West coasts, South Pacific, Gulf of Mexico, Great Lakes, and other locations. Data were...

  15. NOAA marine environmental buoy data from the National Data Buoy Center for 2002-02 (NODC Accession 0000634)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Wind wave spectra and meteorological data were collected from the US East/West coasts, South Pacific, Gulf of Mexico, Great Lakes and other locations. Data were...

  16. NOAA marine environmental buoy data from the National Data Buoy Center for 2001-08 (NODC Accession 0000588)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Wind wave spectra and meteorological data were collected from the US East/West coasts, South Pacific, Gulf of Mexico, Great Lakes and other locations. Data were...

  17. NOAA marine environmental data from moored buoys were acquired from the National Data Buoy Center (NDBC) from 01 December 2000 to 31 December 2000 (NODC Accession 0000380)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Physical and other data were collected from fixed platforms in the Gulf of Mexico, Coastal Waters of Western U.S., Great Lakes, North American Coastline-North, and...

  18. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Relative Humidity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Relative Humidity data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  19. From boots to buoys: promises and challenges of dielectric elastomer energy harvesting

    Science.gov (United States)

    Kornbluh, Roy D.; Pelrine, Ron; Prahlad, Harsha; Wong-Foy, Annjoe; McCoy, Brian; Kim, Susan; Eckerle, Joseph; Low, Tom

    2011-04-01

    Dielectric elastomers offer the promise of energy harvesting with few moving parts. Power can be produced simply by stretching and contracting a relatively low-cost rubbery material. This simplicity, combined with demonstrated high energy density and high efficiency, suggests that dielectric elastomers are promising for a wide range of energy harvesting applications. Indeed, dielectric elastomers have been demonstrated to harvest energy from human walking, ocean waves, flowing water, blowing wind, and pushing buttons. While the technology is promising, there are challenges that must be addressed if dielectric elastomers are to be a successful and economically viable energy harvesting technology. These challenges include developing materials and packaging that sustains long lifetime over a range of environmental conditions, design of the devices that stretch the elastomer material, as well as system issues such as practical and efficient energy harvesting circuits. Progress has been made in many of these areas. We have demonstrated energy harvesting transducers that have operated over 5 million cycles. We have also shown the ability of dielectric elastomer material to survive for months underwater while undergoing voltage cycling. We have shown circuits capable of 78% energy harvesting efficiency. While the possibility of long lifetime has been demonstrated at the watt level, reliably scaling up to the power levels required for providing renewable energy to the power grid or for local use will likely require further development from the material through to the systems level.

  20. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Total Heat Flux

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Total Heat Flux data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  1. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Barometric (Air) Pressure

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Barometric (Air) Pressure data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  2. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Total Heat Flux

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Total Heat Flux data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  3. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Sigma-Theta

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Sigma-Theta (Potential Density Anomaly) data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  4. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, 1989-present, Wind Stress

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Wind Stress data from the TAO/TRITON (Pacific Ocean, https://www.pmel.noaa.gov/gtmba/ ), RAMA (Indian Ocean,...

  5. Technology Refresh of NOAA’s Tropical Atmosphere Ocean (TAO) Buoy System

    Science.gov (United States)

    2006-09-01

    which utilizes an MSP430 microcontroller, controls the collecting of all analog data from sensors. In addition to collecting analog data the...Embedded Workbench and firmware developed for the MSP430 using ANSI C also compiled with IAR Systems Embedded Workbench. The GUI test and

  6. Assessment of wave modeling results with buoy and altimeter deep water waves for a summer monsoon

    Digital Repository Service at National Institute of Oceanography (India)

    Sudheesh, K.; Vethamony, P.; Babu, M.T.; Jayakumar, S.

    32G99G97G112G97G98G105G108G105G116G121G46G32G87G97G118G101G32G104G101G105G103G104G116G115G44G32G105G110G32G103G101G110G101G114G97G108G44G32G97G114G101G32G104G105G103G104G101G114 G105G110G32G116G104G101G32G65G114G97G98G105G97G110G32G83G101G97G32G116G... 32G97G108G108G32G108G111G99G97G116G105G111G110G115G32G40G70G105G103G46G32G54G41G46 G67G111G109G112G97G114G105G115G111G110G32G111G102G32G83G87G72G115G32G102G114G111G109G32G109G111G100G101G108G32G97G110G100G32G84G79G80G69G88G32G102G111G114G32G116G104G...

  7. Development of an Indian Ocean moored buoy array for climate studies

    Digital Repository Service at National Institute of Oceanography (India)

    McPhaden, M.J.; Kuroda, Y.; Murty, V.S.N.

    Environmental Laboratory (PMEL) in collaboration with NIO and the Indian Department of Ocean Development (DOD) deployed 4 ATLAS moorings and 1 ADCP mooring between 80°-90°E from the Ocean Research Vessel Sagar Kanya. These five mooring sites were serviced...

  8. The M3A multi-sensor buoy network of the Mediterranean Sea

    OpenAIRE

    K. NITTIS; C. Tziavos; R. Bozzano; Cardin, V.; Thanos, Y.; Petihakis, G.; Schiano, M. E.; Zanon, F.

    2006-01-01

    A network of three multi-sensor timeseries stations able to deliver real time physical and biochemical observations of the upper thermocline has been developed for the needs of the Mediterranean Forecasting System during the MFSTEP project. They follow the experience of the prototype M3A system that was developed during the MFSPP project and has been tested during a pilot pre-operational period of 22 months (2000–2001). The systems integrate sensors for physical (temperature, salini...

  9. Parametric estimation in the wave buoy analogy - an elaborated approach based on energy considerations

    DEFF Research Database (Denmark)

    Montazeri, Najmeh; Nielsen, Ulrik Dam

    2014-01-01

    the ship’s wave-induced responses based on different statistical inferences including parametric and non-parametric approaches. This paper considers a concept to improve the estimate obtained by the parametric method for sea state estimation. The idea is illustrated by an analysis made on full-scale...

  10. Development of Buoy Mounted Hydrocarbon Vapor Sensors for Use in Local Area Pollution Surveillance Systems

    Science.gov (United States)

    1975-07-01

    10. Emplex + Atlox 1045A 11. Activated Charcoal + NaCl + Atlox 1045A 12. Drenn Shampoo (Proctor and Gamble) 13. Hyamlne 1622 (p...of this report. So far as we are aware, Figaro Engineering, Inc. was the first company to make available commercial sensors of this type. However...the pollutant vapors. The standard test procedure called for exposing the sensor first to water vapor In a closed container and then to the

  11. General Analysis of Directional Ocean Wave Data from Heave Pitch Roll Buoys

    Directory of Open Access Journals (Sweden)

    Donald M. Wiberg

    1984-04-01

    Full Text Available There are many practical applications of lattice form recursive linear least square algorithms (called lattices for short in signal processing, communications, and control systems. The goal of this tutorial is to help practicing engineers to decide if lattices are appropriate for their particular projects.

  12. Study of the directional spectrum of ocean waves using array, buoy and radar measurements

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.

    of frequency from actual field measurements at the Coastal Engineering Research Center’s (CERC) Field Research Facility at Duck, North Carolina, USA, using polygonal and linear arrays respectively in case of swell, sea and surf beat as the directional spread...

  13. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Downgoing Shortwave Radiation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Downgoing Shortwave Radiation data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  14. Fire Safety Analysis of the 180’ WLB Seagoing Buoy Tender.

    Science.gov (United States)

    1998-10-01

    6 M 6666l 0 .010 0 tn- - - - -9 -9 -9 -99 9CicG tn m N 4 J~ 2 O-8I ni 0l dd dd W 010 Td OC -1--- --- O 0ýýq0 0W -- E 0 a) tn In It P It to to s aOa a...Time JEB (x 1000) Plan ID Time lEB (x 1000) Zero-Str 3-9-0-E 3 0.29 7.93 2-9-0-Q 4 0.06 1.54 ext. blkhd. 4 0.06 1.54 ext. blkhd. 4 0.06 1.54 4-30-2-W...Barrier to Adjacent Room ------- FRI P(Loss) RFLIFFS Adj. Room Fail P(Loss) RFLIFFS Opening/ Plan ID Time JEB (x 1000) Plan ID Time

  15. Sea Ice Mass Balance Buoys (IMBs): First Results from a Data Processing Intercomparison Study

    Science.gov (United States)

    Hoppmann, Mario; Tiemann, Louisa; Itkin, Polona

    2017-04-01

    IMBs are autonomous instruments able to continuously monitor the growth and melt of sea ice and its snow cover at a single point on an ice floe. Complementing field expeditions, remote sensing observations and modelling studies, these in-situ data are crucial to assess the mass balance and seasonal evolution of sea ice and snow in the polar oceans. Established subtypes of IMBs combine coarse-resolution temperature profiles through air, snow, ice and ocean with ultrasonic pingers to detect snow accumulation and ice thermodynamic growth. Recent technological advancements enable the use of high-resolution temperature chains, which are also able to identify the surrounding medium through a „heating cycle". The temperature change during this heating cycle provides additional information on the internal properties and processes of the ice. However, a unified data processing technique to reliably and accurately determine sea ice thickness and snow depth from this kind of data is still missing, and an unambiguous interpretation remains a challenge. Following the need to improve techniques for remotely measuring sea ice mass balance, an international IMB working group has recently been established. The main goals are 1) to coordinate IMB deployments, 2) to enhance current IMB data processing and -interpretation techniques, and 3) to provide standardized IMB data products to a broader community. Here we present first results from two different data processing algorithms, applied to selected IMB datasets from the Arctic and Antarctic. Their performance with regard to sea ice thickness and snow depth retrieval is evaluated, and an uncertainty is determined. Although several challenges and caveats in IMB data processing and -interpretation are found, such datasets bear great potential and yield plenty of useful information about sea ice properties and processes. It is planned to include many more algorithms from contributors within the working group, and we explicitly invite other interested scientists to join this promising effort.

  16. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Evaporation Minus Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Evaporation Minus Precipitation data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  17. Sub-inertial variability in the Cretan Sea from the M3A buoy

    Directory of Open Access Journals (Sweden)

    V. Cardin

    2003-01-01

    Full Text Available One year of continuous records of temperature, salinity data at various depths, and currents obtained from by an upward looking acoustic Doppler current profiler (ADCP moored at a site in the Cretan Sea were analyzed. Temperature and salinity data revealed the influence of a multi-scale circulation pattern prevailing in this area. This pattern consists of mesoscale cyclonic and anticyclonic vortices moving together as a dipole, and inducing downwelling and upwelling in the water column. The dipole movements, which control the circulation in the area, have been evidenced from horizontal current variability in the upper 250 m. The basin-scale circulation also shows a prominent seasonal variability. The Empirical Orthogonal Function analysis applied to either zonal or meridional components of the currents, confirmed the prevalence of a depth-independent mode over the baroclinic-like one for the whole period of measurements and for both current components. Nevertheless, the depth-dependent structure indicated the out-of-phase behaviour of the upper 250 m layer with respect to the deeper one. The first mode of the temperature EOF analysis, which accounts for most of the variance, represents the seasonal heating of the water column being principally associated with the surface mixed layer at the level of the seasonal thermocline. Key words. Oceanography: physical (currents, eddies and mesoscale processes, general circulation

  18. Sub-inertial variability in the Cretan Sea from the M3A buoy

    Directory of Open Access Journals (Sweden)

    V. Cardin

    Full Text Available One year of continuous records of temperature, salinity data at various depths, and currents obtained from by an upward looking acoustic Doppler current profiler (ADCP moored at a site in the Cretan Sea were analyzed. Temperature and salinity data revealed the influence of a multi-scale circulation pattern prevailing in this area. This pattern consists of mesoscale cyclonic and anticyclonic vortices moving together as a dipole, and inducing downwelling and upwelling in the water column. The dipole movements, which control the circulation in the area, have been evidenced from horizontal current variability in the upper 250 m. The basin-scale circulation also shows a prominent seasonal variability. The Empirical Orthogonal Function analysis applied to either zonal or meridional components of the currents, confirmed the prevalence of a depth-independent mode over the baroclinic-like one for the whole period of measurements and for both current components. Nevertheless, the depth-dependent structure indicated the out-of-phase behaviour of the upper 250 m layer with respect to the deeper one. The first mode of the temperature EOF analysis, which accounts for most of the variance, represents the seasonal heating of the water column being principally associated with the surface mixed layer at the level of the seasonal thermocline.

    Key words. Oceanography: physical (currents, eddies and mesoscale processes, general circulation

  19. Arctic Ocean Drift Tracks from Ships, Buoys and Manned Research Stations, 1872-1973

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Thirty-four drift tracks in the Arctic Ocean pack ice are collected in a unified tabular data format, one file per track. Data are from drifting ships, manned...

  20. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Wind Stress

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Wind Stress data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean, http://www.pmel.noaa.gov/tao/rama/),...

  1. Wireless Sensor Buoys for Perimeter Security of Military Vessels and Seabases

    Science.gov (United States)

    2015-12-01

    RELATED TECHNOLOGICAL APPLICATIONS ..................................14 1. Wireless Sensor Networks...methods to protect property and assets. Some critical facilities such as military bases, nuclear power plants , and borders have begun to...base, nuclear power plant , border, or other secured area” [1]. The concept of maintaining a secure perimeter is highly relevant in modern times

  2. Comparison of wind data from QuikSCAT and buoys in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Satheesan, K.; Sarkar, A.; Parekh, A.; RameshKumar, M.R.; Kuroda, Y.

    Indian Ocean. The effects of sea surface temperature and air-sea temperature difference on wind residuals were also investigated. In general, QuikSCAT is found to overestimate the winds. It is speculated that low wind speed during rain-free conditions...

  3. TAO/TRITON, RAMA, and PIRATA Buoys, Daily, 1989-present, Wind Stress

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily Wind Stress data from the TAO/TRITON (Pacific Ocean, https://www.pmel.noaa.gov/gtmba/ ), RAMA (Indian Ocean,...

  4. TAO/TRITON, RAMA, and PIRATA Buoys, Daily, Evaporation Minus Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily Evaporation Minus Precipitation data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  5. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, Net Longwave Radiation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly Net Longwave Radiation data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  6. TAO/TRITON, RAMA, and PIRATA Buoys, Daily, Sea Surface Salinity

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily Sea Surface Salinity data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  7. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Evaporation Minus Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Evaporation Minus Precipitation data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  8. Buoy-Tracking Trials Using Decca 916 Radar and Three Types of Reflector.

    Science.gov (United States)

    1985-06-01

    all stages of the tri 1. Mr Trevor Donald of the Bureau of Meteorology , Sydney, provided the Umb.awind fabric reflectors used in the trial. Mr Keith...Donald, Bureau of Meteorology , No 2 Goulburn St., Darlinghurst 2010 34" Mr J.W. Hill 35 Dr M.R. Battaglia 36 AGPS 37 DWSRL 38 %AN- 06

  9. Thirty years of research and development of Lagrangian buoys at the Institute of Marine Sciences

    Directory of Open Access Journals (Sweden)

    Emilio García-Ladona

    2016-09-01

    Full Text Available Since the mid-1980s, physical oceanographers at the Institute of Marine Sciences have been involved in the use of Lagrangian drifters as a complementary technology for their oceanographic research. As Lagrangian observations became more feasible, these researchers continued developing their own drifters in what was to be the seed of current technological activities at the Physical and Technological Oceanography Department. In this paper we overview the work done during the last 30 years with special focus on Lagrangian developments from the initial activities to the latest developments. In addition to basic oceanography research applications, Lagrangian technological developments include prototypes for measuring surface and subsurface ocean properties, for tracking purposes in search and rescue operations and pollution events, and for monitoring ice motion and thickness in the Arctic. The paper emphasizes original and unpublished technical aspects related to the latest developments.

  10. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, 20C Isotherm Depth

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day 20C Isotherm Depth data (the depth at which the ocean temperature is 20C) from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/),...

  11. TAO/TRITON, RAMA, and PIRATA Buoys, Daily, 20C Isotherm Depth

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily 20C Isotherm Depth data (the depth at which the ocean temperature is 20C) from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/),...

  12. TAO/TRITON, RAMA, and PIRATA Buoys, Quarterly, 20C Isotherm Depth

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has quarterly 20C Isotherm Depth data (the depth at which the ocean temperature is 20C) from the TAO/TRITON (Pacific Ocean,...

  13. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, 20C Isotherm Depth

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly 20C Isotherm Depth data (the depth at which the ocean temperature is 20C) from the TAO/TRITON (Pacific Ocean,...

  14. Development of a virtual wave buoy system for the Port of Cape Town, South Africa

    CSIR Research Space (South Africa)

    Rossouw, Marius

    2005-07-01

    Full Text Available The Port of Cape Town is located in Table Bay on the south-west coast of South Africa. Since the port experiences advese weather conditions, especially during the winter period, the monitoring of marine weather and wave conditions forms an integral...

  15. TAO/TRITON, RAMA, and PIRATA Buoys, Monthly, Barometric (Air) Pressure

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has monthly Barometric (Air) Pressure data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  16. TAO/TRITON, RAMA, and PIRATA Buoys, 5-Day, Barometric (Air) Pressure

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has 5-day Barometric (Air) Pressure data from the TAO/TRITON (Pacific Ocean, http://www.pmel.noaa.gov/tao/), RAMA (Indian Ocean,...

  17. Seasonal Ice Mass-Balance Buoys: Adapting Tools to the Changing Arctic

    Science.gov (United States)

    2011-01-01

    challenge for ice mass-balance observations is the objective of this study. An overview of the current IMB design is illustrated in Figure 1. The IMB...Arctic warming through the Fram Strait: oceanic heat transport from 3 years of measurements. J. Geophys. Res., 109(C6), C06026. (10.1029/2003JC001823

  18. Temperature profile data from moored buoy, profiling floats, TAO buoy, and XBT casts in a world-wide distribution from 12 June 2000 to 29 December 2000 (NODC Accession 0000404)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile data were collected from multiple ships from June 12, 2000 to December 29, 2000. Data were submitted by Marine Environmental Data Service (MEDS)...

  19. Temperature profile data from moored buoy, profiling floats, TAO buoy, and XBT casts in a world-wide distribution from 14 April 2000 to 20 February 2001 (NODC Accession 0000406)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile data were collected from multiple ships from April 14, 2000 to February 20, 2001. Data were submitted by Marine Environmental Data Service (MEDS)...

  20. Temperature profile and other data from moored buoy, profiling floats, TAO buoy, and XBT casts in a world-wide distribution from 01 June 2000 to 29 November 2000 (NODC Accession 0000403)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile and other data were collected from multiple ships from June 1, 2000 to November 29, 2000. Data were submitted by Marine Environmental Data...

  1. Temperature profile data from moored buoy, profiling floats, TAO buoy, and XBT casts in a world-wide distribution from 21 October 2000 to 31 January 2001 (NODC Accession 0000405)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Temperature profile data were collected from multiple ships from October 21, 2000 to January 31, 2001. Data were submitted by Marine Environmental Data Service...

  2. Drifting buoy data from SVP Drifting Argos Buoys, deployed by the NOAA Coral Reef Ecosystems Division (CRED) near Guam and the Commonwealth of the Northern Marianas Islands, 2003-2006 (NODC Accession 0067473)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data collection includes data from multiple SVP drifters deployed in the region of the Marianas Archipelago to assess ocean currents and sea surface...

  3. Survey of Technology with Possible Applications to United States Coast Guard Buoy Tenders. Volume 2. Literature Abstracts.

    Science.gov (United States)

    1987-09-01

    Transportation In the Interest of Information i exchange . The United States Government assumes no liability for Its contents or use thereof. The United States...Boat, March 1983, pp 81, 121-2, (’Point Bravo, Chaleur , Liberty, & Normandy’) "Halter’s awesome ’Acadian Commander", The Work Boat, January ". 1982

  4. Modification of wind-wave model WAM and its verification against buoy data in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Polnikov, V.G.; Samiksha, V.; Rashmi, R.; Pogarskii, F.; Sudheesh, K.; Vethamony, P.

    0 . 5 . 1 6 0 5 / 7 6 3 8 9 - . / 9 : 6 3 - 3 5 / 0 4 ; 8 9 < 0 1 0 2 3 4 0 . 5 3 = 3 0 5 ; 4 > ? . @ / 3 4 3 0 5 4 A 9 0 5 / 0 3 5 . 2 9 3 5 B C D E F G C H I J K L M I N O P F G Q R O I J S I T O Q R P F I T U I B C V O W Q G F F I X Y I N Z [ R... C E i b C W \\ · O P f D \\ I x O Q Q \\ D P O E E \\ _ O D K I j k s ½ n B R F D D F f Q I j k s s I j k l ¾ n ] C P \\ E \\ _ O D K I j k k m n o O E Q Q \\ E I p q q m u K ¿ À Á ¿ Â Ã Ä Å Æ Æ Ç È É Ê Ë Ä Ì Í Î Ï Ð Ñ Ò Ó Ô Õ Ó Ö × × Ø Ù Ä Ú Û Ü Ý Ö Ï Æ Å...

  5. A Hyperspectral Tethered Spectral Radiometer Buoy: Ocean Color Algorithm Development in Estuaries, Coastal Waters, and Marginal Seas

    Science.gov (United States)

    1998-09-30

    reflectance was determined for mineralic sands and eel grass meadows (Werdell and Roesler, in prep.). A second model was developed to quantify the contributions...reflectance measured with a TSRB in clear Bahamian waters overlying carbonate sand and turtle grass and in turbid Long Island Sound waters overlying mineralic ...column was too extreme. However, with only one channel of derived benthic reflectance signal, the change from an eel grass meadow to bare mineralic

  6. Estimating the Underwater Diffuse Attenuation Coefficient with a Low-Cost Instrument: The KdUINO DIY Buoy

    Directory of Open Access Journals (Sweden)

    Raul Bardaji

    2016-03-01

    Full Text Available A critical parameter to assess the environmental status of water bodies is the transparency of the water, as it is strongly affected by different water quality related components (such as the presence of phytoplankton, organic matter and sediment concentrations. One parameter to assess the water transparency is the diffuse attenuation coefficient. However, the number of subsurface irradiance measurements obtained with conventional instrumentation is relatively low, due to instrument costs and the logistic requirements to provide regular and autonomous observations. In recent years, the citizen science concept has increased the number of environmental observations, both in time and space. The recent technological advances in embedded systems and sensors also enable volunteers (citizens to create their own devices (known as Do-It-Yourself or DIY technologies. In this paper, a DIY instrument to measure irradiance at different depths and automatically calculate the diffuse attenuation Kd coefficient is presented. The instrument, named KdUINO, is based on an encapsulated low-cost photonic sensor and Arduino (an open-hardware platform for the data acquisition. The whole instrument has been successfully operated and the data validated comparing the KdUINO measurements with the commercial instruments. Workshops have been organized with high school students to validate its feasibility.

  7. Continuous bottom temperature measurements in strategic areas of the Florida Reef Tract at Looe Buoy, 1988 - 2004 (NODC Accession 0002616)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This ongoing project began in 1988. A total of 38 subsurface recording thermographs have been deployed in the Florida Keys National Marine Sanctuary (FKNMS)and at...

  8. Continuous bottom temperature measurements in strategic areas of the Florida Reef Tract at Looe Buoy, 1988 - 2004 (NODC Accession 0002616)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The purpose of this project is to document bottom seawater temperature in strategic areas of the Florida Reef Tract on a continuing basis and make that information...

  9. Verification of Geosat sea surface topography in the Gulf Stream extension with surface drifting buoys and hydrographic measurements

    Science.gov (United States)

    Willebrand, J.; KäSe, R. H.; Stammer, D.; Hinrichsen, H.-H.; Krauss, W.

    1990-03-01

    Altimeter data from Geosat have been analyzed in the Gulf Stream extension area. Horizontal maps of the sea surface height anomaly relative to an annual mean for various 17-day intervals were constructed using an objective mapping procedure. The mean sea level was approximated by the dynamic topography from climatological hydrographic data. Geostrophic surface velocities derived from the composite maps (mean plus anomaly) are significantly correlated with surface drifter velocities observed during an oceanographie experiment in the spring of 1987. The drifter velocities contain much energy on scales less than 100 km which are not resolved in the altimetric maps. It is shown that the composite sea surface height also agrees well with ground verification from hydrographic data along sections in a triangle between the Azores, Newfoundland, and Bermuda, except in regions of high mean gradients.

  10. NODC Standard Format Ocean Wind Time Series from Buoys (F101) Data (1975-1985) (NCEI Accession 0014194)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This file type contains time series measurements of wind and other surface meteorological parameters taken at fixed locations. The instrument arrays may be deployed...

  11. Study of the velocity field of surface currents in the South Atlantic Ocean derived from drifting buoy data

    Directory of Open Access Journals (Sweden)

    Eduardo Marone

    2011-12-01

    Full Text Available A total of 1442 data series of 996 drifters from public and research databases were analyzed in order to decompose and to estimate the velocity field of surface currents between 30º S and 50º S in the South Atlantic Ocean, with emphasis on the South Atlantic Current (SAC. The SAC is the southernmost limit of the South Atlantic Subtropical Gyre and presents strong interaction with other currents such as the Antarctic Circumpolar Current (ACC. The data were processed according to the Taylor's theory. The velocity field map for the study area as well as the mean values of the current intensity and associated standard deviation are presented and discussed. The highest estimated values of the average current velocity are located at the origin of the SAC and at the Malvinas Current (MC. The mean intensity of the SAC is approximately 30 cm.s-1 and the highest intensity values are observed at its origin decaying towards east. The SAC comprises a system containing a main axis and two branches, north and south. The N-SAC feeds the Benguela Current and the S-SAC leaks to the east towards the Indian Ocean. The flow pattern observed for the SAC presents a meandering characteristics and high variability in the regions where it interacts with other currents and mesoscale features.

  12. Mooring Mechanics. A Comprehensive Computer Study. Volume II. Three Dimensional Dynamic Analysis of Moored and Drifting Buoy Systems

    Science.gov (United States)

    1976-12-01

    s-I for that mooring line (vertical excursion ᝼ m). Creep in the synthetic portion of the mooring line was also identified. In addition, we are...CHHABRA, 1973). In its present form no allowance for shrinkage or creep of the synthetic ropes is taken into account, but creep was identified in...T + Pc (aWw) (2.11) P9 Differentiating Te with respect to s 3Te aT Wa - Ww aPc as as 9 as aT - ( - Ww)cos3 (2.12) as Substituting (2.11) and (2.12

  13. Data from a directional waverider buoy off Kailua Bay, Windward Oahu during August 2000 - July 2004 (NODC Accession 0001660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Coastal Data Information Program (CDIP) is an extensive network for monitoring waves along the coastlines of the United States, with a strong emphasis on our...

  14. Directional wave and temperature data from seven buoys at Harvest, CA, 1995-2002 (NODC Accession 0000766)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Coastal Data Information Program (CDIP) is an extensive network for monitoring waves along the coastlines of the United States, with a strong emphasis on our...

  15. Evidence of zooplankton vertical migration from continuous Southern Adriatic buoy current-meter records (E2-M3A)

    Science.gov (United States)

    Ursella, Laura; Cardin, Vanessa; Batistić, Mirna

    2017-04-01

    The E2-M3A Station is deployed in the southern Adriatic Sea, at about 1200 m depth, in the center of the cyclonic gyre where deep convection process takes place, involving both the atmosphere and the ocean dynamics and forming new dense and oxygenated waters, thus triggering the solubility and the biological pump. In particular, the E2M3A is equipped with an upward looking 150 kHz RDI-Acoustic Doppler Current Profiler (ADCP) positioned between 265 and 320 m depth, with a vertical resolution of 5 m and a range of 250-300 m. The mooring line has been in water since November 2006, with an interruption from September 2010 until May 2011. ADCP backscattering signal is very useful in determining zooplankton distribution and variability at various time scales, including seasonal/annual behavior and diel vertical migration (DVM). From ADCP backscattering signal, backscattering strength (Sv) was calculated for the entire dataset. Sv permits to quantify qualitatively the scatters present in the water, i.e. the particulate and/or the phyto/zoo-plankton. Zooplankton distribution is dependent on phytoplankton presence and blooms, which on its own depend on nutrients availability (related to wind-induced vertical mixing), but also on sunlight. The variation in time of Sv together with vertical velocity allows for measuring DVM of zooplankton and its variability with seasons and years. Alternation of high and low values for Sv are present all year long with differences in intensities in particular in the surface layer. Quite high values for Sv are found in spring and summer; in spring they are found along a large part of the water column, while in summer they are detected prevalently in the upper part of the measurement range. This behavior is related to the conditions of the water column, i.e. mixing and nutrients availability, which influence phytoplankton blooms and therefore zooplankton growing and movements. Correlating Net Primary Production obtained from model and Mixed Layer Depth, a delay of two months in the bloom of phytoplankton with respect to deepest mixing is found. Power Spectra of Sv show a major peak at 24 h that corresponds to the classical nocturnal-diurnal migration, and a secondary important peak at the period of 12 hours that indicates a different type of DVM pattern, the twilight migration. The ultimate factor behind DVM seems to be the minimization of the risk of predation from fishes and other carnivorous groups. Calculating the monthly mean daily cycle of the Sv, it is evident that there is a decrease in Sv at sunrise, while it increases at sunset. The highest values in the derivative of Sv, as well as highest values in the vertical velocity (w), coincide in time with sunset and sunrise. In particular, w is negative (downward movement) at sunrise while it is positive (upward movement) at sunset, and in some cases absolute value of w (|w|) reaches 5 cm/s. The hour of occurrence of |w| greater than 4.5 cm/s follows the curves in time of the hours of sunset and sunrise, which are changing throughout the year.

  16. Sword, Shield and Buoys: A History of the NATO Sub-Committee on Oceanographic Research, 1959-1973.

    Science.gov (United States)

    Turchetti, Simone

    2012-08-01

    In the late 1950s the North-Atlantic Treaty Organization (NATO) made a major effort to fund collaborative research between its member states. One of the first initiatives following the establishment of the alliance's Science Committee was the creation of a sub-group devoted to marine science: the Sub-committee on Oceanographic Research.This paper explores the history of this organization, charts its trajectory over the 13 years of its existence, and considers its activities in light of NATO's naval defence strategies. In particular it shows how the alliance's naval commands played a key role in the sub-committee's creation due to the importance of oceanographic research in the tracking of enemy submarines. The essay also scrutinizes the reasons behind the committee's dissolution, with a special focus on the changing landscape of scientific collaboration at NATO. The committee's fall maps onto a more profound shift in the alliance's research agenda, including the re-organization of defence research and the rise of environmentalism.

  17. Sword, Shield and Buoys: A History of the NATO Sub-Committee on Oceanographic Research, 1959–19731

    Science.gov (United States)

    Turchetti, Simone

    2012-01-01

    In the late 1950s the North-Atlantic Treaty Organization (NATO) made a major effort to fund collaborative research between its member states. One of the first initiatives following the establishment of the alliance's Science Committee was the creation of a sub-group devoted to marine science: the Sub-committee on Oceanographic Research.This paper explores the history of this organization, charts its trajectory over the 13 years of its existence, and considers its activities in light of NATO's naval defence strategies. In particular it shows how the alliance's naval commands played a key role in the sub-committee's creation due to the importance of oceanographic research in the tracking of enemy submarines. The essay also scrutinizes the reasons behind the committee's dissolution, with a special focus on the changing landscape of scientific collaboration at NATO. The committee's fall maps onto a more profound shift in the alliance's research agenda, including the re-organization of defence research and the rise of environmentalism. PMID:23935209

  18. Wave hindcast studies using SWAN nested in WAVEWATCH III - comparison with measured nearshore buoy data off Karwar, eastern Arabian Sea

    Digital Repository Service at National Institute of Oceanography (India)

    Amrutha, M.M.; SanilKumar, V.; Sandhya, K.G.; Nair, T.M.B.; Rathod, J.L.

    for the tropical Indian Ocean are 0.8 m/s, 0.57, 2.6 m/s and 52.5, respectively (Harikumar et al., 2013). The earlier studies have shown that bathymetry is also an important factor (Chawla, 2007; Brown and Wolf, 2009). Hence, both bathymetry and wind field.... J. Geophysical Res. 104 (4), 7649- 7666. Brown, Jennifer M., and Judith Wolf., 2009. Coupled wave and surge modellingfor the easern Irish sea and implications for model wind-stress. Continental shelf Res. 29(10), 1329-1342. Cavaleri, L., 1994...

  19. Survey of Technology with Possible Applications to United States Coast Guard Buoy Tenders. Volume 1. Technology Assessment.

    Science.gov (United States)

    1987-09-01

    Classif. (of this report) 20. SECURITY CLASSIF. (of this page) 21. No. of Pages 22. Price %INCLASSIFIED UNCLASSIFIED DOT F 1700.7 (8/72) Reproduction ...23.95 16.40 7.55 1.46 Livita f 226.38 23.79 16.40 7.38 1.45 Wimpey Seahorse f 227.36 23.69 20.67 3.02 1.15 UT 712 f 247.70 22.64 18.37 4.27 1.23

  20. Data from a directional waverider buoy off Waimea Bay, North Shore, Oahu during December 2001 - July 2004 (NODC Accession 0001626)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Coastal Data Information Program (CDIP) is an extensive network for monitoring waves along the coastlines of the United States, with a strong emphasis on our...