EASI: An Air–Sea Interaction Buoy for High Winds

Drennan, William M.; Graber, Hans C.; Collins, Clarence O.; Herrera, A.; Potter, Henry; Ramos, Roberto; Williams, Neil J.

doi:10.1175/jtech-d-13-00201.1

Cited by 28 publications

(30 citation statements)

References 40 publications

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“…We also validated the wind models with observations from the Impact of Typhoons project on the Ocean in the Pacific (ITOP) experiment that took place during August-November 2010 in the Philippine Sea, east of Taiwan (D'Asaro et al, 2014;Drennan et al, 2014). Three typhoons were observed during the ITOP program, including Typhoon Megi.…”

Section: Estimate Of Wind Speedmentioning

confidence: 94%

“…Three typhoons were observed during the ITOP program, including Typhoon Megi. A specially designed extreme air-sea interaction (EASI) buoy was deployed to measure winds and waves locally, in which maximum 30 min winds at the buoys reached 26 ms −1 , and maximum wave heights over 10 m were recorded (Drennan et al, 2014). Figure 10 .…”

Section: Estimate Of Wind Speedmentioning

confidence: 99%

See 1 more Smart Citation

Drag coefficients derived from ocean current and temperature profiles at high wind speeds

Zou

Zhao

Tian

et al. 2018

Tellus A: Dynamic Meteorology and Oceanography

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Two bottom-up methods based on the turbulence closure and bulk model were utilised to estimate drag coefficients at high wind speeds based on ocean current and temperature profiles observed by two subsurface buoys during Typhoon Megi in the South China Sea. A numerical experiment was conducted using the turbulence closure model to test the impact of missing measurements in the upper mixed layer on the wind stress estimate and reconstruction of the upper ocean current. The results were sufficiently robust after several time steps. The wind stresses derived from the two methods were consistent with each other. Wind stress increased quickly with increasing wind force, and then was constant. A parametric typhoon wind model was applied to obtain the wind field and estimate the corresponding drag coefficient. The results showed that the drag coefficient increased to a maximum at a critical wind speed of about 30 m s −1 , and then levelled off with increasing wind speed. The uncertainty of the maximum drag coefficient was about ±0.5 due to neglect of nonlinear terms, the uncertainty of the model wind speeds and the drag coefficient. The critical wind speed for the maximum drag coefficient varied by at least ±5 m s −1 . The uncertainty of the drag coefficient parameterisation was about 24% due to wind speed. These results indicate that a bottom-up method can be used to estimate drag coefficients at the forced stage during the passage of typhoons or hurricanes.

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Section: Estimate Of Wind Speedmentioning

confidence: 94%

Section: Estimate Of Wind Speedmentioning

confidence: 99%

Drag coefficients derived from ocean current and temperature profiles at high wind speeds

Zou

Zhao

Tian

et al. 2018

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

show abstract

“…We calculate attitude and velocity following the complimentary filtering approach of Edson et al (1998); this method is routinely used to motioncorrect ship-borne turbulence measurements (e.g. McGillis et al, 2001;Brooks, 2008;Norris et al, 2012;Yang et al, 2014;Drennan et al, 2014;Landwehr et al, 2015;Prytherch et al, 2015). The ship's mean horizontal velocity when underway can approach 6 m s −1 ; the wave-induced velocity per- Because the lidar Doppler velocities are 2 s averages, we use a corresponding 2 s averaged platform velocity to correct them; the standard deviation of the individual 10 Hz platform velocity measurements is also calculated as a quality control measure, in order to flag measurements for which the ship motion changes substantially during the lidar measurement interval and to provide a measure of the noise added to the Doppler winds by the ship motion.…”

Section: Ship-motion Stabilisationmentioning

confidence: 99%

Measurement of wind profiles by motion-stabilised ship-borne Doppler lidar

Achtert

Brooks

et al. 2015

Atmos. Meas. Tech.

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Abstract. Three months of Doppler lidar wind measurements were obtained during the Arctic Cloud Summer Experiment on the icebreaker Oden during the summer of 2014. Such ship-borne Doppler measurements require active stabilisation to remove the effects of ship motion. We demonstrate that the combination of a commercial Doppler lidar with a custom-made motion-stabilisation platform enables the retrieval of wind profiles in the Arctic atmospheric boundary layer during both cruising and ice-breaking with statistical uncertainties comparable to land-based measurements. This held true particularly within the atmospheric boundary layer even though the overall aerosol load was very low. Motion stabilisation was successful for high wind speeds in open water and the resulting wave conditions. It allows for the retrieval of vertical winds with a random error below 0.2 m s −1 . The comparison of lidar-measured wind and radio soundings gives a mean bias of 0.3 m s −1 (2 • ) and a mean standard deviation of 1.1 m s −1 (12 • ) for wind speed (wind direction). The agreement for wind direction degrades with height. The combination of a motion-stabilised platform with a low-maintenance autonomous Doppler lidar has the potential to enable continuous long-term high-resolution shipbased wind profile measurements over the oceans.

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“…The two buoy pairs were deployed in the Philippine Sea, east of Taiwan, at 21.288N, 126.888E (5450-m depth) and at 19.688N, 127.388E (5500 m). At both sites, a 60-m wire rope tethered an ASIS to an EASI buoy that was moored to the seabed (Graber et al 2000;Drennan et al 2014). Because of heavy weather during RR1015 (it coincided with Typhoon Chaba), the buoys could not be recovered until March 2011.…”

Section: Data Overviewmentioning

confidence: 99%

On Shipboard Marine X-Band Radar Near-Surface Current ‘‘Calibration’’

Lund

Graber

Hessner

et al. 2015

Journal of Atmospheric and Oceanic Technology

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The ocean wave signatures within conventional noncoherent marine X-band radar (MR) image sequences can be used to derive near-surface current information. On ships, an accurate near-real-time record of the near-surface current could improve navigational safety. It could also advance understanding of air-sea interaction processes. The standard shipboard MR near-surface current estimates were found to have large errors (of the same order of magnitude as the signal) that are associated with ship speed and heading. For acoustic Doppler current profilers (ADCPs), ship heading errors are known to induce a spurious cross-track current that is proportional to the ship speed and the sine of the error angle. Conventional mechanical gyrocompasses are very reliable heading sensors, but they are too inaccurate for shipboard ADCPs. Within the ADCP community, it is common practice to correct the gyrocompass measurements with the help of multiantenna carrier-phase differential GPS systems. This study shows how a similar multiantenna GPS-based ship heading correction technique stands to improve the accuracy of MR near-surface current estimates. Changes to the standard MR near-surface current retrieval method that are necessary for high-quality results from ships are also introduced. MR and ADCP data collected from R/V Roger Revelle during the Impact of Typhoons on the Ocean in the Pacific (ITOP) program in 2010 are used to demonstrate the MR currents' accuracy and reliability.

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EASI: An Air–Sea Interaction Buoy for High Winds

Cited by 28 publications

References 40 publications

Drag coefficients derived from ocean current and temperature profiles at high wind speeds

Drag coefficients derived from ocean current and temperature profiles at high wind speeds

Measurement of wind profiles by motion-stabilised ship-borne Doppler lidar

On Shipboard Marine X-Band Radar Near-Surface Current ‘‘Calibration’’

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