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Abstract
Measurements of the momentum, heat, moisture, energy, and scalar variance fluxes are combined with dissipation estimates to investigate the behavior of marine surface layer turbulence. These measurements span a wide range of atmospheric stability conditions and provide estimates of z/L between −8 and 1. Second- and third-order velocity differences are first used to provide an estimate of the Kolmogorov constant equal to 0.53 ± 0.04. The fluxes and dissipation estimates are then used to provide Monin–Obukhov (MO) similarity relationships of the various terms in the turbulent kinetic energy (TKE) and scalar variance (SV) budgets. These relationships are formulated to have the correct limiting forms in extremely stable and convective conditions. The analyses concludes with a determination of updated dimensionless structure function parameters for use with the inertial–dissipation flux method.
The production of TKE is found to balance its dissipation in convective conditions and to exceed dissipation by up to 17% in near-neutral conditions. This imbalance is investigated using the authors’ measurements of the energy flux and results in parameterizations for the energy flux and energy transport term in the TKE budget. The form of the dimensionless energy transport and dimensionless dissipation functions are very similar to previous parameterizations. From these measurements, it is concluded that the magnitude of energy transport (a loss of energy) is larger than the pressure transport (a gain of energy) in slightly unstable conditions.
The dissipation of SV is found to closely balance production in near-neutral conditions. However, the SV budget can only be balanced in convective conditions by inclusion of the transport term. The SV transport term is derived using our estimates of the flux of SV and the derivative approach. The behavior of the derived function represents a slight loss of SV in near-neutral conditions and a gain in very unstable conditions. This finding is consistent with previous investigations.
The similarity between these functions and recent overland results further suggests that experiments are generally above the region where wave-induced fluctuations influence the flow. The authors conclude that MO similarity theory is valid in the marine surface layer as long as it is applied to turbulence statistics taken above the wave boundary layer.
Abstract
Measurements of the momentum, heat, moisture, energy, and scalar variance fluxes are combined with dissipation estimates to investigate the behavior of marine surface layer turbulence. These measurements span a wide range of atmospheric stability conditions and provide estimates of z/L between −8 and 1. Second- and third-order velocity differences are first used to provide an estimate of the Kolmogorov constant equal to 0.53 ± 0.04. The fluxes and dissipation estimates are then used to provide Monin–Obukhov (MO) similarity relationships of the various terms in the turbulent kinetic energy (TKE) and scalar variance (SV) budgets. These relationships are formulated to have the correct limiting forms in extremely stable and convective conditions. The analyses concludes with a determination of updated dimensionless structure function parameters for use with the inertial–dissipation flux method.
The production of TKE is found to balance its dissipation in convective conditions and to exceed dissipation by up to 17% in near-neutral conditions. This imbalance is investigated using the authors’ measurements of the energy flux and results in parameterizations for the energy flux and energy transport term in the TKE budget. The form of the dimensionless energy transport and dimensionless dissipation functions are very similar to previous parameterizations. From these measurements, it is concluded that the magnitude of energy transport (a loss of energy) is larger than the pressure transport (a gain of energy) in slightly unstable conditions.
The dissipation of SV is found to closely balance production in near-neutral conditions. However, the SV budget can only be balanced in convective conditions by inclusion of the transport term. The SV transport term is derived using our estimates of the flux of SV and the derivative approach. The behavior of the derived function represents a slight loss of SV in near-neutral conditions and a gain in very unstable conditions. This finding is consistent with previous investigations.
The similarity between these functions and recent overland results further suggests that experiments are generally above the region where wave-induced fluctuations influence the flow. The authors conclude that MO similarity theory is valid in the marine surface layer as long as it is applied to turbulence statistics taken above the wave boundary layer.
Abstract
Obtaining accurate measurements of wave statistics from research vessels remains a challenge due to the platform motion. One principal correction is the removal of ship heave and Doppler effects from point measurements. Here, open-ocean wave measurements were collected using a laser altimeter, a Doppler radar microwave sensor, a radar-based system, and inertial measurement units. Multiple instruments were deployed to capture the low- and high-frequency sea surface displacements. Doppler and motion correction algorithms were applied to obtain a full 1D (0.035–1.3 ± 0.2 Hz) wave spectrum. The radar-based system combined with the laser altimeter provided the optimal low- and high-frequency combination, producing a frequency spectrum in the range from 0.035 to 1.2 Hz for cruising speeds ≤3 m s−1 with a spectral rolloff of f −4 Hz and noise floor of −20/−30 dB. While on station, the significant wave height estimates were comparable within 10%–15% among instrumentation. Discrepancies in the total energy and in the spectral shape between instruments arise when the ship is in motion. These differences can be quantified using the spectral behavior of the measurements, accounting for aliasing and Doppler corrections. The inertial sensors provided information on the amplitude of the ship’s modulation transfer function, which was estimated to be ~1.3 ± 0.2 while on station and increased while underway [2.1 at ship-over-ground (SOG) speed; 4.3 m s−1]. The correction scheme presented here is adequate for measurements collected at cruising speeds of 3 m s−1 or less. At speeds greater than 5 m s−1, the motion and Doppler corrections are not sufficient to correct the observed spectral degradation.
Abstract
Obtaining accurate measurements of wave statistics from research vessels remains a challenge due to the platform motion. One principal correction is the removal of ship heave and Doppler effects from point measurements. Here, open-ocean wave measurements were collected using a laser altimeter, a Doppler radar microwave sensor, a radar-based system, and inertial measurement units. Multiple instruments were deployed to capture the low- and high-frequency sea surface displacements. Doppler and motion correction algorithms were applied to obtain a full 1D (0.035–1.3 ± 0.2 Hz) wave spectrum. The radar-based system combined with the laser altimeter provided the optimal low- and high-frequency combination, producing a frequency spectrum in the range from 0.035 to 1.2 Hz for cruising speeds ≤3 m s−1 with a spectral rolloff of f −4 Hz and noise floor of −20/−30 dB. While on station, the significant wave height estimates were comparable within 10%–15% among instrumentation. Discrepancies in the total energy and in the spectral shape between instruments arise when the ship is in motion. These differences can be quantified using the spectral behavior of the measurements, accounting for aliasing and Doppler corrections. The inertial sensors provided information on the amplitude of the ship’s modulation transfer function, which was estimated to be ~1.3 ± 0.2 while on station and increased while underway [2.1 at ship-over-ground (SOG) speed; 4.3 m s−1]. The correction scheme presented here is adequate for measurements collected at cruising speeds of 3 m s−1 or less. At speeds greater than 5 m s−1, the motion and Doppler corrections are not sufficient to correct the observed spectral degradation.
Abstract
The NOAA Environmental Technology Laboratory air–sea interaction group and collaborators at the Woods Hole Oceanographic Institution have developed a seagoing measurement system suitable for mounting aboard ships. During its development, it was deployed on three different ships and recently completed three cruises in the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment as well as two cruises off the west coast of the United States. The system includes tower-mounted micrometeorological sensors for direct covariance flux measurements and a variety of remote sensors for profiling winds, temperature, moisture, and turbulence. A sonic anemometer/thermometer and a fast-response infrared hygrometer are used for turbulent fluxes. Winds are obtained from a stabilized Doppler radar (wind profiler) and a Doppler sodar. Returned power and Doppler width from these systems are used to deduce profiles of small-scale turbulence. A lidar ceilometer and a microwave radiometer are used to obtain cloud properties. Radiative fluxes are measured with standard pyranometers and pyrgeometers. A conventional rawinsonde system gives intermittent reference soundings. The system is used to study surface fluxes, boundary layer dynamics, cloud–radiative interactions, and entrainment. It has also proven useful in satellite calibration/validations. Following a description of the systems and methods, various examples of data and results are given from recent deployments in the North Atlantic, off the United States west coast, and in the equatorial Pacific Ocean.
Abstract
The NOAA Environmental Technology Laboratory air–sea interaction group and collaborators at the Woods Hole Oceanographic Institution have developed a seagoing measurement system suitable for mounting aboard ships. During its development, it was deployed on three different ships and recently completed three cruises in the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment as well as two cruises off the west coast of the United States. The system includes tower-mounted micrometeorological sensors for direct covariance flux measurements and a variety of remote sensors for profiling winds, temperature, moisture, and turbulence. A sonic anemometer/thermometer and a fast-response infrared hygrometer are used for turbulent fluxes. Winds are obtained from a stabilized Doppler radar (wind profiler) and a Doppler sodar. Returned power and Doppler width from these systems are used to deduce profiles of small-scale turbulence. A lidar ceilometer and a microwave radiometer are used to obtain cloud properties. Radiative fluxes are measured with standard pyranometers and pyrgeometers. A conventional rawinsonde system gives intermittent reference soundings. The system is used to study surface fluxes, boundary layer dynamics, cloud–radiative interactions, and entrainment. It has also proven useful in satellite calibration/validations. Following a description of the systems and methods, various examples of data and results are given from recent deployments in the North Atlantic, off the United States west coast, and in the equatorial Pacific Ocean.
Abstract
This paper describes two methods for computing direct covariance fluxes from anemometers mounted on moving platforms at sea. These methods involve the use of either a strapped-down or gyro-stabilized system that are used to compute terms that correct for the 1) instantaneous tilt of the anemometer due to the pitch, roll, and heading variations of the platform; 2) angular velocities at the anemometer due to rotation of the platform about its local coordinate system axes; and 3) translational velocities of the platform with respect to a fixed frame of reference. The paper provides a comparison of fluxes computed with three strapped-down systems from two recent field experiments. These comparisons shows that the direct covariance fluxes are in good agreement with fluxes derived using the bulk aerodynamic method. Additional comparisons between the ship system and the research platform FLIP indicate that flow distortion systematically increases the momentum flux by 15%. Evidence suggests that this correction is appropriate for a commonly used class of research vessels. The application of corrections for both motion contamination and flow distortion results in direct covariance flux estimates with an uncertainty of approximately 10%–20%.
Abstract
This paper describes two methods for computing direct covariance fluxes from anemometers mounted on moving platforms at sea. These methods involve the use of either a strapped-down or gyro-stabilized system that are used to compute terms that correct for the 1) instantaneous tilt of the anemometer due to the pitch, roll, and heading variations of the platform; 2) angular velocities at the anemometer due to rotation of the platform about its local coordinate system axes; and 3) translational velocities of the platform with respect to a fixed frame of reference. The paper provides a comparison of fluxes computed with three strapped-down systems from two recent field experiments. These comparisons shows that the direct covariance fluxes are in good agreement with fluxes derived using the bulk aerodynamic method. Additional comparisons between the ship system and the research platform FLIP indicate that flow distortion systematically increases the momentum flux by 15%. Evidence suggests that this correction is appropriate for a commonly used class of research vessels. The application of corrections for both motion contamination and flow distortion results in direct covariance flux estimates with an uncertainty of approximately 10%–20%.
Abstract
In 1996, version 2.5 of the Coupled Ocean–Atmosphere Response Experiment (COARE) bulk algorithm was published, and it has become one of the most frequently used algorithms in the air–sea interaction community. This paper describes steps taken to improve the algorithm in several ways. The number of iterations to solve for stability has been shortened from 20 to 3, and adjustments have been made to the basic profile stability functions. The scalar transfer coefficients have been redefined in terms of the mixing ratio, which is the fundamentally conserved quantity, rather than the measured water vapor mass concentration. Both the velocity and scalar roughness lengths have been changed. For the velocity roughness, the original fixed value of the Charnock parameter has been replaced by one that increases with wind speeds of between 10 and 18 m s−1. The scalar roughness length parameterization has been simplified to fit both an early set of NOAA/Environmental Technology Laboratory (ETL) experiments and the Humidity Exchange Over the Sea (HEXOS) program. These changes slightly increase the fluxes for wind speeds exceeding 10 m s−1. For interested users, two simple parameterizations of the surface gravity wave influence on fluxes have been added (but not evaluated).
This new version of the algorithm (COARE 3.0) was based on published results and 2777 1-h covariance flux measurements in the ETL inventory. To test it, 4439 new values from field experiments between 1997 and 1999 were added, which now dominate the database, especially in the wind speed regime beyond 10 m s−1, where the number of observations increased from 67 to about 800. After applying various quality controls, the database was used to evaluate the algorithm in several ways. For an overall mean, the algorithm agrees with the data to within a few percent for stress and latent heat flux. The agreement is also excellent when the bulk and directly measured fluxes are averaged in bins of 10-m neutral wind speed. For a more stringent test, the average 10-m neutral transfer coefficients were computed for stress and moisture in wind speed bins, using different averaging schemes with fairly similar results. The average (mean and median) model results agreed with the measurements to within about 5% for moisture from 0 to 20 m s−1. For stress, the covariance measurements were about 10% higher than the model at wind speeds over 15 m s−1, while inertial-dissipation measurements agreed closely at all wind speeds. The values for stress are between 8% (for inertial dissipation) and 18% (for covariance) higher at 20 m s−1 than two other classic results. Twenty years ago, bulk flux schemes were considered to be uncertain by about 30%; the authors find COARE 3.0 to be accurate within 5% for wind speeds of 0–10 m s−1 and 10% for wind speeds of between 10 and 20 m s−1.
Abstract
In 1996, version 2.5 of the Coupled Ocean–Atmosphere Response Experiment (COARE) bulk algorithm was published, and it has become one of the most frequently used algorithms in the air–sea interaction community. This paper describes steps taken to improve the algorithm in several ways. The number of iterations to solve for stability has been shortened from 20 to 3, and adjustments have been made to the basic profile stability functions. The scalar transfer coefficients have been redefined in terms of the mixing ratio, which is the fundamentally conserved quantity, rather than the measured water vapor mass concentration. Both the velocity and scalar roughness lengths have been changed. For the velocity roughness, the original fixed value of the Charnock parameter has been replaced by one that increases with wind speeds of between 10 and 18 m s−1. The scalar roughness length parameterization has been simplified to fit both an early set of NOAA/Environmental Technology Laboratory (ETL) experiments and the Humidity Exchange Over the Sea (HEXOS) program. These changes slightly increase the fluxes for wind speeds exceeding 10 m s−1. For interested users, two simple parameterizations of the surface gravity wave influence on fluxes have been added (but not evaluated).
This new version of the algorithm (COARE 3.0) was based on published results and 2777 1-h covariance flux measurements in the ETL inventory. To test it, 4439 new values from field experiments between 1997 and 1999 were added, which now dominate the database, especially in the wind speed regime beyond 10 m s−1, where the number of observations increased from 67 to about 800. After applying various quality controls, the database was used to evaluate the algorithm in several ways. For an overall mean, the algorithm agrees with the data to within a few percent for stress and latent heat flux. The agreement is also excellent when the bulk and directly measured fluxes are averaged in bins of 10-m neutral wind speed. For a more stringent test, the average 10-m neutral transfer coefficients were computed for stress and moisture in wind speed bins, using different averaging schemes with fairly similar results. The average (mean and median) model results agreed with the measurements to within about 5% for moisture from 0 to 20 m s−1. For stress, the covariance measurements were about 10% higher than the model at wind speeds over 15 m s−1, while inertial-dissipation measurements agreed closely at all wind speeds. The values for stress are between 8% (for inertial dissipation) and 18% (for covariance) higher at 20 m s−1 than two other classic results. Twenty years ago, bulk flux schemes were considered to be uncertain by about 30%; the authors find COARE 3.0 to be accurate within 5% for wind speeds of 0–10 m s−1 and 10% for wind speeds of between 10 and 20 m s−1.
Abstract
Previous investigations of the wind stress in the marine surface layer have primarily focused on determining the stress magnitude (momentum flux) and other scalar variables (e.g., friction velocity, drag coefficient, roughness length). However, the stress vector is often aligned with a direction different from that of the mean wind flow. In this paper, the focus is on the study of the stress vector direction relative to the mean wind and surface-wave directions. Results based on measurements made during three field campaigns onboard the R/P Floating Instrument Platform (FLIP) in the Pacific are discussed. In general, the wind stress is a vector sum of the 1) pure shear stress (turbulent and viscous) aligned with the mean wind shear, 2) wind-wave-induced stress aligned with the direction of the pure wind-sea waves, and 3) swell-induced stress aligned with the swell direction. The direction of the wind-wave-induced stress and the swell-induced stress components may coincide with, or be opposite to, the direction of wave propagation (pure wind waves and swell, respectively). As a result, the stress vector may deviate widely from the mean wind flow, including cases in which stress is directed across or even opposite to the wind.
Abstract
Previous investigations of the wind stress in the marine surface layer have primarily focused on determining the stress magnitude (momentum flux) and other scalar variables (e.g., friction velocity, drag coefficient, roughness length). However, the stress vector is often aligned with a direction different from that of the mean wind flow. In this paper, the focus is on the study of the stress vector direction relative to the mean wind and surface-wave directions. Results based on measurements made during three field campaigns onboard the R/P Floating Instrument Platform (FLIP) in the Pacific are discussed. In general, the wind stress is a vector sum of the 1) pure shear stress (turbulent and viscous) aligned with the mean wind shear, 2) wind-wave-induced stress aligned with the direction of the pure wind-sea waves, and 3) swell-induced stress aligned with the swell direction. The direction of the wind-wave-induced stress and the swell-induced stress components may coincide with, or be opposite to, the direction of wave propagation (pure wind waves and swell, respectively). As a result, the stress vector may deviate widely from the mean wind flow, including cases in which stress is directed across or even opposite to the wind.
Abstract
Two distinct backscatter regimes are seen on a European remote sensing satellite ERS-1 C-band (5.6 cm) synthetic aperture radar (SAR) image of the sea surface during a time of fair synoptic-scale weather conditions. One backscatter regime is mottled. In contrast to that, the second backscatter regime is marbled.
The authors hypothesize that the mottled backscatter pattern is a characteristic SAR backscatter pattern linked to the presence of the convective (i.e., statically unstable/convective-eddy containing) marine atmospheric boundary layer (CMABL) and can be used to help determine CMABL structure [convective-eddy type (cellular convection versus longitudinal rolls), eddy wavelength, and CMABL depth (via mixed-layer similarity theory for aspect ratio)]. The hypothesis linking the presence and structure of the CMABL to the mottled backscatter pattern on SAR imagery is validated by analyzing data from a number of sources gathered in the vicinity of the boundary between the mottled and marbled regimes on the SAR image.
Abstract
Two distinct backscatter regimes are seen on a European remote sensing satellite ERS-1 C-band (5.6 cm) synthetic aperture radar (SAR) image of the sea surface during a time of fair synoptic-scale weather conditions. One backscatter regime is mottled. In contrast to that, the second backscatter regime is marbled.
The authors hypothesize that the mottled backscatter pattern is a characteristic SAR backscatter pattern linked to the presence of the convective (i.e., statically unstable/convective-eddy containing) marine atmospheric boundary layer (CMABL) and can be used to help determine CMABL structure [convective-eddy type (cellular convection versus longitudinal rolls), eddy wavelength, and CMABL depth (via mixed-layer similarity theory for aspect ratio)]. The hypothesis linking the presence and structure of the CMABL to the mottled backscatter pattern on SAR imagery is validated by analyzing data from a number of sources gathered in the vicinity of the boundary between the mottled and marbled regimes on the SAR image.
Abstract
A prototype system for the measurement and computation of air–sea fluxes in realtime was tested in the Humidity Exchange Over the Sea (HEXOS) main experiment, HEXMAX. The system used a sonic anemometer/thermometer for wind speed, surface stress and sensible heat flux measurements and a Lyman-α fast hygrometer for latent beat flux. A small desktop computer combining both fast analog to digital (A/D) capabilities, external bus (IEEE-488) operation of a slow voltmeter/scanner unit, and a plug-in board for computation of turbulence spectra by Fast Fourier Transform was used for acquisition of 17 channels of data. At the end of a ten-minute averaging period, air–sea fluxes were computed from the velocity, temperature, and humidity variance spectra using the inertial-dissipation method. A second computer and data acquisition system was used for simultaneous computations of covariance fluxes for comparison.
The sonic anemometer/thermometer proved to be well suited for this application: the velocity data appear to be of good quality and the temperature data wore unaffected by salt contamination. We suggest an infrared hygrometer as a replacement for the Lyman-α. For the six week HEXMAX period the inertial-dissipation flux estimates agreed with covariances computed from the same instruments with a typical average root-mean-square difference of ± 10% for stress and ± 25% for sensible and latent heat.
Abstract
A prototype system for the measurement and computation of air–sea fluxes in realtime was tested in the Humidity Exchange Over the Sea (HEXOS) main experiment, HEXMAX. The system used a sonic anemometer/thermometer for wind speed, surface stress and sensible heat flux measurements and a Lyman-α fast hygrometer for latent beat flux. A small desktop computer combining both fast analog to digital (A/D) capabilities, external bus (IEEE-488) operation of a slow voltmeter/scanner unit, and a plug-in board for computation of turbulence spectra by Fast Fourier Transform was used for acquisition of 17 channels of data. At the end of a ten-minute averaging period, air–sea fluxes were computed from the velocity, temperature, and humidity variance spectra using the inertial-dissipation method. A second computer and data acquisition system was used for simultaneous computations of covariance fluxes for comparison.
The sonic anemometer/thermometer proved to be well suited for this application: the velocity data appear to be of good quality and the temperature data wore unaffected by salt contamination. We suggest an infrared hygrometer as a replacement for the Lyman-α. For the six week HEXMAX period the inertial-dissipation flux estimates agreed with covariances computed from the same instruments with a typical average root-mean-square difference of ± 10% for stress and ± 25% for sensible and latent heat.
Abstract
The integration of plug-in Fast Fourier Transform (FFT) boards in data acquisition computers allows a considerable development in the dynamic calibration of turbulence sensors. The spectral transfer function of a fast and sensitive turbulence sensor can be obtained in situ from a slow sensor having an absolute calibration, by computing in real time either the power spectra of the two signals or their complex cross-spectrum. The real-time spectral method allows calibration of sensors with relatively complex responses and, in most cases, nonlinear transfer functions. When used in conjunction with appropriate control and correction algorithms, this method can take care of numerous sources of error such as electronic noise, line pickup, and sensor malfunctions. This study shows that it can be extended to sensor arrays, including X-wire dual-component anemometers.
Abstract
The integration of plug-in Fast Fourier Transform (FFT) boards in data acquisition computers allows a considerable development in the dynamic calibration of turbulence sensors. The spectral transfer function of a fast and sensitive turbulence sensor can be obtained in situ from a slow sensor having an absolute calibration, by computing in real time either the power spectra of the two signals or their complex cross-spectrum. The real-time spectral method allows calibration of sensors with relatively complex responses and, in most cases, nonlinear transfer functions. When used in conjunction with appropriate control and correction algorithms, this method can take care of numerous sources of error such as electronic noise, line pickup, and sensor malfunctions. This study shows that it can be extended to sensor arrays, including X-wire dual-component anemometers.
Comparison of Direct Covariance Flux Measurements from an Offshore Tower and a BuoyAbstract
Direct covariance flux (DCF) measurements taken from floating platforms are contaminated by wave-induced platform motions that need to be removed before computation of the turbulent fluxes. Several correction algorithms have been developed and successfully applied in earlier studies from research vessels and, most recently, by the use of moored buoys. The validation of those correction algorithms has so far been limited to short-duration comparisons against other floating platforms. Although these comparisons show in general a good agreement, there is still a lack of a rigorous validation of the method, required to understand the strengths and weaknesses of the existing motion-correction algorithms. This paper attempts to provide such a validation by a comparison of flux estimates from two DCF systems, one mounted on a moored buoy and one on the Air–Sea Interaction Tower (ASIT) at the Martha’s Vineyard Coastal Observatory, Massachusetts. The ASIT was specifically designed to minimize flow distortion over a wide range of wind directions from the open ocean for flux measurements. The flow measurements from the buoy system are corrected for wave-induced platform motions before computation of the turbulent heat and momentum fluxes. Flux estimates and cospectra of the corrected buoy data are found to be in very good agreement with those obtained from the ASIT. The comparison is also used to optimize the filter constants used in the motion-correction algorithm. The quantitative agreement between the buoy data and the ASIT demonstrates that the DCF method is applicable for turbulence measurements from small moving platforms, such as buoys.
Abstract
Direct covariance flux (DCF) measurements taken from floating platforms are contaminated by wave-induced platform motions that need to be removed before computation of the turbulent fluxes. Several correction algorithms have been developed and successfully applied in earlier studies from research vessels and, most recently, by the use of moored buoys. The validation of those correction algorithms has so far been limited to short-duration comparisons against other floating platforms. Although these comparisons show in general a good agreement, there is still a lack of a rigorous validation of the method, required to understand the strengths and weaknesses of the existing motion-correction algorithms. This paper attempts to provide such a validation by a comparison of flux estimates from two DCF systems, one mounted on a moored buoy and one on the Air–Sea Interaction Tower (ASIT) at the Martha’s Vineyard Coastal Observatory, Massachusetts. The ASIT was specifically designed to minimize flow distortion over a wide range of wind directions from the open ocean for flux measurements. The flow measurements from the buoy system are corrected for wave-induced platform motions before computation of the turbulent heat and momentum fluxes. Flux estimates and cospectra of the corrected buoy data are found to be in very good agreement with those obtained from the ASIT. The comparison is also used to optimize the filter constants used in the motion-correction algorithm. The quantitative agreement between the buoy data and the ASIT demonstrates that the DCF method is applicable for turbulence measurements from small moving platforms, such as buoys.
