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- Author or Editor: Margaret J. Yelland x
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Abstract
It is proposed that the sea surface roughness z o can be predicted from the height and steepness of the waves, z o /H s = A(H s /L p ) B , where H s and L p are the significant wave height and peak wavelength for the combined sea and swell spectrum; best estimates for the coefficients are A = 1200, B = 4.5. The proposed formula is shown to predict well the magnitude and behavior of the drag coefficient as observed in wave tanks, lakes, and the open ocean, thus reconciling observations that previously had appeared disparate. Indeed, the formula suggests that changes in roughness due to limited duration or fetch are of order 10% or less. Thus all deep water, pure windseas, regardless of fetch or duration, extract momentum from the air at a rate similar to that predicted for a fully developed sea. This is confirmed using published field data for a wide range of conditions over lakes and coastal seas. Only for field data corresponding to extremely young waves (U 10/c p > 3) were there appreciable differences between the predicted and observed roughness values, the latter being larger on average. Significant changes in roughness may be caused by shoaling or by swell. A large increase in roughness is predicted for shoaling waves if the depth is less than about 0.2L p . The presence of swell in the open ocean acts, on average, to significantly decrease the effective wave steepness and hence the mean roughness compared to that for a pure windsea. Thus the predicted open ocean roughness is, at most wind speeds, significantly less than is observed for pure wind waves on lakes. Only at high wind speeds, such that the windsea dominates the swell, do the mean open ocean values reach those for a fully developed sea.
Abstract
It is proposed that the sea surface roughness z o can be predicted from the height and steepness of the waves, z o /H s = A(H s /L p ) B , where H s and L p are the significant wave height and peak wavelength for the combined sea and swell spectrum; best estimates for the coefficients are A = 1200, B = 4.5. The proposed formula is shown to predict well the magnitude and behavior of the drag coefficient as observed in wave tanks, lakes, and the open ocean, thus reconciling observations that previously had appeared disparate. Indeed, the formula suggests that changes in roughness due to limited duration or fetch are of order 10% or less. Thus all deep water, pure windseas, regardless of fetch or duration, extract momentum from the air at a rate similar to that predicted for a fully developed sea. This is confirmed using published field data for a wide range of conditions over lakes and coastal seas. Only for field data corresponding to extremely young waves (U 10/c p > 3) were there appreciable differences between the predicted and observed roughness values, the latter being larger on average. Significant changes in roughness may be caused by shoaling or by swell. A large increase in roughness is predicted for shoaling waves if the depth is less than about 0.2L p . The presence of swell in the open ocean acts, on average, to significantly decrease the effective wave steepness and hence the mean roughness compared to that for a pure windsea. Thus the predicted open ocean roughness is, at most wind speeds, significantly less than is observed for pure wind waves on lakes. Only at high wind speeds, such that the windsea dominates the swell, do the mean open ocean values reach those for a fully developed sea.
Abstract
A synthetic dataset is used to show that apparent variations between different stability classes in the mean drag coefficient, C D10n , to wind speed relationship can be explained by random errors in determining the friction velocity u∗. Where the latter has been obtained by the inertial dissipation method, the variations in C D10n have previously been ascribed to an imbalance between production and dissipation in the turbulent kinetic energy budget. It follows that the application of “imbalance corrections” when calculating u∗ is incorrect and will cause a positive bias in C D10n , by about 10−4.
With no imbalance correction, random errors in u∗ result in scatter in the C D10n values, but for most wind speeds, there is no mean bias. However, in light winds under unstable conditions random errors in u∗ act to positively bias the calculated C D10n values. This is because the stability related effects are nonlinear and also because for some records for which C D10n would be decreased, the iteration scheme does not converge. The threshold wind speed is typically 7 m s−1, less for cleaner datasets. The biased C D10n values can be avoided by using a u∗ value calculated from a mean C D10n –U 10n relationship to determine the stability. The choice of the particular relationship is not critical. Recalculating previously published C D10n values without imbalance correction, but with anemometer response correction, results in a decrease of C D10n but only by about 0.05 × 10−3.
In addition to removing a previous cause of scatter and uncertainty in inertial dissipation data, the results suggest that spurious stability effects and low wind speed biases may be present in C D10n estimates obtained by other methods.
Abstract
A synthetic dataset is used to show that apparent variations between different stability classes in the mean drag coefficient, C D10n , to wind speed relationship can be explained by random errors in determining the friction velocity u∗. Where the latter has been obtained by the inertial dissipation method, the variations in C D10n have previously been ascribed to an imbalance between production and dissipation in the turbulent kinetic energy budget. It follows that the application of “imbalance corrections” when calculating u∗ is incorrect and will cause a positive bias in C D10n , by about 10−4.
With no imbalance correction, random errors in u∗ result in scatter in the C D10n values, but for most wind speeds, there is no mean bias. However, in light winds under unstable conditions random errors in u∗ act to positively bias the calculated C D10n values. This is because the stability related effects are nonlinear and also because for some records for which C D10n would be decreased, the iteration scheme does not converge. The threshold wind speed is typically 7 m s−1, less for cleaner datasets. The biased C D10n values can be avoided by using a u∗ value calculated from a mean C D10n –U 10n relationship to determine the stability. The choice of the particular relationship is not critical. Recalculating previously published C D10n values without imbalance correction, but with anemometer response correction, results in a decrease of C D10n but only by about 0.05 × 10−3.
In addition to removing a previous cause of scatter and uncertainty in inertial dissipation data, the results suggest that spurious stability effects and low wind speed biases may be present in C D10n estimates obtained by other methods.
Abstract
Recent analysis of high quality Solent sonic anemometer spectra revealed a frequency-dependent systematic underestimation of the spectral levels with a minimum at about three-quarters of the Nyquist frequency. It is shown that this is due to the eightfold pulse average with which the Solent sonic achieves its nominal sampling frequency of 21 Hz, combined with the effects of aliasing. An explicit correction curve is developed.
Abstract
Recent analysis of high quality Solent sonic anemometer spectra revealed a frequency-dependent systematic underestimation of the spectral levels with a minimum at about three-quarters of the Nyquist frequency. It is shown that this is due to the eightfold pulse average with which the Solent sonic achieves its nominal sampling frequency of 21 Hz, combined with the effects of aliasing. An explicit correction curve is developed.
Abstract
The concept of an “equivalent surface roughness” over the ocean is useful in understanding the relation between wind speed (at some height) and the net momentum flux from air to sea. The relative performance of different physics-motivated scalings for this roughness can provide valuable guidance as to which mechanisms are important under various conditions. Recently, two quite different roughness length scalings have been proposed. Taylor and Yelland presented a simple formula based on wave steepness, defined as the ratio of significant wave height to peak wavelength, to predict the surface roughness. A consequence of this formula is that roughness changes due to fetch or duration limitations are small, an order of 10%. The wave steepness formula was proposed as an alternative to the classical wave-age scaling first suggested by Kitaigorodskii and Volkov. Wave-age scaling, in contrast to steepness scaling, predicts order-of-magnitude changes in roughness associated with fetch or duration. The existence of two scalings, with different roughness predictions in certain conditions, has led to considerable confusion among certain groups. At several recent meetings, including the 2001 World Climate Research Program/Scientific Committee on Oceanic Research (WCRP/SCOR) workshop on the intercomparison and validation of ocean–atmosphere flux fields, proponents of the two scalings met with the goal of understanding the merits and limitations of each scaling. Here the results of these efforts are presented. The two sea-state scalings are tested using a composite of eight datasets representing a wide range of conditions. In conditions with a dominant wind-sea component, both scalings were found to yield improved estimates when compared with a standard bulk formulation. In general mixed sea conditions, the steepness formulation was preferred over both bulk and wave-age scalings, while for underdeveloped “young” wind sea, the wave-age formulation yields the best results. Neither sea-state model was seen to perform well in swell-dominated conditions where the steepness was small, but the steepness model did better than the wave-age model for swell-dominated conditions where the steepness exceeded a certain threshold.
Abstract
The concept of an “equivalent surface roughness” over the ocean is useful in understanding the relation between wind speed (at some height) and the net momentum flux from air to sea. The relative performance of different physics-motivated scalings for this roughness can provide valuable guidance as to which mechanisms are important under various conditions. Recently, two quite different roughness length scalings have been proposed. Taylor and Yelland presented a simple formula based on wave steepness, defined as the ratio of significant wave height to peak wavelength, to predict the surface roughness. A consequence of this formula is that roughness changes due to fetch or duration limitations are small, an order of 10%. The wave steepness formula was proposed as an alternative to the classical wave-age scaling first suggested by Kitaigorodskii and Volkov. Wave-age scaling, in contrast to steepness scaling, predicts order-of-magnitude changes in roughness associated with fetch or duration. The existence of two scalings, with different roughness predictions in certain conditions, has led to considerable confusion among certain groups. At several recent meetings, including the 2001 World Climate Research Program/Scientific Committee on Oceanic Research (WCRP/SCOR) workshop on the intercomparison and validation of ocean–atmosphere flux fields, proponents of the two scalings met with the goal of understanding the merits and limitations of each scaling. Here the results of these efforts are presented. The two sea-state scalings are tested using a composite of eight datasets representing a wide range of conditions. In conditions with a dominant wind-sea component, both scalings were found to yield improved estimates when compared with a standard bulk formulation. In general mixed sea conditions, the steepness formulation was preferred over both bulk and wave-age scalings, while for underdeveloped “young” wind sea, the wave-age formulation yields the best results. Neither sea-state model was seen to perform well in swell-dominated conditions where the steepness was small, but the steepness model did better than the wave-age model for swell-dominated conditions where the steepness exceeded a certain threshold.
Abstract
Wind speed measurements obtained from ship-mounted anemometers are biased by the presence of the ship, which distorts the airflow to the anemometer. Previous studies have simulated the flow over detailed models of individual research ships in order to quantify the effect of flow distortion at well-exposed anemometers, usually sited on a mast in the ship's bows. In contrast, little work has been undertaken to examine the effects of flow distortion at anemometers sited on other merchant ships participating in the voluntary observing ship (VOS) project. Anemometers are usually sited on a mast above the bridge of VOS where the effects of flow distortion may be severe. The several thousand VOS vary a great deal in shape and size and it would be impractical to study each individual ship.
This study examines the airflow above the bridge of a typical, or generic, tanker/bulk carrier/general cargo ship using computational fluid dynamics models. The results show that the airflow separates at the upwind leading edge of the bridge and a region of severely decelerated flow exists close to the bridge top with a region of accelerated flow above. Large velocity gradients occur between the two regions.
The wind speed bias is highly dependent upon the anemometer location and varies from accelerations of 10% or more to decelerations of 100%. The wind speed bias at particular locations also varies with the relative wind direction, that is, the angle of the ship to the wind. Wind speed biases for various anemometer positions are given for bow-on and beam-on flows.
Abstract
Wind speed measurements obtained from ship-mounted anemometers are biased by the presence of the ship, which distorts the airflow to the anemometer. Previous studies have simulated the flow over detailed models of individual research ships in order to quantify the effect of flow distortion at well-exposed anemometers, usually sited on a mast in the ship's bows. In contrast, little work has been undertaken to examine the effects of flow distortion at anemometers sited on other merchant ships participating in the voluntary observing ship (VOS) project. Anemometers are usually sited on a mast above the bridge of VOS where the effects of flow distortion may be severe. The several thousand VOS vary a great deal in shape and size and it would be impractical to study each individual ship.
This study examines the airflow above the bridge of a typical, or generic, tanker/bulk carrier/general cargo ship using computational fluid dynamics models. The results show that the airflow separates at the upwind leading edge of the bridge and a region of severely decelerated flow exists close to the bridge top with a region of accelerated flow above. Large velocity gradients occur between the two regions.
The wind speed bias is highly dependent upon the anemometer location and varies from accelerations of 10% or more to decelerations of 100%. The wind speed bias at particular locations also varies with the relative wind direction, that is, the angle of the ship to the wind. Wind speed biases for various anemometer positions are given for bow-on and beam-on flows.
Abstract
The authors examine changes in the salinity of the International Association for the Physical Sciences of the Ocean standard seawater (SSW) as used in seven cruises between 1991 and 1997. Ten batches of SSW were used during this time—several more than once—such that a clear demonstration of the effect of aging over months and years on SSW salinity can be made. Thus, the authors demonstrate that simple “offsets” intended to “correct” SSW salinity changes are inappropriate. Interest in intercruise salinity compatibility is high, as scientists attempt to reconcile section measurements made during the World Ocean Circulation Experiment among experiments. SSW salinity changes are one source of salinity differences of O(0.001) between sections. Herein, the authors provide a demonstration of how SSW measurements can be collated to generate a batch-by-batch history of SSW salinity evolution for more accurate sample salinity evaluation.
Abstract
The authors examine changes in the salinity of the International Association for the Physical Sciences of the Ocean standard seawater (SSW) as used in seven cruises between 1991 and 1997. Ten batches of SSW were used during this time—several more than once—such that a clear demonstration of the effect of aging over months and years on SSW salinity can be made. Thus, the authors demonstrate that simple “offsets” intended to “correct” SSW salinity changes are inappropriate. Interest in intercruise salinity compatibility is high, as scientists attempt to reconcile section measurements made during the World Ocean Circulation Experiment among experiments. SSW salinity changes are one source of salinity differences of O(0.001) between sections. Herein, the authors provide a demonstration of how SSW measurements can be collated to generate a batch-by-batch history of SSW salinity evolution for more accurate sample salinity evaluation.
Abstract
The effects of flow distortion created by the ship’s hull and superstructure bias wind speed measurements made from anemometers located on ships. Flow distortion must be taken into account if accurate air–sea flux measurements are to be achieved. Little work has been undertaken to examine the wind speed bias due to flow distortion in wind speed reports from voluntary observing ships (VOS). In this first part of a two-part paper the accuracy of the computational fluid dynamics (CFD) code VECTIS in simulating the airflow over VOS is investigated. Simulations of the airflow over a representation of the bridge of a VOS are compared to in situ wind speed measurements made from six anemometers located above the bridge of the RRS Charles Darwin. The ship’s structure was ideal for reproducing the flow over VOS when the wind is blowing onto either beam. The comparisons showed VECTIS was accurate to within 4% in predicting the wind speed over ships, except in extreme cases such as wake regions or the region close to the bridge top where the flow may be stagnant or reverse direction. The study showed that there was little change in the numerically predicted flow pattern above the bridge with change in Reynolds number between 2 × 105 and 1 × 107. The findings showed that the CFD code VECTIS can reliably be used to determine the mean flow above typical VOS.
Abstract
The effects of flow distortion created by the ship’s hull and superstructure bias wind speed measurements made from anemometers located on ships. Flow distortion must be taken into account if accurate air–sea flux measurements are to be achieved. Little work has been undertaken to examine the wind speed bias due to flow distortion in wind speed reports from voluntary observing ships (VOS). In this first part of a two-part paper the accuracy of the computational fluid dynamics (CFD) code VECTIS in simulating the airflow over VOS is investigated. Simulations of the airflow over a representation of the bridge of a VOS are compared to in situ wind speed measurements made from six anemometers located above the bridge of the RRS Charles Darwin. The ship’s structure was ideal for reproducing the flow over VOS when the wind is blowing onto either beam. The comparisons showed VECTIS was accurate to within 4% in predicting the wind speed over ships, except in extreme cases such as wake regions or the region close to the bridge top where the flow may be stagnant or reverse direction. The study showed that there was little change in the numerically predicted flow pattern above the bridge with change in Reynolds number between 2 × 105 and 1 × 107. The findings showed that the CFD code VECTIS can reliably be used to determine the mean flow above typical VOS.
Abstract
Consideration of the inertial dissipation method for routine wind stress estimation suggests that the most significant errors are likely to be changes in height of the airflow before reaching the anemometers, and errors in estimating the true wind, due either to flow distortion-induced errors in the relative wind estimate or errors in estimating the ship's speed relative to the water. The results from four anemometers—Solent sonic and Kaijo Denki sonic anemometers, and R.M. Young propeller-vane and bivane anemometers—mounted on the foremast of a research ship were compared. The mean bias between the four anemometers in the friction velocity estimates was less than 3% (rms scatter 6%–12%). In contrast the bias and scatter for the drag coefficient was 17%–27% due to flow distortion-induced errors in estimating the true wind speed. It is concluded that, with a reasonably well-exposed anemometer, wind stress can be determined to 5% or better by the dissipation method whereas errors in the bulk aerodynamic method are likely to be between 20% and 30%.
The data from the two sonic anemometers showed the best correlation; the Solent sonic, a relatively new instrument, was comparable in performance to the Kaijo Denki. Comparisons of the two propeller anemometers typically showed twice the scatter compared to the sonic values. Overcorrection for the propeller response at low wind speeds resulted in spuriously high drag coefficient values for wind speeds below 10 m s−1. In contrast, the sonic anemometer data showed no change in the slope of the drag coefficient to wind speed relationship at low wind speed.
Abstract
Consideration of the inertial dissipation method for routine wind stress estimation suggests that the most significant errors are likely to be changes in height of the airflow before reaching the anemometers, and errors in estimating the true wind, due either to flow distortion-induced errors in the relative wind estimate or errors in estimating the ship's speed relative to the water. The results from four anemometers—Solent sonic and Kaijo Denki sonic anemometers, and R.M. Young propeller-vane and bivane anemometers—mounted on the foremast of a research ship were compared. The mean bias between the four anemometers in the friction velocity estimates was less than 3% (rms scatter 6%–12%). In contrast the bias and scatter for the drag coefficient was 17%–27% due to flow distortion-induced errors in estimating the true wind speed. It is concluded that, with a reasonably well-exposed anemometer, wind stress can be determined to 5% or better by the dissipation method whereas errors in the bulk aerodynamic method are likely to be between 20% and 30%.
The data from the two sonic anemometers showed the best correlation; the Solent sonic, a relatively new instrument, was comparable in performance to the Kaijo Denki. Comparisons of the two propeller anemometers typically showed twice the scatter compared to the sonic values. Overcorrection for the propeller response at low wind speeds resulted in spuriously high drag coefficient values for wind speeds below 10 m s−1. In contrast, the sonic anemometer data showed no change in the slope of the drag coefficient to wind speed relationship at low wind speed.
Abstract
Waves and wave breaking play a significant role in the air–sea exchanges of momentum, sea spray aerosols, and trace gases such as CO2, but few direct measurements of wave breaking have been obtained in the open ocean (far from the coast). This paper describes the development and initial deployments on two research cruises of an autonomous spar buoy that was designed to obtain such open-ocean measurements. The buoy was equipped with capacitance wave wires and accelerometers to measure surface elevation and wave breaking, downward-looking still and video digital cameras to obtain images of the sea surface, and subsurface acoustic and optical sensors to detect bubble clouds from breaking waves. The buoy was free drifting and was designed to collect data autonomously for days at a time before being recovered. Therefore, on the two cruises during which the buoy was deployed, this allowed a variety of sea states to be sampled in mean wind speeds, which ranged from 5 to 18 m s−1.
Abstract
Waves and wave breaking play a significant role in the air–sea exchanges of momentum, sea spray aerosols, and trace gases such as CO2, but few direct measurements of wave breaking have been obtained in the open ocean (far from the coast). This paper describes the development and initial deployments on two research cruises of an autonomous spar buoy that was designed to obtain such open-ocean measurements. The buoy was equipped with capacitance wave wires and accelerometers to measure surface elevation and wave breaking, downward-looking still and video digital cameras to obtain images of the sea surface, and subsurface acoustic and optical sensors to detect bubble clouds from breaking waves. The buoy was free drifting and was designed to collect data autonomously for days at a time before being recovered. Therefore, on the two cruises during which the buoy was deployed, this allowed a variety of sea states to be sampled in mean wind speeds, which ranged from 5 to 18 m s−1.
