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- Author or Editor: S. E. Larsen x
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Abstract
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Abstract
The diffusion equation with a wavenumber-dependent diffusivity is derived as an approximation to the statistical theory of plume dispersion from a continuous point source. The travel time explicitly appearing in the statistical theory is implicitly included in the wavenumber-dependent diffusivity K(k). By suitable choice of this function it is possible within a few percent to reproduce the centerline concentration. Due to the approximate nature of the method, however, it yields a non-Gaussian concentration shape close to the source.
Results for the case of a crosswind extended source are compared to other commonly used methods for this problem. Some considerations are presented on the proper choice of averaging time for the concentration field.
Abstract
The diffusion equation with a wavenumber-dependent diffusivity is derived as an approximation to the statistical theory of plume dispersion from a continuous point source. The travel time explicitly appearing in the statistical theory is implicitly included in the wavenumber-dependent diffusivity K(k). By suitable choice of this function it is possible within a few percent to reproduce the centerline concentration. Due to the approximate nature of the method, however, it yields a non-Gaussian concentration shape close to the source.
Results for the case of a crosswind extended source are compared to other commonly used methods for this problem. Some considerations are presented on the proper choice of averaging time for the concentration field.
Abstract
A continuous wave sonic anemometer-thermometer has been developed for simultaneous measurements of vertical velocity and temperature. The phase angle fluctuations are detected by means of a monolithic integrated phase-locked loop, the latter feature providing for inexpensive and accurate electronics. The principle is described and discussed.
Abstract
A continuous wave sonic anemometer-thermometer has been developed for simultaneous measurements of vertical velocity and temperature. The phase angle fluctuations are detected by means of a monolithic integrated phase-locked loop, the latter feature providing for inexpensive and accurate electronics. The principle is described and discussed.
Abstract
Hot wires respond to temperature as well as to velocity, whereas cold wires respond to velocity as well as to temperature. The static and dynamic response characteristics are summarized and it is shown that the frequency transfer functions for the four different responses in general are different. The influence of the transfer characteristics on measurements of turbulence statistics is discussed; it is shown that the nonideal response behavior influences, most strongly, statistics involving the correlation between velocity and temperature and here, most seriously, parameters involving small-scale turbulence.
Abstract
Hot wires respond to temperature as well as to velocity, whereas cold wires respond to velocity as well as to temperature. The static and dynamic response characteristics are summarized and it is shown that the frequency transfer functions for the four different responses in general are different. The influence of the transfer characteristics on measurements of turbulence statistics is discussed; it is shown that the nonideal response behavior influences, most strongly, statistics involving the correlation between velocity and temperature and here, most seriously, parameters involving small-scale turbulence.
Abstract
Based on an interpretation of a field experiment it is argued that, due to breaking of wind waves in deep water, the dissipation of energy is restricted to a range of frequencies ω > ω g , much higher than the frequency ω m of the dominant waves. In this dissipation range the spectrum has the form S(ω) = βg 2ω−5 where g is the acceleration due to gravity and β = 0.025. For spectral wave components at ω ≤ ω g , only a local balance between energy input from the wind and the weak, third-order, nonlinear interaction is important. Asymptotically as ω ≫ ω m the wind input becomes unimportant, and the wave spectrum has the Kitaigorodskii form of a Kolgomorov analog S(ω) = 2aε0 ⅓ g 4/3 ω−4 where ε0 is a constant flow of mean energy per unit surface area through the spectrum dissipated at high frequencies (when multiplied by g and water density ρ w ). From a method of M. S. Longuet-Higgins we estimate the magnitude of the dissipation (due to wave breaking) and find the Kolmogorov constant to be a ≈ 0.6. When a model, explained by Phillips, for wind energy input to the wave spectrum is applied to a simplified spectral model prescribing the scales of dissipation and growth of spectral wave components, good agreement is found with measurements by Donelan et al. of the coefficient 2aε0 ⅓ and its dependence on the frequency ω m of the dominant waves at the spectral peak.
Abstract
Based on an interpretation of a field experiment it is argued that, due to breaking of wind waves in deep water, the dissipation of energy is restricted to a range of frequencies ω > ω g , much higher than the frequency ω m of the dominant waves. In this dissipation range the spectrum has the form S(ω) = βg 2ω−5 where g is the acceleration due to gravity and β = 0.025. For spectral wave components at ω ≤ ω g , only a local balance between energy input from the wind and the weak, third-order, nonlinear interaction is important. Asymptotically as ω ≫ ω m the wind input becomes unimportant, and the wave spectrum has the Kitaigorodskii form of a Kolgomorov analog S(ω) = 2aε0 ⅓ g 4/3 ω−4 where ε0 is a constant flow of mean energy per unit surface area through the spectrum dissipated at high frequencies (when multiplied by g and water density ρ w ). From a method of M. S. Longuet-Higgins we estimate the magnitude of the dissipation (due to wave breaking) and find the Kolmogorov constant to be a ≈ 0.6. When a model, explained by Phillips, for wind energy input to the wave spectrum is applied to a simplified spectral model prescribing the scales of dissipation and growth of spectral wave components, good agreement is found with measurements by Donelan et al. of the coefficient 2aε0 ⅓ and its dependence on the frequency ω m of the dominant waves at the spectral peak.
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.
Abstract
The response of a Kaijo Denki DAT 300 ultrasonic anemometer-thermometer is analyzed with respect to temperature and vertical velocity. The effects of the emitting-receiving cycle of the transducer array on w, T, and CowT spectra are considered. The resulting relations are used to interpret temperature spectra obtained during the HEXMAX (Humidity Exchange over the Sea, Main Experiment) measuring campaign.
Abstract
The response of a Kaijo Denki DAT 300 ultrasonic anemometer-thermometer is analyzed with respect to temperature and vertical velocity. The effects of the emitting-receiving cycle of the transducer array on w, T, and CowT spectra are considered. The resulting relations are used to interpret temperature spectra obtained during the HEXMAX (Humidity Exchange over the Sea, Main Experiment) measuring campaign.
Abstract
The reliability of the wave model (WAM, cycle 4) for predicting waves and wind stress in restricted fetches is investigated using measured data obtained during the Risø Air–Sea Experiment (RASEX) at Vindeby, Denmark. The WAM model includes Janssen’s theory for calculating sea roughness as a function of wave spectra. RASEX is characterized by being located in relatively shallow waters (depths of about 3 to 4 m in an area where the waves are predominantly fetch limited, with a maximum fetch of about 20 km).
Comparison between WAM results and measured data (integral wave parameters and friction velocities) shows fair agreement for moderate winds (U 10 ≃ 10 m s−1) but significant overprediction for strong winds. Analysis of the WAM results for sea roughness yields a trend of increasing dimensionless roughness with inverse wave age, as obtained from field data; however, the WAM values are generally higher than that obtained from field data.
It is shown that inclusion of depth-induced wave breaking does not explain the overprediction of measured wind stress and associated wave heights. Furthermore, it is shown that using the measured wind friction velocities to force the WAM model significantly reduces the wave height overprediction for strong winds.
These investigations indicate that further improvements are required before the WAM model can be reliably used in shallow and fetch-limited areas, such as Vindeby.
Abstract
The reliability of the wave model (WAM, cycle 4) for predicting waves and wind stress in restricted fetches is investigated using measured data obtained during the Risø Air–Sea Experiment (RASEX) at Vindeby, Denmark. The WAM model includes Janssen’s theory for calculating sea roughness as a function of wave spectra. RASEX is characterized by being located in relatively shallow waters (depths of about 3 to 4 m in an area where the waves are predominantly fetch limited, with a maximum fetch of about 20 km).
Comparison between WAM results and measured data (integral wave parameters and friction velocities) shows fair agreement for moderate winds (U 10 ≃ 10 m s−1) but significant overprediction for strong winds. Analysis of the WAM results for sea roughness yields a trend of increasing dimensionless roughness with inverse wave age, as obtained from field data; however, the WAM values are generally higher than that obtained from field data.
It is shown that inclusion of depth-induced wave breaking does not explain the overprediction of measured wind stress and associated wave heights. Furthermore, it is shown that using the measured wind friction velocities to force the WAM model significantly reduces the wave height overprediction for strong winds.
These investigations indicate that further improvements are required before the WAM model can be reliably used in shallow and fetch-limited areas, such as Vindeby.
Abstract
The influence of wind waves on the momentum transfer (wind stress) between the atmosphere and sea surface was studied using new measured data from the RASEX experiment and other datasets compiled by Donelan et al.
Results of the data analysis indicate that errors in wind friction velocity u∗ of about ±10% make it difficult to conclude on the trend in z ch using measured data from a particular dataset. This problem is solved by combining different field data together. This gives a trend of decreasing z ch with wave age, expressed as: z ch = 1.89(c p /u∗)−1.59.
Furthermore, it is shown that calculations of the wind friction velocities using the wave-spectra-dependent expression of Hansen and Larsen agrees quite well with measured values during RASEX. It also gives a trend in Charnock parameter consistent with that found by combining the field data. Last, calculations using a constant Charnock parameter (0.018) also give very good results for the wind friction velocities at the RASEX site.
Abstract
The influence of wind waves on the momentum transfer (wind stress) between the atmosphere and sea surface was studied using new measured data from the RASEX experiment and other datasets compiled by Donelan et al.
Results of the data analysis indicate that errors in wind friction velocity u∗ of about ±10% make it difficult to conclude on the trend in z ch using measured data from a particular dataset. This problem is solved by combining different field data together. This gives a trend of decreasing z ch with wave age, expressed as: z ch = 1.89(c p /u∗)−1.59.
Furthermore, it is shown that calculations of the wind friction velocities using the wave-spectra-dependent expression of Hansen and Larsen agrees quite well with measured values during RASEX. It also gives a trend in Charnock parameter consistent with that found by combining the field data. Last, calculations using a constant Charnock parameter (0.018) also give very good results for the wind friction velocities at the RASEX site.
