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• 1. [Article] Inertial-Convective Subrange Estimates of Thermal Variance Dissipation Rate from Moored Temperature Measurements

  A procedure for estimating thermal variance dissipation rate χ[subscript]T by scaling the inertial-convective subrange of temperature gradient spectra from thermistor measurements on a Tropical Atmosphere ...

  Citation × Citation Citation Abstract Copy Title: Inertial-Convective Subrange Estimates of Thermal Variance Dissipation Rate from Moored Temperature Measurements Author: Zhang, Yanwei, Moum, James N. A procedure for estimating thermal variance dissipation rate χ[subscript]T by scaling the inertial-convective subrange of temperature gradient spectra from thermistor measurements on a Tropical Atmosphere Ocean (TAO) equatorial mooring, maintained by NOAA’s National Data Buoy Center, is demonstrated. The inertial-convective subrange of wavenumbers/frequencies is contaminated by the vertical motion induced by the pumping of the surface float by surface gravity waves through the local vertical temperature gradient. The uncontaminated signal can be retrieved by removing the part of the measured signal that is coherent with the signal induced by surface gravity waves, which must be measured independently. An estimate of χ[subscript]T is then obtained by fitting corrected spectra to theoretical temperature gradient spectra over the inertial-convective subrange (0.05 < ƒ < 0.5 Hz); this estimate is referred to as χ[subscript]T[superscript]IC. Here χ[subscript]T[superscript]IC was calculated over 120-min intervals and compared with estimates of χ[subscript]T[superscript]o determined by scaling temperature gradient spectra at high wavenumbers (viscous-convective and viscous-diffusive subranges). Large differences up to a factor of 20 and of unknown origin occur infrequently, especially when both background currents and vertical temperature gradients are weak, but the results herein indicate that 75% of the data pairs are within a factor of 3 of each other. Tests on 15-, 30-, 60-, 120-min intervals demonstrate that differences between the two methods are nearly random, unbiased, and less than estimates of natural variability determined from unrelated experiments at the same location. Because the inertial-convective subrange occupies a lower-frequency range than is typically used for turbulence measurements, the potential for more routine measurements of χ[subscript]T exists. The evaluation of degraded signals (resampled from original measurements) indicates that a particularly important component of such a measurement is the independent resolution of the surface wave–induced signal. Title: Inertial-Convective Subrange Estimates of Thermal Variance Dissipation Rate from Moored Temperature Measurements Author: Zhang, Yanwei, Moum, James N. A procedure for estimating thermal variance dissipation rate χ[subscript]T by scaling the inertial-convective subrange of temperature gradient spectra from thermistor measurements on a Tropical Atmosphere Ocean (TAO) equatorial mooring, maintained by NOAA’s National Data Buoy Center, is demonstrated. The inertial-convective subrange of wavenumbers/frequencies is contaminated by the vertical motion induced by the pumping of the surface float by surface gravity waves through the local vertical temperature gradient. The uncontaminated signal can be retrieved by removing the part of the measured signal that is coherent with the signal induced by surface gravity waves, which must be measured independently. An estimate of χ[subscript]T is then obtained by fitting corrected spectra to theoretical temperature gradient spectra over the inertial-convective subrange (0.05 < ƒ < 0.5 Hz); this estimate is referred to as χ[subscript]T[superscript]IC. Here χ[subscript]T[superscript]IC was calculated over 120-min intervals and compared with estimates of χ[subscript]T[superscript]o determined by scaling temperature gradient spectra at high wavenumbers (viscous-convective and viscous-diffusive subranges). Large differences up to a factor of 20 and of unknown origin occur infrequently, especially when both background currents and vertical temperature gradients are weak, but the results herein indicate that 75% of the data pairs are within a factor of 3 of each other. Tests on 15-, 30-, 60-, 120-min intervals demonstrate that differences between the two methods are nearly random, unbiased, and less than estimates of natural variability determined from unrelated experiments at the same location. Because the inertial-convective subrange occupies a lower-frequency range than is typically used for turbulence measurements, the potential for more routine measurements of χ[subscript]T exists. The evaluation of degraded signals (resampled from original measurements) indicates that a particularly important component of such a measurement is the independent resolution of the surface wave–induced signal. Close

• 2. [Article] Distribution of Mean Kinetic Energy Around an Isolated Wind Turbine and a Characteristic Wind Turbine of a Very Large Wind Farm

  An isolated wind turbine and a very large wind farm are introduced into large-eddy simulations of an atmospheric boundary layer. The atmospheric flow is forced with a constant geostrophic wind and a time-varying ...

  Citation × Citation Citation Abstract Copy Title: Distribution of Mean Kinetic Energy Around an Isolated Wind Turbine and a Characteristic Wind Turbine of a Very Large Wind Farm Author: Cortina, Gerard, Calaf, Marc, Cal, Raul Bayoan Year: 2016 An isolated wind turbine and a very large wind farm are introduced into large-eddy simulations of an atmospheric boundary layer. The atmospheric flow is forced with a constant geostrophic wind and a time-varying surface temperature extracted from a selected period of the CASES-99 field experiment. A control volume approach is used to directly compare the transfer of mean kinetic energy around a characteristic wind turbine throughout a diurnal cycle considering both scenarios. For the very large wind farm case, results illustrate that the recovery of mean kinetic energy around a wind turbine is dominated by the vertical flux, regardless of atmospheric stratification. Contrarily, for an isolated wind turbine, the recovery is dependent on the background atmospheric stratification and it is produced by a combination of advection, vertical flux, and pressure redistribution. The analysis also illustrates that during the unstable stratification periods vertical entrainment of mean kinetic energy dominates, whereas during the stable regime horizontal entrainment is predominant. Finally, it is observed that in both scenarios, the single wind turbine and the large wind farm cases, turbulent mixing is driven by the background convective stratification during the unstable period and by the effect of the wind turbine during the stable regime. Title: Distribution of Mean Kinetic Energy Around an Isolated Wind Turbine and a Characteristic Wind Turbine of a Very Large Wind Farm Author: Cortina, Gerard, Calaf, Marc, Cal, Raul Bayoan Year: 2016 An isolated wind turbine and a very large wind farm are introduced into large-eddy simulations of an atmospheric boundary layer. The atmospheric flow is forced with a constant geostrophic wind and a time-varying surface temperature extracted from a selected period of the CASES-99 field experiment. A control volume approach is used to directly compare the transfer of mean kinetic energy around a characteristic wind turbine throughout a diurnal cycle considering both scenarios. For the very large wind farm case, results illustrate that the recovery of mean kinetic energy around a wind turbine is dominated by the vertical flux, regardless of atmospheric stratification. Contrarily, for an isolated wind turbine, the recovery is dependent on the background atmospheric stratification and it is produced by a combination of advection, vertical flux, and pressure redistribution. The analysis also illustrates that during the unstable stratification periods vertical entrainment of mean kinetic energy dominates, whereas during the stable regime horizontal entrainment is predominant. Finally, it is observed that in both scenarios, the single wind turbine and the large wind farm cases, turbulent mixing is driven by the background convective stratification during the unstable period and by the effect of the wind turbine during the stable regime. Close