Diurnal and seasonal variability of TKE dissipation rate in the ABL over a tropical station using UHF wind profiler

AbstractIt has been recognized that modulated wave groups trigger wave breaking and generate energy dissipation events on the ocean surface. Quantitative examination of wave-breaking events and associated turbulent kinetic energy (TKE) dissipation rates within a modulated wave group in the open ocean is not a trivial task. To address this challenging topic, a set of laboratory experiments was carried out in an outdoor facility, the Oil and Hazardous Material Simulated Environment Test Tank (203 m long, 20 m wide, 3.5 m deep). TKE dissipation rates at multiple depths were estimated directly while moving the sensor platform at a speed of about 0.53 m s−1 toward incoming wave groups generated by the wave maker. The largest TKE dissipation rates and significant whitecaps were found at or near the center of wave groups where steepening waves approached the geometric limit of waves. The TKE dissipation rate was O(10−2) W kg−1 during wave breaking, which is two to three orders of magnitude larger than before and after wave breaking. The enhanced TKE dissipation rate was limited to a layer of half the wave height in depth. Observations indicate that the impact of wave breaking was not significant at depths deeper than one wave height from the surface. The TKE dissipation rate of breaking waves within wave groups can be parameterized by local wave phase speed with a proportionality breaking strength coefficient dependent on local steepness. The characterization of energy dissipation in wave groups from local wave properties will enable a better determination of near-surface TKE dissipation of breaking waves.

Download Full-text

Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?

10.5194/gmd-2020-16 ◽

2020 ◽

Author(s):

Nicola Bodini ◽

Julie K. Lundquist ◽

Mike Optis

Keyword(s):

Machine Learning ◽

Numerical Weather Prediction ◽

Complex Terrain ◽

Dissipation Rate ◽

Prediction Models ◽

Weather Prediction ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Tke Dissipation Rate ◽

Numerical Weather Prediction Models

Abstract. Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ε) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that a widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ε. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that multivariate polynomial regressions and random forests can eliminate the bias MYNN currently shows in representing ε, while also reducing the average error by up to 30 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ε, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

Download Full-text

Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?

Geoscientific Model Development ◽

10.5194/gmd-13-4271-2020 ◽

2020 ◽

Vol 13 (9) ◽

pp. 4271-4285

Author(s):

Nicola Bodini ◽

Julie K. Lundquist ◽

Mike Optis

Keyword(s):

Machine Learning ◽

Kinetic Energy ◽

Complex Terrain ◽

Dissipation Rate ◽

Prediction Models ◽

Weather Prediction ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Tke Dissipation Rate ◽

Numerical Weather Prediction Models

Abstract. Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ϵ) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that the widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ϵ. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that the models eliminate the bias MYNN currently shows in representing ϵ, while also reducing the average error by up to almost 40 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ϵ, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

Download Full-text

Wind Structure in a Supercell Thunderstorm as Measured by a UHF Wind Profiler

Monthly Weather Review ◽

10.1175/1520-0493(2001)129<1968:wsiast>2.0.co;2 ◽

2001 ◽

Vol 129 (8) ◽

pp. 1968-1986 ◽

Cited By ~ 14

Author(s):

Gregor S. Lehmiller ◽

Howard B. Bluestein ◽

Paul J. Neiman ◽

F. Martin Ralph ◽

Wayne F. Feltz

Keyword(s):

Wind Profiler ◽

Wind Structure ◽

Supercell Thunderstorm ◽

Uhf Wind Profiler

Download Full-text

Physics of Stratocumulus Top (POST): turbulence characteristics

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-9711-2016 ◽

2016 ◽

Vol 16 (15) ◽

pp. 9711-9725 ◽

Cited By ~ 20

Author(s):

Imai Jen-La Plante ◽

Yongfeng Ma ◽

Katarzyna Nurowska ◽

Hermann Gerber ◽

Djamal Khelif ◽

...

Keyword(s):

Dissipation Rate ◽

Richardson Number ◽

Reynolds Numbers ◽

Bulk Richardson Number ◽

Velocity Fluctuations ◽

Turbulence Characteristics ◽

Tke Dissipation Rate ◽

Thermodynamic Variables ◽

Large Eddies ◽

Flight Measurements

Abstract. Turbulence observed during the Physics of Stratocumulus Top (POST) research campaign is analyzed. Using in-flight measurements of dynamic and thermodynamic variables at the interface between the stratocumulus cloud top and free troposphere, the cloud top region is classified into sublayers, and the thicknesses of these sublayers are estimated. The data are used to calculate turbulence characteristics, including the bulk Richardson number, mean-square velocity fluctuations, turbulence kinetic energy (TKE), TKE dissipation rate, and Corrsin, Ozmidov and Kolmogorov scales. A comparison of these properties among different sublayers indicates that the entrainment interfacial layer consists of two significantly different sublayers: the turbulent inversion sublayer (TISL) and the moist, yet hydrostatically stable, cloud top mixing sublayer (CTMSL). Both sublayers are marginally turbulent, i.e., the bulk Richardson number across the layers is critical. This means that turbulence is produced by shear and damped by buoyancy such that the sublayer thicknesses adapt to temperature and wind variations across them. Turbulence in both sublayers is anisotropic, with Corrsin and Ozmidov scales as small as ∼  0.3 and ∼  3 m in the TISL and CTMSL, respectively. These values are ∼  60 and ∼  15 times smaller than typical layer depths, indicating flattened large eddies and suggesting no direct mixing of cloud top and free-tropospheric air. Also, small scales of turbulence are different in sublayers as indicated by the corresponding values of Kolmogorov scales and buoyant and shear Reynolds numbers.

Download Full-text

Preliminary observation of temperature profiles by radio acoustic sounding system (RASS) with a 1280 MHz lower atmospheric wind profiler at Gadanki, India

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-5-4447-2012 ◽

2012 ◽

Vol 5 (3) ◽

pp. 4447-4472 ◽

Cited By ~ 1

Author(s):

T. V. Chandrasekhar Sarma ◽

P. Srinivasulu ◽

T. Tsuda

Keyword(s):

Boundary Layer ◽

Planetary Boundary Layer ◽

Atmospheric Temperature ◽

Precipitation Event ◽

Wind Profiler ◽

Acoustic Sounding ◽

Pulse Transmission ◽

Mst Radar ◽

Uhf Wind Profiler ◽

Virtual Temperature

Abstract. A UHF wind profiler operating at 1280 MHz has been developed at NARL for atmospheric studies in the planetary boundary layer. In order to explore application of radio acoustic sounding system (RASS) technique to this profiler, a suitable acoustic attachment was designed and preliminary experiments were conducted on 27–30 August 2010. Height profiles of virtual temperature, Tv, in the planetary boundary layer were derived with 1 μs and 0.25 μs pulse transmission, corresponding to a height resolution of 150 m and about 40 m, respectively. Diurnal variation of Tv is clearly recognized, and perturbations of Tv are also seen in association with a precipitation event. Simultaneous profiles obtained from the MST Radar-RASS and an onsite 50 m tower demonstrate the capability to continuously profile the atmospheric temperature from near the ground to upper tropospheric altitudes.

Download Full-text