In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer: Implementation of a Neural Network

Kit, E.; Cherkassky, Arkady; Sant, Tonio; Fernando, Harindra J. S.

doi:10.1175/2009jtecha1320.1

Cited by 27 publications

(62 citation statements)

References 23 publications

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“…Integrating the spectrum up to f ¼ f r , we find that TKE is underestimated in lidar measurements by about 7% for the ARL lidar, and 11% for the UU lidar compared with the sonic anemometer. Analogously, the work of Kit et al 40 showed that about 10% of the energy is unaccounted for due to the relatively low resolution of sonic anemometers compared to hot-film probes.…”

Section: Observations Of Large Turbulent Motions Using Dual Doppler Wmentioning

confidence: 89%

Triple Doppler wind lidar observations during the mountain terrain atmospheric modeling and observations field campaign

Wang

Hocut

Hoch

et al. 2016

J. Appl. Remote Sens

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, "Triple Doppler wind lidar observations during the mountain terrain atmospheric modeling and observations field campaign," J. Appl. Remote Sens. 10(2), 026015 (2016), doi: 10.1117/1.JRS.10.026015. . The feasibility of observing large turbulent eddies was investigated by pointing three DWL at an intersecting probe volume adjoining a sonic anemometer mounted on the top of a meteorological tower. The time series and spectra of the sonic anemometer measurement were compared with the lidars. The lidar radial velocities closely followed those of the sonic anemometer, both in time and in the low frequency spectral domain, suggesting that the DWL technique is suitable for observing large turbulent eddies in the ABL. In addition, coordinated scanning triple DWL were used to directly measure the three-dimensional wind vectors, thus circumventing the assumptions required in using single or dual lidar deployments for full velocity measurements. The scanning triple lidar results were in satisfactory agreement with data from towerbased sonic anemometers. Notwithstanding, because of the difficulty of obtaining temporal and spatial synchronizations of the three lidars, the data were scant since a large amount of data had to be rejected in postprocessing. This difficulty is surmountable in the future by employing a robust control system for coordinated scanning.

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Section: Observations Of Large Turbulent Motions Using Dual Doppler Wmentioning

confidence: 89%

Triple Doppler wind lidar observations during the mountain terrain atmospheric modeling and observations field campaign

Wang

Hocut

Hoch

et al. 2016

J. Appl. Remote Sens

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“…The measurements at dissipation scales of SBL remain a challenge because of the low resolution of commonly used probing instruments as well as the changing wind direction and calibration requirements that stymie the use of high-resolution probes such as hot wire/film anemometers. New techniques have been developed to address these issues (Kit et al 2010), which offer promise for delving into SBL dissipation scales. As such, capabilities are becoming available to probe SBL on scales from tens of kilometers to millimeters, and future field experiments must fully capitalize on such multiscale probing capabilities.…”

Section: E V E Lo Pm E Nt a N D D E Ploy M E Nt Challengesmentioning

confidence: 99%

Whither the Stable Boundary Layer?

Fernando¹,

Weil²

2010

Bull. Amer. Meteor. Soc.

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111

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C learly no other part of the atmosphere is more important to Earth's ecosystems than its lowest layer, known as the atmospheric boundary layer (ABL). The land surface exchanges heat, mass, and momentum with the free atmosphere through the ABL, and naturally the ABL is affected by orography, land use, external forcing (e.g., radiation), and Earth's rotation. Environmental changes, whether due to slowly evolving global warming or rapidly dispersing atmospheric releases, permeate through to living organisms via the ABL. During the daytime, the ABL is driven by surface heating [the convective boundary layer (CBL)], whereas radiative cooling near the ground at night leads to the stable boundary layer (SBL). The nocturnal boundary layer (NBL) is the most common manifestation of SBL, with notable exceptions being areas where the urban heat island eliminates the near-surface stable stratification and polar regions where the SBL can persist continuously for days. The SBL breaks down into a CBL during the "morning transition," and the CBL collapses to an SBL during the "evening transition."Over the past half century, the progress in understanding the CBL has far outpaced the SBL; the much stronger forcing in the CBL makes measurement and modeling of turbulence therein much easier. Conversely, the SBL encapsulates a unique mix of processes that are generally much weaker (at least in total) and often difficult to measure at their scales of influence (let alone over multiple scales), study in isolation, or parameterize robustly. These processes interact in nonlinear way such that emerging new phenomena overshadow the contributing processes, and direct parameterizations of the former based on an understanding of the latter may not be viable. A greater emphasis is therefore needed on the interactions of SBL processes and the resulting modification of heat, mass, and momentum fluxes. Modeling of commonly sought meteorological and air quality indicators-surface temperature and wind speed/direction, fog, air pollution, and dispersion of chemical, biological, and radiological contaminants-relies heavily on  Detail of a shallow SBL in a mountain valley. For complete photo, see Fig. 1.

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“…The flow velocity and, if required, the flow direction are systematically changed over a parameter range of interest, thus obtaining input points that are separated at regular intervals. The standard tool for predicting the voltage-velocity relations in this case is polynomial least squares fit (PF; e.g., Kit et al 2010, and references therein). However, obtaining such calibration curves in the atmosphere has been a problem, given the difficulty of calibrating in unfriendly outdoor environments and the necessity for frequent calibrations in view of slowly changing probe performance resulting from contamination.…”

Section: Introductionmentioning

confidence: 99%

In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer: Angular Probability Distribution Properties

Kit

Grits

2011

Journal of Atmospheric and Oceanic Technology

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In a recent paper by Kit et al., a novel algorithm for the calibration of hot-film probes using a collocated sonic anemometer combined with the neural network approach is described. An important step in the algorithm is the generation of a calibration dataset by an appropriate low-pass filtering of the voltage and velocity time series obtained from hot-film probes and a sonic anemometer, correspondingly. Kit et al. report that a polynomial least squares fit that was used to approximate the relations of these voltage-velocity data from the dataset failed while a neural network approach worked satisfactorily. The same polynomial fit worked successfully with a calibration dataset obtained using a standard calibration unit that enables one to generate calibration data at evenly distributed yaw angles, varying in a wide range (2308, 308). In the current study, an attempt is made to uncover the reason for the failure of the polynomial fit algorithm with a sonic anemometer-based calibration dataset (SBS-PF). The probability densities of the velocity angles for the calibration dataset, as well as for a full velocity dataset obtained using the neural network approach, are computed. Also developed are theoretical expressions for the same angular density probability distributions based on the following assumptions: (i) an axisymmetric turbulent velocity field, (ii) Gaussian density probability distribution for velocity components, and (iii) weak correlations between the velocity components (i.e., the probability density distribution of the entire velocity vector is a product of probabilities of its components). The agreement between measured and theoretical angular probability distributions is good. The results herein indicate that the angular density probability of the low-pass-filtered calibration dataset is twice as narrow as that of the full velocity time series. This result can explain the failure of the polynomial fit to reconstruct the full velocity time series satisfactorily as resulting from the intrinsic property of this algorithm to ascribe a large weight to the highly concentrated points and a light weight to the thinly concentrated points while performing fitting.

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In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer: Implementation of a Neural Network

Cited by 27 publications

References 23 publications

Triple Doppler wind lidar observations during the mountain terrain atmospheric modeling and observations field campaign

Triple Doppler wind lidar observations during the mountain terrain atmospheric modeling and observations field campaign

Whither the Stable Boundary Layer?

In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer: Angular Probability Distribution Properties

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