STINHO Structure of turbulent transport under inhomogeneous surface conditions part 1: The micro- scale field experiment

Raabe, Armin; Arnold, Klaus; Ziemann, Astrid; Beyrich, Frank; Leps, Jens-Peter; Bange, Jens; Zittel, Peter; Spiess, T.; Foken, Thomas; Göckede, Mathias; Schröter, M.; Raasch, Siegfried

doi:10.1127/0941-2948/2005/0037

Cited by 17 publications

(16 citation statements)

References 8 publications

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“…9 First, we derive analytical formulas for B TT ͑R 1 , t 1 ; R 2 , t 2 ͒ and B ij ͑R 1 , t 1 ; R 2 , t 2 ͒ for the case of locally frozen turbulence which is a generalization of the hypothesis of frozen turbulence. The obtained formulas for B TT ͑R 1 , t 1 ; R 2 , t 2 ͒ and B ij ͑R 1 , t 1 ; R 2 , t 2 ͒ account for the variance of wind velocity fluctuations V and, hence, are much more realistic than those for the case of frozen turbulence where V is tacitly assumed 0.…”

Section: Sponsoring/monitoring Agency Name(s) and Address(es) 10 Spomentioning

confidence: 99%

“…Most of the algorithms used so far employed a partition of a tomographic volume into grid cells where the values of T and Ṽ are constant, and a subsequent solution of a set of algebraic equations. [3][4][5][6][7][8][9]11,12,14,15 Numerical simulations have shown 12 that these algebraic algorithms can give a good reconstruction of T and Ṽ fields if the number of the grid cells is small enough so that a corresponding inverse problem is overdetermined ͑the number of equations is greater than the number of unknowns͒. However, in two-dimensional ͑2D͒ .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9] Further, some works were done on numerical simulation of acoustic tomography of the atmosphere. [10][11][12][13] One of the main problems in both tomography experiments and numerical simulations is to find a good inverse algorithm for reconstruction of T and Ṽ fields.…”

Section: Introductionmentioning

confidence: 99%

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Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Vecherin

Ostashev

Ziemann

et al. 2007

The Journal of the Acoustical Society of America

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Acoustic travel-time tomography allows one to reconstruct temperature and wind velocity fields in the atmosphere. In a recently published paper ͓S. Vecherin et al., J. Acoust. Soc. Am. 119, 2579 ͑2006͔͒, a time-dependent stochastic inversion ͑TDSI͒ was developed for the reconstruction of these fields from travel times of sound propagation between sources and receivers in a tomography array. TDSI accounts for the correlation of temperature and wind velocity fluctuations both in space and time and therefore yields more accurate reconstruction of these fields in comparison with algebraic techniques and regular stochastic inversion. To use TDSI, one needs to estimate spatial-temporal covariance functions of temperature and wind velocity fluctuations. In this paper, these spatial-temporal covariance functions are derived for locally frozen turbulence which is a more general concept than a widely used hypothesis of frozen turbulence. The developed theory is applied to reconstruction of temperature and wind velocity fields in the acoustic tomography experiment carried out by University of Leipzig, Germany. The reconstructed temperature and velocity fields are presented and errors in reconstruction of these fields are studied.

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Section: Sponsoring/monitoring Agency Name(s) and Address(es) 10 Spomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Vecherin

Ostashev

Ziemann

et al. 2007

The Journal of the Acoustical Society of America

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“…Speakers transmitted two short bursts of a harmonic signal separated by about 20 ms; the frequency range of the transmitted signal was around 1 kHz. Since the mid 1990s, this tomography array has been used in about 10 experimental campaigns (ARNOLD et al, , 2001RAABE et al, 2003RAABE et al, , 2005TETZLAFF et al, 2002;WEINBRECHT et al, 2004;ZIEMANN et al, 1999ZIEMANN et al, , 2001ZIEMANN et al, , 2002). …”

Section: Acoustic Tomography Arraysmentioning

confidence: 99%

Recent progress in acoustic travel-time tomography of the atmospheric surface layer

Ostashev¹,

Vecherin²,

Wilson³

et al. 2009

metz

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Acoustic tomography of the atmospheric surface layer (ASL) is based on measurements of the travel times of sound propagation between sources and receivers which constitute a tomography array. Then, the temperature and wind velocity fields inside the tomographic volume or area are reconstructed using different inverse algorithms. Improved knowledge of these fields is important in many practical applications. Tomography has certain advantages in comparison with currently used instrumentation for measurement of the temperature and wind velocity. In this paper, a short historical overview of acoustic tomography of the atmosphere is presented. The main emphasis is on recent progress in acoustic tomography of the ASL. The tomography arrays that have been used so far are discussed. Inverse algorithms for reconstruction of the temperature and wind velocity fields from the travel times are reviewed. Some results in numerical simulations of acoustic tomography of the ASL and reconstruction of the turbulence fields in tomography experiments are presented and discussed. Zusammenfassung

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“…Acoustic tomography as a measurement and analysis method has been further developed since the late 1990s (Ziemann et al, 1999;Arnold et al, 2001). This method was used to monitor spatially resolved wind and temperature fields for different environmental conditions, e.g., in rural or urban environments (Tetzlaff et al, 2002) with heterogeneous surface properties (Raabe et al, 2005), as well as on different spatial scales, from indoor wind tunnel length scales (Barth and Raabe, 2011;Barth et al, 2007) up to outdoor areas with acoustic path lengths of several hundreds of meters (Arnold et al, 2004). As a result, several inversion techniques were developed and validated regarding 4168 A.…”

Section: Acoustic Travel-time Tomography A-tommentioning

confidence: 99%

Line-averaging measurement methods to estimate the gap in the CO<sub>2</sub> balance closure – possibilities, challenges, and uncertainties

2017

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Abstract. An imbalance of surface energy fluxes using the eddy covariance (EC) method is observed in global measurement networks although all necessary corrections and conversions are applied to the raw data. Mainly during nighttime, advection can occur, resulting in a closing gap that consequently should also affect the CO 2 balances. There is the crucial need for representative concentration and wind data to measure advective fluxes. Ground-based remote sensing techniques are an ideal tool as they provide the spatially representative CO 2 concentration together with wind components within the same voxel structure. For this purpose, the presented SQuAd (Spatially resolved Quantification of the Advection influence on the balance closure of greenhouse gases) approach applies an integrated method combination of acoustic and optical remote sensing. The innovative combination of acoustic travel-time tomography (A-TOM) and open-path Fourier-transform infrared spectroscopy (OP-FTIR) will enable an upscaling and enhancement of EC measurements. OP-FTIR instrumentation offers the significant advantage of real-time simultaneous measurements of lineaveraged concentrations for CO 2 and other greenhouse gases (GHGs). A-TOM is a scalable method to remotely resolve 3-D wind and temperature fields. The paper will give an overview about the proposed SQuAd approach and first results of experimental tests at the FLUXNET site Grillenburg in Germany.Preliminary results of the comprehensive experiments reveal a mean nighttime horizontal advection of CO 2 of about 10 µmol m −2 s −1 estimated by the spatially integrating and representative SQuAd method. Additionally, uncertainties in determining CO 2 concentrations using passive OP-FTIR and wind speed applying A-TOM are systematically quantified. The maximum uncertainty for CO 2 concentration was estimated due to environmental parameters, instrumental characteristics, and retrieval procedure with a total amount of approximately 30 % for a single measurement. Instantaneous wind components can be derived with a maximum uncertainty of 0.3 m s −1 depending on sampling, signal analysis, and environmental influences on sound propagation. Averaging over a period of 30 min, the standard error of the mean values can be decreased by a factor of at least 0.5 for OP-FTIR and 0.1 for A-TOM depending on the required spatial resolution. The presented validation of the joint application of the two independent, nonintrusive methods is in the focus of attention concerning their ability to quantify advective fluxes.

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STINHO Structure of turbulent transport under inhomogeneous surface conditions part 1: The micro- scale field experiment

Cited by 17 publications

References 8 publications

Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Recent progress in acoustic travel-time tomography of the atmospheric surface layer

Line-averaging measurement methods to estimate the gap in the CO<sub>2</sub> balance closure – possibilities, challenges, and uncertainties

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STINHO Structure of turbulent transport under inhomogeneous surface conditions part 1: The micro- scale field experiment

Cited by 17 publications

References 8 publications

Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Recent progress in acoustic travel-time tomography of the atmospheric surface layer

Line-averaging measurement methods to estimate the gap in the CO&lt;sub&gt;2&lt;/sub&gt; balance closure – possibilities, challenges, and uncertainties

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Line-averaging measurement methods to estimate the gap in the CO<sub>2</sub> balance closure – possibilities, challenges, and uncertainties