Acoustic tomography in the atmospheric surface layer

Abstract

Abstract. Acoustic tomography is presented as a technique for remote monitoring of meteorological quantities. This method and a special algorithm of analysis can directly produce area-averaged values of meteorological parameters. As a result consistent data will be obtained for validation of numerical atmospheric micro-scale models. Such a measuring system can complement conventional point measurements over different surfaces. The procedure of acoustic tomography uses the horizontal propagation of sound waves in the atmospheric surface layer. Therefore, to provide a general overview of sound propagation under various atmospheric conditions a two-dimensional ray-tracing model according to a modified version of Snell's law is used. The state of the crossed atmosphere can be estimated from measurements of acoustic travel time between sources and receivers at different points. Derivation of area-averaged values of the sound speed and furthermore of air temperature results from the inversion of travel time values for all acoustic paths. Thereby, the applied straight ray two-dimensional tomographic model using SIRT (simultaneous iterative reconstruction technique) is characterised as a method with small computational requirements, satisfactory convergence and stability properties as well as simple handling, especially, during online evaluation.Key words. Meteorology and atmospheric dynamics (turbulence; instruments and techniques).

Time-dependent stochastic inversion in acoustic travel-time tomography of the atmosphere

“…The probable reason for this is a small effect of the temperature fluctuations on the travel times in comparison to the wind-velocity fluctuations. Indeed, the LES temperature fluctuations were in the range ͓−0.15, 0.35͔ K while, for many meteorological problems, the acceptable error of temperature measurements is ±0.3 K. 7,8 Note that to improve the reconstruction of temperature fluctuations in acoustic tomography, one can use reciprocal transmission of sound waves. The proposed TDSI algorithm also allows one to estimate the temporal mean values of the temperature and wind velocity fluctuations at any given spatial point.…”

“…The inverse algorithms used were the stochastic inversion ͑SI͒ approach 5 and the simultaneous iterative reconstruction technique. [6][7][8][9][10] In the present paper, a generalization of the SI approach in travel-time acoustic tomography of the atmosphere is developed that allows one to effectively increase the number of data without increasing the number of sources and receivers. The idea of travel-time acoustic tomography of the atmosphere is based on the fact that the time required for sound to propagate through a certain volume depends on the adiabatic sound speed ͑and hence on temperature͒ and wind velocity within that volume.…”

“…2, [6][7][8][9][10]12,13 In the opposite case, when the number of unknowns is greater than the number of available data points, such techniques cannot provide a unique solution, and some additional restrictions must be imposed on the sought fields. Since it is unknown in advance whether the actual fields being estimated obey such restrictions, these techniques may yield a spurious solution that perfectly matches the data but has little to do with the real fields.…”

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

“…[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.…”

“…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 .…”