Infrasound propagation in the atmosphere with mesoscale fluctuations induced by internal gravity waves Igor Chunchuzov, Sergey Kulichkov, Oleg Popov, Vitaly Perepelkin Obukhov Institute of Atmospheric Physics, 119017 Moscow, 3 Pyzhevskii Per., Russia Presented at Acoustics’17, June 25-29, 2017, Boston  

ABSTRACT The influence of the mesoscale wind velocity and temperature fluctuations induced by internal gravity waves(IGWs) on infrasound propagation in the atmosphere is studied. The statistical characteristics of the fluctuations in the parameters of infrasonic signals (such as variances and temporal spectra of the fluctuations in travel time and angle of arrival, amplitude and time duration) caused by gravity wave-associated fluctuations are studied based on the nonlinear model of shaping of the 3-D spatial spectrum of the fluctuations. The nonlinear shaping mechanism for the 3-D spectrum is associated with both the non-resonance interactions between IGWs and wave breaking processes caused by the wave-induced shear or convective instabilities. The 1-D wave number spectra (vertical and horizontal) of the mesoscale fluctuations obtained from the 3-D model are compared with the observed spectra derived from the radar,lidar and airplane temperature and wind measurements in the middle atmosphere. The results of theory and numerical modeling of infrasound scattering from gravity wave-associated fluctuations are presented. The vertical profiles of the wind velocity fluctuations in the stratosphere and lower thermosphere up to the altitudes of 130 km are retrieved from the infrasound scattered from the mesoscale wind velocity and temperature fluctuations. The results of acoustic probing of the stably stratified atmospheric boundary layer using detonation source of acoustic pulses are discussed.

Mesoscale wind velocity and temperature fluctuations induced by internal gravity waves (IGWs) in the atmosphere. 3-D spectrum. Effect of IGWs on the parameters of infrasound signals ( travel time, azimuth of propagation, wave form and coherence ) Infrasound probing of the fine-scale layered structure in the stratosphere, mesosphere and lower thermosphere. Probing of the atmospheric boundary layer using acoustic pulse sources Areas of using real time wind data. C O N T E N T

Parameterization of the 3-D IGW spectrum [Chunchuzov I. , 2002: J. Atm Parameterization of the 3-D IGW spectrum [Chunchuzov I., 2002: J. Atm. Sci., 59, 1753-1772] is parameter of anisotropy, is Coriolis parameter is variance of gravity wave-induced wind velocity fluctuations, is BV-frequency, is the vertical scale of IGW sources, is parameter of nonlinearity of IGW wave field, is characteristic vertical wavenumber, horizontal -to –vertical scale ratio for IGW sources, - critical vertical wavenumber at which wave-induced (shear or convective) instability switches on and generates small-scale turbulence.

Vertical wavenumber (1D) spectra -for relative temperature fluctuations Vertical wavenumber (1D) spectra for wind velocity fluctuations for vertical displacements, for the relative temperature fluctuations

Travel time fluctuations  induced by a random internal gravity wave field [Chunchuzov, I.P. “Influence of internal gravity waves on sound propagation in the lower atmosphere.” Meteorol. and Atm. Phys., 34, 1-16, 2003] < 2>= 2 n R0 0 [(2<T2>+<2>)/kZ*]/(3c02) (0) <2>= 41/2 r 0 (2 <T2>+ <2>)/(3k0c02) (0), 0 =0.1-0.3 n-number of total reflections, R0 =z(x0)-1 is the radius of curvature of the ray path z=z(x) taken at a ray turning point (x0, z(x0)), r is horizontal distance. <T2 >= <T‘ 2/(4T02)>, <2> = <Vx 2/c02>, where T'’/T0 and Vx are the fluctuations of the relative temperature and horizontal wind velocity component , respectively. kz*= N/(2<Vх2>)1/2-characteristic vertical wavenumber, N is BV-frequency, k0-characteristic horizontal wavenumber of the 3D spectrum.

Fluctuations of the azimuth of arrival is the sound travel time between two receivers separated by distance is the temporal fluctuation of For the stratospheric arrival at r=200km from a source: For in the range (0.0025 - 0.01), is in the range (2.5 - 10) deg This is the estimate of the IGW-associated error in determining of the azimuth of arrival, which is consistent with the observed azimuth fluctuations from surface explosions [Kulichkov et al., InfraMatics, 18, June 2007]

Transverse coherence function of a plane sound wave is extinction coefficient Ostashev V.E., I.P. Chunchuzov D.K. Wilson. JASA, 2005, V.118 (6)

V. Zhupanovsky, KAMCHATKA

Recordings of infrasound signals at 110km (IS44) and 91 km (PRT) from v. Zhupanovsky

Trace velocity vs time in the shadow zone

Acoustic field (IS44) for the unperturbed atmosphere, f=0 Acoustic field (IS44) for the unperturbed atmosphere, f=0.2 Hz, Oct 11 2014 Acoustic field for the unperturbed atmosphere, f=0.2 Hz, Oct 11 2014

Effect of the N-wave reflection from fine-scale layered Effect of the N-wave reflection from fine-scale layered wind velocity and temperature fluctuations Analytic solution for the reflected wave field is a convolution of the vertical profile of the gradient of relative effective sound speed fluctuations with the wave form of the incident N-wave. Time duration of the reflected signal Vertical sound speed fluctuations obtained from the model by I.P. Chunchuzov. “On the nonlinear shaping mechanism for gravity wave spectrum in the atmosphere.” Ann. Geophys., 27, 4105-4124, 2009 Calculated reflected signal for the incident N-wave with

Retrieval method (Chunchuzov et al. , Izvestiya, Atm Retrieval method (Chunchuzov et al., Izvestiya, Atm. Ocean Physics, 2015, V.51(1), p.69-87) Waveform of the reflected signal Relation between reflected signal and profile of fluctuations , N-wave ( i=1,2,…,n) m is number of discrete values of t within a time duration of N-wave Finding approximate solution X by a least square method

Acoustic field (IS44) for the retrieved profile, f=0.2 Hz, Oct 11 2014

COMMON STRUCTURE OF THE INFRASOUND ARRIVALS

Retrieved profiles from 3 successive signals with the 15-min time interval, v. Tungurahia, July 15, 2006

RETRIEVED PROFILES OF THE FLUCTUATIONS Ceff(z) [Chunchuzov, I. , S RETRIEVED PROFILES OF THE FLUCTUATIONS Ceff(z) [Chunchuzov, I., S. Kulichkov, V. Perepelkin, O. Popov, P. Firstov, J.D. Assink, E. Marchetti, , J. Geophys. Res., 120, doi:10.1002/2015JD023276, 2015] Chunchuzov, I., S. Kulichkov, V. Perepelkin, O. Popov, P. Firstov, J.D. Assink, E. Marchetti, “Study of the wind velocity- layered structure in the stratosphere, mesosphere and lower thermosphere by using infrasound probing of the atmosphere”, J. Geophys. Res., 120, doi:10.1002/2015JD023276, 2015

The comparison of the vertical profiles of wind velocity fluctuations obtained by a) MU radar (reproduced from Tsuda T., 2014, Proc. Jpn. Acad., Ser. B 90, V. 90: 12-27) and infrasound sounding (Chuchuzov et al., 2015, J. Geophys. Res., 120: 8828-8840, doi:10.1002/2015JD023276.)

Vertical wave number spectra of the retrieved Ceff-fluctuations

How could we use retrieved wind profiles in the upper atmosphere How could we use retrieved wind profiles in the upper atmosphere? 1)For monitoring temporal variability of the wind field in the lower thermosphere (90-130 km) . This layer is not studied by other remote sensing methods (radars, lidars and satellites). Wind data in the upper atmosphere are necessary for - climate change modeling, -modeling the transport of atmospheric aerosol, particularly, ash from volcano eruptions, -modeling infrasound propagation through atmosphere with real-time retrieved IGW perturbations. 2) For studying the statistical characteristics of the IGW perturbations (variances, vertical wavenumber spectra and structure functions) to parameterize the effect of IGWs on infrasound signal parameters and wave drag in general circulation models of the atmosphere. What are the main statistical parameters of random IGW perturbations that affect parameters of infrasound signals?

Acoustic pulse generator in Armenia Chunchuzov I.P., Perepelkin V.G., Popov O.E., Kulichkov S.N.,Vardanyan A.A. , Ivazyan G.E., Khachikyan Kh.Z.. Study of the characteristics of the fine-scale layered structure of the lower troposphere by using acoustic pulse remote sensing . Izvestiya Atmospheric and Oceanic Physics, 2016 , Vol. 53, No. 3, pp. 279–293. Chunchuzov I.P., Perepelkin V.G., Popov O.E., Kulichkov S.N.,Vardanyan A.A. , Ivazyan G.E., Khachikyan Kh.Z.. Study the characteristics of the fine-scale layered structure of the lower troposphere by using acoustic pulse remote sensing . Izvestiya Atmospheric and Oceanic Physics, 2016 (in Press) Generator with 3 acoustic cannons. 1- аcoustic cannons; 2 – control panel 3- gas cylinders with an explosive mixture (propane)

Рис. 5. Вверху: Один из сигналов , зарегистрированный в Армении (г Рис.5. Вверху: Один из сигналов , зарегистрированный в Армении (г. Талин) 17.09.15 в 19:38:48 мест. вр. на расстоянии r=2.25 км от источника. На верхней панели указаны волноводный приход сигнала (waveguide) (а), и приходы 1,2 и 3, обнаруженные на “хвосте” сигнала с помощью корреляционного анализа сигналов на приемниках треугольной антенны. На панелях б)-д) , показаны, соответственно: зависимости от текущего времени (горизонтальная ось) спектра всего сигнала (единицы справа в Па2/Гц) в зависимости от частоты (вертикальная ось слева); средней когерентности сигналов между приемниками антенны в зависимости от частоты; азимута прихода сигнала (в град) и его фазовой скорости (единицы в м/c, указаны справа). Внизу: Спектры начального сигнала (20м), его приходов 1,2, 3 (2.25км) и шума перед приходом всего сигнала (е).

(middle panel) by parabolic equation method Upper panels: Vertical profiles of the effective sound speed fluctuations retrieved from the 3 arrivals 1,2 and 3. Lower panel: Calculation of the signal and its spectrum as a function of time (middle panel) by parabolic equation method Upper panels: Vertical profiles of the effective sound speed fluctuations retrieved from the 3 arrivals 1,2 and 3. Lower panel: Calculation of the signal P’ and its spectrum as a function of time (middle panel) by parabolic equation method

Retrieved profiles DСeff(z) obtained from the arrivals 1 and 2 of the signal (middle and right panels) superimposed on the mean profile (left) obtained from sodar and temperature profiler data on Aug 13 2006 near Moscow (ZNS) Retrieved profiles DСeff(z) obtained from the arrivals 1 and 2 of the signal (middle and right panels) superimposed on the mean profile (left) obtained from sodar and temperature profiler data on Aug 13 2006 near Moscow (ZNS)

Calculated signal (Parabolic Equation) P’ in the range 10-100 Hz for the profile Сeff(z) in two cases: а) using sodar profile b) using sodar profile+ retrieved fluctuations in the layers 1 (340-390m) and 2 (560-640m). The retrievals 1 and 2 are predicted only in the case b. Calculated signal (Parabolic Equation) P’ in the range 10-100 Hz for the profile Сeff(z) in two cases: а) using sodar profile б) using sodar profile+ retrieved fluctuations in the layers 1 (340-390m) and 2 (560-640m). The arrivals 1 and 2 are predicted only in the case (б). Recorded arrivals 1 and 2 (в)

CONCLUSIONS The recently developed infrasound probing method allowed us to retrieve vertical profiles of the wind velocity fluctuations in the stratosphere (30-55 km) and MLT (90-140 km). Till present these layers have not been studied well by other remote sensing methods (radars, lidars, satellites). Despite the difference in the locations and time periods for the retrieved wind velocity profiles all of them show common features such as similar power law decays for the vertical wave number spectra in the upper stratosphere in the range of vertical scales from a few kilometers to about 100 m. 3. The obtained vertical wave number spectra show a capability of the infrasound method in studying statistical characteristics of the mesoscale wind velocity fluctuations in the middle and upper atmosphere. These characteristics are necessary for the parameterization of the gravity wave drag in climate change and weather prediction models 4. The incorporation of the real-time wind vertical structure into the infrasound propagation models can significantly improve the localization of the infrasound sources.