Assessing middle atmosphere weather models using infrasound detections from microbaroms

Abstract

SUMMARY The nonlinear interaction of ocean surface waves produces coherent infrasound noise—microbaroms—between 0.1 and 0.5 Hz. Microbaroms propagate through the atmosphere over thousands of kilometres due to low absorption and efficient ducting between the ground and the stratopause. These signals are globally and permanently detected by the International Monitoring System (IMS) infrasound network, which has been established to monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty. At the International Data Centre (IDC) in Vienna, where IMS data are routinely processed, microbarom detections appear in overlapping frequency bands, and are treated as false alarms. Therefore, understanding the variability in microbarom detections is essential to support the IDC in the reduction of the false alarm rate. In this study, microbarom amplitudes and the direction of arrivals at the German infrasound station IS26 were modelled. For the simulations, the source was described by an operational ocean wave interaction model, and the signal amplitude was modelled using a semi-empirical attenuation relation. This relation strongly depends on middle atmosphere (MA; i.e. 15–90 km altitude) dynamics; however, vertical temperature and wind profiles, provided by numerical weather prediction (NWP) models, exhibit significant biases and differences when compared with high-resolution light detection and ranging instrument (lidar) soundings in altitudes where infrasound signals propagate. To estimate uncertainties in the modelled amplitude, a fully autonomous lidar for MA temperature measurements was installed at IS26. Temperature and wind perturbations, considering observed biases and deviations, were added to the operational high-resolution atmospheric model analysis produced by the European Centre for Medium-Range Weather Forecasts. Such uncertainties in horizontal winds and temperature strongly impact propagation conditions, explaining almost 97 per cent of the actual detections, compared to 77 per cent when using the direct output of the NWP model only. Incorporating realistic wind and temperature uncertainties in NWP models can thus significantly improve the understanding of microbarom detections as well as the detection capability of a single station throughout the year.

Characterizing the global ocean ambient noise as recorded by the dense seismo-acoustic Kazakh network

“…Atmospheric specifications are given at the station and are assumed to be constant along the propagation path. This approach shows good results for microbaroms generated in the northern hemisphere similar to those described by De Carlo et al 2018and Hupe et al (2018). However, such an approach does not explain the observed microbarom amplitude for sources located south of the stations.…”

“…Where previous studies analysed microbarom signals at a single station (Hupe et al, 2018), further investigations are conducted in this study by considering a multi-year dataset of continuous records collected by the IGR network. Regional features of both microbaroms and microseisms are highlighted.…”

“…However, such signals are well detected using standard processing techniques, such as beamforming methods used from the sixties (Capon, 1972;Haubrich and McCamy, 1969;ToksoZ and Lacoss, 1968). More recent algorithms are efficient to detect and characterize continuous and global microbarom signals (Evers and Haak, 2001;Garcès, 2004;Landès et al, 2012;Hupe et al, 2018). The above mentioned studies were conducted using IMS infrasound data as well as infrasound records from national networks ( e.g.…”

Microbarom radiation and propagation model assessment using infrasound recordings: a vespagram-based approach

“…well as for long-term assessment of model product uncertainties, particularly when applied to data from a global network of infrasound stations. A key aspect of this approach is that benchmarking between model and infrasound vespagrams considers all back-azimuth directions rather than just the direction of the dominant microbarom source, as done in several previous studies (Garcés et al, 2004;Hupe et al, 2019;De Carlo et al, 2019;Smirnov et al, 2020;De Carlo et al, 2020). The microbarom soundscape at a station is typically a sum of components stemming from a wide spatial distribution of ocean regions, and recently den Ouden et al (2020) demonstrated that an iterative decomposition of the array spatial covariance matrix using the CLEAN algorithm (Högbom, 1974) can be exploited to resolve the back-azimuth and trace velocity of the most coherent wave front arrivals.…”

“…In addition to prospective numerical weather prediction improvements, the suggested vespagram-based approach may be applied in multi-technology studies of atmospheric dynamics, for example initiatives building on the Atmospheric dynamics Research InfraStructure in Europe (ARISE) projects (Blanc et al, 2018(Blanc et al, , 2019. These aim at harvesting from synergies between ground-based infrasound observations, radar and lidar systems, as well as airglow and satellite observations to monitoring the middle atmosphere (Chunchuzov et al, 2015;Le Pichon et al, 2015;Blanc et al, 2018;Hupe et al, 2019;Smets et al, 2019;Hibbins et al, 2019;Assink et al, 2019; Le Pichon et al, 2019).…”