Updated Global Reference Models of Broadband Coherent Infrasound Signals for Atmospheric Studies and Civilian Applications

Kristoffersen, Samuel; Pichon, Alexis Le; Hupe, Patrick; Matoza, Robin S.

doi:10.1029/2022ea002222

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…To this end, a low-frequency (0.15-0.35 Hz) microbarom dataset from Hupe et al (2021Hupe et al ( , 2022 is used. The dataset is generated from IMS infrasound station data using the Progressive Multi-Channel Correlation (PMCC) array signal-processing approach (Cansi, 1995;Cansi & Le Pichon, 2008;Kristoffersen et al, 2022) and output parameters include wavefront characteristics as a function of time under the assumption of a far-field plane-wave model. Among other output parameters, the processing of Hupe et al (2022) provides a quality parameter, Q, for the data at each time sample.…”

Section: Infrasound Datasetmentioning

confidence: 99%

Estimating stratospheric polar vortex strength using ambient ocean‐generated infrasound and stochastics‐based machine learning

Vorobeva,

Eggen,

Midtfjord

et al. 2024

Quart J Royal Meteoro Soc

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There are sparse opportunities for direct measurement of upper stratospheric winds, yet improving their representation in subseasonal‐to‐seasonal prediction models can have significant benefits. There is solid evidence from previous research that global atmospheric infrasound waves are sensitive to stratospheric dynamics. However, there is a lack of results providing a direct mapping between infrasound recordings and polar‐cap upper stratospheric winds. The global International Monitoring System (IMS), which monitors compliance with the Comprehensive Nuclear‐Test‐Ban Treaty, includes ground‐based stations that can be used to characterize the infrasound soundscape continuously. In this study, multi‐station IMS infrasound data were utilized along with a machine‐learning supported stochastic model, Delay‐SDE‐net, to demonstrate how a near‐real‐time estimate of the polar‐cap averaged zonal wind at 1‐hPa pressure level can be found from infrasound data. The infrasound was filtered to a temporal low‐frequency regime dominated by microbaroms, which are ambient‐noise infrasonic waves continuously radiated into the atmosphere from nonlinear interaction between counter‐propagating ocean surface waves. Delay‐SDE‐net was trained on 5 years (2014–2018) of infrasound data from three stations and the ERA5 reanalysis 1‐hPa polar‐cap averaged zonal wind. Using infrasound in 2019–2020 for validation, we demonstrate a prediction of the polar‐cap averaged zonal wind, with an error standard deviation of around 12 m·s compared with ERA5. These findings highlight the potential of using infrasound data for near‐real‐time measurements of upper stratospheric dynamics. A long‐term goal is to improve high‐top atmospheric model accuracy, which can have significant implications for weather and climate prediction.

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Section: Infrasound Datasetmentioning

confidence: 99%

Estimating stratospheric polar vortex strength using ambient ocean‐generated infrasound and stochastics‐based machine learning

Vorobeva,

Eggen,

Midtfjord

et al. 2024

Quart J Royal Meteoro Soc

Self Cite

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On-site infrasound calibration to correct wave parameter estimation

Kristoffersen,

Le Pichon,

Schwardt

et al. 2024

The Journal of the Acoustical Society of America

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The International Monitoring System (IMS) has been established as part of the Comprehensive Nuclear-Test-Ban Treaty to monitor nuclear testing and is comprised of infrasound, hydroacoustic, seismic, and radionuclide stations; it is also used more widely by the scientific community for scientific and civilian applications. For the infrasound stations, on-site calibration provides an accurate measure of the sensor (microbarometer + wind-noise reduction system) frequency response, used to monitor that the sensor response remains within tolerance of the baseline established when the station is certified. However, this on-site calibration can also be used when there are issues/defects with the sensors. As a result, the on-site calibration can be used to correct wave parameter estimations and increase the detection capability of the station. Examples using an experimental sensor at the IMS station IS26 (Germany) and IS47 (South Africa) demonstrate that errors of several degrees and tens of m/s can be introduced, under certain conditions, for the back azimuth and trace velocity, respectively. By using the on-site calibration, these errors are removed, and the correct back azimuth, trace velocity and amplitude are retrieved. This can be especially useful for the identification of infrasound signals, and the localization of their sources.

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Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems

et al. 2023

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To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (<0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.

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Updated Global Reference Models of Broadband Coherent Infrasound Signals for Atmospheric Studies and Civilian Applications

Cited by 4 publications

References 38 publications

Estimating stratospheric polar vortex strength using ambient ocean‐generated infrasound and stochastics‐based machine learning

Estimating stratospheric polar vortex strength using ambient ocean‐generated infrasound and stochastics‐based machine learning

On-site infrasound calibration to correct wave parameter estimation

Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems

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