On 2023 May 20, a ∼3.7 m diameter asteroid entered Earth’s atmosphere over northwest Queensland, Australia, at 27.9 km s ^−1 and underwent catastrophic fragmentation at ∼29 km altitude, producing 1 of the 20 most energetic bolide events reported by NASA’s Center for Near-Earth Object Studies (CNEOS) since 1988 and the most energetic over Australia (∼7.1 kt TNT equivalent). We present a multimodal analysis integrating CNEOS optical data, trajectory determination, infrasound detections at six International Monitoring System stations (up to 8030 km), seismic observations, and Himawari-9 geostationary satellite thermal infrared imagery. Infrasound observations independently confirm a multikiloton source, and the infrasound-derived source altitude (30 ± 5 km) is consistent with the CNEOS-reported peak brightness altitude. We report the first nighttime detection and mass quantification of a bolide dust cloud from geostationary orbit; the cloud persisted for over 1 hr, yielding a dust mass of 10.9–32.8 metric tons (14%–43% of the preatmospheric mass). With an estimated recurrence interval of ∼1.5 yr for events of this energy or greater, the Queensland superbolide provides an end-to-end observational benchmark for atmospheric entry models and planetary defense.

For infrasound propagation most full wave models are restricted to a range-altitude plane and do not model out of plane propagation. This planar approximation is sufficient for many applications. Exceptions include the azimuth deviation produced by cross winds and out of plane ground reflections produced by nontrivial orography. The latter is particularly important because the shadowing effect of mountain ranges is significantly overestimated by the planar approximation. We have been working on extending the capabilities of the NCPAprop's ePape planar parabolic equation (PE) model to 3-D propagation. ePape solves the one way radial wave equation in cylindrical coordinates using a Pade approximant to represent the exponential of the range step operator. The extension to out-of-plane propagation retains the ePape Padé approximation for the range steps, but adds a stable Runge–Kutta algorithm for the azimuthal components. The basic algorithms and approximations as well as the inherent difficulties will be discussed and we will report on progress to date.

Wind-induced turbulent pressure fluctuations, known as “wind noise,” are the dominant noise source in infrasound measurements, limiting detection capability. Wind noise levels are strongly influenced by local topography, yet their relationship remains poorly quantified. This study develops a quantitative framework linking measurable topographic features to wind noise characteristics for improved site selection. Infrasound data collected at the University of Mississippi's Biological Field Station (UMBFS) were analyzed in 0.1–1 Hz and 1–10 Hz frequency bands. Root mean square (RMS) pressure time series were statistically characterized using Kernel density estimation (KDE) to assess probability density functions (PDFs) and corresponding cumulative distribution functions (CDFs). Topographic variables—including ground elevation, vegetation density, and canopy height—were derived from airborne and terrestrial LiDAR. Results have been demonstratingdistinct noise distributions between sites with different topographic characteristics separated by as short as 20-m. These findings support the development of a predictive algorithm that uses topographic inputs to estimate probabilities of wind noise levels remaining below specified thresholds, providing a quantitative basis for improved infrasound site selection and local wind noise mapping.

Simulation of infrasound propagation in a collaborative research environment requires tested and validated numerical models that are freely available, readable enough to parse and understand, and efficient enough to run on a standard workstation. The NCPAprop package was designed to facilitate this kind of research by directly addressing all three of these criteria. Now in its second release version, and with the third release under active development, NCPAprop is an open-source command-line package written in C++ and is widely used in the international infrasound community. It provides two normal-mode models and a parabolic equation model that supports range-dependent atmospheric specifications and the inclusion of orography. The capabilities of the package will be discussed, as well as the status and direction of current development.

Turbulent pressure fluctuations, commonly known as “wind noise”, induced by wind flow in the planetary boundary layer (PBL), close to the ground surface, are the dominant noise source in infrasound measurements, masking signals of interest and limiting detection capability. Therefore, selecting deployment sites with naturally low wind noise levels is critical for maximizing signal-to-noise ratios, independently of applying wind-noise reduction systems in situ or even noise filters in the data processing stage. Noise levels are expected to correlate with local topography, which influences wind flow patterns and turbulence. However, their relationship remains poorly quantified, and site selection still relies on qualitative assessments of topographic features and demands logistically intensive noise tests for long periods of time. To address this gap, the first stage in developing a quantitative framework linking wind noise levels to measurable topographic parameters for wind noise mapping and improved site selection is presented. Wind noise data were collected at multiple test sites in Mississippi and Alabama and analyzed in two frequency bands (0.1–1 Hz and 1–10 Hz). Root Mean Square (RMS) pressure levels were computed to average background pressure fluctuations. Kernel Density Estimation (KDE) was applied to estimate Probability Density Functions (PDFs) of RMS Pressures, and Cumulative Distribution Functions (CDFs) were generated to quantify the probability of noise levels falling below key detection thresholds. Preliminary results demonstrate clear differences in noise distributions between sites separated by distances as short as 20 m, even within forested areas, indicating the need for high resolution noise level mapping.

Wind Observations Through Ambient Noise (WOTAN) technique was first developed in the 1980s to address the limitations of low temporal-resolution satellite data and sparse ship- or surface-buoy-based measurements that can be severely impacted by bad weather. The recent integration of passive acoustic monitoring systems on autonomous platforms (e.g., gliders) has reinvigorated the application of WOTAN for high-resolution, near-surface wind and wave forecasts. This presentation will highlight the opportunities, challenges, and validation efforts associated with using WOTAN in the Northern Gulf of Mexico, a region significantly affected by industrial activities and hurricanes. Raw or processed acoustic data can be assimilated into operational weather-forecasting models (those used by NOAA and the U.S. Navy) to refine cyclone intensification and overall marine weather predictions. These advancements provide the potential of passive acoustic methods to enhance meteorological modeling and advance our understanding of the physics of air-sea interactions in dynamic coastal ocean environments. [Work supported by the Office of the Under Secretary of Defense for Research and Engineering (Award No. FA9550-21-1-0215)]

Noise generated at an infrasound sensor by local turbulent pressure fluctuations in the atmosphere, generally referred to as wind noise, is arguably the greatest impediment to infrasound signal detection. In recent years there has been extensive research on the underlying mechanisms for wind noise generation. While considerable progress has been, and continues to be, made there is still no comprehensive method for estimating, from some first principles, wind noise levels at a given site. At present, the best method for determining wind noise levels at potential deployment sites is to place test sensors at the sites and collect data for an extended period of time. Here we present a protocol we have developed for the analysis of such data. The method entails identifying the frequency band relevant to the proposed application and then estimating the appropriate band limited short time rms pressure levels at the test sensors. This generates a time history of rms pressure levels. We then develop a statistical model for the expected band limited noise levels which can be compared to an appropriate threshold model for the determination of site acceptability.

Turbulent pressure fluctuations around infrasound sensors, known as wind noise, are the primary factor masking infrasound detections of interest, such as signals generated by natural hazards. Identifying deployment sites with sufficiently low wind noise levels is crucial for achieving good signal-to-noise ratios. Noise levels are expected to correlate with local topography, which influences wind flow patterns and turbulence. This study investigates the relationship between wind noise levels and topographic features to support wind noise mapping and optimal sensor site selection. Wind noise data were collected from multiple test sites and analyzed in two frequency bands: 0.1–1 and 1–10 Hz. rms pressure levels were calculated for each band, and Kernel Density Estimation (KDE) was applied to estimate probability density functions (PDFs). The associated cumulative distribution functions (CDFs) provide the probability of noise levels falling below specific thresholds, offering a statistical characterization of wind noise at each site. Topographic data were derived from remote sensing techniques, including LiDAR and satellite imagery, to analyze ground elevation, vegetation density, and canopy height. A framework for understanding wind noise behavior across varying topographic conditions, supporting the development of effective methodologies for wind noise mapping and site selection, is developed.

Long-duration infrasound recordings were performed using several arrays deployed in the southeastern United States during the Spring of 2018. This data has been analyzed via several beamforming methods to detect and locate sources related to severe weather, including tornadic storms. In parallel with this analysis, we also collected weather-related data products from National Weather Service, including their database of severe weather events as well as NEXRAD radar maps covering the entire time period. In this paper, we compare the weather-related data to infrasound activity through time. As previously reported, we found infrasound tracks that are clearly aligned with NWS-identified tornados. Through this new, extended comparison spanning two months of recordings at each site, we have also found that some non-tornadic storms also generate infrasound tracks. The data show that some strong (but non-tornadic) storms generate clear infrasound tracks while others do not. This finding continues to motivate additional research into the relationship between storm structure and infrasound emissions.

How effective are methods for estimating bolide energies from infrasound signal period-yield relationships? A single global period–energy relation can obscure significant variability introduced by parameters such as the atmospheric Doppler wind profile and the bolide’s energy deposition profile as a function of altitude. Bolide speed, entry angle, burst altitude, and multiepisode fragmentation may all play a role in defining the detected period of the shockwave. By leveraging bolide light-curve data from the Center for Near Earth Object Studies, we re-examined the period–energy relation as a function of these parameters. Through a bootstrap approach, we show that various event subsets can deviate from widely cited period–energy models and we identify which specific conditions most strongly reshape the period–energy scaling. The results define both the fidelity and reliability of period–energy relations when no additional data beyond the infrasound record is available and improve the outcome when supporting data from bolide trajectories and light curves are included. Ultimately, these findings expand the scope of earlier models, providing a nuanced and robust framework for infrasound-only yield estimation under a range of bolide scenarios.

Data recorded by several infrasound sensor arrays deployed in the southeastern United States during the spring of 2018 have been analyzed during a period when a storm front passed through and spawned several tornado touchdowns. The tornadoes ranged from EF-0 to EF-2. Accurate bearings have been obtained, corresponding to tornadoes at ranges up to more than 100 km. Earlier in the day, a convective storm cell passed and triggered a tornado warning but no verified tornado touchdowns. During this earlier passage, a weaker signal was detected with bearings that tracked this convective storm cell. The analyses reveal that on the systems deployed, the dominant band of coherent infrasound measured from the tornadic storms was between 2 and 6 Hz. Atmospheric conditions are known to have a significant influence on signal detection because of propagation effects and local wind noise conditions. Propagation modeling and wind noise analysis were undertaken and are observed to be consistent with signal detection from the tornadoes. Because of the static sources, as well as the multiple strong cells that were in the region simultaneously, it was necessary to use array signal processing methods that are capable of resolving multiple sources.

NCPAProp is a command-line-driven, fully open-source software package first released in 2016 and under continuous development since then. It aims to provide a comprehensive set of tested and validated full-wave numerical models to simulate long-range propagation of infrasound through realistic atmospheres, at frequencies nominally in the range of 0.1-10 Hz. The package currently includes two normal-mode models of varying complexity, and a parabolic equation model with the option to include a range-dependent atmosphere and orography. This presentation will focus on a description of the models, a discussion of their use, and a summary of the short-term and long-term ongoing development goals for the overall package.

This presentation describes a method for predicting RMS wind noise levels within a user-specified infrasonic frequency band (e.g., 1–10 Hz) using local topographic features. This capability is especially valuable when performing site selection for infrasound sensor array deployment. The method is based on building models using measured infrasound data from known sensor site locations and the local topographic features corresponding to the site. The presented results correspond to using features such relative terrain elevation and relative vegetation height obtained from publicly available sources. Mean wind speed and direction are also included as predictive parameters and these can be from data measured at the infrasound sensor site or from estimates obtained from a tool such as the Weather Research Forecast (WRF) model as was utilized for this investigation. A novel feature of the approach is that the terrain specification is not uniquely site specific but also depends on wind direction. Essentially, the topographic features are specified relative to wind direction and not in absolute coordinates. This enables a much richer set of samples from which to build the predictive models. While many options are potentially available for model building, this work focused on the use of multiple-layer artificial neural networks as a basis for regression. The results presented correspond to data from several infrasound sensor sites in the U.S Array Project.

The notion that tornadoes emit an infrasonic signal that can be detected from great distances; and thus be used to aid in the detection, monitoring, and potential tracking of active tornadoes; has been discussed for decades. Much evidence has accumulated showing that tornadoes emit acoustic radiation in at least two distinct frequency bands, an infrasonic band and a low frequency audible band. It is believed that the physical mechanisms responsible for the infrasonic and audible emissions are different. We will focus here on the infrasonic emissions. Experimental evidence that tornadoes do indeed emit an infrasonic signal will be reviewed and the current status of the theoretical understanding of the radiation mechanism will be discussed. Data collected during the passage of storm fronts that did not spawn tornadoes were also analyzed to address the possibility that the observed infrasonic signals might be associated with storm fronts rather than tornadoes. Those results will be presented. Finally, our current research campaign and plans will be outlined.

It has been established that tornadoes emit an infrasonic signal that is regularly detected in the 1 to 10 Hz band, although the actual band is expected to be wider. The physical mechanism through which this signal is generated is not yet fully understood and is the subject of current research. Here we discuss some of our experimental efforts, past and present. During the tornado seasons of 2017, 2018, and 2019, we deployed a network of infrasound sensor arrays in northern Alabama, southern Tennessee, and northwestern Georgia. We performed detailed analysis of one particular storm front that spawned at least eight identified tornadoes in northwestern Alabama during its passage. For that storm we find that when propagation modeling and wind noise analyses suggest that the signal should have been detected at a given array it always was. A long term monitoring effort is now underway in Mississippi. The Mississippi network deployment strategy relied on statistical analyses of tornado touchdown probability, signal transmission loss, and local wind noise studies. The network and array design for our Mississippi network was heavily informed by our experiences in Alabama and, we believe, represents the current state-of-the-art. In this presentation we will give an overview of what was learned from our previous deployments in Alabama, what strategies were used for the current deployment in Mississippi, and the current state of our data collection and analysis.

Acoustic reconnaissance of tropical cyclones with underwater gliders is an emerging approach for a comprehensive understanding of the oceanic conditions leading to the formation and intensification of cyclones and for aiding in wind speed forecasting. Shaw et al. (1978) showed that the underwater sound pressure level is in the linear relationship to the logarithm of the moderate local wind speed. Wilson and Makris (2006) proposed an acoustic surface source model for hurricane wind speeds. The model requires a parameterization of the winds’ contribution. The inversion of the acoustic data leads to estimation of the local wind speeds. The Gulf of Mexico acoustic dataset, collected by the bottom-anchored monitoring system during the passage of storm Barry (2019), is analyzed and correlated with the high-resolution wind speed data generated by the Weather Research and Forecast model. The efficacy of gliders outfitted with acoustic system to provide the real-time reporting of the local wind speeds is also discussed based on the flights’ recorded soundscapes. Acoustic glider reconnaissance offers a safe and cost-efficient way of contributing new type data to NOAA and US NAVY weather forecasting models. [Work supported by the Office of the Under Secretary of Defense for Research and Engineering, award# FA9550-21-1-0215.]

The propagation of infrasound from a vertically extended source is investigated. The source is allowed to have azimuthal and vertical variation. The radial extent of the source is assumed to be small compared to an acoustic wavelength. No such assumption is made for the vertical extent. The problem is treated in cylindrical coordinates, as is appropriate for outdoor sound propagation, in a way analogous to the treatment of radiation from a compact source in unbounded three dimensional space. Several equivalent formulations will be presented along with example cases.

A terrain capable parabolic equation (PE) propagation algorithm for long range infrasound propagation modeling has been implemented using Padé approximations for the various operator valued functions that arise in PE algorithms. In this work, the influence of the winds are captured by the effective sound speed approximation and propagation is restricted to the range-altitude plane. The ground topography is included by the addition of an impenetrable fluid below the ground surface. The impedance condition at the ground is handled explicitly, including both vertical and radial components. It is found that including terrain can have a large influence on long range propagation. In particular, reflections from a sufficiently steep slope can change the inclination angle enough to move the propagation path from one atmospheric duct to another.

Infrasound propagation depends critically on the temperature and wind velocity profiles at the time of signal propagation. These vary wildly, showing at best qualitative systematic behavior. It follows that any predictions of signal detection capability are necessarily statistical in nature. One approach is to collect a large number of historical temperature and wind velocity profiles, for a specific time and location, and use this as a sample space from which to generate a statistical model for the atmosphere as a propagation medium. To generate a statistical model for the expected transmission loss one must run a propagation model through a sampling of atmospheric profiles sufficient to reproduce the statistical behavior. The sampling is done using an Empirical Orthogonal Function (EOF) decomposition. The advantages of the use of an EOF decomposition will be discussed and examples from transition zone (ranges less than 100 km) and regional (ranges of several hundreds of kilometers) will be presented. Given a model for the turbulent pressure fluctuation levels near a receiving array, signal detection probability functions can be estimated.