Project

Middle Atmosphere Dynamics: Exploiting Infrasound Using a Multidisciplinary Approach at High Latitudes

Goal: This is a basic research project where we will exploit a combination of infrasonic and meteor radar datasets to characterize the middle atmosphere and to constrain high-top atmospheric models. This will pave the way for improved medium-range weather forecasting.

Funding: Research Council of Norway FRINATEK/FRIPRO basic research grant number 274377.

Date: 1 August 2018 - 1 October 2022

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Project log

Sven Peter Näsholm
added a research item
The 15 January 2022 Hunga, Tonga, volcano's explosive eruption produced the most powerful blast recorded in the last century, with an estimated equivalent TNT yield of 100–200 megatons. The blast energy was propagated through the atmosphere as various wave types. The most prominent wave was a long-period (>2000 s) surface-guided Lamb wave with energy comparable to that of the 1883 Krakatoa Lamb wave; both were clearly observed by pressure sensors (barometers) worldwide. Internal gravity, acoustic-gravity, and infrasound waves were captured in great detail by the entire infrasound component of the International Monitoring System (IMS). For instance, infrasound waves (<300 s period) were seen to circumnavigate Earth up to eight times. Atmospheric waves captured by the IMS infrasound network and selected barometers near the source provide insight on Earth's impulse response at planetary scales.
Sven Peter Näsholm
added 2 research items
This study suggests a stochastic model for time series of daily-zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a sum of a deterministic seasonality function and a L\'evy-driven multidimensional Ornstein-Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model's longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well-represented by normal inverse Gaussian distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the 4th order continuous time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as for example sudden stratospheric warmings. Through stratosphere-troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.
This study suggests a stochastic model for time series of daily zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a combination of a deterministic seasonality function and a Lévy-driven multidimensional Ornstein–Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous-time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model’s longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well represented by normal inverse Gaussian-distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the fourth-order continuous-time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as sudden stratospheric warming. Through stratosphere–troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.
Sven Peter Näsholm
added a research item
Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as Parabolic Equation (PE) codes, often prevents the exploration of a large parameter space, i.e., variations in wind models, source frequency, and source location, when deriving reliable estimates of source or atmospheric properties -- in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00, and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (~5 dB error on average, compared to PE simulations) estimate of the infrasound TL.
Sven Peter Näsholm
added 2 research items
Slide deck for our oral presentation "S51A-04: Predicting infrasound transmission loss using deep learning". This was part of the session "Geophysical and Planetary Seismoacoustics: Capturing the Full Wavefield", chaired by Fransiska Dannemann, Sandia National Laboratories & Jelle Assink, Royal Netherlands Meteorological Institute.
Sven Peter Näsholm
added a research item
This study investigates the use of a vespagram-based approach as a tool for multi-directional comparison between simulated microbarom soundscapes and infrasound data recorded at ground-based array stations. Data recorded at the IS37 station in northern Norway during 2014–2019 have been processed to generate vespagrams (velocity spectral analysis) for five frequency bands between 0.1 and 0.6 Hz. The back azimuth resolution between the vespagram and the microbarom model is harmonized by smoothing the modeled soundscapes along the back azimuth axis with a kernel corresponding to the frequency-dependent array resolution. An estimate of similarity between the output of the microbarom radiation and propagation model and infrasound observations is then generated based on the image-processing approach of the mean square difference. The analysis reveals that vespagrams can monitor seasonal variations in the microbarom azimuthal distribution, amplitude, and frequency, as well as changes during sudden stratospheric warming events. The vespagram-based approach is computationally inexpensive, can uncover microbarom source variability, and has the potential for near-real-time stratospheric diagnostics and atmospheric model assessment.
Ekaterina Vorobeva
added a research item
This study investigates a vespagram-based approach as a tool for multi-direction comparison between simulated microbarom soundscapes and infrasound data recorded at ground-based stations. The used microbarom radiation model takes into consideration both finite ocean-depth and the source radiation dependence on elevation and azimuth angles, while the effects of the atmospheric ducting from the source regions to the station are estimated using a semi-empirical attenuation law. The infrasound data recorded at the IS37 station in northern Norway during 2014-2019 are processed in the framework of the velocity spectrum analysis to generate vespagrams presenting signal power depending on time and back-azimuth direction. The analysis is performed for five frequency bands distributed between 0.1 and 0.6 Hz. The processed infrasound data and the modelled microbarom soundscapes are compared in three different aspects: i) azimuthal distribution of dominating signal, ii) signal amplitude and iii) ability to track atmospheric changes during extreme events such as sudden stratospheric warmings (SSW). The back-azimuth resolution between the vespagrams and the microbarom model output is harmonized by smoothing the modelled soundscapes along the back-azimuth axis with a kernel corresponding to the frequency-dependent array resolution. The time-dependent similarity between the model output and the processed infrasound data is estimated using the image processing approach of mean-square difference. The results reveal good agreement between the model and the infrasound data and demonstrate the ability of vespagrams to monitor the time-dependent microbaroms azimuth distribution, amplitude, and frequency on a seasonal scale, as well as changes during SSWs. The presented vespagram-based approach is computationally low-cost and can uncover microbarom source variability. There is also a potential for near-real-time diagnostics of atmospheric model products and microbarom radiation models, especially when applied to multiple stations, e.g. exploiting the CTBTO International Monitoring System network.
Sven Peter Näsholm
added a research item
This study investigates the use of a vespagram-based approach as a tool for multi-directional comparison between simulated microbarom soundscapes and infrasound data recorded at ground-based array stations. Data recorded at the IS37 station in northern Norway during 2014–2019 have been processed to generate vespagrams (velocity spectral analysis) for five frequency bands between 0.1 and 0.6 Hz. The back-azimuth resolution between vespagrams and a microbarom model is harmonized by smoothing the modelled soundscapes along the back-azimuth axis with a kernel corresponding to the frequency-dependent array resolution. An estimate of similarity between the output of a microbarom radiation and propagation model and infrasound observations is then generated based on the image processing approach of mean-square difference. The analysis revealed that vespagrams can monitor seasonal variations in the microbarom azimuth distribution, amplitude, and frequency, as well as changes during sudden stratospheric warming. The vespagram-based approach is computationally inexpensive, can uncover microbarom source variability, and has potential for near-real-time stratospheric diagnostics and atmospheric model assessment.
Sven Peter Näsholm
added a research item
This paper presents an inversion methodology where acoustic observations of infrasound waves are used to update an atmospheric model. This paper sought a flexible parameterization that permits to incorporate physical and numerical constraints without the need to reformulate the inversion. On the other hand, the optimization conveys an explicit search over the solution space, making the solver computationally expensive. Nevertheless, through a parallel implementation and the use of tight constraints, this study demonstrates that the methodology is computationally tractable. Constraints to the solution space are derived from the spread (variance) of ERA5 ensemble reanalysis members, which summarize the best current knowledge of the atmosphere from assimilated measurements and physical models. Similarly, the initial model temperature and winds for the inversion are chosen to be the average of these parameters in the ensemble members. The performance of the inversion is demonstrated with the application to infrasound observations from an explosion generated by the destruction of ammunition at Hukkakero, Finland. The acoustic signals are recorded at an array station located at 178 km range, which is within the classical shadow zone distance. The observed returns are assumed to come from stratospheric reflections. Thus, the reflection altitude is also an inverted parameter.
Sven Peter Näsholm
added 4 research items
Like seismic waves traveling through the solid earth, infrasound waves traveling through the atmosphere are also sensitive to the medium properties – in particular to temperature and wind. The exploitation of this information is particularly interesting in regions and altitude ranges where other measurements are sparse. In this work, we look at the climatology from first-arrival travel-times using a dataset of infrasound observations from northern Scandinavia, this is, in the context of stratospheric temperatures. The same dataset has recently been exploited to estimate tropospheric and stratospheric cross-winds. This dataset spans 30 years and corresponds to explosions that are due to the destruction of ammunition at a military site in Finland conducted over the months of August and September; hence, it covers the period of transition from summer to winter stratosphere. The transition between summer and winter stratosphere is clear in the data. However, a significant travel-time variation between years produces inconclusive results when inferring stratospheric temperature trends over the 30 years analyzed. Still, when comparing the travel-times against regional stratospheric temperatures represented in atmospheric re-analysis models, there is a correspondence between models and infrasound data.
Polar Mesosphere Winter Echoes (PMWE) are radar echoes that originate from the mesosphere at 50-80 km altitude and are observed with VHF radars during equinox and winter seasons. Strong PMWE are relatively rare phenomena, in most cases they are observed when the lower ionosphere displays high ionisation. Interpretations of observational results concerning PMWE are controversial and the origin of the echoes is still under debate. Especially intriguing is that in some cases of strong PMWE, the measured horizontal speeds of the radar reflecting structures can exceed 300 m/s. Radar reflection (scattering) by infrasound waves at frequencies below about 2 Hz was suggested in order to explain these observations. We will give recent examples of PMWE events of high horizontal speed as observed with the 52 MHz MST radar (ESRAD) located at Esrange (68°N, 21ºE) in northern Sweden. Together with this we will analyse infrasound measurements made at ground-based stations near Kiruna (67.5°N, 20.13ºE) and at the infrasound station IS37 (69°N, 18ºE) in Norway during these events. We discuss prospective relations between PMWE and the microbaroms that are generated by ocean swell in the North Atlantic.
We use acoustical infrasound from explosions to probe an atmospheric wind component from the ground up to stratospheric altitudes. Planned explosions of old ammunition in Finland generate transient infrasound waves that travel through the atmosphere. These waves are partially reflected back towards the ground from stratospheric levels, and are detected at a receiver station located in northern Norway at 178 km almost due North from the explosion site. The difference between the true horizontal direction towards the source and the back-azimuth direction of the incoming infrasound wave-fronts, in combination with the pulse propagation time, are exploited to provide an estimate of the average cross-wind component in the penetrated atmosphere. We perform offline assimilation experiments with an ensemble Kalman filter and these observations, using the ERA5 ensemble reanalysis atmospheric product as background (prior) for the wind at different vertical levels. Information from both sources is combined to obtain analysis (posterior) estimates of cross-winds at different vertical levels of the atmospheric slice between the explosion site and the recording station. The assimilation makes greatest impact at the 12-60 km levels, with some changes with respect to the prior of the order of 0.1-1.0 m/s, which is a magnitude larger than the typical standard deviation of the ERA5 background. The reduction of background variance in the higher levels often reached 2-5%. This is the first study demonstrating techniques to implement assimilation of infrasound data into atmospheric models. It paves the way for further exploration in the use of infrasound observations (especially natural and continuous sources) to probe the middle atmospheric dynamics and to assimilate these data into atmospheric model products.
Sven Peter Näsholm
added a research item
We analyze dataset of infrasound observations from surface military explosions in northern Finland which occur yearly in August and September since 1988. The transient nature of these events allows for identification of returns reflected (or scattered) both from stratospheric and from mesospheric-lower thermospheric (MLT) altitudes. The infrasound data were recorded at Norwegian infrasound-array station around 200 km north of the explosion site. In this study, we use the measured travel-time and backazimuth deviation of the arriving infrasound wavefronts to estimate snapshots of the MLT crosswind averaged along the propagation path. The spatial extent of that averaging process is explored, and the MLT wind estimates retrieved from infrasound data are presented and compared against high-top atmospheric model winds.
Sven Peter Näsholm
added a research item
Manuscript submitted to the Journal of the Acoustical Society of America. This is an author-typeset e-print // Updated on October 2020, including updated DOI web link to most recent version on the ESSOAr server // We present an inversion methodology where acoustic observations of infrasound waves are used to update an atmospheric model. We sought a flexible parameterization that permits to incorporate physical and numerical constraints without the need to reformulate the inversion. On the other hand, the optimization conveys an explicit search over the solution space, making the solver computationally expensive. Nevertheless, through a parallel implementation and the use of tight constraints we demonstrate that the methodology is computationally tractable. Constraints to the solution space are derived from the spread (variance) of ERA5 ensemble reanalysis members, which summarize the best current knowledge of the atmosphere from assimilated measurements and physical models. Similarly, the initial model temperature and winds for the inversion are chosen to be the average of these parameters in the ensemble members. The performance of the inversion is demonstrated with the application to infrasound observations from an explosion generated by the destruction of ammunition at Hukkakero, Finland. The acoustic signals are recorded at an array station located at 178 km range, which is within the classical shadow zone distance. The observed returns are assumed to come from stratospheric reflections. Thus, the reflection altitude is also an inverted parameter.
Sven Peter Näsholm
added a research item
This data assimilation study exploits infrasound from explosions to probe an atmospheric wind component from the ground up to stratospheric altitudes. Planned explosions of old ammunition in Finland generate transient infrasound waves that travel through the atmosphere. These waves are partially reflected back towards the ground from stratospheric levels, and are detected at a receiver station located in northern Norway at 178km almost due North from the explosion site. The difference between the true horizontal direction towards the source and the backazimuth direction (the horizontal direction of arrival) of the incoming infrasound wave-fronts, in combination with the pulse propagation time, are exploited to provide an estimate of the average cross-wind component in the penetrated atmosphere. We perform offline assimilation experiments with an ensemble Kalman filter and these observations, using the ERA5 ensemble reanalysis atmospheric product as background (prior) for the wind at different vertical levels. We demonstrate that information from both sources can be combined to obtain analysis (posterior) estimates of cross-winds at different vertical levels of the atmospheric slice between the explosion site and the recording station. The assimilation makes greatest impact at the 12-60 km levels, with some changes with respect to the prior of the order of 0.1-1.0 m/s, which is a magnitude larger than the typical standard deviation of the ERA5 background. The reduction of background variance in the higher levels often reached 2-5%. This is the first published study demonstrating techniques to implement assimilation of infrasound data into atmospheric models. It paves the way for further exploration in the use of infrasound observations – especially natural and continuous sources – to probe the middle atmospheric dynamics and to assimilate these data into atmospheric model products.
Sven Peter Näsholm
added a research item
Link to arXiv pre-print, typeset by the authors: https://arxiv.org/abs/2004.07972 // Abstract: This data assimilation study exploits infrasound from explosions to probe an atmospheric wind component from the ground up to stratospheric altitudes. Planned explosions of old ammunition in Finland generate transient infrasound waves that travel through the atmosphere. These waves are partially reflected back towards the ground from stratospheric levels, and are detected at a receiver station located in northern Norway at 178 km almost due North from the explosion site. The difference between the true horizontal direction towards the source and the backazimuth direction (the horizontal direction of arrival) of the incoming infrasound wave‐fronts, in combination with the pulse propagation time, are exploited to provide an estimate of the average cross‐wind component in the penetrated atmosphere. We perform offline assimilation experiments with an ensemble Kalman filter and these observations, using the ERA5 ensemble reanalysis atmospheric product as background (prior) for the wind at different vertical levels. We demonstrate that information from both sources can be combined to obtain analysis (posterior) estimates of cross‐winds at different vertical levels of the atmospheric slice between the explosion site and the recording station. The assimilation makes greatest impact at the 12 − 60 km levels, with some changes with respect to the prior of the order of 0.1 − 1.0 m/s, which is a magnitude larger than the typical standard deviation of the ERA5 background. The reduction of background variance in the higher levels often reached 2 − 5%. This is the first published study demonstrating techniques to implement assimilation of infrasound data into atmospheric models. It paves the way for further exploration in the use of infrasound observations – especially natural and continuous sources – to probe the middle atmospheric dynamics and to assimilate these data into atmospheric model products. This article is protected by copyright. All rights reserved.
Erik Mårten Blixt
added a research item
The receiver-to-source backazimuth of atmospheric infrasound signals is biased when crosswinds are present along the propagation path. Infrasound from 598 surface explosions from over 30 years in northern Finland is measured with high spatial resolution on an array 178 km almost due North. The array is situated in the classical shadow-zone distance from the explosions. However, strong infrasound is almost always observed, which is most plausibly due to partial reflections from stratospheric altitudes. The most probable propagation paths are subject to both tropospheric and stratospheric crosswinds , and our wave-propagation modelling yields good correspondence between the observed backazimuth deviation and crosswinds from the ERA-Interim reanalysis product. We demonstrate that atmospheric crosswinds can be estimated directly from infrasound data using propagation time and backazimuth deviation observations. We find these crosswind estimates to be in good agreement with the ERA-Interim reanalysis.
Sven Peter Näsholm
added a research item
The receiver-to-source backazimuth of atmospheric infrasound signals is biased when crosswinds are present along the propagation path. Infrasound from 598 surface explosions from over 30 years in northern Finland is measured with high spatial resolution on an array 178 km almost due North. The array is situated in the classical shadow-zone distance from the explosions. However, strong infrasound is almost always observed, which is most plausibly due to partial reflections from stratospheric altitudes. The most probable propagation paths are subject to both tropospheric and stratospheric crosswinds , and our wave-propagation modelling yields good correspondence between the observed backazimuth deviation and crosswinds from the ERA-Interim reanalysis product. We demonstrate that atmospheric crosswinds can be estimated directly from infrasound data using propagation time and backazimuth deviation observations. We find these crosswind estimates to be in good agreement with the ERA-Interim reanalysis. ------------------------------------------------------------------------------------------------------------------- This article has been accepted by The Journal of the Acoustical Society of America. After it is published, it will be found at http://asa.scitation.org/journal/jas The current e-print was typeset by the authors and can differ in, e.g., pagination, reference numbering, and typographic detail. ------------------------------------------------------------------------------------------------------------------- E-print ID at the arXiv repository: https://arxiv.org/abs/1907.05601
Sven Peter Näsholm
added an update
Our revised manuscript Estimating tropospheric and stratospheric winds using infrasound from explosions has just been submitted to the Journal of the Acoustical Society of America. Let's hope the reviewers are happy with our updates!
Edit on July 11: the paper has been accepted for publication - see the posted preprint.
 
Sven Peter Näsholm
added an update
We are excited to announce that Ekaterina Vorobeva is joining our team on a PhD grant funded by the project!
 
Sven Peter Näsholm
added an update
Please see the following PhD position announcement and apply before April 22:
 
Sven Peter Näsholm
added an update
On November 30, 2017, The Research Council of Norway published a list of funded projects for the “FRINATEK” section of the "FRIPRO" basic research programme and we're extremely happy that our project proposal has been granted funding. Thanks to all partners, colleagues, and others who contributed to making our proposal attractive to the funding agency!
The project will officially start up in August 2018.
The funding includes a PhD scholarship for a candidate jointly based at the Norwegian University of Science and Technology (NTNU) in Trondheim, and at NORSAR in Oslo.
 
Sven Peter Näsholm
added a project goal
This is a basic research project where we will exploit a combination of infrasonic and meteor radar datasets to characterize the middle atmosphere and to constrain high-top atmospheric models. This will pave the way for improved medium-range weather forecasting.
Funding: Research Council of Norway FRINATEK/FRIPRO basic research grant number 274377.