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Recent altimeter retracking (e.g. Tourain et al. 2021) and filtering methods (Quilfen et al. 2019) have considerably reduced the noise level in estimates of the significant wave height (Hs), allowing to study smaller scale processes. Previous studies on the along-track variations of wave height have shown that wave-current interactions may explain most of the variability at scales 20 to 100 km (Ardhuin et al. 2017, Quilfen and Chapron 2019). Here we take advantage of the very low noise level of SWIM nadir beam to explore scales under 10 km, looking at the accuracy of Hs measurements in storms.From theory, we expect that part of the short-scale variability of the estimated Hs is related to wave groups which lead to random variations in wave height at scales of a few kilometers, depending on the sea state. Theory on signal envelopes links the spatial distribution of wave heights to the convolution of the wave spectrum (Rice 1944) thus allowing to estimate the variability linked to wave groups.Here, we use the fact that ocean waves spectra are routinely measured by CFOSAT’s SWIM instrument to evaluate the theoretical contribution of wave groups to the wave height variability within the 80 km² SWIM boxes, using the CFOSAT L2 and L2S products. In this study, we show that, in average, around half the Hs variance at the scale of SWIM boxes can be associated to wave groups.

Oceanic ambient noise (microbaroms) records are examined to retrieve information on the state of the middle atmosphere. We present an approach to compare ground-based infrasound observations with simulated infrasound detections obtained by combining a microbarom source model [1] with a semi-empirical attenuation law. Comparisons using this continuous and global infrasound source are presented for large time periods to assess performances on both seasonal and finer time scales. Infrasound detections obtained with a cross-correlation algorithm (PMCC) as well as with the new MCML (MultiChannel Maximum Likelihood) method [2] are considered. The sensitivity of simulated infrasound detections to the middle atmosphere model and to the propagation model (the transmission loss parametrisation) is evaluated. We discuss how this method may help to assess the performance of an atmospheric model in the middle atmosphere, as well as to select best members in an ensemble reanalysis. [1] De Carlo, M., Accensi, M., Ardhuin, F., and Le Pichon, A.: ARROW (AtmospheRic InfRasound by Ocean Waves): a new real-time product for global ambient noise monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7564, https://doi.org/10.5194/egusphere-egu22-7564, 2022.[2] B Poste, M Charbit, A Le Pichon, C Listowski, F Roueff, J Vergoz, The multichannel maximum-likelihood (MCML) method: a new approach for infrasound detection and wave parameter estimation, Geophysical Journal International, Volume 232, Issue 2, February 2023, Pages 1099–1112, https://doi.org/10.1093/gji/ggac377

Infrasound detections of Mediterranean cyclones known as medicanes (for « Mediterranean hurricanes ») are demonstrated in low- and high- frequency ranges, respectively. We summarize the main findings of a recently published study [1]. We use data from the infrasound station IS48 of the International Monitoring System, in Tunisia, to investigate the infrasound signatures of these meso-cyclones, using a multi-channel correlation algorithm. We discuss cases of detections and non-detections, based on the state of the middle atmosphere and of the wind noise measured at the station. Detections and likely sources are discussed in light of other datasets, comprising satellite observations of deep convection [2] and cloud-to-ground lightning detections from a ground-based network. Detections of infrasound emitted by the cyclone-related swell are modelled using a microbarom source model [3] and are in agreement with observations, comforting the identification of the lower frequency sources. This multi-technology and modelling approach allows to discuss the various sources at plat that may contribute to the monitoring of such extreme meteorological events.[1] Listowski, C.; Forestier, E.; Dafis, S.; Farges, T.; De Carlo, M.; Grimaldi, F.; Le Pichon, A.; Vergoz, J.; Heinrich, P.; Claud, C. Remote Monitoring of Mediterranean Hurricanes Using Infrasound. Remote Sens. 2022, 14, 6162. https://doi.org/10.3390/rs14236162[2] Dafis, S.; Claud, C.; Kotroni, V.; Lagouvardos, K.; Rysman, J. Insights into the convective evolution of Mediterranean tropical-like cyclones. Q. J. R. Meteorol. Soc. 2020, 146, 4147–4169.[3] De Carlo, M.; Accensi, M.; Ardhuin, F.; Le Pichon, A. ARROW (AtmospheRic InfRasound by Ocean Waves): A new real-time product for global ambient noise monitoring. In Proceedings of the EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022

Recent satellite altimeter retracking and filtering methods have considerably reduced the noise level in estimates of the significant wave height (Hs), allowing to study processes with smaller spatial scales. In particular, previous studies have shown that wave-current interactions may explain most of the variability of Hs at scales 20 to 100 km. As the spatial scale of the measurement is reduced, random fluctuations emerge that should be associated to wave groups. Here we quantify the magnitude of this effect, and the contribution of wave groups to the uncertainty in Hs measurements by altimeters, with a particular focus on extreme extra-tropical storms. We take advantage of the low orbit altitude of the China-France Ocean Satellite (CFOSAT), and the low noise level of the nadir beam of the SWIM instrument. Our estimate of wave group effects uses directional wave spectra measured by off-nadir beams on SWIM and signal processing theory that gives statistical properties of the wave envelope, and thus the local wave heights, from the shape of the wave spectrum. We find that the standard deviation of Hs associated to wave groups is a function of satellite altitude, wave height and spectral bandwidth. For CFOSAT these fluctuations generally account for about 25% of the variance measured over a 80 km distance. This fraction is largest in storms and in the presence of long swells. When the estimated effect of wave groups is subtracted from the measured Hs variance, the remaining variability is higher in regions of strong currents.

Between 0.1 and 0.6 Hz, the coherent ambient infrasound noise is dominated worldwide by signals originating from the ocean, the so-called microbaroms. With an energy peaking around 0.2 Hz, microbaroms signals are generated by second order non linear interactions between wind-waves at the ocean surface and are able to propagate all around the globe through the stratosphere and thermosphere. Monitoring these signals allows characterizing the source activity and probing the properties of their propagation medium, the middle atmosphere, assuming that the source is well modelled. A new theoretical description of the mechanism signal generation connecting the amplitude of the pressure signal to the height and frequency wave oscillation has been proposed by De Carlo et al. (2020). This model has been evaluated quantitatively through systematic comparisons with worldwide observations (De Carlo et al., 2021). This model has been implemented by the Laboratoire d’Océanographie Physique et Spatiale (LOPS) research unit of IFREMER in the DATARMOR HPC center (11088 cores - 426 Tflops) which allows big data hosting and intensive computation. We present a technical overview of the ARROW product and its implementation framework for both hindcast and real-time production. In the context of the future verification of the Comprehensive nuclear Test Ban Treaty (CTBT), ARROW offers an opportunity to target infrasonic signals of specific interest interfering with the global ambient coherent noise. This product, based on a state-of-the-art numerical wave model, paves the way for improved medium-range weather forecasting, by building a global and time-dependent reference database used as input to develop innovative remote sensing methods.

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year essentially over the waters of Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructures and lives, because of their strong winds and intense rainfalls. Infrasound technology has already been employed to investigate acoustic signatures of severe weather events. In order to characterize medicane infrasound detections, we use data from the International Monitoring System (IS48 infrasound station, Tunisia), processed with a multi-channel correlation algorithm. For four different medicanes, high and/or low frequency detections are corresponding to these events, and non-detected cases are also discussed. These detections are discussed by considering other datasets such as satellite observations, a surface lightning detection network, and products mapping the intensity of the swell. While convective systems and lightning seem to be the main sources of detections above 1 Hz, hotspots of swell related to the medicanes are evidenced in the 0.1-0.5 Hz range.

The detection capability of the International Monitoring System (IMS) infrasound network is a key concern since ubiquitous coherent noise interfere with the identification of explosive events. The need to distinguish between incoherent wind noise and real coherent infrasonic wave signals has motivated the development of a new parametric stochastic model. We establish the expressions of both the Generalized Likelihood Ratio Test (GLRT) and the Maximum Likelihood Estimator (MLE). The major results are the expression of the asymptotic distribution of the GLRT under null hypothesis, leading to the p-value computation, and the expression of the asymptotic covariance. The Multi-Channel Maximum-Likelihood (MCML) detection is implemented in the time-frequency domain in order to discriminate between interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multi-channel correlation detector algorithms in poor signal-to-noise ratio scenarios. We illustrate the performance of MCML on real IMS data, highlighting its capability to characterize the coherent ocean ambient infrasound noise in high resolution through comparisons with theoretical state-of-the-art numerical wave models as implemented at the LOPS research unit of IFREMER in the DATARMOR HPC center.

Microseisms in the dominant double-frequency band, around 5 s period and their ubiquitous presence makes them an interesting signal for exploring the solid Earth and associated natural hazards (e.g. Olivier et al. 2019). These microseisms are generated by opposing ocean waves of equal frequencies (Hasselmann 1963) that generally arise within the locally generated sea state at high frequencies (class I, Ardhuin et al. 2011), due to coastal reflection (class II) or when swell from a distant storm collides with another wave system, which generally corresponds to the strongest microseism sources (class III). Improving on solid Earth knowledge and natural hazard monitoring can benefit from a better quantitative knowledge of these sources. Similar applications to the study of the stratosphere can use atmospheric infrasound that are generated by the same opposing ocean waves, the microbaroms (Brekhovskikh et al. 1973, De Carlo et al. 2020). So far, very few direct measurements of wave properties have been able to quantify the presence of waves in opposite directions, and the magnitude of microseism sources has relied on numerical simulations. Here we use sun glitter measurements from the Copernicus-Sentinel 2 satellites, as processed in the SARONG project (http://www.sarong.global/). We show that the presence of opposing waves gives a strong anomaly in the phase of co-spectra of optical images from Sentinel 2 (S2). When using only 2 time-lagged images this feature generally limits the possibility to measure surface currents from waves shorter than about 25 m, that always have a significant energy in opposing directions (class I microseism sources).Caption: Processing from Sentinel 2 Level-1c images to phase speeds. Top: data from Copernicus Sentinel 2 on 29 April 2016 off California (See Figs. 3-9 in Kudryavtsev et al. 2017). Bottom: simulated S2 data based on in situ wave spectrum determined from directional moments using the Maximum Entropy Method. The phase speed anomalies, highlighted with the dashed magenta circle near the Nyquist wavelength L = 20 m, disappear when no energy propagates in opposing directions.The same also happens for longer components when strong (class III) microseism sources are present. However this signature is also an opportunity to directly measure the sources of microseisms and quantify the energy in opposing directions using 2 or more different time lags (Ardhuin et al., in 2021). Given its coastal coverage, we find that S2 is particularly well suited for estimating reflection coefficients of waves off the coast, which is a major source of uncertainty for microseism and microbarom source modelling. Ardhuin, F., Stutzmann, E., Schimmel, M., & Mangeney, A. (2011). Ocean wave sources of seismic noise. Journal of Geophysical Research, 116(C9). doi:10.1029/2011jc006952Kudryavtsev, V., Yurovskaya, M., Chapron, B., Collard, F., & Donlon, C. (2017). Sun glitter imagery of ocean surface waves. Part 1: Directional spectrum retrieval and validation. Journal of Geophysical Research: Oceans, 122(2), 1369–1383. doi:10.1002/2016jc012425 Olivier, G., Brenguier, F., Carey, R., Okubo, P., & Donaldson, C. (2019). Decrease in seismic velocity observed prior to the 2018 eruption of Kīlauea volcano with ambient seismic noise interferometry. Geophysical Research Letters. doi:10.1029/2018gl081609

Between 0.1 and 0.6 Hz, the coherent ambient infrasound noise is dominated worldwide by signals, so-called microbaroms, originating from the ocean. With an energy peaking around 0.2 Hz, microbaroms are generated by second order non-linear interactions between wind-waves at the ocean surface and are able to propagate all around the globe through the stratosphere and thermosphere. Monitoring these signals allows characterizing the source activity and probing the properties of their propagation medium, the middle atmosphere. Here we present the first quantitative validation of global microbaroms modelling against worldwide observations. Modelling microbaroms at ground-based stations is a complex process that requires accounting for sea-wave modelling, infrasound generation from wave interactions, infrasound propagation over thousands of kilometers and infrasound detection at stations. In this study, this process was represented by three main parameters: a wave action model, a source model and an attenuation law through the atmosphere. The global modelling is run for two values of each parameter and the results are quantitatively compared with the global reference database of microbaroms detected by the International Monitoring System over seven years. This study demonstrates that the new source model improves the prediction rate of observations by around 20 percent points compared to existing reference models. The performance is enhanced when combining a wind-dependent attenuation and an ocean wave model that includes coastal reflection.

Wave hindcasts of long time series ( > 30 years) have been instrumental in understanding the wave climate. However, it is still difficult to have a consistent reanalysis suitable for study of trends and interannual variability. Here we explore the consistency of wave hindcast with independent observations from moored buoys, satellite altimeters, and microseism data. We use the ECMWF 5th generation re-analysis (ERA5) winds to drive two wave models, using either ECMWF WAM (Bidlot et al. 2019) or WAVEWATCH III (The WAVEWATCH III Develoment Group 2019, Alday et al. 2020). We also use seismic data in the dominant double-frequency band, around 5 s period, that are generated by opposing waves of equal frequencies and compare these to modeled microseims. We find that the inter-platform corrections in the ESA CCI Version 1.1 dataset (Dodet et al. 2020) introduced a trend that differs from the microseism trends. However, the results converge when using a revised correction of this dataset. We also look at the microseism spectral signature of large storms in the North Atlantic and discuss how we may compare the severity of different storms that move over different ocean bathymetry with different wave to microseism conversions. References: Stopa, J. E., Ardhuin, F., Stutzmann, E., & Lecocq, T. (2019). Sea state trends and variability: Consistency between models, altimeters, buoys, and seismic data (1979–2016). Journal of Geophysical Research: Oceans, 124. https://doi.org/10.1029/2018JC014607 Dodet, G., Piolle, J.-F., Quilfen, Y., Abdalla, S., Accensi, M., Ardhuin, F., Ash, E., Bidlot, J.-R., Gommenginger, C., Marechal, G., Passaro, M., Quartly, G., Stopa, J., Timmermans, B., Young, I., Cipollini, P., and Donlon, C.: The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations, Earth Syst. Sci. Data, 12, 1929–1951, https://doi.org/10.5194/essd-12-1929-2020 , 2020.

Signature of the ocean storms at the center of Eurasia are studied using data from Kazakhstani stations. Data from seismic and infrasound arrays that are part of the International Monitoring System of the Comprehensive Test Ban Treaty Organization are used, including PS23-Makanchy, AS058-Kurchatov, and IS31-Aktyubinsk. These data were amended with the local information acquired by the National Nuclear Center of Kazakhstan: seismic arrays ABKAR-Akbulak and KKAR-Karatau, and infrasound arrays KURIS-Kurchatov and MKIAR-Makanchy. Seismic and acoustic signals from ocean storms were detected using standardized correlation based method from 2014 to 2017. A seismo-acoustic source model has been developed to predict seismic and acoustic signals. WAVEWATCH3 data are used for the source model simulation. Microbaroms attenuation was calculated using vertical atmospheric profiles developed by the European Centre for Medium-Range Weather Forecasts. Microseism source parameters were corrected for the bathymetry effect. Afterward, actual and predicted microbarom and microseism parameters are compared and analyzed: data are compared between different arrays. The results show clear seasonal features in recorded microseisms and microbaroms indicating that the sources are of the same origin. Discrepancies are found for the predicted and observed microseism backazimuths. The results of this study combining microbarom and microseism observations reveal the strengths and weaknesses of seismic and acoustic methods while analyzing signals from strong storms, and lead to the conclusion that a fusion of two techniques brings qualitatively new results. In particular, it demonstrates its efficiency for locating a source of seismic noise using infrasound observations, predicting the source amplitude using microseismic observations, correcting seismic propagation anomalies due to heterogeneities in the propagation medium using accurate infrasound backazimuths, and inferring new observational constraints in the middle atmosphere using an enhanced description of the microbarom source. These findings are promising for a better description of the source (localization, intensity, spectral distribution) and coupling mechanisms of the ocean/atmosphere/land interfaces.

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.

In this study, the dense seismo-acoustic network of the Institute of Geophysical Research (IGR), National Nuclear Centre of the Republic of Kazakhstan, is used to characterize the global ocean ambient noise. As the monitoring facilities are collocated, this allows for a joint seismo-acoustic analysis of oceanic ambient noise. Infrasonic and seismic data are processed using a correlation-based method to characterize the temporal variability of microbarom and microseism signals from 2014 to 2017. The measurements are compared with microbarom and microseism source model output that are distributed by the French Research Institute for Exploitation of the Sea (IFREMER). The microbarom attenuation is calculated using a semi-empirical propagation law in a range-independent atmosphere. The attenuation of microseisms is calculated taking into account seismic attenuation and bathymetry effect. Comparisons between the observed and predicted infrasonic and seismic signals confirm a common source mechanism for both microbaroms and microseisms. Multi-year and intra-seasonal parameter variations are analyzed, revealing the strong influence of long-range atmospheric propagation on microbarom predictions. In winter, dominating sources of microbaroms are located in the North Atlantic and in the North Pacific during sudden stratospheric warming events, while signals observed in summer could originate from sources located in the Southern Hemisphere; however, additional analyses are required to consolidate this hypothesis. These results reveal the strengths and weaknesses of seismic and acoustic methods and lead to the conclusion that a fusion of two techniques brought the investigation to a new level of findings. Summarized findings also provide a perspective for a better description of the source (localization, intensity, spectral distribution) and bonding mechanisms of the ocean–atmosphere–land interfaces.

Microbarom signals are generated by wind‐waves at the ocean surface and propagate all around the globe through the stratosphere and ionosphere. Microbaroms dominate the coherent infrasound ambient noise measured worldwide, with a peak around 0.2 Hz. Monitoring these signals allows characterizing the source activity and probing the properties of their propagation medium, the middle atmosphere. Here we show the first quantitative validation of global microbarom modeling based on ocean wave models, a new source model and atmospheric attenuation. For evaluating these parameters’ impact, we compare the modeling results with a global reference database of microbaroms detected by the infrasound International Monitoring System over seven years. This study demonstrates that the new source model improves the prediction rate of observations by around 20 percent points against previous models. The performance is enhanced when the new model is combined with a wind‐dependent attenuation and an ocean wave model that includes coastal reflection. Plain Language Summary Microbaroms are atmospheric ambient noise below the human hearing threshold. They are generated by ocean waves and can be detected by infrasound sensors worldwide. A better understanding is important because microbaroms could hide signals of interest in the context of the Comprehensive Nuclear‐Test‐Ban Treaty, established to unveil clandestine nuclear explosions worldwide. Furthermore, as microbaroms propagate over long‐ranges through the middle atmosphere, a better knowledge of the received signals provides new insights of middle atmosphere dynamics features that are unresolved in global circulation models. In this study, we use a historical database of microbarom detections to evaluate state‐of‐the‐art models and propose a methodology to simulate microbaroms worldwide.

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.

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.

The global International Monitoring System (IMS) network continuously detects coherent ambient infrasound noise between 0.1 and 0.5 Hz. This noise, referred to as microbaroms, is generated by second order non-linear interaction of ocean waves. Various source models have been developed in earlier works; e.g. Brekhovskikh et al. (1973) and Ardhuin & Herbers (2013) who considered a source directivity effect in infinite depth ocean, and Waxler (2007) who investigated the radiation model in finite depth ocean from monopolar sources. De Carlo et al. (2020) proposed a two-dimensional energy spectrum ocean wave model accounting for bathymetry and source directivity effects. First comparisons between the observed and modelled directional microbarom amplitudes at IMS infrasound stations show first order agreement. In order to further evaluate these models, microbarom observations from the Carolina Infrasound secondary payload on board the NASA Ultra Long Duration Balloon flight in 2016 (Bowman and Lees, 2018), that flew over spatially extended microbarom source regions along the Antarctic Circumpolar Current, are compared with the modelled source energy flux. The simulated source strength power spectrum is integrated over an extended source region beneath the balloon and compared with the observed one. The relative importance of modelled source parameters (e.g. bathymetry, launching ray parameters) is assessed. In this presentation, we describe the infrasound observations on the balloon and the supporting microbarom source models, and discuss the implications of these results on the remote estimation of the acoustic energy flux from the ocean surface to the upper atmosphere.

The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) may be used to detect atmospheric explosions and events of interest using infrasound technology. However, ambient noise may affect the detection performance of the station network, and particularly ocean noise known as microbarom, as previously shown by characterizing ambient noise through broadband array processing on IMS data. Indeed, ocean wave interactions generate acoustic noise almost continuously. In this study, we use wave action models and include bathymetry and source directivity effects to model the microbarom sources and perform a global comparison between the synthetic signals obtained from two-dimensional spectrum ocean wave products, and observations. With this study, it is expected to enhance the characterization of the ocean-atmosphere coupling and to discriminate the impact of different features to account for in models. In return, better knowledge of microbarom sources allows to better characterize explosive atmospheric events and to provide information about the middle atmosphere dynamics and disturbances that could be used as model constraints

In routine processing of infrasound data of the International Monitoring System, coherent ocean ambient noise with dominant frequencies ranging from 0.1 to 0.5 Hz appears in overlapping frequency bands. These signals, so-called microbaroms, originate from complex wave interactions. In this study, microbarom detections are used as calibration signals, and their amplitudes at the Argentinian infrasound station IS02 are modelled based on operational ocean wave interaction simulations and a semi-empirical attenuation relation. This relation strongly depends on the middle atmosphere (MA) dynamics. Previous studies have shown that the MA wind and temperature are not properly resolved in numerical weather prediction (NWP) models. Therefore, high-resolution soundings of the Compact Rayleigh Autonomous Lidar (CORAL) are incorporated in the modelling. The infrasound data are processed using the progressive Multi-Channel Correlation (PMCC) algorithm.This sensitivity study focuses on one year of collocated infrasound and lidar measurements in southern Argentina. It highlights the seasonal effects of MA uncertainties on infrasound propagation and detections in 2018.

The Central and Eastern European Infrasound Network (CEEIN) detects significant irregularities in microbarom arrivals between 15 and October 18, 2017. The processes driving the irregular microbarom arrivals are searched in the microbarom source region in the North Atlantic and in the stratospheric waveguide. Generation of microbaroms is simulated using ocean WAVEWATCHIII wave-action model and an updated source theory which combines the effects of both finite depth ocean and source directivity. Signal propagation in a uniform range independent atmosphere is considered. In the studied time interval, a dominant moving microbarom source occurs at the tail of the post-tropical storm Ophelia. The storm Ophelia provides an opportunity to study the development of an intense microbarom source on the open ocean and particularly in coastal waters. Discrepancies between observations and modelling results are identified and discussed. This study shows that the state-of-the-art wave-action models are underestimated in coastal areas during storms which can pose a problem for civil security in coastal areas. The capability of the CEEIN stations to monitor microbaroms is proved. Measurement biases and uncertainties associated with the configurations of the CEEIN stations and current limitations of the processing method are discussed and improvements are suggested.

SUMMARYThe 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.

The global International Monitoring System (IMS) network continuously detects coherent ambient infrasound noise between 0.1 and 0.5 Hz. This noise, referred to as microbaroms, is generated by the second order non-linear interaction of ocean waves, mostly during severe storms. A global and multi-year analyze of microbaroms highlights the strong influence of middle atmospheric conditions on the propagation. Various source models have been developed. Brekhovskikh et al. (1973) and Ardhuin and Herbers (2013) considered a source directivity effect in infinite depth ocean with radiative pressure depending on the wave elevation angle. Waxler and Gilbert (2006) and Waxler (2007) investigated the radiation of infrasound by ocean waves in finite depth ocean by monopolar sources. In this study, the combined effects of non-monopolar source and bathymetry on the radiation are addressed. Beyond theoretical issues, source modelisation and propagation through a realistic atmosphere are carried out. Comparing the predicted signals with the observed ones at all IMS stations shows good agreements for both directional and amplitude information. Building a global reference database of oceanic noise sources opens new perspectives for providing additional integrated constraints on middle atmosphere dynamics and disturbances.The global International Monitoring System (IMS) network continuously detects coherent ambient infrasound noise between 0.1 and 0.5 Hz. This noise, referred to as microbaroms, is generated by the second order non-linear interaction of ocean waves, mostly during severe storms. A global and multi-year analyze of microbaroms highlights the strong influence of middle atmospheric conditions on the propagation. Various source models have been developed. Brekhovskikh et al. (1973) and Ardhuin and Herbers (2013) considered a source directivity effect in infinite depth ocean with radiative pressure depending on the wave elevation angle. Waxler and Gilbert (2006) and Waxler (2007) investigated the radiation of infrasound by ocean waves in finite depth ocean by monopolar sources. In this study, the combined effects of non-monopolar source and bathymetry on the radiation are addressed. Beyond theoretical issues, source modelisation and propagation through a realistic atmosphere are carried out. Comparing the predicte...

The ability of the International Monitoring System (IMS) global infrasound network to detect atmospheric explosions and events of interest strongly depends on station specific ambient noise signatures which include both incoherent wind noise and real coherent infrasonic waves. To characterize the coherent ambient noise, broadband array processing was performed on historical continuous IMS recordings. Ocean wave interactions contribute to the atmospheric coherent ambient noise field, and we apply wave action models to model these microbarom sources. We use two-dimensional energy spectrum ocean wave products to build a global reference database of oceanic noise sources. Then, we compare observed and modeled directional microbarom amplitudes at several stations. The expected benefits of such studies concern the use complementary data to finely characterize the coupling mechanisms at the ocean-atmosphere interface. In return, better knowledge of ambient ocean noise sources opens new perspectives not only by e...