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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

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.

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.

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.