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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.