Using an oceanic acoustic noise model to evaluate simulated atmospheric states

Due to the lack of observations, Numerical Weather Prediction (NWP) models are poorly constrained in the Middle Atmosphere (MA ~10-90km) and thus significantly biased [1]. Infrasounds of oceanic origin (microbaroms) propagate across thousands of kilometers and integrate information on the MA dynamical state and particularly on winds. Thus, we investigate how to assess the performance of NWP models in the MA through simulations and global and continuous observations of microbaroms. 

Infrasound observations are processed using an adaptation of the MCML [2] algorithm to obtain the azimuthal distribution of microbarom amplitudes at the International Monitoring System Norwegian infrasound station I37NO. These observations are compared to simulations where modelled distribution account for the antenna response relative to MCML processing.

Simulations of microbaroms arrival are carried for the year 2021 by combining a microbarom source model [3] and two propagation methods: a semi-empirical law using a single atmospheric profile and Parabolic Equation (PE) range-dependent propagation simulation accounting for the 3D atmosphere. Yearly comparisons through an optimal transport metric using atmospheric specification from different atmospheric models highlight the limitations of the semi-empirical law for a NWP model performance evaluation.

Atmospheric models are thus assessed building on the PE propagation simulations and first conclusions on models relative performances are derived over specific periods of interest, including a sudden stratospheric warming. While the current work focuses on the evaluation of NWP models, it will also allow to define a method relying on microbarom observations to improve these models through Data Assimilation.

 

[1] Le Pichon, A., Assink, J. D., Heinrich, P., Blanc, E., Charlton-Perez, A., Lee, C. F., Keckhut, P., Hauchecorne, A., Rüfenacht, R., Kämpfer, N., et al. (2015), Comparison of co-located independent ground-based middle atmospheric wind and temperature measurements with numerical weather prediction models, J. Geophys. Res. Atmos., 120, 8318–8331, doi:10.1002/2015JD023273.

[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, Pag-es 1099–1112, https://doi.org/10.1093/gji/ggac377

[3] 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.