Evaluating Numerical Weather Prediction Models in the Middle Atmosphere Using Coherent Oceanic Acoustic Noise Observations

Abstract We develop a method to assess numerical weather prediction (NWP) model performances from the mid‐stratosphere to the mesosphere‐lower thermosphere (30–120 km), through comparisons between observed and simulated amplitude of oceanic infrasound known as microbaroms. We adapt a recently published array processing algorithm, the multichannel maximum‐likelihood (MCML), to the 360‐observations of microbaroms. We simulate infrasound propagation using a source model and atmospheric specifications prescribed by NWP models. As this study paves the way for the assimilation of microbarom observations in these models, we assess the different components of our method. The sensitivity of the NWP model assessments to the acoustic propagation is investigated. We demonstrate the limitations of a parametrized attenuation solely driven by atmospheric fields at the infrasound station (a method previously used due to its computational efficiency) by comparing it with an explicit simulation retaining the whole 3D atmospheric fields. Importantly, in the microbarom simulations, we account for the array response to allow one‐to‐one comparisons with observations. We also highlight an observed intermittent semi‐diurnal periodicity, whose occurrence depends on middle‐atmospheric conditions, pointing at arrivals from the mesosphere and lower thermosphere. Hence, its variability needs to be accounted for in the simulations. We use a circular optimal transport metric to quantify differences between simulated and observed microbarom azimuthal distributions in a systematic way. We present NWP models relative performances diagnostics over periods of interest, including a sudden stratospheric warming in January 2021. We discuss how our approach provides insights into the model performances in the middle atmosphere.