Atmospheric model inversion using infrasound signals from the North Korean underground nuclear explosion and the subsequent collapse event in 2017

This study focuses on the infrasound signals from the September 2017 North Korean underground nuclear explosion (UNE17) and subsequent collapse event (SCE17) that occurred close to the autumnal equinox when the atmospheric temperature structure undergoes rapid change. Multiple arrivals, including contributions from local, diffracted and epicentral infrasound, generated by UNE17, were observed at eight infrasound arrays in the Korean Peninsula and one IMS infrasound station (IS45) in Russia while at the closest five arrays for SCE17 only epicentral infrasound was observed. The UNE17 signals provide the opportunity to explore the utility of each distinct arrival in constraining atmospheric conditions during the change associated with the equinox. The observed characteristics of the multiple epicentral infrasonic phases (celerity, backazimuth, phase velocity and spectra) suggest propagation paths through the tropospheric, stratospheric and thermospheric waveguides, although geometric ray paths based on a global atmospheric model at the time of the explosion predict only thermospheric returns. The absence of predicted stratospheric returns may reflect errors in the atmospheric models due to the lack of predicted stratospheric winds which are weak and changing close to the autumnal equinox or the limited resolution of the fine-scale structure not captured by current atmospheric models. The differences between the model predictions and the observations suggest that the numerical weather forecast models need to be modified to fully explain the observations. In order to explore the model space that can explain the UNE17 data set, an inversion scheme is applied to atmospheric wind model parameters constrained by the multi-array observations. Zonal and meridional wind profiles are parametrized using empirical orthogonal functions (EOFs) estimated from 1-yr of Ground-to-Space atmospheric specifications. A best-fitting atmospheric model is estimated using a Bayesian approach that assesses the uncertainty in the inverse solution using a joint likelihood function combining components of azimuth deviation, traveltime and phase velocity. The updated atmospheric models from six different EOFs inversions have up to 20 m s–1 stronger zonal and meridional wind speeds in the stratosphere compared to the original model, and explain the stratospheric observations in the data set. This investigation illustrates that modest changes to atmospheric wind models at the time of autumnal equinox can improve the prediction of stratospheric returns.