Assessing middle atmosphere weather models using infrasound detections from microbaroms

Abstract

SUMMARYThe nonlinear interaction of ocean surface waves produces coherent infrasound noise—microbaroms—between 0.1 and 0.5 Hz. Microbaroms propagate through the atmosphere over thousands of kilometres due to low absorption and efficient ducting between the ground and the stratopause. These signals are globally and permanently detected by the International Monitoring System (IMS) infrasound network, which has been established to monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty. At the International Data Centre (IDC) in Vienna, where IMS data are routinely processed, microbarom detections appear in overlapping frequency bands, and are treated as false alarms. Therefore, understanding the variability in microbarom detections is essential to support the IDC in the reduction of the false alarm rate. In this study, microbarom amplitudes and the direction of arrivals at the German infrasound station IS26 were modelled. For the simulations, the source was described by an operational ocean wave interaction model, and the signal amplitude was modelled using a semi-empirical attenuation relation. This relation strongly depends on middle atmosphere (MA; i.e. 15–90 km altitude) dynamics; however, vertical temperature and wind profiles, provided by numerical weather prediction (NWP) models, exhibit significant biases and differences when compared with high-resolution light detection and ranging instrument (lidar) soundings in altitudes where infrasound signals propagate. To estimate uncertainties in the modelled amplitude, a fully autonomous lidar for MA temperature measurements was installed at IS26. Temperature and wind perturbations, considering observed biases and deviations, were added to the operational high-resolution atmospheric model analysis produced by the European Centre for Medium-Range Weather Forecasts. Such uncertainties in horizontal winds and temperature strongly impact propagation conditions, explaining almost 97 per cent of the actual detections, compared to 77 per cent when using the direct output of the NWP model only. Incorporating realistic wind and temperature uncertainties in NWP models can thus significantly improve the understanding of microbarom detections as well as the detection capability of a single station throughout the year.