Assessing atmospheric gravity wave spectra in the presence of observational gaps

Abstract

Abstract. We present a thorough investigation into the accuracy and reliability of gravity wave (GW) spectral estimation methods when dealing with observational gaps. GWs have a significant impact on atmospheric dynamics, exerting influence over weather and climate patterns. However, empirical atmospheric measurements often suffer from data gaps caused by various factors, leading to biased estimations of the spectral power-law exponent (slope) β. This exponent describes how the energy of GWs changes with frequency over a defined range of GW scales. In this study, we meticulously evaluate three commonly employed estimation methods: the fast Fourier transform (FFT), generalized Lomb–Scargle periodogram (GLS), and Haar structure function (HSF). We assess their performance using time series of synthetic observational data with varying levels of complexity, ranging from a signal with one frequency to a number of superposed sinusoids with randomly distributed wave parameters. By providing a comprehensive analysis of the advantages and limitations of these methods, our aim is to provide a valuable roadmap for selecting the most suitable approach for accurate estimations of β from sparse observational datasets.