Removing spurious inertial instability signals from gravity wave temperature perturbations using spectral filtering methods

Abstract

Abstract. Gravity waves are important drivers of dynamic processes in particular in the middle atmosphere. To analyse atmospheric data for gravity wave signals it is essential to separate gravity wave perturbations from atmospheric variability due to other dynamic processes. Common methods to separate small-scale gravity wave signals from a large-scale background are separation methods depending on filters in either the horizontal or vertical wavelength domain. However, gravity waves are not the only process that could lead to small-scale perturbations in the atmosphere. Recently, concerns have been raised that vertical wavelengths filtering can lead to misinterpretation of other wave-like perturbations, such as inertial instability effects, as gravity wave perturbations. In this paper we assess the ability of different spectral background removal approaches to separate gravity waves and inertial instabilities using artificial inertial instability perturbations, global model data and satellite observations. We investigate a horizontal background removal, which applies a zonal wavenumber filter with additional smoothing of the spectral components in meridional and vertical direction, a sophisticated filter based on 2D time-longitude spectral analysis (see Ern et al. (2011)) and a vertical wavelength Butterworth filter. Critical thresholds for the vertical wavelength and zonal wavenumber are analysed, respectively. Vertical filtering has to cut deep into the gravity wave spectrum in order to remove inertial instability remnants from the perturbations (down to 6 km cutoff wavelength). Horizontal filtering, however, removes inertial instability remnants in global model data at wavenumbers far lower than the typical gravity wave scales for the case we investigated. Specifically, a cutoff zonal wavenumber of 6 in the stratosphere is sufficient to eliminate inertial instability structures. Furthermore, we show that for infrared limb-sounding satellite profiles, it is possible as well to effectively separate perturbations of inertial instabilities from those of gravity waves using a cutoff zonal wavenumber of 6. We generalize the findings of our case study by examining a one-year time series of SABER data.

Superposition of gravity waves with different propagation characteristics observed by airborne and space-borne infrared sounders

“…Fetzer and Gille, 1994;Ern et al, 2006Ern et al, , 2018. As the region of interest in this paper is given by the GLORIA measurement altitude, which is in the lower stratosphere and upper troposphere, zonal filtering with a higher cut-off wave number 18 is required (Strube et al, 2020) and used for all global datasets (ECMWF and ERA5). As this zonal filter still might allocate GW structures with long zonal but short vertical and/or meridional wavelengths to the background, a sliding polynomial smoothing with a Savitzky-Golay filter (SG-filter; Savitzky and Golay, 1964) in the vertical and meridional direction is applied additionally to the background field to suppress these small scale signals: for the analysis and reanalysis model data used in this paper, a 4th order SG-filter over a window of 5 km in the vertical direction and a 3rd order SG-filter over a window of 750 km in the meridional direction are used.…”

Superposition of gravity waves with different propagation characteristics observed by airborne and space-borne infrared sounders

“…Gravity waves (GWs) are an important coupling mechanism in the atmosphere as they can transport energy and momentum over large horizontal and vertical distances. Even though they were discovered in the first half of the 20th century (Wegener, 1906;Trey, 1919), many processes regarding their sources, propagation, and dissipation are still not fully understood (Alexander et al, 2010;Geller et al, 2013;Plougonven and Zhang, 2014). Due to this lack of understanding and because of computational constraints, gravity waves are oversimplified in current numerical weather prediction and climate projection models by employing parameterisation schemes.…”