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

Gravity waves are important drivers of dynamic processes in the middle atmosphere, but not the only process that could lead to small-scale perturbations. To analyse atmospheric data for gravity wave signals, gravity wave perturbations have to be separated from atmospheric variability caused by other dynamic processes. Common methods to separate small-scale gravity wave signals from a large-scale background comprise filtering methods in either the horizontal or vertical wavelength domain. Recently, studies showed that vertical wavelengths filtering can mistake other wave-like perturbations, such as inertial instability effects, for gravity wave perturbations.

We use artificial inertial instability perturbations, global model data and satellite observations to assess different spectral background removal approaches on their ability to separate gravity waves and inertial instabilities. Therefore, we investigate a horizontal background removal, applying 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.

We analyse the results for critical thresholds of the vertical wavelength and zonal wavenumber, respectively. Vertical filtering has to remove a part of the gravity wave spectrum in order to eliminate inertial instability remnants from the perturbations. Horizontal filtering, however, separates the data at scales far beyond the expected gravity wave spectrum for the case we investigated. Furthermore, we show that it is possible to effectively separate inertial instabilities perturbations from gravity waves perturbations for infrared limb-sounding satellite profiles using a cutoff zonal wavenumber of 6.