Antarctica Lidar Studies of Gravity Wave Coupling from 30 to 110 km Using Interleaved Methodologies

Recent developments in data processing methodology have enabled the extension of lidar measurements into higher resolutions and lower signal to noise ratios (SNR). The interleaved method of data processing allows for the calculation of crucial second-order parameters like variance, fluxes, and power spectra such that they intrinsically have no bias or noise floor. Previous methods existed to remove biases and noise floor from data, though existing methods struggled under low SNR, meaning that some resolutions in certain seasons from a given lidar might have previously been inaccessible using existing techniques. We have found that by using the interleaved technique, we can reliably calculate these second-order parameters, with little doubt in the accuracy of the results. The application of the interleaved technique in both variance and spectra to a lidar dataset taken over the last 14 yrs. above McMurdo, Antarctica has revealed new trends in gravity wave measurements throughout the year.
These techniques facilitate new coupling studies by allowing the calculation of gravity wave characteristics covering 30-110 km. This enables comparisons between lower and upper atmosphere gravity wave characteristics which are critical in these coupling studies. Potential energy density profiles calculated using this interleaved approach have shown a different trend than previous studies of this dataset, suggesting an alternative set of physical processes than was previously suspected. Additionally, applying the interleaved method in the spectral domain has significantly increased the resolution at which polar winter (nighttime) data can be resolved, and shown potential to resolve the austral summer (daytime) data much more accurately than when using prior methods. These trends in energy density and spectra are showcased here which are processed using the interleaved method and are compared with previous analyses of the McMurdo lidar dataset.