Lidar Observations of Stratospheric Gravity Waves From 2011 to 2015 at McMurdo (77.84°S, 166.69°E), Antarctica: 2. Potential Energy Densities, Lognormal Distributions, and Seasonal Variations

Atmospheric Gravity Wave Potential Energy Observed by Rayleigh Lidar above Jiuquan (40° N, 95° E), China

Two years of observational data from the 532 nm Rayleigh lidar were used to study the vertical profile characteristics of atmospheric gravity wave potential energy density (GWPED) between 40–80 km above Jiuquan (40° N, 95° E) for the first time. The atmospheric gravity waves (AGWs) characteristics are presented in terms of the atmospheric relative temperature perturbation, along with the estimated annual and seasonal GWPED with high spatial and temporal resolution (0.5 km and 1 h). The annual potential energy mass density Epm and volume density Epv vertical profiles show that the GWPED in the upper mesosphere is close to the adiabatic growth rate. The seasonal vertical profiles result shows that Epm is higher in autumn–winter than in spring–summer in all the observed altitudes. The GWPED approaches adiabatic growth above 61 and 65 km in spring–summer and autumn–winter, respectively. The AGWs severely dissipate below the turning altitudes and transfer energy into the background atmosphere. The GWPED scale heights show that the AGWs dissipation rate of spring–summer is close to that of autumn–winter. Furthermore, based on the wind data from SD–WACCM, the influence of critical level filtering on AGWs is discussed. It plays an important role in affecting the seasonal variation in GWPED.

Mesospheric Gravity Wave Potential Energy Density Observed by Rayleigh Lidar above Golmud (36.25° N, 94.54° E), Tibetan Plateau

Rayleigh lidar data in 2013–2015 is used to describe the characteristics of gravity wave potential energy density in the mesosphere above Golmud (36.25° N, 94.54° E) of the Tibetan Plateau. In this study, the vertical profiles of the atmospheric gravity wave potential energy density between 50–80 km above the region are presented, including the potential energy mass density Epm and the potential energy volume density Epv. It shows the mathematical characteristics of the atmospheric gravity wave potential energy density vertical distribution, which also indicate the gravity waves are obviously dissipated in the lower mesosphere and close to conservative growth in the upper mesosphere (the turning point is around 61 km). A total of 1174 h of data covers seasonal changes, which reveals the seasonal characteristics of the potential energy density. The Epm increases faster with altitude in summer than others. All seasons of the potential energy density profiles show that gravity waves are dissipated in the lower mesosphere, among which spring and winter are the most severe and summer is weakest. The Epm is higher in spring and winter below 55 km. Above 55 km, it is the maximum in winter, followed by summer. Then, the AGWs activities between the location with mid–latitudes and different longitudes are compared and discussed.

Comparison of Three Methodologies for Removal of Random-Noise-Induced Biases From Second-Order Statistical Parameters of Lidar and Radar Measurements

Random-noise-induced biases are inherent issues to the accurate derivation of second-order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude-interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time-interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise-induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high-SNR, yet its accuracy fails at lower-SNR as it often yields negative values. SP is accurate and precise under high-SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low-SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random-noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability.

Eliminating photon noise biases in the computation of second-order statistics of lidar temperature, wind, and species measurements

The precision of lidar measurements is limited by noise associated with the optical detection process. Photon noise also introduces biases in the second-order statistics of the data, such as the variances and fluxes of the measured temperature, wind, and species variations, and establishes noise floors in the computed fluctuation spectra. When the signal-to-noise ratio is low, these biases and noise floors can completely obscure the atmospheric processes being observed. We describe a novel data processing technique for eliminating the biases and noise floors. The technique involves acquiring two statistically independent datasets, covering the same altitude range and time period, from which the various second-order statistics are computed. The efficacy of the technique is demonstrated using Na Doppler lidar observations of temperature in the upper mesosphere and lower thermosphere acquired recently at McMurdo Station, Antarctica. The results show that this new technique enables observations of key atmospheric parameters in regions where the signal-to-noise ratio is far too low to apply conventional processing approaches.

Lidar observations of large-amplitude mountain waves in the stratosphere above Tierra del Fuego, Argentina

Large-amplitude internal gravity waves were observed using Rayleigh lidar temperature soundings above Rio Grande, Argentina (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$54^\circ \; \hbox {S}$$\end{document}54∘S, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$68^\circ \; \hbox {W}$$\end{document}68∘W), in the period 16–23 June 2018. Temperature perturbations in the upper stratosphere amounted to 80 K peak-to-peak and potential energy densities exceeded 400 J/kg. The measured amplitudes and phase alignments agree well with operational analyses and short-term forecasts of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), implying that these quasi-steady gravity waves resulted from the airflow across the Andes. We estimate gravity wave momentum fluxes larger than 100 mPa applying independent methods to both lidar data and IFS model data. These mountain waves deposited momentum at the inner edge of the polar night jet and led to a long-lasting deceleration of the stratospheric flow. The accumulated mountain wave drag affected the stratospheric circulation several thousand kilometers downstream. In the 2018 austral winter, mountain wave events of this magnitude contributed more than 30% of the total potential energy density, signifying their importance by perturbing the stratospheric polar vortex.