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Abstract First characterization of year‐round Na layers from 75 to 150 km is enabled with 7 years (2011–2017) of high‐detection‐sensitivity lidar observations over Boulder (40.13°N, 105.24°W). Clear annual and semiannual oscillations (AO and SAO) are revealed in the nightly‐mean thermosphere‐ionosphere Na (TINa) (∼105–150 km) number density and volume mixing ratio with the summer maximum but spring equinox (March/April) minimum. Such stark contrast to the summer minimum in the main Na layers (∼75–105 km) supports the theory of TINa formed via TINa⁺ion neutralization (). The SAO/AO amplitude ratio profiles (75–150 km) exhibit significant changes (∼0.06–2), linking TINa SAO to thermospheric density SAO and the minimal wave/eddy transport around midlatitude equinoxes which hinders TINa⁺ion production and upward transport via reduced diffusion of the main Na layer. Stronger TINa in autumn than in spring equinox is explained by the maximal (minimal) meteoric influx occurring in September (April). 

The Earth’s upper atmosphere separates interplanetary space from the lower atmosphere and biosphere, absorbs harmful solar radiation, dissipates cosmic dust and energetic particles, and regulates gaseous escape and atmospheric waves, therefore protecting living things on Earth. It is difficult to observe the upper atmosphere, posing challenges to studying these processes. Advancement of lidar technologies and observations over the last decades have revolutionized the research field, significantly extended the profiling altitude ranges and capabilities, and created new potential for exploring space-atmosphere interactions. This article summarizes the principles, technologies, and major discoveries of lidar studies of the upper atmosphere and near-space environment. 

We review the mechanism of multi-step vertical coupling (MSVC) via secondary and higher-order gravity waves (GWs), and its relevance for observed GW perturbations and the circulation in the upper mesosphere and thermosphere. Since the momentum deposition following the breaking or dissipation of a GW packet is localized in space and time, it leads to an imbalance in the ambient flow which in turn results in the generation of secondary or higher-order GWs. This local “body force” (LBF) mechanism is essential for MSVC. We argue that small-scale secondary GWs resulting directly from GW instability form a macro-turbulent cascade that leads to the LBF. We present a simple scale analysis supporting this interpretation with respect to observed GW spectra. Several examples of MSVC are reviewed. These include 1) an explanation of the observed persistent GWs and prevailing eastward winds in the winter mesopause region at middle to high latitudes via secondary GWs, 2) evidence that many of the daytime traveling ionospheric disturbances in the F region during winter and low geomagnetic activity are driven by higher-order GWs from MSVC, 3) the dependence of MSVC during wintertime on the strength of the polar vortex, and 4) the secondary GW disturbances in the thermosphere and ionospheric that were triggered by the Tonga volcanic eruption on January 15, 2022. Furthermore, we describe the GW-resolving whole-atmosphere model that was primarily used in corresponding studies of MSVC, and we discuss some open questions. 

Abstract Observational data inherently contain noise which manifests as uncertainties in the measured parameters and creates positive biases or noise floors in second‐order products like variances, fluxes, and spectra. Historical methods estimate and subsequently subtract noise floors, but struggle with accuracy. Gardner and Chu (2020,doi.org/10.1364/AO.400375) proposed an interleaved data processing method, which inherently eliminates biases from variances and fluxes, and suggested that the method could also eliminate noise floors of power spectra. We investigate the interleaved method for spectral analysis of atmospheric waves through theoretical studies, forward modeling, and demonstration with lidar data. Our work shows that calculating the cross‐power spectral density (CPSD) from two interleaved subsamples does reduce the spectral noise floor significantly. However, only the Co‐PSD (the real part of CPSD) eliminates the noise floor completely, while taking the absolute magnitude of CPSD adds a reduced noise floor back to the spectrum when the sample number is finite. This reduced noise floor can be further minimized through averaging over more observations, completely different from traditional spectrum calculations whose noise floor cannot be reduced by incorporating more samples. We demonstrate the first application of the interleaved method to spectral data, successfully eliminating the noise floor using the Co‐PSD in a forward model and in lidar observations of the vertical wavenumber of gravity waves at McMurdo, Antarctica. This high accuracy is gained by sacrificing precision due to photon‐count splitting, requiring additional observations to counter this effect. We provide quantitative assessment of accuracy and precision as well as application recommendations. 

Abstract We have discovered that the peak phase time of predawn thermosphere‐ionosphere Na (TINa) layers (∼110–150 km altitude) undergoes clear annual variations with the earliest occurrence in summer and latest in winter over Boulder (40.13°N, 105.24°W), which are closely correlated to annual phase variations of sunrise and tidal winds. Such discoveries were enabled by the first characterization of 12 monthly composites of TINa layers from January through December using 7 years of lidar observations (2011–2017). Despite their tenuous densities, the predawn TINa layers have nearly 100% occurrence rate (160 out of 164 nights of observations). Monthly composites show downward‐phase‐progression TINa descending at similar phase speeds as Climatological Tidal Model of the Thermosphere tidal winds. These TINa layers occur in ion convergence but neutral divergence regions, modeled using tidal winds. These results support the formation mechanism (neutralization of converged TINa⁺forming TINa) proposed previously and suggest that migrating tidal winds experience annual phase variations. 

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. 

The discovery of the thermosphere-ionosphere Fe (TIFe) layers has opened a door to exploring the least understood thermosphere and ionosphere region between 100 and 200 km with ground-based lidar instruments. The characteristics of the polar TIFe layers, and the impacts of the atmosphere neutral dynamics, electrodynamics, and metallic chemistry on the formation of TIFe layers deserve further investigation, especially the diurnal cycles of TIFe layers observed by lidar. This paper aims at investigating the major driving forces with 1-D Thermosphere-Ionosphere Fe/Fe + (TIFe) model. A main question to answer is whether neutral dynamics like tidal winds or electrodynamics like the convection electric fields and currents in the magnetosphere and ionosphere are responsible for the diurnal cycle of TIFe layers. 

Abstract 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.