Search for: All records

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 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. 

Abstract We report the first lidar observations of regular occurrence of mid‐latitude thermosphere‐ionosphere Na (TINa) layers over Boulder (40.13°N, 105.24°W), Colorado. Detection of tenuous Na layers (∼0.1–1 cm⁻³from 150 to 130 km) was enabled by high‐sensitivity Na Doppler lidar. TINa layers occur regularly in various months and years, descending from ∼125 km after dusk and from ∼150 km before dawn. The downward‐progression phase speeds are ∼3 m/s above 120 km and ∼1 m/s below 115 km, consistent with semidiurnal tidal phase speeds. One or more layers sometimes occur across local midnight. Elevated volume mixing ratios above the turning point (∼105–110 km) of Na density slope suggest in situ production of the dawn/dusk layers via neutralization of converged Na⁺layers. Vertical drift velocity of TINa⁺calculated with the Ionospheric Connection Explorer Hough Mode Extension tidal winds shows convergent ion flow phases aligned well with TINa, supporting this formation hypothesis. 

Abstract This work presents the first lidar observations of a Quasi‐Biennial Oscillation (QBO) in the interannual variations of stratospheric gravity wave potential energy density (E_pmin 30–50 km) at McMurdo (77.84°S, 166.67°E), Antarctica. This paper also reports the first identification of QBO signals in the distance between McMurdo and the polar vortex edge. Midwinter stratospheric gravity wave activity is stronger during the QBO easterly phase when the June polar vortex expands and the polar night jet shifts equatorward. During the QBO westerly phase, gravity wave activity is weaker when the polar vortex contracts and the polar night jet moves poleward. Nine years of lidar data (2011–2019) exhibit the meanE_pmwinter maxima being ~43% higher during QBO easterly than westerly. The June polar vortex edge at 45 km altitude moves equatorward/poleward during QBO easterly/westerly phases with ~8° latitude differences (39.7°S vs. 47.7°S) as revealed in 21 years of MERRA‐2 data (1999–2019). We hypothesize that an equatorward shifted polar vortex corresponds to less critical level filtering of gravity waves and thus higherE_pmat McMurdo. The critical level filtering is characterized by wind rotation angle (WRA), and we find a linear correlation between the WRA andE_pminterannual variations. The results suggest that the QBO is likely controlling the interannual variations of theE_pmwinter maxima over McMurdo via the critical level filtering. This observationally based study lays the groundwork for a rigorous numerical study that will provide robust statistics to better understand the mechanisms that link the tropical QBO to extratropical waves.