We present midlatitude solar response and linear trend from Colorado State University/Utah State University Na lidar nocturnal temperature observations between 1990 and 2017. Along with the nightly mean temperatures (_Ngt), we also use the corresponding 2‐hr means centered at midnight (_2MN), resulting in vertical trend profiles similar in shapes as those previously published. The 28‐year trend from _Ngt (_2MN) data set starts from a small warming at 85 km, to cooling at 87 (88) km, reaching a maximum of 1.85 ± 0.53 (1.09 ± 0.74) at 92 (93) km and turns positive again at 102 (100) km. The 6‐month winter trend is much cooler than the 4‐month summer trend with comparable solar response varying around 5 ± 1 K/100 SFU throughout the profile (85–105 km) with higher summer values. We explore the observed summer/winter trend difference in terms of observed gravity wave heat flux heating rate at a nearby station and the long‐term trend of gravity wave variance at a midlatitude. Between 89 and 100 km, the lidar trends are within the error bars of the Leibniz Middle Atmosphere (LIMA) summer trends (1979–2013), which are nearly identical to the lidar‐Ngt trend. We address the need of long data set for reliable analysis on trend, the extent of trend uncertainty due to possible tidal bias, the effect of a Pinatubo/episodic function, and the impact of stratospheric ozone recovery.

In 2011, Chu et al. reported the observation of neutral Fe layers reaching 155 km. Since then, there are five additional reports of layers extending above 130 km to as high as 170 km, none from central middle latitudes (35°–55°), a large atmospheric region of considerable importance. Here we report the first concurrent observations of thermospheric Na layers from two nearby lidar stations so located, Yanqing (40.5°N, 116.0°E) and Pingquan (41.0°N, 118.7°E). From one‐year data set, we found four such layers, including an unprecedented one reaching 200 km with highest density of 35 cm⁻³ and fastest descending rate. While the main Na layers were comparable, in three nights, these thermospheric layers were observed only in one station (Yanqing), suggesting that these layers often occur locally with a horizontal scale less than ~250 km. We tabulate, compare, and discuss the principle characteristics of all the reported thermospheric layers.

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively.

The climatology of earth's Na density over Fort Collins, CO (41°N, 105°W) based on nocturnal Na lidar observations between 1990 and 1999 was reported by She et al. (2000, https://doi.org/10.1029/2000gl003825). Based on a continued 28‐year data set between 1990 and 2017 with the latter part observed over Logan, UT (42N, 112W), we update the seasonal variations between 80 and 110 km. This data set is also used to deduce long‐term responses of Na density (profile) between 75 and 110 km, showing a positive linear trend between 75 and 93 km (with maximum ∼2.87 × 10⁸ m⁻³/decade at 87 km); it turns negative before approaching zero at 110 km (with minimum ∼−2.96 × 10⁷ m⁻³/decade at 100 km). The associated solar response is also positive for the altitude range in question (with maximum ∼5.20 × 10⁶ m⁻³/SFU at 91 km). We also derived the 28‐year mean Na layer column abundance, centroid altitude, and root mean square width to be 3.92 ± 2.14 10¹³ m⁻², 91.3 ± 1.0 km, and 4.62 ± 0.56 km, respectively, and deduced long‐term trend and solar cycle responses of column abundance and centroid altitude, respectively to be 7.81 ± 1.63%/decade and 16.9 ± 2.8%/100SFU, and −355 ± 35 m/decade and −1.94 ± 0.69 m/SFU. We explained conceptually how positive long‐term responses in Na density led to positive responses in column abundance and negative responses in centroid altitude.

Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency.

Meteor radar data collected over Langfang, China (39.4° N, 116.7° E) were used to estimate the momentum flux of short-period (less than 2 h) gravity waves (GWs) in the mesosphere and lower thermosphere (MLT), using the Hocking (2005) analysis technique. Seasonal variations in GW momentum flux exhibited annual oscillation (AO), semiannual oscillation (SAO), and quasi-4-month oscillation. Quantitative estimations of GW forcing toward the mean zonal flow were provided using the determined GW momentum flux. The mean flow acceleration estimated from the divergence of this flux was compared with the observed acceleration of zonal winds displaying SAO and quasi-4-month oscillations. These comparisons were used to analyze the contribution of zonal momentum fluxes of SAO and quasi-4-month oscillations to zonal winds. The estimated acceleration from high-frequency GWs was in the same direction as the observed acceleration of zonal winds for quasi-4-month oscillation winds, with GWs contributing more than 69%. The estimated acceleration due to Coriolis forces to the zonal wind was studied; the findings were opposite to the estimated acceleration of high-frequency GWs for quasi-4-month oscillation winds. The significance of this study lies in estimating and quantifying the contribution of the GW momentum fluxes to zonal winds with quasi-4-month periods over mid-latitude regions for the first time.