Data from the Cloud Imaging and Particle Size experiment on the Aeronomy of Ice in the Mesosphere (AIM) satellite are employed to study the impact of small‐scale gravity wave (GW) on albedo, ice water content (IWC), and particle radius (PR) of polar mesospheric clouds. Overall, 23,987 eligible GW events, with a horizontal wavelength of 20–150 km are eventually extracted from Cloud Imaging and Particle Size level 2 orbit albedo maps during 2007–2011. The overall statistical results show that when small‐scale GWs travel horizontally in polar mesospheric clouds, they can amplify the albedo and IWC by a rate of 10.0–22.6%, while reducing the PR by as much as −7.01%. Owing to the strong temporal and spatial dependences, the albedo and IWC variations are larger on an average during the core of the season, while they decrease during the initial and final periods of the season. The obvious zonal asymmetries are also found. The albedo variations show a positive linear relation with the GW amplitudes in albedo, as opposed to a negative linear relation with GW horizontal wavelengths. In most of the GW events, the periodic variation in the trend of albedo exhibits an anticorrelation with that of PR. Combining previous research studies with our results, we deduce that the rapid change in particle concentration and the upward movement of water vapor by GWs may be very important aspects for explaining the influence mechanism.

Double-layer structures in polar mesospheric clouds (PMCs) are observed by using Solar Occultation for Ice Experiment (SOFIE) data between 2007 and 2014. We find 816 and 301 events of double-layer structure with percentages of 10.32 and 7.25 % compared to total PMC events, and the mean distances between two peaks are 3.06 and 2.73 km for the Northern Hemisphere (NH) and Southern Hemisphere (SH) respectively. Double-layer PMCs almost always have less mean ice water content (IWC) than daily IWC during the core of the season, but they are close to each other at the beginning and the end. The result by averaging over all events shows that the particle concentration has obvious double peaks, while the particle radius exhibits an unexpected monotonic increase with decreasing altitude. By further analysis of the background temperature and water vapour residual profiles, we conclude that the lower layer is a reproduced one formed at the bottom of the upper layer. 56.00 and 47.51 % of all double-layer events for the NH and SH respectively have temperature enhancements larger than 2 K locating between their double peaks. The longitudinal anti-correlation between the gravity waves' (GWs') potential energies and occurrence frequencies of double-layer PMCs suggests that the double-layer PMCs tend to form in an environment where the GWs have weaker intensities.

Effective design of the lubrication path greatly influences the durability of any transmission system. However, it is experimentally impossible to estimate the internal distribution of the automotive transmission fluid (ATF) to different parts of the transmission system due to its structural complexities. Hybrid vehicle transmission systems usually consist of different types of bearings (ball bearings, thrust bearings, roller bearings, etc.) in conjunction with gear systems. It is a perennial challenge to computationally simulate such complicated rotating systems. Hence, one-dimensional models have been the state of the art for designing these intricate transmission systems. Though quantifiable, the 1D models still rely heavily on some testing data. Furthermore, HEVs (hybrid electric vehicles) desire a more efficient lubrication system compared to their counterparts (Internal combustion engine vehicles) to extend the range of operation on a single charge. Thus, this paper includes a detailed, transient, three-dimensional CFD analysis of the lubricating oil flow path in an HEV transmission system using the commercial CFD software Simerics-MP+. The modeled transmission system includes scores of bearings, rotating components, and planetary gear systems. Using this modeling framework, we can predict the lubrication state of the various components of the transmission system. Furthermore, this paper reveals the effect of the centrifugal force on the oil distribution and the wetting fraction of different components. Additionally, two different designs of lubricating flow paths inside the roller bearings are explored to study the effect on the wetting of the rollers. The current simulation framework adopts the volume of fluid (VOF) technique to accurately model the two-phase interface development in the rotating systems.

In this study, the effect of day/night factor on the detection performance of the FY4A lightning mapping imager (LMI) is evaluated using the Bayesian theorem, and by comparing it to the measurements made by a ground-based low-frequency magnetic field lightning location system. Both the datasets were collected in the summers of 2019–2020 in Hainan, China. The results show that for the observed summer thunderstorms in Hainan, the daytime detection efficiencies of LMI (DE_LMI) were 20.41~35.53% lower than the nighttime DE_LMI. Compared to other space-based lightning sensors (lightning imaging sensors/optical transient detectors (LIS/OTD) and geostationary lightning mapper (GLM)), the detection performance of LMI is more significantly influenced by the day/night factor. The DE_LMI rapidly dropped within about four hours after sunrise while it increased before sunset. For the storms that formed at night and lasted for an entire day, the DE_LMI remained relatively low during the daytime, even as the thunderstorms intensified. The poor detection performance of LMI during daytime is probably because of the sunlight reflection by clouds and atmosphere, which results in larger background radiative energy density (RED) than that at night. During night, LMI captured the lightning signals well with low RED (8.38~10.63 μJ sr⁻¹ m⁻² nm⁻¹). However, during daytime, signals with RED less than 77.12 μJ sr⁻¹ m⁻² nm⁻¹ were filtered, thus lightning groups could rarely be identified by LMI, except those with extremely high RED. Due to the limitations of the Bayesian theorem, the obtained DE in this study was “relative” DE rather than “absolute” DE. To obtain the absolute DE of LMI, the total lightning density is necessary but can hardly be measured. Nonetheless, the results shown here clearly indicate the strong impact of day/night factor on the detection performance of LMI, and can be used to improve the design and post-processing method of LMI.

The greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data‐driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border‐intensified agricultural development on China side (CNSP) versus largely little‐disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p < 0.05), with the annual variability reaching values of 9.8 × 10⁻³ yr⁻¹ and 11.3 × 10⁻³ yr⁻¹, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8‐day periods from 26 June to 28 July. During this period, the LAI trend of CNSP is much higher than that of RUSP, at 92.7 × 10⁻³ yr⁻¹ (p < 0.01) and 43.8 × 10⁻³ yr⁻¹ (p < 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.