Search Results (7)

Equatorial plasma bubbles (EPBs) occur frequently in low-latitude areas and have a non-negligible impact on navigation satellite signals. To systematically analyze the effects of a single EPB event on multi-frequency signals of GPS, Galileo, GLONASS, and BDS, all-sky airglow images over South China are jointly used to visually determine the EPB structure and depletion degree. The results reveal that scintillations, or GNSS signal fluctuations, are directly linked to EPBs and that the intensity of scintillation is positively correlated with the airglow depletion intensity. The center of the airglow depletion often corresponds to stronger GNSS scintillation, while the edge of the bubble, which is considered to have the largest density gradient, corresponds to relatively smaller scintillation instead. This work also systematically analyzes the responses of multi-constellation and multi-frequency signals to EPBs. The results show that the L2 and L5 frequencies are more susceptible than the L1 frequency is. For different constellations, Galileo’s signal has the best tracking stability during an EPB event compared with GPS, GLONASS, and BDS. The results provide a reference for dual-frequency signal selection in precise positioning or TEC calculation, that is, L1C and L2L for GPS, L1C and L5Q for Galileo, L1P and L2C for GLONASS, and L1P and L5P for BDS. Notably, BDS-2 is significantly weaker than BDS-3. And inclined geosynchronous orbit (IGSO) satellites have abnormal data error rates, which should be related to the special signal path trajectory of the IGSO satellite. Full article

All-sky airglow imagers (ASAIs) are used in the Meridian Project to observe the airglow in the middle and upper atmosphere to study the atmospheric perturbation. However, the ripples of airglow caused by the perturbation are only visible in the airglow images taken on a clear night. It is a problem to effectively select images suitable for scientific analysis from the enormous amount of airglow images captured under various environments due to the low efficiency and subjectivity of traditional manual classification. We trained a classification model based on convolutional neural network to distinguish between airglow images from clear nights and unclear nights. The data base contains 1688 images selected from the airglow images captured at Xinglong station (40.4° N, 30.5° E). The entire training process was tracked by feature maps which visualized every resulting classification model. The classification models with the clearest feature maps were saved for future use. We cropped the central part of the airglow images to avoid disturbance from the artificial lights at the edge of the vision field according to the feature maps of our first training. The accuracy of the saved model is 99%. The feature maps of five categories also indicate the reliability of the classification model. Full article

Unique observation of a long-lasting meteor trail of about half an hour duration is described. The trail resulted from a burning meteor from the Leonid storm flux in the middle latitudes over eastern Siberia. We describe three-dimensional morphological characteristics of both the meteor and the long-lasting trail using data from wide-angle CCD cameras. Additionally, we present the meteor and the trail radiolocation characteristics obtained with a meteor radar and ionosonde. The background dynamics of the upper atmosphere at the height where the long-lasting trail developed were observed using data from the meteor radar and Fabry-Perot interferometer. The obtained results allowed the conclusion that the dynamics of a long-lasting trail are conditioned by the wind. However, during the first minutes of trail development, it is possible that a high-speed component is present, resulting from explosion of the meteor body in the atmosphere. A primitive spectral analysis of the long-lasting trail’s optical emissions and earlier studies point to hydroxyl molecules as a possible source of the glow. We believe the enhanced hydroxyl emission could be related to interaction of excited O(1D) oxygen atoms with meteor body water in the Earth’s upper atmosphere. Full article

630.0 nm all-sky imaging data are used to detect airglow depletions associated with equatorial spread F. Pairs of imagers located at geomagnetically conjugate locations in the American sector at low and mid-latitudes provide information on the occurrence rate and zonal motion of airglow depletions. Airglow depletions are seen extending to magnetic latitudes as high as 25°. An asymmetric extension is observed with structures in the northern hemisphere reaching higher latitudes. By tracking the zonal motion of airglow depletions, zonal plasma drifts in the thermosphere can be inferred and their simultaneous behavior in both hemispheres investigated. Case studies using El Leoncito and Mercedes imagers in the southern hemisphere, and the respective magnetically conjugate imagers at Villa de Leyva and Arecibo, provide consistent evidence of the influence of the South Atlantic Magnetic Anomaly on the dynamics and characteristics of the thermosphere–ionosphere system at low and mid-latitudes. Full article

An algorithm has been developed to isolate the gravity waves (GWs) of different scales from airglow images. Based on the discrete wavelet transform, the images are decomposed and then reconstructed in a series of mutually orthogonal spaces, each of which takes a Daubechies (db) wavelet of a certain scale as a basis vector. The GWs in the original airglow image are stripped to the peeled image reconstructed in each space, and the scale of wave patterns in a peeled image corresponds to the scale of the db wavelet as a basis vector. In each reconstructed image, the extracted GW is quasi-monochromatic. An adaptive band-pass filter is applied to enhance the GW structures. From an ensembled airglow image with a coverage of 2100 km × 1200 km using an all-sky airglow imager (ASAI) network, the quasi-monochromatic wave patterns are extracted using this algorithm. GWs range from ripples with short wavelength of 20 km to medium-scale GWs with a wavelength of 590 km. The images are denoised, and the propagating characteristics of GWs with different wavelengths are derived separately. Full article

Amplitude growth rates of quasi-monochromatic gravity waves were estimated and compared from multiple instrument measurements carried out in Brazil. Gravity wave parameters, such as the wave amplitude and growth rate in distinct altitudes, were derived from sodium lidar density and nightglow all-sky images. Lidar observations were carried out in São Jose dos Campos (23 ${}^{\circ }$ S, 46 ${}^{\circ }$ W) from 1994 to 2004, while all-sky imagery of multiple airglow layers was conducted in Cachoeira Paulista (23 ${}^{\circ }$ S, 45 ${}^{\circ }$ W) from 1999–2000 and 2004–2005. We have found that most of the measured amplitude growth rates indicate dissipative behavior for gravity waves identified in both lidar profiles and airglow image datasets. Only a small fraction of the observed wave events (4% imager; 9% lidar) are nondissipative (freely propagating waves). Our findings also show that imager waves are strongly dissipated within the mesosphere and lower thermosphere region (MLT), decaying in amplitude in short distances (<12 km), while lidar waves tend to maintain a constant amplitude within that region. Part of the observed waves (16% imager; 36% lidar) showed unchanging amplitude with altitude (saturated waves). About 51.6% of the imager waves present strong attenuation (overdamped waves) in contrast with 9% of lidar waves. The general saturated or damped behavior is consistent with diffusive filtering processes imposing limits to amplitude growth rates of the observed gravity waves. Full article

With the development of ground-based all-sky airglow imager (ASAI) technology, a large amount of airglow image data needs to be processed for studying atmospheric gravity waves. We developed a program to automatically extract gravity wave patterns in the ASAI images. The auto-extraction program includes a classification model based on convolutional neural network (CNN) and an object detection model based on faster region-based convolutional neural network (Faster R-CNN). The classification model selects the images of clear nights from all ASAI raw images. The object detection model locates the region of wave patterns. Then, the wave parameters (horizontal wavelength, period, direction, etc.) can be calculated within the region of the wave patterns. Besides auto-extraction, we applied a wavelength check to remove the interference of wavelike mist near the imager. To validate the auto-extraction program, a case study was conducted on the images captured in 2014 at Linqu (36.2°N, 118.7°E), China. Compared to the result of the manual check, the auto-extraction recognized less (28.9% of manual result) wave-containing images due to the strict threshold, but the result shows the same seasonal variation as the references. The auto-extraction program applies a uniform criterion to avoid the accidental error in manual distinction of gravity waves and offers a reliable method to process large ASAI images for efficiently studying the climatology of atmospheric gravity waves. Full article