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19 pages, 1634 KiB  
Article
A New Method for Determining the Wave Turbopause Based on SABER/TIMED Data
by Zewei Wang, Cunying Xiao, Xiong Hu, Junfeng Yang, Xuan Cheng, Kuan Li, Luo Xiao, Xiaoqi Wu, Yang Yu and Hao Li
Remote Sens. 2025, 17(4), 623; https://doi.org/10.3390/rs17040623 - 12 Feb 2025
Viewed by 408
Abstract
The determination of the wave turbopause is vital for understanding the dynamics of atmospheric processes in the Mesosphere and Lower Thermosphere (MLT). In this study, we introduce a novel approach for identifying the wave turbopause, using SABER/TIMED temperature data and number density data, [...] Read more.
The determination of the wave turbopause is vital for understanding the dynamics of atmospheric processes in the Mesosphere and Lower Thermosphere (MLT). In this study, we introduce a novel approach for identifying the wave turbopause, using SABER/TIMED temperature data and number density data, addressing the limitations associated with traditional linear fitting methods that can lead to ambiguities in results. Our approach is grounded in the conservation-of-energy principle, which facilitates the introduction of an energy index to effectively delineate the boundaries of the turbopause layer. This method allows us to define several key parameters: the lower boundary height, upper boundary height, turbopause height, and turbopause layer thickness. Analyzing long-term SABER data specifically over Beijing, we observed that the turbopause layer exhibited significant seasonal and inter-annual variations. Our findings indicated that the average height of the lower boundary was approximately 69.17 km, while the average height of the upper boundary was around 93.85 km. The energy index provided a comprehensive assessment of atmospheric wave activity, revealing periodic variations at different altitudes within the turbopause layer. The proposed method not only offers a more precise and applicable characterization of the turbopause but also enhances our capacity for atmospheric modeling and empirical investigations. Future work will focus on extending this methodology, to analyze the comprehensive SABER data collected globally. We aim to uncover insights into the seasonal characteristics of the turbopause across various geographic regions, allowing for a more detailed understanding of its behavior under different climatic conditions, ultimately contributing to a deeper understanding of MLT dynamics. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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Figure 1
<p>The picture on the right is the daily mean temperature profile at 40°N on 15 September 2002, and the picture on the left is the temperature standard deviation profile and fitted lines for different height segments. The solid black line shows the temperature standard deviation for the 40°N latitude circle and the dashed black line shows the temperature standard deviation for the Beijing grid (40° ± 2.5°, 120° ± 10°). The colored lines represent the results of fitting the standard deviation of the temperature of the latitude circle at 40°N with different altitude segments.</p>
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<p>Energy index and its slope change with the height of the Beijing grid on 21 March, 21 June, 21 September, and 15 December 2006 for different colors. The black line represents the annual average at 50 km of the energy index.</p>
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<p>In the graph above, the solid line represents the slope of the energy index, and the dashed line represents the fit slope of the energy index from its current height to its apex. The chart below shows the difference between the two.</p>
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<p>Temperature standard deviation(blue) and energy index(red) change with height on 15 September 2002. The two colored straight lines are fitted to 30–65 km and 91–110 km, respectively.</p>
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<p>The energy index of the Beijing grid varied with season and height.</p>
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<p>Normalized spectral graphs of energy indices at different altitudes. The black lines represent areas with confidence levels above 0.99.</p>
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<p>The position of the upper boundary of the turbopause layer, the position of the lower boundary of the turbopause layer, the position of turbopause, and the thickness of the turbopause layer change with months. The values for each month are the average of approximately 21 years for each month.</p>
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<p>Lomb–Scargle diagram of the upper boundary of the turbopause layer, the lower boundary of the turbopause layer, and the position of the turbopause. These straight lines represent their respective positions with 99% confidence.</p>
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<p>Variation of zonal wind with height and season. The black line means the zonal wind is zero. The red line represents the upper boundary and the blue line represents the lower boundary.</p>
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16 pages, 17053 KiB  
Article
Adding to Our Knowledge on the Diatom and Green Algae Biodiversity of Egypt: Some New-to-Science, Poorly Known, and Newly Recorded Species
by Abdullah A. Saber, Mostafa M. El-Sheekh, Olfat M. A. Salem, Zlatko Levkov, Marco Cantonati, Modhi O. Alotaibi and Hani Saber
Water 2025, 17(3), 446; https://doi.org/10.3390/w17030446 - 5 Feb 2025
Viewed by 567
Abstract
During our research on the diversity of diatoms and green microalgae from Egypt, four new-to-science, newly recorded, and poorly known species were retrieved from different Egyptian habitats. The new benthic diatom species Halamphora shaabanii A.A. Saber, El-Sheekh, Levkov, H. Saber et Cantonati sp. [...] Read more.
During our research on the diversity of diatoms and green microalgae from Egypt, four new-to-science, newly recorded, and poorly known species were retrieved from different Egyptian habitats. The new benthic diatom species Halamphora shaabanii A.A. Saber, El-Sheekh, Levkov, H. Saber et Cantonati sp. nov., which could not be identified using the currently available literature, was described from the high-conductivity, oasis lake Abu Nuss in the El-Farafra Oasis, located in the Western Desert of Egypt, employing both light (LM) and scanning electron (SEM) microscopy observations. A detailed comparison of the biometrically distinctive traits, and ecological preferences, of this new diatom species revealed sufficient differentiations from its morphologically most closely related species: H. atacamana, H. caribaea, H. ectorii, H. gasseae, H. halophila, H. mosensis, H. poianensis, and H. vantushpaensis. Ecologically, Halamphora shaabanii can tolerate relatively high nutrients (N and P) and prefers saline inland environments with NaCl water types. The araphid diatom Pseudostaurosiropsis geocollegarum was observed in the epilithic diatom assemblages of the River Nile Damietta Branch and identified on the basis of LM and SEM. From an ecological standpoint, P. geocollegarum seems to prefer elevated nutrient concentrations (meso-eutraphentic species), reflecting different human influences on the freshwater River Nile Damietta Branch. Based on the available literature, this is the first documentation of this freshwater diatom species for Egypt, and the second record for the African continent. Two green motile microalgae, Chlamydomonas proboscigera and Gonium pectorale, were isolated and identified from the terrestrial biomes of the arid habitat “Wadi El-Atshan” in the Eastern Desert of Egypt. C. proboscigera is reported herein for the first time in the Egyptian algal flora, while G. pectorale is poorly documented in the available literature. In light of our findings, the Egyptian habitats, particularly the isolated desert ecosystems, are interesting biodiversity hotspots and have a richer algal microflora than earlier anticipated. Furthermore, more in-depth taxonomic studies, using a combined polyphasic approach, are needed not only to foster our knowledge of the Egyptian and African algal and cyanobacterial diversity and biogeography, but also to be further used in applied environmental sciences. Full article
(This article belongs to the Special Issue Biodiversity of Freshwater Ecosystems: Monitoring and Conservation)
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Graphical abstract

Graphical abstract
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<p>Sampling sites in the present studies. (<b>A</b>) the desert oasis lake Abu Nuss in the El-Farafra Oasis, the Western Desert of Egypt; (<b>B</b>) the Damietta Branch of the River Nile, note the epilithon where the diatom samples were collected; and (<b>C</b>) the arid valley “Wadi El-Atshan”, located in the Eastern Desert of Egypt.</p>
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<p>(<b>A</b>–<b>H</b>) Type material of <span class="html-italic">Halamphora shaabanii</span> sp. nov. from the high-conductivity, oasis lake Abu Nuss in the El-Farafra Oasis, the Western Desert of Egypt. LM micrographs showing the size range of the species. Scale bar 10 μm.</p>
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<p>(<b>A</b>–<b>H</b>) SEM micrographs of the type population of <span class="html-italic">Halamphora shaabanii</span> sp. nov.; (<b>A</b>–<b>C</b>) girdle views of whole frustules. Notice the numerous girdle bands; (<b>D</b>) external view of whole valve; (<b>E</b>) external detailed view of mid-valve showing the raphe ledge and proximal raphe ends; (<b>F</b>,<b>G</b>) internal views of whole valves; (<b>H</b>,<b>J</b>) internal detailed views of distal raphe ends and helictoglossae; and (<b>I</b>) internal detailed view of mid-valve showing fused central helictoglossa and detail of areolae. Scale bars = 5 µm (<b>A</b>–<b>D</b>), 3 µm (<b>E</b>–<b>G</b>), and 1 µm (<b>H</b>–<b>J</b>).</p>
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<p><span class="html-italic">Pseudostaurosiropsis geocollegarum</span> sampled from the River Nile-Damietta Branch. (<b>A</b>–<b>D</b>) LM micrographs presenting external details of the valves, and (<b>E</b>–<b>G</b>) SEM micrographs showing external and internal details of the valve, sternum, and striae. Notice striae are commonly composed of two rows of areolae and occluded with disc-like volae (arrowheads). Spines simple, bifurcate. Internally, notice the absence of rimoportulae and details of apical pore fields. Scale bars = 10 µm (<b>A</b>–<b>D</b>), and 1 µm (<b>E</b>–<b>G</b>).</p>
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<p>Light micrographs of <span class="html-italic">Chlamydomonas proboscigera</span> showing its detailed morphotaxonomic features and lifecycle stages. (<b>A</b>–<b>F</b>) Subspherical to ellipsoidal vegetative cells. Notice flattened papilla (arrowheads) and stigma located anteriorly in the chloroplast; (<b>G</b>,<b>H</b>) autofluorescent cup-shaped chloroplasts; (<b>I</b>,<b>J</b>) palmelloid stages; and (<b>K</b>) asexual zoosporangium. Scale bars = 5 μm.</p>
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<p>Light micrographs of <span class="html-italic">Gonium pectorale</span> showing the general diagnostic features. (<b>A</b>–<b>E</b>) Details of coenobia and the vegetative cells; (<b>F</b>) cells stained with Lugol’s iodine showing the pyrenoids and flagella. Scale bar = 5 μm.</p>
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13 pages, 2080 KiB  
Communication
Mesosphere and Lower Thermosphere (MLT) Density Responses to the May 2024 Superstorm at Mid-to-High Latitudes in the Northern Hemisphere Based on Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) Observations
by Ningtao Huang, Jingyuan Li, Jianyong Lu, Shuai Fu, Meng Sun, Guanchun Wei, Mingming Zhan, Ming Wang and Shiping Xiong
Remote Sens. 2025, 17(3), 511; https://doi.org/10.3390/rs17030511 - 31 Jan 2025
Viewed by 640
Abstract
The thermospheric density response during geomagnetic storms has been extensively explored, but with limited studies on the density response in the Mesosphere and Lower Thermosphere (MLT) region. In this study, the density response in the MLT region at mid-to-high latitudes of the Northern [...] Read more.
The thermospheric density response during geomagnetic storms has been extensively explored, but with limited studies on the density response in the Mesosphere and Lower Thermosphere (MLT) region. In this study, the density response in the MLT region at mid-to-high latitudes of the Northern Hemisphere during the intense geomagnetic storm in May 2024 is investigated using density data from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite. The results indicate that during the geomagnetic storm, the density response exhibits both significant decreases and increases; specifically, approximately 25.2% of the observation points show a notable reduction within a single day, with the maximum decrease exceeding −59.9% at 105 km. In contrast, around 16.5% of the observation points experience a significant increase over the same period, with the maximum increase surpassing 82.4% at 105 km. The distribution of density changes varies with altitudes. The magnitude of density increases diminishes with decreasing altitude, whereas the density decreases exhibit altitude-dependent intensity variations. Density decreases are primarily concentrated in high-latitude regions, especially in the polar cap, while density increases are mainly observed between 50°N and 70°N. The intensity of density response is generally stronger in the dusk sector than in the dawn sector. These results suggest that atmospheric expansion and uplift driven by temperature variations are the primary factors underlying the observed density change. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>Geomagnetic indices and concurrent SABER density differences from 9 to 13 May 2024. (<b>a</b>) Hourly Dst index. The blue dashed lines indicate the main phase of the storm. (<b>b</b>) Three-hourly <span class="html-italic">Kp</span> index. The gray dashed lines denote intervals of intense geomagnetic disturbances during the recovery phase. (<b>c</b>) Neutral density profiles at 65–75°N. (<b>d</b>) Polar perspective of significant density response points at 105 km, 45–83°N. Points are shown with color representing response intensity, with panels from left to right showing conditions on 9–13 May.</p>
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<p>Significant response points in the 45–83°N MLT region from 9 to 13 May 2024. (<b>a</b>) Significant density response points at 105 km. (<b>b</b>,<b>c</b>) are the same as (<b>a</b>), but for altitudes of 100 and 95 km, respectively. The left y-axis indicates the proportion of significant response points relative to the total daily observation points, corresponding to the short horizontal lines in the figure. The red line represents the proportion of significant density increases, while the blue line represents the proportion of density decreases. The point’s color represents the response intensity. The blue dashed line marks the storm’s main phase, and the gray dashed lines denote periods of intense geomagnetic activity during the recovery phase.</p>
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<p>Latitude distribution of significant response points in the 45–83°N MLT region from 9 to 13 May 2024. A two-dimensional KDE is applied to the latitude–time distribution of significant points. Color intensity indicates the concentration of points within each region and does not represent specific values. (<b>a</b>) The significant response points at 105 km. (<b>b</b>,<b>c</b>) are the same as (<b>a</b>), but for altitudes of 100 and 95 km, respectively. The upper (red) plots indicate significant density increases and the lower (blue) plots indicate significant decreases. The black dashed lines enclose the densest 10% of significant points. The blue dashed line represents the storm’s main phase and the gray dashed line marks periods of intense geomagnetic disturbance during the recovery phase.</p>
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<p>Statistical distribution of significant density response points in the dawn and dusk sectors in the 45–83°N MLT region from 17 UT on 10 to 14 May 2024. (<b>a</b>) The local time distribution of data profiles corresponding to the SABER ascending node. (<b>b</b>) is the same as (<b>a</b>) but for the descending node. (<b>c</b>,<b>e</b>,<b>g</b>) Significant density responses in the dawn sector (orange border) at 105 km, 100 km, and 95 km, respectively. (<b>d</b>,<b>f</b>,<b>h</b>) are the same as (<b>c</b>,<b>e</b>,<b>g</b>), but in the dusk sector (green border). The numbers in the figure represent the proportion (prop) and mean response intensity (avg) for density decreases (blue) and increases (red).</p>
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15 pages, 7833 KiB  
Article
Spatial and Temporal Characterization of Near Space Temperature and Humidity and Their Driving Influences
by Wenhui Luo, Jinji Ma, Miao Li, Haifeng Xu, Cheng Wan and Zhengqiang Li
Remote Sens. 2024, 16(22), 4307; https://doi.org/10.3390/rs16224307 - 19 Nov 2024
Viewed by 852
Abstract
Near space refers to the atmospheric region 20–100 km above Earth’s surface, encompassing the stratosphere, mesosphere, and part of the thermosphere. This region is susceptible to surface and upper atmospheric disturbances, and the atmospheric temperature and humidity profiles can finely characterize its complex [...] Read more.
Near space refers to the atmospheric region 20–100 km above Earth’s surface, encompassing the stratosphere, mesosphere, and part of the thermosphere. This region is susceptible to surface and upper atmospheric disturbances, and the atmospheric temperature and humidity profiles can finely characterize its complex environment. To analyze the relationship between changes in temperature and humidity profiles and natural activities, this study utilizes 18 years of temperature and water vapor data from the TIMED/SABER and AURA/MLS instruments to investigate the variations in temperature and humidity with altitude, time, and spatial distribution. In addition, multiple linear regression analysis is used to examine the impact mechanisms of solar activity, the El Niño–Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO) on temperature and humidity. The results show that in the mid- and low-latitude regions, temperature and water vapor reach their maxima at an altitude of 50 km, with values of 265 K and 8–9 × 10⁻⁶ ppmv, respectively; the variation characteristics differ across latitudes and altitudes, with a clear annual cycle; the feedback effects of solar activity and the ENSO index on temperature and humidity in the 20–40 km atmospheric layer are significantly different. Among these factors, solar activity is the most significant influence on temperature and water vapor, with response coefficients of −0.2 to −0.16 K/sfu and 0.8 to 4 × 10⁻⁶ ppmv/sfu, respectively. Secondly, in the low-latitude stratospheric region, the temperature response to ENSO is approximately −1.5 K/MEI, while in the high-latitude region, a positive response of 3 K/MEI is observed. The response of water vapor to ENSO varies between −1 × 10⁻⁷ and −4 × 10⁷ ppmv/sfu. In the low-latitude stratospheric region, the temperature and humidity responses to the QBO index exhibit significant differences, ranging from −1.8 to −0.6 K/10 m/s. Additionally, there are substantial differences in responses between the polar regions and the low-latitude equatorial region. Finally, a three-dimensional model coefficient was constructed to illustrate the influence of solar activity, ENSO, and QBO on temperature and humidity in the near space. The findings of this study contribute to a deeper understanding of the temperature and humidity variation characteristics in near space and provide valuable data and model references for predicting three-dimensional parameters of temperature and humidity in this region. Full article
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<p>TIMED/SABER temperature data.</p>
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<p>Aura/MLS temperature data.</p>
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<p>Time series of solar, ENSO, and QBO activity data from January 2005 to December 2022. (<b>a</b>) Time series of solar activity data from January 2005 to December 2022; (<b>b</b>) Time series of ENSO-MEI data from January 2005 to December 2022; (<b>c</b>) Time series of QBO data from January 2005 to December 2022.</p>
Full article ">Figure 3 Cont.
<p>Time series of solar, ENSO, and QBO activity data from January 2005 to December 2022. (<b>a</b>) Time series of solar activity data from January 2005 to December 2022; (<b>b</b>) Time series of ENSO-MEI data from January 2005 to December 2022; (<b>c</b>) Time series of QBO data from January 2005 to December 2022.</p>
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<p>Monthly data for temperature and water vapor concentration. (<b>a</b>) Monthly data of temperature data in the mid-latitude region of the Northern Hemisphere from 2005 to 2022; (<b>b</b>) Monthly data of water vapor data in the mid-latitude region of the Northern Hemisphere from 2005 to 2022.</p>
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<p>Range of temperature and humidity profile extrema. (<b>a</b>) Range of temperature profile extrema; (<b>b</b>) Range of humidity profile extrema.</p>
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<p>Temperature and water vapor response to solar activity at 180°E. (<b>a</b>) temperature response to solar activity at 180°E; (<b>b</b>) water vapor response to solar activity at 180°E.</p>
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<p>Temperature and water vapor response to ENSO at 180°E. (<b>a</b>) temperature response to ENSO at 180°E; (<b>b</b>) water vapor response to ENSO at 180°E.</p>
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<p>Temperature and water vapor response to QBO at 180° E. (<b>a</b>) temperature response to the QBO; (<b>b</b>) water vapor response to the QBO.</p>
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<p>Box plot of coefficients at different altitudes. (<b>a</b>) Box plot of TMP-solar coefficients at different altitudes; (<b>b</b>) Box plot of TMP-ENSO coefficients at different altitudes; (<b>c</b>) Box plot of TMP-QBO coefficients at different altitudes; (<b>d</b>) Box plot of H<sub>2</sub>O-solar coefficients at different altitudes; (<b>e</b>) Box plot of H<sub>2</sub>O-ENSO coefficients at different altitudes; (<b>f</b>) Box plot of H<sub>2</sub>O-QBO coefficients at different altitudes.</p>
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<p>3D scatter plot of coefficients. (<b>a</b>) 3D scatter plot of temperature response coefficients to solar activity; (<b>b</b>) 3D scatter plot of temperature response coefficients to ENSO; (<b>c</b>) 3D scatter plot of temperature response coefficients to QBO; (<b>d</b>) 3D scatter plot of water vapor response coefficients to solar activity; (<b>e</b>) 3D scatter plot of water vapor response coefficients to ENSO; (<b>f</b>) 3D scatter plot of water vapor response coefficients to QBO.</p>
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13 pages, 1833 KiB  
Article
Amplifying Cognitive Functions in Amateur Esports Athletes: The Impact of Short-Term Virtual Reality Training on Reaction Time, Motor Time, and Eye–Hand Coordination
by Maciej Lachowicz, Anna Serweta-Pawlik, Alicja Konopka-Lachowicz, Dariusz Jamro and Grzegorz Żurek
Brain Sci. 2024, 14(11), 1104; https://doi.org/10.3390/brainsci14111104 - 30 Oct 2024
Viewed by 1513
Abstract
Objectives: Electronic sports (esports) have grown into a major competitive field in today’s digital landscape, attracting the interest of established companies and evolving into a fast-growing industry. Cognitive function, including reaction time, motor time, and eye–hand coordination, plays a crucial role in e-athlete [...] Read more.
Objectives: Electronic sports (esports) have grown into a major competitive field in today’s digital landscape, attracting the interest of established companies and evolving into a fast-growing industry. Cognitive function, including reaction time, motor time, and eye–hand coordination, plays a crucial role in e-athlete performance. This study aims to examine the impact of VR training on these cognitive functions in amateur e-athletes. Methods: The study involved 66 amateur e-athletes (45 men and 21 women, aged 19–41, with a mean age of 23.96 ± 3.90 years) who reported active, non-professional involvement in esports. Participants were randomly assigned to an experimental group (E) (n = 32) and a control group (C) (n = 34), with initial comparisons confirming no significant differences in daily gaming habits, esports experience, or age between groups. The E group completed 15-minute daily training sessions using the VR game Beat Saber over eight consecutive days. Results: The results demonstrated that VR training significantly improved eye–hand coordination in the experimental group, although there were no notable effects on reaction time or motor time. Conclusions: These findings suggest that VR training may be an effective method to enhance certain cognitive functions, specifically eye–hand coordination, among amateur e-athletes. This could offer a valuable approach for performance improvement in this rapidly growing field. Full article
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<p>Distribution of participants during subsequent tests.</p>
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<p>Progress chart for measurement protocol.</p>
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<p>Results of the Reaction Test across three measurement points.</p>
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<p>Results of 2HAND test across three measurement points.</p>
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<p>Significant within-groups comparisons. Note: * Significant at <span class="html-italic">p</span> &lt; 0.05, ** Significant at <span class="html-italic">p</span> &lt; 0.01, *** Significant at <span class="html-italic">p</span> &lt; 0.001.</p>
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20 pages, 8775 KiB  
Article
Response of NO 5.3 μm Emission to the Geomagnetic Storm on 24 April 2023
by Hongshan Liu, Hong Gao, Zheng Li, Jiyao Xu, Weihua Bai, Longchang Sun and Zhongmu Li
Remote Sens. 2024, 16(19), 3683; https://doi.org/10.3390/rs16193683 - 2 Oct 2024
Viewed by 721
Abstract
The response of NO emission at 5.3 μm in the thermosphere to the geomagnetic storm on 24 April 2023 is analyzed using TIMED/SABER observations and TIEGCM simulations. Both the observations and the simulations indicate a significant enhancement in NO emission during the storm. [...] Read more.
The response of NO emission at 5.3 μm in the thermosphere to the geomagnetic storm on 24 April 2023 is analyzed using TIMED/SABER observations and TIEGCM simulations. Both the observations and the simulations indicate a significant enhancement in NO emission during the storm. Observations show two peaks around 50°S/N in the altitude–latitude distribution of NO emission and its relative variation. Additionally, the peak emission and enhancement are stronger on the nightside compared with the dayside. The peak altitude in the Northern Hemisphere is approximately 2–10 km higher than in the Southern Hemisphere; meanwhile, the peak altitude on the dayside is approximately 2–8 km higher than that on the nightside. Simulations reveal three peaks around 50°S, the equator, and 65°N, with peak altitudes at higher latitudes being slightly lower than those observed. In general, the altitude–latitude distribution structure of the relative variation in simulated NO emission matches observations, with two peaks around 50°S/N. TIEGCM simulations suggest that the increase in NO density and temperature during a geomagnetic storm can lead to an increase in NO emission at most altitudes and latitudes. Furthermore, the significant enhancement around 50°S/N is mainly attributed to the changes in NO density. Full article
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<p>Temporal distribution of (<b>a</b>) By, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">B</mi> <mi mathvariant="normal">z</mi> </mrow> </semantics></math>, (<b>c</b>) solar wind speed, (<b>d</b>) Dst index, and (<b>e</b>) F10.7 index from 22–26 April 2023.</p>
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<p>Altitude–latitude distribution of NO VER observed by the TIMED/SABER satellite on the dayside (<b>a1</b>–<b>e1</b>) and nightside (<b>a2</b>–<b>e2</b>) from 22–26 April 2023.</p>
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<p>Altitude–latitude distribution of relative variations in NO VER observed by the TIMED/SABER satellite on the dayside (<b>a1</b>–<b>e1</b>) and nightside (<b>a2</b>–<b>e2</b>) from 22–26 April 2023.</p>
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<p>Altitude–latitude distribution of NO VER on the dayside (<b>a1</b>–<b>e1</b>) and nightside (<b>a2</b>–<b>e2</b>) from 22–26 April 2023, simulated by the TIEGCM.</p>
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<p>Altitude–latitude distribution of NO VER relative variation on the dayside (<b>a1</b>–<b>e1</b>) and nightside (<b>a2</b>–<b>e2</b>) from 22–26 April 2023, simulated by the TIEGCM.</p>
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<p>Temporal dayside distribution of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">t</mi> </mrow> </semantics></math> index (<b>a</b>), altitude–latitude distributions of relative variations in the TIEGCM-simulated NO density (<b>a1</b>–<b>e1</b>), O density (<b>a2</b>–<b>e2</b>), and temperature (<b>a3</b>–<b>e3</b>) from 22–26 April 2023.</p>
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<p>Same as <a href="#remotesensing-16-03683-f006" class="html-fig">Figure 6</a> but for the nightside, Temporal nightside distribution of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">t</mi> </mrow> </semantics></math> index (<b>a</b>), altitude–latitude distributions of relative variations in the TIEGCM-simulated NO density (<b>a1</b>–<b>e1</b>), O density (<b>a2</b>–<b>e2</b>), and temperature (<b>a3</b>–<b>e3</b>) from 22–26 April 2023.</p>
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<p>Altitude–latitude distributions of the TIEGCM-simulated NO VER on 22 April (<b>a1</b>,<b>a2</b>), variations in NO VER respectively caused by changes in NO density (<b>b1</b>,<b>b2</b>), O density (<b>c1</b>,<b>c2</b>), and temperature (<b>d1</b>,<b>d2</b>) on 24 April compared with 22 April, and the TIEGCM-simulated NO VER on 24 April (<b>e1</b>,<b>e2</b>) on both the dayside (<b>upper row</b>) and nightside (<b>lower row</b>).</p>
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<p>Altitude–latitude distributions of the TIEGCM-simulated NO VER relative variations on 22 April (<b>a1</b>,<b>a2</b>), variations in NO VER relative variations respectively caused by changes in NO density (<b>b1</b>,<b>b2</b>), O density (<b>c1</b>,<b>c2</b>), and temperature (<b>d1</b>,<b>d2</b>) on 24 April compared with 22 April, and the TIEGCM-simulated NO VER relative variations on 24 April (<b>e1</b>,<b>e2</b>) on both the dayside (<b>upper row</b>) and nightside (<b>lower row</b>).</p>
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<p>The external forcing of the TIEGCM and options (based on a block diagram given in a poster from Solomon et al. [<a href="#B39-remotesensing-16-03683" class="html-bibr">39</a>]). In this study, the options displayed in blue are used.</p>
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19 pages, 1909 KiB  
Article
Comparison of Virtual Reality Exergames and Nature Videos on Attentional Performance: A Single-Session Study
by Elena Rodríguez-Rodríguez, Joaquín Castillo-Escamilla and Francisco Nieto-Escamez
Brain Sci. 2024, 14(10), 972; https://doi.org/10.3390/brainsci14100972 - 26 Sep 2024
Viewed by 865
Abstract
Background/Objectives: This study aimed to investigate the acute effects of a single session of a VR exergame (Beat Saber) and a VR nature video (Ireland 4K) on attentional performance, using the Flanker and Attentional Blink (AB) tasks. The objective was to [...] Read more.
Background/Objectives: This study aimed to investigate the acute effects of a single session of a VR exergame (Beat Saber) and a VR nature video (Ireland 4K) on attentional performance, using the Flanker and Attentional Blink (AB) tasks. The objective was to assess whether these VR interventions could enhance attentional control, as measured by improvements in response times and accuracy. Methods: A total of 39 psychology students, aged 19–25, were randomly assigned to one of three groups: VR exergame, VR nature video, or control. Participants completed the Flanker and AB tasks before and after the intervention. A repeated measures design was employed to analyze changes in response times and accuracy across pre- and post-test sessions. Results: The study revealed significant improvements in response times and accuracy across all groups in the post-test measures, indicating a strong training effect. In the AB task, shorter stimulus onset asynchrony (SOA) led to decreased accuracy and slower response times, emphasizing the difficulty in processing closely spaced targets. The interaction between Type and Group in response times for target stimuli suggested that the intervention types differentially influenced processing speed in specific conditions. Conclusions: The findings suggest that while brief VR interventions did not produce significant differences between groups, the training effect observed highlights the influence of task-specific factors such as SOA and target presence. Further research is needed to explore whether longer or repeated VR sessions, as well as the optimization of task-specific parameters, might lead to more pronounced cognitive benefits. Full article
(This article belongs to the Special Issue Effects of Cognitive Training on Executive Function and Cognition)
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<p>Stimuli and target conditions. Experimental process. Each stimulus sequence has a duration of 1700 ms. If the correct key is pressed, a green cross appears for 100 ms. If the wrong key is pressed, a red cross appears for 400 to 1600 ms. After the feedback, the next trial begins.</p>
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<p>Stimulus sequence. Example trial where the target symbol in the shape of the letter ‘L’ is present as the first stimulus (T1).</p>
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<p>Accuracy by trial type in the Attentional Blink task. For trials type 1 to type 4, T1 refers to when the target stimulus appears in the first position, and T2 refers to when the target stimulus appears in the second position. The SOAs were either less than 300 ms or greater than 300 ms. Trials without target stimuli (type 5, type 6) showed significantly lower accuracy compared to the other types. Performance was also significantly worse in intervals &lt;300 ms, where the Attentional Blink occurs. Mean ± SEM.</p>
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<p>Accuracy by group for the Attentional Blink task. The VR video group demonstrated significantly higher accuracy than the Control group (* <span class="html-italic">p</span> &lt; 0.05). Mean ± SEM.</p>
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<p>Response times by SOA in the Attentional Blink task. For the six types of trials, T1 refers to when the target stimulus appears in the first position, and T2 refers to when the target stimulus appears in the second position. The SOAs were either less than 300 ms or greater than 300 ms. Mean ± SEM.</p>
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<p>Response time by trial type in the Attentional Blink task. Significant differences were observed between types with SOAs &lt; 300 ms, resulting in slower response times compared to SOAs &gt; 300 ms. For the four types of trials, T1 refers to when the target stimulus appears in the first position, and T2 refers to when the target stimulus appears in the second position. The SOAs were either less than 300 ms or greater than 300 ms. Mean ± SEM.</p>
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<p>Analysis of the Type × Group interaction in the Attentional Blink task. For the four types of trials, T1 refers to when the target stimulus appears in the first position, and T2 refers to when the target stimulus appears in the second position. The SOAs were either less than 300 ms or greater than 300 ms. Mean ± SEM.</p>
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15 pages, 2926 KiB  
Article
Solar Cycle Dependence of Migrating Diurnal Tide in the Equatorial Mesosphere and Lower Thermosphere
by Shuai Liu, Guoying Jiang, Bingxian Luo, Jiyao Xu, Ruilin Lin, Yajun Zhu and Weijun Liu
Remote Sens. 2024, 16(18), 3437; https://doi.org/10.3390/rs16183437 - 16 Sep 2024
Viewed by 696
Abstract
Atmospheric migrating diurnal tide (DW1) is one of the prominent variabilities in the mesosphere and lower thermosphere (MLT). The existence of the solar cycle dependence of DW1 is debated, and there exist different and even opposite findings at different latitudes. In this paper, [...] Read more.
Atmospheric migrating diurnal tide (DW1) is one of the prominent variabilities in the mesosphere and lower thermosphere (MLT). The existence of the solar cycle dependence of DW1 is debated, and there exist different and even opposite findings at different latitudes. In this paper, the solar cycle dependence of temperature DW1 in the equatorial mesosphere and lower thermosphere (MLT) is investigated using temperature global observations from TIMED/SABER spanning 22 years (2002–2023). The results show that (a) the solar cycle dependence of temperature DW1 is seen very clearly at the equator. The maximum correlation coefficient between DW1 and the F10.7 index occurs at 87km, with 0.72; the second maximum coefficient occurs at 99 km, with 0.62. The coefficient could reach 0.87 at 87 km and 0.67 at 99 km after dropping the years influenced by the Stratosphere Quasi-biennial oscillation (SQBO) disruption event. (b) DW1 shows a lag response to the solar cycle at the equator. DW1 amplitudes show a 1-year lag to the F10.7 index at 87 km and a 2-year lag to the F10.7 index at 99 km. Full article
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<p>Full processing procedure from SABER/TIMED observations to migrating tide amplitudes.</p>
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<p>Indices used in the multiple linear regression: F10.7 (<b>top</b>), NINO3.4 (<b>middle</b>), and QBO (<b>bottom</b>). Black solid lines show where the zero winds are.</p>
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<p>Temperature migrating diurnal tide amplitude structure as a function of altitude (65–100 km) and latitude (50°S–50°N) obtained by the average from 2002 to 2023. The vertical dashed lines give the latitudes corresponding to the peaks of theoretical calculation results.</p>
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<p>Temperature DW1 amplitude response to (<b>a</b>) solar activity (F10.7 index) and (<b>b</b>) ENSO (NINO3.4) as a function of height (65–100 km) and latitude (50°S–50°N) revealed using the MLR method. The oblique line region indicates the coefficients over the 95% significance level. The contour parts represent the coefficient. The vertical green dashed lines give the latitudes, which give the latitude corresponding to the peak of the theoretical calculation.</p>
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<p>Relationships between the temperature DW1 amplitude, which removes QBO component using the MLR method and F10.7 index. (<b>a</b>–<b>c</b>) annual mean results of temperature DW1 amplitude and F10.7 index at 87 km, 93 km, and 99 km on the equator. The years in red represent the series influenced by the 2015–2016 SQBO disruption event.</p>
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<p>Scatter plots of temperature DW1 amplitude and F10.7 index (<b>a</b>) at 87 km (blue dots) and (<b>c</b>) 99 km (green dots) on the equator. The red dots represent the years influenced by 2015–2016 SQBO disruption event. Series drop amplitude from 2015 to 2018 (<b>b</b>) at 87 km and (<b>d</b>) at 99 km. C_C represents the correlation coefficient.</p>
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<p>Comparison of temperature DW1 amplitude from original data (blue lines) and the data removing the QBO component using the MLR (orange) and EEMD (green lines) methods at 87 km and 99 km on the equator.</p>
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<p>The same as <a href="#remotesensing-16-03437-f005" class="html-fig">Figure 5</a> but with the QBO component removed using the EEMD method at (<b>a</b>) 87 (blue stars) and (<b>b</b>) 99 km (green stars). The red stars represent the years influenced by 2015–2016 SQBO disruption event.</p>
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<p>Same as <a href="#remotesensing-16-03437-f006" class="html-fig">Figure 6</a> but remove QBO component by EEMD method at (<b>a</b>) 87 (blue stars) and (<b>c</b>) 99 km (green starts). The red stars represent the years influenced by 2015–2016 SQBO disruption event. Series drop amplitude from 2015 to 2018 (<b>b</b>) at 87 km and (<b>d</b>) at 99 km.</p>
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17 pages, 12797 KiB  
Article
Study on the Momentum Flux Spectrum of Gravity Waves in the Tropical Western Pacific Based on Integrated Satellite Remote Sensing and In Situ Observations
by Zhimeng Zhang, Yang He, Yuyang Song and Zheng Sheng
Remote Sens. 2024, 16(14), 2550; https://doi.org/10.3390/rs16142550 - 11 Jul 2024
Viewed by 849
Abstract
Gravity wave (GW) momentum flux spectra help to uncover the mechanisms through which GWs influence momentum transfer in the atmosphere and provide crucial insights into accurately characterizing atmospheric wave processes. This study examines the momentum flux spectra of GWs in the troposphere (2–14 [...] Read more.
Gravity wave (GW) momentum flux spectra help to uncover the mechanisms through which GWs influence momentum transfer in the atmosphere and provide crucial insights into accurately characterizing atmospheric wave processes. This study examines the momentum flux spectra of GWs in the troposphere (2–14 km) and stratosphere (18–28 km) over Koror Island (7.2°N, 134.3°W) using radiosonde data from 2013–2018. Utilizing hodograph analysis and spectral methods, the characteristics of momentum flux spectra are discussed. Given that the zonal momentum flux spectra of low-level atmospheric GWs generally follow a Gaussian distribution, Gaussian fitting was applied to the spectral structures. This fitting further explores the seasonal variations of the zonal momentum flux spectra and the average spectral parameters for each month. Additionally, the GW energy is analyzed using SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) satellite data and compared with the results of the momentum flux spectra from radiosonde data, revealing the close negative correlation between wave energy and wave momentum for stratospheric GW changing with time. The findings indicate that the Gaussian peak shifts more eastward in both the troposphere and stratosphere, primarily due to the absorption of eastward-propagating GWs by the winter tropospheric westerly jet and critical layer filtering. The full width at half maximum (FWHM) in the stratosphere is larger than in the troposphere, especially in June and July, as the spectrum broadens due to propagation effects, filtering, and interactions among waves. The central phase speed in the stratosphere exceeds that in the troposphere, reflecting the influences of Doppler effects and background wind absorption. The momentum flux in the stratosphere is lower than in the troposphere, which is attributed to jet absorption, partial reflection, or the dissipation of GWs. Full article
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<p>Timeheight cross-sections over the Koror station from 2013 to 2018 (<b>a</b>–<b>d</b>).</p>
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<p>Momentum flux spectrum and Gaussian fitting spectrum at the Koror station: (<b>a</b>) Tropospheric meridional, (<b>b</b>) Tropospheric zonal, (<b>c</b>) Stratospheric meridional, and (<b>d</b>) Stratospheric zonal phase speed.</p>
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<p>Vertical profiles of the seasonal average zonal wind at the Koror station for each of the four seasons.</p>
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<p>Seasonal variation in tropospheric zonal momentum flux spectra in Koror Station by Gaussian fitting.</p>
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<p>Seasonal variation in stratospheric zonal momentum flux spectra in Koror Station by Gaussian fitting.</p>
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<p>Line chart of tropospheric Gaussian fitting parameters for each month after the 6-year average of Koror Station; (<b>a</b>) peak value, (<b>b</b>) Gaussian central phase velocity, and (<b>c</b>) FWHM.</p>
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<p>Line chart of stratospheric Gaussian fitting parameters for each month after the 6-year average of Koror Station; (<b>a</b>) peak value, (<b>b</b>) Gaussian central phase velocity, and (<b>c</b>) FWHM.</p>
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<p>Distribution of stratospheric potential energy by month in the Koror region from 2013 to 2018, from SABER.</p>
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<p>Stratospheric zonal Gaussian fitting parameters for each season from 2013 to 2018 in Koror region, (<b>a</b>) peak value, and (<b>b</b>) FWHM.</p>
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16 pages, 3346 KiB  
Technical Note
Spatial and Temporal Variation Patterns of NO 5.3 µm Infrared Radiation during Two Consecutive Auroral Disturbances
by Fan Wu, Congming Dai, Shunping Chen, Cong Zhang, Wentao Lian and Heli Wei
Remote Sens. 2024, 16(8), 1420; https://doi.org/10.3390/rs16081420 - 17 Apr 2024
Viewed by 909
Abstract
The variation in key parameters of the solar–terrestrial space during two consecutive auroral disturbances (the magnetic storm index, Dst index = −422 nT) that occurred during the 18–23 November 2003 period was analyzed in this paper, as well as the spatiotemporal characteristics of [...] Read more.
The variation in key parameters of the solar–terrestrial space during two consecutive auroral disturbances (the magnetic storm index, Dst index = −422 nT) that occurred during the 18–23 November 2003 period was analyzed in this paper, as well as the spatiotemporal characteristics of NO 5.3 μm radiation with an altitude around the location of 55°N 160°W. The altitude was divided into four regions (50–100 km, 100–150 km, 150–200 km, and 200–250 km), and it was found that the greatest amplification occurs at the altitude of 200–250 km. However, the radiance reached a maximum of 3.38 × 10−3 W/m2/sr at the altitude of 123 km during the aurora event, which was approximately 10 times higher than the usual value during “quiet periods”. Based on these findings, the spatiotemporal variations in NO 5.3 μm radiance within the range of latitude 51°S–83°N and longitude of 60°W–160°W were analyzed at 120 km, revealing an asymmetry between the northern and southern hemispheres during the recovery period. Additionally, the recovery was also influenced by the superposition of a second auroral event. The data used in this study were obtained from the OMNI database and the SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) infrared radiometer onboard the TIMED (Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics) satellite. Finally, the correlation of NO 5.3 μm radiance at 120 km with temperature, solar wind speed, auroral electrojet index (AE index), and Dst index were analyzed. It was found that only the Dst index had a good correlation with the radiance value. Furthermore, the correlation between the Dst index and radiance at different altitudes was also analyzed, and the highest correlation was found at 170 km. Full article
(This article belongs to the Special Issue Earth Radiation Budget and Earth Energy Imbalance)
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<p>Temporal variations in solar wind speed V<sub>sm</sub>, the interplanetary magnetic field IMF B<sub>Z</sub>, disturbance storm time index Dst, and the auroral electrojet index AE (from top to bottom) during 18–23 November 2003.</p>
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<p>Variation in NO 5.3 μm radiance values over time in the altitude region of 50–250 km during 18–23 November 2003, measured by TIMED/SABER at about 55°N 160°W.</p>
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<p>STD (black squares), MIN (red dots), and MAX (blue triangles) values for four different altitude regions.</p>
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<p>Longitudinal and latitudinal distribution of NO 5.3 µm radiance during 18–23 November 2003. Measurement dates and times are: (<b>a</b>) 11-18 23:06 UT; (<b>b</b>): 11-19 23:17 UT; (<b>c</b>): 11-20 13:51 UT; (<b>d</b>): 11-20 15:27 UT; (<b>e</b>): 11-20 18:42 UT; (<b>f</b>): 11-20 20:19 UT; (<b>g</b>): 11-20 21:57 UT; (<b>h</b>): 11-20 23:34 UT; (<b>i</b>): 11-21 01:11 UT; (<b>j</b>): 11-22 11:10 UT; (<b>k</b>): 11-23 14:42 UT; (<b>l</b>): 11-23 17:57 UT.</p>
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<p>Variation in the maximum (blue dots) and minimum (black squares) values of NO 5.3 μm radiance for the 17 selected groups of orbits.</p>
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<p>Variation in and values of NO 5.3 μm radiance for the 17 selected events.</p>
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<p>Correlation between the measured radiance of NO 5.3 μm at 120 km around 55°S 160°W of SABER and temperature, V<sub>sw</sub>, AE index, and Dst index during 18–23 November 2003.</p>
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<p>Correlation of Dst index with NO 5.3 µm radiance at different altitudes around 55°S 160°W of SABER during 18–23 November 2003.</p>
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<p>The adjusted R<sup>2</sup> of the fitting curves corresponding to different altitudes during 18–23 November 2003.</p>
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42 pages, 25177 KiB  
Review
Climatology of the Nonmigrating Tides Based on Long-Term SABER/TIMED Measurements and Their Impact on the Longitudinal Structures Observed in the Ionosphere
by Dora Pancheva, Plamen Mukhtarov and Rumiana Bojilova
Atmosphere 2024, 15(4), 478; https://doi.org/10.3390/atmos15040478 - 12 Apr 2024
Viewed by 1151
Abstract
This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the [...] Read more.
This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the global TEC maps generated with the NASA JPL for the interval of 2002–2022. As the main drivers of the longitudinal structures are mainly nonmigrating tides, this study first investigates the climatology of those nonmigrating tides, which are the main contributors of the considered longitudinal structures; these are nonmigrating diurnal DE3, DE2, and DW2, and semidiurnal SW4 and SE2 tides. The climatology of WN4, WN3, and WN2 structures in the lower thermosphere reveals that WN4 is the strongest one with a magnitude of ~20 K observed at 10° S in August, followed by WN2 with ~13.9 K at 10° S in February, and the weakest is WN3 with ~12.4 K observed over the equator in July. In the ionosphere, WN3 is the strongest structure with a magnitude of 5.9 TECU located at −30° modip latitude in October, followed by WN2 with 5.4 TECU at 30 modip in March, and the last is WN4 with 3.7 TECU at −30 modip in August. Both the climatology of the WSA and the features of its drivers are investigated as well. Full article
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Figure 1
<p>(<b>a</b>) Latitude-time structures of the climatologically mean (2002–2022) DE3 (<b>left</b> plot, in K), DE2 (<b>middle</b> plot), and DW2 (<b>right</b> plot) tidal amplitude calculated at altitude of 110 km; (<b>b</b>) altitude-latitude structures of the amplitudes (<b>upper</b> row of plots, in K) and phases (<b>bottom</b> row of plots, in degrees) of the above-mentioned diurnal tides arranged in the same way as in (<b>a</b>); DE3 is presented in August, while DE2 and DW2 is presented in June and December, respectively.</p>
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<p>Latitude-time structures of the monthly mean DE3 (<b>upper</b> plot), DE2 (<b>middle</b> plot), and DW2 (<b>bottom</b> plot) tidal amplitudes at an altitude of 110 km.</p>
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<p>(<b>a</b>) Latitude-time structures of the AM (<b>upper</b> plot), AC (<b>middle</b> plot), and SAC (<b>bottom</b> plot) separated from the monthly mean DE3 amplitudes at altitude of 110 km; (<b>b</b>) the same as (<b>a</b>), but for DE2; (<b>c</b>) the same as (<b>a</b>), but for DW2.</p>
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<p>(<b>a</b>, <b>left</b> column of plots) Cross correlation functions between the QBO and the AM (<b>upper</b> plot), AC (<b>middle</b> plot) and SAC (<b>bottom</b> plot) of the DE3; (<b>b</b>, <b>middle</b> column of plots) the same as (<b>a</b>), but for the DE2, and (<b>c</b>, <b>right</b> column of plots) the same as (<b>a</b>), but for the DW2.</p>
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<p>(<b>a</b>) Latitude-time structures of the climatologically mean amplitudes (2002–2022) of SW4 (<b>left</b> plot, in K) and SE2 (<b>right</b> plot) at altitude of 110 km; (<b>b</b>) altitude–latitude structures of the amplitudes (<b>upper</b> row of plots, in K), and phases (<b>bottom</b> row of plots, in degrees) of the above-mentioned semidiurnal tides, arranged in the same way as in (<b>a</b>).</p>
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<p>Latitude-time structures of the monthly mean SW4 (<b>upper</b> plot) and SE2 (<b>bottom</b> plot) amplitudes at altitude of 110 km.</p>
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<p>(<b>a</b>) Latitude-time structures of the AM (<b>upper</b> plot), AC (<b>middle</b> plot), and SAC (<b>bottom</b> plot) of the SW4 monthly mean amplitudes at altitude of 110 km; (<b>b</b>) the same as (<b>a</b>), but for the SE2.</p>
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<p>(<b>a</b>, <b>left</b> column of plots) Crosscorrelation functions between the QBO and the AM (<b>upper</b> plot), AC (<b>middle</b> plot), and SAC (<b>bottom</b> plot) of the SW4; and (<b>b</b>, <b>right</b> column of plots) the same as (<b>a</b>), but for the SE2.</p>
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<p>(<b>a</b>) LT variability of the climatologically mean (2002–2022) SABER WN4 structure at different latitudes, shown in the upper side of each plot, during August; (<b>b</b>) latitudinal dependence of the tidal and SPW4 contribution in months when the SABER WN4 structure has large amplitudes: August (<b>upper left</b> plot), September (<b>upper right</b> plot), March (<b>bottom left</b> plot), and November (<b>bottom right</b> plot).</p>
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<p>(<b>a</b>) LT variability of the climatologically mean (2002–2022) SABER WN3 structure at different latitudes, shown in the upper side of each plot, during July; (<b>b</b>) latitudinal dependence of the tidal and SPW3 contribution in months when the SABER WN3 structure has large amplitudes: July (<b>upper left</b> plot), June (<b>upper right</b> plot), December (<b>bottom left</b> plot), and February (<b>bottom right</b> plot).</p>
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<p>(<b>a</b>) LT variability of the climatologically mean (2002–2022) SABER WN2 structure at different latitudes and during different winter months; the latitudes and months are shown in the upper side of each plot; (<b>b</b>) latitudinal dependence of the tidal and SPW2 contribution in months when the SABER WN2 structure has large amplitudes: November (<b>upper left</b> plot), February (<b>upper right</b> plot), October (<b>bottom left</b> plot), and January (<b>bottom right</b> plot).</p>
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<p>(<b>a</b>) LT variability of the climatologically mean (2002–2022) TEC WN4 structure at different latitudes, shown in the upper side of each plot, during August; (<b>b</b>) latitudinal dependence of the tidal and SPW4 contribution in months when the TEC WN4 structure has large amplitudes: August (<b>upper left</b> plot), September (<b>upper right</b> plot), March (bottom left plot), and June (<b>bottom right</b> plot); and (<b>c</b>) latitude-time climatological structure of the TEC SPW4 amplitude (<b>left</b> plot) and the TEC SE2 amplitude (<b>right</b> plot).</p>
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<p>(<b>a</b>) LT variability of the climatologically mean (2002–2022) TEC WN3 structure at different latitudes, shown in the upper side of each plot, during March; (<b>b</b>) latitudinal dependence of the tidal and SPW3 contribution in months when the TEC WN3 structure has large amplitudes: March (<b>upper left</b> plot), April (<b>upper right</b> plot), October (<b>bottom left</b> plot), and September (<b>bottom right</b> plot); and (<b>c</b>) latitude-time climatological structure of the TEC SPW3 amplitude (<b>left</b> plot) and the TEC DW4 amplitude (<b>right</b> plot).</p>
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<p>(<b>a</b>) LT variability of the climatologically mean (2002–2022) TEC WN2 structure at different latitudes, shown in the upper side of each plot, during October (<b>left</b> column of plots) and April (<b>right</b> column of plots); (<b>b</b>) latitudinal dependence of the tidal and SPW2 contribution in months when the TEC WN2 structure has large amplitudes: October (<b>left</b> plot) and April (<b>right</b> plot); and (<b>c</b>) latitude-time climatological structure of the TEC SPW2 amplitude (<b>upper left</b> plot), the TEC DE1 amplitude (<b>upper right</b> plot), the TEC DW3 amplitude <b>(bottom left</b> plot), and the TEC SW4 amplitude (<b>bottom right</b> plot).</p>
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<p>(<b>a</b>) Altitude-latitude structure of the DW2 amplitude (<b>upper</b> plot, in MHz) and phase (<b>bottom</b> plot, in degree) extracted from the FORMOSAT-3/COSMIC electron density profiles, and (<b>b</b>) latitude-time climatological structure of the TEC DW2 tidal amplitude.</p>
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<p>(<b>a</b>) Latitude-longitude structures of the median TEC for December 2008 at universal times: 05 UT (<b>uppermost</b> plot), 06 UT (second from <b>above</b> plot), 07 UT (second from <b>below</b> plot), and 08 UT (<b>bottom</b> plot); (<b>b</b>) latitude-longitude structure of the full TEC reconstruction for December 2008 at the above mentioned UTs, arranged in the same way as in (<b>a</b>); and (<b>c</b>) latitude-longitude structure of the WSA reconstructed by the superposition of D0 + SPW1 + DW2 + DE1 at the above-mentioned UTs, arranged in the same way as in (<b>a</b>); actually, the real WSA is situated at the western part of the southern middle and high latitudes.</p>
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<p>(<b>a</b>) Latitude-UT structures of the nonmigrating tidal D0 (<b>left</b> plot) DW2 (<b>middle</b> plot) and DE1 (<b>right</b> plot) amplitude at latitude of 60° S and longitude of 90°W for December 2008, and (<b>b</b>) latitudinal dependence of the tidal and SPW1 contribution to the WSA generation in December 2008 at UTs: 05 UT (<b>left</b> plot), 06 UT (second from the <b>left</b> plot), 07 UT (second from the <b>right</b> plot), and 08 UT (<b>right</b> plot).</p>
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<p>(<b>a</b>) Latitude-longitude structures of the climatological mean (2002–2022) TEC (<b>left</b> column of plots) and its full reconstructions (<b>right</b> column of plots) at universal times: 05 UT (<b>uppermost</b> plots), 06 UT (second from <b>above</b> plots), 07 UT (second from <b>below</b> plots), and 08 UT (<b>bottom</b> plots); (<b>b</b>) latitude-longitude structures of the climatological mean WSA (<b>left</b> column of plots), reconstructed by the superposition of the climatologically mean D0 + SPW1 + DW2 + DE1 and the latitudinal dependence of the contribution of these WSA components (<b>right</b> column of plots) at the above mentioned UTs, arranged in the same way as in (<b>a</b>).</p>
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15 pages, 3759 KiB  
Article
Hardware Design and Implementation of a Lightweight Saber Algorithm Based on DRC Method
by Weifang Zheng, Huihong Zhang, Yuejun Zhang, Yongzhong Wen, Jie Lv, Lei Ni and Zhiyi Li
Electronics 2023, 12(11), 2525; https://doi.org/10.3390/electronics12112525 - 3 Jun 2023
Cited by 2 | Viewed by 2087
Abstract
With the development of quantum computers, the security of classical cryptosystems is seriously threatened, and the Saber algorithm has become one of the potential candidates for post-quantum cryptosystems (PQCs). To address the problems of long delay and the high power consumption of Saber [...] Read more.
With the development of quantum computers, the security of classical cryptosystems is seriously threatened, and the Saber algorithm has become one of the potential candidates for post-quantum cryptosystems (PQCs). To address the problems of long delay and the high power consumption of Saber algorithm hardware implementation, a lightweight Saber algorithm hardware design scheme based on the joint optimization of data readout and clock (DRC) was proposed. Firstly, an analysis was carried out on the hardware architecture, timing overhead and power consumption distribution of the Saber algorithm, and the key circuits that limit the performance of the algorithm were identified; secondly, a dual-port SRAM parallel reading method was adopted to improve the data reading efficiency and reduce the timing overhead of double data reading in the multiplier module. Then, a clock gating technology was used to reduce the dynamic flipping probability of internal registers and reduce the hardware power consumption of the Saber algorithm; finally, data reading and clock gating were jointly optimized to design a high-speed and low-power Saber algorithm hardware IP core. Lightweight IP cores were integrated into RISC-V SoC systems via APB bus in a TSMC 65 nm process to complete the digital back-end design. The experimental results show an IP core area of 0.99 mm2 and power consumption of 8.49 mW, which is 33% lower than that reported in the related literature. Under 72 MHz & 1 V operating conditions, the number of clock cycles for the Saber algorithm’s key generation, encryption and decryption are 3315, 9204 and 1420, respectively. Full article
(This article belongs to the Special Issue Computer-Aided Design for Hardware Security and Trust)
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<p>Overall hardware IP architecture.</p>
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<p>Saber hardware IP diagram.</p>
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<p>Memory module.</p>
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<p>Hardware circuit structure for random number generation.</p>
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<p>Output timing diagram of the encrypted IP.</p>
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<p>Saber algorithm IP core SoC architecture.</p>
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<p>Hardware IP core back-end implementation diagram: (<b>a</b>) chip architecture diagram, (<b>b</b>) process information diagram, and (<b>c</b>) power consumption ratio of each module.</p>
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<p>Power consumption profiles: (<b>a</b>) power consumption trends of IP cores at different process angles, (<b>b</b>) power consumption profiles with and without clock gating.</p>
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<p>Power consumption area curves: (<b>a</b>) power consumption curves for each module of the IP core, (<b>b</b>) area curves for each module of the IP core.</p>
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13 pages, 4586 KiB  
Article
Thermospheric NO Cooling during an Unusual Geomagnetic Storm of 21–22 January 2005: A Comparative Study between TIMED/SABER Measurements and TIEGCM Simulations
by Tikemani Bag, Diptiranjan Rout, Yasunobu Ogawa and Vir Singh
Atmosphere 2023, 14(3), 556; https://doi.org/10.3390/atmos14030556 - 14 Mar 2023
Cited by 7 | Viewed by 1988
Abstract
The geomagnetic storm is the manifestation of the solar wind–magnetosphere interaction. It deposits huge amount of the solar energy into the magnetosphere–ionosphere–thermosphere (MIT) system. This energy creates global perturbations in the chemistry, dynamics, and energetics of the MIT system. The high latitude energy [...] Read more.
The geomagnetic storm is the manifestation of the solar wind–magnetosphere interaction. It deposits huge amount of the solar energy into the magnetosphere–ionosphere–thermosphere (MIT) system. This energy creates global perturbations in the chemistry, dynamics, and energetics of the MIT system. The high latitude energy deposition results in the Joule and particle heating that subsequently increases the thermospheric temperature. The thermospheric temperature is effectively regulated by the process of thermospheric cooling emission by nitric oxide via 5.3 µm. A peculiar, intense geomagnetic storm (Dst = −105 nT) occurred during 21–22 January 2005, where the main phase developed during the northward orientation of the z-component of interplanetary magnetic field. We utilized the nitric oxide 5.3 µm infrared emission from the NCAR’s Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIEGCM) simulation and the Sounding of Atmosphere using Broadband Emission Radiometry (SABER) onboard the thermosphere–ionosphere–mesosphere energetic and dynamics satellite to investigate its response to this anomalous geomagnetic storm. We compared the model results with the observations on both the local and global scales. It is observed that the model results agree very well with the observations during quiet times. However, the model severely underestimates the cooling emission by one-fourth of the observations, although it predicts an enhancement in the thermospheric temperature and densities of atomic oxygen and nitric oxide during the geomagnetic storm. Full article
(This article belongs to the Special Issue Structure and Dynamics of Mesosphere and Lower Thermosphere)
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<p>Comparison of TIMED/SABER and TIEGCM nitric oxide cooling flux during 20–22 November 2003 storm. The TIEGCM overestimates NO cooling emission during storm period.</p>
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<p>Solar wind parameters during 21–22 January 2005, storm. (<b>a</b>) IMF Bz, (<b>b</b>) B, (<b>c</b>) solar wind density, (<b>d</b>) solar wind speed, (<b>e</b>) dynamics pressure, and (<b>f</b>) Sym-H. The vertical redlines represent the time of sudden storm commencements.</p>
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<p>Temporal variation of (<b>a</b>) Joule heating Power, (<b>b</b>) NOAA-observed hemispheric power and (<b>c</b>) orbit-averaged NO cooling flux from the TIEGCM simulation and TIMED-SABER observations.</p>
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<p>Latitude–longitude cross-section of nitric oxide flux from (<b>a</b>,<b>b</b>) the TIEGCM simulation and (<b>c</b>,<b>d</b>) TIMED-SABER satellite observation.</p>
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<p>Orbit-wise comparison between (<b>top</b>) SABER observations and (<b>bottom</b>) model results.</p>
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<p>Latitudinal variation of cooling flux (<b>a</b>–<b>e</b>) for different latitude regions and (<b>f</b>) relative (%) change with respect to SABER observations. The solid and dashed lines, respectively, represent the observation and model results.</p>
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<p>TIEGCM simulated latitudinal–longitude cross-section of (<b>a</b>,<b>b</b>) temperature, (<b>c</b>,<b>d</b>) atomic oxygen, and (<b>e</b>,<b>f</b>) nitric oxide density along SABER satellite track corresponding to 130 km.</p>
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<p>Temporal variation of DMSP particle precipitation during 21–22 January 2005.</p>
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<p>GUVI observations of O/N<sub>2</sub> ratio during 21–22 January 2005.</p>
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20 pages, 9976 KiB  
Article
Limb Sounders Tracking Tsunami-Induced Perturbations from the Stratosphere to the Ionosphere
by Xiangxiang Yan, Tao Yu and Chunliang Xia
Remote Sens. 2022, 14(21), 5543; https://doi.org/10.3390/rs14215543 - 3 Nov 2022
Viewed by 1813
Abstract
In this study, we employ three types of satellite data from two different limb sounders: the FORMOSAT-3/COSMIC (F3/C) radio occultation (RO) technique and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument to study the vertical coupling of the 16-09-2015 Chile [...] Read more.
In this study, we employ three types of satellite data from two different limb sounders: the FORMOSAT-3/COSMIC (F3/C) radio occultation (RO) technique and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument to study the vertical coupling of the 16-09-2015 Chile tsunami-induced perturbations from the stratosphere to the ionosphere. All three types of datasets, including temperature profiles from 10 to 55 km and 16 to 107 km, and electron density profiles from 120 to 550 km, recognized perturbations of different scales at different heights after the Chile tsunami. The vertical scales identified by the wavelet analysis are from 1–2 km, 5–9 km, and 25–50 km in the stratosphere, mesosphere, and ionosphere, respectively. Meanwhile, as a comparison and validation of the reliability, we also revisited the 11-03-2011 Tohoku earthquake/tsunami-related perturbations from the stratosphere to the ionosphere using the same data. It is believed that the two tsunamis both disturbed the whole atmosphere space, and the scale of these signals gradually increases with the increase in altitude but decreases with time. In addition, the tsunami-related ionospheric gravity wavefronts are examined by the F3/C observations. Another interesting point is that the temperature perturbations recorded by the SABER from 70–100 km altitude are found to arrive earlier than the 2015 tsunami wavefront. The findings in this study suggest that the limb-sounding technique is a useful instrument for detecting the tsunami-coupling gravity wave and benefits the tsunami warning system. Full article
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<p>Data sources of the 16-09-2015 Chile tsunami. (<b>a</b>) Maximum wave amplitude with travel-time contours of the tsunami. Filled colors show the maximum computed tsunami amplitude in cm during 24 h of wave propagation (<a href="https://nctr.pmel.noaa.gov/chile20150916/" target="_blank">https://nctr.pmel.noaa.gov/chile20150916/</a> (accessed on 1 July 2022)). The red rectangular box marks the main focus area of this study. (<b>b</b>–<b>d</b>) The projections of the tangent points 15 h after the tsunami for the F3/C atmPrf, SABER, and F3/C ionPrf datasets, respectively. Blue and red curves mark the ground track of the tangent point location on DOY 259 and 260 of 2015, respectively. The thick red point shows the epicenter.</p>
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<p>Same as <a href="#remotesensing-14-05543-f001" class="html-fig">Figure 1</a> but for the 11-03-2011 Tohoku tsunami. The maximum wave amplitude with travel-time contours of the tsunami in (<b>a</b>) is from <a href="https://nctr.pmel.noaa.gov/honshu20110311/" target="_blank">https://nctr.pmel.noaa.gov/honshu20110311/</a> (accessed on 1 July 2022).</p>
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<p>(<b>a</b>) Temperature profiles detected by the F3/C atmPrf datasets on DOY 259 (blue) and 260 (red) of 2015. (<b>b</b>) The corresponding temperature perturbation profiles. (<b>c</b>) The corresponding wavelet power spectrum differences between DOY 259 and 260 of 2015.</p>
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<p>(<b>a</b>) Temperature profiles detected by the SABER datasets on DOY 259 (blue) and 260 (red) of 2015. (<b>b</b>) The corresponding temperature perturbation profiles. (<b>c</b>) The corresponding wavelet power spectrum differences between DOY 259 and 260 of 2015.</p>
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<p>(<b>a</b>) Electron density profiles detected by the F3/C ionPrf datasets on DOY 259 (blue) and 260 (red) of 2015. (<b>b</b>) The corresponding electron density perturbation profiles. (<b>c</b>) The corresponding wavelet power spectrum differences between DOY 259 and 260 of 2015.</p>
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<p>The wavelet power spectrum differences between DOY 69 and 70 of 2011 of (<b>a</b>) temperature profiles detected by the F3/C atmPrf datasets, (<b>b</b>) temperature profiles detected by the SABER datasets, and (<b>c</b>) electron density profiles detected by the F3/C ionPrf datasets.</p>
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<p>(<b>a</b>) Ground-based GNSS dTEC map at 01:54 UT, 17−09−2015. The TEC data is provided by SIMuRG (<a href="https://simurg.iszf.irk.ru/" target="_blank">https://simurg.iszf.irk.ru/</a> (accessed on 22 October 2022)) [<a href="#B49-remotesensing-14-05543" class="html-bibr">49</a>]. The pale blue area marks the contributing observation area of an RO podTec profile, which is used to track the wavefront after the 2015-09-16 earthquake/tsunami. Red curves represent the projections of the tangent points for the podTec profile. (<b>b</b>) The TEC profile in (<b>a</b>) with height probed by the F3/C. The blue arrow indicates a descending or ascending scan. The red curve shows the detrended TEC using a Savitzky–Golay filter. The Y-axis scale on the right panel indicates the hours after the tsunami. (<b>c</b>) The corresponding WPS. The magenta contour is at the 95% confidence level. The regions outside the black curve are the “cone of influence”, where edge effects become important.</p>
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<p>(<b>a</b>) Data used to track the wavefront after the 2011-03-11 earthquake/tsunami. Red curves represent the projections of the tangent points for F3/C profiles P1, P2, and P3, respectively. The magenta triangles mark the tangent points at a height of 300 km. See details about the ground-based GNSS data observations in the <a href="#app1-remotesensing-14-05543" class="html-app">supplementary materials</a>. Panels (<b>b</b>,<b>d</b>,<b>f</b>) show the TEC profiles P1, P2, and P3 with height probed by the F3/C, respectively. Panels (<b>c</b>,<b>e</b>,<b>g</b>) show the corresponding WPS for the three TEC profiles, respectively. The legends in the figure are similar to those in <a href="#remotesensing-14-05543-f007" class="html-fig">Figure 7</a>.</p>
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<p>The observed vertical perturbation scales (blue dots mark the 2015 case and black dots mark the 2011 case) and the fitted curve (red line marks the 2015 case and pink line marks the 2011 case) with 95% confidence bounds (dashed lines).</p>
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<p>(<b>a</b>) The projections of the tangent points 5 h after the tsunami for the F3/C ionPrf dataset. Blue and red lines mark the ground track of the tangent point location on DOY 259 and 260 of 2015, respectively. The thick red point shows the epicenter. (<b>b</b>) The corresponding wavelet power spectrum differences between DOY 259 and 260 of 2015.</p>
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<p>(<b>a</b>) The projection of the tangent points of track 3 on DOY 260 of 2015. The color denotes the observation time. (<b>b</b>) SABER temperature perturbations that are made by a 2−D cubic interpolation in altitude and latitude over the coherent variations of limb scans of track 3. (<b>c</b>) The corresponding wavelet power spectrum of track 3.</p>
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<p>(<b>a</b>–<b>c</b>) The tsunami waves (<a href="http://nctr.pmel.noaa.gov/chile20150916/" target="_blank">http://nctr.pmel.noaa.gov/chile20150916/</a> (accessed on 1 July 2022)) at three adjacent moments (7h56m, 7h58m, and 8h00m), the red arrows represent the four observations (7h57m, 7h58m, 7h59m, and 8h00m) of SABER closest to the above three moments. (<b>d</b>) Panels from left to right record the temperature perturbations at seven different moments, respectively.</p>
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<p>(<b>a</b>) The tsunami waves (<a href="http://nctr.pmel.noaa.gov/chile20150916/" target="_blank">http://nctr.pmel.noaa.gov/chile20150916/</a> (accessed on 1 July 2022)) at 09h40m, the red arrows represent the projections of the tangent point of ten observations by SABER around the above moment. (<b>b</b>) Panels from left to right correspond to the temperature perturbations of the ten observations from top to bottom in (<b>a</b>), respectively.</p>
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17 pages, 3792 KiB  
Article
Clouds in the Vicinity of the Stratopause Observed with Lidars at Midlatitudes (40.5–41°N) in China
by Shaohua Gong, Yuru Wang, Jianchun Guo, Weipeng Chen, Yuhao Zhang, Faquan Li, Yuchang Xun, Jiyao Xu, Xuewu Cheng and Guotao Yang
Remote Sens. 2022, 14(19), 4938; https://doi.org/10.3390/rs14194938 - 3 Oct 2022
Cited by 1 | Viewed by 1746
Abstract
Based on long-term lidar (light detection and ranging) observations at Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E), cloud events occurred in the vicinity of the stratopause above Beijing were reported for the first time. These events occurred with tenuous and sparse layers within [...] Read more.
Based on long-term lidar (light detection and ranging) observations at Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E), cloud events occurred in the vicinity of the stratopause above Beijing were reported for the first time. These events occurred with tenuous and sparse layers within the altitude range of 33–65 km, and the maximum VBSC value ranged from 1×1010m1sr1 to 5.5×109m1sr1. Considering temperature and water vapor measurements from SABER/TIMED, the occurrence mechanism of these lidar-observed cloud events was examined. It was found that some cloud layers resulted from the nucleation of water vapor due to the local meteorological changes in the middle atmosphere, while other lidar-observed clouds could comprise floating clusters of cosmic dust, hydrate droplets, volcanic ash, space traffic exhaust, etc. These cloud events are rare cloud-like phenomena in the middle atmosphere observed by lidars at midlatitudes in China; they differ from NLCs and PSCs in terms of altitude distribution and seasonal variation, and the relevant microphysics processes behind their occurrence are likely meaningful to meteorology at midlatitudes. Full article
(This article belongs to the Special Issue Atmospheric Dynamics with Radar Observations)
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<p>(<b>a</b>) Histogram of the lidar observation nights and cloud event occurrence times during the different months; (<b>b</b>) Statistics of the peak altitude and maximum volume backscatter coefficient of the cloud layers. The solid dots (<span style="color:#4874c4">•</span>) indicate the peak altitude, and the altitude distribution range of each cloud layer is correspondingly represented with a vertical bar. The red circles (<span style="color:#ED7D31">o</span>) indicate the value of the maximum volume backscatter coefficient value of the cloud layer.</p>
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<p>(<b>a</b>) Photon count profile sequence and (<b>b</b>) the volume backscatter coefficient, reflecting the temporal evolution of cloud layers observed with the lidar at Yanqing (40.5°N, 116°E) at dawn on 30 October 2018.</p>
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<p>(<b>a</b>) Photon count profile sequence and (<b>b</b>) the volume backscatter coefficient, reflecting the temporal evolution of the cloud events observed with the lidar over Yanqing (40.5°N, 116°E) at twilight on 30 October 2018.</p>
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<p>(<b>a</b>) Photon count profile sequence and (<b>b</b>) the volume backscatter coefficient, reflecting the temporal evolution of the cloud events observed with the lidar at Pingquan (41°N, 118.7°E) at night on 30 October 2018.</p>
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<p>Photon count profile sequence (on a logarithmic scale) and the volume backscatter coefficient of the cloud layers, reflect the temporal evolution of the cloud event observed with the lidar at Yanqing (40.5°N, 116°E) on 17–18 September 2017.</p>
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<p>(<b>a</b>) Mesospheric temperature structure derived from lidar observations over Yanqing (40.5°N, 116°E) before the onset of cloud events; (<b>b</b>) Comparison between the lidar-observed atmospheric temperature (blue line with dots) and the frost-point temperature of water vapor (red line). The frost-point temperature profile was estimated according to the SABER-measured water vapor (green dashed line with circles) at the footprint (40.8°N, 119.7°E) at 23:09 LT on 29 October 2018. The horizontal bars indicate the measurement uncertainty.</p>
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<p>(<b>a</b>) Temperature and water vapor profile sequences measured by SABER from October 28 to November 1 when the TIMED satellite swept the footprints near Beijing; (<b>b</b>) Temperature (blue line with circles) and water vapor (red dashed line with crosses) profiles obtained at the footprint (41.8°N, 119.7°E) at 23:09 LT on 29 October 2018, and the estimated frost-point temperature of water vapor (red line). The green dashed line with dots indicates the temperature profile simultaneously measured with lidar at Yanqing (40.5°N, 116°E), and the horizontal bars indicate the measurement uncertainty.</p>
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<p>(<b>a</b>) Temperature and water vapor profile sequences measured by SABER from 16–19 September 2017 when the TIMED satellite swept the footprints above Beijing; (<b>b</b>) Temperature (green line) and water vapor (red dashed line) profiles measured by SABER at the footprint (41.42°N, 105.37°E) at 22:57 LT on 17 September 2017.</p>
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<p>(<b>a</b>) Temperature structure measured by SABER/TIMED within the latitude range of 38–43°N; (<b>b</b>) Background temperature obtained via least harmonic fitting with zonal wavenumbers ranging from 0 to 7; (<b>c</b>) Residual (temperature perturbation) calculated by subtracting the fitted background temperature from the observed temperature structure; (<b>d</b>) Wave structure (solid line) obtained from SABER measurements at the footprint (41.42°N, 105.37°E) at 22:57 LT on 17 September 2017. The red dashed line indicates the wave profile reconstructed via wavelet analysis.</p>
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