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Search Results (169)

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10 pages, 4340 KiB  
Article
Study on the Thermal Control Performance of Mg-Li Alloy Micro-Arc Oxidation Coating in High-Temperature Environments
by Wentao Zhang, Shigang Xin, Qing Huang and Haiyang Jiao
Surfaces 2024, 7(4), 969-978; https://doi.org/10.3390/surfaces7040063 - 8 Nov 2024
Viewed by 735
Abstract
This paper reports on the successful preparation of a low absorption–emission thermal control coating on the surface of LAZ933 magnesium–lithium alloy using the micro-arc oxidation method. This study analyzed the microstructure, phase composition, and thermal control properties of the coating using Scanning Electron [...] Read more.
This paper reports on the successful preparation of a low absorption–emission thermal control coating on the surface of LAZ933 magnesium–lithium alloy using the micro-arc oxidation method. This study analyzed the microstructure, phase composition, and thermal control properties of the coating using Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), UV–visible near-infrared spectroscopy (UV-VIS-NIR) and infrared emissivity measurements. The results indicate that the hemispherical emissivity of the coating remains unaffected with an increase in temperature and holding time, while the solar absorption ratio gradually increases. The thermal control performance of the coating after a high-temperature experiment was found to be related to the diffusion of the Li metal element in the magnesium lithium alloy matrix, as determined by X-ray photoelectron spectroscopy (XPS), flame graphite furnace atomic absorption spectrometry (GFAAS) and Glow Discharge Optical Emission Spectroscopy (GD-OES). As the holding time is extended, the coating structure gradually loosens under thermal stress. The Li metal element in the substrate diffuses outward and reacts with O2, H2O and CO2 in the air, forming LiO2, LiOH, Li2CO3 and other products. This reaction affects the coating’s solar absorption ratio in the end. Full article
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Figure 1

Figure 1
<p>Reflectance curves of the coating after different holding times.</p>
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<p>XRD spectra of coatings after different times at 200 °C.</p>
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<p>Surface topographies of the coatings after different high-temperature exposure times: (<b>a</b>) following 0 h of high temperature, (<b>b</b>) following 48 h of high temperature, (<b>c</b>) following 144 h of high temperature, and (<b>d</b>) following 288 h of high temperature.</p>
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<p>Cross-sectional topography of the coating after different high-temperature exposure times: (<b>a</b>) following 0 h of high temperature, (<b>b</b>) following 48 h of high temperature, (<b>c</b>) following 144 h of high temperature, and (<b>d</b>) following 288 h of high temperature.</p>
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<p>XPS spectra of the coating after different high-temperature exposure times: (<b>a</b>) Si 2p, (<b>b</b>) O 1s, (<b>c</b>) Mg 1s, and (<b>d</b>) Li 1s.</p>
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<p>Spectral test spectrum of glow power generation: (<b>a</b>) Mg, (<b>b</b>) Li, (<b>c</b>) Al, and (<b>d</b>) Zn.</p>
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20 pages, 5807 KiB  
Article
Unfixed Seasonal Partition Based on Symbolic Aggregate Approximation for Forecasting Solar Power Generation Using Deep Learning
by Minjin Kwak, Tserenpurev Chuluunsaikhan, Azizbek Marakhimov, Jeong-Hun Kim and Aziz Nasridinov
Electronics 2024, 13(19), 3871; https://doi.org/10.3390/electronics13193871 - 30 Sep 2024
Cited by 1 | Viewed by 854
Abstract
Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related [...] Read more.
Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related situations. Most existing studies rely on fixed season partitions, such as meteorological and astronomical, where the start and end dates are specific. However, even if the countries are in the same Northern or Southern Hemisphere, seasonal changes can occur due to abnormal climates such as global warming. Therefore, we propose a novel unfixed seasonal data partition based on Symbolic Aggregate Approximation (SAX) to forecast solar power generation. Here, symbolic representations generated by SAX are used to select seasonal features and obtain seasonal criteria. We then employ two-layer stacked LSTM and combine predictions from various seasonal features and partitions through ensemble methods. The datasets used in the experiments are from real-world solar panel plants such as in Gyeongju, South Korea; and in California, USA. The results of the experiments show that the proposed methods perform better than non-partitioned or fixed-partitioned solar power generation forecasts. They outperform them by 2.2% to 3.5%; and 1.6% to 6.5% in the Gyeongju and California datasets, respectively. Full article
(This article belongs to the Special Issue Big Data and AI Applications)
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Figure 1
<p>Overview of the proposed methodology. The symbols a–c represent the division of the data into equal sized areas or bins.</p>
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<p>Temperature of the Gyeongju dataset in hourly and daily intervals.</p>
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<p>Example of smoothed data by Temperature in Gyeongju dataset.</p>
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<p>PAA transformation and SAX transformation process. The symbols a–e are represent the division of the data into equal sized areas or bins.</p>
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<p>Assigning SAX symbols to temperature data in Gyeongju dataset. The symbols a–c represent the division of the data into equal sized areas or bins.</p>
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<p>Example of unfixed seasonal partition based on Temperature feature of the Gyeongju dataset.</p>
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<p>Hidden layer structure of LSTM.</p>
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<p>Seasonal solar power generation forecasting by ensemble LSTM method.</p>
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<p>Comparison experimental results of the Gyeongju dataset.</p>
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<p>Comparison of experimental results of the California dataset.</p>
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12 pages, 9339 KiB  
Article
Correlation between Peak Height of Polar Mesospheric Clouds and Mesopause Temperature
by Yuxin Li, Haiyang Gao, Shaoyang Sun and Xiang Li
Atmosphere 2024, 15(10), 1149; https://doi.org/10.3390/atmos15101149 - 25 Sep 2024
Viewed by 606
Abstract
Polar mesospheric clouds (PMCs) are ice crystal clouds formed in the mesosphere of high-latitude regions in both the northern (NH) and southern hemispheres (SH). Peak height is an important physical characteristic of PMCs. Satellite observation data from solar occultation for ice experiments (SOFIE) [...] Read more.
Polar mesospheric clouds (PMCs) are ice crystal clouds formed in the mesosphere of high-latitude regions in both the northern (NH) and southern hemispheres (SH). Peak height is an important physical characteristic of PMCs. Satellite observation data from solar occultation for ice experiments (SOFIE) during seven PMC seasons from 2007 to 2014 show that the difference between the height of the mesopause and the peak height of the PMCs (Zmes-Zmax) were inversely correlated with the atmospheric mesopause temperature. The Zmes-Zmax averages for all seasons for the NH and SH were 3.54 km and 2.66 km, respectively. They were smaller at the starting and ending stages of each PMC season and larger in the middle stages. Analysis of the individual cases and statistical results simulated by the PMCs 0-D model also revealed the inverse correlations between the Zmes-Zmax and mesopause temperature, with correlation coefficients of −0.71 and −0.62 for the NH and SH, respectively. The corresponding rates of change of Zmes-Zmax with respect to mesopause temperature were found to be −0.21 km/K and −0.14 km/K, respectively. The formation mechanism of PMCs suggests that a lower temperature around the mesopause can lead to a greater distance and longer time for ice crystals to condense and grow in clouds. Thus, ice crystals sediment to a lower height, making the peak height of the PMCs further away from the mesopause. In addition, disturbances in small-scale dynamic processes tend to weaken the impact of temperature on the peak height of PMCs. Full article
(This article belongs to the Special Issue The 15th Anniversary of Atmosphere)
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Figure 1
<p>Latitude of SOFIE observation data for 7 PMC seasons in the NH (<b>a</b>) and SH (<b>b</b>), respectively, from 2007 to 2014.</p>
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<p>The distributions of daily temperature profile (rainbow background), mesopause height (blue curves), peak height of PMCs (red curves), and cloud top and cloud bottom (black curves) observed by SOFIE for 7 PMC seasons in the NH (<b>a</b>–<b>g</b>) and SH (<b>h</b>–<b>n</b>) from 2007 to 2014, respectively. Error bars show the standard deviation of the daily mean (Grey vertical line). Note that the blank area in panel (<b>g</b>) indicates no observation data from SOFIE during those days.</p>
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<p>The distributions of daily mean ice mass density, <span class="html-italic">Z</span><sub>mes</sub>-<span class="html-italic">Z</span><sub>max</sub>, and mesopause temperature in the NH season of 2012 (<b>a</b>–<b>c</b>) and the SH season of 2012~2013 (<b>d</b>–<b>f</b>). Error bars show the standard deviation of the daily mean (grey vertical line).</p>
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<p>The correlation between <span class="html-italic">Z</span><sub>mes</sub>-<span class="html-italic">Z</span><sub>max</sub> and mesopause temperature using SOFIE observation data; (<b>a</b>) represents the NH seasons, (<b>b</b>) represents the SH seasons; the red lines represent the linear fitting results.</p>
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<p>Three cases simulated based on the 0-D model under different background atmospheric conditions: (<b>a</b>) for Case 1, (<b>b</b>) for Case 2, (<b>c</b>) for Case 3.</p>
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<p>The correlation between <span class="html-italic">Z</span><sub>mes</sub>-<span class="html-italic">Z</span><sub>max</sub> using the PMC 0-D model and mesopause temperature observed by SOFIE; (<b>a</b>) represents the NH seasons, (<b>b</b>) represents the SH seasons; The blue lines represent the linear fitting results, while the red lines are the same as <a href="#atmosphere-15-01149-f004" class="html-fig">Figure 4</a>; the dashed curves represent the confidence limits of 95%.</p>
Full article ">
24 pages, 10714 KiB  
Article
A Potential Link between Space Weather and Atmospheric Parameters Variations: A Case Study of November 2021 Geomagnetic Storm
by Mauro Regi, Alessandro Piscini, Patrizia Francia, Marcello De Lauretis, Gianluca Redaelli and Giuseppina Carnevale
Remote Sens. 2024, 16(17), 3318; https://doi.org/10.3390/rs16173318 - 7 Sep 2024
Viewed by 1144
Abstract
On 4 November 2021, during the rising phase of solar cycle 25, an intense geomagnetic storm (Kp = 8−) occurred. The effects of this storm on the outer magnetospheric region up to the ionospheric heights have already been examined in previous investigations. This [...] Read more.
On 4 November 2021, during the rising phase of solar cycle 25, an intense geomagnetic storm (Kp = 8−) occurred. The effects of this storm on the outer magnetospheric region up to the ionospheric heights have already been examined in previous investigations. This work is focused on the analysis of the solar wind conditions before and during the geomagnetic storm, the high-latitude electrodynamics conditions, estimated through empirical models, and the response of the atmosphere in both hemispheres, based on parameters from the ECMWF ERA5 atmospheric reanalysis dataset. Our investigations are also supported by counter-test analysis and Monte Carlo tests. We find, for both hemispheres, a significant correspondence, within 1–2 days, between high-latitude electrodynamics variations and changes in the temperature, specific humidity, and meridional and zonal winds, in both the troposphere and stratosphere. The results indicate that, in the complex solar wind–atmosphere relationship, a significant role might be played by the intensification of the polar cap potential. We also study the reciprocal relation between the ionospheric Joule heating, calculated from a model, and two adiabatic invariants used in the analysis of solar wind turbulence. Full article
(This article belongs to the Section Earth Observation Data)
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Figure 1
<p>(From top to bottom) The interplanetary parameter conditions <math display="inline"><semantics> <msub> <mi>B</mi> <mi>Y</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>B</mi> <mi>Z</mi> </msub> </semantics></math> (panels <b>a1</b>–<b>a3</b>), <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>S</mi> <mi>W</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>n</mi> <mrow> <mi>S</mi> <mi>W</mi> </mrow> </msub> </semantics></math> (panels <b>b1</b>–<b>b3</b>), used by the W05 model; the hemispheric average in the northern hemisphere of polar cap potential <span class="html-italic">E</span> (panels <b>c1</b>–<b>c3</b>) and FACs intensities <span class="html-italic">F</span> (panels <b>d1</b>–<b>d3</b>) for both positive (black curves) and negative (blue curves) components, as well as the <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (red curves). The same quantities for the southern hemisphere are very similar to those shown here and are not reported for simplicity; the geomagnetic activity indices (panels <b>e1</b>–<b>e3</b>) Dst (black curves) and Kp (red segments) during 3–6 November for the three years 2019, 2020 and 2021.</p>
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<p>Relative temperature deviation <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative specific humidity <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>Q</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
Full article ">Figure 4
<p>Relative zonal wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>U</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
Full article ">Figure 5
<p>Relative meridional wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>V</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
Full article ">Figure 6
<p>Results of the counter-test analysis conducted for the time interval 3–6 November 2020. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line) and height average atmospheric parameters (green line) and filtered (black line) at tropospheric heights for T (panels <b>a1</b>–<b>a3</b>), Q (panels <b>c1</b>–<b>c3</b>), U (panels <b>e1</b>–<b>e3</b>), and V (panels <b>g1</b>–<b>g3</b>). The result of moving the correlation analysis among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with filtered height average atmospheric parameters (panels <b>b1</b>–<b>b3</b>,<b>d1</b>–<b>d3</b>,<b>f1</b>–<b>f3</b>,<b>h1</b>–<b>h3</b>). Horizontal red lines in panels <b>b</b>,<b>d</b>,<b>f</b>,<b>h</b> represent the 95% confidence interval.</p>
Full article ">Figure 7
<p>Relative temperature deviation <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative specific humidity <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>Q</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative zonal wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>U</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative meridional wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>V</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Cross-covariance analysis results among filtered averaged atmospheric parameters (from top to bottom) of T, Q, U and V and for the troposphere (left columns) and stratosphere (right columns) and in both the southern and northern hemispheres: in each panel, the results for the disturbed (black curves) and quiet (gray curves) time intervals are reported, together with the 99% confidence threshold (red dashed curves) estimated through the MC test. The selected maximum and reliable correlations are marked with green triangles (see text for details).</p>
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<p>The latitudinal dependence of the absolute value of the correlation between <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math>. Horizontal dashed lines indicate the correlation threshold for different confidence levels. The vertical line indicates the average geographic latitude of the predicted auroral electron flux maximum.</p>
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<p>Comparison between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>C</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>a</b>) and between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>R</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>c</b>) during 2–5 November 2021. Logarithmic of normalized absolute value cross-wavelet spectrum (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>x</mi> <mi>y</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> </semantics></math>, color scales) computed between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>C</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>b</b>) and between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>R</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>d</b>). The 95% significance thresholds are highlighted with white curves in panels b and d, while the cones of influence are reported in light blue. Geomagnetic activity index SYM-H (panel <b>e</b>). The vertical dashed lines in all panels refer to the beginning of the SSC (<math display="inline"><semantics> <msub> <mi>t</mi> <mrow> <mi>S</mi> <mi>S</mi> <mi>C</mi> </mrow> </msub> </semantics></math>), and the first (<math display="inline"><semantics> <msub> <mi>t</mi> <mn>1</mn> </msub> </semantics></math>) and second (<math display="inline"><semantics> <msub> <mi>t</mi> <mn>2</mn> </msub> </semantics></math>) minima of the main phase of the storm.</p>
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14 pages, 1633 KiB  
Article
The Response of Stratospheric Gravity Waves to the 11-Year Solar Cycle
by Cong Wang, Qianchuan Mi, Fei He, Wenjie Guo, Xiaoxin Zhang and Junfeng Yang
Remote Sens. 2024, 16(17), 3239; https://doi.org/10.3390/rs16173239 - 1 Sep 2024
Viewed by 768
Abstract
Atmospheric gravity waves are one of the important dynamic processes in near space and are widely present in the atmosphere. They play a crucial role in the transfer of energy and momentum between different regions of the atmosphere. The Sun, as the ultimate [...] Read more.
Atmospheric gravity waves are one of the important dynamic processes in near space and are widely present in the atmosphere. They play a crucial role in the transfer of energy and momentum between different regions of the atmosphere. The Sun, as the ultimate source of gravity wave energy, significantly influences the intensity of gravity wave disturbances through its activity variations. This paper utilizes data from the Global Navigation Satellite System Occultation Sounder (GNOS) onboard the Fengyun-3C (FY-3C) satellite to invert global stratospheric gravity wave disturbances. It provides the global stratospheric gravity wave distribution from 2015 to 2023, nearly covering one solar activity cycle, and focuses on analyzing the response of gravity waves at different latitudes, altitudes, and wavelengths to the solar activity cycle. We found that short-wavelength gravity waves respond more noticeably to solar activity compared to long-wavelength gravity waves. Through analyzing the intensity of stratospheric gravity wave disturbances across different latitude bands, we found that in high-latitude regions, stratospheric gravity wave disturbances are most sensitive and respond most quickly to variations in solar activity. Furthermore, the Southern Hemisphere exhibits a stronger response to the current year’s solar activity changes compared to the Northern Hemisphere. In the mid-latitude and equatorial regions, the response to changes in solar activity intensity is delayed. The correlation gradually strengthens with this lag, reaching a very strong level after a 2-year lag. Additionally, the correlation between the Southern Hemisphere and solar activity is generally higher than that of the Northern Hemisphere. Full article
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Figure 1

Figure 1
<p>Response of short-wavelength gravity waves to solar activity at an altitude of 25 km. The top graph shows the variation in gravity wave disturbance intensity over time in different latitude intervals. Significance levels are indicated as follows: <span class="html-italic">p</span>-value &gt; 0.1 is not marked, <span class="html-italic">p</span>-value &lt; 0.1 is marked with *, <span class="html-italic">p</span>-value &lt; 0.05 is marked with **, and <span class="html-italic">p</span>-value &lt; 0.01 is marked with ***. In the bottom graph, the black line represents the monthly mean sunspot number, and the red line represents the 13-month mean sunspot number.</p>
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<p>Response of short-wavelength gravity waves to solar activity at an altitude of 35 km. The legend is the same as in <a href="#remotesensing-16-03239-f001" class="html-fig">Figure 1</a>.</p>
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<p>Response of short-wavelength gravity waves to solar activity at an altitude of 45 km. The legend is the same as in <a href="#remotesensing-16-03239-f001" class="html-fig">Figure 1</a>.</p>
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<p>Response of long-wavelength gravity waves at an altitude of 25 km to solar activity. The legend is the same as in <a href="#remotesensing-16-03239-f001" class="html-fig">Figure 1</a>.</p>
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<p>Response of long-wavelength gravity waves at an altitude of 35 km to solar activity. Significance levels are indicated as follows: <span class="html-italic">p</span>-value &gt; 0.1 is not marked, <span class="html-italic">p</span>-value &lt; 0.05 is marked with **, and <span class="html-italic">p</span>-value &lt; 0.01 is marked with ***.</p>
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<p>Response of long-wavelength gravity waves at an altitude of 45 km to solar activity. The legend is the same as in <a href="#remotesensing-16-03239-f001" class="html-fig">Figure 1</a>.</p>
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<p>Response of gravity waves across all wavelengths at an altitude of 25 km to solar activity. The legend is the same as in <a href="#remotesensing-16-03239-f001" class="html-fig">Figure 1</a>.</p>
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<p>Response of gravity waves across all wavelengths at an altitude of 35 km to solar activity. The legend is the same as in <a href="#remotesensing-16-03239-f001" class="html-fig">Figure 1</a>.</p>
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<p>Response of gravity waves across all wavelengths at an altitude of 45 km to solar activity. Significance levels are indicated as follows: <span class="html-italic">p</span>-value &gt; 0.1 is not marked, <span class="html-italic">p</span>-value &lt; 0.05 is marked with **, and <span class="html-italic">p</span>-value &lt; 0.01 is marked with ***.</p>
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16 pages, 3659 KiB  
Article
Applying and Improving Pyranometric Methods to Estimate Sunshine Duration for Tropical Site
by Tovondahiniriko Fanjirindratovo, Didier Calogine, Oanh Chau and Olga Ramiarinjanahary
Solar 2024, 4(3), 455-470; https://doi.org/10.3390/solar4030021 - 29 Aug 2024
Viewed by 599
Abstract
The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, [...] Read more.
The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, one of these methods will be optimized to effectively fit tropical sites. Five methods in the literature (Step algorithm, Carpentras Algorithm, Slob and Monna Algorithm, Slob and Monna 2 Algorithm, and linear algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, with regard to its original form, the step algorithm is in the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical sites in the southern hemisphere. Full article
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Figure 1
<p>Scatter plot with linear fitting: (<b>a</b>) Step algorithm vs. reference; (<b>b</b>) Meteo France Algorithm vs. Reference.</p>
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<p>Scatter plot with linear fitting: (<b>a</b>) Slob and Monna Algorithm vs. Reference; (<b>b</b>) Slob and Monna 2 Algorithm vs. Reference.</p>
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<p>Scatter plot of linear algorithm vs. reference with linear fitting.</p>
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<p>Total and relative cumulative differences between algorithms and the reference: (<b>a</b>) For the eleven years; (<b>b</b>) Zoom for the first three years.</p>
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<p>Histogram and statistical parameters: (<b>a</b>) Step algorithm; (<b>b</b>) Meteo France Algorithm.</p>
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<p>Histogram and statistical parameters: (<b>a</b>) Slob and Monna Algorithm; (<b>b</b>) Slob and Monna 2 Algorithm.</p>
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<p>Histogram and statistical parameters for linear algorithm.</p>
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<p>Relative cumulative differences for the 70 possible combinations of A and B values.</p>
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<p>Total cumulative differences for the Improved Meteo France Algorithm compared to the other models: (<b>a</b>) All methods over the eleven years; (<b>b</b>) Zoom for the step algorithm and Improved Meteo France Algorithm.</p>
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<p>Improved Meteo France Algorithm: (<b>a</b>) Scatter plot and linear fit; (<b>b</b>) Histogram.</p>
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17 pages, 2679 KiB  
Article
Field-Aligned Currents during the Strong December 2023 Storm: Local Time and Hemispheric Differences
by Hui Wang, Chengzhi Wang and Zhiyue Leng
Remote Sens. 2024, 16(17), 3130; https://doi.org/10.3390/rs16173130 - 24 Aug 2024
Viewed by 752
Abstract
This study investigates field-aligned currents (FACs) during strong magnetic storms in December 2023, analyzing variations in different local times and in the Northern (NH) and Southern Hemispheres (SH). Peak FAC densities were approximately 7.8 times higher than nominal values, with the most equatorward [...] Read more.
This study investigates field-aligned currents (FACs) during strong magnetic storms in December 2023, analyzing variations in different local times and in the Northern (NH) and Southern Hemispheres (SH). Peak FAC densities were approximately 7.8 times higher than nominal values, with the most equatorward FACs reaching −52° magnetic latitude (MLat). In the summer hemisphere, the daytime FACs were stronger than the nighttime FACs, with the daytime westward Polar Electrojet (PEJ) surpassing nighttime levels. In the winter hemisphere, the nighttime FACs and westward PEJ were stronger than daytime. Generally, the FACs and westward PEJ were stronger in the SH than in the NH across most local time sectors, attributed to greater solar illumination. The NH pre-midnight currents were stronger than for the SH, indicating enhanced substorm currents during winter nights. The nighttime FACs occurred at lower MLat than daytime, with pre-noon FACs at a higher MLat than post-noon. The NH FACs were positioned more equatorward than their SH counterparts. In the NH, the mean FACs correlated most strongly with the merging electric field (Em) at pre-noon, post-noon, and post-midnight and with the SMU (SuperMAG Electrojet Upper Index) at pre-midnight. In the SH, the mean FACs correlated best with the SMU at pre-midnight/pre-noon, with the SML (SuperMAG Electrojet Lower Index) at post-midnight, and Em at post-noon. The mean MLat of the peak FACs show the strongest correlation with Em across most local times and hemispheres. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
Show Figures

Figure 1

Figure 1
<p>Typical storm-time solar wind parameters, including the IMF By (<b>a</b>) and Bz (<b>b</b>) components in the GSM coordinate system; solar wind dynamic pressure, Pd (<b>c</b>); merging electric field, Em (<b>d</b>); Dst (<b>e</b>); AsyH (<b>f</b>); and SMU and SML (<b>g</b>) variations on 30 November–3 December 2023. Storm-time (ST) means individual hours preceding or beginning at 00:00 UT on 1 December 2023. The black vertical dashed line marks the onset of the storm, while the blue vertical dashed line indicates the time of minimum Dst.</p>
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<p>Storm-time and latitudinal variation in the FACs compared with the Dst Index in both hemispheres. The top two panels (<b>a</b>–<b>d</b>) depict data from Swarms A/C, while the bottom two panels (<b>e</b>–<b>h</b>) depict data from Swarm B. The left panels (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) represent the Northern Hemisphere, and the right panels (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) represent the Southern Hemisphere. From top to bottom are the pre-noon (<b>a</b>,<b>b</b>), pre-midnight (<b>c</b>,<b>d</b>), post-noon (<b>e</b>,<b>f</b>), and post-midnight (<b>g</b>,<b>h</b>) sectors.</p>
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<p>Correlation of the upward and downward FACs between the daytime and nighttime sectors for both hemispheres. The correlation coefficients and mean current densities are displayed in each panel. The top two panels (<b>a</b>–<b>d</b>) present data from Swarms A/C in the pre-noon and pre-midnight sectors, while the bottom two panels (<b>e</b>–<b>h</b>) show data from Swarm B in the post-noon and post-midnight sectors. Panels (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) shown the Northern Hemisphere, and panels (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) show the Southern Hemisphere. The subscripts ‘up’ and ‘down’ denote the FACs flowing up from and down into the ionosphere, respectively.</p>
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<p>Correlations between the Northern and Southern Hemispheric peak upward and downward FACs. The correlation coefficient and mean current densities are shown in each panel. The top two panels show the upward and downward FACs from Swarms A/C in the pre-midnight (<b>a</b>,<b>c</b>) and pre-noon (<b>b</b>,<b>d</b>) sectors. The bottom two panels display the upward and downward FACs from Swarm B in the post-noon (<b>e</b>,<b>g</b>) and post-midnight (<b>f</b>,<b>h</b>) sectors.</p>
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<p>The Storm-Time Indexes, Dst, along the orbit segments observed by Swarm in the four local time sectors: (<b>a</b>) pre-midnight, (<b>b</b>) pre-noon, (<b>c</b>) post-noon, (<b>d</b>) post-midnight. Black represents the Dst values during the Northern Hemisphere sampling, and the blue shows the Dst values for the Southern Hemisphere observations.</p>
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<p>Scatter plots of the peak westward PEJ observed by the Swarms A and B satellites on the dayside (pre-noon and post-noon) and nightside (pre-midnight and post-midnight). The left panel (<b>a</b>) represents the Northern Hemisphere, and the right panel (<b>b</b>) represents the Southern Hemisphere.</p>
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<p>Correlation between Northern and Southern Hemispheric peak westward PEJ. The correlation coefficient and mean current densities are shown in each panel. The top panels show the PEJ from Swarm A in the pre-midnight (<b>a</b>) and pre-noon (<b>b</b>) sectors. The bottom panels display the PEJ from Swarm B in the post-noon (<b>c</b>) and post-midnight (<b>d</b>) sectors.</p>
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<p>The correlation coefficients between the averaged peak density of the FACs and the solar wind IMF and geomagnetic indices. Panels (<b>a</b>,<b>b</b>) correspond to the Northern Hemisphere, (<b>c</b>,<b>d</b>) correspond to the Southern Hemisphere. Panels (<b>a</b>–<b>c</b>) represent sectors in the local time of pre-noon (blue) and pre-midnight (red), while panels (<b>b</b>–<b>d</b>) represent sectors in the local time of post-noon (red) and post-midnight (blue). The highest correlation coefficients are denoted by yellow circles.</p>
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<p>The correlation coefficients between the mean MLat of peak density of the FACs and solar wind IMF and geomagnetic indices. Panels (<b>a</b>,<b>b</b>) correspond to the Northern Hemisphere, (<b>c</b>,<b>d</b>) to the Southern Hemisphere. Panels (<b>a</b>–<b>c</b>) represent sectors in local time of pre-noon (blue) and pre-midnight (red), while panels (<b>b</b>–<b>d</b>) represent sectors in local time of post-noon (red) and post-midnight (blue). The highest correlation coefficients are denoted by yellow circles.</p>
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14 pages, 2119 KiB  
Article
Sun Declination and Distribution of Natural Beam Irradiance on Earth
by José A. Rueda, Sergio Ramírez, Miguel A. Sánchez and Juan de Dios Guerrero
Atmosphere 2024, 15(8), 1003; https://doi.org/10.3390/atmos15081003 - 20 Aug 2024
Viewed by 1465
Abstract
The daily path of the Sun across longitude yields night and day, but the Sun also travels across latitude on a belt 47° wide. The solar meridian declination explains the latitudinal budget of natural beam irradiance (NBI), which is defined as [...] Read more.
The daily path of the Sun across longitude yields night and day, but the Sun also travels across latitude on a belt 47° wide. The solar meridian declination explains the latitudinal budget of natural beam irradiance (NBI), which is defined as the irradiance delivered to the Earth’s surface as a normal projection from the Sun. Data for the Sun meridian declination were obtained from the Spencer model, known as the geometric model. The distribution of NBI was weighed for the latitudinal belt between the Tropics of Cancer and Capricorn. The variation in the parameters of solar meridian declination were found to be analogous to that of pendular motion. The joint distributions of the solar meridian declination against its own velocity, or that of the velocity against the acceleration of solar meridian declination, displayed circular functions. The NBI budget that a particular latitude gathers, fluctuates in inverse proportion to the velocity of solar meridian declination, yielding 18 sun-paths per degree for latitudes above 20°, or 6 sun-paths per degree of latitude for latitudes under 20°. At an average Sun–Earth distance of 1 AU, all sites of the planet, whose latitude coincides, whether within or between hemispheres, accumulate an equivalent budget of NBI. Full article
(This article belongs to the Section Planetary Atmospheres)
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Figure 1
<p>Meridian declination (<math display="inline"><semantics> <mi mathvariant="bold-italic">δ</mi> </semantics></math>), angular velocity (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">ω</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">α</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>), and angular acceleration (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">ω</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>) of the Sun for two subsequent Gregorian years built from the geometric model of solar declination. Equinoxes are tagged on the Equator (solid circle) and solstices on the Tropics of Cancer and Capricorn (open circle).</p>
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<p>Sun meridian declination (<math display="inline"><semantics> <mi mathvariant="bold-italic">δ</mi> </semantics></math>) against the velocity of declination (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">ω</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">δ</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>) throughout a Gregorian year. Equinoxes (solid circle) are tagged in the Equator and solstices (open circle) in the Tropics of Cancer and Capricorn.</p>
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<p>Acceleration of declination (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">ω</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>) against the velocity of solar meridian declination (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">ω</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">δ</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>) throughout a Gregorian year. Equinoxes (solid circle) are tagged in the Equator and solstices (open circle) in the Tropics of Cancer and Capricorn.</p>
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<p>Angular velocity (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">ω</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">δ</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>) and angular acceleration (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">α</mi> <mo>=</mo> <mo>∂</mo> <mi mathvariant="bold-italic">ω</mi> <mo>/</mo> <mo>∂</mo> <mi mathvariant="bold-italic">t</mi> </mrow> </semantics></math>) of the Sun meridian declination (<math display="inline"><semantics> <mi mathvariant="bold-italic">δ</mi> </semantics></math>) in association with the Equation of Time (<b>E</b>, minutes of time) within a Gregorian year. Equinoxes (solid circle) and solstices (open circle) are tagged on the analemma (<math display="inline"><semantics> <mi mathvariant="bold-italic">δ</mi> </semantics></math>) at the three main parallels.</p>
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<p>Budget of <span class="html-italic">natural beam irradiance</span> <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi mathvariant="bold-italic">Γ</mi> </mrow> </semantics></math>) expressed as a percentage of the budget annually available (100%) that is delivered per arcdeg of latitude, where <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">Γ</mi> <mo>=</mo> <mn>60</mn> <mo> </mo> <msub> <mi mathvariant="bold-italic">Γ</mi> <mi>d</mi> </msub> <mo>/</mo> <mi mathvariant="bold-italic">ω</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">Γ</mi> <mi>d</mi> </msub> <mo>=</mo> <mrow> <mo>[</mo> <mrow> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>/</mo> <mn>365</mn> <mrow> <mtext> </mtext> <mi>day</mi> </mrow> </mrow> <mo>)</mo> </mrow> <mo>∗</mo> <mn>100</mn> </mrow> <mo>]</mo> </mrow> <mo>%</mo> </mrow> </semantics></math> = 0.274% day<sup>−1</sup>. The function <math display="inline"><semantics> <mi mathvariant="bold-italic">Γ</mi> </semantics></math> is presented separately for each season (green, yellow, brown, and blue circles).</p>
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14 pages, 4413 KiB  
Technical Note
Latitudinal Characteristics of Nighttime Electron Temperature in the Topside Ionosphere and Its Dependence on Solar and Geomagnetic Activities
by Jianyun Liang, Jiyao Xu, Kun Wu and Ji Luo
Remote Sens. 2024, 16(16), 2946; https://doi.org/10.3390/rs16162946 - 12 Aug 2024
Viewed by 768
Abstract
This study investigates the latitudinal characteristics of the nighttime electron temperature, as observed by the Defense Meteorological Satellite Program F16 satellite, and its dependence on solar and geomagnetic activities between 2013 and 2022 in the topside ionosphere, only for the winter hemispheres. The [...] Read more.
This study investigates the latitudinal characteristics of the nighttime electron temperature, as observed by the Defense Meteorological Satellite Program F16 satellite, and its dependence on solar and geomagnetic activities between 2013 and 2022 in the topside ionosphere, only for the winter hemispheres. The electron temperature in both hemispheres exhibited a low-temperature zone at the equator and a double high-temperature zone at the sub-auroral and auroral latitudes along the magnetic latitude. In addition, we further studied the temperature crest/trough positions in the temperature zone at different latitudes. As the solar activity intensity decreased (increased), the temperature trough position at the equator shifted from the Southern (Northern) to the Northern (Southern) Hemisphere, and the temperature double-crest positions at the sub-auroral and auroral latitudes gradually approached (moved away from) each other. Furthermore, during the geomagnetic disturbance time, the temperature double-crest positions both moved toward lower latitudes, but the temperature trough position was not sensitive to geomagnetic activity. Our analysis demonstrates that the values and correlations of the electron temperature and density varied in different temperature characteristic zones (the temperature crest/trough positions ±2°), possibly due to the different energy control factors of the electrons at different latitudes. This may also indirectly indicate the energy coupling process between the topside ionosphere and different regions at different latitudes. Full article
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Figure 1
<p>Global distribution (<b>a</b>) of DMSP observations and magnetic latitude variation (<b>b</b>) in the electron temperature observed by the DMSP (black solid line) and simulated by the IRI-2020 model (black dash line) at nighttime in the winter hemisphere during geomagnetic quiet time using 2015 as an example. The black, red, and blue dots represent the temperature trough position at the magnetic equator and the crest positions at the middle and high magnetic latitudes. The white solid line represents the magnetic equator, and the dashed lines represent the magnetic latitude line at 20° intervals.</p>
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<p>Variation in the temperature characteristic positions with the solar activity year at the equator (<b>a2</b>), the sub-auroral latitude (<b>b</b>) in the NH (<b>b1</b>) and SH (<b>b2</b>), and the auroral latitude (<b>c</b>) in the NH (<b>c1</b>) and SH (<b>c2</b>) for local winter conditions during the geomagnetic quiet (circle solid line) and disturbance (triangle dash line) time. (<b>a1</b>) represents the annual average F10.7.</p>
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<p>Variations in the electron temperature (red line) and density (blue line) within the temperature characteristic zones (crest/trough positions ±2°) at the equator (<b>a2</b>), the sub-auroral latitudes (<b>b</b>) in the NH (<b>b1</b>) and SH (<b>b2</b>), and the auroral latitudes (<b>c</b>) in the NH (<b>c1</b>) and SH (<b>c2</b>) for local winter conditions during the geomagnetic quiet (circle solid line) and disturbance (triangle dash line) time. (<b>a1</b>) represents the annual average F10.7.</p>
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<p>Variations in the electron temperature (red line) and density (blue line) for the IRI-2020 simulations within the temperature characteristic zones (crest/trough positions ±2°) at the equator (<b>a2</b>), the sub-auroral latitudes (<b>b</b>) in the NH (<b>b1</b>) and SH (<b>b2</b>), and the auroral latitudes (<b>c</b>) in the NH (<b>c1</b>) and SH (<b>c2</b>) for local winter conditions during the geomagnetic quiet (circle solid line) and disturbance (triangle dash line) time. (<b>a1</b>) represents the annual average F10.7.</p>
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<p>Pearson’s correlation coefficient between the electron temperature and density in the temperature characteristic zones at the equator (<b>a2</b>), the sub-auroral latitude (<b>b</b>) in the NH (<b>b1</b>) and SH (<b>b2</b>), and the auroral latitude (<b>c</b>) in the NH (<b>c1</b>) and SH (<b>c2</b>) for local winter conditions during the geomagnetic quiet (circle solid line) and disturbance (triangle dash line) times. (<b>a1</b>) represents the annual average F10.7. All coefficient correlations have a confidence level of 95%, as shown in <a href="#app2-remotesensing-16-02946" class="html-app">Appendix B</a>.</p>
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<p>The latitudinal distribution of the monthly data during the nighttime with a local solar zenith angle (SZA) ≥100° from 2013 to 2022.</p>
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11 pages, 1242 KiB  
Article
Mesospheric Ozone Depletion during 2004–2024 as a Function of Solar Proton Events Intensity
by Grigoriy Doronin, Irina Mironova, Nikita Bobrov and Eugene Rozanov
Atmosphere 2024, 15(8), 944; https://doi.org/10.3390/atmos15080944 - 6 Aug 2024
Viewed by 1760
Abstract
Solar proton events (SPEs) affect the Earth’s atmosphere, causing additional ionization in the high-latitude mesosphere and stratosphere. Ionization rates from such solar proton events maximize in the stratosphere, but the formation of ozone-depleting nitrogen and hydrogen oxides begins at mesospheric altitudes. The destruction [...] Read more.
Solar proton events (SPEs) affect the Earth’s atmosphere, causing additional ionization in the high-latitude mesosphere and stratosphere. Ionization rates from such solar proton events maximize in the stratosphere, but the formation of ozone-depleting nitrogen and hydrogen oxides begins at mesospheric altitudes. The destruction of mesospheric ozone is associated with protons with energies of about 10 MeV and higher and will strongly depend on the intensity of the flux of these particles. Most studies investigating the impact of SPEs on the characteristics of the middle atmosphere have been based on either simulations or reanalysis datasets, and some studies have used satellite observations to validate model results. We study the impact of SPEs on cold-season ozone loss in both the northern and southern hemispheres using Aura MLS mesospheric ozone measurements over the 2004 to 2024 period. Here, we show how strongly SPEs can deplete polar mesospheric ozone in different hemispheres and attempt to evaluate this dependence on the intensity of solar proton events. We found that moderate SPEs consisting of protons with an energy of more than 10 MeV and a flux intensity of more than 100 pfu destroy mesospheric ozone in the northern hemisphere up to 47% and in the southern hemisphere up to 33%. For both hemispheres, the peak of winter ozone loss was observed at about 76 km. In the northern hemisphere, maximum winter ozone loss was observed on the second day after a solar proton event, but in the southern hemisphere, winter ozone depletion was already detected on the first day. In the southern hemisphere, mesospheric ozone concentrations return to pre-event levels on the ninth day after a solar proton event, but in the northern hemisphere, even on the tenth day after a solar proton event, the mesospheric ozone layer may not be fully recovered. The strong SPEs with a proton flux intensity of more than 1000 pfu lead to a maximum winter ozone loss of up to 85% in the northern hemisphere, and in the southern hemisphere winter, ozone loss reaches 73%. Full article
(This article belongs to the Special Issue Cosmic Rays, Ozone Depletion and Climate Change)
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Figure 1
<p>Results of superposed epoch analysis of Aura MLS ozone altitudinal profiles over 60–80 NH before and after SPEs, which are summarized in <a href="#atmosphere-15-00944-t001" class="html-table">Table 1</a>.</p>
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<p>Results of superposed epoch analysis of Aura MLS ozone altitudinal profiles over 60–80 SH before and after SPEs, which are summarized in <a href="#atmosphere-15-00944-t002" class="html-table">Table 2</a>.</p>
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<p>Northern hemisphere ozone depletion (in %) after SPEs compared to the average ozone concentration observed before SPEs, which are summarized in <a href="#atmosphere-15-00944-t001" class="html-table">Table 1</a>. The ozone profile for each day is obtained using superposed epoch analysis of Aura MLS ozone altitudinal profiles for 60–80 NH after moderate SPEs with a proton flux intensity of more than 100 pfu.</p>
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<p>Northern hemisphere ozone depletion (in %) after SPEs compared to the average ozone concentration observed before SPEs, which are summarized in <a href="#atmosphere-15-00944-t001" class="html-table">Table 1</a>. The ozone profile for each day is obtained using superposed epoch analysis of Aura MLS ozone altitudinal profiles for 60–80 NH after strong SPEs with a proton flux intensity of more than 1000 pfu.</p>
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<p>Southern hemisphere ozone depletion (in %) after SPEs compared to the average ozone concentration observed before SPEs, which are summarized in <a href="#atmosphere-15-00944-t002" class="html-table">Table 2</a>. Each day ozone profile—results superposed epoch analysis of Aura MLS ozone altitudinal profiles for 60–80 SH after solar proton events. Moderate SPEs—with a proton flux intensity of more than 100 pfu.</p>
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<p>Southern hemisphere ozone depletion (in %) after SPEs compared to the average ozone concentration observed before SPEs, which are summarized in <a href="#atmosphere-15-00944-t002" class="html-table">Table 2</a>. Each day ozone profile—results superposed epoch analysis of Aura MLS ozone altitudinal profiles for 60–80 SH after SPE. Strong solar proton events—with a proton flux intensity of more than 1000 pfu.</p>
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27 pages, 6282 KiB  
Article
Solar Energy Received on Flat-Plate Collectors Fixed on 2-Axis Trackers: Effect of Ground Albedo and Clouds
by Harry D. Kambezidis, Kosmas A. Kavadias and Ashraf M. Farahat
Energies 2024, 17(15), 3721; https://doi.org/10.3390/en17153721 - 28 Jul 2024
Viewed by 756
Abstract
This study investigates the performance of isotropic and anisotropic diffuse models to estimate the total solar energy received on flat-plate collectors fixed on dual-axis trackers. These estimations are applied at twelve sites selected in both hemispheres with different terrain and environmental conditions. The [...] Read more.
This study investigates the performance of isotropic and anisotropic diffuse models to estimate the total solar energy received on flat-plate collectors fixed on dual-axis trackers. These estimations are applied at twelve sites selected in both hemispheres with different terrain and environmental conditions. The diffuse (or transposition) models used in this study are the isotropic Liu-Jordan (L&J), Koronakis (KOR), Badescu (BAD), and Tian (TIA), and the anisotropic Hay (HAY), Reindl (REI), Klucher (KLU), Skartveit and Olseth (S&O), and Steven and Unsworth (S&U). These models were chosen because of their simplicity in the calculations and minimum number of input values. The results show that a single transposition model is not efficient for all sites; therefore, the most appropriate models are selected for each site under all, clear, intermediate, and overcast conditions in skies. On the other hand, an increase in the ground albedo in the vicinity of the solar installation can increase the annual inclined solar availability on a two-axis tracker by at least 9% on average. Further, a linear dependence of the annual inclined solar energy on the variation of the ground albedo was found. Also, a linear relationship exists between the annual diffuse-fraction and cloud-modification factor values at the 12 sites. Full article
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Figure 1
<p>Location of the 12 selected sites (red circles). The circled numbers correspond to those in column 1 of <a href="#energies-17-03721-t001" class="html-table">Table 1</a>.</p>
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<p>Annual sums of the total inclined solar energies, H<sub>g,t,AS,ρ</sub>, on flat-plate collectors fixed on dual-axis trackers at the 12 sites as a function of the ground albedo, ρ, of the sites under AS conditions. The dashed straight lines are the best-fit linear curves to the data points for each site.</p>
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<p>Annual sums of the total inclined solar energies, H<sub>g,t,AS,ρg</sub>, on flat-plate collectors fixed on dual-axis trackers at the 12 sites as a function of the diffuse fraction, k<sub>d,AS</sub>, of the sites under AS conditions.</p>
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<p>Annual sums of the total inclined solar energies, H<sub>g,t,AS,ρg</sub>, on flat-plate collectors fixed on dual-axis trackers at the 12 sites as a function of the cloud-modification factor, CMF, of the sites under AS conditions.</p>
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<p>Variation in the annual mean diffuse fractions, k<sub>d,AS</sub>, over the corresponding CMF values at the 12 sites under AS conditions.</p>
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<p>Variation in the annual mean correction factors, CF<sub>ρr</sub>, with the ground-albedo ratio, ρ<sub>r</sub>, at the 12 sites under AS conditions. The data points next to those for CF<sub>ρr</sub> = ρ<sub>r</sub> = 1 correspond to ρ<sub>r</sub> = 0.95, which is the average ρ<sub>r</sub> of all 12 sites.</p>
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<p>Variation in the annual mean correction factors, CF<sub>ρr</sub>, with the absolute geographical latitude, |φ|, at various ρ<sub>r</sub> values along the 12 sites under AS conditions.</p>
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<p>Variation in the annual mean correction factors, CF<sub>ρr</sub>, with the absolute geographical latitude, |φ|, at various ρ<sub>r</sub> values along the 12 sites under CS conditions.</p>
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<p>Monthly solar energy gains, SEG, for the 12 sites under AS conditions and taking into account the near-real ρ<sub>g</sub> values of the sites in all calculations; t = month in the range 1 (January) to 12 (December).</p>
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<p>Contour plots of the inclined solar energies, H<sub>g,t,AS,ρg</sub> (in kWhm<sup>−2</sup>), received by flat-plate collectors fixed on dual-axis trackers at the 12 sites under AS conditions. The x-axis is time (in months); the y-axis accommodates the monthly mean near-real ground-albedo, ρ<sub>g</sub>, values of the sites. The panels are for (<b>a</b>) ATH year 2000, (<b>b</b>) BOU year 1999, (<b>c</b>) CAR year 2018, (<b>d</b>) DAA year 2017, (<b>e</b>) GAN year 2015, (<b>f</b>) ILO year 2003, (<b>g</b>) KIS year 2020, (<b>h</b>) LER year 2003, (<b>i</b>) LIN year 2018, (<b>j</b>) PAY year 2013, (<b>k</b>) REG year 2003, and (<b>l</b>) SOV year 2002.</p>
Full article ">Figure 10 Cont.
<p>Contour plots of the inclined solar energies, H<sub>g,t,AS,ρg</sub> (in kWhm<sup>−2</sup>), received by flat-plate collectors fixed on dual-axis trackers at the 12 sites under AS conditions. The x-axis is time (in months); the y-axis accommodates the monthly mean near-real ground-albedo, ρ<sub>g</sub>, values of the sites. The panels are for (<b>a</b>) ATH year 2000, (<b>b</b>) BOU year 1999, (<b>c</b>) CAR year 2018, (<b>d</b>) DAA year 2017, (<b>e</b>) GAN year 2015, (<b>f</b>) ILO year 2003, (<b>g</b>) KIS year 2020, (<b>h</b>) LER year 2003, (<b>i</b>) LIN year 2018, (<b>j</b>) PAY year 2013, (<b>k</b>) REG year 2003, and (<b>l</b>) SOV year 2002.</p>
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<p>Contour plots of the inclined solar energies, H<sub>g,t,CS,ρg</sub> (in kWhm<sup>−2</sup>), received by flat-plate collectors fixed on dual-axis trackers at the 12 sites under CS conditions and near-real ground-albedo, ρ<sub>g</sub>, values. The x-axis is time (in months); the y-axis accommodates the monthly mean cloud-modification factors, CMF, of the sites. The panels are for (<b>a</b>) ATH year 2000, (<b>b</b>) BOU year 1999, (<b>c</b>) CAR year 2018, (<b>d</b>) DAA year 2017, (<b>e</b>) GAN year 2015, (<b>f</b>) ILO year 2003 (not shown because of the absence of clear skies in 9 months), (<b>g</b>) KIS year 2020, (<b>h</b>) LER year 2003 (absence of clear skies in December, January, February), (<b>i</b>) LIN year 2018, (<b>j</b>) PAY year 2013, (<b>k</b>) REG year 2003, and (<b>l</b>) SOV year 2002.</p>
Full article ">Figure 11 Cont.
<p>Contour plots of the inclined solar energies, H<sub>g,t,CS,ρg</sub> (in kWhm<sup>−2</sup>), received by flat-plate collectors fixed on dual-axis trackers at the 12 sites under CS conditions and near-real ground-albedo, ρ<sub>g</sub>, values. The x-axis is time (in months); the y-axis accommodates the monthly mean cloud-modification factors, CMF, of the sites. The panels are for (<b>a</b>) ATH year 2000, (<b>b</b>) BOU year 1999, (<b>c</b>) CAR year 2018, (<b>d</b>) DAA year 2017, (<b>e</b>) GAN year 2015, (<b>f</b>) ILO year 2003 (not shown because of the absence of clear skies in 9 months), (<b>g</b>) KIS year 2020, (<b>h</b>) LER year 2003 (absence of clear skies in December, January, February), (<b>i</b>) LIN year 2018, (<b>j</b>) PAY year 2013, (<b>k</b>) REG year 2003, and (<b>l</b>) SOV year 2002.</p>
Full article ">Figure 11 Cont.
<p>Contour plots of the inclined solar energies, H<sub>g,t,CS,ρg</sub> (in kWhm<sup>−2</sup>), received by flat-plate collectors fixed on dual-axis trackers at the 12 sites under CS conditions and near-real ground-albedo, ρ<sub>g</sub>, values. The x-axis is time (in months); the y-axis accommodates the monthly mean cloud-modification factors, CMF, of the sites. The panels are for (<b>a</b>) ATH year 2000, (<b>b</b>) BOU year 1999, (<b>c</b>) CAR year 2018, (<b>d</b>) DAA year 2017, (<b>e</b>) GAN year 2015, (<b>f</b>) ILO year 2003 (not shown because of the absence of clear skies in 9 months), (<b>g</b>) KIS year 2020, (<b>h</b>) LER year 2003 (absence of clear skies in December, January, February), (<b>i</b>) LIN year 2018, (<b>j</b>) PAY year 2013, (<b>k</b>) REG year 2003, and (<b>l</b>) SOV year 2002.</p>
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<p>Intro-annual variation of the cloud-modification factor, CMF, at the 12 sites under AS conditions and near-real ground-albedo, ρ<sub>g</sub>, calculations; t = month in the range 1 (January) to 12 (December).</p>
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18 pages, 1340 KiB  
Article
Assessment of the Sunscreen Properties of Sesame Oil Using the Hemispherical Directional Reflectance Method
by Małgorzata Bożek, Julia Trybała, Agata Lebiedowska, Anna Stolecka-Warzecha, Paula Babczyńska and Sławomir Wilczyński
Appl. Sci. 2024, 14(15), 6545; https://doi.org/10.3390/app14156545 - 26 Jul 2024
Viewed by 826
Abstract
Sesame oil has been widely used for centuries. It is not only used as a kitchen ingredient, but it is also used to apply to the skin. Sesame oil contains natural compounds such as sesamol, sesamolin and sesamide, which have the ability to [...] Read more.
Sesame oil has been widely used for centuries. It is not only used as a kitchen ingredient, but it is also used to apply to the skin. Sesame oil contains natural compounds such as sesamol, sesamolin and sesamide, which have the ability to reflect or absorb certain UV rays. These substances can act as UV filters, helping to minimize the effects of harmful UV radiation on the skin. The aim of the study was to investigate the radioprotective/sun protection properties of sesame oil. The influence of sesame oils from different manufacturers on the directional reflectance of the skin was analyzed at various time intervals. To assess the sunscreen properties of the oil, a new technique was used: the 410-Solar hemispherical directional reflectometer. Sesame oil can be used in sunscreen preparations, but only when combined with other, more powerful ingredients. The oil itself is not sufficient protection against solar radiation. The study revealed no significant disparities in performance between the tested sesame oils from diverse manufacturers. Full article
23 pages, 1353 KiB  
Article
Scaling Properties of Magnetic Field Fluctuations in the High-Latitude Ionosphere
by Simone Mestici, Fabio Giannattasio, Paola De Michelis, Francesco Berrilli and Giuseppe Consolini
Remote Sens. 2024, 16(11), 1928; https://doi.org/10.3390/rs16111928 - 27 May 2024
Viewed by 979
Abstract
Space plasma turbulence plays a relevant role in several plasma environments, such as solar wind and the Earth’s magnetosphere–ionosphere system, and is essential for describing their complex coupling. This interaction gives rise to various phenomena, including ionospheric irregularities and the amplification of magnetospheric [...] Read more.
Space plasma turbulence plays a relevant role in several plasma environments, such as solar wind and the Earth’s magnetosphere–ionosphere system, and is essential for describing their complex coupling. This interaction gives rise to various phenomena, including ionospheric irregularities and the amplification of magnetospheric and ionospheric currents. The structure and dynamics of these currents have relevant implications, for example, in studying ionospheric heating and the nature of electric and magnetic field fluctuations in the auroral and polar environments. In this study, we investigate the nature of small-scale fluctuations characterizing the ionospheric magnetic field in response to different geomagnetic conditions. We use high-resolution (50 Hz) magnetic data from the ESA’s Swarm mission, collected during a series of high-latitude crossings, to probe the scaling features of magnetic field fluctuations in auroral and polar cap regions at spatial scales still poorly explored. Our findings reveal that magnetic field fluctuations in field-aligned currents (FACs) and polar cap regions across both hemispheres are characterized by different scaling properties, suggesting a distinct driver of turbulence. Furthermore, we find that geomagnetic activity significantly influences the nature of energy dissipation in FAC regions, leading to more localized filamentary structures toward smaller scales. Full article
(This article belongs to the Special Issue Ionosphere Monitoring with Remote Sensing (3rd Edition))
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Figure 1

Figure 1
<p>An example of the magnetic field components of external origin along the main field’s parallel and perpendicular directions during a series of high-latitude crossings. The first and second panels show two Northern and Southern Hemisphere crossings that occurred on 27 February 2023, while the third and fourth panels correspond to crossings that occurred on 29 October 2021. The magenta dotted lines denoted the three distinct regions traversed by the satellite: FACs dayside region, polar cap, and FACs nightside region.</p>
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<p>The upper panel provides an illustration of the Northward magnetic field component originating from external sources, observed during a high-latitude crossing on 27 February 2023. In contrast, the remaining two panels illustrate the signal decomposition using the EMD method. Specifically, the middle panel represents the combination of the first 9 modes, depicting fluctuations of smaller scale within the signal, while the lower panel showcases fluctuations on larger scales. Magenta vertical lines denote the dayside (left) and nightside (right) field-aligned current regions, identified using the standard deviation filter described in <a href="#sec3-remotesensing-16-01928" class="html-sec">Section 3</a>.</p>
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<p>Example of the three filtered external magnetic field components for the same Northern crossing reported in the first panel of <a href="#remotesensing-16-01928-f001" class="html-fig">Figure 1</a>. The top panel illustrates the behavior of the filtered perpendicular components, while the bottom panel showcases the behavior of the filtered external magnetic field component aligned parallel to the main field. The vertical lines in magenta highlight the FACs dayside zone (left), polar cap (middle), and FACs nightside region (right), detected by the standard deviation filter defined in <a href="#sec3-remotesensing-16-01928" class="html-sec">Section 3</a>.</p>
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<p>Average power spectral densities of magnetic field fluctuations along three distinct directions within three chosen sectors in the Northern Hemisphere. The red, blue, and green outlines represent PSDs during disturbed conditions, while orange, violet, and green-lime denote quiet conditions, as explained in the legend. The dashed black line represents a power-law function with an exponent of <math display="inline"><semantics> <mi>β</mi> </semantics></math> = 2.</p>
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<p>Example of the trace of <span class="html-italic">q</span>th-order average structure functions Tr(<math display="inline"><semantics> <msubsup> <mi mathvariant="normal">S</mi> <mi>q</mi> <mo>⊥</mo> </msubsup> </semantics></math>(<math display="inline"><semantics> <mi>τ</mi> </semantics></math>)) = <math display="inline"><semantics> <msub> <mo>∑</mo> <mi>i</mi> </msub> </semantics></math><math display="inline"><semantics> <msubsup> <mi mathvariant="normal">S</mi> <mi>q</mi> <mi>i</mi> </msubsup> </semantics></math>(<math display="inline"><semantics> <mi>τ</mi> </semantics></math>) for the FACs dayside sector, derived from the increments of the external magnetic field perpendicular components recorded by Swarm A satellite during all its transit on 27 February 2023 in the Northern Hemisphere. The panels from top to bottom highlight the behavior of the first, second, third, and fourth moment orders <span class="html-italic">q</span>, respectively. The dashed black lines represent the best linear fit for each moment order. The vertical red lines mark the considered time interval for the fitting process.</p>
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<p>Scaling exponents <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </semantics></math> relative to the generalized structure functions of the external magnetic field increments for parallel and north directed perpendicular component in the dayside, nightside, and polar cap regions. The triangular markers refers to quiet conditions while the circular ones to disturbed conditions. The dashed lines mark the line passing through the zero and second-order data points for the disturbed geomagnetic conditions.</p>
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<p>Comparison between the average scaling exponents for the Northern and Southern Hemispheres in disturbed (<b>top</b>) and quiet (<b>bottom</b>) geomagnetic conditions for each region (dayside: red, nightside: blue, polar cap: green). The dashed line marks the bisector of the plane where <math display="inline"><semantics> <mrow> <msubsup> <mi>ξ</mi> <mo>⊥</mo> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>ξ</mi> <mo>⊥</mo> <mi>S</mi> </msubsup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Cumulative distributions of <math display="inline"><semantics> <mo>Θ</mo> </semantics></math>, which serves as a proxy measure of intermittency, obtained for the Northern (<math display="inline"><semantics> <msub> <mo>Θ</mo> <mrow> <mi>N</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>) and Southern (<math display="inline"><semantics> <msub> <mo>Θ</mo> <mrow> <mi>S</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>) Hemispheres. The distributions associated with quiet and disturbed geomagnetic conditions are highlighted in blue and red, respectively, for each high-latitude region.</p>
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<p>Kernel density estimates comparing the values of <math display="inline"><semantics> <mo>Θ</mo> </semantics></math>, i.e., the proxy measure of intermittency, obtained for the Northern (<math display="inline"><semantics> <msub> <mo>Θ</mo> <mrow> <mi>N</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>) and Southern (<math display="inline"><semantics> <msub> <mo>Θ</mo> <mrow> <mi>S</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>) Hemispheres. The distribution associated with quiet and disturbed geomagnetic conditions are denoted in blue and red for each high-latitude region, respectively. The innermost contours depict the regions containing 90% of the probability mass of the distribution, while subsequent outward contours correspond to 80%, 70%, 60%, 50%, 40%, and 30% probability levels. The dashed black line indicates the bisector of the plane where <math display="inline"><semantics> <msub> <mo>Θ</mo> <mrow> <mi>N</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> equals <math display="inline"><semantics> <msub> <mo>Θ</mo> <mrow> <mi>S</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>.</p>
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25 pages, 4138 KiB  
Article
An EOF-Based Global Plasmaspheric Electron Content Model and Its Potential Role in Vertical-Slant TEC Conversion
by Fengyang Long, Chengfa Gao, Yanfeng Dong and Zhenhao Xu
Remote Sens. 2024, 16(11), 1857; https://doi.org/10.3390/rs16111857 - 23 May 2024
Viewed by 758
Abstract
Topside total electron content (TEC) data measured by COSMIC/FORMAT-3 during 2008 and 2016 were used to analyze and model the global plasmaspheric electron content (PEC) above 800 km with the help of the empirical orthogonal function (EOF) analysis method, and the potential role [...] Read more.
Topside total electron content (TEC) data measured by COSMIC/FORMAT-3 during 2008 and 2016 were used to analyze and model the global plasmaspheric electron content (PEC) above 800 km with the help of the empirical orthogonal function (EOF) analysis method, and the potential role of the proposed PEC model in helping Global Navigation Satellite System (GNSS) users derive accurate slant TEC (STEC) from existing high-precision vertical TEC (VTEC) products was validated. A uniform gridded PEC dataset was first obtained using the spherical harmonic regression method, and then, it was decomposed into EOF basis modes. The first four major EOF modes contributed more than 99% of the total variance. They captured the pronounced latitudinal gradient, longitudinal differences, hemispherical differences, diurnal and seasonal variations, and the solar activity dependency of global PEC. A second-layer EOF decomposition was conducted for the spatial pattern and amplitude coefficients of the first-layer EOF modes, and an empirical PEC model was constructed by fitting the second-layer basis functions related to latitude, longitude, local time, season, and solar flux. The PEC model was designed to be driven by whether solar proxy or parameters derived from the Klobuchar model meet the real-time requirements. The validation of the results demonstrated that the proposed PEC model could accurately simulate the major spatiotemporal patterns of global PEC, with a root-mean-square (RMS) error of 1.53 and 2.24 TECU, improvements of 40.70% and 51.74% compared with NeQuick2 model in 2009 and 2014, respectively. Finally, the proposed PEC model was applied to conduct a vertical-slant TEC conversion experiment with high-precision Global Ionospheric Maps (GIMs) and dual-frequency carrier phase observables of more than 400 globally distributed GNSS sites. The results of the differential STEC (dSTEC) analysis demonstrated the effectiveness of the proposed PEC model in aiding precise vertical-slant TEC conversion. It improved by 18.52% in dSTEC RMS on a global scale and performed better in 90.20% of the testing days compared with the commonly used single-layer mapping function. Full article
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Figure 1
<p>(<b>a</b>) Daily description of PodTec dataset measured by COSMIC/FORMAT-3 satellites; (<b>b</b>) daily mean geomagnetic activity indices and daily solar activity conditions from 2006 to 2019. The black dotted lines denote the altitude of 800 km, ap of 30 nT and <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mn>10.7</mn> </mrow> </msub> </semantics></math> of 80 sfu respectively.</p>
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<p>(<b>a</b>) Original PodTec dataset and (<b>b</b>) gridded PEC data at 12:00 LT in March 2014.</p>
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<p>(<b>a</b>–<b>d</b>) Global distribution of <math display="inline"><semantics> <msub> <mi>E</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>E</mi> <mn>4</mn> </msub> </semantics></math> and (<b>e</b>–<b>h</b>) variations of <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>P</mi> <mn>4</mn> </msub> </semantics></math> with month and local time in 2008∼2016.</p>
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<p>Normalized <math display="inline"><semantics> <msub> <mi>e</mi> <mn>1</mn> </msub> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <msub> <mi>u</mi> <mn>1</mn> </msub> </semantics></math> (<b>right</b>) after a second-layer EOF decomposition for <math display="inline"><semantics> <msub> <mi>E</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>(<b>a</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>1</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>v</mi> <mn>1</mn> </msub> </semantics></math> after a second-layer decomposition from <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math> and (<b>c</b>) scatter plot and fitting curve for <math display="inline"><semantics> <msub> <mi>M</mi> <mn>10</mn> </msub> </semantics></math> against <math display="inline"><semantics> <msub> <mi>v</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Variances of EOF modes through a second-layer decomposition for (<b>a</b>) <math display="inline"><semantics> <msub> <mi>E</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>E</mi> <mn>4</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>P</mi> <mn>4</mn> </msub> </semantics></math>.</p>
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<p>(<b>a</b>) Fitting results and (<b>b</b>) residuals of <math display="inline"><semantics> <msub> <mi>E</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>E</mi> <mn>4</mn> </msub> </semantics></math> respectively.</p>
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<p>(<b>a</b>) Fitting results and (<b>b</b>) residuals of <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>P</mi> <mn>4</mn> </msub> </semantics></math>, respectively.</p>
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<p>(<b>a</b>) Cumulate variance for different numbers of major EOF modes; (<b>b</b>) the correlation coefficients and RMS errors of the PEC model compared to those of the gridded PEC dataset when using different numbers of major EOF modes; (<b>c</b>) histograms of model residuals when three first-layer major EOF modes are considered.</p>
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<p>(<b>a</b>) Daily <math display="inline"><semantics> <msub> <mi>M</mi> <mn>10</mn> </msub> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>K</mi> <mi>p</mi> <mi>a</mi> <mi>r</mi> </mrow> </semantics></math> indices in 2003∼2022 and (<b>b</b>) scatter plot and fit curve of <math display="inline"><semantics> <mrow> <mi>K</mi> <mi>p</mi> <mi>a</mi> <mi>r</mi> </mrow> </semantics></math> against <math display="inline"><semantics> <msub> <mi>M</mi> <mn>10</mn> </msub> </semantics></math>.</p>
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<p>(<b>a</b>,<b>d</b>) Histograms of model residuals; (<b>b</b>,<b>e</b>) daily bias; and (<b>c</b>,<b>f</b>) daily RMS errors of different competing models in 2009 and 2014, respectively.</p>
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<p>Daily average PEC distributed with geomagnetic latitude of PodTec dataset, NeQuick2 model, PECM-M10 model, and PECM-Kpar model (<b>left</b>) and daily RMS error at different geomagnetic latitudes for the three competing models (<b>right</b>) in 2009.</p>
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<p>Similar to <a href="#remotesensing-16-01857-f012" class="html-fig">Figure 12</a> but for 2014.</p>
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<p>The monthly average PEC computed with PodTec dataset, NeQuick2 model, PECM-M10 model, and PECM-Kpar model in (<b>a</b>) July and (<b>b</b>) December 2009.</p>
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<p>Similar to <a href="#remotesensing-16-01857-f014" class="html-fig">Figure 14</a> but for 2014.</p>
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<p>Monthly mean deviations of PEC models compared to the PodTec dataset. The red lines represent the linear fitting of the residuals, and the linear trends per decade for different local times are also shown in each subgraph.</p>
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<p>Diagrammatic sketch of the PECM-2L mapping function.</p>
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<p>Daily RMS errors and <math display="inline"><semantics> <mi>dRMS</mi> </semantics></math> for (<b>a</b>,<b>c</b>) low-elevation signals and (<b>b</b>,<b>d</b>) full data in ORID and ALIC station when using SLM450 and PECM-2L, respectively. The <math display="inline"><semantics> <msub> <mi>dRMS</mi> <mi>better</mi> </msub> </semantics></math> and mean <math display="inline"><semantics> <mi>dRMS</mi> </semantics></math> throughout 2014 are also shown at the bottom of each subgraph.</p>
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<p>The dRMS distribution of global GNSS sites for (<b>a</b>) low-elevation signals and (<b>b</b>) full data in 2014, 2016, and 2018 when using SLM450 and PECM-2L, respectively.</p>
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<p>Similar to <a href="#remotesensing-16-01857-f019" class="html-fig">Figure 19</a> but for <math display="inline"><semantics> <msub> <mi>dRMS</mi> <mi>better</mi> </msub> </semantics></math> parameter.</p>
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31 pages, 10270 KiB  
Article
Study and Modelling of the Impact of June 2015 Geomagnetic Storms on the Brazilian Ionosphere
by Oladayo O. Afolabi, Claudia Maria Nicoli Candido, Fabio Becker-Guedes and Christine Amory-Mazaudier
Atmosphere 2024, 15(5), 597; https://doi.org/10.3390/atmos15050597 - 14 May 2024
Viewed by 1497
Abstract
This study investigated the impact of the June 2015 geomagnetic storms on the Brazilian equatorial and low-latitude ionosphere by analyzing various data sources, including solar wind parameters from the advanced compositional explorer satellite (ACE), global positioning satellite vertical total electron content (GPS-VTEC [...] Read more.
This study investigated the impact of the June 2015 geomagnetic storms on the Brazilian equatorial and low-latitude ionosphere by analyzing various data sources, including solar wind parameters from the advanced compositional explorer satellite (ACE), global positioning satellite vertical total electron content (GPS-VTEC), geomagnetic data, and validation of the SAMI2 model-VTEC with GPS-VTEC. The effect of geomagnetic disturbances on the Brazilian longitudinal sector was examined by applying multiresolution analysis (MRA) of the maximum overlap discrete wavelet transform (MODWT) to isolate the diurnal component of the disturbance dynamo (Ddyn), DP2 current fluctuations from the ionospheric electric current disturbance (Diono), and semblance cross-correlation wavelet analysis for local phase comparison between the Sq and Diono currents. Our findings revealed that the significant fluctuations in DP2 at the Brazilian equatorial stations (Belem, dip lat: −0.47° and Alta Floresta, dip lat: −3.75°) were influenced by IMF Bz oscillations; the equatorial electrojet also fluctuated in tandem with the DP2 currents, and dayside reconnection generated the field-aligned current that drove the DP2 current system. The short-lived positive ionospheric storm during the main phase on 22 June in the Southern Hemisphere in the Brazilian sector was caused by the interplay between the eastward prompt penetration of the magnetospheric convection electric field and the westward disturbance dynamo electric field. The negative ionospheric storms that occurred during the recovery phase from 23 to 29 June 2015, were attributed to the westward disturbance dynamo electric field, which caused the downward E × B drift of the plasma to a lower height with a high recombination rate. The comparison between the SAMI2 model-VTEC and GPS-VTEC indicates that the SAMI2 model underestimated the VTEC within magnetic latitudes of −9° to −24° in the Brazilian longitudinal sector from 6 to 17 June 2015. However, it demonstrated satisfactory agreement with the GPS-VTEC within magnetic latitudes of −9° to 10° from 8 to 15 June 2015. Conversely, the SAMI2 model overestimated the VTEC between ±10° magnetic latitudes from 16 to 28 June 2015. The most substantial root mean square error (RMSE) values, notably 10.30 and 5.48 TECU, were recorded on 22 and 23 June 2015, coinciding with periods of intense geomagnetic disturbance. Full article
(This article belongs to the Section Upper Atmosphere)
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Figure 1
<p>Map of GNSS receivers’ stations.</p>
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<p>Map of magnetometer stations.</p>
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<p>State of the interplanetary medium from 6 to 30 June 2015. (<b>a</b>) Velocity of the solar wind (Vsw). In addition, (<b>b</b>) shows the IMF Bz (interplanetary magnetic field in the Z direction), (<b>c</b>) presents the y-component of interplanetary electric field, (<b>d</b>) portrays the aurora electrojet (AL: aurora lower boundary, AU: aurora upper boundary), (<b>e</b>) demonstrates the H-component symmetry of Earth’s magnetic field observed at various low latitudes, and (<b>f</b>) illustrates <span class="html-italic">DP2</span> currents in Belem (dip lat: <math display="inline"><semantics> <mo>−</mo> </semantics></math>0.47°) and Alta Floresta (<math display="inline"><semantics> <mo>−</mo> </semantics></math>3.75°). Further, (<b>g</b>) shows the day-to-day variations of EEJ current (red legend) and magnetically five-quiet day average (black legend).</p>
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<p>Phase comparison between <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> currents at Belem from 6 to 30 June 2015. (<b>a</b>) <span class="html-italic">Sq</span> current in Belem from 6 to 30 June 2015. (<b>b</b>) Continuous wavelet transforms (<span class="html-italic">CWT</span>) of the <span class="html-italic">Sq</span> current at Belem, and (<b>c</b>) <span class="html-italic">Diono</span> current at Belem from 6–30 June 2015. (<b>d</b>) Continuous wavelet transforms (<span class="html-italic">CWT</span>) of the <span class="html-italic">Diono</span> current at Belem. (<b>e</b>) Semblance between <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> current. The value <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> represents the <span class="html-italic">anti-Sq</span> current, and +1 represents the phase correction of the <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> currents. (<b>f</b>) Amplitude of semblance analysis between <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> currents.</p>
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<p>Variation in diurnal component of <span class="html-italic">Ddyn</span> alongside the AE index, Akasofu index, and ASYM-H index from 6 to 30 June 2015. (<b>a</b>) Day-to-day variation of the AE index (right-hand side of (<b>a</b>), presented in the black legend) and Akasofu index left-hand side of (<b>a</b>) presented in the red legend (<b>b</b>) ASYM-H index presented in (<b>b</b>). (<b>c</b>) Diurnal component of <span class="html-italic">Ddyn</span> at magnetic stations in Belem represented in the red legend and Alta Floresta represented in the black legend.</p>
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<p>Development of <span class="html-italic">anti-Sq</span> amplitude computed from semblance cross-correlation wavelet analysis between <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> currents in relation to AE index activity and Akasofu index from 6 to 30 June 2015. (<b>a</b>) AE index plotted on the right-hand side (black legend) and Akasofu index plotted on the left-hand side (red legend) in (<b>a</b>). (<b>b</b>) <span class="html-italic">Anti-Sq</span> of semblance analysis of local phase comparison between <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> current at Belem (dip lat: <math display="inline"><semantics> <mo>−</mo> </semantics></math>0.47°). (<b>c</b>) <span class="html-italic">Anti-Sq</span> of semblance analysis of local phase comparison between <span class="html-italic">Sq</span> and <span class="html-italic">Diono</span> currents at Alta Floresta (dip lat: <math display="inline"><semantics> <mo>−</mo> </semantics></math>3.75°).</p>
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<p>Temporal variation of VTEC alongside the SYM-H index and <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>×</mo> <mi>B</mi> </mrow> </semantics></math> drift velocity from 6 to 30 June 2015. (<b>a</b>) Day-to-day variability of SYM-H index from 6 to 30 June 2015. (<b>b</b>) Day-to-day variability of vertical drift velocity (<span class="html-italic">DVDV</span>: red legend) and average of five magnetically quiet days in June 2015 (<span class="html-italic">QVDV</span>; black legend). (<b>c</b>–<b>l</b>) Day-to-day variations of VTEC from 6 to 30 June 2015 (red legend) and average of five magnetically quiet days in June 2015 (black legend).</p>
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<p>Spatial-temporal variation of VTEC from 8 to 30 June 2015. (<b>a</b>) Change in EEJ (ΔEEJ) from 6 to 30 June 2015, plotted on left-hand-side of (<b>a</b>) (black legend) and <span class="html-italic">Diono</span> current from 6–30 June 2015, plotted on right-hand side of Panel a of <a href="#atmosphere-15-00597-f008" class="html-fig">Figure 8</a> (red legend). (<b>b</b>) Day-to-day variability of VTEC from 6 to 30 June 2015 shown on TEC-MAP and day-to-day variability of SYM-H index plotted on right-hand side of (<b>b</b>) (red legend). (<b>c</b>) Change in VTEC (ΔVTEC) from 6 to 30 June 2015, shown on TEC-MAP and day-to-day variability of SYM-H index plotted on right-hand side (red legend) from 6 to 30 June 2015.</p>
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<p><span class="html-italic">SAMI2 model-VTEC</span> comparison with <span class="html-italic">GPS-VTEC</span> from 6 to 19 June 2015.</p>
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<p>SAMI2 model comparison with <span class="html-italic">GPS-VTEC</span> from 20 to 30 June 2015.</p>
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<p>IMF Bz Fluctuations alongside <span class="html-italic">DP2</span> and EEJ from 7–8 June 2015.</p>
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<p><span class="html-italic">IMF Bz</span> fluctuations alongside the <span class="html-italic">DP2</span> and <span class="html-italic">EEJ</span> currents from 22 to 23 June 2015.</p>
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