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Atmosphere, Volume 13, Issue 9 (September 2022) – 191 articles

Cover Story (view full-size image): Atmospheric gravity waves (GWs) are believed to be the seeding mechanism for the occurrence of equatorial ionospheric irregularities; however, it was not known which range of the waves are responsible for the occurrence of the irregularity. In this study, the wavelengths of the GWs responsible for seeding the irregularity are identified using the TIMED/SABER and C/NOFS satellites’ observations. The results showed that GWs with vertical wavelengths (VWs) between about 1 and 13 km are found to dissipate energy in the Earth’s lower thermosphere every day. The zonal wavelengths estimated to correspond to these GWs and the oscillation of ion density perturbations have shown excellent agreement. In general, the results imply that GWs with VWs between 1 and 13 km are seeding the post-sunset equatorial ionospheric irregularity. View this paper
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13 pages, 8020 KiB  
Article
Directivity of Coseismic Ionospheric Disturbances Propagation Following the 2016 West Sumatra Earthquake Using Three-Dimensional Tomography GNSS-TEC
by Mokhamad Nur Cahyadi, Deasy Arisa, Ihsan Naufal Muafiry, Buldan Muslim, Ririn Wuri Rahayu, Meilfan Eka Putra, Mega Wulansari, Bambang Setiadi, Andria Arisal, Pakhrur Razi and Syachrul Arief
Atmosphere 2022, 13(9), 1532; https://doi.org/10.3390/atmos13091532 - 19 Sep 2022
Cited by 2 | Viewed by 2146
Abstract
Ionospheric disturbances caused by the 2016 West Sumatra earthquake have been studied using total electron content (TEC) measurements by Global Navigation Satellite System (GNSS) observation stations evenly distributed in Sumatra and Java, Indonesia. Previous observation focused on the coseismic ionospheric disturbances (CID) detected [...] Read more.
Ionospheric disturbances caused by the 2016 West Sumatra earthquake have been studied using total electron content (TEC) measurements by Global Navigation Satellite System (GNSS) observation stations evenly distributed in Sumatra and Java, Indonesia. Previous observation focused on the coseismic ionospheric disturbances (CID) detected 11–16 min after the earthquake. The maximum TEC amplitude measured was 2.9 TECU (TEC Unit) with speed between 1 and 1.72 km/s. A comprehensive analysis needs to be done to see how the growth and direction of the movement of the CID due to the earthquake is using the 3D tomography method. The dimensions of 3D tomographic model are setup to 1° × 1.2° × 75 km. The continuity constraints were used to stabilize the solution, and multiple resolution tests with synthetic data were conducted to evaluate the precision of the results. This research focuses on the anomalous movement of the ionosphere observed in three dimensions. From the model, the positive anomaly initially appeared 11 min after the earthquake at the altitude of 300 km, which is the highest ionization layer and correspond to the electron density profile using IRI model. The anomalous movement appeared 12 min after the mainshock and moved 1° toward the geomagnetic field every minute. The density anomaly of the ionosphere began to weaken 8 min after the appearance of CID. To check the accuracy of the 3D tomography model, we carried out two types of tests, namely checkerboard resolution test and the second resolution test. Full article
(This article belongs to the Special Issue Ionospheric Science and Ionosonde Applications)
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Figure 1
<p>(<b>a</b>) shows the Tomographic voxel setup and the distribution of observation stations on the island of Sumatra, with (<b>b</b>) the distribution of LoS when the earthquake occurred. The small black triangles indicate the location of the GNSS observation stations, while the white grids indicate the tomographic voxel setup. The colorful straight lines indicate the LoS distribution, and the small yellow star indicates the location of the earthquake epicenter.</p>
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<p>Resolution test with the classic checkerboard pattern. (<b>a</b>) Assumed electron density as the input and (<b>b</b>) output of 3D tomography are given in map view and north–south, east–west profile.</p>
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<p>Second resolution test for a compact pair of positive and negative anomalies over Sumatra. The left and right panels are horizontal views and latitudinal profiles of the assumed pattern anomalies (<b>a</b>) and the output from the 3D tomography (<b>b</b>).</p>
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<p>The histograms of raw (upper panel) and calculated STEC anomaly data (lower panel) at three-time epochs: (<b>a</b>) 15 min before mainshock, (<b>b</b>) at mainshock, and (<b>c</b>) 15 min after mainshock.</p>
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<p>Electron density anomalies at heights from 100 km to 600 km after the 2016 west Sumatera earthquake derived by the 3-D tomography, that is, (<b>a</b>) at the time of earthquake, (<b>b</b>) 16 min after earthquake, and (<b>c</b>) 21 min after earthquake. The white curves show coastlines and nation boundaries, and the yellow star indicates the epicenter.</p>
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<p>Electron density anomalies at heights from 100 km to 600 km before the 2016 West Sumatra earthquake were derived by the 3-D tomography at six epochs, that is, (<b>a</b>–<b>f</b>) 10–20 min after the earthquake. The white curves show coastlines and national boundaries, and the yellow star indicates the epicenter.</p>
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<p>The vertical electron density profiles reconstructed over the epicenter at CID occurred (13:06 UT) from the IRI model (<a href="https://irimodel.org/" target="_blank">https://irimodel.org/</a>, accessed on 23 June 2022).</p>
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<p>Tomography results of the 2016 West Sumatra Earthquake at an altitude of 300 km. The yellow star is the epicenter. The light blue indicates the beginning of the emergence of CID and followed by the dark blue circle and the black arrow indicate the direction and area of movement the positive CID. The positive anomaly starts to increase at 13:02 UT.</p>
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<p>The tomographic 3D model profile is viewed from the longitude and latitude point of view.</p>
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<p>Directivity of CID at an altitude of 300 Km. The light blue indicates the beginning of the emergence of CID and followed by the dark blue circle and the black arrow indicate the direction and area of movement the positive CID. The positive anomaly starts to increase at 13:02 UT.</p>
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17 pages, 6316 KiB  
Article
Characteristics of Propagation of Meteorological to Hydrological Drought for Lake Baiyangdian in a Changing Environment
by Shan He, Enze Zhang, Junjun Huo and Mingzhi Yang
Atmosphere 2022, 13(9), 1531; https://doi.org/10.3390/atmos13091531 - 19 Sep 2022
Cited by 6 | Viewed by 1910
Abstract
The analysis of drought propagation has garnered mounting attention in the changing global environment. The current studies tend to focus on the propagation characteristics from meteorological to hydrological drought in rivers. Lakes, despite being a key component of watershed ecosystems, have received little [...] Read more.
The analysis of drought propagation has garnered mounting attention in the changing global environment. The current studies tend to focus on the propagation characteristics from meteorological to hydrological drought in rivers. Lakes, despite being a key component of watershed ecosystems, have received little attention to their response to meteorological and hydrological droughts. To this end, here, we investigated the characteristics of propagation from meteorological to hydrological drought for a lake in a changing environment. To determine the drought propagation time from meteorological to hydrological drought, we analyzed correlations between the standardized precipitation index (SPI), standardized runoff index (SRI), and standardized water level index (SWI). Lake Baiyangdian in China served as the case study. The results showed that meteorological droughts occur at high frequency but are short in duration, indicating that not every meteorological drought will necessarily lead to a hydrological drought. By contrast, lake hydrological droughts have low frequency and long duration and feature more severe consequences. Comparing drought characteristics before and after a changing environment, we found a reduced frequency of the SPI, SRI, and SWI, yet their duration was prolonged. For the SWI especially, these results were even more pronounced, which suggests the changing environment enabled further intensification of the lake hydrological drought. In addition, more time was needed for a meteorological drought to transition into a lake hydrological drought after a changing environment. Full article
(This article belongs to the Special Issue Hydrological Responses under Climate Changes)
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<p>Map of the study area showing Lake Baiyangdian and the extent of the Baiyangdian Basin in China. Wangkuai Reservoir hydrological station (WRHS) and Xidayang Reservoir hydrological station (XRHS).</p>
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<p>Schematic illustration of drought events and their characteristics (<span class="html-italic">I</span> denotes the intensity of a drought).</p>
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<p>Boxplots of the goodness-of-fit for the runoff and water levels (S<sub>d</sub> is the standard deviation, R<sub>co</sub> is the Pearson correlation coefficient, and R<sub>na</sub> is the Nash coefficient between the observed values and the simulated values). (<b>a</b>) Wangkuai Reservoir runoff, (<b>b</b>) Water level of Lake Baiyangdian.</p>
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<p>Results for the SPI, SRI<sub>Xi</sub>, SRI<sub>Wan</sub>, and SWI at different timescales from 1956 to 2010.</p>
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<p>Drought characteristics of the SPI, SRI<sub>Xi</sub>, SRI<sub>Wang</sub>, and SWI at different timescales (PI denotes the absolute values).</p>
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<p>The MK (Mann–Kendall) test results for precipitation, runoff, and water level.</p>
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<p>Change in the gross domestic product (GDP) of Baoding, Hebei Province, China, from 1952 to 2020.</p>
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<p>Drought characteristics of the SPI, SRI<sub>Xi</sub>, SRI<sub>Wang</sub>, and SWI for different timescales before a changing environment (PI denotes the absolute values.).</p>
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<p>Drought characteristics of the SPI, SRI<sub>Xi</sub>, SRI<sub>Wang</sub>, and SWI at different timescales after a changing environment (PI denotes the absolute values).</p>
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<p>Correlation coefficients between the meteorological drought and hydrological drought series at various timescales: (<b>a</b>) before a changing environment and (<b>b</b>) after a changing environment in the study area. Black circles show the locations of the most suitable propagation time.</p>
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<p>Correlation coefficients between the meteorological drought and hydrological drought series at various timescales: (<b>a</b>) before a changing environment and (<b>b</b>) after a changing environment in the study area. Black circles show the locations of the most suitable propagation time.</p>
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15 pages, 5702 KiB  
Article
Interdecadal Change of Ural Blocking Highs and Its Atmospheric Cause in Winter during 1979–2018
by Yao Lu, Yan Li, Quan Xia, Qingyi Yang and Chenghai Wang
Atmosphere 2022, 13(9), 1530; https://doi.org/10.3390/atmos13091530 - 19 Sep 2022
Cited by 2 | Viewed by 1595
Abstract
The Ural blocking (UB) high is a weather system closely related to the cold air process during winter, which could trigger extreme cold events in East Asia. By retrieving five single blocking indexes, including accumulation frequency, central latitude, blocking intensity, mean duration and [...] Read more.
The Ural blocking (UB) high is a weather system closely related to the cold air process during winter, which could trigger extreme cold events in East Asia. By retrieving five single blocking indexes, including accumulation frequency, central latitude, blocking intensity, mean duration and north rim, it is found that the UB in winter occurs more frequently, grows stronger, lasts longer and is located more northward after 2002, compared with 1985–2001. In order to describe the UB comprehensively, a new comprehensive blocking index (CBI) is developed based on the above five blocking indexes. The CBI can also reflect the interdecadal change of UB synthetically. Analysis on the corresponding atmospheric circulation shows that the relationship between the UB and atmospheric circulation, such as the polar vortex and jet, is closer in 2002–2018 than in 1985–2001. Compared with the atmospheric circulation in 1985–2001, the most prominent feature in 2002–2018 is that the intensity of the polar vortex is weaker at 100 hPa, and that the subtropical jet moves northward. Meanwhile, the East Asian trough downstream of the Urals deepens at 500 hPa and the Siberian high strengthens, indicating that the East Asia winter monsoon is stronger during 2002–2018. Further analysis on atmospheric waves and baroclinicity demonstrates that the meridional circulation of planetary waves strengthens, especially the 2-waves, which may increase the frequency of the UB and shift its location northward after 2002. Additionally, the baroclinicity (T/y) in the mid-high latitudes is weakened during winter since 2002, which is also beneficial for the establishment of meridional circulation, causing a stronger intensity and longer duration of the UB. Full article
(This article belongs to the Special Issue Atmospheric Blocking and Weather Extremes)
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<p>Interannual change characteristics of the UB in winter from 1979 to 2019. (Normalized: Fq (<b>a</b>), CL (<b>b</b>), IB (<b>c</b>), DU (<b>d</b>), NR (<b>e</b>) and blocking index (<b>f</b>), comprehensive blocking index (<b>g</b>). The solid black line represents the result of a 9-point smoothing average, and the black dotted line represents the trend.)</p>
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<p>The moving <span class="html-italic">t</span>-test for each blocking index of the UB in winter over the period of 1979–2019. (Normalized: Fq (<b>a</b>), CL (<b>b</b>), BI (<b>c</b>), DU (<b>d</b>), NR (<b>e</b>), blocking index (<b>f</b>), comprehensive blocking index (<b>g</b>). The black lines represent the significance test of 95%).</p>
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<p>The distribution of geopotential heights at 100 hPa ((<b>a1</b>,<b>a2</b>), units: dagpm), zonal winds at 200 hPa ((<b>b1</b>,<b>b2</b>), units: m·s<sup>−1</sup>), geopotential heights at 500 hPa ((<b>c1</b>,<b>c2</b>), units: dagpm), sea level pressure ((<b>d1</b>,<b>d2</b>), units: hPa) during the P1 and P2 periods and their differences between the two periods (P2–P1, (<b>a3</b>,<b>b3</b>,<b>c3</b>,<b>d3</b>)). (The bold solid black lines in Figure (<b>a1</b>,<b>a2</b>) represent the 1628 dapgm geopotential heights. The bold solid black lines in Figure (<b>b1</b>,<b>b2</b>) represent the 30 m·s<sup>−1</sup>. The bold solid black lines in Figure (<b>c1</b>,<b>c2</b>) represent the 588 dapgm geopotential heights. The bold solid black lines in Figure (<b>d1</b>,<b>d2</b>) represent the 1023 hPa. The dotted areas represent the differences which pass the significance test of 90%. The outermost latitude is 0°).</p>
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<p>The linear regression of geopotential heights at 100 hPa ((<b>a1</b>,<b>a2</b>), unit: gpm, contour interval: 10 gpm), zonal winds on 200 hPa ((<b>b1</b>,<b>b2</b>), unit: <math display="inline"> <semantics> <mrow> <mi mathvariant="normal">m</mi> <mi>·</mi> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics> </math>, contour interval: 1 m·s<sup>−1</sup>), geopotential heights at 500 hPa ((<b>c1</b>,<b>c2</b>), unit: gpm, contour interval: 5 gpm) and sea level pressure ((<b>d1</b>,<b>d2</b>), unit: Pa, contour interval: 40 Pa) in winter against the comprehensive blocking indexes during P1 and P2. (The red lines represent that the regression coefficient is positive, and the blue is negative. The shaded area indicates that the regression coefficients have passed the significance test of 90%).</p>
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<p>The distribution of 1 wave (<b>a1</b>,<b>a2</b>), 2 wave (<b>b1</b>,<b>b2</b>), 3 wave (<b>c1</b>,<b>c2</b>), 1–3 wave (<b>d1</b>,<b>d2</b>) and differences (P2–P1, (<b>a3</b>,<b>b3</b>,<b>c3</b>,<b>d3</b>)) on 500 hPa in two periods. (Units: gpm, and the dotted areas represent that the differences pass the significance test of 95%. The outermost latitude is 15° N.)</p>
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<p>The distribution of <math display="inline"> <semantics> <mrow> <msubsup> <mstyle mathsize="60%" displaystyle="true"> <mo>∫</mo> </mstyle> <mrow> <msub> <mi>P</mi> <mi>b</mi> </msub> </mrow> <mrow> <msub> <mi>P</mi> <mi>t</mi> </msub> </mrow> </msubsup> <mo>−</mo> <mo>∂</mo> <mi>T</mi> <mo>/</mo> <mo>∂</mo> <mi>y</mi> <mi>d</mi> <mi>p</mi> </mrow> </semantics> </math> between surface-500 hPa (<b>a1</b>,<b>a2</b>) and 500–100 hPa (<b>b1</b>,<b>b2</b>) during the P1 and P2 periods and their differences between the two periods (P2–P1, (<b>a3</b>,<b>b3</b>)). (Units: hPa·K·m<sup>−1</sup>·10<sup>−4</sup>. The dotted areas represent that the differences pass the significance test of 95%. The outermost latitude is 15° N.)</p>
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15 pages, 11256 KiB  
Article
A Circulation Weather Type Analysis of Urban Effects on Daily Thermal Range for Milan (Italy)
by Giuseppe Colangelo, Giovanni Sanesi, Luigi Mariani, Simone G. Parisi and Gabriele Cola
Atmosphere 2022, 13(9), 1529; https://doi.org/10.3390/atmos13091529 - 19 Sep 2022
Cited by 1 | Viewed by 1450
Abstract
We present a first attempt to analyse the effect of a large urban park (Parco Nord Milano—PNM) on the Urban Heat Island (UHI) of the city area of Milan. Specifically, analysis of the effect of three cyclonic and three anticyclonic circulation weather types [...] Read more.
We present a first attempt to analyse the effect of a large urban park (Parco Nord Milano—PNM) on the Urban Heat Island (UHI) of the city area of Milan. Specifically, analysis of the effect of three cyclonic and three anticyclonic circulation weather types (CWTs) on the frequency distribution of the daily thermal range (DTR) of five weather stations in Milan shows the stabilizing effect of the city on the DTR when compared with suburban and rural areas, generating a modal class of 4 °C in winter and 9 °C in summer. In parallel, a temperature transect of the urban park Parco Nord Milano was performed by bicycle during a day of anticyclonic summer weather in order to understand the effect of the park on the UHI. This investigation highlighted the homogenization effect on temperatures induced by the thermal turbulence triggered by intense sunshine. Full article
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<p>Position of the five weather stations. Blue area = metropolitan city; grey area = Milan municipality; green area = Parco Nord.</p>
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<p>(<b>a</b>,<b>b</b>) Location of Parco Nord in the context of the urban area of Milan. (<b>c</b>) Transects of the bike monitoring activity (transect 1—red line, transect 2—blue line).</p>
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<p>Distribution of the seasonal daily thermal range (DTR) for the five weather stations.</p>
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<p>Transect 1—charts of single-lap measurements. LCZs are represented with different background colours: 2—dark grey; 9—light grey; B—green; Y axis reports air temperatures in °C.</p>
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<p>Transect 2—charts of single-lap measurements. LCZs are represented with different background colours: 2—dark grey; 9—light grey; B—green; Y axis reports air temperatures in °C.</p>
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<p>Hourly statistics of LCZ temperature, considering all the sampling points of the two transects; Y axis reports air temperatures in °C. For each value X represents the average, the horizontal line is the median, the box extends from upper to lower quartile. The whiskers (vertical lines outside the box) represent data variability outside the upper and lower quartiles. Points outside the whisker line represent the outlier data.</p>
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18 pages, 8370 KiB  
Article
Numerical Investigation on Mixing Characteristics and Mechanism of Natural Gas/Air in a Super-Large-Bore Dual-Fuel Marine Engine
by Long Liu, Shihai Liu, Qian Xia, Bo Liu and Xiuzhen Ma
Atmosphere 2022, 13(9), 1528; https://doi.org/10.3390/atmos13091528 - 19 Sep 2022
Cited by 4 | Viewed by 1766
Abstract
Premixed combustion mode dual-fuel (DF) engines are widely used in large-bore marine engines due to their great potential to solve the problem of CO2 emissions. However, detonation is one of the main problems in the development of marine engines based on the [...] Read more.
Premixed combustion mode dual-fuel (DF) engines are widely used in large-bore marine engines due to their great potential to solve the problem of CO2 emissions. However, detonation is one of the main problems in the development of marine engines based on the premixed combustion mode, which affects the popularization of liquefied natural gas (LNG) engines. Due to the large bore and long stroke, marine dual-fuel engines have unique flow characteristics and a mixture mechanism of natural gas and air. Therefore, the purpose of this study is to present a simulated investigation on the influence of swirl on multiscale mixing and the concentration field, which provides a new supplement for mass transfer theory and engineering applications. It is suggested that the phenomenon of abnormal combustion occurs on account of the distribution of the mixture being uneven in a super-large-bore dual-fuel engine. Further analysis showed that the level of swirl at the late compression stage and the turbulence intensity are the decisive factors affecting the transmission process of natural gas (NG) and distribution of methane (CH4) concentration. Finally, a strategy of improving mixture quality and the distribution of the mixture was proposed. Full article
(This article belongs to the Special Issue Shipping Emissions and Air Pollution)
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<p>The geometric model of engine.</p>
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<p>The mesh refinement regions in the model.</p>
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<p>Comparison of cylinder pressure for different base grid size.</p>
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<p>Comparison of simulation and experiment spray penetration. (<b>a</b>) Spray penetration under non-evaporating condition. (<b>b</b>) Spray penetration under evaporating condition.</p>
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<p>Comparisons between the calculated and experimental cylinder pressure and HRR. The arrow represents the corresponding ordinate of the curve, which is a representation for cylinder pressure and HRR.</p>
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<p>Equivalence ratio distribution in narrow-equivalence ratio range in cylinder at −10ATDC. (<b>a</b>–<b>d</b>) is four different positions of sections, which can show that equivalence ratio distribution in the cylinder and has been explanted in the manuscript. And (<b>a</b>) is longitudinal section of model, which is a common slice location.</p>
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<p>1200 K temperature iso-surfaces in combustion chamber.</p>
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<p>The variation of flow field during the scavenging process and compression process. (<b>a</b>) The evolution of SR with crank angle. (<b>b</b>) The evolution of TR with crank angle. (<b>c</b>) The evolution of mean TKE with crank angle.</p>
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<p>Velocity distribution on cross section of natural gas injection valve.</p>
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<p>The calculated SR for different NG injection conditions. (<b>a</b>) Different injection pressure. (<b>b</b>) Different injection angle. (<b>c</b>) Different number of nozzles.</p>
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<p>The calculated mean TKE for different NG injection conditions. (<b>a</b>) Different injection pressure. (<b>b</b>) Different injection angle. (<b>c</b>) Different number of nozzles.</p>
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<p>Distribution of in-cylinder CH<sub>4</sub> for different NG injection conditions at −10° ATDC.</p>
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<p>The distribution of CH<sub>4</sub> concentrations under conditions of swirl and no swirl at the initial moment.</p>
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<p>The density probability distribution of equivalence ratios under different swirl states.</p>
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<p>Distribution of CH<sub>4</sub> concentrations and velocity vectors of Z-direction under the condition of no swirl motion in-cylinder.</p>
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<p>The position and structure of the jet valve.</p>
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<p>Comparison of the calculated change of in-cylinder SR and TKE with crankshaft angles for two different air injection times. (<b>a</b>) In-cylinder mean SR curves. (<b>b</b>) In-cylinder mean TKE curves.</p>
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<p>The density probability distribution of equivalence ratios at different air injection conditions. (<b>a</b>) Original model without air injection. (<b>b</b>) Air and NG injection at the same time. (<b>c</b>) Air injection after the end of NG injection.</p>
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<p>Equivalence ratio distribution in narrow-equivalence ratio range for different air injection conditions at −10 ATDC. (<b>a</b>) Origin model without air injection. (<b>b</b>) Air and NG injection at the same time. (<b>c</b>) Air injection after the end of NG injection.</p>
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<p>A temperature isosurface of 1200 K in the case of air injection after the end of NG injection.</p>
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16 pages, 5712 KiB  
Article
Air Pollution and Economic Impact from Ships Operating in the Port of Varna
by Yordan Garbatov and Petar Georgiev
Atmosphere 2022, 13(9), 1526; https://doi.org/10.3390/atmos13091526 - 19 Sep 2022
Cited by 7 | Viewed by 2187
Abstract
The present work develops a multidisciplinary approach for evaluating the air pollution and economic impact from ships operating in the port of Varna. The work collects and analyses automatic identification system (AIS) data of the arriving and queuing dry cargo ships in the [...] Read more.
The present work develops a multidisciplinary approach for evaluating the air pollution and economic impact from ships operating in the port of Varna. The work collects and analyses automatic identification system (AIS) data of the arriving and queuing dry cargo ships in the seaport of Varna in identifying the statistical descriptors of the length of the ships, gross tonnage (GT), and ship engine power. The queueing theory (QT) is employed to analyse the ship operations in a single queue and is processed by three parallel terminals, satisfying the port regulations. The Gaussian dispersion model (GDM) is adopted to predict the pollution concentration from ships arriving at the seaport, queuing, approaching, waiting, processing at the berth, and departing. The gas emission is estimated as a function of the ship movement trajectory, and the time duration at any stage is defined by QT for the most critical surrounding areas, considering the wind speed, as well as horizontal and vertical dispersion as a function of the location of the ship, accounting for the effective emission height, weather conditions, and speed. To mitigate the potential impact on health, the gas emissions of oxides of nitrogen (NOx), sulphur dioxide (SO2), and air-borne particles (PM10) generated by ships during the queuing and port operation are evaluated. Potential cleaning measures for any ship are implemented to satisfy the maximum allowable concentrations (MAC) in surrounding areas. The implemented ship pollution cleaning measures and overall ship and terminal operating costs are minimised to identify the most efficient berth operation. The developed approach is flexible and can be used for any particular conditions for ships operating in ports. Full article
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<p>Varna port canals.</p>
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<p>Ship length (<b>left</b>) and GT (<b>right</b>).</p>
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<p>Ship engine power.</p>
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<p>Ship interarrival (<b>left</b>) and arriving (<b>right</b>) time.</p>
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<p>Spatio-temporal path for an ongoing moving ship.</p>
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<p>Berth queuing system configurations.</p>
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<p>Time duration of ship operation in port.</p>
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<p>Total cost, cost of berth service, cost of air pollution cleaning, and cost of waiting as a function of traffic intensity (<b>left</b>) and terminal efficiency factor (<b>right</b>).</p>
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<p>Air pollution, μg/m<sup>3</sup> at receptors <span class="html-italic">R</span><sub>1</sub> to <span class="html-italic">R</span><sub>25</sub>, from ship sources <span class="html-italic">S</span><sub>1</sub> to <span class="html-italic">S</span><sub>13</sub> and queuing port and terminal, before ship emission reduction, <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi mathvariant="normal">x</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Air pollution, μg/m<sup>3</sup> at receptors <span class="html-italic">R</span><sub>1</sub> to <span class="html-italic">R</span><sub>25</sub>, after ship emission cleaning, <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi mathvariant="normal">x</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Total cost as a function of traffic intensity, conditional on service time.</p>
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12 pages, 2843 KiB  
Article
Increasing Wind Speeds Fuel the Wider Spreading of Pollution Caused by Fires over the IGP Region during the Indian Post-Monsoon Season
by Vinay Kumar, Rupesh Patil, Rohini L. Bhawar, P.R.C. Rahul and Subbarao Yelisetti
Atmosphere 2022, 13(9), 1525; https://doi.org/10.3390/atmos13091525 - 18 Sep 2022
Cited by 1 | Viewed by 1813
Abstract
Every year, forest fires and harvest harnessing produce atmospheric pollution in October and November over the Indo-Gangetic Plain (IGP). The fire count data (MODIS) shows a decreasing/increasing trend of fire counts in all confidence ranges in October/November over Northern India. There is a [...] Read more.
Every year, forest fires and harvest harnessing produce atmospheric pollution in October and November over the Indo-Gangetic Plain (IGP). The fire count data (MODIS) shows a decreasing/increasing trend of fire counts in all confidence ranges in October/November over Northern India. There is a widespread increase in fires with a confidence level above 60 to 80% over the whole Northern Indian region. The Aerosol Optical Index (AOD) also shows an increase with values > 0.7 over the northwestern and IGP regions. There have been some startling results over the lower IGP belt, where there has been increasing trend in AOD during October ~56% and during November, the increase was by a whopping ~116%. However, in November, a slight turning of the winds towards central India might be transporting the AOD towards the central Indian region. Hence, during November, it is inferred that due to the low wind speed over the lower IGP belt and increased fires, the AODs in the polluted air tend to hover for a long time. During recent years from 2010, the winds have become stronger, indicating more transport of AOD is occurring over the lower IGP belt as compared to previous years till 2009, especially in October. Full article
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<p>AOD climatology 2002–2019 from MODIS-Aqua (<b>a</b>) October, Boxes here: Northwestern region (70° E–76° E, 29° N–32° N), Delhi region (76° E–78° E, 27° N–31° N), Lucknow region (78° E–81.5° E, 26° N–28° N), and Patna region (82° E–85° E, 25° N–27° N). The dashed line in black marks the boundary of the IGP region. (<b>b</b>) November months over the Indian region (60° E–90° E, 20° N–40° N).</p>
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<p>Time series of AOD over (<b>a</b>) Indian region 60° E–90° E, 20° N–40° N), (<b>b</b>) Northwestern region (70° E–76° E, 29° N–32° N), (<b>c</b>) Delhi region (76° E–78° E, 27° N–31° N), (<b>d</b>) Lucknow region (78° E–81.5° E, 26° N–28° N), and (<b>e</b>) Patna region (82° E–85° E, 25° N–27° N).</p>
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<p>Spatial distribution of Fire confidence from 2002 to 2019 for October and November (<b>a</b>,<b>b</b>) ≥50 and ≤60, (<b>c</b>,<b>d</b>) ≥60 and ≤70, (<b>e</b>,<b>f</b>) ≥70 and ≤80, (<b>g</b>,<b>h</b>) ≥80 and ≤90, (<b>i</b>,<b>j</b>) ≥90 and ≤100.</p>
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<p>Time series of the number of fire counts in the different fire confidence range in the month of October (<b>a</b>) and November (<b>b</b>) over the Indian region (20–40° N and 60–90° E). Similarly, (<b>c</b>,<b>d</b>) Northwestern region (70° E–76° E, 29° N–32° N), (<b>e</b>,<b>f</b>) Delhi region (76° E–78° E, 27° N–31° N), (<b>g</b>,<b>h</b>) Lucknow (78° E–81.5° E, 26° N–28N) and (<b>i</b>,<b>j</b>) Patna region (82° E–85° E, 25° N–27° N) respectively.</p>
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<p>October (<b>a</b>) Vertical velocity (Omega at 900 mb) (<b>b</b>) Winds for two periods and difference in the magnitude of 2002–2009 (wind in red) to 2011–2018 (wind in green).</p>
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<p>November (<b>a</b>) Vertical velocity (Omega at 900 mb) (<b>b</b>) Winds for two periods and difference in the magnitude of 2002–2009 to 2011–2018.</p>
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<p>Averaged variables over selected regions of IGP for October and November from 2002 to 2021 (<b>a</b>) Rainfall anomaly (mm/day) (<b>b</b>) Moisture anomaly (%).</p>
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18 pages, 4645 KiB  
Article
Attention-Based BiLSTM Model for Pavement Temperature Prediction of Asphalt Pavement in Winter
by Shumin Bai, Wenchen Yang, Meng Zhang, Duanyang Liu, Wei Li and Linyi Zhou
Atmosphere 2022, 13(9), 1524; https://doi.org/10.3390/atmos13091524 - 18 Sep 2022
Cited by 6 | Viewed by 1880
Abstract
Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. [...] Read more.
Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. To solve this problem, a bidirectional extended short-term memory network model based on the attention mechanism (Att-BiLSTM) was proposed to improve the prediction performance by using the time series features of pavement temperature and meteorological factors. Pavement temperature data and climatic data were collected from a road weather station in Yunnan, China. The results show that the MAE, MSE, and MAPE of the proposed Att-BiLSTM model were 0.330, 0.339, and 10.1%, respectively, which were better than the other baseline models. It was shown that 93.4% of the predicted values had an error less than 1 °C, and 82.1% had an error less than 0.5 °C, indicating that the proposed Att-BiLSTM model enables significant performance improvement. In addition, this paper quantified and analyzed the effects of parameters such as the size of the sliding window, the number of hidden layer neurons, and the optimizer on the performance of the prediction model. Full article
(This article belongs to the Special Issue Advances in Transportation Meteorology)
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<p>General outline of the research methodology.</p>
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<p>Distribution of each measured variable. Pavement temperature (<b>first</b>), air temperature (<b>second</b>), visibility (<b>third</b>), relative humidity (<b>fourth</b>), wind direction (<b>fifth</b>), wind speed (<b>sixth</b>) and rainfall (<b>last</b>).</p>
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<p>Distribution of each measured variable. Pavement temperature (<b>first</b>), air temperature (<b>second</b>), visibility (<b>third</b>), relative humidity (<b>fourth</b>), wind direction (<b>fifth</b>), wind speed (<b>sixth</b>) and rainfall (<b>last</b>).</p>
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<p>The data processing flow.</p>
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<p>Sliding window approach.</p>
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<p>Framework of the proposed model.</p>
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<p>Long short-term memory neural network.</p>
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<p>Bidirectional long short-term memory neural network.</p>
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<p>Structure of attention mechanism.</p>
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<p>Correlation coefficients between pavement temperature and various meteorological factors.</p>
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<p>Errors comparison with different hours. <span class="html-italic">MAE</span> (<b>left</b>), <span class="html-italic">MSE</span> (<b>middle</b>), <span class="html-italic">MAPE</span> (<b>right</b>).</p>
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<p>Performance of the model during training and validation error.</p>
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<p>Comparison of pavement temperature truth and predicted values in the test set. Data points 1–300 in the test set (<b>first</b>), Data points 301–600 in the test set (<b>second</b>), Data points 601–866 in the test set (<b>third</b>).</p>
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<p>The error of the predicted and observed values.</p>
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<p>Distribution of errors.</p>
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17 pages, 7115 KiB  
Article
Seismo-Ionospheric Effects Prior to Two Earthquakes in Taiwan Detected by the China Seismo-Electromagnetic Satellite
by Yufan Guo, Xuemin Zhang, Jiang Liu, Muping Yang, Xing Yang, Xiaohui Du, Jian Lü and Jian Xiao
Atmosphere 2022, 13(9), 1523; https://doi.org/10.3390/atmos13091523 - 18 Sep 2022
Cited by 4 | Viewed by 3049
Abstract
In this paper, we focused on the characteristics of the seismo-ionospheric effects related to two successive earthquakes, namely, the earthquakes in 2022 in Taitung Sea, Taiwan, China, with magnitudes (M) of 6.7 and 6.3, at 23.45° N, 121.55° E and 23.39° N, 121.52° [...] Read more.
In this paper, we focused on the characteristics of the seismo-ionospheric effects related to two successive earthquakes, namely, the earthquakes in 2022 in Taitung Sea, Taiwan, China, with magnitudes (M) of 6.7 and 6.3, at 23.45° N, 121.55° E and 23.39° N, 121.52° E and with the same focal depth of 20 km, which were detected by the China Seismo-Electromagnetic Satellite (CSES). By applying the sliding interquartile range method to electron density (Ne) data acquired by the Langmuir probe (LAP) onboard the CSES and the grid total electron content (TEC) data obtained from the Center for Orbit Determination in Europe (CODE), positive anomalies were found under quiet geomagnetic conditions on 2–3 March and 8–9 March 2022—that is, 19–20 and 13–14 d before the earthquakes, respectively, and the global ionospheric mapping (GIM) TEC data suggested that anomalies may also have been triggered in the magnetic conjugate area 13–14 d prior to the earthquakes occurrences. In addition, the CSES Ne data showed enhancements 3 and 5 d before the earthquakes occurred. Furthermore, 138 earthquakes with M ≥ 5.0 that occurred in Taiwan and the surrounding region during the period February 2019 to March 2022 were statistically analyzed using the CSES Ne data. The results show that most of the Ne anomalies were positive. Moreover, the greater the earthquake magnitude, the greater the frequency of the anomalies; however, the amplitude of the anomalies did not increase with the earthquake magnitude. The anomalies were concentrated during the period of 10 d before to 5 d after the earthquakes. No increase in the amplitude of anomalies was observed as the time of the earthquakes approached. Finally, based on evidence relating to earthquake precursor anomalies, we conclude that it is possible that earthquakes in Taiwan and the surrounding region affect the ionosphere through the geochemical, acoustic, and electromagnetic channels, as described by the lithosphere–atmosphere–ionosphere coupling (LAIC) model, and that the two studied earthquakes in Taiwan may have induced ionospheric effects through the geochemical channel. Full article
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<p>(<b>A</b>) Spatial map of Taiwan and the surrounding region and spatial distribution of the seismic activity in map view. The red dots are 138 events (M ≥ 5.0) that are chosen from the earthquake catalogue (February 2019–March 2022) of CENC. The yellow box shows the location of the schematic diagram (<b>B</b>); (<b>B</b>) a schematic diagram exhibiting the structure of the subducting Eurasian and Philippine Sea slab along the Ryukyu trench and Manila trench. The red box indicates the spatial distribution of two consecutive earthquakes; (<b>C</b>) spatial distribution of the 2022 M 6.7 (EQ1) and M 6.3 (EQ2) Taitung Sea earthquakes.</p>
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<p>Space weather from 21 February to 22 March 2022, where (<b>A</b>–<b>C</b>) denote <span class="html-italic">Kp</span>, <span class="html-italic">Dst</span>, and F<sub>10.7</sub> indices, respectively. The red lines represent the thresholds used to judge whether the space weather is quiet or not.</p>
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<p>(<b>A</b>) Time-series analysis of CSES Ne variations from 21 February to 22 March 2022. The blue line represents the observed Ne value. The black line represents the median value. The pink lines represent the upper and lower boundaries of the <span class="html-italic">IQR</span>; (<b>B</b>) time-series analysis of CSES Ne variations and associated anomalies from 21 February to 22 March 2022. The blue line represents the relative change in Ne, the dotted red lines represent the thresholds (±60%) used to judge whether a Ne anomaly exists, and the red circles mark the Ne anomalies.</p>
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<p>GIM TEC global ionospheric anomaly maps. TEC anomalies occurred from 14:00 (<b>top left</b>) to 16:00 UT (<b>top middle</b>) on 2 March; from 04:00 (<b>top right</b>) to 06:00 (<b>upper middle left</b>) and at 14:00 (<b>upper middle centered</b>) and 18:00 (<b>upper middle right</b>) UT on 3 March; from 08:00 (<b>upper lower left</b>), 10:00 (<b>upper</b> <b>lower centered</b>) to 12:00 (<b>upper lower right</b>) UT on March 8; and from 08:00 (<b>bottom left</b>) to 10:00 (<b>bottom right</b>) UT on 9 March 2022. The green ellipse is the earthquake preparation zone of EQ1. The blue line is the magnetic equator. The TEC is measured in TECU units (1 TECU = 10<sup>16</sup> el/m<sup>2</sup>).</p>
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<p>(<b>A</b>–<b>D</b>) Relationship between the number of days before or after the earthquakes with magnitudes of 5.0–5.4, 5.5–5.9, 6.0–6.4, 6.5–6.9, respectively, and the number of anomalies. The blue lines represent the accumulation of anomalies. The dotted green lines represent the trend of the accumulation of anomalies. (<b>E</b>–<b>H</b>) Relationship between the number of days before or after the earthquakes with magnitudes of 5.0–5.4, 5.5–5.9, 6.0–6.4, 6.5–6.9, respectively, and the amplitude of anomalies.</p>
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<p>The 3D model of lithosphere-atmosphere-ionosphere coupling in Taiwan and the surrounding region.</p>
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24 pages, 12297 KiB  
Article
Impact of Radar and Surface Data Assimilation on the Forecast of a Nocturnal Squall Line in the Yangtze–Huaihe River
by Zongmei Pan, Shuwen Zhang and Weidong Zhang
Atmosphere 2022, 13(9), 1522; https://doi.org/10.3390/atmos13091522 - 17 Sep 2022
Viewed by 1861
Abstract
The impact of radar and surface data assimilation on the forecast of a nocturnal squall line initiated above the stable boundary layer in the Yangtze–Huaihe River is investigated by the Weather Research and Forecasting (WRF) model and its three-dimensional variational assimilation system (WRFDA [...] Read more.
The impact of radar and surface data assimilation on the forecast of a nocturnal squall line initiated above the stable boundary layer in the Yangtze–Huaihe River is investigated by the Weather Research and Forecasting (WRF) model and its three-dimensional variational assimilation system (WRFDA 3DVar). Results show that the assimilation of radar and surface data can improve the prediction of the convection initiation time, height and vertical ascending motion during the early stage of the squall line formation by adjusting the thermodynamic structure, circulation patterns, water vapor conditions and hydrometeor mixing ratios. Although the radar and surface data assimilation can improve the forecast of the location of the squall line to a certain extent, the squall line is stronger in the radar data assimilation than that in the surface data assimilation, leading to stronger radar reflectivity and heavier precipitation. The assimilation of both radar and surface data has a more positive impact on the forecast skill than the assimilation of either type of data. Moreover, during the mature stage of the squall line, radar and surface data assimilation can enhance the intensity of the surface cold pool. Specifically, radar data assimilation or assimilating the two data simultaneously can produce a stronger cold pool than only assimilating surface data, which is more conducive to the maintenance and development of the squall line. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Model domain, distribution of radar and surface observation stations, the geographic information of the area of interest and other geographic conditions. The big blue spots represent the locations of the radar stations from Zhumadian (ZMD), Nanyang (NY), Suizhou (SZ), Fuyang (FY), Hefei (HF), Bengbu (BB), Zhengzhou (ZZ), Wuhan (WH), Yichang (YC). The small black spots represent the locations of surface data stations in the left panel. The orange box represents the area of interest, the black dot represents the location of Xinyang City, Henan Province, and the black triangle represents the location of the Nanyang Sounding Station in the right panel.</p>
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<p>Flowchart for noDA, radarDA, surfaceDA and bothDA experiments.</p>
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<p>The mosaics of radar composite reflectivity (shaded, units: dBZ) at (<b>a</b>) 1230, (<b>b</b>) 1330, (<b>c)</b> 1530, (<b>d</b>) 1700, (<b>e)</b> 1730, (<b>f</b>) 1800, (<b>g</b>) 1830, (<b>h</b>) 1900, (<b>i</b>) 2200 UTC 11 July 2014.</p>
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<p>Weather charts from the fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5, 0.25° × 0.25°) data and the sounding chart from observation at 1200 UTC 11 July 2014, (<b>a</b>) 500 hPa and (<b>b</b>) 850 hPa geopotential height (blue solid lines, units: dagpm) and wind field (barbs, units: m/s), the red dashed line was the potential temperature of 345 K which indicated the location of MeiYu front in (<b>b</b>). (<b>c</b>) The sounding chart was taken at Nanyang station. The green line represents the dew point temperature profile, the black line represents the lifting curve, the red line represents the temperature profile and the black represents the lifting condensation level in (<b>c</b>). The location of Nanyang station is shown in <a href="#atmosphere-13-01522-f001" class="html-fig">Figure 1</a>.</p>
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<p>Radar composite reflectivity (shaded, units: dBZ) at (<b>a</b>–<b>e</b>) 1530 UTC and (<b>f</b>–<b>i</b>) 1730 UTC 11 July 2014 from the (<b>a</b>,<b>f</b>) observation, (<b>b</b>,<b>g</b>) noDA, (<b>c</b>,<b>h</b>) radarDA, (<b>d</b>,<b>i</b>) surfaceDA and (<b>e</b>,<b>j</b>) bothDA experiments.</p>
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<p>The averaged fractions skill scores (FSS) of the radar composite reflectivity at different thresholds (10, 15, 20, 25, 30, 35, 40, 45, 50 dBZ) from 1500 UTC to 1900 UTC 11 July 2014 for noDA (black dotted line), radarDA (blue dotted line), surfaceDA (green dotted line) and bothDA (red dotted line) experiments. The score area is the same as the panel area in <a href="#atmosphere-13-01522-f005" class="html-fig">Figure 5</a>.</p>
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<p>Distribution of 6h (1500 UTC to 2100 UTC 11 July 2014) accumulated precipitation (shaded, units: mm) from the (<b>a</b>) observation, (<b>b</b>) noDA, (<b>c</b>) radarDA, (<b>d</b>) surfaceDA and (<b>e</b>) bothDA experiments.</p>
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<p>The performance diagram of 6 h (1500 UTC to 2100 UTC 11 July 2014) accumulated precipitation for noDA (blue markers), radarDA (green markers), surfaceDA (orange markers) and bothDA (red markers) experiments. The horizontal coordinates represent the success ratio (SR). The longitudinal coordinates represent the probability of detection (POD). Yellow curves represent the threat score (TS), and the black dashed lines represent the bias score (BIAS). The filled circle, down-pointing triangle, square, pentagon, plus and star makers represent different precipitation thresholds. The score area is the same as the panel area in <a href="#atmosphere-13-01522-f007" class="html-fig">Figure 7</a>.</p>
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<p>The surface temperature increments of the three assimilation experiments (shaded, unit: °C) at (<b>a–c</b>) 1300, (<b>d–f</b>) 1400 and (<b>g–i</b>) 1500 UTC 11 July 2014. The black box in (<b>g</b>–<b>i</b>) is where the squall line initiated.</p>
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<p>The Root Mean Squared Error (RMSE) for the surface temperature from 1200 to 1500 UTC 11 July 2014 for noDA (black dotted line), radarDA (green dotted line), surfaceDA (blue dotted line) and bothDA (red dotted line) experiments. The score area is the same as the panel area in <a href="#atmosphere-13-01522-f009" class="html-fig">Figure 9</a>.</p>
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<p>The area-averaged soundings taken in the initiation area of the squall line (denoted by the black box in <a href="#atmosphere-13-01522-f009" class="html-fig">Figure 9</a>) at 1530 UTC 11 July 2014 for noDA (green line), radarDA (blue line), surfaceDA (red line) and bothDA (orange line) experiments. The dotted lines represent the dew point temperature, and the realizations describe the temperature.</p>
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<p>The horizontal distribution of 800 hPa wind fields (quiver, units: m/s) and divergence (shaded, units:10<sup>−5</sup>∙s<sup>−1</sup>) at 1530 UTC 11 July 2014 for (<b>a</b>) noDA, (<b>b</b>) radarDA, (<b>c</b>) surfaceDA and (<b>d</b>) bothDA experiments, the black box in the figure is the same as that in <a href="#atmosphere-13-01522-f009" class="html-fig">Figure 9</a>.</p>
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<p>The time–height cross sections of area-averaged vertical velocity (shaded, units: 10<sup>−1</sup>∙m/s) and divergence (contour, units:10<sup>−5</sup>∙s<sup>−1</sup>) from 1200 UTC to 1900 UTC 11 July 2014 for (<b>a</b>) noDA, (<b>b</b>) radarDA, (<b>c</b>) surfaceDA and (<b>d</b>) bothDA experiments. The averaged area is denoted by the black box in <a href="#atmosphere-13-01522-f012" class="html-fig">Figure 12</a>. The red dotted line represents the time of the squall line initiation, and the two black dotted lines represent the time near the squall line initiation.</p>
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<p>As in <a href="#atmosphere-13-01522-f013" class="html-fig">Figure 13</a> but for the specific humidity (shaded, units: g∙kg<sup>−1</sup>) and the water vapor flux divergence (contour, units: 10<sup>−5</sup>∙s<sup>−1</sup>) for (<b>a</b>) noDA, (<b>b</b>) radarDA, (<b>c</b>) surfaceDA and (<b>d</b>) bothDA experiments.</p>
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<p>The vertical profiles of the area-averaged (<b>a</b>) water vapor mixing ratio (QVAPOR), (<b>b</b>) rainwater mixing ratio (QRAIN), (<b>c</b>) cloud water mixing ratio (QCLOUD), (<b>d</b>) graupel mixing ratio (QGRAUP), (<b>e</b>) snow mixing ratio (QSNOW), (<b>f</b>) ice mixing ratio (QICE) at 1530 UTC 11 July 2014 for the noDA (black line), radarDA (green line), surfaceDA (blue line) and bothDA (red line) experiments. The black box in <a href="#atmosphere-13-01522-f012" class="html-fig">Figure 12</a> denotes the averaged area.</p>
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<p>The horizontal distribution of wind field at 10 m (quiver, unit: m/s), and surface perturbation temperature (shaded, unit: °C) at 1530, 1700 and 1800 UTC 11 July 2014 for (<b>a</b>) noDA, (<b>b</b>) radarDA, (<b>c</b>) surfaceDA and (<b>d</b>) bothDA experiments. The black boxes in (<b>a</b>–<b>d</b>) represent the location of initiation of the squall line (same as in <a href="#atmosphere-13-01522-f009" class="html-fig">Figure 9</a>), and the black boxes in (<b>c</b>–<b>l</b>) represent the location of the main body during the mature of the squall line.</p>
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<p>The south–north cross sections of in-plane flow vectors (quiver, units: m∙s−1; vertical motion amplified by a factor of 5) and perturbation equivalent potential temperature (shaded, units: K) along 114.6° E at 1800 UTC 11 July 2014 for (<b>a</b>) noDA, (<b>b</b>) radarDA, (<b>c</b>) surfaceDA and (<b>d</b>) bothDA experiments.</p>
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11 pages, 2652 KiB  
Article
Highly Efficient Removal of CO2 Using Water-Lean KHCO3/Isopropanol Solutions
by Lei Wang, Mohammad Saeed, Jianmin Luo, Anna Lee, Rowan Simonet, Zhao Sun, Nigel Walker, Matthew Aro, Richard Davis, Mohammad Abu Zahra, Malek Alkasrawi and Sam Toan
Atmosphere 2022, 13(9), 1521; https://doi.org/10.3390/atmos13091521 - 17 Sep 2022
Cited by 1 | Viewed by 2672
Abstract
The use of aqueous carbonate as an inorganic absorbent is not only inexpensive but also stable and environmentally friendly. However, the regeneration processes for aqueous carbonate sorbents require high regeneration heat duty; this energy intensity makes their wide utilization unaffordable. In this work, [...] Read more.
The use of aqueous carbonate as an inorganic absorbent is not only inexpensive but also stable and environmentally friendly. However, the regeneration processes for aqueous carbonate sorbents require high regeneration heat duty; this energy intensity makes their wide utilization unaffordable. In this work, a low-temperature, energy-saving, and environmentally friendly carbon dioxide desorption method has been investigated in potassium bicarbonate-water-alcohol solutions. The addition of alcohol, particularly isopropanol, to the potassium bicarbonate-water solution can significantly increase carbon dioxide desorption capacity. The potassium bicarbonate-water-isopropanol solution used in this study (36 wt % isopropanol) resulted in 15.2 mmol of carbon dioxide desorption within 2400 s at 80 °C, which was 2000-fold higher than the potassium bicarbonate-water-solution. This research demonstrates a water-lean solvent-based carbon dioxide removal route with the potential to be economical, environmentally safe, and energy-efficient. CO2 sequestration, capture, and utilization technologies will play a key role in reducing CO2 emissions. The excellent desorption kinetics and relatively moderate desorption temperatures (80 °C) of water-lean solvent could help in reducing the cost of CO2 capture, particularly in terms of the heat demand at the regenerator. Full article
(This article belongs to the Special Issue CO2 Sequestration, Capture and Utilization)
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<p>CO<sub>2</sub> capture experiment setup. (1) N<sub>2</sub> cylinder, (2) mixed gas cylinder, (3) mass flow controller, (4) desiccator, (5) thermostatic bath, (6) 500 mL three-necked flask, (7) condenser, (8) thermocouple, (9) heater/stirrer, (10) gas analyzer, (11) paperless recorder, (12) laptop.</p>
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<p>The effects of different alcohols on CO<sub>2</sub> absorption in KHCO<sub>3</sub>−H<sub>2</sub>O and KHCO<sub>3</sub>−H<sub>2</sub>O−45 wt % alcohol solutions: (<b>a</b>) effects of alcohols on the quantities of absorbed CO<sub>2</sub>. (<b>b</b>) The rates of CO<sub>2</sub> absorption.</p>
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<p>Effect of different alcohols on CO<sub>2</sub> desorption in KHCO<sub>3</sub>−H<sub>2</sub>O and KHCO<sub>3</sub>−H<sub>2</sub>O−alcohol solutions.</p>
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<p>CO<sub>2</sub> desorption rates in KHCO<sub>3</sub>−H<sub>2</sub>O and KHCO<sub>3</sub>−H<sub>2</sub>O−alcohol solutions.</p>
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<p>Effect on IPA concentration on CO<sub>2</sub> desorption in KHCO<sub>3</sub>−H<sub>2</sub>O and KHCO<sub>3</sub>−H<sub>2</sub>O−IPA solutions.</p>
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<p>CO<sub>2</sub> desorption rates in KHCO<sub>3</sub>−H<sub>2</sub>O and KHCO<sub>3</sub>−H<sub>2</sub>O−IPA solutions.</p>
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<p>Raman spectra of CO<sub>2</sub> desorption. Measurements were taken at five-minute intervals from time 0 min to 40 min. (<b>a</b>) KHCO<sub>3</sub>−water−IPA solution (36 wt % IPA). (<b>b</b>) KHCO<sub>3</sub>−water solution.</p>
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<p>Major reaction pathways of CO<sub>2</sub> capture with KHCO<sub>3</sub>−H<sub>2</sub>O and KHCO<sub>3</sub>−H<sub>2</sub>O−IPA solutions.</p>
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<p>Stability of KHCO<sub>3</sub>−H<sub>2</sub>O−IPA solution (36 wt % IPA).</p>
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13 pages, 2468 KiB  
Article
Evaluation of ECMWF Lightning Flash Forecast over Indian Subcontinent during MAM 2020
by Rituparna Sarkar, Parthasarathi Mukhopadhyay, Peter Bechtold, Philippe Lopez, Sunil D. Pawar and Kaustav Chakravarty
Atmosphere 2022, 13(9), 1520; https://doi.org/10.3390/atmos13091520 - 17 Sep 2022
Cited by 4 | Viewed by 2122
Abstract
During the pre-monsoon season (March–April–May), the eastern and northeastern parts of India, Himalayan foothills, and southern parts of India experience extensive lightning activity. Mean moisture, surface and upper-level winds, the sheared atmosphere in the lower level, and high positive values of vertically integrated [...] Read more.
During the pre-monsoon season (March–April–May), the eastern and northeastern parts of India, Himalayan foothills, and southern parts of India experience extensive lightning activity. Mean moisture, surface and upper-level winds, the sheared atmosphere in the lower level, and high positive values of vertically integrated moisture flux convergence (VIMFC) create favorable conditions for deep convective systems to occur, generating lightning. From mid-2018, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) operationally introduced lightning flash density on a global scale. This study evaluates the ECMWF lightning forecasts over India during the pre-monsoon season of 2020 using the Indian Institute of Tropical Meteorology (IITM) Lightning Location Network (LLN) observation data. Qualitative and quantitative analysis of the ECMWF lightning forecast has shown that the lightning forecast with a 72-h lead time can capture the spatial and temporal variation of lightning with a 90% skill score. Full article
(This article belongs to the Special Issue Precipitation and Convection: From Observation to Simulation)
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<p>The locations of Indian Institute of Tropical Meteorology (IITM) Lightning Location Network (LLN) stations (marked by white stars). Overlaid colored boxes are 5 thunderstorm-prone regions (as defined by [<a href="#B26-atmosphere-13-01520" class="html-bibr">26</a>]).</p>
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<p>Pre-monsoon (March–April–May or MAM) mean flash rates (flash count km<sup>−</sup><sup>2</sup> day<sup>−</sup><sup>1</sup>) obtained from Lightning Imaging Sensor (LIS)/Optical Transient Detector (OTD) Gridded 1995–2015 Climatology data sets [<a href="#B38-atmosphere-13-01520" class="html-bibr">38</a>]. Colored boxes highlight five thunderstorm-prone regions as defined by [<a href="#B26-atmosphere-13-01520" class="html-bibr">26</a>].</p>
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<p>2020 MAM mean (<b>a</b>) relative humidity (%; shaded), sea-level pressure (hPa; contours in red) and 850 hPa wind vector; (<b>b</b>) low-level wind shear between 850 and 700 hPa (m s<sup>−</sup><sup>1</sup>; shaded) and 200 hPa mean wind vector; and (<b>c</b>) vertically integrated moisture flux convergence (VIMFC; ×10<sup>−</sup><sup>5</sup> kg m<sup>−</sup><sup>2</sup> s<sup>−</sup><sup>1</sup>). Plotted using ECMWF reanalysis (ERA5) hourly data [<a href="#B47-atmosphere-13-01520" class="html-bibr">47</a>].</p>
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<p>MAM 2020 mean flash density (flash count km<sup>−</sup><sup>2</sup> day<sup>−</sup><sup>1</sup>) (<b>a</b>) observed by LLN and forecasted by IFS for (<b>b</b>) Day 1, (<b>c</b>) Day 2, and (<b>d</b>) Day 3 lead times. (<b>e</b>–<b>g</b>) shows the forecast bias (flash count km<sup>−</sup><sup>2</sup> day<sup>−</sup><sup>1</sup>) for Day 1, Day 2, and Day 3 lead times, respectively.</p>
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<p>Mean diurnal cycle of flash density (×10<sup>−</sup><sup>3</sup> km<sup>−</sup><sup>2</sup> day<sup>−</sup><sup>1</sup>) during MAM 2020 for LLN (black) and IFS Day 1 (blue), Day 2 (red), and Day 3 (green) lead times, spatially averaged over (<b>a</b>) the Indian subcontinent and (<b>b</b>–<b>f</b>) over each predefined subregion.</p>
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<p>Mean daily time-series of observed and IFS forecast lightning flash density (km<sup>−</sup><sup>2</sup> day<sup>−</sup><sup>1</sup>) for MAM 2020 over (<b>a</b>) All India, (<b>b</b>) Box 1: NEI, (<b>c</b>) Box 2: SP, (<b>d</b>) Box 3: CI, (<b>e</b>) Box 4: ECI, and (<b>f</b>) Box 5: NWI.</p>
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<p>MAM 2020 mean monthly total lightning flash density (km<sup>−</sup><sup>2</sup>) averaged over (<b>a</b>) All India (40° N–10° S, 50° E–100° E), (<b>b</b>) Box 1: NEI, (<b>c</b>) Box 2: SP, (<b>d</b>) Box 3: CI, (<b>e</b>) Box 4: ECI and (<b>f</b>) Box 5: NWI.</p>
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<p>Forecast Skills—(<b>a</b>) probability of detection (POD), (<b>b</b>) false alarm ratio (FAR), (<b>c</b>) Frequency Bias (FB), and (<b>d</b>) Symmetric Extremal Dependence Index (SEDI) for Yes/No forecast over all India and five thunderstorm-prone regions for Day 1 (blue), Day 2 (red) and Day 3 (green) lead time.</p>
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13 pages, 1374 KiB  
Article
Holiday Climate Index: Urban—Application for Urban and Rural Areas in Romania
by Liliana Velea, Alessandro Gallo, Roxana Bojariu, Anisoara Irimescu, Vasile Craciunescu and Silvia Puiu
Atmosphere 2022, 13(9), 1519; https://doi.org/10.3390/atmos13091519 - 17 Sep 2022
Cited by 6 | Viewed by 2383
Abstract
Nature, landscape, relaxation, and outdoor activities are important motivations when choosing rural destinations for vacations. Therefore, when selecting a rural area as a vacation destination, we assume that climate features are important. We investigated the appropriateness of the holiday climate index: urban (HCI:urban) [...] Read more.
Nature, landscape, relaxation, and outdoor activities are important motivations when choosing rural destinations for vacations. Therefore, when selecting a rural area as a vacation destination, we assume that climate features are important. We investigated the appropriateness of the holiday climate index: urban (HCI:urban) in quantitatively describing the relationship between climate and tourism fluxes in such destinations. We employed data from 94 urban and rural tourist destinations in Romania and correlated the monthly mean HCI:urban values with sectoral data (overnight tourists) for 2010–2018. The results show that weather and climate influenced tourism fluxes similarly in rural and urban destinations, supporting the hypothesis that HCI:urban may be used for rural areas as well. The information derived from HCI:urban may be useful for tourists when planning their vacations as well as for tourism investors in managing their businesses and reducing the weather and climate-related seasonality in tourism fluxes. Full article
(This article belongs to the Section Biometeorology)
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<p>(<b>a</b>) Touristic reception establishments with functions for tourist accommodations; (<b>b</b>) number of tourists accommodated (in the structure of tourist reception). Data source: National Institute for Statistics.</p>
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<p>Map of the selected touristic destinations.</p>
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<p>Percentage of the number of beds in accommodation units in various touristic destination types in Romania (bars), from the total number of beds at the national level (line) for 1994–2021. (source: processed data from NIS <a href="http://www.insse.ro" target="_blank">www.insse.ro</a>, accessed on 29 June 2022).</p>
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23 pages, 8220 KiB  
Article
Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms
by Robson Argolo dos Santos, Everardo Chartuni Mantovani, Elpídio Inácio Fernandes-Filho, Roberto Filgueiras, Rodrigo Dal Sasso Lourenço, Vinícius Bof Bufon and Christopher M. U. Neale
Atmosphere 2022, 13(9), 1518; https://doi.org/10.3390/atmos13091518 - 17 Sep 2022
Cited by 4 | Viewed by 2509
Abstract
The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ETrF (Evapotranspiration Fraction) [...] Read more.
The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ETrF (Evapotranspiration Fraction) of sugar can crop using the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model with data from the Sentinel-2 satellites constellation. In order to achieve this goal, images from the MSI sensor (MultiSpectral Instrument) from the Sentinel-2 and the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors from the Landsat-8 were acquired nearly at the same time between the years 2018 and 2020 for sugar cane crops. Images from OLI and TIR sensors were intended to calculate ETrF through METRIC (target variable), while for the MSI sensor images, the explanatory variables were extracted in two approaches, using 10 m (approach 1) and 20 m (approach 2) spatial resolution. The results showed that the algorithms were able to identify patterns in the MSI sensor data to predict the ETrF of the METRIC model. For approach 1, the best predictions were XgbLinear (R2 = 0.80; RMSE = 0.15) and XgbTree (R2 = 0.80; RMSE = 0.15). For approach 2, the algorithm that demonstrated superiority was the XgbLinear (R2 = 0.91; RMSE = 0.10), respectively. Thus, it became evident that machine learning algorithms, when applied to the MSI sensor, were able to estimate the ETrF in a simpler way than the one that involves energy balance with the thermal band used in the METRIC model. Full article
(This article belongs to the Special Issue Agrometeorology)
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<p>Location of the study area with the central pivots used for training, testing, and applying the models highlighted.</p>
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<p>Climatological normal of the study region extracted from station 83,386 of INMET (Instituto Nacional de Meteorologia).</p>
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<p>Acquisition of data from sensors onboard Landsat-8 and Sentinel-2 for training and testing in machine learning algorithms.</p>
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<p>Statistical results for the selection of predictor variables when applying the RFE in approach 1.</p>
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<p>Statistical results for the selection of predictor variables when applying the RFE in approach 2.</p>
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<p>Statistical results of the test of approach 1.</p>
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<p>Statistical results of the test of approach 2.</p>
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<p>Residual analysis of the estimated ET<sub>r</sub><span class="html-italic">F</span> through machine learning models.</p>
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<p>Spatial variability of ET<sub>r</sub><span class="html-italic">F</span> for approaches 1, 2, and Metric for 02/12/2020.</p>
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<p>Comparison of K<sub>c</sub> values between approaches 1 and 2, METRIC, and the ones recommended by the FAO’s 56 reports.</p>
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<p>Sugar cane temporal-spatial actual evapotranspiration in three different spatial resolutions evincing, through rectangles, coincident dates between Sentinel-2 and Landsat-8.</p>
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15 pages, 2763 KiB  
Article
Variability of Equatorial Ionospheric Bubbles over Planetary Scale: Assessment of Terrestrial Drivers
by Lalit Mohan Joshi, Lung-Chih Tsai, Shin-Yi Su and Abhijit Dey
Atmosphere 2022, 13(9), 1517; https://doi.org/10.3390/atmos13091517 - 17 Sep 2022
Viewed by 1800
Abstract
Nighttime F-region field-aligned irregularities (FAIs) associated with equatorial plasma bubbles (EPBs) are impacted by terrestrial factors, such as solar irradiance and geomagnetic activity. This paper examines the impact of the planetary-scale periodic variability of terrestrial processes on EPB activity. Continual observations of the [...] Read more.
Nighttime F-region field-aligned irregularities (FAIs) associated with equatorial plasma bubbles (EPBs) are impacted by terrestrial factors, such as solar irradiance and geomagnetic activity. This paper examines the impact of the planetary-scale periodic variability of terrestrial processes on EPB activity. Continual observations of the Equatorial Atmosphere Radar (EAR) have been utilized to derive the intra-seasonal variability of nighttime F-region FAIs in the context of the terrestrial factors mentioned above. A periodicity analysis using wavelet and Lomb–Scargle (LS) spectral analysis indicated significant amplitudes of the long-period planetary-scale variability in the F-region FAI signal-to-noise ratio (SNR), 10.7 cm flux, and geomagnetic indices, as well as a shorter period of variability. Interestingly, a careful inspection of the time series indicated the planetary-scale variability of F-region FAIs to be reasonably out of phase with the periodic geomagnetic variability. EPB occurrence and the FAI signal-to-noise ratio presented a systematic decrease with an increase in the level of geomagnetic activity. Non-transient quiet-time geomagnetic activity has been found to suppress both the occurrence as well as the strength of F-region FAIs. The impacts of planetary-scale geomagnetic activity appear to be non-identical in the summer and equinoctial EPBs. The results highlight the importance of periodic terrestrial processes in driving the planetary-scale variability of EPBs. Full article
(This article belongs to the Special Issue Monitoring and Forecasting of Ionospheric Space Weather)
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<p>(<b>a</b>) Height–Time–SNR maps of night-time EAR observation recorded on 25 September 2012 and 16 September 2012. Radar plumes representing plasma bubbles were observed on 25 September, while on 16 September plasma bubbles were not observed. (<b>b</b>) Nocturnal (18–24 LT) SNR variability during days 240–330 in 2012. Here, the grey region indicates the data gap.</p>
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<p>(<b>a</b>) Local-time day-of-year map of radar SNR from a height of 300 km in 2012–2013 (rill mid-July). (<b>b</b>) Day-of-Year variation in nocturnal average SNR for the same period. (<b>c</b>) Same as (<b>b</b>), but with data gaps filled with spline interpolation. (<b>d</b>) Wavelet scalogram of SNR derived from day series in panel (<b>c</b>). (<b>e</b>,<b>f</b>) Wavelet scalogram of F10.7 and the AE index, respectively. White dashed lines indicate the cone of influence.</p>
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<p>Wavelet coherence between terrestrial driver proxies and nocturnal average SNR at 300 km. (<b>a</b>) Wavelet coherence between F10.7 and SNR. (<b>b</b>) Wavelet coherence between the AE index and SNR. White dashed lines indicate the cone of influence.</p>
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<p>Intra-seasonal variation in nocturnal SNR in equinoctial and summer months. (<b>a</b>) Nocturnal variation in SNR at 300 km in vernal equinox months (Febuary, March, and April) in 2012. (<b>b</b>) The day-of-year series of nocturnal average SNR in vernal equinox months at 300 km and 400 km is indicated in blue and red, respectively. (<b>c</b>,<b>d</b>) The Lomb–Scargle spectra of SNR variation at 300 km and 400 km, respectively, are presented and derived using the DOY series shown in (<b>b</b>). (<b>e</b>–<b>h</b>) present the same as (<b>a</b>–<b>d</b>), but for the summer months (June, July, and Augst) in 2012. (<b>i</b>–<b>l</b>) present the same as (<b>a</b>–<b>d</b>), but for the autumnal equinox months (September, October, and November) in 2012. (<b>m</b>–<b>p</b>) present the same as (<b>a</b>–<b>d</b>), but for the vernal equinox months in 2013.</p>
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<p>Lomb–Scargle spectra of geomagnetic and solar irradiance proxies. (<b>a</b>–<b>c</b>) present daily average F10.7, AE, and Ap, respectively, during the vernal equinox period in 2012. (<b>d</b>–<b>f</b>) presents the Lomb–Scargle spectrum of F10.7, AE, and Ap, respectively, derived from the DOY series shown in (<b>a</b>–<b>c</b>). (<b>g</b>–<b>l</b>) present the same as (<b>a</b>–<b>f</b>), but for the summer months in 2012. (<b>m</b>–<b>r</b>) present the same as (<b>a</b>–<b>f</b>), but for the autumnal equinox months in 2012. (<b>s</b>–<b>x</b>) present the same as (<b>a</b>–<b>f</b>), but for the vernal equinox months in 2012.</p>
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<p>Day-of-year variation in nocturnal average SNR at 300 km, daily averaged AE and F10.7 during (<b>a</b>) FMA 2012, (<b>b</b>) JJA 2012, (<b>c</b>) SON 2012, and (<b>d</b>) FMA 2013. Top, middle, and bottom sub-panels in each of these show SNR, AE, and F10.7, respectively.</p>
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<p>(<b>a</b>–<b>d</b>) scatter plot analysis of AE and SNR (nocturnal average, 300 km) for FMA 2012, JJA 2012, SON 2012, and FMA 2013, respectively. (<b>e</b>–<b>h</b>) Same, but for F10.7 and SNR.</p>
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<p>(<b>a</b>) Scatter plot of daily AE and nocturnal average SNR at 300 km for the entire equinoctial period considered in the study. Orange and blue colors indicate EPB and non-EPB nights, respectively. (<b>b</b>) EPB occurrence in the piece-wise incremental level of daily AE. Note, the box adjacent to each stem indicates the no. of EPB cases/total cases in each window. (<b>c</b>) Nocturnal average SNR (300 km) in the piece-wise incremental level of daily AE. Note that the average SNR is calculated only by considering EPB nights (no. indicated in adjacent box).</p>
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15 pages, 5904 KiB  
Article
The Performance of S2S Models on Predicting the 21.7 Extreme Rainfall Event in Henan China
by Xiaojuan Wang, Shuai Li, Li Liu, Huimin Bai and Guolin Feng
Atmosphere 2022, 13(9), 1516; https://doi.org/10.3390/atmos13091516 - 17 Sep 2022
Cited by 2 | Viewed by 2433
Abstract
Extreme rainfall may cause meteorological disasters and has tremendous impact on societies and economics. Assessing the capability of current dynamic models for rainfall prediction, especially extreme rainfall event prediction, at sub-seasonal to seasonal (S2S) scale and diagnosing the probable reasons are quite important [...] Read more.
Extreme rainfall may cause meteorological disasters and has tremendous impact on societies and economics. Assessing the capability of current dynamic models for rainfall prediction, especially extreme rainfall event prediction, at sub-seasonal to seasonal (S2S) scale and diagnosing the probable reasons are quite important topics in the current climate study field. This study analyzes the formation mechanisms of the extreme rainfall event during 18–22 July 2021 in Henan Province and introduces the Tanimoto Coefficient (TC) to evaluate the prediction performance of S2S models. The results show that confrontation between low-latitude typhoon “In-Fa” and subtropical highs leads to sufficient water vapor transporting to Henan, and that remarkable upward air motion causes strong convergence of water vapor, thereby providing atmospheric conditions for this extreme rainfall event. Furthermore, five S2S models showed limited capability in predicting this extreme rainfall event 20 days in advance with the TCs of four models being below 0.1. Models could capture this event signal 6 days ahead with most TCs above 0.2. The performances of model prediction for this extreme rainfall event were closely related to the fact that the water vapor convergence, vertical movements, relative vorticity, and geopotential height predicted by the NCEP model 20 days ahead were close to the actual situation, in contrast to the other four models 6 days in advance. This study implies that S2S model predictions for this extreme rainfall event show obvious differences, and the application of S2S models in the prediction of extreme events needs to fully consider their prediction uncertainties. The capability of the models to properly reproduce local water vapor convergence and vertical motions is also shown to be crucial for correctly simulating the extreme event, which might provide some hints for the further amelioration of models. Full article
(This article belongs to the Special Issue Climate Extremes in China)
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<p>Spatial distribution of rainfall in the Henan Province from 18 to 22 July 2021: (<b>a</b>) cumulative rainfall (unit: mm); (<b>b</b>) percentage of accumulated rainfall anomaly (unit: %).</p>
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<p>Time series of rainfall in Henan Province: (<b>a</b>) interannual variation of average rainfall (unit: mm) during 18–22 July from 1951 to 2021; (<b>b</b>) daily average rainfall of 2021 and climatic normal during 1–31 July (unit: mm); (<b>c</b>) daily rainfall anomalies (unit: mm) during 1–31 July 2021.</p>
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<p>The average and daily water vapor flux (vector, unit: 10 × 10<sup>2</sup> kg/m·s) and convergence anomalies (shading, unit: 10 × 10<sup>−5</sup>) kg/(m<sup>2</sup>·s)) during 18–22 July 2021. The purple region is Henan province. (<b>a</b>) The average and (<b>b</b>–<b>f</b>) the daily variation during 18–22 July 2021, respectively.</p>
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<p>Same as <a href="#atmosphere-13-01516-f003" class="html-fig">Figure 3</a>, but for the 500 hPa geopotential height (contour, unit: gpm) and vertical velocity anomalies (shading, unit: p/s). (<b>a</b>) The average and (<b>b</b>–<b>f</b>) the daily variation during 18–22 July 2021, respectively.</p>
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<p>Same as <a href="#atmosphere-13-01516-f003" class="html-fig">Figure 3</a>, but for the 850 hPa wind (vector, <math display="inline"><semantics> <mrow> <mi>unit</mi> <mo>:</mo> <mrow> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) and the relative vorticity anomalies (shading, <math display="inline"><semantics> <mrow> <mi>unit</mi> <mo>:</mo> <msup> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>). (<b>a</b>) The average and (<b>b</b>–<b>f</b>) the daily variation during 18–22 July 2021, respectively.</p>
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<p>Spatial distribution of accumulated rainfall (unit: mm) during 18–22 July in Henan Province predicted by the five models 20 days, 13 days, 6 days, and 3 days in advance, respectively: (<b>a1</b>–<b>a4</b>) CMA model; (<b>b1</b>–<b>b4</b>) ECMWF model; (<b>c1</b>–<b>c4</b>) KMA model; (<b>d1</b>–<b>d4</b>) NCEP model; (<b>e1</b>–<b>e4</b>) UKMO model.</p>
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<p>The daily average rainfall of Henan from 15 to 25 July and the predictions of the five models 20 days, 13 days, 6 days, and 3 days in advance, respectively (<b>a1</b>–<b>a4</b>).</p>
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<p>The spot distribution of predictions and observations for the cumulative rainfall during 18–22 July 2021 in Henan. Prediction (<b>a</b>) 20 days, (<b>b</b>) 13 days, (<b>c</b>) 6 days, and (<b>d</b>) 3 days in advance.</p>
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<p>Spatial distribution of the difference in 500 hPa geopotential height (counter, <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math>) and vertical velocity (shading, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">p</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) between model prediction and observation during 18–22 July in Henan Province.</p>
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<p>Spatial distribution of the difference in average water vapor fluxes (vector, <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>2</mn> <mtext> </mtext> </mrow> </msup> <mrow> <mtext> </mtext> <mi>kg</mi> </mrow> <mo>/</mo> <mo stretchy="false">(</mo> <mi mathvariant="normal">m</mi> <mo>·</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>)) and their convergence/divergence (shading, <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mrow> <mtext> </mtext> <mi>kg</mi> </mrow> <mo>/</mo> <mo stretchy="false">(</mo> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> <mo>·</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>)) between model prediction and observation during 18–22 July in Henan Province.</p>
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17 pages, 1917 KiB  
Article
Comprehensive Evaluation of Odor-Causing VOCs from the Painting Process of the Automobile Manufacturing Industry and Its Sustainable Management
by Vanitchaya Kultan, Sarawut Thepanondh, Nattaporn Pinthong, Jutarat Keawboonchu and Mark Robson
Atmosphere 2022, 13(9), 1515; https://doi.org/10.3390/atmos13091515 - 16 Sep 2022
Cited by 5 | Viewed by 3240
Abstract
Automotive manufacturing is one of the potential sources of air pollution particularly involving volatile organic compounds (VOCs). This study intensively evaluated VOC emissions and their dispersion from the industry. The measured VOCs were speciated for further evaluation of their odor threats according to [...] Read more.
Automotive manufacturing is one of the potential sources of air pollution particularly involving volatile organic compounds (VOCs). This study intensively evaluated VOC emissions and their dispersion from the industry. The measured VOCs were speciated for further evaluation of their odor threats according to the characteristics of each compound. Mathematical emission and air dispersion models were applied to assist in elaborating the source–receptor relationship allowing the determining of existing business-as-usual conditions with proposed mitigation measures to manage the pollution of the factory studied in this paper. Seven VOC species potentially caused odor problems to the surrounding community, including 1-butanol, ethyl benzene, toluene, m,p xylene, o xylene, methyl ethyl ketone, and methyl isobutyl ketone. The results from the AERMOD dispersion model revealed that the smell from these chemicals could reach up to about 800 m from the source. Analysis of mitigation measures indicated that two interesting scenarios should be considered according to their effectiveness. The concentrations of VOCs can decrease by up to 4.7, 14.0 and 24.9% from increasing the physical stack height by +1, +3 and +5 m from its existing height, respectively. Modification of the aeration tank of the wastewater treatment unit to a closed system also helped to reduce about 27.8% of emissions resulting in about a 27.6% decreased ambient air concentration. This study provided useful information on the characteristics of VOCs emitted by the automobile manufacturing industry. It also demonstrated the relevant procedures and highlights the necessity to comprehensively analyze the source–receptor relationship to evaluate the most appropriate measures in managing industrial air pollution. Full article
(This article belongs to the Special Issue Air Pollution in Industrial Regions)
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<p>Schematic diagram of WWTs (abbreviation of unit type is denoted in <a href="#atmosphere-13-01515-t002" class="html-table">Table 2</a>).</p>
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<p>Scope and planned view sampling location of the factory.</p>
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<p>Wind direction from WRPLOT view.</p>
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<p>Maximum ground-level concentration at discrete receptors for different source types (a1–a2 were the maximum receptors for stack sources and b1–b2 were the maximum receptors for WWTs source).</p>
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<p>Distribution patterns according to wind direction classified by odor-potential substances.</p>
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<p>Source identification of each substance at receptors.</p>
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11 pages, 7188 KiB  
Communication
Global Distribution of Clouds over Six Years: A Review Using Multiple Sensors and Reanalysis Data
by Lerato Shikwambana
Atmosphere 2022, 13(9), 1514; https://doi.org/10.3390/atmos13091514 - 16 Sep 2022
Cited by 2 | Viewed by 2190
Abstract
A six-year global study of cloud distribution and cloud properties obtained from observations of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), the Atmospheric Infrared Sounder (AIRS), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data is presented [...] Read more.
A six-year global study of cloud distribution and cloud properties obtained from observations of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), the Atmospheric Infrared Sounder (AIRS), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data is presented in this study. From the CALIPSO observations, the highest clouds for both daytime and night-time were found in the Inter Tropical Convergence Zone (ITCZ) region. The lowest cloud heights were found towards the poles due to the decrease in the tropopause height. Seasonal studies also revealed a high dominance of clouds in the 70 °S–80 °S (Antarctic) region in the June–July–August (JJA) season and a high dominance of Arctic clouds in the December–January–February (DJF) and September–October–November (SON) seasons. The coldest cloud top temperatures (CTT) were mostly observed over land in the ITCZ and the polar regions, while the warmest CTTs were mostly observed in the mid-latitudes and over the oceans. Regions with CTTs greater than 0 °C experienced less precipitation than regions with CTTs less than 0 °C. Full article
(This article belongs to the Special Issue Feature Papers in Meteorological Science)
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<p>Schematic illustration of the formation of cirrus clouds by two mechanisms. (<b>a</b>) Dissipation of cumulonimbus outflow anvils and (<b>b</b>) in situ nucleation.</p>
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<p>Daytime (<b>left panels</b>) and night-time (<b>right panels</b>) latitudinal distribution of cloud heights for the period of 2011–2016 using CALIPSO.</p>
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<p>Seasonal averaged (2011–2016) latitudinal distribution cloud detection using CALIPSO.</p>
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<p>Seasonal averaged distribution of cloud fraction for low (<b>left panel</b>), middle (<b>middle panel</b>), and high (<b>right panel</b>) clouds using the MERRA-2 model data.</p>
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<p>S Seasonal averaged AIRS cloud top temperature for the period 2011–2016.</p>
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<p>Seasonal averaged total precipitation land for the period 2011–2016.</p>
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15 pages, 5644 KiB  
Article
Optimization Design of Velocity Distribution in the Airways of the Fluidized Bed Based on CFD and Taguchi Algorithm
by Hao Yan, Shisong Liu, Fei Wang, Wei Xu, Jian Li, Tengzhou Xie and Yishan Zeng
Atmosphere 2022, 13(9), 1513; https://doi.org/10.3390/atmos13091513 - 16 Sep 2022
Cited by 1 | Viewed by 1355
Abstract
A vital component that is frequently employed in the industrial powder conveying sector is the fluidized bed. In the light of powder unloading with a fluidized bed as the research object, an orthogonal experiment with two factors and four levels was established for [...] Read more.
A vital component that is frequently employed in the industrial powder conveying sector is the fluidized bed. In the light of powder unloading with a fluidized bed as the research object, an orthogonal experiment with two factors and four levels was established for the structural parameters of the fluidized bed. In the case of different noise factors, 16 schemes are designed and all schemes via computational fluid dynamics numerical simulation. The Taguchi method and regression analysis are used to analyze the response. Finally, the accuracy of the optimization results is tested. The results show that gas velocity decreases sharply at the airway’s entrance and, then, gas flows to the second half of the airway and velocity decreases steadily and uniformly. Airway arc length L exerts a greater effect on the signal-to-noise ratio (SNR) than airway height H. The parameter combination of 180 mm L and 17 mm H for obtaining the optimal velocity distribution uniformity is determined. The test results indicate that the overall fluidization effect of the fluidized bed with the optimal parameters is better. The linked research findings can be used as a guide when designing a fluidized bed system for transporting comparable powder. Full article
(This article belongs to the Special Issue CFD Modeling in Gas-Liquid Separators)
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<p>Physical model of fluidized bed for powder tank truck.</p>
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<p>CFD computational domain and grid diagram. (<b>a</b>) CFD computational domain; (<b>b</b>) Airway grid.</p>
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<p>Grid independence validation. (<b>a</b>) <span class="html-italic">V<sub>u</sub></span>; (<b>b</b>) <span class="html-italic">R</span><sup>2</sup>.</p>
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<p>(<b>a</b>) Mean main effect; (<b>b</b>) SNR main effect.</p>
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<p>SNR residual diagram.</p>
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<p>(<b>a</b>) Pareto chart for two factors at α = 0.05; (<b>b</b>) Scatter plot.</p>
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<p>Gas velocity analysis of the fluidized bed airway. (<b>a</b>–<b>c</b>) Gas velocity attenuation curve and velocity field diagram of Scheme 1, Scheme 6, and Scheme 12; (<b>d</b>) Standard deviation of velocity distribution for the three schemes.</p>
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<p>Unloading experimental device. (<b>a</b>) Scaled model; (<b>b</b>) Airway display; (<b>c</b>) Breathable cloth.</p>
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<p>Effects of the two schemes for powder unloading. (<b>a</b>) Scheme 1; (<b>b</b>) Scheme 12.</p>
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17 pages, 5773 KiB  
Article
Impact of PM10 Particles on the Measurement Error of SO2 Electrochemical Gas Sensor
by Wei Chen, Shijing Wu, Dongmei Liao and Hanping Zhang
Atmosphere 2022, 13(9), 1512; https://doi.org/10.3390/atmos13091512 - 16 Sep 2022
Viewed by 1305
Abstract
To address the problems of poor measurement accuracy and long service life of SO2 electrochemical gas sensors when used in thermal power plant areas, fly ash emitted from a thermal power plant in China was used as the research object. Based on [...] Read more.
To address the problems of poor measurement accuracy and long service life of SO2 electrochemical gas sensors when used in thermal power plant areas, fly ash emitted from a thermal power plant in China was used as the research object. Based on the analysis of the morphological characteristics of fly ash particles, theoretical calculations were used to obtain the settling speed of fly ash particles and the amount of fly ash deposited at different times, and then the impact of fly ash on the measurement error of a SO2 electrochemical gas sensor was investigated by experimental tests. The research results show that the particle size distribution of fly ash is 2–11 μm, the average settling speed of fly ash particles is 1.34 × 10−3 m/s, and the deposition amount of fly ash on the surface of the sensor inlet film is 0.95 mg per day. The deposition time of fly ash affects the sensor measurement error, and the longer the deposition time, the larger the sensor measurement error, which is due to the reduction of gas diffusion area S and diffusion coefficient K in the sensor caused by fly ash deposition. Fly ash deposition has a greater impact on the sensor when measuring low concentration gases. The higher the gas concentration, the lower the measurement error, because the higher the gas concentration, the faster the gas reaches the working electrode area and the higher the effective SO2 concentration detected in the limited response time. When using SO2 electrochemical sensors in environments with high concentrations of fly ash or dust, it is recommended to install dust-proof devices (such as air-permeable filter membranes with a pore size of less than 4 μm) and regularly clean the deposited fly ash, which can improve the accuracy of the sensor measurement and extend the service life. Full article
(This article belongs to the Special Issue Industrial Air Pollution Control in China)
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<p>MIRA3 field emission scanning electron microscope.</p>
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<p>Structure diagram of SO<sub>2</sub> electrochemical gas sensor.</p>
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<p>Schematic diagram of the electrochemical gas sensor.</p>
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<p>Fly ash sample.</p>
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<p>Fly ash drying process. (<b>a</b>) Tray size. (<b>b</b>) Electric constant temperature blast drying oven. (<b>c</b>) The comparison of dried before and dried after fly ash.</p>
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<p>Fly ash drying process. (<b>a</b>) Tray size. (<b>b</b>) Electric constant temperature blast drying oven. (<b>c</b>) The comparison of dried before and dried after fly ash.</p>
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<p>SO<sub>2</sub> electrochemical gas sensor test chamber.</p>
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<p>Fly ash particle morphology.</p>
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<p>The settling motion process and forces on fly ash particles in air.</p>
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<p>Sensor measurements at different concentrations of SO<sub>2</sub> gas and different fly ash deposition times.</p>
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<p>Measurement errors of sensors with different deposition times for the same concentration of SO<sub>2</sub> gas: (<b>a</b>) 10 ppm measurement error; (<b>b</b>) 20 ppm measurement error; (<b>c</b>) 30 ppm measurement error; (<b>d</b>) 40 ppm measurement error; (<b>e</b>) 50 ppm measurement error; (<b>f</b>) 60 ppm measurement error; (<b>g</b>) 70 ppm measurement error; (<b>h</b>) 80 ppm measurement error.</p>
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<p>Measurement errors of sensors with different deposition times for the same concentration of SO<sub>2</sub> gas: (<b>a</b>) 10 ppm measurement error; (<b>b</b>) 20 ppm measurement error; (<b>c</b>) 30 ppm measurement error; (<b>d</b>) 40 ppm measurement error; (<b>e</b>) 50 ppm measurement error; (<b>f</b>) 60 ppm measurement error; (<b>g</b>) 70 ppm measurement error; (<b>h</b>) 80 ppm measurement error.</p>
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<p>Measurement equipment around thermal power plants.</p>
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14 pages, 7651 KiB  
Article
Aerosol Property Analysis Based on Ground-Based Lidar in Sansha, China
by Deyi Kong, Hu He, Jingang Zhao, Jianzhe Ma and Wei Gong
Atmosphere 2022, 13(9), 1511; https://doi.org/10.3390/atmos13091511 - 16 Sep 2022
Cited by 2 | Viewed by 1667
Abstract
Marine aerosol is one of the most important natural aerosols. It has a significant impact on marine climate change, biochemical cycling and marine ecosystems. Previous studies on marine aerosols, especially in the South China Sea, were carried out by satellite and shipborne measurements. [...] Read more.
Marine aerosol is one of the most important natural aerosols. It has a significant impact on marine climate change, biochemical cycling and marine ecosystems. Previous studies on marine aerosols, especially in the South China Sea, were carried out by satellite and shipborne measurements. The above methods have drawbacks, such as low temporal–spatial resolution and signal interference. However, lidar has high accuracy and high temporal–spatial resolution, so it is suitable for high-precision long-term observations. In this work, we obtain marine aerosol data using Mie Lidar in Sansha, an island in the South Chain Sea. Firstly, by comparing boundary layer height (BLH) between Sansha and Hefei, we found that Sansha’s boundary layer height has significant differences with that of inland China. Secondly, we compare the aerosol extinction coefficients and their variation with height in Sansha and Hefei. Finally, we obtain hourly averaged aerosol optical depth at Sansha and explore its relation with weather. To analyze the AOD–weather relation, we select three meteorological factors (sea surface temperature, mean sea level pressure and 10 m u-component of wind) based on their feature importance, which is determined by random forest regression. We also analyze the relationship between AOD and the above meteorological factors in each season separately. The results show that there is a strong relation between the meteorological factors and AOD in spring and summer, while there is no clear correlation in fall and winter. These analyses can provide valid data for future researches on marine aerosols in the South China Sea. Full article
(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing)
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<p>Monthly average of boundary layer height in Sansha and Hefei. The absence of data in December in Sansha is due to the lack of lidar data.</p>
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<p>Daily average of boundary layer height in Sansha.</p>
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<p>Daily average of boundary layer height in Hefei.</p>
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<p>Aerosol extinction coefficient on 29 January 2021, 2:00 am, local time.</p>
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<p>Pseudo-color map for aerosol extinction coefficient (<math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mo> </mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) in Sansha on 11 February 2021.</p>
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<p>Pseudo color map for aerosol extinction coefficient (<math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mo> </mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) in Hefei on 11 February 2021.</p>
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<p>Seasonal average AOD in Sansha.</p>
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<p>Feature importance of candidate meteorological factors.</p>
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<p>AOD-meteorological relation in spring.</p>
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<p>AOD-meteorological relation in summer.</p>
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<p>AOD-meteorological relation in fall.</p>
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<p>AOD-meteorological relation in winter.</p>
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<p>Feature importance of candidate meteorological factors (<b>a</b>) fall; (<b>b</b>) winter.</p>
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21 pages, 8181 KiB  
Article
Particle Number and Size Distributions (PNSD) from a Hybrid Electric Vehicle (HEV) over Laboratory and Real Driving Emission Tests
by Daisy Thomas, Hu Li, Xin Wang, Karl Ropkins, Alison S. Tomlin, Chris D. Bannister and Gary Hawley
Atmosphere 2022, 13(9), 1510; https://doi.org/10.3390/atmos13091510 - 16 Sep 2022
Cited by 1 | Viewed by 1711
Abstract
Particle number (PN) emissions from hybrid electric vehicles (HEV) during engine ignition and re-ignition events are an important but scarcely reported area. The objectives of the present work are to study the effects of drive cycle properties on the engine behaviour of a [...] Read more.
Particle number (PN) emissions from hybrid electric vehicles (HEV) during engine ignition and re-ignition events are an important but scarcely reported area. The objectives of the present work are to study the effects of drive cycle properties on the engine behaviour of a hybrid electric vehicle (HEV) and to investigate how this impacts the tailpipe PN emissions and their size distributions (PNSD). Worldwide harmonised light vehicles test cycle (WLTC) testing was conducted, as well as chassis dynamometer emission measurements over a realistic real driving emissions (RDE) speed pattern, using a Euro 5 Toyota Prius HEV with a Cambustion DMS500 sampling PN concentrations at the tailpipe. It is shown that the number of vehicle stops during a test cycle has a direct impact on the re-ignition activity for the HEV. 64 ± 3% of the total PN from WLTC testing was produced during engine re-ignition events while only 6 ± 1% was from stabilised engine operation. Similar proportions were observed for the RDE-style test cycle. The majority of engine reignition and destabilised activity, and hence PN emission, was during the low-speed sections of the drive cycles used. The average PNSD across cycle phases was different between cycles, due to the influence of dynamic properties on engine behaviour and hence the PN emission profile. The PNSD at the engine re-ignition and destabilised events had a merged wide peak with a maximum at 60 nm diameter and a shoulder at 12 nm diameter. The HEV had increased emissions of particles smaller than 23 nm under cold start, but similar overall PN emission values, compared to a warm start. The results of this work highlight the importance of controlling HEV PN emissions to limit human exposure to PN in urban environments where the majority of PN emissions occur. The sensitivity of HEV PN emission factors and PNSD to engine behaviour and, in turn, test cycle dynamic properties, is important to note when considering legislative test cycles, particularly with reference to the freedoms afforded by the RDE test cycle. The results also indicate that substantial improvements to air quality could be made by reducing the particle measurement protocol PN cut-off size to 10 nm. Full article
(This article belongs to the Special Issue Traffic Related Emission)
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<p>Speed profile of the WLTC.</p>
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<p>(<b>a</b>) Speed profiles of the shortened RDE-style chassis dynamometer drive cycle and (<b>b</b>) full RDE-style drive.</p>
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<p>Percentage of time covered with the engine in different states of operation. Error bars represent the standard error of the repeats (two tests for each WLTC phase, three tests for each RDE phase).</p>
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<p>Percentage of distance covered with the engine in different states of operation. Error bars represent the standard error of the repeats (two tests for each WLTC phase, three tests for each RDE phase).</p>
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<p>Number of engine re-ignition events per km for the total WLTC and RDE-style test cycles and their individual phases. RDE phases are called phase 1 (urban), phase 2 (rural) and phase 3 (motorway) for comparison with the WLTC cycle phases. Error bars represent the standard error of the repeats (two tests for WLTC, three tests for RDE).</p>
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<p>Average PN emissions per kilometre across the WLTC and RDE test cycles. Individual phase and total cycle results are given, along with the Euro 6 PN limit of 6 × 10<sup>11</sup> #/km (dotted line). Error bars represent the standard error of the repeats (two tests for WLTC, three tests for RDE).</p>
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<p>Modal (i.e., transient) and cumulative PN emissions over (<b>a</b>) the WLTC and (<b>b</b>) the shortened RDE-style cycle. Transient values are multiplied by 10 for scale. Vehicle speed is indicated on the secondary axis, with engine RPM indicated qualitatively in grey.</p>
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<p>Modal (i.e., transient) and cumulative PN emissions over (<b>a</b>) the WLTC and (<b>b</b>) the shortened RDE-style cycle. Transient values are multiplied by 10 for scale. Vehicle speed is indicated on the secondary axis, with engine RPM indicated qualitatively in grey.</p>
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<p>HEV PN emission factor versus the number of engine re-ignition events per kilometre.</p>
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<p>WLTC total and phased average PNSD.</p>
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<p>RDE total and phased average PNSD.</p>
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<p>Average WLTC and RDE PNSD during engine re-ignition event, engine stabilised and engine destabilised states.</p>
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<p>Example of one RDE test PNSD with time for all engine re-ignition events. Engine RPM is indicated in blue at 3 × 10<sup>4</sup> times scale.</p>
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<p>Example of one RDE test PNSD with time for all engine stabilised operation. Engine RPM is indicated in blue at 3 × 10<sup>3</sup> times scale.</p>
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<p>Total HEV PN emission rate alongside engine RPM and lambda (the actual air–fuel ratio divided by the stoichiometric air–fuel ratio).</p>
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<p>Average size distributed PN emission factors from individual phases and total WLTC cycles.</p>
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<p>Average size distributed PN emission factors from individual phases and total RDE cycles.</p>
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<p>Comparison of the average PNSD over the first 300 s of cold and warm start WLTCs.</p>
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<p>Transient PNSD over the first 300 s of a cold start WLTC. Engine RPM is indicated qualitatively in blue on the back wall.</p>
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<p>Transient PNSD over the first 300 s of a warm start WLTC. Engine RPM is indicated qualitatively in blue on the back wall.</p>
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<p>PN values comparing diameter cut-offs at 23 nm, 10 nm and 5 nm, across sections of (<b>a</b>) the WLTC and (<b>b</b>) the shortened RDE-style cycles. Error bars represent the standard error of the repeats (two tests for each WLTC phase, three tests for each RDE phase).</p>
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<p>PN values comparing diameter cut-offs at 23 nm, 10 nm and 5 nm, across sections of (<b>a</b>) the WLTC and (<b>b</b>) the shortened RDE-style cycles. Error bars represent the standard error of the repeats (two tests for each WLTC phase, three tests for each RDE phase).</p>
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14 pages, 4013 KiB  
Article
Study on Enhancing Shale Oil Recovery by CO2 Pre-Pad Energized Fracturing in A83 Block, Ordos Basin
by Yang Xiao, Zhigang Li, Jiahao Wang, Jinyuan Yang, Zhonghui Ma, Shuyun Liu and Chenhui Han
Atmosphere 2022, 13(9), 1509; https://doi.org/10.3390/atmos13091509 - 15 Sep 2022
Cited by 3 | Viewed by 1700
Abstract
The Ordos Basin is rich in shale oil resources. The main targeted layers of blocks A83 and X233 are the Chang 7 member of the Yanchang Formation. Due to extremely low permeability, a fracturing technique was required to enhance oil recovery. However, after [...] Read more.
The Ordos Basin is rich in shale oil resources. The main targeted layers of blocks A83 and X233 are the Chang 7 member of the Yanchang Formation. Due to extremely low permeability, a fracturing technique was required to enhance oil recovery. However, after adopting the stimulated reservoir volume-fracturing technology, the post-fracturing production of the A83 block is significantly lower than that of the X233 block. For this problem, the dominating factors of productivity of the two blocks were analyzed using the Pearson correlation coefficient (PCC) and the Spearman rank correlation coefficient (SRCC), showing that the main reason for the lower production of the A83 block is its insufficient formation energy. To solve this problem, the CO2 pre-pad energized fracturing method was proposed. To study the feasibility of CO2 pre-pad energized fracturing in the A83 block, an integrated reservoir numerical simulation model of well A83-1 was established based on the idea of integration of geology and engineering. Additionally, the productions within five years after conventional volume fracturing and CO2 pre-pad energized fracturing were compared. The results show that compared with conventional volume fracturing, the cumulative oil production of CO2 pre-pad energized fracturing increases by 11.8%, and the water cut decreases by 16.5%. The research results can guide the subsequent reservoir reconstruction operation in the A83 block and provide new ideas for fracturing in the future. Full article
(This article belongs to the Special Issue CO2 Geological Storage and Utilization)
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<p>The workflow of establishing integrated model of geology and engineering.</p>
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<p>Comparison of horizontal section length (<b>a</b>) and dynamic reserves per 100 m (<b>b</b>) of blocks A83 and X233.</p>
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<p>The dynamic 3D geomechanical model of the Chang 7 member of the Yanchang Formation in the A83 block: (<b>a</b>) the minimum principal stress model before production and (<b>b</b>) the minimum principal stress model after one year’s production.</p>
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<p>The complex artificial fracture network model of well A83-1.</p>
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<p>The integrated numerical simulation model of well A83-1 by using unstructured grid.</p>
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<p>History matching of well A83-1 after actual volume fracturing.</p>
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<p>Production prediction of well A83-1 after actual volume fracturing.</p>
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<p>Production prediction of well A83-1 after CO<sub>2</sub> pre-pad energized fracturing.</p>
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17 pages, 6389 KiB  
Article
Transmission of Seeding Agent for Aircraft Precipitation Enhancement Based on the HYSPLIT Model
by Xiuzhu Sha, Ronghao Chu, Meng Li, Yao Xiao, Jianfang Ding and Lisha Feng
Atmosphere 2022, 13(9), 1508; https://doi.org/10.3390/atmos13091508 - 15 Sep 2022
Cited by 1 | Viewed by 1721
Abstract
The precipitation enhancement operation data of aircraft from 2014 to 2019 and the global data assimilation system (NCEP GDAS) were used in this study. The transport process of the transmission of artificial precipitation enhancement seeding agents for aircraft was successfully simulated by the [...] Read more.
The precipitation enhancement operation data of aircraft from 2014 to 2019 and the global data assimilation system (NCEP GDAS) were used in this study. The transport process of the transmission of artificial precipitation enhancement seeding agents for aircraft was successfully simulated by the HYSPLIT model. The purpose of the study was to explore the applicability of the model in determining the artificial precipitation enhancement influence area and provide a technical method for evaluating the effect of artificial precipitation enhancement. The results show that (1) the HYSPLIT model can be used to track the transmission of aircraft precipitation enhancement seeding agents hourly. Suppose the seeding route satisfies the condition that the route and its interval area are the effective seeding area within 3 h after the end of the seeding agent. In that case, the seeding area’s boundary points can be used as dynamic change markers in the influence area. (2) The HYSPLIT model was used to simulate 24 aircraft precipitation enhancement seeding agent transmission processes. The transmission path for the seeding agent influence altitude layer was mostly southwest or west; the angle ranged from 225° to 268°; the horizontal transport distance of the seeding agent for three hours was 100–200 km; the vertical transmission direction was mostly upward; the range was 0–1200 m; the influence area decreased at the third h of seeding agent transport for 71% of the precipitation enhancement operations. (3) Based on the dynamic variations of 24 aircraft precipitation affected areas determined by the HYSPLIT model, and the contrast area selected by the similarity measurement method, 15 (63%) aircraft precipitation operations contributed to the increase in precipitation. Full article
(This article belongs to the Section Meteorology)
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<p>Borders of China with the study area.</p>
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<p>Overlay of the airline and radar echo plane (<b>a1</b>–<b>a4</b>), vertical radar profile (<b>b1</b>–<b>b4</b>) of No.1–4 aircraft precipitation enhancement operations.</p>
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<p>Overlay of the airline and radar echo plane (<b>a1</b>–<b>a4</b>), vertical radar profile (<b>b1</b>–<b>b4</b>) of No.1–4 aircraft precipitation enhancement operations.</p>
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<p>The transmission of boundary points of affected areas within 3 h after seeding for 24 aircraft precipitation enhancement operations simulated by HYSPLIT model (<b>1</b>–<b>24</b>). Note: The upper part of each graph represents the horizontal transport path of the seeding point, the horizontal and vertical coordinates are longitude and latitude, respectively. The lower part represents the vertical transmission height of the seeding point, the horizontal and vertical coordinates are time (h) and altitude (m), respectively. Red, blue, and green lines indicate the transport paths of different seeding points.</p>
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<p>The transmission of boundary points of affected areas within 3 h after seeding for 24 aircraft precipitation enhancement operations simulated by HYSPLIT model (<b>1</b>–<b>24</b>). Note: The upper part of each graph represents the horizontal transport path of the seeding point, the horizontal and vertical coordinates are longitude and latitude, respectively. The lower part represents the vertical transmission height of the seeding point, the horizontal and vertical coordinates are time (h) and altitude (m), respectively. Red, blue, and green lines indicate the transport paths of different seeding points.</p>
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<p>Direction and angle of seeding agent transmission path. Note: Take the example of querying the south wind, which indicates that the end point of the wind direction arrow is in the middle “+”, then the starting point of the arrow is at 180° marked on the circle, the angle of the south wind is 180°.</p>
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<p>Three-dimensional geospatial configuration of the airline and the hourly affected area within 3 h after seeding for 24 aircraft precipitation enhancement operations (<b>1</b>–<b>24</b>). Note: The solid red line is the airline path, the blue area in the sky is the influence area, the blue area on the ground is the projection of the influence area in the air.</p>
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<p>Three-dimensional geospatial configuration of the airline and the hourly affected area within 3 h after seeding for 24 aircraft precipitation enhancement operations (<b>1</b>–<b>24</b>). Note: The solid red line is the airline path, the blue area in the sky is the influence area, the blue area on the ground is the projection of the influence area in the air.</p>
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<p>Mean hourly precipitation of influence area, mean change rate of hourly precipitation of affected area, and contrast area for 3 h after 24 aircraft precipitation enhancement operations.</p>
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21 pages, 6306 KiB  
Article
Parameterization of a Rising Smoke Plume for a Large Moving Ship Based on CFD
by Jingqian Li, Jihong Song, Yine Xu, Qi Yu, Yan Zhang and Weichun Ma
Atmosphere 2022, 13(9), 1507; https://doi.org/10.3390/atmos13091507 - 15 Sep 2022
Cited by 5 | Viewed by 2088
Abstract
The plume rising height of a ship will directly affect the maximum ground concentration and distance from the source caused by flue gas emission. Ship movement has an important effect on plume rising, but it is often ignored in previous studies. We simulated [...] Read more.
The plume rising height of a ship will directly affect the maximum ground concentration and distance from the source caused by flue gas emission. Ship movement has an important effect on plume rising, but it is often ignored in previous studies. We simulated the weakening effect caused by ship movement by considering the influence of four main parameters (wind speed, ship speed, flue gas exit velocity, and flue gas exit temperature) on the smoke plume rising height, using the computational fluid dynamics (CFD) model (PHOENICS version 6.0 CHAM, London, UK). The main parameters affecting the difference in plume rising height between stationary and moving sources for the same parameter settings are the wind speed and the ship speed. Therefore, we established two simplified calculation methods that corrected the flue gas exit velocity (Vexit) and the flue gas exit temperature (T) for approximately simulating the smoke plume rising height of the moving ship using the formula of a stationary ship. Verification cases indicated that the corrected Vexit (the average of relative error is 5.48%) and the corrected T(the average of relative error is 60.07%) not only saved calculation time but also improved the simulation accuracy compared with the uncorrected stationary source scheme (the average of relative error is 135.38%). Of these correction methods, the scheme with corrected Vexit is more effective. The intention is to provide some references for the field experimentation of moving ship plume rising in different ports in the future and to further study the mechanism of moving ship plume rising. Full article
(This article belongs to the Section Air Quality)
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<p>The ship model, the domain, and the grids in the CFD model: (<b>a</b>) model setting on the X–Z plane; (<b>b</b>) model setting on the Y–Z plane.</p>
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<p>The rising height (H<sub>+</sub>) of the smoke plume of the T2 group: (<b>a</b>) stationary source and (<b>b</b>) moving source.</p>
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<p>The rising height (H<sub>+</sub>) of the smoke plume of the V2 group: (<b>a</b>) stationary source and (<b>b</b>) moving source.</p>
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<p>The relative rate of change (R<sub>H+</sub>) in the H<sub>+</sub> for the simulation schemes with influencing parameters: (<b>a</b>) stationary source and (<b>b</b>) the moving source (red dots indicate reference scenario: S2−V2−T2 scenario at a wind speed of 3 m/s).</p>
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<p>The rising height difference dH<sub>+</sub> between the moving source scheme and the stationary source scheme at different wind speeds in the V2−T2 scenario.</p>
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<p>The rising height difference (dH<sub>+</sub>) between the moving source scheme and the stationary source scheme at different emission levels in the T2 scenario with a wind speed of 3 m/s.</p>
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<p>The rising height difference (dH<sub>+</sub>) between the moving source scheme and the stationary source scheme at different emission temperatures in the V2 scenario with a wind speed of 3 m/s.</p>
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<p>The vertical velocity contours in the S4−V2−T2 scenario: (<b>a</b>) stationary source when the wind speed is 3 m/s; (<b>b</b>) moving source when the wind speed is 3 m/s; (<b>c</b>) stationary source when the wind speed is 12 m/s; and (<b>d</b>) moving source when the wind speed is 12 m/s.</p>
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<p>The vertical velocity contours in the V2−T2 scenario with a wind speed of 3 m/s: (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) are the stationary sources and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) are the moving sources.</p>
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<p>The vertical velocity contours influenced only by exhaust outlet movement in the V2−T2 scenario at wind speeds of 3 m/s: (<b>a</b>–<b>d</b>) are S1, S2, S3, and S4 scenarios, respectively.</p>
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<p>The vertical velocity contours near the hull in the V2−T2 scenario with wind speeds of 3 m/s: (<b>a</b>–<b>d</b>) are S1, S2, S3, and S4 scenarios, respectively.</p>
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<p>The vertical temperature contours in the S4-V2 scenario with a wind speed of 3 m/s: (<b>a</b>) stationary source of T1; (<b>b</b>) moving source of T1; (<b>c</b>) stationary source of T3; and (<b>d</b>) moving source of T3.</p>
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<p>The vertical velocity contours in the S4−V2 scenario with a wind speed of 3 m/s: (<b>a</b>) stationary source of T1; (<b>b</b>) moving source of T1; (<b>c</b>) stationary source of T3; and (<b>d</b>) moving source of T3.</p>
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<p>The comparison between the smoke plume rising height (H<sub>+</sub>) at 1 km downwind simulated by the three stationary source schemes (without correction; correction 1 that corrected <math display="inline"><semantics> <mrow> <msubsup> <mi>V</mi> <mrow> <mi>e</mi> <mi>x</mi> <mi>i</mi> <mi>t</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math>; correction 2 that corrected <math display="inline"><semantics> <msup> <mi>T</mi> <mo>′</mo> </msup> </semantics></math>) and the results of the moving source scheme in the validation cases (the dashed line is the 1:1 line as a reference).</p>
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17 pages, 2998 KiB  
Article
Assessing the Drought Variability in Northeast China over Multiple Temporal and Spatial Scales
by Lin Xue, Martin Kappas, Daniel Wyss and Birgitta Putzenlechner
Atmosphere 2022, 13(9), 1506; https://doi.org/10.3390/atmos13091506 - 15 Sep 2022
Cited by 5 | Viewed by 1838
Abstract
Long-term drought variation provides a scientific foundation for water resource planning and drought mitigation. However, the spatiotemporal variation characteristics of drought in northeast China (NEC) are unclear. We conducted a comprehensive assessment of drought status and trends based on the Standardized Precipitation Evapotranspiration [...] Read more.
Long-term drought variation provides a scientific foundation for water resource planning and drought mitigation. However, the spatiotemporal variation characteristics of drought in northeast China (NEC) are unclear. We conducted a comprehensive assessment of drought status and trends based on the Standardized Precipitation Evapotranspiration Index (SPEI) in NEC from 1990 until 2018. The findings show that: (1) the drying trend peaked in 2001, and then exhibited a mitigation tendency before drying again after 2013. The implementation of ecological restoration projects is primarily responsible for drought mitigation. (2) The areas with wetting and drying trends in the future would cover 86% and 17% of NEC, respectively. (3) There is a time lag between improved vegetation and the trend shift from dry to wet. (4) Spring and winter revealed wet trends within 71% and 84% of NEC, respectively, showing high sensitivity and resilience to drought, while 92–93% of NEC displayed dry tendencies during the summer and autumn seasons. The drought-affected area was the highest in summer and lowest in autumn. (5) The interannual drought severity was highest in May and June. (6) The highest drought impacts and trends occur within shrub and grass and sparsely vegetated land, as well as middle-temperate semiarid regions (M-semiarid). (7) The warmer the temperature zone, the more sensitive it is towards drought under the same hydrological conditions, showing a high drought-affected area. The drier the land, the higher the drought-affected area within the same temperature zone, with pronounced drought trends during the spring and summer seasons. Our findings highlight the need for the government to more explicitly develop drought mitigation strategies in accordance with NEC’s spatiotemporal drought variations and specifically the need to concentrate on droughts in M-semiarid regions occurring in summer, particularly in May and June. Full article
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<p>Location (<b>a</b>), elevation and climate regions (<b>b</b>), and land cover types (<b>c</b>) of northeast China (NEC). The elevation data in a 30 m spatial resolution was obtained from the Data Center for Resource and Environmental Sciences of the Chinese Academy of Sciences (<a href="http://www.resdc.cn/" target="_blank">http://www.resdc.cn/</a>, accessed on 11 March 2021). Land cover data were derived from the “plant functional type map” of China in a 1 km spatial resolution, which was obtained from the National Tibetan Plateau Data Center (<a href="https://data.tpdc.ac.cn/" target="_blank">https://data.tpdc.ac.cn/</a>, accessed on 11 March 2021).</p>
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<p>Maps of dry–wet conditions in northeast China (NEC) from 1990 to 2018 every five years. (<b>a</b>) 1990–1994; (<b>b</b>) 1995–1999; (<b>c</b>) 2000–2004; (<b>d</b>) 2005–2009; (<b>e</b>) 2010–2014; (<b>f</b>) 2015–2018.</p>
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<p>Annual dry–wet conditions (<b>a</b>) and annual light drought area percentage (SPEI12 &lt; −0.5), (<b>b</b>) in northeast China (NEC) from 1990 to 2018.</p>
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<p>Percentage of monthly light and quarterly moderate drought area SPEI03 &lt; −1, (<b>a</b>); SPEI01 &lt; −1.5, (<b>b</b>) in northeast China (NEC) from 1990 to 2018.</p>
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<p>Percentage of annual light drought area (SPEI12 &lt; −0.5) over different climate regions and land cover types in northeast China (NEC) from 1990 to 2018.</p>
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<p>Spatial variation trends of SPEI12 (<b>a</b>) and spatial distribution of Hurst exponent value of SPEI12 (<b>b</b>) in northeast China from 1990 to 2018. S- in the figure is the abbreviation for significant. Black crosses indicate significant trends with <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Mutation point (<span class="html-italic">p</span> &lt; 0.1, (<b>a</b>)) of the spatial variation trend of SPEI12 and the change slope before (<b>b</b>) and after (<b>c</b>) the mutation point in northeast China (NEC) from 1990 to 2018.</p>
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<p>Quarterly variations of spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) based on SPEI03 in northeast China (NEC) from 1990 to 2018.</p>
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<p>Mann–Kendall statistical test for quarterly variations in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) based on SPEI03 in northeast China (NEC) from 1990 to 2018.</p>
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<p>Spatial distribution of quarterly trends in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>) based on SPEI03 in northeast China (NEC) from 1990 to 2018.</p>
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<p>Average annual and quarterly changing trend over different climate regions and land cover types in northeast China (NEC) from 1990 to 2018.</p>
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<p>Relationship between annual mean temperature (<b>a</b>), potential evapotranspiration (<b>b</b>), precipitation (<b>c</b>) and annual SPEI12.</p>
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<p>Annual mean Normalized Difference Vegetation Index (NDVI) of the areas where the SPEI12 trend turned during the period 1990–2018.</p>
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14 pages, 4902 KiB  
Article
Analysis of Ozone Vertical Profiles over Wuyishan Region during Spring 2022 and Their Correlations with Meteorological Factors
by Tianfu Zhu, Huiying Deng, Jinhong Huang, Yulan Zheng, Ziliang Li, Rui Zhao and Hong Wang
Atmosphere 2022, 13(9), 1505; https://doi.org/10.3390/atmos13091505 - 15 Sep 2022
Cited by 3 | Viewed by 1694
Abstract
Understanding the vertical structure of ozone concentrations in different seasons and their correlations with the associated meteorological conditions is crucial for exploring atmospheric ozone variability and improving the accuracy of regional ozone prediction. In this study, an ozone-sounding experiment was carried out at [...] Read more.
Understanding the vertical structure of ozone concentrations in different seasons and their correlations with the associated meteorological conditions is crucial for exploring atmospheric ozone variability and improving the accuracy of regional ozone prediction. In this study, an ozone-sounding experiment was carried out at the Shaowu sounding Station in Fujian from November 2021 to May 2022 in order to obtain vertical profiles of ozone concentrations and synoptic variables. Based on these observations, we examined the characteristics of tropospheric ozone profiles in spring over the Wuyishan region and their comparison with wintertime ozone. The results show that compared with winter, the total ozone column (TOC) in spring has increased by 64.4%, with an enhancement of 23.8% for the troposphere and a greater increment of 69.1% for the stratosphere. The sub-peaks of tropospheric ozone below 12 km are found in both spring and winter of 2022, which are accompanied by lower relative humidity (<10% in winter and <15% in spring), temperature inversions in some cases, and intensive westerly winds. Furthermore, we investigated the relationship between ozone volume mixing ratio (OVMR) and synoptic conditions in the Wuyishan region and concluded that OVMR above 1.5 km is negatively correlated with temperature and relative humidity but positively correlated with wind speed. Additionally, springtime OVMR in the middle and upper troposphere exhibits a “funnel” distribution, showing a higher OVMR on the day of sounding observations and one day before and after that on adjacent days with low-level southwesterly winds and updrafts. While in winter, the strong downdrafts dominate on the sounding observation day. Full article
(This article belongs to the Special Issue Remote Sensing and Multiple Observations of Air Quality in China)
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<p>The geographical location of (<b>a</b>) the six national meteorological observation stations in mainland China (blue triangles) and (<b>b</b>) the Wuyishan and Shaowu stations (red dots).</p>
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<p>Major sounding instruments: (<b>a</b>) CTY-1 ozone sounding sensor and (<b>b</b>) Beidou Satellite navigator.</p>
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<p>Vertical profiles of (<b>a</b>) ozone partial pressure and (<b>b</b>) OVMR for the average of all observations (red line), individual profiles (grey line) and the height corresponding to the average value (blue dotted line) during spring 2022 at Shaowu station.</p>
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<p>Profiles of ozone partial pressure at (<b>a</b>) the entire detection levels and (<b>b</b>) within the troposphere, averaged across the spring (red line) and winter (blue line). The error bars represent the standard deviation of the individual observations.</p>
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<p>Same as <a href="#atmosphere-13-01505-f004" class="html-fig">Figure 4</a>, but for OVMR.</p>
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<p>The TOC over the stratosphere (grey bar), the troposphere (yellow bar), and boundary layer (blue bar), as well as the trend line (black line), derived from each ozonesonde observation from November 2021 to May 2022. The vertical blue dash line means to distinguish winter from spring. (The boundary layer: 0–1.5 km; troposphere (including boundary layer): 0—tropopause height (km); stratosphere: tropopause height (km)—balloon burst height (km)).</p>
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<p>Profiles of tropospheric OVMR (black line), air temperature (red line), relative humidity (blue line), inversion structure (blue dash circles) and horizontal winds (vectors) measured on (<b>a</b>) 1 December 2021; (<b>b</b>) 15 December 2021; (<b>c</b>) 29 December 2021 and (<b>d</b>) 19 January 2022. The sub-peak of each profile is highlighted in blue background and the corresponding relative humidity is also shown.</p>
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<p>Same as <a href="#atmosphere-13-01505-f007" class="html-fig">Figure 7</a>, but for the spring.</p>
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<p>The distribution of vertical velocity (shading, units: Pa∙s<sup>−1</sup>) and wind divergence (black contours, units: 10<sup>−5</sup>∙s<sup>−1</sup>) at the Shaowu station during 29–30 May 2022 as a function of hours (data from ERA-5).</p>
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<p>Variations of the monthly mean vertical velocity (shading, units: Pa∙s<sup>−1</sup>), horizontal wind (vectors, units: m∙s<sup>−1</sup>), and relative humidity (green contours, units: %) at the Shaowu city as a function of time and height, derived from ERA5 reanalysis (the ERA5 grid data used is located at 117.50° E, 27.25° N).</p>
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<p>The evolution of (<b>a</b>) daily OVMR (shading) and horizontal winds (vectors), and (<b>b</b>) vertical velocity (shading) and relative humidity (contours) at different heights during the three days before and after the day of sounding observation in spring, based on ERA5 reanalysis. (<b>c</b>,<b>d</b>) shows the same as (<b>a</b>,<b>b</b>) but for the winter.</p>
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17 pages, 11570 KiB  
Article
Application of a Hybrid CEEMD-LSTM Model Based on the Standardized Precipitation Index for Drought Forecasting: The Case of the Xinjiang Uygur Autonomous Region, China
by Yan Ding, Guoqiang Yu, Ran Tian and Yizhong Sun
Atmosphere 2022, 13(9), 1504; https://doi.org/10.3390/atmos13091504 - 15 Sep 2022
Cited by 4 | Viewed by 2846
Abstract
Accurate forecasting of droughts can effectively reduce the risk of drought. We propose a hybrid model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) to improve drought prediction accuracy. Taking the Xinjiang Uygur Autonomous Region as an example, [...] Read more.
Accurate forecasting of droughts can effectively reduce the risk of drought. We propose a hybrid model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) to improve drought prediction accuracy. Taking the Xinjiang Uygur Autonomous Region as an example, the prediction accuracy of the LSTM and CEEMD-LSTM models for the standardized precipitation index (SPI) on multiple timescales was compared and analyzed. Multiple evaluation metrics were used in the comparison of the models, such as the Nash–Sutcliffe efficiency (NSE). The results show that (1) with increasing timescale, the prediction accuracy of the LSTM and CEEMD-LSTM models gradually improves, and both reach their highest accuracy at the 24-month timescale; (2) the CEEMD can effectively stabilize the time-series, and the prediction accuracy of the hybrid model is higher than that of the single model at each timescale; and (3) the NSE values for the hybrid CEEMD-LSTM model at SPI24 were 0.895, 0.930, 0.908, and 0.852 for Fuhai, Kuerle, Yutian, and Hami station, respectively. This indicates the applicability of the hybrid model in the forecasting of drought. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Geographical information and meteorological station distribution in Xinjiang Uygur Autonomous Region.</p>
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<p>Structure diagram of long short-term memory.</p>
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<p>Workflow of the CEEMD-LSTM combined model.</p>
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<p>Observed SPI values at the 1-, 3-, 6-, 9-, 12-, and 24-month timescales of the example stations.</p>
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<p>Forecast of multi-timescale SPI values of the LSTM and CEEMD-LSTM model at Fuhai Station (2008–2019).</p>
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<p>Forecast of multi-timescale SPI values of the LSTM and CEEMD-LSTM model at Kuerle Station (2008–2019).</p>
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<p>Forecast of multi-timescale SPI values of the LSTM and CEEMD-LSTM model at Yutian Station (2008–2019).</p>
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<p>Forecast of multi-timescale SPI values of the LSTM and CEEMD-LSTM model at Hami Station (2008–2019).</p>
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<p>The CEEMD decomposition results of SPI3 sequence.</p>
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<p>Spatial distributions of SPI in 2011, using LSTM and CEEMD-LSTM model.</p>
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21 pages, 8229 KiB  
Article
A Quantizing Method for Atmospheric Environment Impact Post-Assessment of Highways Based on Computational Fluid Dynamics Model
by Xiaochun Qin, Dongxiao Yang, Shu Liu, Xiaoqing Yu and Vicky Wangechi Wangari
Atmosphere 2022, 13(9), 1503; https://doi.org/10.3390/atmos13091503 - 15 Sep 2022
Cited by 1 | Viewed by 1886
Abstract
The post-assessment of highway atmospheric environmental impacts was limited by the traditional air pollution prediction model, which cannot adapt to complex terrain and complex obstacle scenes. The traditional model has a single evaluation index, which cannot accurately evaluate and predict the transient and [...] Read more.
The post-assessment of highway atmospheric environmental impacts was limited by the traditional air pollution prediction model, which cannot adapt to complex terrain and complex obstacle scenes. The traditional model has a single evaluation index, which cannot accurately evaluate and predict the transient and long-term emissions of various pollutants. Based on the computational fluid dynamics model, this work establishes a post-assessment method of the atmospheric environment impact of the Beijing–Chengde Expressway construction project. The main pollution factors NOx and CO of highway traffic for transmission and diffusion simulation analysis were selected. The influence law of traffic function, environmental impact, meteorological conditions, and landform on the diffusion of pollution factors in complex tunnel sections were analyzed. It concludes that the pollution within 200 m along the expressway is severe and mainly concentrated in the tunnel entrance and gully area. The NOx concentration is generally higher than CO. The environmental quality is not up to standard and has a diffusion trend with increased traffic flow, operation time, wind speed, wind temperature, and wind direction frequency. The research results can provide theoretical guidance and technical support for the scientific post-assessment of highway environmental impact under complex conditions. Full article
(This article belongs to the Special Issue Aerosols in Residential, School, and Vehicle Environments)
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<p>Research route.</p>
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<p>The map of a typical section and the main control points of the study case.</p>
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<p>Flow boundary condition.</p>
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<p>Rose map of wind direction and wind speed.</p>
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<p>Topographic map of the study case.</p>
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<p>Hourly average concentration distribution of NO<sub>X</sub> at a peak period in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) 7:00–8:00; (<b>b</b>) 17:00–18:00.</p>
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<p>Hourly average concentration distribution of CO at a peak period in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) 7:00–8:00; (<b>b</b>) 17:00–18:00.</p>
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<p>Hourly average concentration distribution of NO<sub>X</sub> and CO at an off-peak period in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) NO<sub>X</sub>; (<b>b</b>) CO.</p>
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<p>Annual average concentrations of NO<sub>X</sub> and CO vary with the distance in the Hengchengzi No. 2 Tunnel section.</p>
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<p>Annual average concentrations of NO<sub>X</sub> and CO vary with the distance in the Hengchengzi No. 2 Tunnel section.</p>
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<p>Winter and annual average concentration distribution of NOx in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) Average concentration distribution in winter; (<b>b</b>) Annual average concentration distribution.</p>
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<p>Winter and annual average concentration distribution of CO in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) Average concentration distribution in winter; (<b>b</b>) Annual average concentration distribution.</p>
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<p>Concentration distribution of NO<sub>X</sub> and CO under the dominant wind direction in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) NO<sub>X</sub>; (<b>b</b>) CO.</p>
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<p>Concentration distribution of NO<sub>X</sub> under different wind speeds in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) Low wind speed (0.5 m/s); (<b>b</b>) Medium wind speed (2.7 m/s); (<b>c</b>) High wind speed (6.0 m/s).</p>
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<p>Concentration distribution of CO under different wind speeds in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) Low wind speed (0.5 m/s); (<b>b</b>) Medium wind speed (2.7 m/s); (<b>c</b>) High wind speed (6.0 m/s).</p>
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<p>Average concentration distribution of NOx in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) Low temperature (−6 °C); (<b>b</b>) High temperature (3 °C).</p>
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<p>Average concentration distribution of CO in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) Low temperature (−6 °C); (<b>b</b>) High temperature (3 °C).</p>
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<p>Concentration distribution of NO<sub>X</sub> and CO in the Hengchengzi No. 2 Tunnel section. (<b>a</b>) NO<sub>X</sub>; (<b>b</b>) CO.</p>
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20 pages, 3778 KiB  
Article
Organic Acids in Sequential Volume-Based Rainwater Samples in Shanghai: Seasonal Variations and Origins
by Zhixiong Xie, Huayun Xiao and Yu Xu
Atmosphere 2022, 13(9), 1502; https://doi.org/10.3390/atmos13091502 - 15 Sep 2022
Cited by 1 | Viewed by 1885
Abstract
Organic acids were investigated in the rain sequence. Samples were collected in Shanghai (East China) over a one-year period using an automatic volume-based sequential rain sampler designed by ourselves. Organic acids significantly contributed (17.8 ± 10.2%) to the acidity of rainfall events in [...] Read more.
Organic acids were investigated in the rain sequence. Samples were collected in Shanghai (East China) over a one-year period using an automatic volume-based sequential rain sampler designed by ourselves. Organic acids significantly contributed (17.8 ± 10.2%) to the acidity of rainfall events in Shanghai. We observed that the concentration of each water-soluble ion in the sequential volume-based rainwater samples did not change significantly after the cumulative rainfall reached ~1.2 mm, on average. The volume-weighted mean (VWM) concentrations of formic acid, acetic acid, and oxalic acid were 13.54 μeq L−1, 8.32 μeq L−1, and 5.85 μeq L−1, respectively. Organic acids might mostly come from fine particles, which was the reason for the differences in acid concentrations in rainfall events, cloud water, and early sequences of rainfall events. The VWM concentrations of organic acids in rainfall events, cloud water, and early sequences of rainfall events were highest in spring and lowest in winter. Further analysis, including positive matrix factorization (PMF), suggested that vehicle exhaust and secondary emission sources were dominant contributors of organic acids in rainfall events (40.5%), followed by biological emission sources (37.3%), and biomass combustion sources (18.6%). The overall results not only reveal the critical role of organic acids in cloud water and rainfall events but also indicate organic acids might pose an ecological threat to the local surface ecosystem. Full article
(This article belongs to the Section Climatology)
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<p>Conceptual diagram showing an automatic volume-based sequential rain sampler. When the blue liquid level reaches the sensing line (corresponding to the purple liquid height), the horizontal conduit goes to the next point.</p>
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<p>The change patterns of water-soluble ions in rainfall event with rainfall intensity and rainfall in Shanghai: (<b>a</b>) pH and EC, (<b>b</b>) Na<sup>+</sup> and Cl<sup>−</sup>, (<b>c</b>) K<sup>+</sup>, Mg<sup>2+</sup>, and Ca<sup>2+</sup>, (<b>d</b>) acetic, formic, MSA, glutaric, succinic, and oxalic acid, (<b>e</b>) NO<sub>2</sub><sup>−</sup>, NO<sub>3</sub><sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, NH<sub>4</sub><sup>+</sup>, and F<sup>−</sup>.</p>
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<p>The changes in concentrations of (<b>a</b>) Ca<sup>2+</sup>, (<b>b</b>) NH<sub>4</sub><sup>+</sup>, (<b>c</b>) SO<sub>4</sub><sup>2−</sup>, (<b>d</b>) formic acid, (<b>e</b>) acetic acid, and (<b>f</b>) oxalic acid with accumulated rainfall. The ion concentrations were divided into four percentiles to observe the ion concentrations in each concentration interval (0–30%, 30–50%, 50–70%, 70–90%).</p>
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<p>Seasonal variations of (<b>a</b>) water-soluble inorganic ions and (<b>b</b>) organic acids in rainfall events.</p>
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<p>Backward trajectory of the major air mass clusters arriving at the sampling site and pie chart of ion concentrations in rainfalls in (<b>a</b>,<b>b</b>) spring, (<b>c</b>,<b>d</b>) summer, (<b>e</b>,<b>f</b>) autumn, and (<b>g</b>,<b>h</b>) winter.</p>
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<p>The [F/A]<sub>M</sub> judgment equation curve and [F/A]<sub>M</sub> of rainfall events for Spring, Summer, Autumn, and Winter. The proportion [F/A]<sub>M</sub> &gt; 1 in each season is represented in pie charts.</p>
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<p>The percentages and concentrations of species in rainfall events in the six factors identified by PMF are illustrated. Total organic acids are represented by TOA, total ion concentrations are represented by TIC. The bar graphs corresponded to the left ordinate (percentage of component), and the grey points corresponded to the right ordinate (concentration of component). Six factors at this site were identified: (<b>a</b>) marine sources, (<b>b</b>) vehicle exhaust and secondary emission sources, (<b>c</b>) secondary sulfate sources, (<b>d</b>) biological emission sources, (<b>e</b>) biomass combustion sources, and (<b>f</b>) dust sources.</p>
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<p>Illustrations of the percentage of factor contributions to organic acids in (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter.</p>
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<p>Illustrations of the percentage of factor contributions to organic acids in (<b>a</b>) cloud water and (<b>b</b>) early sequences of rainfall events.</p>
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