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26 pages, 416 KiB  
Perspective
Foundational Issues in Dynamical Casimir Effect and Analogue Features in Cosmological Particle Creation
by Jen-Tsung Hsiang and Bei-Lok Hu
Universe 2024, 10(11), 418; https://doi.org/10.3390/universe10110418 - 7 Nov 2024
Viewed by 464
Abstract
Moving mirrors as analogue sources of Hawking radiation from black holes have been explored extensively but less so with cosmological particle creation (CPC), even though the analogy between the dynamical Casimir effect (DCE) and CPC based on the mechanism of the parametric amplification [...] Read more.
Moving mirrors as analogue sources of Hawking radiation from black holes have been explored extensively but less so with cosmological particle creation (CPC), even though the analogy between the dynamical Casimir effect (DCE) and CPC based on the mechanism of the parametric amplification of quantum field fluctuations has also been known for a long time. This ‘perspective’ essay intends to convey some of the rigor and thoroughness of quantum field theory in curved spacetime, which serves as the theoretical foundation of CPC, to DCE, which enjoys a variety of active experimental explorations. We have selected seven issues of relevance to address, starting from the naively simple ones, e.g., why one should be bothered with ‘curved’ spacetime when performing a laboratory experiment in ostensibly flat space, to foundational theoretical ones, such as the frequent appearance of nonlocal dissipation in the system dynamics induced by colored noises in its field environment, the existence of quantum Lenz law and fluctuation–dissipation relations in the backreaction effects of DCE emission on the moving atom/mirror or the source, and the construction of a microphysics model to account for the dynamical responses of a mirror or medium. The strengthening of the theoretical ground for DCE is not only useful for improving conceptual clarity but needed for the development of the proof-of-concept type of future experimental designs for DCE. The results from the DCE experiments in turn will enrich our understanding of quantum field effects in the early universe because they are, in the spirit of analogue gravity, our best hopes for the verification of these fundamental processes. Full article
(This article belongs to the Special Issue Quantum Physics including Gravity: Highlights and Novelties)
33 pages, 7753 KiB  
Review
State-of-the-Art Review of the Simulation of Dynamic Recrystallization
by Xin Liu, Jiachen Zhu, Yuying He, Hongbin Jia, Binzhou Li and Gang Fang
Metals 2024, 14(11), 1230; https://doi.org/10.3390/met14111230 - 28 Oct 2024
Viewed by 733
Abstract
The evolution of microstructures during the hot working of metallic materials determines their workability and properties. Recrystallization is an important softening mechanism in material forming that has been extensively researched in recent decades. This paper comprehensively reviews the basic methods and their applications [...] Read more.
The evolution of microstructures during the hot working of metallic materials determines their workability and properties. Recrystallization is an important softening mechanism in material forming that has been extensively researched in recent decades. This paper comprehensively reviews the basic methods and their applications in numerical simulations of dynamic recrystallization (DRX). The advantages and shortcomings of simulation methods are evaluated. Mean field models are used to implicitly describe the DRX process and are embedded into a finite element (FE) program for forming. These models provide recrystallization volume fraction and average grain size in the FE results without requiring extra computational resources. However, they do not accurately describe the microphysical mechanism, leading to a lower simulation accuracy. On the other hand, full field methods explicitly predict grain topology on a mesoscopic scale, fully considering the microscopic physical mechanism. This enhances the simulation accuracy but requires a significant amount of computational resources. Recently, the coupling of full field methods with polycrystal plasticity models and precipitation models has rapidly developed, considering more influencing factors of recrystallization on a microscale. Furthermore, integration with evolving machine learning methods has the potential to significantly improve the accuracy and efficiency of recrystallization simulation. Full article
(This article belongs to the Special Issue Modeling, Simulation and Experimental Studies in Metal Forming)
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Figure 1

Figure 1
<p>Schematic of dynamic recrystallization.</p>
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<p>Models of numerical simulation for microstructure evolution.</p>
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<p>Hot compression simulation results for 80MnSi8-6 Steel at temperature of 1000 °C and strain rate of 10 s<sup>−1</sup>: (<b>a</b>) strain intensity distribution, (<b>b</b>) DRX fraction distribution, and (<b>c</b>) average grain size distribution [<a href="#B23-metals-14-01230" class="html-bibr">23</a>]. In this simulation, the JMAK model for grain growth and DRX was developed, and DRX kinetics were determined. The compression was simulated on QForm implemented with coefficients for the JMAK model.</p>
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<p>Flow diagram of the unified constitutive modeling framework [<a href="#B58-metals-14-01230" class="html-bibr">58</a>].</p>
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<p>Comparisons between the experimental (<b>a</b>–<b>c</b>) and the CA-simulated (<b>d</b>–<b>f</b>) microstructures of the magnesium alloy ZM21 under various deformation conditions: (<b>a</b>,<b>d</b>) at 450 °C and 0.01 s<sup>−1</sup>; (<b>b</b>,<b>e</b>) at 450 °C and 1 s<sup>−1</sup>; (<b>c</b>,<b>f</b>) 350 °C and 1 s<sup>−1</sup> [<a href="#B91-metals-14-01230" class="html-bibr">91</a>]. Simulated white regions (<b>d</b>) indicate the deformed and un-recrystallized grains, consistent with the corresponding grains marked with red arrows (<b>c</b>).</p>
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<p>CA-simulated 3D grain topology of microalloyed steel in a drawn wire [<a href="#B108-metals-14-01230" class="html-bibr">108</a>].</p>
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<p>(<b>a</b>) Predicted results using a phase-field model and (<b>b</b>) measured results of grain microstructures at different compression strains of the magnesium alloy AZ80 [<a href="#B121-metals-14-01230" class="html-bibr">121</a>].</p>
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<p>CPFEM coupled with LS−FE simulated the orientation, average stress, and dislocation density of recrystallized grains of 304 L stainless steel [<a href="#B151-metals-14-01230" class="html-bibr">151</a>].</p>
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<p>Multiscale framework for fully coupled CPFEM-CA approach simulating the DRX of AISI 304LN stainless steel during the hot compression of AISI 304 LN stainless steel [<a href="#B163-metals-14-01230" class="html-bibr">163</a>].</p>
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<p>(<b>a</b>) Simulated results of the dislocation density distribution using CPFEM of pure aluminum during tensile test; (<b>b</b>,<b>c</b>) are the dislocation density distribution on a small area before and after data mapping [<a href="#B166-metals-14-01230" class="html-bibr">166</a>].</p>
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<p>Schematic diagram of multilevel CA space for the concurrent simulation of DRX and dynamic precipitation of magnesium alloy [<a href="#B190-metals-14-01230" class="html-bibr">190</a>].</p>
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<p>Microstructural morphology of compressed magnesium alloy at a strain of 0.7 from experiments (<b>a</b>–<b>c</b>) and simulations (<b>d</b>–<b>f</b>): (<b>a</b>,<b>d</b>) 300 °C and 0.01 s<sup>−1</sup>, (<b>b</b>,<b>e</b>) 300 °C and 0.1 s<sup>−1</sup>, (<b>c</b>,<b>f</b>) 270 °C and 0.1 s<sup>−1</sup> [<a href="#B190-metals-14-01230" class="html-bibr">190</a>].</p>
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22 pages, 13393 KiB  
Article
Microphysical Characteristics of Monsoon Precipitation over Yangtze-and-Huai River Basin and South China: A Comparative Study from GPM DPR Observation
by Zelin Wang, Xiong Hu, Weihua Ai, Junqi Qiao and Xianbin Zhao
Remote Sens. 2024, 16(18), 3433; https://doi.org/10.3390/rs16183433 - 16 Sep 2024
Viewed by 555
Abstract
It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, [...] Read more.
It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h−1) than over YHRB (2.27 mm/h, 3.22 h−1) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)
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Figure 1

Figure 1
<p>(<b>a</b>) China: Located in the Northern Hemisphere, within the latitudes of 0°N to 53°N and longitudes of 70°E to 140°E. Red rectangle: the central and eastern regions of China. (<b>b</b>) The elevation distribution and topography of Yangtze–Huai River Basin (110°E–122°E, 26.5°N–35°N) and South China (105°E–120°E, 18°N–26.5°N, unit: m).</p>
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<p>The frequency and amount contribution (%) of different intensity (heavy, extreme, the most extreme) (<b>a</b>), different types (convective, stratiform, and shallow) (<b>b</b>), different PEI (low, moderate, and high) (<b>c</b>) precipitation to total precipitation over YHRB and SC. The left/right six columns are for precipitation frequency/amount contributions. “Freq” is an abbreviation for frequency. Probability density functions (PDFs) (%) of the (<b>d</b>) precipitation efficiency index (PEI) over YHRB and SC (red and blue line).</p>
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<p>(<b>Top</b>) Convective (red) and stratiform (purple) precipitation contributions by frequency and amount across precipitation PEI categories over (<b>a</b>,<b>c</b>) YHRB and (<b>b</b>,<b>d</b>) SC. From the inside to the outside, circles represent the precipitation cases: low, moderate, and high. (<b>Middle</b>) The same for different precipitation intensities over (<b>e</b>,<b>g</b>) YHRB and (<b>f</b>,<b>h</b>) SC. (<b>Bottom</b>) Low (red), moderate (purple), and high (green) PEI precipitation contributions by intensity over (<b>i</b>,<b>k</b>) YHRB and (<b>j</b>,<b>l</b>) SC. From the inside to the outside, circles denote precipitation intensity cases: heavy, extreme, and most extreme.</p>
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<p>Distribution of <span class="html-italic">D<sub>m</sub></span> versus <span class="html-italic">N<sub>w</sub></span> pairs for convective (red dots), stratiform (blue dots), and shallow (green dots) rain types at 1 km height during the rainy season over YHRB (<b>a</b>) and SC (<b>b</b>) and (<b>c</b>) averaged <span class="html-italic">D<sub>m</sub></span> versus <span class="html-italic">N<sub>w</sub></span> pairs of different precipitation types for the two regions. The <span class="html-italic">D<sub>m</sub></span> and <span class="html-italic">N<sub>w</sub></span> are from 2ADPR product, which belongs to GPM’s L2-level product. The square, triangle, cross, dot, and star represent convective precipitation, stratiform precipitation, shallow precipitation, and all precipitation.</p>
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<p>Probability density functions (PDFs) of (<b>a</b>,<b>d</b>) <span class="html-italic">D<sub>m</sub></span>; (<b>b</b>,<b>e</b>) <span class="html-italic">N<sub>w</sub></span> at 1 km, and (<b>c</b>,<b>f</b>) PEI over YHRB (<b>the first row</b>) and SC (<b>the second row</b>) during the rainy seasons (2014–2023). Abbreviations: PDF—probability density function; PEI—precipitation efficiency index.</p>
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<p>The vertical profiles of (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>) <span class="html-italic">D<sub>m</sub></span> (mm), (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) <span class="html-italic">N<sub>w</sub></span> (dB), and (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) <span class="html-italic">Z<sub>e</sub></span> (dBZ) (radar reflectivity factor) for (<b>a</b>–<b>f</b>) convective precipitation and (<b>g</b>–<b>l</b>) stratiform precipitation in terms of different PEIs and intensities in rainy season over (<b>a</b>–<b>c</b>), (<b>g</b>–<b>i</b>) YHRB and (<b>d</b>–<b>f</b>), (<b>j</b>–<b>l</b>) SC. Blue/yellow/pink lines represent heavy/extreme/the most extreme precipitation. Solid/dotted/dashed lines represent low/moderate/high PEI precipitation.</p>
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<p>The box–whisker plots the vertical structure of rain rate (RR) within the ice phase layer (IPL) for convective (<b>a</b>–<b>c</b>) and stratiform precipitation (<b>d</b>–<b>f</b>) precipitation in terms of different PEIs (low, moderate, and high; from the left column to the right column) in the rainy season over YHRB (red boxes) and SC (blue boxes). The <span class="html-italic">y</span>-axis depicts the relative IPL height, with 0 representing the height of the minimum IPL bottom. The center of the box represents the 50% percentile value, the lower quartile (25%) and the upper quartile (75%) are the left and right boundaries of the box, and the whiskers correspond to the 5% and 95% values.</p>
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<p>Histogram of the STH (<b>a</b>–<b>f</b>) and MLT (<b>g</b>–<b>l</b>) for (<b>a</b>–<b>c</b>), (<b>g</b>–<b>i</b>) convective precipitation and (<b>d</b>–<b>f</b>), (<b>j</b>–<b>l</b>) stratiform precipitation in terms of different PEIs (low, moderate, and high; from the left column to the right column) in rainy season precipitation systems over YHRB and SC from 2014 to 2023.</p>
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<p>The spatial distributions of melting layer top height (MLTH) (<b>a</b>,<b>b</b>), melting layer bottom height (MLBH) (<b>c</b>,<b>d</b>), melting layer width (MLW) (<b>e</b>,<b>f</b>), and storm top height (STH) (<b>g</b>,<b>h</b>), CPAE (<b>i</b>,<b>j</b>) over YHRB (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>), and SC (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>).</p>
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<p>The frequency pattern in two-dimensional space of Δ<span class="html-italic">D<sub>m</sub></span> and Δ<span class="html-italic">Z<sub>e</sub></span> for (<b>a</b>–<b>f</b>) convective precipitation and (<b>g</b>–<b>l</b>) stratiform precipitation at different PEI of (<b>a</b>,<b>e</b>) 0–3 h<sup>−1</sup>, (<b>b</b>,<b>f</b>) 3–6 h<sup>−1</sup>, and (<b>c</b>,<b>g</b>) &gt;6 h<sup>−1</sup> in rainy season precipitation systems over YHRB (<b>a</b>–<b>c</b>,<b>g</b>–<b>i</b>) and SC (<b>d</b>–<b>f</b>,<b>j</b>–<b>l</b>) from 2014 to 2023.</p>
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<p>The violin plots for the IPL width of convective (<b>left</b>) and stratiform precipitation (<b>right</b>) in the rainy season over YHRB (red) and SC (blue).</p>
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<p>The amount contribution of low (pink), moderate (purple), and high (red) IPL width precipitation in different types of precipitation ((<b>a</b>–<b>c</b>) are convective precipitation, (<b>d</b>–<b>f</b>) are stratiform precipitation) under different PEIs (low, moderate, and high, from the top to the bottom) over YHRB (inside circle) and SC (outside circle). (<b>g</b>) The box–whisker plots for the distribution of convective and stratiform near-surface precipitation rates with different IPL widths under different PEIs. LC, MC, HC, LS, MS, and HS represent low convective, moderate convective, high convective, low stratiform, moderate stratiform, and high stratiform, respectively. For instance, in LCL, where the first L stands for PEI grade, the C stands for convective precipitation, and the last L stands for IPL thickness grade. LCL is characterized by low PEI and low IPL width in convective precipitation.</p>
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<p>Two-dimensional kernel density estimation of ice phase layer (IPL) width and near-surface rain (nsRR) for (<b>a</b>–<b>f</b>) convective precipitation and (<b>g</b>–<b>l</b>) stratiform precipitation in terms of different PEIs (from left to right, low PEI, moderate PEI, and high PEI) in rainy season precipitation systems over YHRB (<b>a</b>–<b>c</b>,<b>g</b>–<b>i</b>) and SC (<b>d</b>–<b>f</b>,<b>j</b>–<b>l</b>) from 2014 to 2023.</p>
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<p>Two-dimensional kernel density estimation of the radar reflectivity (Δ<span class="html-italic">Z<sub>e</sub></span>) and near-surface rain rate (nsRR) for (<b>a</b>–<b>f</b>) convective precipitation and (<b>g</b>–<b>l</b>) stratiform precipitation in terms of different PEIs (from left to right, low PEI, moderate PEI, and high PEI) in rainy season precipitation systems over YHRB (<b>a</b>–<b>c</b>,<b>g</b>–<b>i</b>) and SC (<b>d</b>–<b>f</b>,<b>j</b>–<b>l</b>) from 2014 to 2023.</p>
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<p>The vertical velocity (<span class="html-italic">w</span>) of environmental wind applied in convective precipitation (<b>a</b>–<b>c</b>) and stratiform precipitation (<b>d</b>–<b>f</b>) with different PEIs (from left to right, low, moderate, and high). The negative direction of velocity is from the ground to the sky.</p>
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27 pages, 14463 KiB  
Article
Numerical Investigation of Track and Intensity Evolution of Typhoon Doksuri (2023)
by Dieu-Hong Vu, Ching-Yuang Huang and Thi-Chinh Nguyen
Atmosphere 2024, 15(9), 1105; https://doi.org/10.3390/atmos15091105 - 11 Sep 2024
Viewed by 647
Abstract
This study utilized the WRF model to investigate the track evolution and rapid intensification (RI) of Typhoon Doksuri (2023) as it moved across the Luzon Strait and through the South China Sea (SCS). The simulation results indicate that Doksuri has a smaller track [...] Read more.
This study utilized the WRF model to investigate the track evolution and rapid intensification (RI) of Typhoon Doksuri (2023) as it moved across the Luzon Strait and through the South China Sea (SCS). The simulation results indicate that Doksuri has a smaller track sensitivity to the use of different physics schemes, while having a greater intensity sensitivity. Sensitivity numerical experiments with different physics schemes can well capture its northwestward movement in the first two days, but they predict less westward track deflection as the typhoon moves across the Luzon Strait and through the SCS. Moreover, all the experiments successfully simulated Doksuri’s RI, albeit with quite different rates and a time lag of 12 h. Among different combinations of physics schemes, there exists an optimal set of cumulus parameterization and cloud microphysics schemes for track and intensity predictions. Doksuri’s track changes as the typhoon moved across the Luzon Strait and through the SCS were influenced by the topographic effects of the terrain of the Philippines and Taiwan, to different extents. The track changes of Doksuri are explained by the wavenumber-one potential vorticity (PV) tendency budget from different physical processes, highlighting that the horizontal PV advection dominates the PV tendency throughout most of the simulation time due to the offset of vertical PV advection and differential diabatic heating. In addition, this study applies the extended Sawyer–Eliassen (SE) equation to compare the transverse circulations of the typhoon induced by various forcing sources. The SE solution indicates that radial inflow was largely driven in the lower-tropospheric vortex by strong diabatic heating, while being significantly enhanced in the lower boundary layer due to turbulent friction. All other physical forcing terms were relatively insignificant for the induced transverse circulation. The coordinated radial inflow at low levels may have led to the eyewall development in unbalanced dynamics. Intense diabatic heating thus was vital to the severe RI of Doksuri under a weak vertical wind shear. Full article
(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))
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Figure 1

Figure 1
<p>The two nested domains of the WRF model for Doksuri during the simulation time. The outermost box and white box denote the outer and the inner domains, respectively. The black line with color dots at intervals of 12 h indicates the best track from the IBTrACS from 1200 UTC 23 July to 1200 UTC 28 July 2023. The color of the dots represents different typhoon intensity categories according to the Saffir–Simpson scale for Typhoon Doksuri (2023).</p>
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<p>Tracks of Typhoon Doksuri including the best track data from IBTrACS (solid black line) and JMA (dashed black line) as well as simulated tracks from sensitivity experiments that combined the CPSs (<b>a</b>) KF, (<b>b</b>) GF, (<b>c</b>) GD, and (<b>d</b>) New Tiedtke with different MPSs (solid colored lines) during the period from 1200 UTC 23 July to 1200 UTC 28 July 2023 (0 to 120 forecast hours). The circle symbols indicate the time every 24 h. (<b>e</b>–<b>h</b>) as in (<b>a</b>–<b>d</b>), respectively, but for the 10 m maximum wind speed (V<sub>max</sub>, m s<sup>−1</sup>). The blue, green, magenta, yellow, red, and cyan lines denote the cloud microphysics schemes Lin, WSM6, Goddard, Thompson, NSSL2, and P3, respectively.</p>
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<p>(<b>a</b>) Track of Typhoon Doksuri including the best track data from IBTrACS (solid black line) and JMA (dashed black line) as well as simulated tracks from the sensitivity experiments using the GF cumulus schemes combined with nine different MPSs (solid colored lines) during the period from 1200 UTC 23 July to 1200 UTC 28 July 2023 (0 to 120 forecast hours). The circle symbols indicate the time every 24 h. The 9 different MPSs are listed in <a href="#atmosphere-15-01105-t001" class="html-table">Table 1</a>. (<b>b</b>) as in (<b>a</b>) but for the 10 m maximum wind speed (V<sub>max</sub>, m s<sup>−1</sup>). (<b>c</b>) as in (<b>a</b>) but for the track error (km) for all the sensitivity experiments combining the GF cumulus scheme with the fifteen different MPSs, as noted in <a href="#atmosphere-15-01105-t001" class="html-table">Table 1</a>.</p>
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<p>Accumulated hourly precipitation (mm) from GSMaP in (<b>a</b>) 12 h (from 0000 UTC to 0059 UTC 24 July, (<b>b</b>) 24 h (from 1200 UTC to 1259 UTC 24 July), (<b>c</b>) 36 h (from 0000 UTC to 0059 UTC 25 July), and (<b>d</b>) 48 h (from 1200 UTC to 1259 UTC 25 July). (<b>e</b>–<b>h</b>) as in (<b>a</b>–<b>d</b>), respectively, but for simulated rainfall (mm) for CTL.</p>
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<p>The simulated horizontal wind speed (shaded colors, m s<sup>−1</sup>) averaged in 1–8 km height for CTL at (<b>a</b>) 36 h, (<b>b</b>) 48 h, (<b>c</b>) 60 h, and (<b>d</b>) 72 h. (<b>e</b>–<b>h</b>) as in (<b>a</b>–<b>d</b>) respectively, but for vertical velocity (colors shaded, m s<sup>−1</sup>). The vector shows the simulated horizontal wind averaged in 1–8 km height.</p>
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<p>Hovmöller plots of (<b>a</b>) horizontal wind speed (m s<sup>−1</sup>), (<b>b</b>) vertical velocity (m s<sup>−1</sup>), and (<b>c</b>) diabatic heating (K h<sup>−1</sup>) averaged in 1–8 km height and radii of 0.5°–1.5° concerning azimuth for Doksuri.</p>
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<p>Time evolution of the translation velocity (vectors, m s<sup>−1</sup>) induced by different PV budget terms, including differential diabatic heating (HDIA), vertical PV advection (VADV), horizontal PV advection (HADV), and the sum of the former three terms (SUM), during the forecast time of 120 h for CTL.</p>
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<p>Simulated tracks for CTL (red line), NoTW (blue line), NoPhi (green line), NoPhi-TW (magenta line), and the best track from IBTrACS (black line). The simulated time was from 1200 UTC 23 July to 1200 UTC 28 July 2023. The circle symbols indicate the time every 24 h.</p>
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<p>As in <a href="#atmosphere-15-01105-f007" class="html-fig">Figure 7</a> but for (<b>a</b>) NoTW and (<b>b</b>) NoPhi.</p>
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<p>LHF (shaded colors, W m<sup>−2</sup>) for CTL at (<b>a</b>) 3 h, (<b>b</b>) 12 h, (<b>c</b>) 18 h, (<b>d</b>) 24 h, (<b>e</b>) 36 h, and (<b>f</b>) 48 h. Solid blue circles mark the 100 and 200 km radii from the typhoon center. The vector at the typhoon center denotes the VWS averaged within the radius of 0–800 km. The number in the top-right inset of each panel denotes the magnitude of VWS.</p>
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<p>Azimuthal-mean diabatic heating (shaded colors, K h<sup>−1</sup>) and tangential wind (contours, at intervals of 5 m s<sup>−1</sup>) for CTL at (<b>a</b>) 3 h, (<b>b</b>) 12 h, (<b>c</b>) 18 h, (<b>d</b>) 24 h, (<b>e</b>) 36 h, and (<b>f</b>) 48 h.</p>
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<p>Azimuthal-mean radial velocity (shaded colors, m s<sup>−1</sup>) at 24 h from (<b>a</b>) the nonlinear simulation, (<b>b</b>) the SE solution with the total sources, (<b>c</b>) as in (<b>b</b>) but with symmetric diabatic heating only, (<b>d</b>) as in (<b>b</b>) but with turbulent momentum diffusion only, (<b>e</b>) as in (<b>b</b>) but with asymmetric eddy heating only, (<b>f</b>) as in (<b>b</b>) but with asymmetric eddy momentum only, (<b>g</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>U</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>W</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only, (<b>h</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only. The wind vectors (m s<sup>−1</sup>) induced by the total forcing sources overlapped in each panel indicate the radial and vertical wind components (m s<sup>−1</sup>) with their reference vectors given at the lower right corner.</p>
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<p>As in <a href="#atmosphere-15-01105-f012" class="html-fig">Figure 12</a> but for vertical velocity (m s<sup>−1</sup>) at 24 h for (<b>a</b>) the nonlinear simulation, (<b>b</b>) the SE solution with the total sources, (<b>c</b>) as in (<b>b</b>) but with symmetric diabatic heating only, (<b>d</b>) as in (<b>b</b>) but with turbulent momentum diffusion only, (<b>e</b>) as in (<b>b</b>) but with asymmetric eddy heating only, (<b>f</b>) as in (<b>b</b>) but with asymmetric eddy momentum only, (<b>g</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>U</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>W</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only, (<b>h</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only.</p>
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<p>As in <a href="#atmosphere-15-01105-f012" class="html-fig">Figure 12</a> but at 48 h for (<b>a</b>) the nonlinear simulation, (<b>b</b>) the SE solution with the total sources, (<b>c</b>) as in (<b>b</b>) but with symmetric diabatic heating only, (<b>d</b>) as in (<b>b</b>) but with turbulent momentum diffusion only, (<b>e</b>) as in (<b>b</b>) but with asymmetric eddy heating only, (<b>f</b>) as in (<b>b</b>) but with asymmetric eddy momentum only, (<b>g</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>U</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>W</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only, (<b>h</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only.</p>
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<p>As in <a href="#atmosphere-15-01105-f013" class="html-fig">Figure 13</a> but for vertical velocity (m s<sup>−1</sup>) at 48 h for (<b>a</b>) the nonlinear simulation, (<b>b</b>) the SE solution with the total sources, (<b>c</b>) as in (<b>b</b>) but with symmetric diabatic heating only, (<b>d</b>) as in (<b>b</b>) but with turbulent momentum diffusion only, (<b>e</b>) as in (<b>b</b>) but with asymmetric eddy heating only, (<b>f</b>) as in (<b>b</b>) but with asymmetric eddy momentum only, (<b>g</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>U</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>W</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only, (<b>h</b>) as in (<b>b</b>) but with <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> only.</p>
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<p>Contoured frequency by altitude diagrams (CFAD, %) of vertical velocity within a radius of 150 km from the typhoon center at 3 h for (<b>a</b>) WDM6 and (<b>b</b>) NSSL2. (<b>c</b>,<b>d</b>) as in (<b>a</b>,<b>b</b>), respectively, but at 24 h.</p>
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<p>Vertical profiles of hydrometeors (10<sup>−3</sup> kg kg<sup>−1</sup>) averaged in a 200 km radius of the typhoon center for (<b>a</b>) cloud water mixing ratio; (<b>b</b>) rainwater mixing ratio; (<b>c</b>) total of ice mixing ratio, snow mixing ratio, and graupel mixing ratio at 3 h. (<b>d</b>–<b>f</b>) as in (<b>a</b>–<b>c</b>), respectively, but at 24 h.</p>
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18 pages, 9930 KiB  
Article
A Comparative Study of Cloud Microphysics Schemes in Simulating a Quasi-Linear Convective Thunderstorm Case
by Juan Huo, Yongheng Bi, Hui Wang, Zhan Zhang, Qingping Song, Minzheng Duan and Congzheng Han
Remote Sens. 2024, 16(17), 3259; https://doi.org/10.3390/rs16173259 - 2 Sep 2024
Viewed by 863
Abstract
An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant [...] Read more.
An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain’s topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes. Full article
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<p>Satellite images of the weather event observed by Himawari-8, obtained from observations at three visible bands (blue: 0.47 micron; green: 0.51 micron; red: 0.64 micron): (<b>a</b>–<b>i</b>) 1 h intervals from local time 10:00 to 18:00. The red dots represent the location of the Beijing urban area.</p>
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<p>Sites of all automatic weather stations and the measured rainfalls and wind speeds in Beijing. (<b>a</b>) Distribution of all AWS sites, the five selected representative sites are presented by green, black, blue, red and purple solid dots. (<b>b</b>) Variations of rainfall observed every minute from 14:00 to 15:00 at the five sites, the value 0 at the <span class="html-italic">X</span>-axis means 14:00 and 120 means 15:01. (<b>c</b>) Distribution of rainfall observed at the time of 14:55; (<b>d</b>) Variations of the wind speed observed every minute from 14:00 to 15:00 at the five sites. Unit of rainfall is mm and unit of the wind speed is ms<sup>−1</sup>.</p>
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<p>Distribution of 500 hPa (<b>a</b>–<b>c</b>), 750 hPa (<b>d</b>–<b>f</b>), and 950 hPa (<b>g</b>–<b>i</b>) geopotential heights (contours, unit in dagpm), positive vorticity (filled color, unit in 10<sup>−5</sup> s<sup>−1</sup>), and winds during the event. Left column: at 09:00; middle column: at 12:00; right column: at 15:00. The two red areas in (<b>e</b>) denote the low-vortex centers.</p>
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<p>Distribution of relative humidity (filled color, unit: %), and wind (blue arrows; unit: ms<sup>−1</sup>) at 500 hPa (<b>a</b>–<b>c</b>), 750 hPa (<b>d</b>–<b>f</b>), and 950 hPa (<b>g</b>–<b>i</b>) at different moments. Left column: 09:00; middle column: 12:00; right column: 15:00. Blue circle denotes water vapor transport path at 750 hPa.</p>
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<p>Topographic map with the red solid dots marking the location of Beijing.</p>
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<p>Comparisons of radar reflectivity observed at 14:30 on 30 May 2024 simulated with five cloud microphysics schemes. (<b>a</b>) Observed, (<b>b</b>) Lin scheme, (<b>c</b>) WSM6 scheme, (<b>d</b>) Goddard scheme, (<b>e</b>) Morrison scheme, and (<b>f</b>) WDM6 scheme. Unit: dBZ.</p>
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<p>Rainfall accumulation of the event on 30 May 2024 (accumulated 13:00~16:00, unit mm) observed by AWS and simulated by model. (<b>a</b>) Observed, (<b>b</b>) Lin scheme, (<b>c</b>) WSM6 scheme, (<b>d</b>) Goddard scheme, (<b>e</b>) Morrison scheme, and (<b>f</b>) WDM6 scheme.</p>
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<p>Quantitative contrast of the rainfall accumulation between simulations and observations: (<b>a</b>) MAE is the mean absolute error; (<b>b</b>) CSI is the critical success index.</p>
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<p>The vertical distribution of mixing ratios of the cloud droplet, rain, ice, snow and graupel at different stages simulated with five cloud microphysics schemes. Left column: at 14:00, middle column: 14:30, and right column: 15:00. (<b>a</b>–<b>c</b>) Lin scheme, (<b>d</b>–<b>f</b>) WSM6 scheme, (<b>g</b>–<b>i</b>) Goddard scheme, (<b>j</b>–<b>l</b>) Morrison scheme, (<b>m</b>–<b>o</b>) WDM6 scheme. Unit of the mixing ratio in g kg<sup>−1</sup>.</p>
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18 pages, 4297 KiB  
Article
The Preliminary Application of Spectral Microphysics in Numerical Study of the Effects of Aerosol Particles on Thunderstorm Development
by Yi Yang, Ji ming Sun, Zheng Shi, Wan shun Tian, Fu xing Li, Tian yu Zhang, Wei Deng, Wenhao Hu and Jun Zhang
Remote Sens. 2024, 16(12), 2117; https://doi.org/10.3390/rs16122117 - 11 Jun 2024
Cited by 1 | Viewed by 696
Abstract
Progress in numerical models and improved computational capabilities have significantly advanced our comprehension of how aerosol particles impact thunderstorm clouds. Yet, much of this research has focused on employing bulk microphysics models to explain the impacts of aerosol particles acting as cloud condensation [...] Read more.
Progress in numerical models and improved computational capabilities have significantly advanced our comprehension of how aerosol particles impact thunderstorm clouds. Yet, much of this research has focused on employing bulk microphysics models to explain the impacts of aerosol particles acting as cloud condensation nuclei (CCN) on electrical activities in thunderstorm clouds. The bulk thunderstorm models use mean sizes of particles and terminal-fall velocities. This causes calculation deviation in the electrification simulation, which in turn leads to deviations in the simulation of lightning processes. Developing this further, we established a three-dimensional high-resolution cloud–aerosol bin thunderstorm model with electrification and lightning to provide more accurate microphysics and dynamic fields for studying electrical activities. For evaluating the impacts of aerosol particles, specifically CCN, on the properties of continental thunderclouds, aerosols from both clean and polluted continental environments were selected. Cloud simulations indicate that droplets develop a narrower spectrum in polluted continental conditions, and weakened ice crystal growth increases the number of small ice crystals compared to clean conditions. Smaller droplets and ice crystals result in less effective riming and decreased graupel concentration and mass. Consequently, a significant decrease in large ice particles leads to a weakened process of charge separation under conditions of pollution. As a direct result, there is about a 43% reduction in lightning frequency and a delay of approximately 5 min in the lightning process under polluted conditions. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>Topography of the simulation domains.</p>
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<p>Distribution of observed and simulated composite radar reflectivity (dBZ) at 1300 UTC on 20 July 2021: (<b>a</b>) observation; (<b>b</b>) bulk microphysics scheme; (<b>c</b>) spectral microphysics scheme.</p>
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<p>Distribution of observed and simulated cumulative precipitation (mm) from 0000 UTC on 20 July 2021 to 0000 UTC on 21 July 2021: (<b>a</b>) observation; (<b>b</b>) bulk simulation; (<b>c</b>) spectral bin simulation.</p>
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<p>Initial aerosol spectrum for the continental clean and polluted backgrounds.</p>
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<p>The initial vertical temperature and dew point profiles.</p>
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<p>The concentration and mass mixing ratio distribution of droplets at T = 11 min in C_N and P_N: (<b>a</b>,<b>c</b>) C_N; (<b>b</b>,<b>d</b>) P_N.</p>
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<p>The concentration and mass mixing ratio distribution of droplets at T = 11 min in C_N and P_N: (<b>a</b>,<b>c</b>) C_N; (<b>b</b>,<b>d</b>) P_N.</p>
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<p>Number density distribution functions of droplets for C_N (solid line) and P_N (dotted line) cases at the center of the thunderstorms, 2 km high (just above the thunderstorm base) and at T = 11 min.</p>
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<p>Ice crystal concentration and mass mixing ratio distribution at T = 22 min in C_N and P_N cases: (<b>a</b>,<b>c</b>) C_N; (<b>b</b>,<b>d</b>) P_N.</p>
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<p>Number density distribution functions of ice crystals for C_N (solid line) and P_N (dotted line) cases at the center of the thunderstorms at 8.5 km high and T = 22 min.</p>
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<p>The concentration and mass mixing ratio distribution of graupel at T = 22 min in C_N and P_N cases: (<b>a</b>,<b>c</b>) C_N; (<b>b</b>,<b>d</b>) P_N.</p>
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<p>Number density distribution functions of graupel for C_N (solid line) and P_N (dotted line) cases at the center of the thunderstorms at 8.5 km high and T = 22 min.</p>
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<p>The total charge density distribution at T = 22 min in C_N and P_N cases: (<b>a1</b>) C_N; (<b>a2</b>) P_N.</p>
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<p>Evolution of lightning frequency in C_N and C_P cases.</p>
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17 pages, 3601 KiB  
Article
Simulation and Diagnosis of Physical Precipitation Process of Local Severe Convective Rainstorm in Ningbo
by Tingting Lu, Yeyi Ding, Zan Liu, Fan Wu, Guoqiang Xue, Chengming Zhang and Yuan Fu
Atmosphere 2024, 15(6), 658; https://doi.org/10.3390/atmos15060658 - 30 May 2024
Viewed by 550
Abstract
On 31 July 2021, Ningbo, an eastern coast city in China, experienced a severe convective rainstorm, characterized by intense short-duration precipitation extremes with a maximum rainfall rate of 130 mm h−1. In this research, we first analyzed this rainstorm using Doppler [...] Read more.
On 31 July 2021, Ningbo, an eastern coast city in China, experienced a severe convective rainstorm, characterized by intense short-duration precipitation extremes with a maximum rainfall rate of 130 mm h−1. In this research, we first analyzed this rainstorm using Doppler radar and precipitation observation and then conducted high-resolution simulation for it. A three-dimensional precipitation diagnostic equation is introduced to quantitatively analyze the microphysical processes during the rainstorm. It is shown that this rainstorm was triggered and developed locally in central Ningbo under favorable large-scale quasi-geostrophic conditions and local conditions. In the early stage, the precipitation increase is mainly driven by the strong convergence of water vapor, and a noticeable increase in both the intensity and spatial extent of uplift promotes the upward transportation of water vapor. As the water vapor flux and associated convergence weaken in the later stage, the precipitation reduces accordingly. Cloud microphysical processes are also important in the entire precipitation process. The early stage updraft supports the escalations in raindrops, with the notable fluctuations in raindrop concentrations directly linked to variations in ground precipitation intensity. The behavior of graupel particles is intricately connected to their melting as they fall below the zero-degree layer. Although cloud water and snow exhibit changes during this period, the magnitudes of these adjustments are considerably less pronounced than those in raindrops and graupels, highlighting the differentiated response of various condensates to the convective dynamics. These results can help deepen the understanding of local severe rainstorms and provide valuable scientific references for practical forecasting. Full article
(This article belongs to the Special Issue Characteristics of Extreme Climate Events over China)
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<p>Model nested domains configuration.</p>
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<p>Distribution of the accumulated rainfall amount (shaded; unit: mm) during the rainstorm event for (<b>a</b>) observation from automatic weather stations and (<b>b</b>) simulated rainfall amount.</p>
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<p>The 500 hPa geopotential height (thick blue lines, unit: gpm) and 850 hPa wind fields (wind bar, unit: m s<sup>−1</sup>) at (<b>a</b>) 0800 BST 31 July, (<b>b</b>)1400 BST 31 July, (<b>c</b>) 2000 BST 31 July, and (<b>d</b>) 0200 BST 1 August 2021.</p>
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<p>The same as <a href="#atmosphere-15-00658-f003" class="html-fig">Figure 3</a>, but for the simulations (<b>a</b>–<b>d</b>).</p>
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<p>The combined radar reflectivity observed in Ningbo (shaded, unit: dBZ) from 1500 BST to 2230 BST 31 July in 2021.</p>
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<p>Same as <a href="#atmosphere-15-00658-f005" class="html-fig">Figure 5</a>, but for the simulated radar reflectivity.</p>
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<p>Distribution of hourly rainfall in Ningbo (shaded, units: mm h<sup>−1</sup>) from 1500 BST 31 July to 1800 BST 31 July 2021. The left column shows the observations (<b>a1</b>–<b>d1</b>) and the right column shows the simulations (<b>a2</b>–<b>d2</b>).</p>
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<p>Temporal evolutions of the area-averaged (29.2°~29.5° N, 121.0°~121.4° E) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi mathvariant="normal">S</mi> </mrow> </msub> </mrow> </semantics></math>, moisture-related processes (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi mathvariant="normal">W</mi> <mi mathvariant="normal">V</mi> </mrow> </msub> </mrow> </semantics></math>), change rates for hydrometeor-related processes (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> </mrow> </semantics></math>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">I</mi> </mrow> </msub> </mrow> </semantics></math> from 1500 BST to 1900 BST 31 July 2021.</p>
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<p>Area-averaged (29.2°~29.5° N, 121.0°~121.4° E) vertical profiles of hydrometeor mixing ratio (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math> for graupel, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> for snow, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> for ice, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> for raindrops, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> for cloud water, units: 10<sup>−3</sup> kg/kg; w for vertical speed, unit: m/s) from 1500 LST (notation in the sub-figures: 1500) to 1830 LST 31 July 2021.</p>
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30 pages, 8701 KiB  
Article
Use of CAMS near Real-Time Aerosols in the HARMONIE-AROME NWP Model
by Daniel Martín Pérez, Emily Gleeson, Panu Maalampi and Laura Rontu
Meteorology 2024, 3(2), 161-190; https://doi.org/10.3390/meteorology3020008 - 26 Apr 2024
Viewed by 1053
Abstract
Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model [...] Read more.
Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration. Full article
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<p>Cross− sections between <math display="inline"><semantics> <mrow> <mn>43.0</mn> </mrow> </semantics></math> N <math display="inline"><semantics> <mrow> <mn>10.0</mn> </mrow> </semantics></math> W and <math display="inline"><semantics> <mrow> <mn>43.0</mn> </mrow> </semantics></math> N <math display="inline"><semantics> <mrow> <mn>7.0</mn> </mrow> </semantics></math> W for the CAMSNRT experiment at 12 UTC on the 16 February 2020. (<b>Top left</b>): total condensation nuclei, (<b>top right</b>): supersaturation, (<b>bottom left</b>): cloud droplet number concentration, (<b>bottom right</b>): cloud water content.</p>
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<p>Instantaneous precipitation forecast at 12 UTC on 16 February 2020 over Galicia in the northwest of the Iberian Peninsula. (<b>left</b>): REFERENCE, (<b>right</b>): CAMSNRT. The line plotted between 43.0 N 10.0 W and 43.0 N 7.0 W is the position of the cross−section shown in <a href="#meteorology-03-00008-f001" class="html-fig">Figure 1</a> and <a href="#meteorology-03-00008-f003" class="html-fig">Figure 3</a>.</p>
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<p>Cross−section of instantaneous precipitation at 12 UTC on 16 February 2020 between <math display="inline"><semantics> <mrow> <mn>43.0</mn> </mrow> </semantics></math> N <math display="inline"><semantics> <mrow> <mn>10.0</mn> </mrow> </semantics></math> W and <math display="inline"><semantics> <mrow> <mn>43.0</mn> </mrow> </semantics></math> N <math display="inline"><semantics> <mrow> <mn>7.0</mn> </mrow> </semantics></math> W. (<b>left</b>): REFERENCE, (<b>right</b>): CAMSNRT.</p>
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<p>Snowfall intensity at 12 UTC on 23 February 2023. (<b>top left</b>): REFERENCE, (<b>top right</b>): CAMSNRT, and (<b>bottom centre</b>): CAMSNRT-REFERENCE. The line plotted between 40.5 N 5.5 W and 42.5 N 5.5 W is for the cross sections shown in <a href="#meteorology-03-00008-f005" class="html-fig">Figure 5</a> and <a href="#meteorology-03-00008-f006" class="html-fig">Figure 6</a>.</p>
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<p>Cross− sections of the intensity of the snow (<b>top row</b>) and the cloud water content (<b>bottom row</b>) at 12 UTC on 23 February 2023 between 40.5 N 5.5 W and 42.5 N 5.5 W, as plotted in <a href="#meteorology-03-00008-f004" class="html-fig">Figure 4</a>. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT.</p>
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<p>Cross-sections of the total condensation nuclei and the supersaturation for CAMSNRT (<b>top row</b>), and the CDNC for CAMSNRT and CDNC for the REFERENCE (<b>bottom row</b>) at 12 UTC on 23 February 2023 between 40.5 N 5.5 W and 42.5 N 5.5 W, as plotted in <a href="#meteorology-03-00008-f004" class="html-fig">Figure 4</a>. Notice that the scale for CDNC for the REFERENCE is different to that for CAMSNRT to allow us to show the vertical variation. The CDNC is calculated only where the cloud water content is greater than <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>20</mn> </mrow> </msup> </semantics></math>.</p>
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<p>Daily global radiation plots. (<b>Left</b>): dust intrusion case, 31 March 2021. (<b>Center</b>): cloudy case, 22 October 2020. (<b>Right</b>): clear sky case, 12 October 2020. Measurements of global radiation from 29 stations over the peninsular Spanish territory have been used in these plots, except for the clear sky case for which only 19 stations were selected.</p>
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<p>Dust case on 31 March 2021. Accumulated SW global radiation over 24 h. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT.</p>
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<p>Cloudy Case: accumulated global radiation over 24 h for 22 October 2020. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT.</p>
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<p>Clear sky case: accumulated global radiation over 24 h for 12 October 2020. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT.</p>
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<p>Fog 14 April 2021. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT. The (<b>top row</b>) corresponds to the analysis from the 00 Z run on 14 April and the (<b>bottom row</b>) corresponds to the 06 h forecast from the 00 Z run on 14 April.</p>
Full article ">Figure 11 Cont.
<p>Fog 14 April 2021. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT. The (<b>top row</b>) corresponds to the analysis from the 00 Z run on 14 April and the (<b>bottom row</b>) corresponds to the 06 h forecast from the 00 Z run on 14 April.</p>
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<p>Fog 13 May 2023. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT. The (<b>top row</b>) corresponds to the analysis from the 00 Z run on 13 May and the (<b>bottom row</b>) corresponds to the 06 h forecast from the 00 Z run on 13 May.</p>
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<p>Equitable threat score for 12 h precipitation. Iberian Peninsula. (<b>left</b>): AUTUMN; (<b>right</b>): SPRING.</p>
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<p>Scatter plots for 24 h precipitation for the spring period. Iberian Peninsula. (<b>left</b>): REFERENCE; (<b>right</b>): CAMSNRT.</p>
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<p>Histogram of clear sky index (CSI) for (<b>left</b>) spring and (<b>right</b>) autumn based on 2-week periods where the results from HARMONIE-AROME CY46 experiments conducted using TEGEN and CAMS NRT aerosols are compared to observations.</p>
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<p>Two-dimensional Histograms of the clear sky index (CSI) for spring and autumn (rows) based on a 2-week period where the results from HARMONIE-AROME CY46 experiments conducted using TEGEN (<b>left column</b>) and CAMS NRT (<b>right column</b>) aerosols are compared to observations.</p>
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<p>SW bias for spring and autumn (rows) based on a 2−week period where the results from HARMONIE-AROME CY46 experiments conducted using TEGEN (<b>left column</b>) and CAMS NRT (<b>right column</b>) aerosols are compared to observations.</p>
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17 pages, 6147 KiB  
Article
Upgraded Three-Wavelength Lidar for Real-Time Observations of Volcanic Aerosol Optical and Microphysical Properties at Etna (Italy): Calibration Procedures and Measurement Tests
by Matteo Manzo, Gianpiero Aiesi, Antonella Boselli, Salvatore Consoli, Riccardo Damiano, Guido Di Donfrancesco, Benedetto Saraceno and Simona Scollo
Sensors 2024, 24(6), 1762; https://doi.org/10.3390/s24061762 - 8 Mar 2024
Viewed by 886
Abstract
An innovative mobile lidar device, developed to monitor volcanic plumes during explosive eruptions at Mt. Etna (Italy) and to analyse the optical properties of volcanic particles, was upgraded in October 2023 with the aim of improving volcanic plume retrievals. The new configuration of [...] Read more.
An innovative mobile lidar device, developed to monitor volcanic plumes during explosive eruptions at Mt. Etna (Italy) and to analyse the optical properties of volcanic particles, was upgraded in October 2023 with the aim of improving volcanic plume retrievals. The new configuration of the lidar allows it to obtain new data on both the optical and the microphysical properties of the atmospheric aerosol. In fact, after the upgrade, the lidar is able to measure three backscattering coefficients, two extinction coefficients and two depolarisation ratios in a configuration defined as “state-of-the-art lidar”, where properties such as particle size distribution and the refractive index can be derived. During the lidar implementation, we were able to test the system’s performance through specific calibration measurements. A comparison in an aerosol-free region (7.2–12 km) between lidar signals at 1064 nm, 532 nm and 355 nm and the corresponding pure molecular profiles showed a relative difference of <1% between them for all the wavelengths, highlighting the good dynamic of the signals. The overlap correction allowed us to reduce the underestimation of the backscattering coefficient from 50% to 10% below 450 m and 750 m at both 355 and 532 nm, respectively. The correct alignment between the laser beam and the receiver optical chain was tested using the signal received from the different quadrants of the telescope, and the relative differences between the four directions were comparable to zero, within the margin of error. Finally, the first measurement results are shown and compared with results obtained by other instruments, with the aim of proving the ability of the upgraded system to more precisely characterise aerosol optical and microphysical properties. Full article
(This article belongs to the Section Radar Sensors)
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Figure 1

Figure 1
<p>Experimental set-up of the polychromator unit after the lidar system upgrade: P stands for ‘Pinhole’, M for ‘Mirror’, D for ‘Dichroic mirror’, PBS for ‘Polarizing Beam Splitter’, L for ‘Lens’, IF (λ nm) for ‘Interferential Filter’. The servos (1–3) are used to insert the attenuators or the depolarizer plates along the optical path.</p>
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<p>Results of Rayleigh fit tests for the 355 nm wavelength: (<b>a</b>) fit between the Range-Corrected lidar Signal (RCS) and a pure molecular profile; (<b>b</b>) relative differences between the RCS and the pure molecular profile.</p>
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<p>Results of Rayleigh fit tests for the 532 nm wavelength: (<b>a</b>) fit between the Range-Corrected lidar Signal (RCS) and a pure molecular profile; (<b>b</b>) relative differences between the RCS and the pure molecular profile.</p>
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<p>Results of Rayleigh fit tests for the 1064 nm wavelength: (<b>a</b>) fit between the Range-Corrected lidar Signal (RCS) and a pure molecular profile; (<b>b</b>) relative differences between the RCS and the pure molecular profile.</p>
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<p>Results for the depolarization calibration at 355 nm: (<b>a</b>) comparison between P (blue line) and S (light blue line) signals in a series of measurements with the depolarizer plate; (<b>b</b>) gain ratio at 355 nm (violet line); (<b>c</b>) total depolarization ratio percentage at 355 nm (red line). The average value of δ is about 0.5% in the aerosol-free region 8–14 km.</p>
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<p>Results for the depolarization calibration at 532 nm: (<b>a</b>) comparison between P (green line) and S (light green line) signals in a series of measurements using the depolarizer plate; (<b>b</b>) gain ratio at 532 nm (dark green line); (<b>c</b>) total depolarization ratio percentage at 532 nm (red line). The average value of δ was about 0.37% in the aerosol-free region of 8–14 km.</p>
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<p>Comparison between β<sub>KLETT</sub> and β<sub>RAMAN</sub> at 355 nm before and after overlap correction: (<b>a</b>) backscattering coefficients before overlap correction; (<b>b</b>) backscattering coefficients matching after just 2 iterations of the method.</p>
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<p>Comparison between β<sub>KLETT</sub> and β<sub>RAMAN</sub> at 532 nm before and after overlap correction: (<b>a</b>) backscattering coefficients before overlap correction; (<b>b</b>) backscattering coefficients matching after 2 iterations of the method.</p>
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<p>(<b>a</b>) Comparison between RCS at 355 nm before and after the overlap correction; (<b>b</b>) overlap function at 355 nm.</p>
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<p>(<b>a</b>) Comparison between RCS at 532 nm before and after the overlap correction; (<b>b</b>) overlap function at 532 nm.</p>
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<p>The Telecover test at 355 nm (<b>a</b>), 532 nm (<b>b</b>) and 1064 nm (<b>c</b>). The different colours identify different sectors of the telescope.</p>
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<p>Colour maps of the lidar signals measured at 355 nm (<b>a</b>), 532 nm (<b>b</b>) and 1064 nm (<b>c</b>), showing the spatio-temporal variation of a thin cirrus layer observed in the atmospheric column up to 25 km of altitude. The colour scale units are arbitrary (a.u.).</p>
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<p>Aerosol backscattering coefficient profiles measure at 355 nm (purple line), 532 nm (green line) and 1064 nm (red line) from 17:30 to 18:00 UTC.</p>
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<p>Water Vapour Mixing Ratio (g/Kg) derived by Raman lidar measurements (green line) and radiosounding data from Pratica di Mare (blue line). Error bars are reported only from lidar data due to the lack of the corresponding information for the radio-sounding data.</p>
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<p>Particles size distributions retrieved by a co-located AERONET sun-photometer (<b>a</b>) and Lidar (<b>b</b>) measurements.</p>
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18 pages, 4033 KiB  
Article
Cloud Characteristics during Intense Cold Air Outbreaks over the Barents Sea Based on Satellite Data
by Alexandra Narizhnaya and Alexander Chernokulsky
Atmosphere 2024, 15(3), 317; https://doi.org/10.3390/atmos15030317 - 2 Mar 2024
Cited by 1 | Viewed by 1236
Abstract
The Arctic experiences remarkable changes in environmental parameters that affect fluctuations in the surface energy budget, including radiation and sensible and latent heat fluxes. Cold air masses and cloud transformations during marine cold air outbreaks (MCAOs) substantially influence the radiative fluxes, thereby shaping [...] Read more.
The Arctic experiences remarkable changes in environmental parameters that affect fluctuations in the surface energy budget, including radiation and sensible and latent heat fluxes. Cold air masses and cloud transformations during marine cold air outbreaks (MCAOs) substantially influence the radiative fluxes, thereby shaping the link between large-scale dynamics, sea ice conditions, and the surface energy budget. In this study, we investigate various cloud characteristics during intense MCAOs over the Barents Sea from 2000 to 2018 using satellite data. We identify 72 intense MCAO events that propagated southward using reanalysis data of the surface temperature and potential temperature at the 800 hPa level. We investigate the macro- and microphysical parameters and radiative properties of clouds within selected MCAOs, their dependence on sea ice concentration, and their initial air mass properties using satellite data. A significant increase in low-level clouds near the ice edge (up to +25% anomalies) and a smooth transition to upper-level clouds is revealed. The total cloud top height during intense MCAOs is generally 500–700 m lower than under neutral conditions. MCAOs induce a positive net cloud radiative effect, which peaks at +20 W m−2 (100 km from the ice edge) and gradually decreases towards the continent (−2.3 W m−2 per 100 km). Our study provides evidence for the importance of changes in the cloud radiative effect within MCAOs, which should be accurately simulated in regional and global climate models. Full article
(This article belongs to the Section Climatology)
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<p>An example of a marine cold air outbreak on 19 March 2003: (<b>a</b>) the surface analysis map at 03:00 UTC retrieved from Ref. [<a href="#B35-atmosphere-15-00317" class="html-bibr">35</a>] (the red frame outlines the area shown in the satellite image in (<b>b</b>)); (<b>b</b>) cloud cover over the Barents Sea on 19 March 2003 (around 10:00 UTC) obtained from NASA Worldview (based on the Terra/Aqua MODIS satellite images [<a href="#B36-atmosphere-15-00317" class="html-bibr">36</a>]).</p>
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<p>Analysis region (65–86° N, 10–80 ° E) and areas used for sample selection or specific analysis for MCAO: in box #1, the wind condition is obtained; in box #2, MCAO severity is noted, and the sea surface temperature is calculated; box #3 is used for averaging cloud radiative characteristics for further composite analysis; box #4 is used for averaging the moisture content of the intruding air masses. Dotted lines show mean sea ice concentration values at 15%, 50%, and 95% (data from NSIDC archive [<a href="#B37-atmosphere-15-00317" class="html-bibr">37</a>,<a href="#B38-atmosphere-15-00317" class="html-bibr">38</a>]), averaged over the analyzed sample of intense MCAOs. The orange line shows the section of the 38th meridian extending south of the 95% sea ice edge position for the analysis of the composite profiles of the cloud radiative parameters.</p>
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<p>(<b>a</b>) Schematic example of the grid cell structure of an MCAO; (<b>b</b>) a distribution of the M-index within an example of an intense MCAO case for 19 March 2003, 12:00 UTC (the same case as that used in <a href="#atmosphere-15-00317-f001" class="html-fig">Figure 1</a>).</p>
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<p>Composite maps of absolute values of several cloud characteristics for 72 intense MCAO events: (<b>a</b>) low-level cloud fraction (%); (<b>b</b>) low-level cloud top height (km); (<b>c</b>) cloud particle phase for low-level clouds (where 1 is liquid and 2 is ice); (<b>d</b>) low-level cloud optical depth; (<b>e</b>) low-level cloud base pressure (hPa); (<b>f</b>) net cloud radiative effect (W m<sup>−2</sup>). The solid and dashed lines mark the position of the 95% and 30% ice concentration boundaries, respectively, averaged for the sample of the 72 analyzed MCAO episodes.</p>
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<p>Composite maps of anomaly values of various cloud and radiation characteristics for 72 intense MCAO events: (<b>a</b>) low-level cloud fraction (%), (<b>b</b>) total cloud fraction (%), (<b>c</b>) low-level cloud particle phase (where 1 is liquid and 2 is ice), (<b>d</b>) total cloud top height (km), (<b>e</b>) low-level cloud base pressure (hPa), (<b>f</b>) low-level cloud liquid water path (g m<sup>−2</sup>), (<b>g</b>) net surface radiation balance (W m<sup>−2</sup>), and (<b>h</b>) net cloud radiative effect on the surface (W/m<sup>2</sup>). The solid and dashed lines mark the position of the 95% and 30% ice concentration boundaries, respectively, averaged for the sample of the 72 analyzed MCAO episodes. Dots indicate significant anomalies at the 0.05 significance level.</p>
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<p>Changes in cloud characteristics along 38 °E from the sea ice margin (95% of sea ice concentration) for the analyzed sample of intense MCAOs: (<b>a</b>) the fraction of low-level clouds (%), (<b>b</b>) the fraction of total cloud cover (%), (<b>c</b>) the optical depth of low-level clouds; (<b>d</b>) the optical depth of total cloud cover; (<b>e</b>) the cloud particle phase of low-level clouds (where 1 is liquid and 2 is ice); (<b>f</b>) the cloud particle phase of total cloud cover (where 1 is liquid and 2 is ice); (<b>g</b>) the low-level cloud top height (km); (<b>h</b>) the low-level cloud base pressure (hPa); (<b>i</b>) the low-level cloud liquid water path (g m<sup>−2</sup>); and (<b>j</b>) the net cloud radiative effect on the surface (W m<sup>−2</sup>). Light grey areas show values in the 5th–95th percentile range, dark grey areas show values in the 25th–75th percentile range, and black dotted lines show the median value. Turquoise curves are averages over all selected MCAOs. Yellow dashed and small dotted lines show a linear trend with a 95% confidence interval.</p>
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<p>Cloud characteristics averaged for 19 MCAO events based on MODIS (Terra) data, for total cloud cover: (<b>a</b>) cloud fraction (%), (<b>b</b>) cloud optical thickness, (<b>c</b>) cloud top temperature (K), (<b>d</b>) cloud top height (km), (<b>e</b>) cloud top pressure (hPa), and (<b>f</b>) cloud droplet effective radius (μm). The lines represent the 25th, 50th, and 75th percentiles (turquoise, pink, and yellow lines, respectively) of the ice boundary (15% concentration), averaged for the sample of the 19 MCAO episodes.</p>
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24 pages, 15061 KiB  
Article
A Case Study on Two Differential Reflectivity Columns in a Convective Cell: Phased-Array Radar Observation and Cloud Model Simulation
by Gang Ren, Yue Sun, Hongping Sun, Yaning Dong, Yonglong Yang and Hui Xiao
Remote Sens. 2024, 16(3), 460; https://doi.org/10.3390/rs16030460 - 25 Jan 2024
Viewed by 1038
Abstract
A convective cell storm containing two differential reflectivity (ZDR) columns was observed with a dual-polarization phased-array radar (X-PAR) in Xixian County. Since a ZDR column is believed to correspond to a strong updraft and a single convective cell is considered [...] Read more.
A convective cell storm containing two differential reflectivity (ZDR) columns was observed with a dual-polarization phased-array radar (X-PAR) in Xixian County. Since a ZDR column is believed to correspond to a strong updraft and a single convective cell is considered to have a simple dynamic structure with one updraft core, how these two ZDR columns form and coexist is the focus of this study. The dynamic and microphysical structures around the two ZDR columns are studied under the mutual confirmation of the X-PAR observations and a cloud model simulation. The main ZDR column forms and maintains in an updraft whose bottom corresponds to a convergence of low-level and mid-level flow; it lasts from the early stages to the later stages. The secondary ZDR column emerges at the rear of the horizontal reflectivity (ZH) core relative to the moving direction of the cell; it forms in the middle stages and lasts for a shorter period, and its formation is under an air lifting forced by the divergent outflow of precipitation. Therefore, the secondary ZDR column is only a by-product in the middle stages of the convection rather than an indicator of a new or enhanced convection. Full article
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<p>The topography around the X-PAR and the nearest operational radar (C-band).</p>
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<p>Local weather conditions near the radar site from ERA5 reanalysis data on 8 July 2021: (<b>a</b>) wind and air temperature and (<b>b</b>) convective available potential energy (CAPE). The wind barb and contour lines in (<b>a</b>) represent horizontal wind and air temperature (with 10 °C intervals), respectively; the bold black line represents the 0 °C level (approximately 4.73 km height). The vertical red line in (<b>b</b>) represents the emerging moment (09:18 UTC) of the convective cell observed with the X-PAR. The local standard time is Beijing Time (UTC + 8), and the time used in this paper is UTC.</p>
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<p>Example of a comparison of X-PAR and C-band radar on the same PPI surface. Data near 09:51 are used from these two radars. (<b>a</b>) Z<sub>H</sub> in the original 4th-level PPI of the C-band radar at 3.4° elevation. (<b>b</b>) The same as (<b>a</b>) but zoomed at the target cloud, and the X and Y coordinates are converted to be relative to the X-PAR. (<b>c</b>) The scatter plot of data in (<b>b</b>,<b>d</b>), where both of them have valid data points. (<b>d</b>) Z<sub>H</sub> of X-PAR that was interpolated to the same PPI surface of (<b>b</b>). The dashed lines in (<b>a</b>) mark beam ranges of the azimuth of the C-band radar, which cover the target cloud.</p>
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<p>Examples of original RHIs observed with the X-PAR, which contain Z<sub>DR</sub> columns: (<b>a</b>) Z<sub>e</sub> and the direction mark of the RHIs; (<b>b</b>) Z<sub>H</sub> at 72° azimuth; and (<b>c</b>) Z<sub>H</sub> at 79.2° azimuth; (<b>d</b>–<b>f</b>) are the same as (<b>a</b>–<b>c</b>) but for Z<sub>DRC</sub> or Z<sub>DR</sub>. The dashed lines in (<b>a</b>,<b>d</b>) represent 72° (upper) and 79.2° (lower) azimuths.</p>
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<p>Evolution of Z<sub>e</sub>, Z<sub>DRC</sub>, and Z<sub>DRw</sub> observed with X-PAR. X and Y represent west–east and south–north distances relative to the radar site. Lines A–F are the locations of selected typical vertical profiles, which will be analyzed in the following. The front side and rear side relative to the moving direction of the cloud are marked in the upper left sub-figure. The time is UTC.</p>
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<p>Evolution of Z<sub>e</sub>, Z<sub>DRC</sub>, and Z<sub>DRW</sub> simulated with the cloud model. X and Y represent west–east and south–north distances of the model domain. Lines A–F are the locations of selected typical vertical profiles, which will be analyzed in the following.</p>
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<p>Variables in profiles AA″ observed with the X-PAR and simulated with the cloud model. (<b>a</b>) Observed Z<sub>H</sub>, (<b>b</b>) observed Z<sub>DR</sub>, (<b>c</b>) observed RVD, (<b>d</b>) simulated Z<sub>H</sub>, (<b>e</b>) simulated Z<sub>DR</sub>, and (<b>f</b>) simulated HWD. The dashed lines represent the height where the background air temperature is 0 °C. The outlines of the simulated echo are limited to 10 dBZ. Black vectors are the simulated wind field.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles BB″.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles CC″.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles DD″.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles DD″.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles EE″.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles FF″.</p>
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<p>Same as <a href="#remotesensing-16-00460-f007" class="html-fig">Figure 7</a> but for profiles FF″.</p>
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<p>Typical horizontal distribution in the early stages. The selected height is where the background temperature is near 0 °C. (<b>a</b>) Observed Z<sub>H</sub> and Z<sub>DR</sub>; (<b>b</b>) simulated Z<sub>H</sub> and Z<sub>DR</sub>; and (<b>c</b>) simulated vertical air velocity. The thinner black line in (<b>c</b>) is 0 dBZ for Z<sub>H</sub>, and the thicker black line is 0.2 dB for Z<sub>DR</sub>, which are the same in (<b>b</b>).</p>
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<p>Same as <a href="#remotesensing-16-00460-f013" class="html-fig">Figure 13</a> but for the middle stages. The black thin line in (<b>c</b>) is 0 dBZ for Z<sub>H</sub>, and the thicker black line is 0.2 dB for Z<sub>DR</sub>, which are the same in (<b>b</b>).</p>
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<p>Demonstration of the secondary Z<sub>DR</sub> column splitting from the main Z<sub>DR</sub> column: (<b>a</b>) observed Z<sub>DR</sub> and (<b>b</b>) simulated Z<sub>DR</sub>. The unit of the shading is dB. The selected height is where the background temperature is near 0 °C. The depicted outlines of the cloud correspond to Z<sub>H</sub> = 0 dBZ.</p>
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<p>Relative distribution of different hydrometeors in profiles (<b>a</b>) AA″, (<b>b</b>) BB″, (<b>c</b>) CC″, (<b>d</b>) DD″, (<b>e</b>) EE″, and (<b>f</b>) FF″. The hydrometeors include ice crystals (I), snow (S), graupel (G), hail (H), frozen drops (FD), cloud drops (C)l and raindrops (R). The bold black lines are the outlines of Z<sub>H</sub> = 10 dBZ. Colored contour lines are based on simulated water content and are automatically produced according to their own value range in a specific time and profile instead of a fixed value. The scales of these contour lines are seen in <a href="#remotesensing-16-00460-t003" class="html-table">Table 3</a>.</p>
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<p>Same as <a href="#remotesensing-16-00460-f016" class="html-fig">Figure 16</a> but for the simulated vertical flux of the water content of raindrops.</p>
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<p>Same as <a href="#remotesensing-16-00460-f016" class="html-fig">Figure 16</a> but for the hydrometeor classification retrieved from X-PAR data.</p>
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13 pages, 7587 KiB  
Article
The Dynamics and Microphysical Characteristics of the Convection Producing the Record-Breaking Hourly Precipitation on 20 July 2021 in Zhengzhou, China
by Kun Zhao, Xin Xu, Ming Xue, Zhe-Min Tan, Hao Huang, Ang Zhou, Xueqi Fan, Qiqing Liu, Chenli Wang, Juan Fang, Wen-Chau Lee, Qinghong Zhang, Fan Zhang, Gang Chen and Ji Yang
Remote Sens. 2023, 15(18), 4511; https://doi.org/10.3390/rs15184511 - 13 Sep 2023
Cited by 3 | Viewed by 1792
Abstract
An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using [...] Read more.
An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using disdrometer and polarimetric radar observations aided by retrievals from the Variational Doppler Radar Analysis System. The synoptic flow forcing brought abundant moisture from the oceans and converged at Zhengzhou; then, the extreme rainfall was produced by a slow-moving convective storm that persisted throughout the hour over Zhengzhou. Unusually high concentrations of raindrops of all sizes (showing combined properties of maritime and continental convection) are revealed by the disdrometer data, whereas the polarimetric radar data suggest that both ice-based and warm rain processes were important contributors to the total rainfall. High precipitation efficiency was achieved with an erect updraft at the low levels, whereas enhanced easterly inflows kept the storm moving slowly. The interaction between convective-scale and mesoscale dynamics and microphysical processes within the favorable synoptic conditions led to this extremely heavy rainfall. Full article
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<p>(<b>a</b>) Distribution map of ground instruments on the digital terrain elevation map around ZZ. Triangles denote the locations of four operational radars. Blue, red, and purple contours indicate the 24-h accumulated rainfall over 100, 250, and 400 mm on 20 July, respectively. The time series of the hourly rainfall from the ZZ national surface station (indicated by a red cross) is also shown in the bottom right corner of the figure. (<b>b</b>) Circulation fields at 0800 LST on 20 July 2021. Gray contours represent geopotential height at 500 hPa, and the bold line indicates the location of the western Pacific subtropical high (WPSH). Vectors are vertically integrated water vapor flux (in the layer of 1000–300 hPa) larger than 150 kg m<sup>−1</sup> s<sup>−1</sup>, and black dots indicate the wind divergence at 200 hPa that is larger than 10<sup>−5</sup> s<sup>−1</sup>. Shading represents the 10.8 μm infrared brightness temperature from FY-4A. The black cross indicates the location of the ZZ national surface station.</p>
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<p>(<b>a</b>) Perturbation temperature (shading) and horizontal wind vectors at 0.6 km MSL in the VDRAS analysis at 1200 LST on 20 July 2021. Yellow contours are the horizontal convergence starting from −1 × 10<sup>−3</sup> s<sup>−1</sup> at an interval of −2 × 10<sup>−3</sup> s<sup>−1</sup>. Black lines are 600 m terrain elevation contours and the purple triangle indicates the location of the record-breaking hourly precipitation rain gauge station in ZZ. (<b>b</b>) As (<b>a</b>) but at 3 km. The curved arrow represents the mesoscale vortex. (<b>c</b>) As (<b>a</b>) but at 1600 LST. (<b>d</b>) 0–3 km moisture fluxes through the 4 borders of a 60 × 60 km box region covering the ZZ city (red box in (<b>a</b>,<b>c</b>)) and net flux into the box (positive into the box), based on the VADRS analysis. (<b>e</b>) Inflow environment sounding located at 150 km southeast of ZZ (yellow X in (<b>a</b>)) extracted from the VDRAS analysis at 1200 LST with the red, green, and black solid lines representing the temperature profile, dew point profile, and the parcel ascent curve, respectively. (<b>f</b>) Perturbation temperature (shading) and wind vectors in the west–east vertical cross section through the center of ZZ city (line AB in (<b>c</b>)) in VADRAS analysis at 1600 LST. Red contours denote 40 dBZ radar reflectivity. (<b>g</b>) Evolution of cold pool density current and low-level easterlies between 0 and 1 km calculated within the red and black boxes in (<b>c</b>). (<b>h</b>) Evolution of the southerly and northerly wind components to the south and north of the storm (see the white boxes in (<b>c</b>)) over ZZ, respectively.</p>
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<p>Observed radar reflectivity (color shading; units: dBZ) and horizontal winds at 600 m MSL at (<b>a</b>) 1200 LST, (<b>b</b>) 1500 LST, (<b>c</b>) 1600 LST, and (<b>d</b>) 1700 LST on 20 July 2021. A terrain height of 200 m is indicated by the bold brown contours. The black line represents the urban area of ZZ, and the triangle indicates the national surface station with the hourly extreme rainfall in ZZ. The blue pluses indicate that the lightning occurred 30 min before and after the radar time. SM indicates the Song Mountain. West–east cross sections of (<b>e</b>) Z<sub>H</sub> (color shading) and Z<sub>DR</sub> (contours) and (<b>f</b>) hydrometer types based on hydrometeor classification algorithm (shading) and K<sub>DP</sub> (contours) across the rainfall center at 1600 LST.</p>
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<p>Time series of (<b>a</b>) Z<sub>H</sub>, (<b>b</b>) Z<sub>DR</sub>, and (<b>c</b>) K<sub>DP</sub> fractions in specified bins at 2.0 km MSL in the 30 × 30 km box centered on the CS (labeled in <a href="#remotesensing-15-04511-f003" class="html-fig">Figure 3</a>) from 1200 to 2000 LST. Average profiles of (<b>d</b>) polarimetric radar variables and (<b>e</b>) hydrometer types (shading), LWC (black solid), and IWC (red dashed) for K<sub>DP</sub> values higher than 4° km<sup>−1</sup> near the ZZ national surface station between 1600 and 1700 LST. The label for IWC/LWC is shown at the top of the panel.</p>
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<p>(<b>a</b>) Time series of 1 min DSDs from the OTT disdrometer at the ZZ national surface station. Color shading represents the DSD in logarithmic units of mm<sup>−1</sup> m<sup>−3</sup> and the <span class="html-italic">y</span>-axis indicates the equivalent volume diameter (mm) of raindrops; the instant rain rate is plotted as the red line. (<b>b</b>) Scatters of <span class="html-italic">N<sub>w</sub></span> (<span class="html-italic">D<sub>m</sub></span>) from the ZZ extreme hourly rainfall between 1600 and 1700 LST (red) and DSD samples with an instant rain rate over 20 mm<sup>−1</sup> h<sup>−1</sup> from 1200 to 2000 LST (blue), respectively. The two gray rectangles represent the maritime and continental convective clusters reported by [<a href="#B24-remotesensing-15-04511" class="html-bibr">24</a>]. The blue, green, black, and red crosses represent the mean values of convective rain in different regions from previous studies [<a href="#B50-remotesensing-15-04511" class="html-bibr">50</a>,<a href="#B51-remotesensing-15-04511" class="html-bibr">51</a>,<a href="#B52-remotesensing-15-04511" class="html-bibr">52</a>,<a href="#B53-remotesensing-15-04511" class="html-bibr">53</a>].</p>
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<p>The conceptual model of the maintenance and precipitation microphysics of the convective storm resulting in extreme hourly rainfall in ZZ. The blue line with triangles indicates the cold pool gust front. The red arrows represent the prevalent winds.</p>
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33 pages, 19921 KiB  
Article
Combined Characterization of Airborne Saharan Dust above Sofia, Bulgaria, during Blocking-Pattern Conditioned Dust Episode in February 2021
by Zahari Peshev, Anatoli Chaikovsky, Tsvetina Evgenieva, Vladislav Pescherenkov, Liliya Vulkova, Atanaska Deleva and Tanja Dreischuh
Remote Sens. 2023, 15(15), 3833; https://doi.org/10.3390/rs15153833 - 1 Aug 2023
Cited by 3 | Viewed by 1647
Abstract
The wintertime outbreaks of Saharan dust, increasing in intensity and frequency over the last decade, have become an important component of the global dust cycle and a challenging issue in elucidating its feedback to the ongoing climate change. For their adequate monitoring and [...] Read more.
The wintertime outbreaks of Saharan dust, increasing in intensity and frequency over the last decade, have become an important component of the global dust cycle and a challenging issue in elucidating its feedback to the ongoing climate change. For their adequate monitoring and characterization, systematic multi-instrument observations and multi-aspect analyses of the distribution and properties of desert aerosols are required, covering the full duration of dust events. In this paper, we present observations of Saharan dust in the atmosphere above Sofia, Bulgaria, during a strong dust episode over the whole of Europe in February 2021, conditioned by a persistent blocking weather pattern over the Mediterranean basin, providing clear skies and constant measurement conditions. This study was accomplished using different remote sensing (lidar, satellite, and radiometric), in situ (particle analyzing), and modeling/forecasting methods and resources, using real measurements and data (re)analysis. A wide range of columnar and range/time-resolved optical, microphysical, physical, topological, and dynamical characteristics of the detected aerosols dominated by desert dust are obtained and profiled with increased accuracy and reliability by combining the applied approaches and instruments in terms of complementarity, calibration, and normalization. Vertical profiles of the aerosol/dust total and mode volume concentrations are presented and analyzed using the LIRIC-2 inversion code joining lidar and sun-photometer data. The results show that interactive combining and use of various relevant approaches, instruments, and data have a significant synergistic effect and potential for verifying and improving theoretical models aimed at complete aerosol/dust characterization. Full article
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Figure 1
<p>Geographic maps of (<b>a</b>) the Balkan Peninsula (source: <a href="https://commons.wikimedia.org/w/index.php?curid=110933799" target="_blank">https://commons.wikimedia.org/w/index.php?curid=110933799</a>, accessed on 7 March 2023) and (<b>b</b>) Sofia (Google Maps image). The positions are indicated of the Sofia IE-BAS station (magenta star) and the Kopitoto automated measuring station (AMS) (orange triangle).</p>
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<p>GFS analysis map of the Polar jet stream at 300 mb level (<b>a</b>); NCEP/NCAR reanalysis diagrams at 500 mb level for the Northern Hemisphere of the vector winds (<b>b</b>) and the geopotential height (<b>c</b>), in the period 21–22 February 2021. Sofia’s location is marked by a red circle.</p>
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<p>NCEP/NCAR maps of the geopotential height composite mean (colored) and wind vectors (black arrows), at 700 mb pressure level, for the period 22–27 February 2021. Sofia’s location is marked by a blue circle.</p>
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<p>NCEP/NCAR color maps of air temperature composite anomalies at a 700-mb level with respect to the climatological means for the period 1981–2010 over the region of North Africa, the Mediterranean, and Europe (20 N–60 N, 10 W–50 E) for the period 22–27 February 2021.</p>
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<p>Forecast color maps of the total dust load of Saharan dust over North Africa, the Mediterranean, and Europe for 22–27 February 2021, obtained by the NKUA SKIRON model. Sofia’s location is marked by a red circle.</p>
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<p>MODIS-Terra true-color images of the dust-cloud plume spread for (<b>a</b>) 21 February 2021, (<b>b</b>) 22 February 2021, (<b>c</b>) 23 February 2021, (<b>d</b>) 24 February 2021, (<b>e</b>) 25 February 2021, and (<b>f</b>) 26 February 2021. The red symbol indicates the position of Sofia station. The images are downloaded from the NASA Worldview website (<a href="https://worldview.earthdata.nasa.gov/" target="_blank">https://worldview.earthdata.nasa.gov/</a>, accessed on 29 April 2021) using the NASA Worldview Snapshots application.</p>
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<p>Time series of the dust/aerosol vertical distribution in terms of range-corrected lidar signals for the measurement dates.</p>
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<p>BSC-DREAM8b dust-concentration profiles and NOAA HYSPLIT model backward trajectories ending in Sofia (inset) for the time of lidar measurements in the period 22–27 February 2021. The corresponding time durations and ending heights of the trajectories are noted at the bottom of the insets.</p>
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<p>Time-averaged vertical profiles of the aerosol: BSC at 532 nm and 1064 nm (<b>left</b>), BAE (<b>middle</b>), radiosonde, and modeling meteorological parameters (Temperature, Dew Point, Relative Humidity) (<b>right</b>) for the days of lidar measurements.</p>
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<p>Height profiles of the aerosol volume concentration for fine and coarse particle size modes and the resulting total aerosol concentration as retrieved by the LIRIC-2 inversion code.</p>
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<p>Aerosol optical parameters derived from the AERONET radiometric data and interpolated ones at wavelengths 532 and 1064 nm.</p>
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<p>Particle size distributions derived from AERONET radiometric measurements performed from 22–27 February 2021.</p>
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<p>Hourly mean values of the PM<sub>10</sub> mass concentrations (red lines) measured by the Kopitoto AMS (BG0070A) in the periods of Saharan dust intrusions over Sofia from 21–28 February 2021. The green stripes mark the intervals of lidar measurements.</p>
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22 pages, 16280 KiB  
Article
A Preliminary Analysis of Typical Structures and Microphysical Characteristics of Precipitation in Northeastern China Cold Vortexes
by Jingshi Wang, Xiaoyong Zhuge, Fengjiao Chen, Xu Chen and Yuan Wang
Remote Sens. 2023, 15(13), 3399; https://doi.org/10.3390/rs15133399 - 4 Jul 2023
Cited by 2 | Viewed by 1220
Abstract
The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain [...] Read more.
The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious. Full article
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Figure 1
<p>(<b>a</b>) The 10.4 μm infrared brightness temperature of the Himawari-7 satellite (shading, k), (<b>b</b>) the rain type identified by GPM DPR (shading, unitless), with yellow (blue) representing convection (stratiform), (<b>c</b>) the storm-top height (shading, km), and (<b>d</b>) the reflectivity at 6 km height (shading, dBZ) within 2000 km distance of the NCCV center at 1200 UTC on 12 June 2014 with GPM orbit No.001631. (The purple lines represent the swath of GPM DPR; the ‘+’ represents the NCCV center; and lines AB, CD, EF, and GH represent the section position in <a href="#remotesensing-15-03399-f002" class="html-fig">Figure 2</a>).</p>
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<p>Vertical sections of the radar echo along lines (<b>a</b>) AB, (<b>b</b>) CD, (<b>c</b>) EF, and (<b>d</b>) GH in <a href="#remotesensing-15-03399-f001" class="html-fig">Figure 1</a>d. The <span class="html-italic">X</span> axis represents the latitude along the cross-section direction. The yellow and blue dots at the 11 km altitude represent the convective and stratiform precipitation retrieved from the dual-frequency method in GPM, respectively.</p>
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<p>The distribution of (<b>a</b>–<b>c</b>) near-surface rain rate (shading, mm h<sup>−1</sup>), (<b>d</b>–<b>f</b>) precipitation frequency (shading, %), and (<b>g</b>–<b>i</b>) storm-top height (shading, km) for total, stratiform, and convective precipitation at each 50 km × 50 km grid in the NCCV coordinate system, derived from GPM DPR for 2014–2019. (The black dots represent the NCCV center; 6432 NCCVs are included in the dynamic composite analysis).</p>
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<p>The distribution of (<b>a</b>,<b>b</b>) precipitation contribution (shading, %), and (<b>c</b>,<b>d</b>) precipitation frequency contribution (shading, %) for total, stratiform, and convective precipitation at each 50 km × 50 km grid in the NCCV coordinate system, derived from GPM DPR for 2014–2019. (The black dots represent the NCCV center; 6432 NCCVs are included in the dynamic composite analysis).</p>
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<p>The distribution of near-surface (<b>a</b>–<b>c</b>) droplet mass-weighted mean diameter <span class="html-italic">D<sub>m</sub></span> (shading, mm), (<b>d</b>–<b>f</b>) particle concentration parameter dB<span class="html-italic">N<sub>w</sub></span> (shading, no unit), and (<b>g</b>–<b>i</b>) average reflectivity (shading, dBZ) for total, stratiform, and convective precipitation at each 50 km × 50 km grid in the NCCV coordinate system, derived from GPM DPR for 2014–2019. (The black dots represent the NCCV center; 6432 NCCVs are included in the dynamic composite analysis).</p>
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<p>The azimuthal distributions of (<b>a</b>) precipitation frequency, (<b>b</b>) near-surface rain rate, (<b>c</b>) precipitation frequency contribution, (<b>d</b>) precipitation contribution, and (<b>e</b>) storm-top height for total (black solid lines), stratiform (blue solid lines), and convective (red solid lines) precipitation in the NCCV coordinate system, derived from GPM DPR for 2014–2019. (A total of 6432 NCCVs are included in the dynamic composite analysis; samples should be within 2000 km distance of the NCCV center).</p>
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<p>The azimuthal distributions of near-surface (<b>a</b>) droplet mass-weighted mean diameter <span class="html-italic">D<sub>m</sub></span>, and (<b>b</b>) particle concentration parameter dB<span class="html-italic">N<sub>w</sub></span> for total (black solid lines), stratiform (blue solid lines), and convective (red solid lines) precipitation in the NCCV coordinate system, derived from GPM DPR for 2014–2019. (A total of 6432 NCCVs are included in the dynamic composite analysis; samples should be within 2000 km distance of the NCCV center).</p>
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<p>The frequency pattern (shading, %) in two-dimensional space of <span class="html-italic">D<sub>m</sub></span> and dB<span class="html-italic">N<sub>w</sub></span> at 2.5 km in the (<b>a</b>,<b>e</b>) northeast, (<b>b</b>,<b>f</b>) northwest, (<b>c</b>,<b>g</b>) southwest, and (<b>d</b>,<b>h</b>) southeast regions within 2000 km distance of the NCCV center for stratiform and convective precipitation. (The black (white) solid line represents the frequency higher than 4% (36%)).</p>
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<p>The CFADs (shading, %) of the Ku-band reflectivity for (<b>a</b>) total, (<b>b</b>) stratiform, and (<b>c</b>) convective precipitation within 2000 km distance of the NCCV center, derived from GPM DPR for 2014–2019.</p>
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<p>The CFADs (shading, %) of the Ku-band reflectivity in the (<b>a</b>,<b>e</b>) northeast, (<b>b</b>,<b>f</b>) northwest, (<b>c</b>,<b>g</b>) southwest, and (<b>d</b>,<b>h</b>) southeast regions within 2000 km distance of the NCCV center for stratiform and convective precipitation, derived from GPM DPR for 2014–2019.</p>
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<p>The azimuthal distribution of Ku-band reflectivity (shading, dBZ) for (<b>a</b>) total, (<b>b</b>) stratiform, and (<b>c</b>) convective precipitation within 2000 km distance of the NCCV center, derived from GPM DPR for 2014–2019. (The sample size in the gray area is less than 0.1% of the maximum sample size).</p>
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<p>The contoured frequency (shading, %) by altitude diagram of <span class="html-italic">D<sub>m</sub></span> and <math display="inline"><semantics><mrow><mi mathvariant="normal">d</mi><mi mathvariant="normal">B</mi><msub><mrow><mi>N</mi></mrow><mrow><mi>w</mi></mrow></msub></mrow></semantics></math> for (<b>a</b>,<b>d</b>) total, (<b>b</b>,<b>e</b>) stratiform, and (<b>c</b>,<b>f</b>) convective precipitation within 2000 km distance of the NCCV center, derived from GPM DPR for 2014–2019.</p>
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41 pages, 8589 KiB  
Article
Profiling of Aerosols and Clouds over High Altitude Urban Atmosphere in Eastern Himalaya: A Ground-Based Observation Using Raman LIDAR
by Trishna Bhattacharyya, Abhijit Chatterjee, Sanat K. Das, Soumendra Singh and Sanjay K. Ghosh
Atmosphere 2023, 14(7), 1102; https://doi.org/10.3390/atmos14071102 - 30 Jun 2023
Viewed by 1827
Abstract
Profiles of aerosols and cloud layers have been investigated over a high-altitude urban atmosphere in the eastern Himalayas in India, for the first time, using a Raman LIDAR. The study was conducted post-monsoon season over Darjeeling (latitude 27°01 N longitude 88°36 [...] Read more.
Profiles of aerosols and cloud layers have been investigated over a high-altitude urban atmosphere in the eastern Himalayas in India, for the first time, using a Raman LIDAR. The study was conducted post-monsoon season over Darjeeling (latitude 27°01 N longitude 88°36 E, 2200 masl), a tourist destination in north-eastern India. In addition to the aerosols and cloud characterization and atmospheric boundary layer detection, the profile of the water vapor mixing ratio has also been analyzed. Effects of atmospheric dynamics have been studied using the vertical profiles of the normalized standard deviation of RCS along with the water vapor mixing ratio. The aerosol optical characteristics below and above the Atmospheric Boundary Layer (ABL) region were studied separately, along with the interrelation of their optical and microphysical properties with synoptic meteorological parameters. The backscatter coefficient and the extinction coefficient were found in the range from 7.15×1010 m1 sr1 to 3.01×105 m1 sr1 and from 1.02×105 m1 to 2.28×103 m1, respectively. The LIDAR ratio varies between 3.9 to 78.39 sr over all altitudes. The variation of the linear depolarization ratio from 0.19 to 0.32 indicates the dominance, of non-spherical particles. The periodicity observed in different parameters may be indicative of atmospheric wave phenomena. Cloud parameters, such as scattering coefficients, top and bottom height, and optical depth for different cloud phases, have been evaluated. A co-located Micro Rain Radar has been used with LIDAR for cloud life cycle study. Full article
(This article belongs to the Section Aerosols)
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Figure 1
<p>Satellite image showing geographical position of Darjeeling and the topographical features of the study site is shown inset.</p>
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<p>The slope of <math display="inline"><semantics><mrow><mo form="prefix">ln</mo><mo>(</mo><mi>R</mi><mi>C</mi><mi>S</mi><mo>)</mo></mrow></semantics></math> and Raw signal of 355 nm channel is shown. The peak and base marked by the arrow is used to determine Rat.</p>
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<p>Sample plot of mean RCS of 355 nm and variance of 30-min LIDAR profile is shown by black and red curves, respectively. The layer heights have been calculated from cluster analysis of the normalized data of RCS and variance.</p>
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<p>Scatter plot of uncalibrated WVMR from LIDAR data and the specific humidity from Reanalysis data in kg/kg along with linear fit is shown. The correlation coefficient (<span class="html-italic">R</span>), probability of significance (<span class="html-italic">p</span>), and slope are shown in the figure.</p>
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<p>Variation of different quantities with altitude over ground level. First row: 27 October 2016 1700 UT without aerosol layer, Second row: 13 September 2016 1900 UT with multiple aerosol layers, and third row: 15 September 2016 1900 UT with a cloud layer. First column: <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> and <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>387</mn></msub></mrow></semantics></math>, second column: <math display="inline"><semantics><msub><mi>α</mi><mi>a</mi></msub></semantics></math> calculated by ‘averaged’, ‘linear’, and ‘quadratic’ method, third column: <math display="inline"><semantics><msub><mi>β</mi><mi>a</mi></msub></semantics></math> and fourth column: LR. All the data are of 120 m resolution and 30 min averaged. The axis titles in the first column and third row are the same for all rows and columns.</p>
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<p>(<b>a</b>) The MLD (LIDAR) on 28 October 2016 from 0000 UT to 1400 UT is shown. (<b>b</b>) The linear correlation of MLD derived from LIDAR and MLD from HYSPLIT is shown. The <span class="html-italic">R</span> and <span class="html-italic">p</span> values are given in the figure.</p>
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<p>Variation of average stable boundary layer and residual layer during 1700 UT–2300 UT for the study period.</p>
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<p>Altitude variation of averaged quantities of groups 1 and 2 within the ABL region. First row shows, from left, variation of <math display="inline"><semantics><msub><mi>α</mi><mi>a</mi></msub></semantics></math>, <math display="inline"><semantics><msub><mi>β</mi><mi>a</mi></msub></semantics></math> and LR, respectively. Second row shows, from left, variation of AE, LDR, and WVMR, respectively. The red and black color has been used to indicate “group 1” and “group 2”, respectively. Here “group 1” <math display="inline"><semantics><msub><mi>α</mi><mi>a</mi></msub></semantics></math> and <math display="inline"><semantics><msub><mi>β</mi><mi>a</mi></msub></semantics></math> are multiplied by 10.</p>
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<p>Altitude variation of averaged quantities of groups 1 and 2 above the ABL region. First row shows, from left, variation of <math display="inline"><semantics><msub><mi>α</mi><mi>a</mi></msub></semantics></math>, <math display="inline"><semantics><msub><mi>β</mi><mi>a</mi></msub></semantics></math> and LR, respectively. Second row shows, from the left, variation of AE, LDR, and WVMR, respectively. The red and black color has been used to indicate “group 1” and “group 2”, respectively. The <math display="inline"><semantics><msub><mi>α</mi><mi>a</mi></msub></semantics></math> and <math display="inline"><semantics><msub><mi>β</mi><mi>a</mi></msub></semantics></math> for “group 1” are multiplied by 10.</p>
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<p>The CCN concentration on Diwali and non-Diwali days with local time is plotted in the figure. The increase in CCN concentration at night on Diwali day is shown.</p>
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<p>The figure shows <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> and NSD variation of two days. The color map beside each figure corresponds to the <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> for (<b>a</b>,<b>b</b>), and NSD for (<b>c</b>,<b>d</b>). Figure (<b>a</b>,<b>c</b>) corresponds to 27 October 2016 1700–1730 UT, whereas figure (<b>b</b>,<b>d</b>) corresponds to 13 September 2016 1900–1930 UT.</p>
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<p>The figure shows <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> and NSD variation of two days. The color map beside each figure corresponds to the <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> for (<b>a</b>,<b>b</b>), and NSD for (<b>c</b>,<b>d</b>). Figure (<b>a</b>,<b>c</b>) corresponds to 27 October 2016 1700–1730 UT, whereas figure (<b>b</b>,<b>d</b>) corresponds to 13 September 2016 1900–1930 UT.</p>
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<p>The variation of <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> on 4 October 2016 from 1900 UT to 2018 UT to show the precipitation layer from the growth to dispersion stage of its total life cycle.</p>
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<p>(<b>a</b>) The variation of RADAR reflectivity with altitude and time on 4 October 2016 from 1900 UT to 2030 UT. The precipitation initiation time and height are indicated by the increase in RADAR reflectivity at 2018 UT. (<b>b</b>) RCS of 355 nm is shown for 4 October 2016 from 2000 UT to 2026 UT to show the RCS increment and precipitation initiation time (2018 UT) clearly.</p>
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<p>Figure shows the variation of <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math>, NSD, normalized WVMR for the growth, maturation, and dispersion phase of the cloud. (<b>a</b>) <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> of growth phase, (<b>b</b>) <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> of mature phase, (<b>c</b>) <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> of dispersion phase, (<b>d</b>) NSD of growth phase, (<b>e</b>) NSD of mature phase, (<b>f</b>) NSD of dispersion phase, (<b>g</b>) WVMR of growth phase, (<b>h</b>) WVMR of mature phase, (<b>i</b>) WVMR of dispersion phase. The color map at right to each figure corresponds to the <math display="inline"><semantics><mrow><mi>R</mi><mi>C</mi><msub><mi>S</mi><mn>355</mn></msub></mrow></semantics></math> for (<b>a</b>–<b>c</b>), NSD for (<b>d</b>–<b>f</b>), WVMR for (<b>g</b>–<b>i</b>).</p>
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