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Volume 10
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Atmosphere, Volume 10, Issue 9 (September 2019) – 84 articles

Cover Story (view full-size image): Motor vehicle emissions are gaining increasing interest due to impacts on human health and the environment, and its associated economic costs. Accurate measurement of vehicle emissions in real-world conditions is therefore essential. This paper is one in a series of papers that use remote sensing to measure on-road vehicle emissions but is innovative in the sense that it includes a range of additional measurement devices to verify and augment the data. The paper examines the relevance of cold start conditions on high emitting vehicles using a thermal camera. It also identifies poor real-world emissions performance for Euro 4/5 diesel cars, which may potentially lead to air quality issues in Australia. View this paper.
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54 pages, 21822 KiB  
Review
On Evolution of Young Wind Waves in Time and Space
by Lev Shemer
Atmosphere 2019, 10(9), 562; https://doi.org/10.3390/atmos10090562 - 19 Sep 2019
Cited by 23 | Viewed by 4518
Abstract
The mechanisms governing the evolution of the wind-wave field in time and in space are not yet fully understood. Various theoretical approaches have been offered to model wind-wave generation. To examine their validity, detailed and accurate experiments under controlled conditions have to be [...] Read more.
The mechanisms governing the evolution of the wind-wave field in time and in space are not yet fully understood. Various theoretical approaches have been offered to model wind-wave generation. To examine their validity, detailed and accurate experiments under controlled conditions have to be carried out. Since it is next to impossible to get the required control of the governing parameters and to accumulate detailed data in field experiments, laboratory studies are needed. Extensive previously unavailable results on the spatial and temporal variation of wind waves accumulated in our laboratory under a variety of wind-forcing conditions and using diverse measuring techniques are reviewed. The spatial characteristics of the wind-wave field were determined using stereo video imaging. The turbulent airflow above wind waves was investigated using an X-hot film. The wave field under steady wind forcing as well as evolving from rest under impulsive loading was studied. An extensive discussion of the various aspects of wind waves is presented from a single consistent viewpoint. The advantages of the stochastic approach suggested by Phillips over the deterministic theory of wind-wave generation introduced by Miles are demonstrated. Essential differences between the spatial and the temporal analyses of wind waves’ evolution are discussed, leading to examination of the applicability of possible approaches to wind-wave modeling. Full article
(This article belongs to the Special Issue Wind-Wave Interaction)
►▼ Show Figures

Figure 1

Figure 1
<p>General view of the experimental facility in the TAU Water Waves Laboratory.</p> Full article ">Figure 2
<p>Vertical profiles of the mean air velocity and the logarithmic fits performed for locations satisfying <span class="html-italic">z/δ &lt;</span> 0.4 (solid lines): (<b>a</b>) <span class="html-italic">x</span>= 260 cm; (<b>b</b>) <span class="html-italic">x</span> = 340 cm.</p> Full article ">Figure 3
<p>The vertical profile of the relative intensity of velocity fluctuations: (<b>a</b>) longitudinal <span class="html-italic">u</span>′: for various wind velocities at fetch <span class="html-italic">x</span> = 300 cm; (<b>b</b>) <span class="html-italic">u</span>′ for various fetches <span class="html-italic">x</span> and U<sub>max</sub> = 7.7 m/s; (<b>c</b>) as in (<b>a</b>) for vertical velocity fluctuations w′; (<b>d</b>) as in (<b>b</b>) for w′. The results of Klebanoff [<a href="#B72-atmosphere-10-00562" class="html-bibr">72</a>] in a turbulent boundary layer over a flat plate, and of Corssin and Kistler [<a href="#B73-atmosphere-10-00562" class="html-bibr">73</a>] for longitudinal velocity fluctuations over rough and smooth plates are plotted as well.</p> Full article ">Figure 4
<p>Distribution of the Reynolds stress <math display="inline"><semantics> <mrow> <mo>−</mo> <mover accent="true"> <mrow> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>w</mi> <mo>′</mo> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> above the mean surface level for various wind velocities. The solid lines show the linear fit for each fetch. Panels (<b>a</b>–<b>d</b>) correspond to different wind velocities.</p> Full article ">Figure 5
<p>Measurements of the friction velocity <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mo>*</mo> </msub> </mrow> </semantics></math>. (<b>a</b>) Comparison of friction velocities obtained by logarithmic fit of profiles as shown in <a href="#atmosphere-10-00562-f002" class="html-fig">Figure 2</a> and by the eddy correlation method, as shown in <a href="#atmosphere-10-00562-f004" class="html-fig">Figure 4</a>; various symbols and colors denote fetches and wind velocities, respectively. (<b>b</b>) Dependence of friction velocities measured from the logarithmic velocity profiles on wind velocity <span class="html-italic">U</span><sub>max</sub>.</p> Full article ">Figure 6
<p>Variation of elevation power spectra along the test section: (<b>a</b>) <span class="html-italic">U</span><sub>max</sub> = 5.5 m/s; (<b>b</b>) <span class="html-italic">U</span><sub>max</sub> = 12.3 m/s; (<b>c</b>) in semi-log coordinates for different fetches at <span class="html-italic">U</span><sub>max</sub> = 6.6 m/s; (<b>d</b>) normalized spectra for various wind velocities at <span class="html-italic">x</span> = 260 cm.</p> Full article ">Figure 7
<p>(<b>a</b>) High-frequency part of the surface elevation power spectrum for <span class="html-italic">U</span><sub>max</sub> = 8.9 m/s and various fetches; (<b>b</b>) the value of <span class="html-italic">n</span> in the power law in Equation (6) for the spectral tails.</p> Full article ">Figure 8
<p>Dependence of various dimensionless statistical parameters on the dimensionless fetch <math display="inline"><semantics> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>: (<b>a</b>) characteristic wave amplitude <math display="inline"><semantics> <mover accent="true"> <mi>η</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>; (<b>b</b>) peak wave frequency <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> <mi>p</mi> </msub> </mrow> </semantics></math>, symbols as in (<b>a</b>); (<b>c</b>) spectral width υ.</p> Full article ">Figure 9
<p>Deviation of random wind waves from Gaussianity. (<b>a</b>) The skewness coefficient; (<b>b</b>) the kurtosis coefficient; (<b>c</b>) wave height exceedance <span class="html-italic">F</span>(<span class="html-italic">H</span>) for <span class="html-italic">U</span><sub>max</sub> = 3.3 m/s for selected fetches; (<b>d</b>) as in (<b>c</b>) for <span class="html-italic">U</span><sub>max</sub> = 12.3 m/s.</p> Full article ">Figure 10
<p>Characterization of wave shape asymmetry: (<b>a</b>) Asymmetry <span class="html-italic">A</span>(<span class="html-italic">η</span>) of the surface elevation; (<b>b</b>) exceedance distributions of crest heights (<span class="html-italic">η<sub>c</sub></span>), trough depths (<span class="html-italic">η<sub>t</sub></span>), and crest-to-trough heights (<span class="html-italic">h</span>) at <span class="html-italic">x</span> = 220 cm, <span class="html-italic">U</span> = 8.5 m/s; (<b>c</b>) as in (<b>b</b>) for <span class="html-italic">x</span> = 340 cm, <span class="html-italic">U</span> = 10.5 m/s.</p> Full article ">Figure 11
<p>Characterization of wave-coherent air velocity fluctuations. (<b>a</b>) Cross-correlation coefficients <span class="html-italic">r</span><sub>ηu</sub> at various elevations <span class="html-italic">z</span> above the highest crest at <span class="html-italic">x</span> = 260 cm and <span class="html-italic">U</span><sub>max</sub> = 11.2 m/s; (<b>b</b>) as in (<b>a</b>) for <span class="html-italic">r</span><sub>ηw,</sub> <span class="html-italic">x</span> = 340 cm and <span class="html-italic">U</span><sub>max</sub> = 11.2 m/s; (<b>c</b>) Phase shifts <span class="html-italic">θ</span><sub>ηu</sub> and <span class="html-italic">θ</span><sub>ηw</sub> in the vicinity of the peak frequency at <span class="html-italic">x</span> = 260 cm and <span class="html-italic">U</span><sub>max</sub> = 11.2 m/s; (<b>d</b>) Phase shifts between the two velocity components and the surface elevation at the peak wind-wave frequency for various fetches and wind velocities.</p> Full article ">Figure 12
<p>Correlation between fluctuations of the static pressure in the air above the water and the surface elevation. (<b>a</b>) Cross-correlation coefficients <span class="html-italic">r</span><sub>pη</sub> between the static pressure fluctuations at various heights, and the surface elevation variations, for <span class="html-italic">U</span><sub>max</sub> = 11.2 m/s at fetch <span class="html-italic">x</span> = 340 cm; (<b>b</b>) Phase shifts around the peak frequency at <span class="html-italic">U</span><sub>max</sub> = 11.2 m/s = 300 cm; (<b>c</b>) as in (<b>b</b>) for <span class="html-italic">x</span> = 340 cm.</p> Full article ">Figure 13
<p>Reconstruction of instantaneous surface elevation from stereo imaging centered at <span class="html-italic">x</span> = 220 cm for wind velocity <span class="html-italic">U</span><sub>max</sub> = 8.5 m/s. The color bar defines instantaneous local <span class="html-italic">η</span> in meters.</p> Full article ">Figure 14
<p>The spatial autocorrelation functions in the along wind (<b>a</b>–<b>c</b>) and crosswind (<b>d</b>–<b>f</b>) directions for three fetches <span class="html-italic">x</span> and three wind velocities <span class="html-italic">U</span><sub>max</sub> = 8.5 m/s.</p> Full article ">Figure 15
<p>The normalized by the local dominant wavelength <span class="html-italic">λ</span><sub>d</sub> integral length scales in the (<b>a</b>) along-wind and (<b>b</b>) cross-wind directions.</p> Full article ">Figure 16
<p>Directional spectra obtained from stereo video reconstruction; upper row: <span class="html-italic">x</span> = 120 cm; middle row: <span class="html-italic">x</span> = 220 cm; bottom row: <span class="html-italic">x</span> = 340 cm. Color bar scale in m<sup>4</sup>.</p> Full article ">Figure 17
<p>Probability density function of the instantaneous slope inclination direction <span class="html-italic">θ:</span> (<b>a</b>) for <span class="html-italic">U</span><sub>max</sub> = 8.5 m/s and three fetches; (<b>b</b>) for <span class="html-italic">x</span> = 220 cm and different wind velocities <span class="html-italic">U</span><sub>max.</sub></p> Full article ">Figure 18
<p>Power spectra at fetch <span class="html-italic">x</span> = 220 cm of (<b>a</b>) the surface elevation <span class="html-italic">η</span>; (<b>b</b>) surface slope in the downwind direction, ∂<span class="html-italic">η/</span>∂x; (<b>c</b>) surface slope in the crosswind direction, ∂<span class="html-italic">η/</span>∂y.</p> Full article ">Figure 19
<p>Comparison of exponents of the spectral tails for surface elevation and two components of the surface slope.</p> Full article ">Figure 20
<p>Higher order statistical parameters of surface slope components <span class="html-italic">η</span><sub>x</sub> and <span class="html-italic">η</span><sub>y</sub>: (<b>a</b>) the skewness coefficients <span class="html-italic">λ</span><sub>3</sub>; (<b>b</b>) the kurtosis coefficients <span class="html-italic">λ</span><sub>4</sub>.</p> Full article ">Figure 21
<p>Mean wind velocity (empty symbols) and the blower output voltage (filled symbols) during the initial stages of experimental runs.</p> Full article ">Figure 22
<p>(<b>a</b>) Instantaneous surface elevation <span class="html-italic">η(t)</span> and surface slope components, ∂<span class="html-italic">η</span>/∂<span class="html-italic">x</span>(<span class="html-italic">t</span>) and ∂<span class="html-italic">η</span>/∂<span class="html-italic">y</span>(<span class="html-italic">t</span>) under wind accelerated from rest to the <span class="html-italic">U</span> = 8.5 m/s at fetch <span class="html-italic">x</span> = 220 cm; (<b>b</b>) corresponding ensemble-averaged values for <span class="html-italic">U</span> = 10.5 m/s and <span class="html-italic">x</span> = 340 cm.</p> Full article ">Figure 23
<p>The ensemble-averaged wave parameters as a function of time elapsed since the activation of the blower.</p> Full article ">Figure 24
<p>Comparison of the temporal variation of the ensemble-averaged wave amplitudes and the characteristic dominant frequencies at two fetches and two wind velocities. The broken lines indicate the steady-state values at the shorter fetch. Panels (<b>a</b>,<b>b</b>) correspond to two fetches at <span class="html-italic">U</span> = 7.5 m/s; panels (<b>c</b>,<b>d</b>) show the amplitudes at the same fetches for <span class="html-italic">U</span> = 9.5 m/s</p> Full article ">Figure 25
<p>The calculated according to the model duration of the initial wave growth process, <span class="html-italic">t</span><sub>gr</sub>, compared to the measured growth duration, <span class="html-italic">t</span><sub>tot</sub>.</p> Full article ">Figure 26
<p>Definition of characteristic transition times based on slope changes in the plot of variation of the ensemble-averaged root mean square (RMS) value of the surface elevation with time (<span class="html-italic">x</span> = 340 cm; <span class="html-italic">U</span> = 7.5 m/s).</p> Full article ">Figure 27
<p>Appearance of initial ripples: (<b>a</b>) Exponential growth of ripples’ energy; (<b>b</b>) the growth of ripples’ slope components. Vertical broken lines denote transition instants <span class="html-italic">t</span><sub>1</sub> and <span class="html-italic">t</span><sub>2</sub>, as defined in <a href="#atmosphere-10-00562-f026" class="html-fig">Figure 26</a>.</p> Full article ">Figure 28
<p>(<b>a</b>) Initial stage that occurs during <span class="html-italic">t</span><sub>2</sub> &lt; <span class="html-italic">t</span> &lt; <span class="html-italic">t</span><sub>3</sub>, dashed lines for <span class="html-italic">x</span> = 220 cm; dotted lines for <span class="html-italic">x</span> = 220 cm, straight lines: linear fit; (<b>b</b>) normalized by <span class="html-italic">U</span><sup>4</sup> wave energy variation during the “principal development stage” according to Phillips, <span class="html-italic">t</span><sub>3</sub> &lt; <span class="html-italic">t</span> &lt; <span class="html-italic">t</span><sub>4</sub>. Straight lines denote linear fit. (<b>c</b>) The growth of wave energy with time at two fetches, normalized by steady-state values. The solid lines denote the averaging over different wind velocities; broken lines represent the three highest wind velocities.</p> Full article ">Figure 29
<p>Probability density function of the instantaneous azimuthal angle <span class="html-italic">θ</span> at several instants during the wave growth stage. Panels (<b>a</b>) and (<b>b</b>) correspond to different fetches and wind velocities, as shown.</p> Full article ">Figure 30
<p>Averaged over different fetches normalized power spectra of the surface elevation as a function of normalized deviation from the peak frequency.</p> Full article ">Figure 31
<p>(<b>a</b>) Symbols–dimensionless spatial exponential growth rates under steady wind forcing estimated from experiments for different frequency spectral harmonics, lines–empirical fit by Plant [<a href="#B104-atmosphere-10-00562" class="html-bibr">104</a>] and Miles [<a href="#B23-atmosphere-10-00562" class="html-bibr">23</a>] theory; (<b>b</b>) Measured spectra at <span class="html-italic">U</span> = 7.5 m/s and three fetches.</p> Full article ">
17 pages, 694 KiB  
Article
Evolution of Turbulence in the Kelvin–Helmholtz Instability in the Terrestrial Magnetopause
by Francesca Di Mare, Luca Sorriso-Valvo, Alessandro Retinò, Francesco Malara and Hiroshi Hasegawa
Atmosphere 2019, 10(9), 561; https://doi.org/10.3390/atmos10090561 - 18 Sep 2019
Cited by 11 | Viewed by 3230
Abstract
The dynamics occurring at the terrestrial magnetopause are investigated by using Geotail and THEMIS spacecraft data of magnetopause crossings during ongoing Kelvin–Helmholtz instability. Properties of plasma turbulence and intermittency are presented, with the aim of understanding the evolution of the turbulence as a [...] Read more.
The dynamics occurring at the terrestrial magnetopause are investigated by using Geotail and THEMIS spacecraft data of magnetopause crossings during ongoing Kelvin–Helmholtz instability. Properties of plasma turbulence and intermittency are presented, with the aim of understanding the evolution of the turbulence as a result of the development of Kelvin–Helmholtz instability. The data have been tested against standard diagnostics for intermittent turbulence, such as the autocorrelation function, the spectral analysis and the scale-dependent statistics of the magnetic field increments. A quasi-periodic modulation of different scaling exponents may exist along the direction of propagation of the Kelvin–Helmholtz waves along the Geocentric Solar Magnetosphere coordinate system (GSM), and it is visible as a quasi-periodic modulation of the scaling exponents we have studied. The wave period associated with such oscillation was estimated to be approximately 6.4 Earth Radii ( R E ). Furthermore, the amplitude of such modulation seems to decrease as the measurements are taken further away from the Earth along the magnetopause, in particular after X ( G S M ) ≲ − 15 R E . The observed modulation seems to persist for most of the parameters considered in this analysis. This suggests that a kind of signature related to the development of the Kelvin–Helmholtz instabilities could be present in the statistical properties of the magnetic turbulence. Full article
(This article belongs to the Special Issue Turbulence, Waves and Transport in Stratified, Rotating Fluid and Plasma Flows)
►▼ Show Figures

Figure 1

Figure 1
<p>Locations of rolled-up Kelvin–Helmholtz events identified from Geotail (light-blue/blue dot) and THEMIS (red diamond).</p> Full article ">Figure 2
<p>The autocorrelation function for three component of magnetic field, related to the event E2 in the left panel and the event O at the right.</p> Full article ">Figure 3
<p>Two examples of the one-dimensional power spectral density (PSD) of the magnetic field. We show here the PSD of <math display="inline"><semantics> <msub> <mi>B</mi> <mi>x</mi> </msub> </semantics></math> for the dataset E2 (left panel) and of <math display="inline"><semantics> <msub> <mi>B</mi> <mi>z</mi> </msub> </semantics></math> for the dataset O (right panel). A Kolmogorov-like spectrum is observed at the MHD scale, while a steeper power law is suggested below ion scales. The vertical black dashed lines indicate the frequency <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>d</mi> <mi>i</mi> </mrow> </msub> </semantics></math> related to the ion inertial length <math display="inline"><semantics> <msub> <mi>d</mi> <mi>i</mi> </msub> </semantics></math>, and the frequency related to the correlation time <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 4
<p>Histograms of the total number of events with the spectral exponent <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mi>o</mi> <mi>l</mi> </mrow> </msub> </semantics></math>, distributed around the Kolmogorov value 5/3 and the exponent <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>i</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </semantics></math>, distributed around the value <math display="inline"><semantics> <mrow> <mn>2.44</mn> </mrow> </semantics></math>. The exponents refer to all three components of the field.</p> Full article ">Figure 5
<p>Probability distribution functions of the normalized increments <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>B</mi> <mi>τ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </msubsup> </mrow> </semantics></math> are shown in the left panel for sample E2 and in the right panel for sample O. The black dashed line is a Gaussian distribution used as reference.</p> Full article ">Figure 6
<p>The scaling dependence of the kurtosis <span class="html-italic">K</span> for two samples, E2 on the left and O on the right. The Gaussian value <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> is indicated, as well as power-law fit in the inertial range for the two cases. The vertical black dashed lines indicate the inertial period <math display="inline"><semantics> <msub> <mi>τ</mi> <mrow> <mi>d</mi> <mi>i</mi> </mrow> </msub> </semantics></math> related to the frequency <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>d</mi> <mi>i</mi> </mrow> </msub> </semantics></math>, and the correlation time <math display="inline"><semantics> <msub> <mi>τ</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 7
<p>The correlation scale of the component <math display="inline"><semantics> <msub> <mi>B</mi> <mi>z</mi> </msub> </semantics></math> (left-top panel), the fitted power-law index (component <span class="html-italic">z</span> of magnetic field), at MHD scales (right-top panel) and below ion scale of energy spectra (left-bottom panel), as a function of -<span class="html-italic">X</span> coordinate. The value expected for a Kolmogorov-like spectrum is –5/3 that corresponds to the horizontal green line in the top panel. The small-scale reference value is <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>2.44</mn> </mrow> </semantics></math> [<a href="#B43-atmosphere-10-00561" class="html-bibr">43</a>,<a href="#B57-atmosphere-10-00561" class="html-bibr">57</a>,<a href="#B58-atmosphere-10-00561" class="html-bibr">58</a>,<a href="#B59-atmosphere-10-00561" class="html-bibr">59</a>]. A modulation is suggested, during the departure along -<span class="html-italic">X</span> coordinate, consistently because the error that affect measures are significantly smaller than the <math display="inline"><semantics> <mi>α</mi> </semantics></math>-index values. In the right-bottom panel, the fitted power-law index of the kurtosis is plotted as a function of -<span class="html-italic">X</span> coordinate. The reference value is <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0.101</mn> </mrow> </semantics></math>, which is typical value observed in Navier–Stokes turbulence [<a href="#B48-atmosphere-10-00561" class="html-bibr">48</a>].</p> Full article ">Figure 8
<p>All fitted power-law index, i.e., <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mi>o</mi> <mi>l</mi> <mi>m</mi> </mrow> </msub> </semantics></math> at MHD scales (blue symbols), <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>i</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </semantics></math> at ion scales (red symbols) and <math display="inline"><semantics> <mi>κ</mi> </semantics></math> scaling exponent of the kurtosis (green symbols), as a function of -<span class="html-italic">X</span>, <span class="html-italic">Y</span>, <span class="html-italic">Z</span> coordinate. Different shades of color and symbol shape refer to the different component (see legend). The mean value of each sample is reported as grey or black square. The overall fluctuating behaviour is seen for all three indexes.</p> Full article ">Figure 9
<p>The fluctuation of the fitted power-law index for component z of energy spectra at MHD scales, i.e., <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mi>o</mi> <mi>l</mi> <mi>m</mi> </mrow> </msub> </semantics></math> (blue dots) and the fluctuation of the power-law index for component z of the scaling exponent <math display="inline"><semantics> <mi>κ</mi> </semantics></math> of the kurtosis (red dots) as a function of -<span class="html-italic">X</span> coordinate.</p> Full article ">Figure 10
<p>Top left panel: the <span class="html-italic">z</span>-component of the scaling exponent <math display="inline"><semantics> <mi>κ</mi> </semantics></math> as a function of the same component of the scaling exponent <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mi>o</mi> <mi>l</mi> </mrow> </msub> </semantics></math>. Top right panel: the <span class="html-italic">z</span>-component of the scaling exponent <math display="inline"><semantics> <mi>κ</mi> </semantics></math> as a function of the same component of the scaling exponent <math display="inline"><semantics> <msub> <mi>α</mi> <mrow> <mi>i</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </semantics></math>. Bottom left panel: the <span class="html-italic">x</span>-component of the scaling exponent <math display="inline"><semantics> <mi>κ</mi> </semantics></math> as a function of the same component of the velocity field. Bottom right panel: the <span class="html-italic">z</span>-component of the scaling exponent <math display="inline"><semantics> <mi>κ</mi> </semantics></math> as a function of the magnetic field correlation timescale <math display="inline"><semantics> <mi>τ</mi> </semantics></math>. For each pair of parameters, the largest correlation coefficients, Pearson’s (<math display="inline"><semantics> <msub> <mi>C</mi> <mi>P</mi> </msub> </semantics></math>) or Spearman’s (<math display="inline"><semantics> <msub> <mi>C</mi> <mi>S</mi> </msub> </semantics></math>), is indicated in the corresponding panel.</p> Full article ">
21 pages, 3367 KiB  
Article
Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction
by Xiaotong Sun and Wei Xu
Atmosphere 2019, 10(9), 560; https://doi.org/10.3390/atmos10090560 - 18 Sep 2019
Cited by 8 | Viewed by 3225
Abstract
Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) [...] Read more.
Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions. Full article
(This article belongs to the Special Issue Air Quality Control and Planning)
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<p>The air quality forecasting framework. Legend: MODIS, moderate-resolution imaging spectroradiometer.</p> Full article ">Figure 2
<p>Illustration of spatial-temporal feature engineering. (<b>a</b>) Spatial dependence; (<b>b</b>) temporal dependence.</p> Full article ">Figure 3
<p>The long short-term memory network.</p> Full article ">Figure 4
<p>The fluctuation of air pollutant concentration trends. This figure shows the hourly concentration fluctuation for eight major pollutants at one monitoring site in Shenyang (from January 1st 2017 to March 31st 2017).</p> Full article ">Figure 5
<p>Performances of prediction models using different baselines.</p> Full article ">Figure 6
<p>Performances of prediction models for different air pollutants.</p> Full article ">Figure 7
<p>Performances of forecasting PM<sub>2.5</sub> in sub-regions.</p> Full article ">Figure 8
<p>Performances of forecasting PM<sub>2.5</sub> in different seasons.</p> Full article ">
24 pages, 10386 KiB  
Article
Evaluation of Different WRF Parametrizations over the Region of Iași with Remote Sensing Techniques
by Iulian-Alin Roșu, Silvia Ferrarese, Irina Radinschi, Vasilica Ciocan and Marius-Mihai Cazacu
Atmosphere 2019, 10(9), 559; https://doi.org/10.3390/atmos10090559 - 18 Sep 2019
Cited by 9 | Viewed by 3379
Abstract
This article aims to present an evaluation of the Weather Research and Forecasting (WRF) model with multiple instruments when applied to a humid continental region, in this case, the region around the city of Iași, Romania. A series of output parameters are compared [...] Read more.
This article aims to present an evaluation of the Weather Research and Forecasting (WRF) model with multiple instruments when applied to a humid continental region, in this case, the region around the city of Iași, Romania. A series of output parameters are compared with observed data, obtained on-site, with a focus on the Planetary Boundary Layer Height (PBLH) and on PBLH-related parametrizations used by the WRF model. The impact of each different parametrization on physical quantities is highlighted during the two chosen measurement intervals, both of them in the warm season of 2016 and 2017, respectively. The instruments used to obtain real data to compare to the WRF simulations are: a lidar platform, a photometer, and ground-level (GL) meteorological instrumentation for the measurement of temperature, average wind speed, and pressure. Maps of PBLH and 2   m above ground-level (AGL) atmospheric temperature are also presented, compared to a topological and relief map of the inner nest of the WRF simulation. Finally, a comprehensive simulation performance evaluation of PBLH, temperature, wind speed, and pressure at the surface and total precipitable water vapor is performed. Full article
(This article belongs to the Special Issue Atmospheric Composition and Cloud Cover Observations)
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<p>Three nested model domains used in this study (yellow lines). 1st nest: contains the entirety of Romania and Republic of Moldova, along with the area around it and sections of neighboring countries. 2nd nest: contains larger Moldova and Bucovina region of Romania, and the majority of the Republic of Moldova. 3rd nest: contains area around the city of Iași. Number 3 (in yellow circle) represents the city of Iași.</p> Full article ">Figure 2
<p>(<b>a</b>) Satellite map of the 3rd nest with latitude/longitude grid; border between Romania (right) and Moldova (left) delineated by the Prut river (yellow line); Google Earth. (<b>b</b>) Relief map of the 3rd nest with latitude/longitude grid; border between Romania (right) and Moldova (left) delineated by the Prut river (yellow line); altitude colormap: ~5 m above ground-level (AGL) dark green, ≤300 m AGL bright yellow; Google Earth.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) Satellite map of the 3rd nest with latitude/longitude grid; border between Romania (right) and Moldova (left) delineated by the Prut river (yellow line); Google Earth. (<b>b</b>) Relief map of the 3rd nest with latitude/longitude grid; border between Romania (right) and Moldova (left) delineated by the Prut river (yellow line); altitude colormap: ~5 m above ground-level (AGL) dark green, ≤300 m AGL bright yellow; Google Earth.</p> Full article ">Figure 3
<p>Example of a Weather Research and Forecasting (WRF) output map (PBLH); 3rd nest, in center of nest: Iași; 17/05/2017, 08:00 UTC; YSU parametrization.</p> Full article ">Figure 4
<p>Example of a WRF output map (PBLH); 3rd nest, in center of nest: Iași; 04/04/2016, 08:00 UTC; YSU parametrization.</p> Full article ">Figure 5
<p>Example of a WRF output map (temperature at 2 m AGL); 3rd nest, in center of nest: Iași; 17/05/2017, 08:00 UTC; YSU parametrization.</p> Full article ">Figure 6
<p>Example of a WRF output map (temperature at 2 m AGL); 3rd nest, in center of nest: Iași; 04/04/2016, 08:00 UTC; YSU parametrization.</p> Full article ">Figure 7
<p>(<b>a</b>) Lidar-obtained RCS profile, 17/05/2017, 08:00 UTC, black: real data, blue: Savitsky–Golay smoothing of real data. (<b>b</b>) Lidar-obtained RCS profile, 04/04/2016, 08:00 UTC, black: real data, blue: Savitsky–Golay smoothing of real data.</p> Full article ">Figure 8
<p>(<b>a</b>) Simulated PBLH timeseries compared with lidar-retrieved PBLH timeseries, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization. (<b>b</b>) Simulated PBLH timeseries compared with lidar-retrieved PBLH timeseries, zoomed-in, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 9
<p>(<b>a</b>) Simulated PBLH timeseries compared with lidar-retrieved PBLH timeseries, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization. (<b>b</b>) Simulated PBLH timeseries compared with lidar-retrieved PBLH timeseries, zoomed-in, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 10
<p>Simulated T2M timeseries compared with real GL T2M timeseries, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 11
<p>Simulated T2M timeseries compared with real GL T2M timeseries, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 12
<p>Simulated U10M timeseries compared with real GL U10M timeseries, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 13
<p>Simulated U10M timeseries compared with real GL U10M timeseries, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 14
<p>Simulated P2M timeseries compared with real GL P2M timeseries, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 15
<p>Simulated P2M timeseries compared with real GL P2M timeseries, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 16
<p>(<b>a</b>) Simulated WV timeseries compared with photometer-retrieved WV timeseries, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization. (<b>b</b>) Simulated WV timeseries compared with photometer-retrieved WV timeseries, zoomed-in, 17/05/2017, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 17
<p>(<b>a</b>) Simulated WV timeseries compared with photometer-retrieved WV timeseries, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization. (<b>b</b>) Simulated WV timeseries compared with photometer-retrieved WV timeseries, zoomed-in, 04/04/2016, dotted grey: real data, black: Savitsky–Golay smoothing of real data, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 18
<p>Simulated bulk Richardson number profiles compared with one another, 04/04/2016, 09:30, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">Figure 19
<p>Simulated bulk Richardson number profiles compared with one another, 04/04/2016, 14:00, red: simulated data with YSU parametrization, blue: simulated data with BouLac parametrization, green: simulated data with ACM2 parametrization, orange: simulated data with ShinHong parametrization, yellow: simulated data with TEMF parametrization, purple: simulated data with MYJ parametrization.</p> Full article ">
17 pages, 3081 KiB  
Article
Climate Change Influences of Temporal and Spatial Drought Variation in the Andean High Mountain Basin
by Dario Zhiña, Martín Montenegro, Lisseth Montalván, Daniel Mendoza, Juan Contreras, Lenin Campozano and Alex Avilés
Atmosphere 2019, 10(9), 558; https://doi.org/10.3390/atmos10090558 - 18 Sep 2019
Cited by 21 | Viewed by 4287
Abstract
Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate [...] Read more.
Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981–2005) and future period (2011–2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades. Full article
(This article belongs to the Special Issue Meteorological and Hydrological Droughts)
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<p>Study area location.</p> Full article ">Figure 2
<p>Scheme of drought characterization. M = Magnitude, D = Duration, Severity = M/D.</p> Full article ">Figure 3
<p>Variation in the base period and future periods of (<b>a</b>) Precipitation; (<b>b</b>) Temperature.</p> Full article ">Figure 4
<p>(<b>a</b>) Precipitation and (<b>b</b>) temperature variation maps of the base period and future periods.</p> Full article ">Figure 5
<p>Frequencies of drought categories: (<b>a</b>) moderate drought, (<b>b</b>) severe drought, and (<b>c</b>) extreme drought.</p> Full article ">Figure 6
<p>Ratio change of drought features of scenario RCP 4.5 with respect to the base period (1981–2005): (<b>a</b>) Future 1 (2011–2040) and (<b>b</b>) Future 2 (2041–2070).</p> Full article ">Figure 7
<p>Ratio change of drought features of scenario RCP 8.5 with respect to the base period (1981–2005): (<b>a</b>) Future 1 (2011–2040) and (<b>b</b>) Future 2 (2041–2070).</p> Full article ">Figure A1
<p><span class="html-italic">p</span>-values of the goodness of fit test for GEV distribution in (<b>a</b>) RCP 4.5 and (<b>b</b>) 8.5 (base period and Future 1 and 2). White color indicates zones where the <span class="html-italic">p</span>-value is less to 0.05.</p> Full article ">
18 pages, 9920 KiB  
Article
Air Quality Impacts of Smoke from Hazard Reduction Burns and Domestic Wood Heating in Western Sydney
by Maximilien Desservettaz, Frances Phillips, Travis Naylor, Owen Price, Stephanie Samson, John Kirkwood and Clare Paton-Walsh
Atmosphere 2019, 10(9), 557; https://doi.org/10.3390/atmos10090557 - 17 Sep 2019
Cited by 13 | Viewed by 6288
Abstract
Air quality was measured in Auburn, a western suburb of Sydney, Australia, for approximately eighteen months during 2016 and 2017. A long open-path infrared spectrometer sampled path-averaged concentrations of several gaseous species, while other pollutants such as PM 2.5 and PM 10 were [...] Read more.
Air quality was measured in Auburn, a western suburb of Sydney, Australia, for approximately eighteen months during 2016 and 2017. A long open-path infrared spectrometer sampled path-averaged concentrations of several gaseous species, while other pollutants such as PM 2.5 and PM 10 were sampled by a mobile air quality station. The measurement site was impacted by a number of indoor wood-heating smoke events during cold winter nights as well as some major smoke events from hazard reduction burning in the spring of 2017. In this paper we compare the atmospheric composition during these different smoke pollution events and assess the relative overall impact on air quality from domestic wood-heaters and prescribed forest fires during the campaign. No significant differences in the composition of smoke from these two sources were identified in this study. Despite the hazard reduction burning events causing worse peak pollution levels, we find that the overall exposure to air toxins was greater from domestic wood-heaters due to their higher frequency and total duration. Our results suggest that policy-makers should place a greater focus on reducing wood-smoke pollution in Sydney and on communicating the issue to the public. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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<p>Locations over map (Source: Google Earth) of the Auburn measurement site, the retro-reflectors for the open-paths (400 m) and the Auburn train station for geographic reference.</p> Full article ">Figure 2
<p>Example Multiple Atmospheric Layer Transmission (MALT) spectra fittings for (<b>a</b>) CH<sub>2</sub>O, (<b>b</b>) CH<sub>3</sub>OH, (<b>c</b>) C<sub>2</sub>H<sub>4</sub> and (<b>d</b>) C<sub>2</sub>H<sub>2</sub>.</p> Full article ">Figure 3
<p>(<b>a</b>) MODIS image and thermal anomalies over Sydney on 14 August 2017 (Source: <a href="https://worldview.earthdata.nasa.gov" target="_blank">https://worldview.earthdata.nasa.gov</a>) and (<b>b</b>) picture of smoke in Sydney from a news article citing the hazard reduction burns as the cause (Photo by John Grainger reproduced with kind permission from photographer. Source: News Corp Australia).</p> Full article ">Figure 4
<p>Location of hazard reduction burns and measurement site within the greater Sydney region (source: OEH; MODIS on <a href="https://worldview.earthdata.nasa.gov/" target="_blank">https://worldview.earthdata.nasa.gov/</a>. Google Earth.)</p> Full article ">Figure 5
<p>Time series of CO and PM<math display="inline"><semantics> <msub> <mrow/> <mrow> <mn>2.5</mn> </mrow> </msub> </semantics></math> during August and first half of September 2017. Shading highlights hazard reduction burn smoke events.</p> Full article ">Figure 6
<p>Time series of CO, PM<math display="inline"><semantics> <msub> <mrow/> <mrow> <mn>2.5</mn> </mrow> </msub> </semantics></math>, evening temperature and wind speed (averaged between 16:00 and 22:00) during June and July 2017. Shading highlights domestic wood heating smoke events.</p> Full article ">
15 pages, 2188 KiB  
Article
Brake Wear Particle Emissions of a Passenger Car Measured on a Chassis Dynamometer
by Marcel Mathissen, Theodoros Grigoratos, Tero Lahde and Rainer Vogt
Atmosphere 2019, 10(9), 556; https://doi.org/10.3390/atmos10090556 - 17 Sep 2019
Cited by 49 | Viewed by 6766
Abstract
Brake wear emissions with a special focus on particle number (PN) concentrations were investigated during a chassis dynamometer measurement campaign. A recently developed, well-characterized, measurement approach was applied to measure brake particles in a semi-closed vehicle setup. Implementation of multiple particle measurement devices [...] Read more.
Brake wear emissions with a special focus on particle number (PN) concentrations were investigated during a chassis dynamometer measurement campaign. A recently developed, well-characterized, measurement approach was applied to measure brake particles in a semi-closed vehicle setup. Implementation of multiple particle measurement devices allowed for simultaneous measurement of volatile and solid particles. Estimated PN emission factors for volatile and solid particles differed by up to three orders of magnitude with an estimated average solid particle emission factor of 3∙109 # km−1 brake−1 over a representative on-road brake cycle. Unrealistic high brake temperatures may occur and need to be ruled out by comparison with on-road temperature measurements. PN emissions are strongly temperature dependent and this may lead to its overestimation. A high variability for PN emissions was found when volatile particles were not removed. Volatiles were observed under high temperature conditions only which are not representative of normal driving conditions. The coefficient of variation for PN emissions was 1.3 without catalytic stripper and 0.11 with catalytic stripper. Investigation of non-braking sections confirmed that particles may be generated at the brake even if no brakes are applied. These “off-brake-event” emissions contribute up to about 30% to the total brake PM10 emission. Full article
(This article belongs to the Section Air Quality)
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<p>Top-view of the instrumented vehicle on the chassis dynamometer. Brake particles are generated at the front left enclosed wheel (right). Bottom–A schematic overview of all measurement devices. Dashed lines indicate variations of the setup that were used within the measurement campaign.</p> Full article ">Figure 2
<p>3h- Los Angeles City Traffic (LACT) and LACT-20 (Highlighted) speed/time trace (top) and vehicle brake disc temperature as measured by a sliding thermocouple on a test-track and at the chassis dynamometer (Bottom).</p> Full article ">Figure 3
<p>Brake disc temperatures recorded by sliding thermocouples at the front right wheel (enclosed) and at the front left wheel (reference/unmodified) during 3h-LACT cycle on the test track and on the chassis dynamometer.</p> Full article ">Figure 4
<p>Overview of the PN emissions during chassis dyno LACT-20 repeatability measurement. Top—Velocity trace and brake disc temperatures. Left—Time trace. Solid lines refer to measurements with CS and dashed w/o CS. Right—Stop averaged total PN concentration for each run plotted against mean brake disc temperature per stop with and without CS.</p> Full article ">Figure 5
<p>PN EFs estimated for eight runs of the LACT-20 on the chassis dynamometer based on the EEPS (w/o CS) and CPC2<sub>10nm</sub> (CS). The EEPS EFs during runs one to four is the lower EF limit since the particle concentration exceeded the measurement limit. As reference, EF from EEPS and CPC2 are shown during first 450 s where the temperatures are &lt;155 °C.</p> Full article ">Figure 6
<p>Time trace of second 3h-LACT run. From top to bottom: Vehicle speed and brake disc temperature measured at enclosed wheel. PM concentration as measured by DustTrak. Particle size distribution as measured by APS. Total PN concentration as measured by both EEPS. Total PN concentration as measured by CPCs.</p> Full article ">Figure 7
<p>Timeline of pyramid-cycle without any brake application. Left side (<b>a</b>) refers to chassis dyno measurements. Right side (<b>b</b>) refers to brake dyno testing. Top to bottom: Velocity profile (black, left axis); PM<sub>10</sub> concentration (TSI DustTrak); PN size distribution (dN/d log dP) (TSI APS); and PN concentration (CPCs with (CPC34nm, CPC210nm) and without (CPC110nm) CS. In all cases dotted lined indicate speed trace as reference.</p> Full article ">
18 pages, 4902 KiB  
Article
Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images
by Hongguang Chen, Xing Zhang, Yintian Liu and Qiangyu Zeng
Atmosphere 2019, 10(9), 555; https://doi.org/10.3390/atmos10090555 - 16 Sep 2019
Cited by 13 | Viewed by 5261
Abstract
Improving the resolution of degraded radar echo images of weather radar systems can aid severe weather forecasting and disaster prevention. Previous approaches to this problem include classical super-resolution (SR) algorithms such as iterative back-projection (IBP) and a recent nonlocal self-similarity sparse representation (NSSR) [...] Read more.
Improving the resolution of degraded radar echo images of weather radar systems can aid severe weather forecasting and disaster prevention. Previous approaches to this problem include classical super-resolution (SR) algorithms such as iterative back-projection (IBP) and a recent nonlocal self-similarity sparse representation (NSSR) that exploits the data redundancy of radar echo data, etc. However, since radar echoes tend to have rich edge information and contour textures, the textural detail in the reconstructed echoes of traditional approaches is typically absent. Inspired by the recent advances of faster and deeper neural networks, especially the generative adversarial networks (GAN), which are capable of pushing SR solutions to the natural image manifold, we propose using GAN to tackle the problem of weather radar echo super-resolution to achieve better reconstruction performance (measured in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)). Using authentic weather radar echo data, we present the experimental results and compare its reconstruction performance with the above-mentioned methods. The experimental results showed that the GAN-based method is capable of generating perceptually superior solutions while achieving higher PSNR/SSIM results. Full article
(This article belongs to the Section Meteorology)
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<p>Low-resolution imaging model of weather radar echo.</p> Full article ">Figure 2
<p>Reconstruction process of low-resolution (LR) echo.</p> Full article ">Figure 3
<p>Architecture of the generative adversarial network (GAN)-based method with corresponding kernel size (k), number of feature maps (n), and stride (s) indicated for each convolutional layer. We used 23 residual-in-residual dense blocks (RRDBs) in the generator network and seven Conv-BN-LReLU blocks in the discriminator network. (Nomenclature: Conv–convolutional layers; LReLU–Leaky Rectified Linear Units [<a href="#B42-atmosphere-10-00555" class="html-bibr">42</a>]; BN–batch normalization [<a href="#B38-atmosphere-10-00555" class="html-bibr">38</a>]).</p> Full article ">Figure 4
<p>Example test radar echo images with selected patches for comparison shown in Plan Position Indicator (PPI). (<b>a</b>,<b>b</b>) are the first elevation cut of the reflectivity and radial velocity data of the S-band China New-Generation Weather Radar (CINRAD-SA) at Beijing, China on 19 May 2018 at 09:36 (BJT), which has 360 radials with 460 range bins per radial direction for reflectivity, and 920 range bins per radial direction for radial velocity; (<b>c</b>–<b>e</b>) are the first elevation cut of reflectivity, radial velocity, and differential reflectivity data of the X-band dual-polarization Radar (XPRAD) radar at Xinfeng, Guangdong on 28 May 2016 at 07:39 (BJT), which has 360 radials with 600 range bins per radial direction.</p> Full article ">Figure 5
<p>Example of a high-resolution echo image patch (<a href="#atmosphere-10-00555-f004" class="html-fig">Figure 4</a>a) and the output low-resolution patch from the degradation process.</p> Full article ">Figure 6
<p>Qualitative reconstructed results of generative adversarial networks (GAN)-based methods and comparison with bicubic, iterative back-projection (IBP), and nonlocal self-similarity sparse representation (NSSR) using images from <a href="#atmosphere-10-00555-f004" class="html-fig">Figure 4</a> with upscale factors of ×4. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results are shown below individual patch. GAN-based methods produces more crisp edges and details than other methods. (<b>a</b>–<b>e</b>) reconstructed result patch using image from <a href="#atmosphere-10-00555-f004" class="html-fig">Figure 4</a>a–e (×4).</p> Full article ">Figure 7
<p>Same with <a href="#atmosphere-10-00555-f006" class="html-fig">Figure 6</a>. Cropped patch of reconstructed results with the upscaling factor of ×2. (<b>a</b>–<b>e</b>) reconstructed result patch using image from <a href="#atmosphere-10-00555-f004" class="html-fig">Figure 4</a>a–e (×2).</p> Full article ">
12 pages, 2462 KiB  
Article
Atmospheric Monitoring of Methane in Beijing Using a Mobile Observatory
by Wanqi Sun, Liangchun Deng, Guoming Wu, Lin Wu, Pengfei Han, Yucong Miao and Bo Yao
Atmosphere 2019, 10(9), 554; https://doi.org/10.3390/atmos10090554 - 16 Sep 2019
Cited by 15 | Viewed by 4246
Abstract
Cities have multiple fugitive emission sources of methane (CH4) and policies adopted by China on replacing coal with natural gas in recent years can cause fine spatial heterogeneities at the range of kilometers within a city and also contribute to the [...] Read more.
Cities have multiple fugitive emission sources of methane (CH4) and policies adopted by China on replacing coal with natural gas in recent years can cause fine spatial heterogeneities at the range of kilometers within a city and also contribute to the CH4 inventory. In this study, a mobile observatory was used to monitor the real-time CH4 concentrations at fine spatial and temporal resolutions in Beijing, the most important pilot city of energy transition. Results showed that: several point sources, such as a liquefied natural gas (LNG) power plant which has not been included in the Chinese national greenhouse gas inventory yet, can be identified; the ratio “fingerprints” (CH4:CO2) for an LNG carrier, LNG filling station, and LNG power plant show a shape of “L”; for city observations, the distribution of CH4 concentration, in the range of 1940–2370 ppbv, had small variations while that in the rural area had a much higher concentration gradient; significant correlations between CO2 and CH4 concentrations were found in the rural area but in the urban area there were no such significant correlations; a shape of “L” of CH4:CO2 ratios is obtained in the urban area in wintertime and it is assigned to fugitive emissions from LNG sources. This mobile measurement methodology is capable of monitoring point and non-point CH4 sources in Beijing and the observation results could improve the CH4 inventory and inform relevant policy-making on emission reduction in China. Full article
(This article belongs to the Section Air Quality)
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<p>Schematic of the mobile observatory. The viewing angle is from the passenger side of the car.</p> Full article ">Figure 2
<p>CH<sub>4</sub> concentration map and ratio “fingerprints” (CH<sub>4</sub>:CO<sub>2</sub>) of five point sources as a function of location: (<b>A</b>) an liquified natural gas (LNG) carrier; (<b>B</b>) an LNG filling station; (<b>C</b>) a residential area; (<b>D</b>) an LNG power station; (<b>E</b>) a refuse landfill. In A–D, the red dots represent the measurements of which high levels of CH<sub>4</sub> were associated with low CO<sub>2</sub> concentrations and black dots represent that the measurements where high CO<sub>2</sub> concentrations were associated with low CH<sub>4</sub> concentrations. The red and black lines are the linear regression results corresponding to the two groups of dots, respectively. The linear regressions are as the basis of ordinary least squares.</p> Full article ">Figure 3
<p>CH4 concentration maps of the 2nd–4th and the 6th ring roads as a function of location in non-wintertime. Note: The directions and magnitudes of arrows show the wind directions and speeds when the meteorological station was at these locations.</p> Full article ">Figure 4
<p>CH<sub>4</sub> concentration map as a function of location in wintertime.</p> Full article ">Figure 5
<p>Correlation analysis of CH<sub>4</sub> and CO<sub>2</sub> enhanced concentrations in the non-wintertime in the urban area (<b>A</b>) and in the rural area (<b>B</b>) and in the wintertime in the urban area (<b>C</b>) and in the rural area (<b>D</b>) and of CH<sub>4</sub> and CO enhanced concentrations in the wintertime in the urban area (<b>E</b>) and in the rural area (<b>F</b>). For the rural area measurement, three measurement data were merged for each figure. The slope, the regression coefficient (r) and the confidence level (<span class="html-italic">p</span>), the number of observations (n), are also shown. Note that the scales are different for urban and rural areas. The red lines are the linear regression results. In B, C, and E, the red dots represent the measurements of obvious fugitive CH<sub>4</sub> and they were not used in the regression analysis.</p> Full article ">
21 pages, 8765 KiB  
Article
Anatomy of a Cyclonic Eddy in the Kuroshio Extension Based on High-Resolution Observations
by Yongchui Zhang, Xi Chen and Changming Dong
Atmosphere 2019, 10(9), 553; https://doi.org/10.3390/atmos10090553 - 16 Sep 2019
Cited by 16 | Viewed by 3814
Abstract
Mesoscale eddies are common in the ocean and their surface characteristics have been well revealed based on altimetric observations. Comparatively, the knowledge of the three-dimensional (3D) structure of mesoscale eddies is scarce, especially in the open ocean. In the present study, high-resolution field [...] Read more.
Mesoscale eddies are common in the ocean and their surface characteristics have been well revealed based on altimetric observations. Comparatively, the knowledge of the three-dimensional (3D) structure of mesoscale eddies is scarce, especially in the open ocean. In the present study, high-resolution field observations of a cyclonic eddy in the Kuroshio Extension have been carried out and the anatomy of the observed eddy is conducted. The temperature anomaly exhibits a vertical monopole cone structure with a maximum of −7.3 °C located in the main thermocline. The salinity anomaly shows a vertical dipole structure with a fresh anomaly in the main thermocline and a saline anomaly in the North Pacific Intermediate Water (NPIW). The cyclonic flow displays an equivalent barotropic structure. The mixed layer is deep in the center of the eddy and thin in the periphery. The seasonal thermocline is intensified and the permanent thermocline is upward domed by 350 m. The subtropical mode water (STMW) straddled between the seasonal and permanent thermoclines weakens and dissipates in the eddy center. The salinity of NPIW distributed along the isopycnals shows no significant difference inside and outside the eddy. The geostrophic relation is approximately set up in the eddy. The nonlinearity—defined as the ratio between the rotational speed to the translational speed—is 12.5 and decreases with depth. The eddy-wind interaction is examined by high resolution satellite observations. The results show that the cold eddy induces wind stress aloft with positive divergence and negative curl. The wind induced upwelling process is responsible for the formation of the horizontal monopole pattern of salinity, while the horizontal transport results in the horizontal dipole structure of temperature in the mixed layer. Full article
(This article belongs to the Special Issue Disentangling Atmosphere-Ocean Interactions, from Weather to Climate)
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<p>Surface characteristics. (<b>a</b>) Mean dynamic topography (MDT) in the northwestern North Pacific Ocean. The mean Kuroshio Extension axis is represented as bold black line, which is 120 cm of MDT. The blue line shows the eddy movement trajectory. The overlaid magenta rectangle is the study area (156.25–157.75° E, 30.33–32° N). (<b>b</b>) Sea level anomaly (SLA) and survey stations. Shaded areas indicate the SLA and the magenta line is the −0.1 m contour of SLA, which indicates the boundary of the cyclonic eddy. The black lines are the cruising tracks, from south to north named S1, S2, S3, S4, S5 and S6. The squares overlaid on the tracks are the stations with Expendable conductivity–temperature–depth (XCTD) probes deployed. The red and blue squares indicate the stations inside and outside the eddy based on the eddy boundary. (<b>c</b>) Sea surface temperature observed by the satellite. The magenta line is the boundary as the same in (<b>b</b>).</p> Full article ">Figure 2
<p>Temperature anomalies at the six sections. (<b>a</b>), (<b>b</b>), (<b>c</b>), (<b>d</b>), (<b>e</b>) and (<b>f</b>) are for the sections of S1, S2, S3, S4, S5 and S6, respectively.</p> Full article ">Figure 3
<p>Same as in <a href="#atmosphere-10-00553-f002" class="html-fig">Figure 2</a> but for salinity anomaly.</p> Full article ">Figure 4
<p>Velocity observed by the 38 kHz shipboard acoustic Doppler current profilers (ADCP) at various depths. Shaded areas show the magnitude of the velocity. Vectors show the flows direction and magnitude. The red and blue full (dotted) lines indicate the zero lines of observed (geostrophic) meridional and zonal velocities, respectively.</p> Full article ">Figure 5
<p>Observed velocity and geostrophic velocity. Shaded areas indicate the observed velocity, which were measured by the 300 kHz and 38 kHz ADCP at depths shallower and deeper than 90 m, respectively. The contours show the geostrophic currents, the solid and dotted lines indicate positive and negative velocities and the black (magenta) thick line is the observed (geostrophic) zero-velocity contour in the upper and middle panels and the 0.4 m s<sup>−1</sup> contour in the bottom panel. Zonal velocity in the S1, S2, S3, S4, S5, S6 sections are shown in upper panels from left to right. Meridional velocity in the S1, S2, S3, S4, S5, S6 sections are shown in middle panels from left to right. Velocity magnitude in the S1, S2, S3, S4, S5, S6 sections are shown in bottom panels from left to right.</p> Full article ">Figure 6
<p>Mixed layer, seasonal and permanent thermoclines. (<b>a</b>) Mixed layer depth (MLD), which is also the upper boundary of the seasonal thermocline. (<b>b</b>) The bottom of the seasonal thermocline. (<b>c</b>) The thickness of the seasonal thermocline. (<b>d</b>) The depth corresponding to the upper boundary of the permanent thermocline, which is along the contour of 25.5 <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>. (<b>e</b>) The depth corresponding to the lower boundary of the permanent thermocline, which is along the contour of 26.7 <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>. (<b>f</b>) The thickness of the permanent thermocline. The MLD is determined as the depth with temperature change of 0.5 °C from the ocean surface to this depth. The seasonal and permanent thermoclines are determined here by vertical temperature gradients larger than 0.05 <math display="inline"><semantics> <mrow> <mo>℃</mo> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> and 0.02 <math display="inline"><semantics> <mrow> <mo>℃</mo> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, respectively.</p> Full article ">Figure 7
<p>Thermocline. (<b>a</b>), (<b>b</b>), (<b>c</b>), (<b>d</b>), (<b>e</b>) and (<b>f</b>) are for the sections of S1, S2, S3, S4, S5 and S6, respectively. The vertical gradient of temperature (<math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>z</mi> </msub> </mrow> </semantics></math>) is used to identify the thermocline. <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>z</mi> </msub> </mrow> </semantics></math> larger than 0.05 <math display="inline"><semantics> <mrow> <mo>℃</mo> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> and 0.02 <math display="inline"><semantics> <mrow> <mo>℃</mo> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> are deemed to be the seasonal thermocline and permanent thermocline. The magenta and red lines denote the upper and lower boundaries of the seasonal thermocline. The magenta line also shows the MLD. The black lines are the isopycnals of 25.5 and 26.7 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>, which correspond to the upper and lower boundaries of the permanent thermocline.</p> Full article ">Figure 8
<p>Subtropical Mode Water (STMW). (<b>a</b>), (<b>b</b>), (<b>c</b>), (<b>d</b>), (<b>e</b>) and (<b>f</b>) are for the sections of S1, S2, S3, S4, S5 and S6, respectively. The potential vorticity smaller than <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> <msup> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> is used to identify STMW between 25.3 and 25.5 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>, which is nattier blue shaded. The gray line denotes the climatological potential vorticity of <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> <msup> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>STMW and North Pacific Intermediate Water (NPIW). (<b>a</b>) Upper and (<b>b</b>) lower boundaries of the STMW. (<b>c</b>) Intensity of the STMW. (<b>d</b>) Upper and (<b>e</b>) lower boundaries of the NPIW. (<b>f</b>) The thickness of the NPIW.</p> Full article ">Figure 10
<p>NPIW. (<b>a</b>), (<b>b</b>), (<b>c</b>), (<b>d</b>), (<b>e</b>) and (<b>f</b>) are for the sections of S1, S2, S3, S4, S5 and S6, respectively. Shaded areas indicate the observed salinity with 33.9PSU-34.0PSU. The black lines indicate 26.7–26.8 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>, which are used to characterize the range of the NPIW. The gray lines are the climatological isopycnals of 26.7–26.8 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Temperature-Salinity diagram. The blue and red lines are the averages of temperature and salinity inside and outside the eddy. The shaded regions are their standard deviations, respectively. The STMW and NPIW are cyan and blue color shaded, which correspond to 25.3 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>−25.5 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math> and 26.7 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math> −26.8 <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>, respectively.</p> Full article ">Figure 12
<p>(<b>a</b>) The skill of the geostrophic currents. The black, red and blue lines are the magnitude, zonal and meridional velocities, respectively. The grey shaded in the bottom is the barotropical velocity which is added to the geostrophic velocity. (<b>b</b>) Nonlinearity defined by translational speed divided by the rotational speed.</p> Full article ">Figure 13
<p>Vertical relative vorticity scaled by the planetary vorticity. (<b>a</b>), (<b>b</b>), (<b>c</b>), (d), (<b>e</b>) and (<b>f</b>) are for the sections of S1, S2, S3, S4, S5 and S6, respectively. The black line is the zero contour.</p> Full article ">Figure 14
<p>(<b>a</b>) The wind stress (vectors) and water temperature (color shaded) at 5 m depth. (<b>b</b>) Wind stress magnitude (shaded) and water temperature (contours). (<b>c</b>) Wind stress divergence. (<b>d</b>) Wind stress curl.</p> Full article ">Figure 15
<p>Temperature, salinity and velocity at different depths. Shaded areas indicate temperature. The vectors are velocities measured by the 300 kHz ADCP.</p> Full article ">Figure 16
<p>Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) diagnoses. (<b>a</b>) SST induced by the advection. (<b>b</b>) SST induced by the Ekman downwelling. (<b>c</b>) SST induced by the sum of the horizontal and vertical processes. (<b>d</b>) SSS induced by the advection. (<b>e</b>) SSS induced by the Ekman upwelling. (<b>f</b>) SSS induced by the sum of the horizontal and vertical processes.</p> Full article ">
23 pages, 10020 KiB  
Article
The Green Infrastructure in Cities as A Tool for Climate Change Adaptation and Mitigation: Slovakian and Polish Experiences
by Ingrid Belčáková, Małgorzata Świąder and Małgorzata Bartyna-Zielińska
Atmosphere 2019, 10(9), 552; https://doi.org/10.3390/atmos10090552 - 16 Sep 2019
Cited by 42 | Viewed by 7561
Abstract
Climate change could be seen as a 21st century phenomenon. This topic has been taken up equally by professionals as well as the general public. Adaptation and mitigation actions are needed, especially in cities where the concentration of population and an increased demand [...] Read more.
Climate change could be seen as a 21st century phenomenon. This topic has been taken up equally by professionals as well as the general public. Adaptation and mitigation actions are needed, especially in cities where the concentration of population and an increased demand for resources (e.g., water, food, land) are expected in the coming years. Already, 400 cities have been declared to be in a “climate emergency” state. There are no longer any doubts that current environmental state requires actions and solutions for both the alarming climate situation and urban quality life development. If such action is not going to be taken, the environmental state will deteriorate. One possible solution could be the use of green infrastructure. This research compares approaches to green areas and green infrastructure development in Bratislava (Slovakia) and Wrocław (Poland). A comparison was made for projects realized between 2013 and 2018—i.e., since the publication of the European Union (EU) Strategy on Adaptation to Climate Change in 2013. The research presents an overview of delivered projects regarding land use. The overview, which is supported by a density map of implemented green projects, verifies whether the new greenery fits and fills in the existing natural areas. Secondly, the green projects were analyzed according to years and land use types using Tableau software. Moreover, the legislation of climate adaptation mechanisms and practical aspects of green infrastructure implementation are shown. Finally, actions concerning the greening of the cities were categorized into practical, educational, and participatory ones, and the potential of green infrastructure as a positive landscape, micro-climate, health, and aesthetic influence was examined. Full article
(This article belongs to the Special Issue Effects of Urban Areas on Climate Change Conditions)
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<p>The study area of Bratislava (Slovakia) and Wrocław (Poland). Source: Own elaboration using ArcGIS (Esri, Redlands, CA, United States).</p> Full article ">Figure 2
<p>The green projects realized during period 2015–2018 in Bratislava. Top side of the figure shows the overview and density of projects regarding to natural areas in city (top-right figure shows the zoom to the city center). The lower section of the figure shows the overview and density map of projects [No. of projects per square kilometer]. Source: Own elaboration using ArcGIS software.</p> Full article ">Figure 3
<p>The green projects realized during period 2013–2018 in Wrocław. Top side of the figure shows the overview and density of projects regarding to natural areas in city (top-right figure shows the zoom to the city center). The lower section of the figure shows the overview and density map of projects [No. of projects per square kilometer]. Source: Own elaboration using ArcGIS software.</p> Full article ">Figure 4
<p>The comparison of projects implemented in Bratislava and Wrocław according to land use type. Source: Own elaboration using Tableau software.</p> Full article ">Figure 5
<p>The comparison of projects realized in Bratislava and Wrocław according to year and land use type. Source: Own elaboration using Tableau software.</p> Full article ">
27 pages, 4830 KiB  
Article
North Sea Wave Database (NSWD) and the Need for Reliable Resource Data: A 38 Year Database for Metocean and Wave Energy Assessments
by George Lavidas and Henk Polinder
Atmosphere 2019, 10(9), 551; https://doi.org/10.3390/atmos10090551 - 16 Sep 2019
Cited by 19 | Viewed by 5347
Abstract
The study presents a newly generated hindcast database of metocean conditions for the region of the North Sea by parametrising the newly introduced ST6 physics in a nearshore wave model. Exploring and assessing the intricacies in wave generation are vital to produce a [...] Read more.
The study presents a newly generated hindcast database of metocean conditions for the region of the North Sea by parametrising the newly introduced ST6 physics in a nearshore wave model. Exploring and assessing the intricacies in wave generation are vital to produce a reliable hindcast. The new parametrisations perform better, though they have a higher number of tuneable options. Parametrisation of the white capping coefficient within the ST6 package improved performance with significant differences ≈±20–30 cm. The configuration which was selected to build the database shows a good correlation ≈95 % for H m 0, has an overall minimal bias with the majority of locations being slightly over-estimated ±0.5–1 cm. The calibrated model was subsequently used to produce a database for 38 years, analysing and discussing the metocean condition. In terms of wave energy resource, the North Sea has not received attention due to its perceived “lower” resource. However, from analysing the long-term climatic data, it is evident that the level of metocean conditions, and subsequently wave power, can prove beneficial for development. The 95th percentile indicates that the majority of the time H m 0 should be expected at 3.4–5 m, and the wave energy period T e at 5–7 s. Wave power resource exceeds 15 kW/m at locations very close to the coast, and it is uniformly reduced as we move to the Southern parts, near the English Channel, with values there being ≈5 kW/m, with most energetic seas originating from the North East. Results by the analysis show that in the North Sea, conditions are moderate to high, and the wave energy resource, which has been previously overlooked, is high and easily accessible due to the low distance from coasts. The study developed a regional high-fidelity model, analysed metocean parameters and properly assessed the energy content. Although, the database and its results can have multiple usages and benefit other sectors that want to operate in the harsh waters of the North Sea. Full article
(This article belongs to the Special Issue Waves and Wave Climate Analysis and Modeling)
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<p>Developed domain for the study, depth in meters.</p> Full article ">Figure 2
<p>Performance of wind drag coefficient (with author’s permission [<a href="#B33-atmosphere-10-00551" class="html-bibr">33</a>]).</p> Full article ">Figure 3
<p>Configuration for the calibration phase.</p> Full article ">Figure 4
<p>Bathymetry domain depth in meters and locations as numbered in <a href="#atmosphere-10-00551-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 5
<p>Histograms of <math display="inline"><semantics> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </semantics></math> indices for all compared locations by all calibration models.</p> Full article ">Figure 6
<p>Histograms of <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>m</mi> <mn>02</mn> </mrow> </msub> </semantics></math> indices for all compared locations by all calibration models.</p> Full article ">Figure 7
<p>Comparison of calibrating models, with dotted lines are the mean of each quantity for all models.</p> Full article ">Figure 8
<p><math display="inline"><semantics> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </semantics></math> of the “good model” based on ST6 parametrisation.</p> Full article ">Figure 9
<p>Comparison of in-situ data with “good” configuration model.</p> Full article ">Figure 10
<p><math display="inline"><semantics> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </semantics></math> differences of means in meters for <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>T</mi> <msub> <mi>H</mi> <mn>123</mn> </msub> </mrow> </semantics></math> versus the <math display="inline"><semantics> <msub> <mi>K</mi> <mn>1</mn> </msub> </semantics></math>.</p> Full article ">Figure 11
<p><math display="inline"><semantics> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mn>0</mn> </mrow> </msub> </semantics></math> statistics.</p> Full article ">Figure 12
<p><math display="inline"><semantics> <msub> <mi>T</mi> <mi>e</mi> </msub> </semantics></math> statistics.</p> Full article ">Figure 13
<p><math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>w</mi> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> </semantics></math> NSWD 1980-2017 (38 years).</p> Full article ">Figure 14
<p><math display="inline"><semantics> <mrow> <mi>P</mi> <msub> <mi>k</mi> <mrow> <mi>D</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> at indicative Northern (<b>a</b>) and Southern (<b>b</b>) locations.</p> Full article ">Figure 15
<p>Accessibility in percentage of time, based on different thresholds.</p> Full article ">
13 pages, 5302 KiB  
Article
Atmospheric Characterization Based on Relative Humidity Control at Optical Turbulence Generator
by Jhonny Villamizar, Manuel Herreño, Omar Tíjaro and Yezid Torres
Atmosphere 2019, 10(9), 550; https://doi.org/10.3390/atmos10090550 - 16 Sep 2019
Cited by 3 | Viewed by 2792
Abstract
In atmospheric turbulence, relative humidity has been almost a negligible variable due to its limited effect, compared with temperature and air velocity, among others. For studying the horizontal path, a laser beam was propagated in a laboratory room, and an Optical Turbulence Generator [...] Read more.
In atmospheric turbulence, relative humidity has been almost a negligible variable due to its limited effect, compared with temperature and air velocity, among others. For studying the horizontal path, a laser beam was propagated in a laboratory room, and an Optical Turbulence Generator (OTG) was built and placed along the optical axis. Additionally, there was controlled humidity inside the room and measuring of some physical variables inside the OTG device for determining its effects on the laser beam. The experimental results show the measurements of turbulence parameters C n 2 , l o , and σ I 2 from beam centroids fluctuations, where increases in humidity generated stronger turbulence. Full article
(This article belongs to the Special Issue Atmospheric Turbulence Measurements and Calibration)
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<p>Signal processing scheme to register characteristics of beam centroid on an electronic embedded system. Source: Authors.</p> Full article ">Figure 2
<p>Transversal shifts of laser propagation, the illustration shows two observers placed at <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mn>1</mn> </msub> </mrow> </semantics></math> (solid line) and <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mn>2</mn> </msub> </mrow> </semantics></math> (dashed lines), respectively. We assumed a centered beam at time <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, and beam wander effects at time <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mn>2</mn> </msub> </mrow> </semantics></math> due to laser cavity (in polar coordinates). Source: Authors.</p> Full article ">Figure 3
<p>Experimental setup. A#: Density neutral filter. BS#: Beam Splitter #. CMOS#: CMOS Camera to acquire beam at <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>1</mn> </msub> </mrow> </semantics></math>=1.18 [m], <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>2</mn> </msub> </mrow> </semantics></math> =1.45 [m] and <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>3</mn> </msub> </mrow> </semantics></math> =3.97 [m]. OTG: Optical Turbulence Generator. RHG: Relative Humidity Generator. Source: Authors.</p> Full article ">Figure 4
<p>Scheme to compute the angle of centroids. Source: Authors.</p> Full article ">Figure 5
<p>Designed board to add humidity signal to the microcontroller (ADC). Source: Authors.</p> Full article ">Figure 6
<p>Signal processing scheme to register humidity fluctuations on an embedded system. Source: Authors.</p> Full article ">Figure 7
<p>Temporal distribution, in cartesian coordinates, of centroid fluctuations to Pattern Test. Up: Measured at distance <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, Down: estimated at distance <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math> from distance <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>2</mn> </msub> </mrow> </semantics></math>. (<b>a</b>) X-axis. (<b>b</b>) Y-axis. Source: Authors.</p> Full article ">Figure 8
<p>2D histogram of temporal fluctuations in the Pattern Test (X and Y movements are in pixels). (<b>a</b>) Measured at distance <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math>. (<b>b</b>) Estimated at <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math> from distance <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>2</mn> </msub> </mrow> </semantics></math>. Source: Authors.</p> Full article ">Figure 9
<p>2D histogram of temporal fluctuations measured at <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math>: (<b>a</b>) Pattern Test. (<b>b</b>) Test #1. Source: Authors.</p> Full article ">Figure 10
<p>2D histogram of temporal fluctuations measured at <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math> plane: (<b>a</b>) Pattern Test. (<b>b</b>) Test #2. Source: Authors.</p> Full article ">Figure 11
<p>2D histogram of temporal fluctuations measured at <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math> plane: (<b>a</b>) Pattern Test. (<b>b</b>) Test #3. Source: Authors.</p> Full article ">Figure 12
<p>Temporal distribution for the angle of the centroid for the Pattern Test, measured at the <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>3</mn> </msub> </mrow> </semantics></math> plane. Source: Authors.</p> Full article ">Figure 13
<p>Fluctuations for the angle of centroid (<math display="inline"><semantics> <mi>θ</mi> </semantics></math>) according to Test: (<b>a</b>) Pattern. (<b>b</b>) Test #1. (<b>c</b>) Test #2. (<b>d</b>) Test #3. Source: Authors.</p> Full article ">Figure 14
<p>Scintillation index (<math display="inline"><semantics> <mrow> <msubsup> <mi>σ</mi> <mi>I</mi> <mn>2</mn> </msubsup> </mrow> </semantics></math>) according to Test: (<b>a</b>) Pattern. (<b>b</b>) Test #1. (<b>c</b>) Test #2. (<b>d</b>) Test #3. Source: Authors.</p> Full article ">Figure 15
<p>Inner Scale (<math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>0</mn> </msub> </mrow> </semantics></math>) according to Test: (<b>a</b>) Pattern. (<b>b</b>) Test #1. (<b>c</b>) Test #2. (<b>d</b>) Test #3. Source: Authors.</p> Full article ">Figure 16
<p>Refraction index structure constant <math display="inline"><semantics> <mrow> <msubsup> <mi>C</mi> <mi>n</mi> <mn>2</mn> </msubsup> </mrow> </semantics></math> according to Test: (<b>a</b>) Pattern. (<b>b</b>) Test #1. (<b>c</b>) Test #2. (<b>d</b>) Test #3. Source: Authors.</p> Full article ">
10 pages, 4518 KiB  
Article
Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
by Beatrice Augere, Matthieu Valla, Anne Durécu, Agnès Dolfi-Bouteyre, Didier Goular, François Gustave, Christophe Planchat, Didier Fleury, Thierry Huet and Claudine Besson
Atmosphere 2019, 10(9), 549; https://doi.org/10.3390/atmos10090549 - 16 Sep 2019
Cited by 8 | Viewed by 3997
Abstract
A three-dimensional (3D) wind profiling Lidar, based on the latest high power 1.5 µm fiber laser development at Onera, has been successfully flown on-board a SAFIRE (Service des Avions Français Instrumentés pour la Recherche en Environnement) ATR42 aircraft. The Lidar called LIVE (LIdar [...] Read more.
A three-dimensional (3D) wind profiling Lidar, based on the latest high power 1.5 µm fiber laser development at Onera, has been successfully flown on-board a SAFIRE (Service des Avions Français Instrumentés pour la Recherche en Environnement) ATR42 aircraft. The Lidar called LIVE (LIdar VEnt) is designed to measure wind profiles from the aircraft down to ground level, with a horizontal resolution of 3 km, a vertical resolution of 100 m and a designed accuracy on each three wind vector components better than 0.5 m.s−1. To achieve the required performance, LIVE Lidar emits 410 µJ laser pulses repeating at 14 KHz with a duration of 700 ns and uses a conical scanner of 30° total opening angle and a full scan time of 17 s. Full article
(This article belongs to the Special Issue Atmospheric Applications of Lidar)
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<p><b>Left</b>: picture of the power amplifier. <b>Right</b>: temporal shape of laser pulse.</p> Full article ">Figure 2
<p>Diagram of the LIVE lidar installation inside the airplane. V<sub>1</sub>, V<sub>2</sub> … represent radial wind speed measured by lidar, and V<sub>x</sub>, V<sub>y</sub> and V<sub>z</sub> are the components of the retrieved wind vector.</p> Full article ">Figure 3
<p>ATR42 aircraft, with trapdoor localization and lidar scanning representation.</p> Full article ">Figure 4
<p>Spectrogram data at a low carrier to noise ratio (CNR), and presence of the lidar signal (black dotted trail) in accordance with the maximum of the function of the accumulated spectra (MFAS) algorithm estimation.</p> Full article ">Figure 5
<p>Doppler compensated spectrogram and presence of lidar signal (black dotted trail) in accordance with MFAS estimation (<b>left</b>). Function of accumulated spectra (<b>right</b>).</p> Full article ">Figure 6
<p>Picture of LIVE lidar installed inside the ATR42.</p> Full article ">Figure 7
<p>(<b>a</b>) Line of sight CNR real time display. (<b>b</b>) Line of sight velocity real time display for the conical scan. Range zero is the aircraft altitude while range 3500 m is the ground echo.</p> Full article ">Figure 8
<p>(<b>a</b>) Aircraft trajectories during measurements (north–south and west–east) over Fauga. (<b>b</b>–<b>d</b>) respectively z (zenith), x (north) and y (west) components of wind measured by airborne lidar LIVE (red squares) and WindCube lidar (blue dots).</p> Full article ">Figure 9
<p>(<b>a</b>–<b>c</b>) respectively x (north), y (west) and z (zenith) components of wind measured by airborne lidar LIVE at three different altitudes and each altitude at two different headings.</p> Full article ">
15 pages, 2457 KiB  
Article
Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area
by Xinpeng Tian and Zhiqiang Gao
Atmosphere 2019, 10(9), 548; https://doi.org/10.3390/atmos10090548 - 14 Sep 2019
Cited by 22 | Viewed by 3510
Abstract
The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined [...] Read more.
The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area. Full article
(This article belongs to the Special Issue Urban Atmospheric Aerosols: Sources, Analysis and Effects)
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<p>The geographical location of the study area. (<b>a</b>) The locations of the 23 AERONET sites are used for the evaluation of the satellite-based AOD products by circulars, and (<b>b</b>) the spatial distribution of all AERONET sites in the world. Background map is global annual average surface-level PM<sub>2.5</sub> concentration derived from MODIS and MISR AOD satellite data sets (2001–2010) [<a href="#B32-atmosphere-10-00548" class="html-bibr">32</a>].</p> Full article ">Figure 2
<p>Validation of MOD04 C6.1 DT (<b>a</b>), DB (<b>b</b>), and combined DT/DB (<b>c</b>) AOD retrievals at 10 km resolution against AERONET measurements for the years 2000–2016. The dashed lines = EE lines, black solid line = 1:1 line, and red solid line = regression line.</p> Full article ">Figure 3
<p>The percentage within in (PWE), above (PAE) and below (PBE) the expected error for the DT (<b>a</b>), DB (<b>b</b>), and DT/DB (<b>c</b>) products in each AOD bin over heavy aerosol loading area for the years 2000–2016.</p> Full article ">Figure 4
<p>The percentage within in (PWE), above (PAE) and below (PBE) the expected error and the number of collocations for the DT, DB and DT/DB products in the four seasons over heavy aerosol loading area for the years 2000–2016.</p> Full article ">Figure 5
<p>Error statistics for DT, DB, and DT/DB products against AERONET AOD ground-observed measurements in the four seasons.</p> Full article ">Figure 6
<p>The percentage within in (PWE), above (PAE) and below (PBE) the expected error and the number of collocations for the DT, DB and DT/DB products over different land cover types for the years 2000–2016.</p> Full article ">Figure 7
<p>Error statistics for DT, DB, and DT/DB products against AERONET AOD ground-observed measurements over different land cover types.</p> Full article ">
21 pages, 13025 KiB  
Article
Frontal Wind Field Retrieval Based on UHF Wind Profiler Radars and S-Band Radars Network
by Min-Seong Kim, Bernard Campistron and Byung Hyuk Kwon
Atmosphere 2019, 10(9), 547; https://doi.org/10.3390/atmos10090547 - 14 Sep 2019
Cited by 3 | Viewed by 4315
Abstract
The three-dimensional wind field (WPR3D) and the multiple WPR3D (M-WPR3D) associated with the passage of a stationary front was derived from observations made by a network of eight wind profiler radars (WPR) being operated by the Korea Meteorological Administration during the summer “Jangma” [...] Read more.
The three-dimensional wind field (WPR3D) and the multiple WPR3D (M-WPR3D) associated with the passage of a stationary front was derived from observations made by a network of eight wind profiler radars (WPR) being operated by the Korea Meteorological Administration during the summer “Jangma” season. The effectiveness of the WPR3D was determined through numerical model analysis and wind profilers at three sites, and the accuracy of the M-WPR3D was validated by comparing the trajectory of the radiosonde. The discontinuity of the wind field near the frontal interface was clearly retrieved and the penetration of the air mass in the southern front was detected. Compared with either the wind vector of three single wind profiler or a local data assimilation and predication system, the WPR3D wind field showed a wind speed accuracy of approximately 70% at an altitude of 1.5 km and underestimated the wind speed by 0.5–1.5 m s−1. The M-WPR3D with three S-band Doppler radars successfully retrieved the backing wind field as well as the pre-Jangma-frontal jet. The results of this study showed that severe weather can be effectively analyzed using a three-dimensional wind field generated on the basis of a remote sensing network. Full article
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<p>Scheme of the research to construct the three-dimensional wind field (WPR3D) and the multiple WPR3D (M-WPR3D) wind field. Observational sites of 8 wind profilers and 4 S-band radars are indicated by P0–P7 and W1–W4, respectively. R5 is the WPR3D reference point of the triangle P1–P2–P4.</p> Full article ">Figure 2
<p>(<b>a</b>) positions of wind profilers at the P0, P1, P2, and P3 sites (red points) and the other five wind profilers (black points) superimposed on a topographical map of the South Korea peninsula. Here, 50-km intervals range circles are centered on P0, the profiler reference site. (<b>b</b>) a multiple-instrument network constructed by wind profilers (P1–P8) and S-band Doppler weather radars (W1–W4). Green points (R1–R11) indicate the center of the triangle from which the wind fields are derived.</p> Full article ">Figure 3
<p>Surface weather maps show Jangma front movement. The green line and the hatched zone indicate the area where the dew point temperature is 20 °C or higher.</p> Full article ">Figure 3 Cont.
<p>Surface weather maps show Jangma front movement. The green line and the hatched zone indicate the area where the dew point temperature is 20 °C or higher.</p> Full article ">Figure 4
<p>Wind vectors observed by wind profilers (<b>a</b>) at P1, (<b>b</b>) at P0, and (<b>c</b>) at P3 with an inverse time axis. Red dashed lines indicate the arrival of front at each site.</p> Full article ">Figure 5
<p>Composed radar images during Jangma front migration.</p> Full article ">Figure 5 Cont.
<p>Composed radar images during Jangma front migration.</p> Full article ">Figure 6
<p>One hour accumulated rainfall from 17 to 20 June 2013 at P0, P1, and P3.</p> Full article ">Figure 7
<p>Comparison of wind components U, V, and WPR3D wind speed with those (<b>a</b>) from WPR at P0, and (<b>b</b>) from Local Data Assimilation and Prediction System (LDAPS) around P0 at an altitude of 1.5 km.</p> Full article ">Figure 8
<p>Skill scores with height used for validating the wind determined from WPR3D against the wind (<b>a</b>) from the single wind profiler at P0 and (<b>b</b>) from LDAPS.</p> Full article ">Figure 9
<p>Storm relative helicity (SRH) produced from wind profiler observations (<b>a</b>) at P1, (<b>b</b>) at P0, and (<b>c</b>) at P3. The rainfall period is shaded in blue.</p> Full article ">Figure 10
<p>Same as <a href="#atmosphere-10-00547-f008" class="html-fig">Figure 8</a> but for convective available potential energy (CAPE) (<b>a</b>) at P1, (<b>b</b>) at P0, and (<b>c</b>) at P3.</p> Full article ">Figure 11
<p>Wind vectors (<b>a</b>) from WPR3D and (<b>b</b>) from LDAPS at the P0 station.</p> Full article ">Figure 12
<p>SRH (<b>a</b>) from WPR3D and (<b>b</b>) from LDAPS. The blue shading indicates the rainfall period with rainfall over 15 mm h<sup>−1</sup>.</p> Full article ">Figure 13
<p>Wind vector from WPR3D (red) and LDAPS (black) at the 1.5-km level. P0 is shown by the red point in the center.</p> Full article ">Figure 14
<p>Comparison of the horizontal wind fields (<b>a</b>) from the S-band radar near R5 with those (<b>b</b>) from the WPR at P4 on 26–27 October 2013.</p> Full article ">Figure 15
<p>Comparison of the horizontal trajectories from M-WPR3D with those from the GPS radiosonde launched simultaneously (<b>a</b>) at P1 and (<b>b</b>) at P6 from 11 UTC to 23 UTC on 18 June 2013.</p> Full article ">Figure 16
<p>Comparison of the horizontal wind fields between the M-WPR3D (red arrow) and the LDAPS (blue arrow) at an altitude of 1.5 km.</p> Full article ">
11 pages, 1018 KiB  
Article
Method for Determining Neutral Wind Velocity Vectors Using Measurements of Internal Gravity Wave Group and Phase Velocities
by Andrey V. Medvedev, Konstantin G. Ratovsky, Maxim V. Tolstikov, Roman V. Vasilyev and Maxim F. Artamonov
Atmosphere 2019, 10(9), 546; https://doi.org/10.3390/atmos10090546 - 13 Sep 2019
Cited by 8 | Viewed by 3496
Abstract
This study presents a new method for determining a neutral wind velocity vector. The basis of the method is measurement of the group velocities of internal gravity waves. Using the case of the Boussinesq dispersion relation, we demonstrated the ability to measure a [...] Read more.
This study presents a new method for determining a neutral wind velocity vector. The basis of the method is measurement of the group velocities of internal gravity waves. Using the case of the Boussinesq dispersion relation, we demonstrated the ability to measure a neutral wind velocity vector using the group velocity and wave vector data. An algorithm for obtaining the group velocity vector from the wave vector spectrum is proposed. The new method was tested by comparing the obtained winter wind pattern with wind data from other sources. Testing the new method showed that it is in quantitative agreement with the Fabry–Pérot interferometer wind measurements for zonal and vertical wind velocities. The differences in meridional wind velocities are also discussed here. Of particular interest were the results related to the measurement of vertical wind velocities. We demonstrated that two independent methods gave the presence of vertical wind velocities with amplitude of ~20 m/s. Estimation of vertical wind contribution to plasma drift velocity indicated the importance of vertical wind measurements and the need to take them into account in physical and empirical models of the ionosphere and thermosphere. Full article
(This article belongs to the Special Issue Atmospheric Acoustic-Gravity Waves)
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<p>Group velocity (<b>red</b>) and wave (<b>green</b>) vectors for different wind (<b>blue</b>) cases: (<b>a</b>) No-wind, (<b>b</b>) horizontal downwind, (<b>c</b>) horizontal upwind, (<b>d</b>) horizontal perpendicular wind, (<b>e</b>) vertical upward wind, and (<b>f</b>) vertical downward wind. In figures (<b>b</b>–<b>f</b>), dashed lines show the no-wind case.</p> Full article ">Figure 2
<p>Elevation distributions for wave vector (<b>green</b>) and group velocity (<b>red</b>).</p> Full article ">Figure 3
<p>Wintertime diurnal variations in zonal (<b>positive eastward, left panel</b>), meridional (<b>positive southward, central panel</b>), and vertical (<b>positive upward, right panel</b>) winds obtained in various ways: The new method based on measurement of the group velocities of internal gravity waves (IGWs) (<b>black</b>), the Fabry–Pérot interferometer (FPI) (<b>red</b>), the HWM2007 (<b>blue</b>), and a previously developed method based on wind projection measurements (<b>cyan</b>).</p> Full article ">Figure 4
<p>Comparison of meridional wind contribution (<b>left, black</b>), vertical wind contribution (<b>central, black</b>), and total contribution (<b>right, black</b>) to plasma drift velocity with peak height (<b>blue</b>) from the Irkutsk ionosonde.</p> Full article ">
17 pages, 2692 KiB  
Article
Precipitation Diurnal Cycle in Germany Linked to Large-Scale Weather Circulations
by Wael Ghada, Ye Yuan, Clemens Wastl, Nicole Estrella and Annette Menzel
Atmosphere 2019, 10(9), 545; https://doi.org/10.3390/atmos10090545 - 13 Sep 2019
Cited by 9 | Viewed by 3754
Abstract
The precipitation diurnal cycle (PDC) varies with the season and location. Its link to large-scale weather circulations has been studied in different regions. However, comparable information is lacking for Central Europe. Two decades of hourly precipitation data were combined with records of objective [...] Read more.
The precipitation diurnal cycle (PDC) varies with the season and location. Its link to large-scale weather circulations has been studied in different regions. However, comparable information is lacking for Central Europe. Two decades of hourly precipitation data were combined with records of objective weather patterns over Germany, focusing on the general atmospheric wind directions (WD). The PDC is characterized by the frequency and the average amount of hourly precipitation. The precipitation frequency generally has two peaks: one in the morning and the other in the afternoon. The morning peak of the precipitation amount is small compared to that of the afternoon peak. Remarkably, WD has a prominent influence on the PDC. Days with southwesterly WD have a high afternoon peak and a lower morning peak, while days with northwesterly WD have a high morning peak and a lower afternoon peak. Furthermore, the seasonal variations of PDC are dominated by the seasonal frequency of WD classes. This study presents a general overview of the PDC in Germany with regard to its variation with seasonality, geographical location, elevation, and WD. Full article
(This article belongs to the Section Meteorology)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geographical distribution of the meteorological stations with the corresponding elevation classes. The two horizontal lines divide the stations into northern, central, and southern stations. The vertical lines divide the stations into western, central, and eastern stations.</p> Full article ">Figure 2
<p>Precipitation diurnal cycles (PDC) represented by the long-term averages of the hourly precipitation occurrence frequency (<b>upper panel</b>) and the long-term averages of the hourly precipitation amount (<b>lower panel</b>) with shaded 95% confidence intervals in different (<b>a</b>) latitudes, (<b>b</b>) longitudes, and (<b>c</b>) elevations (see <a href="#atmosphere-10-00545-t001" class="html-table">Table 1</a> for the definitions of the classes; N—north, C—central, and S—south (<b>a</b>); W—west, C—central, and E—east (<b>b</b>); and H—high, M—medium, and L—low (<b>c</b>)).</p> Full article ">Figure 3
<p>Precipitation diurnal cycles (PDC) for each season represented by the long-term averages of (<b>a</b>) the hourly precipitation occurrence frequency and (<b>b</b>) the hourly precipitation amount with the shaded 95% confidence intervals.</p> Full article ">Figure 4
<p>Total number of days per wind direction (WD) class in each season. The numbers in the legend indicate the percentage of days for each WD class for the whole study period, regardless of season. The WD classes are XX: no prevailing direction, NE: northeasterly, SE: southeasterly, SW: southwesterly, and NW: northwesterly. See <a href="#sec2dot1-atmosphere-10-00545" class="html-sec">Section 2.1</a>.</p> Full article ">Figure 5
<p>Percentage of wet days per wind direction (WD) in different seasons. The boxplots represent the distribution of this percentage across all stations. The horizontal colored lines and associated numbers in the legend represent the mean percentage across all years and stations, regardless of season. For the WD classes, see <a href="#sec2dot1-atmosphere-10-00545" class="html-sec">Section 2.1</a>. In each boxplot, outliers are represented by black dots, the median is represented by the horizontal thick line, and the lower and upper hinges represent the first and third quartiles, while the upper (lower) whisker extends from the upper (lower) hinge to the largest (smallest) value no further than 1.5 times the interquartile range from the hinge [<a href="#B57-atmosphere-10-00545" class="html-bibr">57</a>].</p> Full article ">Figure 6
<p>Average number of hours per wet day per wind direction (WD) in each season. The boxplots represent the distribution of the number of wet hours across all stations colored by the WD class. The horizontal colored lines and the associated numbers in the legend represent the mean number of wet hours per wet day across all years and stations for each WD class, regardless of the season. For the WD classes, see <a href="#sec2dot1-atmosphere-10-00545" class="html-sec">Section 2.1</a>. The representation of the boxplot is same as explained in <a href="#atmosphere-10-00545-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 7
<p>Boxplot of precipitation sums per wind direction (WD) in each season averaged over the measurement years for all stations. The numbers in the legend are the mean precipitation sums per year for each WD averaged over all stations in mm. For the WD classes, see <a href="#sec2dot1-atmosphere-10-00545" class="html-sec">Section 2.1</a>. The representation of the boxplot is same as explained in <a href="#atmosphere-10-00545-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 8
<p>Precipitation diurnal cycle in different wind direction (WD) classes represented by the long-term averages of the hourly precipitation occurrence frequency (<b>a</b>) and the hourly precipitation amount (<b>b</b>) with the shaded 95% confidence intervals.</p> Full article ">Figure 9
<p>Precipitation diurnal cycle variation in different wind direction (WD) classes and geographical locations represented by the long-term averages of the hourly precipitation occurrence frequency (<b>a</b>,<b>c</b>) and the hourly precipitation amount (<b>b</b>,<b>d</b>) with the shaded 95% confidence intervals. The colors represent the four WD classes, while the panels represent the station class by latitude (<b>a</b>,<b>b</b>) and longitude (<b>c</b>,<b>d</b>).</p> Full article ">Figure 10
<p>Precipitation diurnal cycle variation in different wind direction (WD) classes and seasons represented by the long-term averages of the hourly precipitation occurrence frequency (<b>a</b>) and the hourly precipitation amount (<b>b</b>) with the shaded 95% confidence intervals. The colors represent the four WD classes, while the panels represent the four seasons.</p> Full article ">
16 pages, 2756 KiB  
Article
Differences in Model Performance and Source Sensitivities for Sulfate Aerosol Resulting from Updates of the Aqueous- and Gas-Phase Oxidation Pathways for a Winter Pollution Episode in Tokyo, Japan
by Syuichi Itahashi, Kazuyo Yamaji, Satoru Chatani and Hiroshi Hayami
Atmosphere 2019, 10(9), 544; https://doi.org/10.3390/atmos10090544 - 12 Sep 2019
Cited by 8 | Viewed by 3521
Abstract
During the Japanese intercomparison study, Japan’s Study for Reference Air Quality Modeling (J-STREAM), it was found that wintertime SO42– concentrations were underestimated over Japan with the Community Multiscale Air Quality (CMAQ) modeling system. Previously, following two development phases, model performance was [...] Read more.
During the Japanese intercomparison study, Japan’s Study for Reference Air Quality Modeling (J-STREAM), it was found that wintertime SO42– concentrations were underestimated over Japan with the Community Multiscale Air Quality (CMAQ) modeling system. Previously, following two development phases, model performance was improved by refining the Fe- and Mn-catalyzed oxidation pathways and by including an additional aqueous-phase pathway via NO2 oxidation. In a third phase, we examined a winter haze period in December 2016, involving a gas-phase oxidation pathway whereby three stabilized Criegee intermediates (SCI) were incorporated into the model. We also included options for a kinetic mass transfer aqueous-phase calculation. According to statistical analysis, simulations compared well with hourly SO42– observations in Tokyo. Source sensitivities for four domestic emission sources (transportation, stationary combustion, fugitive VOC, and agricultural NH3) were investigated. During the haze period, contributions from other sources (overseas and volcanic emissions) dominated, while domestic sources, including transportation and fuel combustion, played a role in enhancing SO42– concentrations around Tokyo Bay. Updating the aqueous phase metal catalyzed and NO2 oxidation pathways lead to increase contribution from other sources, and the additional gas phase SCI chemistry provided a link between fugitive VOC emission and SO42– concentration via changes in O3 concentration. Full article
(This article belongs to the Special Issue Air Pollution Modelling: Local-, Regional-, and Global-Scale Application)
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<p>Spatial distribution of (<b>left</b>) SO<sub>4</sub><sup>2–</sup> concentrations simulated by the CMAQ base-case simulation for domain 1 and (<b>right</b>) changes in SO<sub>4</sub><sup>2–</sup> concentrations for chemistry updates A and KMT averaged over the period 15–25 December 2016.</p> Full article ">Figure 2
<p>Spatial distribution of SO<sub>4</sub><sup>2–</sup> concentrations simulated by the CMAQ chemistry updates B as changes from chemistry updates A averaged over the period 15–25 December 2016 in domain 1. The inclusion of SCI was tested incrementally as (<b>top-left</b>) only SCI1 with a higher rate constant for H<sub>2</sub>O (SCI1(H)), (<b>top-center</b>) the addition of SCI2 to SCI1(H), and (<b>top-right</b>) the addition of SCI3 to SCI1(H) and SCI2, (<b>bottom-left</b>) SCI1 with a lower rate constant of H<sub>2</sub>O (SCI1(L)), (<b>bottom-center</b>) the addition of SCI2 to SCI1(L), and (<b>bottom-right</b>) the addition of SCI3 to SCI(L) and SCI2.</p> Full article ">Figure 3
<p>Spatial distribution of (<b>top</b>) ambient concentrations of SO<sub>4</sub><sup>2–</sup> and (<b>bottom</b>) wet deposition simulated by (<b>left</b>) CMAQ base-case simulation and (<b>right</b>) changes by chemistry updates A, B, and KMT relative to the base-case averaged over the period 15–25 December 2016 over domain 4. The observation site at Tokyo is indicated by the gray circle.</p> Full article ">Figure 4
<p>Temporal variation of (<b>top</b>) hourly observed and modelled ambient concentrations of SO<sub>4</sub><sup>2</sup>, and (<b>bottom</b>) hourly modelled precipitation at the Tokyo site for domain 4. The inset in bottom figure is the daily accumulated SO<sub>4</sub><sup>2–</sup> wet deposition for the observed and modelled results.</p> Full article ">Figure 5
<p>Spatial distribution of SO<sub>4</sub><sup>2–</sup> concentrations simulated by the CMAQ base-case simulation averaged over (<b>a</b>) P1, (<b>b</b>) P2, and (<b>c</b>) P3 for domain 4.</p> Full article ">Figure 6
<p>Spatial distribution of (<b>left</b>) all SO<sub>2</sub> emission sources used in this study and SO<sub>2</sub> emissions from sources g01 (<b>center</b>) and g02 (<b>right</b>) over domain 4. Note that emissions are shown in two dimensions and most of g01 is distributed on the surface layer of the model, whereas g02 is distributed over the upper layer of the model, considering the stack heights.</p> Full article ">Figure 7
<p>Temporal variation of source contributions of g01, g02, g03, g04, and others calculated for the CMAQ base-case simulation.</p> Full article ">Figure 8
<p>Spatial distributions of source contributions g01, g02, and other sources calculated by CMAQ base-case simulation, averaged during the analyzed periods defined as (<b>a</b>) P1, (<b>b</b>) P2, and (<b>c</b>) P3. Note that the color scale for g02 is different from that of g01 and other sources.</p> Full article ">Figure 9
<p>Summary of the source contributions calculated by the four CMAQ simulations averaged during (<b>a</b>) P1, (<b>b</b>) P2, and (<b>c</b>) P3.</p> Full article ">Figure 10
<p>Source contributions of O<sub>3</sub> from four domestic sources of g01, g02, g03, and g04, calculated by chemistry updates B, averaged during P1.</p> Full article ">
14 pages, 8025 KiB  
Article
Inverting the East Asian Dust Emission Fluxes Using the Ensemble Kalman Smoother and Himawari-8 AODs: A Case Study with WRF-Chem v3.5.1
by Tie Dai, Yueming Cheng, Daisuke Goto, Nick A. J. Schutgens, Maki Kikuchi, Mayumi Yoshida, Guangyu Shi and Teruyuki Nakajima
Atmosphere 2019, 10(9), 543; https://doi.org/10.3390/atmos10090543 - 12 Sep 2019
Cited by 5 | Viewed by 3851
Abstract
We present the inversions (back-calculations or optimizations) of dust emissions for a severe winter dust event over East Asia in November 2016. The inversion system based on a fixed-lag ensemble Kalman smoother is newly implemented in the Weather Research and Forecasting model and [...] Read more.
We present the inversions (back-calculations or optimizations) of dust emissions for a severe winter dust event over East Asia in November 2016. The inversion system based on a fixed-lag ensemble Kalman smoother is newly implemented in the Weather Research and Forecasting model and is coupled with Chemistry (WRF-Chem). The assimilated observations are the hourly aerosol optical depths (AODs) from the next-generation geostationary satellite Himawari-8. The posterior total dust emissions (2.59 Tg) for this event are 3.8 times higher than the priori total dust emissions (0.68 Tg) during 25–27 November 2016. The net result is that the simulated aerosol horizontal and vertical distributions are both in better agreement with the assimilated Himawari-8 observations and independent observations from the ground-based AErosol RObotic NETwork (AERONET), the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The developed emission inversion approach, combined with the geostationary satellite observations, can be very helpful for properly estimating the Asian dust emissions. Full article
(This article belongs to the Special Issue Soil/Mineral Dust Aerosols in the Earth System)
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<p>The flow chart of the Ensemble Kalman smoother system with a fixed-lag value N = 1 for dust emission inversions. The white and dark shaded boxes denote the first guess and final optimized dust emission ensembles, respectively. The light shaded boxes denote the intermediate optimized dust emission ensembles that need to be optimized in the next cycle. The number in parentheses indicates how many times the dust emissions have been optimized.</p> Full article ">Figure 2
<p>Model simulation domain with (<b>a</b>) the terrain height and (<b>b</b>) soil erodibility. The soil erodibility ranges from 0 to 1, with 0 indicating no erodibility (i.e., no dust source). The pink dot depicts the location of the Beijing site of Aerosol Robotic Network (AERONET) used for independent validation.</p> Full article ">Figure 3
<p>Horizontal distributions of the (<b>a</b>) priori and (<b>b</b>–<b>f</b>) posterior accumulated dust emissions for the five assimilation experiments during 25–27 November 2016. (<b>g</b>) The time series of the hourly total dust emissions over the region 30° N–52° N and 80° E–140° E.</p> Full article ">Figure 4
<p>The horizontal distributions of the daily mean Himawari-8 aerosol optical depths (AODs) and the daily mean differences in the simulated AODs minus the Himawari-8 observed AODs for the six experiments.</p> Full article ">Figure 5
<p>The daily averaged normalized model error covariances of the aerosol optical depths (AODs) on 25, 26, and 27 November 2016 directly estimated from the ensemble perturbations. The model error covariances are estimated with respect to the point of largest dust emission flux at 06:00 UTC on 25 November 2016, denoted by the black dot. The data between 30° N–52° N and 80° E–140° E are used. The red circle shows the distance to the black dot point of 1000 km.</p> Full article ">Figure 6
<p>The scatter plots of the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra-observed AODs and the simulated AODs with (<b>a</b>) priori dust emissions and (<b>b</b>) posterior dust emissions over the dust-dominated region for the period of 25–27 November 2018. The number of samples (N), statistical metrics (Bias, RMSE, R, IOA and Skill), and linear equations are also shown. The solid lines show the best linear fit between the variables in the x and y axes. The 1:1 ratio is represented by the dotted line, and the 2:1 ratio is represented by the dashed lines. (<b>c</b>) and (<b>d</b>) are similar to (<b>a</b>) and (<b>b</b>) except for the comparisons of MODIS Aqua. (<b>e</b>) The time series of the simulated AODs with priori and posterior emissions are compared with the Aerosol RObotic NETwork (AERONET)-observed AODs. The AERONET-retrieved fine and coarse mode AODs are also shown.</p> Full article ">Figure 7
<p>Comparisons of the model-simulated and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-observed vertical aerosol extinction coefficients over two orbit paths. The first row shows the CALIPSO orbit paths and the horizontal distributions of the simulated AODs in the L300kmT2d experiment. The second row shows the CALIPSO-observed vertical aerosol extinction coefficients, whereas the third and fourth rows show the model-simulated ones with priori and posterior dust emissions. The bottom row shows the CALIPSO-detected aerosol types. (<b>a</b>) CALIPSO orbit at 19:00:00, 25 November 2016; (<b>b</b>) CALIPSO orbit at 06:00:00, 26 November 2016.</p> Full article ">
15 pages, 4880 KiB  
Article
Spatiotemporal Variations of Meteorological Droughts and the Assessments of Agricultural Drought Risk in a Typical Agricultural Province of China
by Mengjing Guo, Jing Li, Yongsheng Wang, Qiubo Long and Peng Bai
Atmosphere 2019, 10(9), 542; https://doi.org/10.3390/atmos10090542 - 12 Sep 2019
Cited by 15 | Viewed by 3278
Abstract
Drought is one of the most common natural disasters on a global scale and has a wide range of socioeconomic impacts. In this study, we analyzed the spatiotemporal variations of meteorological drought in a typical agricultural province of China (i.e., Shaanxi Province) based [...] Read more.
Drought is one of the most common natural disasters on a global scale and has a wide range of socioeconomic impacts. In this study, we analyzed the spatiotemporal variations of meteorological drought in a typical agricultural province of China (i.e., Shaanxi Province) based on the Standard Precipitation Evapotranspiration Index (SPEI). We also investigated the response of winter wheat and summer maize yields to drought by a correlation analysis between the detrended SPEI and the time series of yield anomaly during the crop growing season. Moreover, agricultural drought risks were assessed across the province using a conceptual risk assessment model that emphasizes the combined role of drought hazard and vulnerability. The results indicated that droughts have become more severe and frequent in the study area after 1995. The four typical timescales of SPEI showed a consistent decreasing trend during the period 1960–2016; the central plains of the province showed the most significant decreasing trend, where is the main producing area of the province’s grain. Furthermore, the frequency and intensity of drought increased significantly after 1995; the most severe drought episodes occurred in 2015–2016. Our results also showed that the sensitivity of crop yield to drought varies with the timescales of droughts. Droughts at six-month timescales that occurred in March can explain the yield losses for winter wheat to the greatest extent, while the yield losses of summer maize are more sensitive to droughts at three-month timescales that occurred in August. The assessment agricultural drought risk showed that some areas in the north of the province are exposed to a higher risk of drought and other regions are dominated by low risk. Full article
(This article belongs to the Section Meteorology)
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<p>The location and terrain of the study area (<b>a</b>) and the spatial patterns of aridity index (<b>b</b>) and land use (<b>c</b>) across the study area. The study area includes ten prefecture-level cities (see <a href="#atmosphere-10-00542-f001" class="html-fig">Figure 1</a>b), namely Yulin (①), Yan’an (②), Tongchuan (③), Xianyang (④), Weinan (⑤), Baoji (⑥), Xi’an (⑦), Hanzhong (⑧), Ankang (⑨), and Shanluo (⑩), respectively.</p> Full article ">Figure 2
<p>The inter-annual variability of standard yield residual series (SYRS) for winter wheat (black line) and summer maize (red line) in the Shaanxi Province.</p> Full article ">Figure 3
<p>(<b>a</b>) Spatiotemporal evolution of SPEI series with 1- to 12-month lags from 1960 to 2016 and (<b>b–e</b>) temporal evolution of SPEI at 1-, 3-, 6-, and 12-month lags across the Shanxi Province.</p> Full article ">Figure 4
<p>Spatial patterns of frequency in moderate drought months (a–d, –1.0 ≤ SPEI &lt; –1.5), severe drought months (e–h, –2.0 &lt; SPEI ≤ –1.5), and extreme drought months (i-l, SPEI ≤ –2.0) across the study area.</p> Full article ">Figure 5
<p>Spatial patterns in the annual trend of SPEI at 1-, 3-, 6-, and 12-month lags (<b>a</b>–<b>d</b>), and inter-annual variations and annual trends in SPEI time series at four typical timescales (<b>e</b>).</p> Full article ">Figure 6
<p>Spearman’s correlation coefficients (<span class="html-italic">r)</span> between the monthly detrended SPEI during 1- to 12-month lags and the SYRS of winter wheat (<b>a</b>) and summer maize (<b>b</b>) from 1989 to 2016. The number marked with the white font denotes the maximum correlation between the detrended SPEI and SYRS of winter wheat or summer maize.</p> Full article ">Figure 7
<p>Spatial patterns of drought hazard index for four typical timescales of SPEI, i.e., the SPEI-01 (<b>a</b>), SPEI-03(<b>b</b>), SPEI-06 (<b>c</b>), and SPEI-12 (<b>d</b>).</p> Full article ">Figure 8
<p>Grid-based maps of drought vulnerability indicators: (<b>a</b>) available soil water-holding capacity (AWC, mm) and (<b>b</b>) the percentage of area equipped for irrigation (IRR, %). (<b>c</b>) The spatial pattern of agricultural drought vulnerability index (DVI). The assessment indicator AI (aridity index) has been shown in <a href="#atmosphere-10-00542-f001" class="html-fig">Figure 1</a>b and thus is not presented in this figure.</p> Full article ">Figure 9
<p>Spatial patterns in drought risk index (DRI) under four typical timescales of SPEI: the SPEI-01 (<b>a</b>), SPEI-03 (<b>b</b>), SPEI-06 (<b>c</b>), and SPEI-12 (<b>d</b>).</p> Full article ">
21 pages, 8316 KiB  
Article
Developing Spatially Accurate Rainfall Predictions for the San Francisco Bay Area through Case Studies of Atmospheric River and other Synoptic Events
by Alison F.C. Bridger, Dung Nguyen and Sen Chiao
Atmosphere 2019, 10(9), 541; https://doi.org/10.3390/atmos10090541 - 12 Sep 2019
Cited by 2 | Viewed by 2763
Abstract
Rainfall patterns in the San Francisco Bay Area (SFBA) are highly influenced by local topography. It has been a forecasting challenge for the main US forecast models. This study investigates the ability of the Weather Research and Forecasting (WRF) model to improve upon [...] Read more.
Rainfall patterns in the San Francisco Bay Area (SFBA) are highly influenced by local topography. It has been a forecasting challenge for the main US forecast models. This study investigates the ability of the Weather Research and Forecasting (WRF) model to improve upon forecasts, with particular emphasis on the rain shadow common to the southern end of the SFBA. Three rain events were evaluated: a mid-season atmospheric river (AR) event with copious rains; a typical non-AR frontal passage rain event; and an area-wide rain event in which zero rain was recorded in the southern SFBA. The results show that, with suitable choices of parameterizations, the WRF model with a resolution around 1 km can forecast the observed rainfall patterns with good accuracy, and would be suitable for operational use, especially to water and emergency managers. Additionally, the three synoptic situations were investigated for further insight into the common ingredients for either flooding rains or strong rain shadow events. Full article
(This article belongs to the Section Meteorology)
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<p>SFBA (San Francisco Bay Area) terrain features. Shown are locations of the three airports (KOAK, KSFO, KSJC—Oakland, San Francisco, San Jose), and the peak of Mt Umunhum where the NWS–Monterey radar is located. The Santa Clara Valley (SCV) is at the southern end of the Bay Area, with San Jose at its heart.</p> Full article ">Figure 2
<p>Composite radar for SFBA on 20 February 2017 at 1055Z. See [<a href="#B16-atmosphere-10-00541" class="html-bibr">16</a>] for definition and description of product.</p> Full article ">Figure 3
<p>Cumulative rainfall (inches) across SFBA for 12Z–12Z, 20–21 February 2017.</p> Full article ">Figure 4
<p>Domain used for our WRF simulations. Inner domain (“d02”) is designed to cover both the SFBA, and the immediate offshore waters.</p> Full article ">Figure 5
<p>WRF-simulated radar product valid at 13Z on 20 February 2017.</p> Full article ">Figure 6
<p>WRF-simulated total rainfall (inches) from 12Z–12Z on 20–21 February 2017.</p> Full article ">Figure 7
<p>(<b>a</b>) observed radiosonde sounding at Oakland (KOAK) at 12Z on 20 February 2017; (<b>b</b>) WRF-simulated sounding at gridpoint closest to OAK at same time; (<b>c</b>) Same as (<b>b</b>), but at a gridpoint to the southwest of Oakland over the ocean (away from local terrain influences).</p> Full article ">Figure 8
<p>Profiler data from Bodega Bay (northern side of SFBA) for 20–22 February 2017. Black dots indicate a bright band (BB) level around 1.9–2.2 km during the AR event.</p> Full article ">Figure 9
<p>Composite radar for SFBA on 30 October 2016 at 1725Z.</p> Full article ">Figure 10
<p>WRF-simulated radar product valid at 18Z on 30 October 2016.</p> Full article ">Figure 11
<p>As <a href="#atmosphere-10-00541-f003" class="html-fig">Figure 3</a> but for 12Z–12Z, 30–31 October 2016.</p> Full article ">Figure 12
<p>(<b>a</b>) left: As <a href="#atmosphere-10-00541-f006" class="html-fig">Figure 6</a> but for 12Z–12Z on 30–31 October 2016; (<b>b</b>) right: same as above except contour level separating green from blue shading altered to emphasize rainfall pattern as opposed to amount.</p> Full article ">Figure 13
<p>WRF-simulated soundings at grid point closest to OAK (left, <b>a</b>) and to the southwest of Oakland over the ocean (right, <b>b</b>). Both valid at 16 Z on 30 October 2016. Red dashed line in (<b>b</b>) indicates the presence of CAPE offshore at the time.</p> Full article ">Figure 14
<p>Observed (left, <b>a</b>) and WRF-simulated (right, <b>b</b>) accumulated for 12Z–12Z, 8–9 December 2016.</p> Full article ">Figure 15
<p>WRF-simulated soundings at gridpoint closest to OAK (<b>a</b>) and to the southwest of Oakland over the ocean (<b>b</b>). Both are valid at 15Z on 8 December 2016.</p> Full article ">Figure 16
<p>Left panels (<b>a</b>–<b>c</b>): 500 hPa height and thickness distributions at 12Z for 20 February 2017 (upper), 30 October 2016 (middle), and 8 December 2016 (lower). Right panels (<b>d</b>–<b>f</b>): same, but 200 hPa. Jet streaks are shaded.</p> Full article ">Figure 17
<p>Maximum and minimum vertical wind speeds (cm s<sup>−1</sup>) measured during each of the three WRF simulations. Blue line = February (AR) case; red line = October case; green line = December case.</p> Full article ">Figure 18
<p>Simulated 24 h accumulated rain (inches) for each case when only a single (4 km) WRF domain is used. (<b>a</b>) (upper left) 20 February 2017 case; (<b>b</b>) (upper right) 30 October 2016 case; (<b>c</b>) (lower middle) 8 December 2016 case.</p> Full article ">
13 pages, 886 KiB  
Article
Optical Energy Variability Induced by Speckle: The Cases of MERLIN and CHARM-F IPDA Lidar
by Vincent Cassé, Fabien Gibert, Dimitri Edouart, Olivier Chomette and Cyril Crevoisier
Atmosphere 2019, 10(9), 540; https://doi.org/10.3390/atmos10090540 - 11 Sep 2019
Cited by 5 | Viewed by 3595
Abstract
In the context of the FrenchGerman space lidar mission MERLIN (MEthane Remote LIdar missioN) dedicated to the determination of the atmospheric methane content, an end-to-end mission simulator is being developed. In order to check whether the instrument design meets the performance requirements, simulations [...] Read more.
In the context of the FrenchGerman space lidar mission MERLIN (MEthane Remote LIdar missioN) dedicated to the determination of the atmospheric methane content, an end-to-end mission simulator is being developed. In order to check whether the instrument design meets the performance requirements, simulations have to count all the sources of noise on the measurements like the optical energy variability induced by speckle. Speckle is due to interference as the lidar beam is quasi monochromatic. Speckle contribution to the error budget has to be estimated but also simulated. In this paper, the speckle theory is revisited and applied to MERLIN lidar and also to the DLR (Deutsches Zentrum für Luft und Raumfahrt) demonstrator lidar CHARM-F. Results show: on the signal path, speckle noise depends mainly on the size of the illuminated area on ground; on the solar flux, speckle is fully negligible both because of the pixel size and the optical filter spectral width; on the energy monitoring path a decorrelation mechanism is needed to reduce speckle noise on averaged data. Speckle noises for MERLIN and CHARM-F can be simulated by Gaussian noises with only one random draw by shot separately for energy monitoring and signal paths. Full article
(This article belongs to the Special Issue Atmospheric Applications of Lidar)
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<p>Observation geometry scheme.</p> Full article ">Figure 2
<p>Locations on optic calibration path from where speckle can contribute to noise.</p> Full article ">Figure A1
<p>Speckle pattern geometry [<a href="#B53-atmosphere-10-00540" class="html-bibr">53</a>].</p> Full article ">
15 pages, 7961 KiB  
Article
Use of Low-Cost Ambient Particulate Sensors in Nablus, Palestine with Application to the Assessment of Regional Dust Storms
by Abdelhaleem Khader and Randal S. Martin
Atmosphere 2019, 10(9), 539; https://doi.org/10.3390/atmos10090539 - 11 Sep 2019
Cited by 8 | Viewed by 4159
Abstract
Few air pollutant studies within the Palestinian territories have been reported in the literature. In March–April and May–June of 2018, three low-cost, locally calibrated particulate monitors (AirU’s) were deployed at different elevations and source areas throughout the city of Nablus in Northern West [...] Read more.
Few air pollutant studies within the Palestinian territories have been reported in the literature. In March–April and May–June of 2018, three low-cost, locally calibrated particulate monitors (AirU’s) were deployed at different elevations and source areas throughout the city of Nablus in Northern West Bank, Palestine. During each of the three-week periods, high but site-to-site similar particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) and less than 10 µm (PM10) concentrations were observed. The PM2.5 concentrations at the three sampling locations and during both sampling periods averaged 38.2 ± 3.6 µg/m3, well above the World Health Organization’s (WHO) 24 h guidelines. Likewise, the PM10 concentrations exceeded or were just below the WHO’s 24 h guidelines, averaging 48.5 ± 4.3 µg/m3. During both periods, short episodes were identified in which the particulate levels at all three sites increased substantially (≈2×) above the regional baseline. Air mass back trajectory analyses using U.S. National Oceanic and Atmospheric Administration’s (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model suggested that, during these peak episodes, the arriving air masses spent recent days over desert areas (e.g., the Saharan Desert in North Africa). On days with regionally low PM2.5 concentrations (≈20 µg/m3), back trajectory analysis showed that air masses were directed in from the Mediterranean Sea area. Further, the lower elevation (downtown) site often recorded markedly higher particulate levels than the valley wall sites. This would suggest locally derived particulate sources are significant and may be beneficial in the identification of potential remediation options. Full article
(This article belongs to the Special Issue Ambient Aerosol Measurements in Different Environments)
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<p>(<b>a</b>) Location of the city of Nablus (country map source: [<a href="#B17-atmosphere-10-00539" class="html-bibr">17</a>]); (<b>b</b>) the locations of the sampling sites, the mountain summits, and the site where the pictures in <a href="#atmosphere-10-00539-f002" class="html-fig">Figure 2</a> were taken (aerial photograph, city boundary, road map, and building map source: [<a href="#B18-atmosphere-10-00539" class="html-bibr">18</a>]).</p> Full article ">Figure 2
<p>The visibility in the city of Nablus: (<b>a</b>) during a dust storm in March 2014; (<b>b</b>) during a clear day in April 2014. The pictures were taken from a location southwest of the city (<a href="#atmosphere-10-00539-f001" class="html-fig">Figure 1</a>b) facing northeast (Mount Ebal).</p> Full article ">Figure 3
<p>The AirU sensors as deployed at the first (SW), second (DT), and third (NE) sites, respectively.</p> Full article ">Figure 4
<p>Calibration results of the locally calibrated particulate monitors (AirUs) against the Mini-Vol. The different colors (and markers) represent different AirUs. The lowest MiniVol concentration was measured at the indoor (WESI) site, the middle 5 were measured at ambient sites, and the highest concentration was measured at a smoke shop (indoor site).</p> Full article ">Figure 5
<p>Daily average of concentrations of particulate matter less than 2.5 µm in aerodynamic diameter (PM<sub>2.5</sub>) in the three locations—DT, SW, NE—compared to WHO guidelines (<b>a</b>) during the March–April period; (<b>b</b>) during the May–June period. (The shaded areas represent the standard deviations of the 1-min measurements in the 24 h period).</p> Full article ">Figure 6
<p>Daily average of concentrations of particulate matter less than 2.5 µm in aerodynamic diameter (PM<sub>10</sub>) in the three locations—DT, SW, NE—compared to WHO guidelines (<b>a</b>) during the March–April period; (<b>b</b>) during the May–June period. (The shaded areas represent the standard deviations).</p> Full article ">Figure 7
<p>Air mass trajectory and PM concentrations in the three locations during 23–25 March period (<b>a</b>) NOAA-HYSPLIT model results (GDAS—top, GFSG—middle, and CDC1—bottom). The colored lines represent the following arrival times: yellow—23 March at 12:00, magenta—23 March at 18:00, sky blue—24 March at 00:00, green—24 March at 06:00, blue—24 March at 12:00, and red—24 March at 18:00. The marks on the lines represent six-hour steps; (<b>b</b>) hourly PM<sub>2.5</sub>; (<b>c</b>) hourly PM<sub>10</sub>.</p> Full article ">Figure 7 Cont.
<p>Air mass trajectory and PM concentrations in the three locations during 23–25 March period (<b>a</b>) NOAA-HYSPLIT model results (GDAS—top, GFSG—middle, and CDC1—bottom). The colored lines represent the following arrival times: yellow—23 March at 12:00, magenta—23 March at 18:00, sky blue—24 March at 00:00, green—24 March at 06:00, blue—24 March at 12:00, and red—24 March at 18:00. The marks on the lines represent six-hour steps; (<b>b</b>) hourly PM<sub>2.5</sub>; (<b>c</b>) hourly PM<sub>10</sub>.</p> Full article ">Figure 8
<p>Air mass trajectory and PM concentrations in the three locations during 10–13 June period (<b>a</b>) NOAA-HYSPLIT model results. The colored lines represent the following arrival times: sky blue—10 June at 12:00, green—11 June at 00:00, blue—11 June at 12:00, and red—12 June at 00:00; (<b>b</b>) hourly PM<sub>2.5</sub>; (<b>c</b>) hourly PM<sub>10</sub>.</p> Full article ">Figure 8 Cont.
<p>Air mass trajectory and PM concentrations in the three locations during 10–13 June period (<b>a</b>) NOAA-HYSPLIT model results. The colored lines represent the following arrival times: sky blue—10 June at 12:00, green—11 June at 00:00, blue—11 June at 12:00, and red—12 June at 00:00; (<b>b</b>) hourly PM<sub>2.5</sub>; (<b>c</b>) hourly PM<sub>10</sub>.</p> Full article ">
15 pages, 2418 KiB  
Article
Air/Surface Exchange of Gaseous Elemental Mercury at Different Landscapes in Mississippi, USA
by James V. Cizdziel, Yi Jiang, Divya Nallamothu, J. Stephen Brewer and Zhiqiang Gao
Atmosphere 2019, 10(9), 538; https://doi.org/10.3390/atmos10090538 - 11 Sep 2019
Cited by 12 | Viewed by 3334 | Correction
Abstract
Mercury (Hg) is a global pollutant with human health and ecological impacts. Gas exchange between terrestrial surfaces and the atmosphere is an important route for Hg to enter and exit ecosystems. Here, we used a dynamic flux chamber to measure gaseous elemental Hg [...] Read more.
Mercury (Hg) is a global pollutant with human health and ecological impacts. Gas exchange between terrestrial surfaces and the atmosphere is an important route for Hg to enter and exit ecosystems. Here, we used a dynamic flux chamber to measure gaseous elemental Hg (GEM) exchange over different landscapes in Mississippi, including in situ measurements for a wetland (soil and water), forest floor, pond, mowed field and grass-covered lawn, as well as mesocosm experiments for three different agricultural soils. Fluxes were measured during both the summer and winter. Mean ambient levels of GEM ranged between 0.93–1.57 ng m−3. GEM emission fluxes varied diurnally with higher daytime fluxes, driven primarily by solar radiation, and lower and more stable nighttime fluxes, dependent mostly on temperature. GEM fluxes (ng m−2 h−1) were seasonally dependent with net emission during the summer (mean 2.15, range 0.32 to 4.92) and net deposition during the winter (−0.12, range −0.32 to 0.12). Total Hg concentrations in the soil ranged from 17.1 ng g−1 to 127 ng g−1 but were not a good predictor of GEM emissions. GEM flux and soil temperature were correlated over the forest floor, and the corresponding activation energy for Hg emission was ~31 kcal mol−1 using the Arrhenius equation. There were significant differences in GEM fluxes between the habitats with emissions for grass > wetland soil > mowed field > pond > wetland water ≈ forest ≈ agriculture soils. Overall, we demonstrate that these diverse landscapes serve as both sources and sinks for airborne Hg depending on the season and meteorological factors. Full article
(This article belongs to the Special Issue Atmospheric Mercury: Sources, Sinks, and Transformations)
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<p>Photos showing the dynamic flux chamber for air/water and soil/air measurements. Also shown is the direction of air flow, light meter, ambient air inlet and YSI water quality probe.</p> Full article ">Figure 2
<p>Relationship between gaseous elemental mercury (GEM) flux and solar radiation (<b>top left</b>), wind speed (<b>top right</b>), air temperature (<b>lower left</b>), and humidity (<b>lower right</b>) at a loblolly pine forest in Mississippi during the summer.</p> Full article ">Figure 3
<p>Concurrent gaseous elemental mercury (GEM) fluxes over a pond and adjacent mowed field at the University of Mississippi (UM) Field Station.</p> Full article ">Figure 4
<p>Gaseous elemental mercury (GEM) emissions from the pond were highly correlated with solar radiation, with the greatest flux at maximum solar radiation (star).</p> Full article ">Figure 5
<p>Examples of seasonal differences for gaseous elemental mercury (GEM) fluxes over the course of a day. Pond water/air fluxes (<b>left</b>) and agricultural soil/air fluxes (<b>right</b>).</p> Full article ">Figure 6
<p>Arrhenius relationship between GEM flux and soil temperature over a loblolly pine forest floor in Mississippi during the summer.</p> Full article ">
18 pages, 7824 KiB  
Article
Added Value of Atmosphere-Ocean Coupling in a Century-Long Regional Climate Simulation
by Fanni Dóra Kelemen, Cristina Primo, Hendrik Feldmann and Bodo Ahrens
Atmosphere 2019, 10(9), 537; https://doi.org/10.3390/atmos10090537 - 11 Sep 2019
Cited by 15 | Viewed by 4566
Abstract
A twentieth century-long coupled atmosphere-ocean regional climate simulation with COSMO-CLM (Consortium for Small-Scale Modeling, Climate Limited-area Model) and NEMO (Nucleus for European Modelling of the Ocean) is studied here to evaluate the added value of coupled marginal seas over continental regions. The interactive [...] Read more.
A twentieth century-long coupled atmosphere-ocean regional climate simulation with COSMO-CLM (Consortium for Small-Scale Modeling, Climate Limited-area Model) and NEMO (Nucleus for European Modelling of the Ocean) is studied here to evaluate the added value of coupled marginal seas over continental regions. The interactive coupling of the marginal seas, namely the Mediterranean, the North and the Baltic Seas, to the atmosphere in the European region gives a comprehensive modelling system. It is expected to be able to describe the climatological features of this geographically complex area even more precisely than an atmosphere-only climate model. The investigated variables are precipitation and 2 m temperature. Sensitivity studies are used to assess the impact of SST (sea surface temperature) changes over land areas. The different SST values affect the continental precipitation more than the 2 m temperature. The simulated variables are compared to the CRU (Climatic Research Unit) observational data, and also to the HOAPS/GPCC (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, Global Precipitation Climatology Centre) data. In the coupled simulation, added skill is found primarily during winter over the eastern part of Europe. Our analysis shows that, over this region, the coupled system is dryer than the uncoupled system, both in terms of precipitation and soil moisture, which means a decrease in the bias of the system. Thus, the coupling improves the simulation of precipitation over the eastern part of Europe, due to cooler SST values and in consequence, drier soil. Full article
(This article belongs to the Special Issue Regional Climate Modeling: Ocean–Atmosphere Coupling)
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<p>Connection between SST (seas surface temperature), and precipitation and 2 m temperature.</p> Full article ">Figure 2
<p>Time evolution (2000–2003) of precipitation (left column) and 2 m temperature (right column) field mean over the Mediterranean Sea (<b>a</b>,<b>b</b>), the Baltic and the North Seas (<b>c</b>,<b>d</b>) and over land (<b>e</b>,<b>f</b>) in the sensitivity experiments and in the coupled and uncoupled simulation.</p> Full article ">Figure 3
<p>Difference between the SST_0 and the SST – 2 (left column, <b>a</b>,<b>c</b>) or the SST + 2 (right column, <b>b</b>,<b>d</b>) simulations. The first row (<b>a</b>,<b>b</b>) shows the difference of the precipitation sums during the whole time period (2000–2003) and the second row (<b>c</b>,<b>d</b>) shows the differences in 2 m temperature mean.</p> Full article ">Figure 4
<p>Mean winter precipitation sum: (<b>a</b>) Mean square error skill score (MSESS) values comparing the coupled system to the uncoupled with respect to CRU (Climatic Research Unit) (1901–2009) and (<b>b</b>) with respect to HOAPS/GPCC (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, Global Precipitation Climatology Centre) (1988–2008), (<b>c</b>) bias of the coupled system with respect to CRU and (<b>d</b>) HOAPS/GPCC, (<b>e</b>) difference between the coupled and the uncoupled system for the time period 1901–2009 and for (<b>f</b>) 1988–2008.</p> Full article ">Figure 5
<p>The difference of mean soil moisture content during winter between the coupled and the uncoupled simulation.</p> Full article ">Figure 6
<p>Positive feedback mechanism between precipitation, soil moisture and evapotranspiration.</p> Full article ">Figure 7
<p>Winter (left column) and summer (right column) mean 2 m air temperature (<b>a</b>,<b>b</b>) MSESS (<b>c</b>,<b>d</b>) bias compared to CRU data and (<b>e</b>,<b>f</b>) difference between coupled and uncoupled simulation. (<b>g</b>,<b>h</b>) Seasonal mean SST difference between coupled and uncoupled simulation.</p> Full article ">
14 pages, 439 KiB  
Article
Carbonaceous Particulate Matter Emitted from a Pellet-Fired Biomass Boiler
by Michael D. Hays, John Kinsey, Ingrid George, William Preston, Carl Singer and Bakul Patel
Atmosphere 2019, 10(9), 536; https://doi.org/10.3390/atmos10090536 - 11 Sep 2019
Cited by 7 | Viewed by 6713
Abstract
Biomass pellets are a source of renewable energy; although, the air pollution and exposure risks posed by the emissions from burning pellets in biomass boilers (BBs) are uncertain. The present study examines the organic species in fine particle matter (PM) emissions from an [...] Read more.
Biomass pellets are a source of renewable energy; although, the air pollution and exposure risks posed by the emissions from burning pellets in biomass boilers (BBs) are uncertain. The present study examines the organic species in fine particle matter (PM) emissions from an BB firing switchgrass (SwG) and hardwood (HW) biomass pellets using different test cycles. The organic and elemental carbon (OC and EC) content and select semivolatile organic compounds (SVOCs) in filter-collected PM were identified and quantified using thermal-optical analysis and gas chromatography–mass spectrometry (GC–MS), respectively. Fine PM emissions from the BB ranged from 0.4 g/kg to 2.91 g/kg of pellets burned of which 40% ± 17% w/w was carbon. The sum of GC–MS quantified SVOCs in the PM emissions varied from 0.13 to 0.41 g/g OC. Relatively high levels of oxygenated compounds were observed in the PM emissions, and the most predominant individual SVOC constituent was levoglucosan (12.5–320 mg/g OC). The effect of boiler test cycle on emissions was generally greater than the effect due to pellet fuel type. Organic matter emissions increased at lower loads, owing to less than optimal combustion performance. Compared with other types of residential wood combustion studies, pellet burning in the current BB lowered PM emissions by nearly an order of magnitude. PM emitted from burning pellets in boilers tested across multiple studies also contains comparatively less carbon; however, the toxic polycyclic aromatic hydrocarbons (PAH) in the PM tested across these pellet-burning studies varied substantially, and produced 2–10 times more benzo[k]fluoranthene, dibenz[a,h]anthracene and indeno[1,2,3-c,d]pyrene on average. These results suggest that further toxicological evaluation of biomass pellet burning emissions is required to properly understand the risks posed. Full article
(This article belongs to the Special Issue Real World Air Pollutant Emissions from Combustion Sources)
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<p>Quantile box plots of individual SVOC concentrations pooled by compound class. Levoglucosan is the anhydrosugar. The line in the box is at the median. The whiskers indicate the 10% and 90% quantiles. SVOC data populations (<a href="#atmosphere-10-00536-t002" class="html-table">Table 2</a>): aliphatic diacid (<span class="html-italic">n</span> = 81); alkanoic acid (<span class="html-italic">n</span> = 160); anhydrosugar (<span class="html-italic">n</span> = 15); aromatic acid (<span class="html-italic">n</span> = 103); <span class="html-italic">b</span>-alkane (<span class="html-italic">n</span> = 30); fatty acid (<span class="html-italic">n</span> = 48); methoxy-phenol (<span class="html-italic">n</span> = 84); <span class="html-italic">n</span>-alkane (<span class="html-italic">n</span> = 296); polycyclic aromatic hydrocarbons (PAH) (<span class="html-italic">n</span> = 332); resin acid (<span class="html-italic">n</span> = 38).</p> Full article ">Figure 2
<p>Filter-based OC–EC ratios in PM for individual tests sorted by heat load demand profile and fuel type. Panel A pools the OC–EC ratios by fuel type, whereas panel B pools them by operational mode. Data populations: Full load (<span class="html-italic">n</span> = 10), low load (<span class="html-italic">n</span> = 18), and Syracuse load (<span class="html-italic">n</span> = 11).</p> Full article ">Figure 3
<p>Concentration sums (<math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math>g/gOC) for individual tests sorted by compound class, test load conditions, and fuel type (HW: hardwood pellet; SwG: switchgrass pellet).</p> Full article ">Figure 4
<p>Comparison of PAH concentrations in PM (mg/g PM) emitted from wood- and pellet-burning appliances. Data populations for individual compounds ranged from <span class="html-italic">n</span> = 57 to <span class="html-italic">n</span> = 92. <a href="#app1-atmosphere-10-00536" class="html-app">Figure S5</a> shows the calculated means with standard error and median values for individual PAH concentrations gathered across the multiple studies. These values are provided in an effort to highlight differences and provide consensus.</p> Full article ">
18 pages, 6610 KiB  
Article
Characterization of Real-World Pollutant Emissions and Fuel Consumption of Heavy-Duty Diesel Trucks with Latest Emissions Control
by Christos Keramydas, Leonidas Ntziachristos, Christos Tziourtzioumis, Georgios Papadopoulos, Ting-Shek Lo, Kwok-Lam Ng, Hok-Lai Anson Wong and Carol Ka-Lok Wong
Atmosphere 2019, 10(9), 535; https://doi.org/10.3390/atmos10090535 - 10 Sep 2019
Cited by 9 | Viewed by 4166
Abstract
Heavy-duty diesel trucks (HDDTs) comprise a key source of road transport emissions and energy consumption worldwide mainly due to the growth of road freight traffic during the last two decades. Addressing their air pollutant and greenhouse gas emissions is therefore required, while accurate [...] Read more.
Heavy-duty diesel trucks (HDDTs) comprise a key source of road transport emissions and energy consumption worldwide mainly due to the growth of road freight traffic during the last two decades. Addressing their air pollutant and greenhouse gas emissions is therefore required, while accurate emission factors are needed to logistically optimize their operation. This study characterizes real-world emissions and fuel consumption (FC) of HDDTs and investigates the factors that affect their performance. Twenty-two diesel-fueled, Euro IV to Euro VI, HDDTs of six different manufacturers were measured in the road network of the Hong Kong metropolitan area, using portable emission measurement systems (PEMS). The testing routes included urban, highway and mixed urban/highway driving. The data collected corresponds to a wide range of driving, operating, and ambient conditions. Real-world distance- and energy-based emission levels are presented in a comparative manner to capture the effect of after-treatment technologies and the role of the evolution of Euro standards on emissions performance. The emission factors’ uncertainty is analyzed. The impact of speed, road grade and vehicle weight loading on FC and emissions is investigated. An analysis of diesel particulate filter (DPF) regenerations and ammonia (NH3) slip events are presented along with the study of Nitrous oxide (N2O) formation. The results reveal deviations of real-world HDDTs emissions from emission limits, as well as the significant impact of different operating and driving factors on their performance. The occasional high levels of N2O emissions from selective catalytic reduction equipped HDDTs is also revealed, an issue that has not been thoroughly considered so far. Full article
(This article belongs to the Special Issue Transport Emissions and the Atmosphere)
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<p>Examples of heavy-duty diesel truck (HDDTs) test routes. The green route (mean speed 60 km/h) corresponds mostly to highway driving, the cyan one (mean speed 40 km/h) represents mixed driving including rural areas, and the red one (mean speed 20 km/h) is in the built environment.</p> Full article ">Figure 2
<p>HDDT in test setup configuration. Left photo: GPS (global positioning system), SEMTECΗ EFM-2 or EFM-HS flowmeter (Sensors, Inc., USA), AVL M.O.V.E. GAS/PM PEMS analyzers, speedometer (Peiseler GmbH, Germany), weather station. Right photo: AVL M.O.V.E. GAS PEMS and AVL M.O.V.E. PM PEMS.</p> Full article ">Figure 3
<p>Sample mean and ± one standard error limits of the distance-based NO<sub>x</sub> emission levels over speed (500 m driving sequences). Numbers next to each dot correspond to number of vehicles that have been averaged.</p> Full article ">Figure 4
<p>Comparison of the energy-based emission factors (median value of speed-bins) per vehicle class to the respective Euro standard limits. Error bars correspond to ± one standard error limits.</p> Full article ">Figure 5
<p>Example of a typical on-road trip of the Euro IV (DOC/EGR/SCR) and Euro V (DPF/SCR) vehicles. Profiles of NH<sub>3</sub>, N<sub>2</sub>O, and NO<sub>x</sub> mass rates, and exhaust gas temperature and vehicle speed signals.</p> Full article ">Figure 6
<p>Total hydrocarbons (THC) and exhaust gas temperature overtime for three indicative diesel particulate filter (DPF) regeneration events.</p> Full article ">Figure 7
<p>Median value and 10th and 90th percentiles of the calculated ratios (per regeneration) of the emission levels during regeneration over the respective levels before regeneration (at no regeneration conditions and same average speed) for THC, CO, NO, NO<sub>2</sub>, and PM pollutants.</p> Full article ">Figure 8
<p>NO<sub>x</sub> emissions and fuel consumption (FC) ratios of uphill (road grade &gt; +0.5%) and downhill (road grade &lt; −0.5%) driving over flat road driving (|grade| &lt; 0.5%) as a function of absolute road grade classes for Euro IV, Euro V, and Euro VI HDDTs (500 m driving sequences data).</p> Full article ">Figure 9
<p>Average NO<sub>x</sub> and PM emissions ratios and FC ratio over the respective 30% loading reference values for the 30%, 50%, and 100% weight loadings, the error bars correspond to the respective ± one standard deviation limits.</p> Full article ">Figure A1
<p>Sample mean and ± one standard error limits of the distance-based PM emission levels over speed (500 m driving sequences).</p> Full article ">Figure A2
<p>Sample mean and ± one standard error limits of the distance-based THC emission levels over speed (500 m driving sequences).</p> Full article ">Figure A3
<p>Sample mean and ± one standard error limits of the distance-based CO emission levels over speed (500 m driving sequences).</p> Full article ">Figure A4
<p>Sample mean and ± one standard error limits of the distance-based FC levels over speed (500 m driving sequences).</p> Full article ">
22 pages, 2475 KiB  
Article
The Spatiotemporal Dynamics and Socioeconomic Factors of SO2 Emissions in China: A Dynamic Spatial Econometric Design
by Zhimin Zhou
Atmosphere 2019, 10(9), 534; https://doi.org/10.3390/atmos10090534 - 10 Sep 2019
Cited by 8 | Viewed by 3117
Abstract
With the great strides of China’s economic development, air pollution has become the norm that is a cause of broad adverse influence in society. The spatiotemporal patterns of sulfur dioxide (SO2) emissions are a prerequisite and an inherent characteristic for SO [...] Read more.
With the great strides of China’s economic development, air pollution has become the norm that is a cause of broad adverse influence in society. The spatiotemporal patterns of sulfur dioxide (SO2) emissions are a prerequisite and an inherent characteristic for SO2 emissions to peak in China. By exploratory spatial data analysis (ESDA) and econometric approaches, this study explores the spatiotemporal characteristics of SO2 emissions and reveals how the socioeconomic determinants influence the emissions in China’s 30 provinces from 1995 to 2015. The study first identifies the overall space- and time-trend of regional SO2 emissions and then visualizes the spatiotemporal nexus between SO2 emissions and socioeconomic determinants through the ESDA method. The determinants’ impacts on the space–time variation of emissions are also confirmed and quantified through the dynamic spatial panel data model that controls for both spatial and temporal dependence, thus enabling the analysis to distinguish between the determinants’ long- and short-term spatial effects and leading to richer and novel empirical findings. The study emphasizes close spatiotemporal relationships between SO2 emissions and the socioeconomic determinants. China’s SO2 emissions variation is the multifaceted result of urbanization, foreign direct investment, industrial structure change, technological progress, and population in the short run, and it is highlighted that, in the long run, the emissions are profoundly affected by industrial structure and technology. Full article
(This article belongs to the Section Air Quality)
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<p>SO<sub>2</sub> emissions in (<b>a</b>) 1995, (<b>b</b>) 2005, and (<b>c</b>) 2015 (Tons).</p> Full article ">Figure 1 Cont.
<p>SO<sub>2</sub> emissions in (<b>a</b>) 1995, (<b>b</b>) 2005, and (<b>c</b>) 2015 (Tons).</p> Full article ">Figure 2
<p>Spatiotemporal significant spots of SO<sub>2</sub> emissions.</p> Full article ">Figure 3
<p>Spatiotemporal significant spots of foreign direct investment (FDI).</p> Full article ">Figure 4
<p>Spatiotemporal significant spots of industrial structure <span class="html-italic">(stru).</span></p> Full article ">Figure 5
<p>Spatiotemporal significant spots of urbanization level <span class="html-italic">(urb)</span>.</p> Full article ">Figure 6
<p>Comprehensive spatiotemporal significant spots.</p> Full article ">
25 pages, 4228 KiB  
Article
The Hydrometeorology Testbed–West Legacy Observing Network: Supporting Research to Applications for Atmospheric Rivers and Beyond
by Andrea J. Ray and Allen B. White
Atmosphere 2019, 10(9), 533; https://doi.org/10.3390/atmos10090533 - 10 Sep 2019
Cited by 2 | Viewed by 3295
Abstract
An observing network has been established along the United States west coast that provides up to 20 years of observations to support early warning, preparedness and studies of atmospheric rivers (ARs). The Hydrometeorology Testbed–West Legacy Observing Network, a suite of upper air and [...] Read more.
An observing network has been established along the United States west coast that provides up to 20 years of observations to support early warning, preparedness and studies of atmospheric rivers (ARs). The Hydrometeorology Testbed–West Legacy Observing Network, a suite of upper air and surface observing instruments, is now an official National Oceanic and Atmospheric Administration (NOAA) observing system with real-time data access provided via publicly available websites. This regional network of wind profiling radars and co-located instruments also provides observations of boundary layer processes such as complex-terrain flows that are not well depicted in the current operational rawindsonde and radar networks, satellites, or in high-resolution models. Furthermore, wind profiling radars have been deployed ephemerally for projects or campaigns in other areas, some with long records of observations. Current research uses of the observing system data are described as well as experimental products and services being transitioned from research to operations and applications. We then explore other ways in which this network and data library provide valuable resources for the community beyond ARs, including evaluation of high-resolution numerical weather prediction models and diagnosis of systematic model errors. Other applications include studies of gap flows and other terrain-influenced processes, snow level, air quality, winds for renewable energy and the predictability of cloudiness for solar energy industry. Full article
(This article belongs to the Special Issue Atmospheric Rivers)
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<p>HMT–West LON observing network map. GPS-Met refers to Global Positioning Station meterological stations. Inset shows the “picket fence,” of Atmospheric River Observatories (ATmos. River Obs. Or ARO) including Oregon and Washington coastal AROs. Two inland ARO stations were added after the 2017 flooding. Adapted from [<a href="#B1-atmosphere-10-00533" class="html-bibr">1</a>,<a href="#B25-atmosphere-10-00533" class="html-bibr">25</a>].</p> Full article ">Figure 2
<p>Water vapor flux tool. Water vapor flux analysis and verification for Bodega Bay (BBY) and Cazadero (CZC), CA stations, 25–27 February 2019 (note that time increases from the right to the left along the x-axis). <b>Top panel:</b> observed and forecasted wind profiles (flags = 50 kts; barbs = 10 kts; half-barbs = 5 kts; wind speed is color coded). The vertical bar in the top plot separates observed from forecast variables; this also applies to the middle and bottom plots. The observed snow level (km or kft; black dots) and adjusted forecasted freezing level (km or kft; black dashed line). <b>Center panel:</b> observed and forecasted wind speed averaged over the controlling wind layer, as designated by the horizontal lines in the top panel at 750 m and 1250 m. MSL: observed upslope wind speed (m s<sup>−1</sup>; purple bars and forecasted upslope wind speed (m s<sup>−1</sup> T-post symbols). Observed and forecasted IWV (in; solid and dashed blue lines, respectively). <b>Bottom panel:</b> observed and forecasted precipitation (in; green bars and T-post symbols, respectively) and observed and forecasted IWV flux (solid and dashed dark blue lines, respectively). The online tool is provided in mostly English units because of the preferences of the major users in weather forecast offices and water management agencies. Data/image provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at <a href="http://www.esrl.noaa.gov/psd/ data/obs/datadisplay/" target="_blank">http://www.esrl.noaa.gov/psd/ data/obs/datadisplay/</a>. This tool is available to forecasters and the general public online by selecting the Integrated Water Vapor Flux Plot at <a href="https://www.esrl.noaa.gov/psd/data/obs/datadisplay/tools/ActiveSites.html" target="_blank">https://www.esrl.noaa.gov/psd/data/obs/datadisplay/tools/ActiveSites.html</a>, then selecting a location from the active station list.</p> Full article ">Figure 3
<p>Snow level detection and verification tool comparing observed snow levels with forecasted snow levels by the HRRR. <b>Upper panel:</b> radar-based snow level observations (km; black dots) and HRRR snow level forecasts (km; colored lines for different lead times). <b>Middle panel:</b> snow level radar observations (black dots) compared to forecasts (colored lines for the current forecast). <b>Lower panel:</b> Five graphs show the historical forecast verification for snow level for at forecast lead times from 0- to 18-h. This tool is available to forecasters and the general public online by selecting the Snow Level Verification Plot at <a href="https://www.esrl.noaa.gov/psd/data/obs/datadisplay/tools/ActiveSites.html" target="_blank">https://www.esrl.noaa.gov/psd/data/obs/datadisplay/tools/ActiveSites.html</a>, then selecting a location from the active station list. Data/image provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at <a href="http://www.esrl.noaa.gov/psd/data/obs/datadisplay" target="_blank">http://www.esrl.noaa.gov/psd/data/obs/datadisplay</a>.</p> Full article ">Figure 4
<p>The WFIP2 real-time model-observation evaluation tool. The top panel shows the observed data, the virtual temperature by the RASS, with the winds measured by the 915-MHZ WP radar over-imposed). The middle panel shows the corresponding model data, the ESRL HRRR model forecast of virtual temperature. The bottom panel shows the difference between observed and model forecast. Inset map shows in red the location of the selected station (Wasco, OR). In-situ instrument data are displayed as time-series of the observations overlaid with the model forecasts (not shown). On the left of the web page, users can select other instrument types, the model initialization time (the start hour of the images), and the observing site and date of interest. On the top right, users can select the model to compare for validation. The cold pool event discussed in the text is illustrated. Data and image provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA: <a href="http://wfip.esrl.noaa.gov/psd/programs/wfip2/" target="_blank">http://wfip.esrl.noaa.gov/psd/programs/wfip2/</a>.</p> Full article ">Figure 5
<p>Gap flow events through the Columbia River Gorge, shown in the Winter Gap Flow tool. <b>Top panel</b> shows the time–height section of hourly radar wind profiles (flag: 50 kt, barb: 10 kt, half-barb: 5 kt) color coded by wind speed (kt). Hourly measurements of the snow level and the top of the gap flow are marked with stars and solid black dots, respectively. <b>Second panel:</b> time–height section of 3-min-resolution color-coded radar reflectivity (dBZ) from the vertical beam. The snow level and gap-flow top are marked as in the top panel. Third panel: time–height section of hourly radar wind profiles (flags and barbs are as in the top panel and hourly color-coded RASS virtual potential temperature (K)). The snow level and gap-flow top are marked as in the top panel. <b>Bottom panel:</b> The Surface Meteorology panel consists of two graphs. The temperature graph shows 2-min-resolution temperature (red; °C) and wet-bulb temperature (blue; °C). The horizontal dashed green line marks 0 °C. In the same graph, surface time series of hourly wind velocities are shown, with flags and barbs as in the top panel; black dots portray observed wind speeds. The precipitation graph shows color-coded hourly precipitation type (across top of panel; color key at bottom), tipping-bucket hourly precipitation rate (in. hr<sup>−1</sup>; blue bars), and 2-min-resolution precipitation accumulation (in.; pink line). Data and image provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA: <a href="http://www.esrl.noaa.gov/psd/data/obs/datadisplay/" target="_blank">http://www.esrl.noaa.gov/psd/data/obs/datadisplay/</a>. The real-time, web-based data product can be accessed on the WFVT webpage at by selecting a location from the station list, and then the “Precipitation Hazard Plot” link. As in the other graphics, English units are provided because of the preferences of the primary users of the products.</p> Full article ">
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