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Atmosphere
Volume 11
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Atmosphere, Volume 11, Issue 2 (February 2020) – 100 articles

Cover Story (view full-size image): The increasing global energy demand and the target of reducing anthropogenic emissions affecting the climate are leading to an era of renewable energy. Countries with long coastlines exposed to open sea such as Norway have the potential to harvest wave energy in order to partially satisfy domestic and European energy demands. Our study aims to shed new light on the spatiotemporal assessment of wave energy flux in the Nordic Seas, individually for wind-sea and swell conditions, using a 59-year hindcast (NORA10). The highest wave energy flux is evidenced in winter, in the Norwegian Sea, where swell conditions are dominant. During calm wind conditions, reduced wind power production could be compensated by wave power from swell originating from the Atlantic Ocean. View this paper.
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6 pages, 316 KiB  
Communication
A Collaborative Approach between Japan and China for Implementing Interlaboratory Evaluation of Olfactometry
by Takaya Higuchi, Weifang Li, Jing Geng, Gen Wang and Kumiko Shigeoka
Atmosphere 2020, 11(2), 221; https://doi.org/10.3390/atmos11020221 - 22 Feb 2020
Cited by 1 | Viewed by 3033
Abstract
Odor measurement is a crucial element of odor management and regulation. This paper introduced a collaborative implementation of interlaboratory evaluation of olfactometry between Japan and China. An international comparison of olfactometry using the triangular odor bag method was carried out for the first [...] Read more.
Odor measurement is a crucial element of odor management and regulation. This paper introduced a collaborative implementation of interlaboratory evaluation of olfactometry between Japan and China. An international comparison of olfactometry using the triangular odor bag method was carried out for the first time between Japan and China in 2018. A total of 134 olfactometry laboratories (130 Japanese and 4 Chinese) participated in the test, and the odor index of the test odorant (dimethyl disulfide with a concentration of 10.7 ppm) was measured three times at each laboratory. In the interlaboratory evaluation, a reference value and repeatability and reproducibility standard deviations were determined on the basis of measurement results of 13 ‘excellent qualified laboratories’ designated by the Japan Association on Odor Environment. Evaluation results of trueness and precision of the 133 laboratories that conducted duplicate or triplicate measurements showed that 110 (108 Japanese and 2 Chinese) and 104 (102 Japanese and 2 Chinese) laboratories (82.7% and 78.2%) conformed to the criterion of trueness and precision, respectively, and 87 (86 Japanese and 1 Chinese) laboratories (65.4%) conformed to both. Based on the meaningful experiences in 2018, a continuous international collaboration between Japan and China in the field of olfactometry should be implemented for the improvement of the quality of olfactometry laboratories and the reliability of odor measurement in both countries. Full article
(This article belongs to the Special Issue Environmental Odour: Emission, Dispersion, and the Assessment of Annoyance)
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<p>Distribution of mean odor index of 134 laboratories.</p> Full article ">
21 pages, 17305 KiB  
Article
Impact of Global Warming on Extreme Heavy Rainfall in the Present Climate: Case Study of Heavy Rainfall in Kinugawa, Japan (2015)
by Kenji Taniguchi and Yuto Minobe
Atmosphere 2020, 11(2), 220; https://doi.org/10.3390/atmos11020220 - 21 Feb 2020
Cited by 2 | Viewed by 2791
Abstract
Hazardous heavy rainfall and wide-scale inundation occurred in the Kinugawa River basin, north of Tokyo, in 2015. In this study, ensemble hindcast and non-global warming (NGW) simulations of this heavy rainfall event were implemented. In the NGW simulations, initial and boundary conditions were [...] Read more.
Hazardous heavy rainfall and wide-scale inundation occurred in the Kinugawa River basin, north of Tokyo, in 2015. In this study, ensemble hindcast and non-global warming (NGW) simulations of this heavy rainfall event were implemented. In the NGW simulations, initial and boundary conditions were generated by using the outputs of natural forcing historical experiments by twelve different global climate models. The results of the hindcast and NGW simulations indicated the high likelihood of the generation of linear heavy rainfall bands and the intensification of Kinugawa heavy rainfall due to anthropogenic greenhouse gas emissions. However, in some NGW simulations, the total rainfall was greater than in the hindcast. In addition, the maximum total rainfall was greater in many NGW simulations. Lower atmospheric temperature, sea surface temperature (SST), and precipitable water content (PWC) under the initial conditions can cause less rainfall in the NGW simulations. However, some discrepancies were found in the initial conditions and simulated rainfall; less rainfall with higher atmospheric temperature, SST and PWC, and vice versa. A detailed investigation of simulated atmospheric conditions explained the simulated rainfall. These results indicate that it is not sufficient to examine climatological anomalies to understand individual extreme weather events, but that detailed simulations are useful. Full article
(This article belongs to the Section Climatology)
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<p>Target domains for the weather research and forecasting (WRF) simulations. (<b>a</b>) Parent domain; (<b>b</b>) intermediate domain; (<b>c</b>) child domain. The blue, red, and yellow areas represent the parent, intermediate, and child domains, respectively. The red line and open rectangle shown in (<b>c</b>) indicate the Kanto area and area A, respectively. The red circle in (<b>c</b>) is the location of Nikko city.</p> Full article ">Figure 2
<p>Total rainfall from 8 to 11 September 2015. (<b>a</b>) Result by Radar-AMeDAS precipitation (RAP); (<b>b</b>) ensemble mean total rainfall of the hindcast. Open circle indicates the location of the Nikko city. The unit of the color bar is mm.</p> Full article ">Figure 3
<p>(<b>a</b>) Time-series of hourly rain rate averaged for the area around the Nikko city; (<b>b</b>) scatter plot of the maximum rain rate and total rainfall averaged for the area around the Nikko city.</p> Full article ">Figure 4
<p>3-hourly rainfall by Radar-AMeDAS precipitation (RAP). Result of (<b>a</b>) 01–03 UTC, (<b>b</b>) 04–06 UTC, (<b>c</b>) 07–09 UTC, (<b>d</b>) 10–12 UTC, (<b>e</b>) 13–15 UTC, (<b>f</b>) 16–18 UTC, (<b>g</b>) 19–21 UTC on 9 September. (<b>h</b>) Result of 22 UTC on 9 September–00 UTC on 10 September. The unit of the color bar is mm/3 h. Filled black circle indicate the location of the Nikko city.</p> Full article ">Figure 5
<p>3-hourly rainfall by the hindcast simulation (a member starts at 00 UTC 7 September). Result of (<b>a</b>) 01–03 UTC, (<b>b</b>) 04–06 UTC, (<b>c</b>) 07–09 UTC, (<b>d</b>) 10–12 UTC, (<b>e</b>) 13–15 UTC, (<b>f</b>) 16–18 UTC, (<b>g</b>) 19–21 UTC on 9 September. (<b>h</b>) Result of 22 UTC on 9 September–00 UTC on 10 September. The unit of the color bar is mm/3 h. Filled black circle indicate the location of the Nikko city.</p> Full article ">Figure 6
<p>Spatial distribution of the ensemble mean total rainfall in D03 for non-global warming (NGW) simulations. Result of (<b>a</b>) NGW-1, (<b>b</b>) NGW-2, (<b>c</b>) NGW-3, (<b>d</b>) NGW-4, (<b>e</b>) NGW-5, (<b>f</b>) NGW-6, (<b>g</b>) NGW-7, (<b>h</b>) NGW-8, (<b>i</b>) NGW-9, (<b>j</b>) NGW-10, (<b>k</b>) NGW-11, (<b>l</b>) NGW-12. The unit of the color bar is mm. Open circle indicates the location of the Nikko city.</p> Full article ">Figure 7
<p>Latitude-time cross section of hourly rainfall averaged for the 139.9° E–140.1° E band. Ensemble mean values are shown for each panel. Result of (<b>a</b>) hindcastm, (<b>b</b>) NGW-1, (<b>c</b>) NGW-2, (<b>d</b>) NGW-3, (<b>e</b>) NGW-4, (<b>f</b>) NGW-5, (<b>g</b>) NGW-6, (<b>h</b>) NGW-7, (<b>i</b>) NGW-8, (<b>j</b>) NGW-9, (<b>k</b>) NGW-10, (<b>l</b>) NGW-11, (<b>m</b>) NGW-12. The unit of the color bar is mm/h.</p> Full article ">Figure 8
<p>Probability density curves of the mean total rainfall for D03 (R<sub>T,3</sub>). The horizontal axis is R<sub>T,3</sub> (mm). The vertical axis is the probability density.</p> Full article ">Figure 9
<p>Probability density curves of the mean total rainfall for area A (R<sub>T,A</sub>). The horizontal axis is R<sub>T,3</sub> (mm). The vertical axis is the probability density.</p> Full article ">Figure 10
<p>Probability density curves of the maximum total rainfall in D03 (R<sub>MAX</sub>). The horizontal axis is R<sub>MAX</sub> (mm). The vertical axis is the probability density.</p> Full article ">Figure 11
<p>Difference in atmospheric temperature 2 m above the Earth’s surface under initial conditions between each NGW simulation and the hindcast. Result (<b>a</b>) NGW-1, (<b>b</b>) NGW-2, (<b>c</b>) NGW-3, (<b>d</b>) NGW-4, (<b>e</b>) NGW-5, (<b>f</b>) NGW-6, (<b>g</b>) NGW-7, (<b>h</b>) NGW-8, (<b>i</b>) NGW-9, (<b>j</b>) NGW-10, (<b>k</b>) NGW-11, (<b>l</b>) NGW-12. The unit of the color bar is Kelvin.</p> Full article ">Figure 12
<p>Difference in sea surface temperature (SST) under initial conditions between each NGW simulation and the hindcast. Result of (<b>a</b>) NGW-1, (<b>b</b>) NGW-2, (<b>c</b>) NGW-3, (<b>d</b>) NGW-4, (<b>e</b>) NGW-5, (<b>f</b>) NGW-6, (<b>g</b>) NGW-7, (<b>h</b>) NGW-8, (<b>i</b>) NGW-9, (<b>j</b>) NGW-10, (<b>k</b>) NGW-11, (<b>l</b>) NGW-12. The unit of the color bar is Kelvin.</p> Full article ">Figure 13
<p>Difference in precipitable water content (PWC) under initial conditions between each NGW simulation and the hindcast. Result (<b>a</b>) NGW-1, (<b>b</b>) NGW-2, (<b>c</b>) NGW-3, (<b>d</b>) NGW-4, (<b>e</b>) NGW-5, (<b>f</b>) NGW-6, (<b>g</b>) NGW-7, (<b>h</b>) NGW-8, (<b>i</b>) NGW-9, (<b>j</b>) NGW-10, (<b>k</b>) NGW-11, (<b>l</b>) NGW-12. The unit of the color bar is mm.</p> Full article ">Figure 14
<p>Difference in PWC at 18 UTC 9 September between each NGW simulation and the hindcast. Result of (<b>a</b>) NGW-1, (<b>b</b>) NGW-2, (<b>c</b>) NGW-3, (<b>d</b>) NGW-4, (<b>e</b>) NGW-5, (<b>f</b>) NGW-6, (<b>g</b>) NGW-7, (<b>h</b>) NGW-8, (<b>i</b>) NGW-9, (<b>j</b>) NGW-10, (<b>k</b>) NGW-11, (<b>l</b>) NGW-12. The unit of the color bar is mm.</p> Full article ">Figure 15
<p>Spatial distribution of divergence (<b>shaded</b>), wind (<b>arrows</b>), and sea level pressure (<b>contours</b>) at 18 UTC 9 September. Divergence and wind are averaged for the layers from 1000 to 700 hPa. Result (<b>a</b>) hindcast, (<b>b</b>) NGW-1, (<b>c</b>) NGW-2, (<b>d</b>) NGW-3, (<b>e</b>) NGW-4, (<b>f</b>) NGW-5, (<b>g</b>) NGW-6, (<b>h</b>) NGW-7, (<b>i</b>) NGW-8, (<b>j</b>) NGW-9, (<b>k</b>) NGW-10, (<b>l</b>) NGW-11, (<b>m</b>) NGW-12. The unit of the color bar is 10<sup>−5</sup> /s.</p> Full article ">
12 pages, 2937 KiB  
Article
The Global Distribution of Cirrus Clouds Reflectance Based on MODIS Level-3 Data
by Fengmei Zhao, Chaoli Tang, Congming Dai, Xin Wu and Heli Wei
Atmosphere 2020, 11(2), 219; https://doi.org/10.3390/atmos11020219 - 21 Feb 2020
Cited by 6 | Viewed by 3897
Abstract
Cirrus clouds are crucially important to weather, climate and earth energy balance studies. The distribution of cirrus reflectance with latitude and season is an interesting topic in atmospheric sciences. The monthly mean Level-3 MODIS cirrus reflectance is used to analyze the global distribution [...] Read more.
Cirrus clouds are crucially important to weather, climate and earth energy balance studies. The distribution of cirrus reflectance with latitude and season is an interesting topic in atmospheric sciences. The monthly mean Level-3 MODIS cirrus reflectance is used to analyze the global distribution of cirrus clouds, which covers a period from 1 March 2000 to 28 February 2018. The latitude, from 90° S to 90° N, is divided into 36 latitude zones with 5° interval. Data in each latitude zone are analyzed. The research results show that the slopes of cirrus reflectance variation in the Northern and Southern Hemisphere are −1.253 × 10−4/year and –1.297 × 10−4/year, respectively. The yearly-average cirrus reflectance reveals strong negative correlation with time in the Northern Hemisphere, i.e., the correlation coefficient is −0.761. Then the statistical analysis of cirrus reflectance is performed in different seasons, the results show that cirrus reflectance varies obviously with seasonal change. Additionally, for the [30°, 90°] latitude regions, cirrus reflectance reaches the minimum in summer and the maximum in winter in the Southern and Northern Hemisphere. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>The flow chart of quality control for cirrus clouds data preprocesses: Obtain the effective samples.</p> Full article ">Figure 2
<p>The 18-year average cirrus reflectance vs. latitude and longitude.</p> Full article ">Figure 3
<p>The global average of cirrus reflectance.</p> Full article ">Figure 4
<p>The variations of the yearly-average cirrus reflectance vs. latitude from 2000 to 2017.</p> Full article ">Figure 5
<p>The hemispheric cirrus reflectance vs. year.</p> Full article ">Figure 6
<p>The seasonal-average cirrus reflectance vs. latitude from 2000 to 2017. (<b>a</b>) March-May, (<b>b</b>) June-August, (<b>c</b>) September-November, (<b>d</b>) December-February. Note that there are no data for (<b>b</b>) and (<b>d</b>) in the [80 °S, 90 °S], [80 °N, 90 °N] regions, respectively, due to the Polar nights or very short sunshine time.</p> Full article ">Figure 6 Cont.
<p>The seasonal-average cirrus reflectance vs. latitude from 2000 to 2017. (<b>a</b>) March-May, (<b>b</b>) June-August, (<b>c</b>) September-November, (<b>d</b>) December-February. Note that there are no data for (<b>b</b>) and (<b>d</b>) in the [80 °S, 90 °S], [80 °N, 90 °N] regions, respectively, due to the Polar nights or very short sunshine time.</p> Full article ">Figure 7
<p>The seasonal distribution of the 18-year average cirrus reflectance vs. latitude. Note that: due to the polar nights in the South and Arctic, data in the region [80°,90°] in winter and [−90°,−80°] in summer does not exist.</p> Full article ">
18 pages, 4763 KiB  
Article
Influence of Internal Structure and Composition on Head’s Local Thermal Sensation and Temperature Distribution
by Shuai He, Yinghua Zhang, Zhian Huang, Ge Zhang and Yukun Gao
Atmosphere 2020, 11(2), 218; https://doi.org/10.3390/atmos11020218 - 21 Feb 2020
Viewed by 2807
Abstract
A personalized thermal environment is an effective way to ensure a good thermal sensation for individuals. Since local thermal sensation and temperature distribution are affected by individual physiological differences, it is necessary to study the effects of physiological parameters. The purpose of this [...] Read more.
A personalized thermal environment is an effective way to ensure a good thermal sensation for individuals. Since local thermal sensation and temperature distribution are affected by individual physiological differences, it is necessary to study the effects of physiological parameters. The purpose of this study was to investigate the effects of internal structures and tissue composition on head temperature distribution and thermal sensation. A new mathematical model based on fuzzy logic control was established, the internal structure and tissue composition of the head were obtained by magnetic resonance imaging (MRI), and the local thermal sensation (LTS) index was used to evaluate the thermal sensation. Based on the mathematical model and the real physiological data, the head temperature and local sensation changes under different parameters were investigated, and the sensitivity of thermal sensation relative to the differences in tissue thickness was analyzed. The results show that skin tissue had the highest influence ( C s k i n = 0.0180 ) on head temperature, followed by muscle tissue ( C m u s c l e = 0.0127 ), and the influence of adipose tissue ( C f a t = 0.0097 ) was the lowest. LTS was most sensitive to skin thickness variation, with an average sensitivity coefficient of 1.58, while the muscle tissue had an average sensitivity coefficient of 0.2, and the sensitivity coefficient of fat was relatively small, at a value of 0.04. Full article
(This article belongs to the Special Issue Indoor Thermal Comfort)
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<p>(<b>a</b>) The structure of fuzzy control model; (<b>b</b>) membership functions of fuzzy control model.</p> Full article ">Figure 2
<p>The structures of different sections of the head obtained by MRI. In (<b>a</b>), the two left images are the front and side view of the head. Sub images of 1, 2, 3, and 4 on the right side show the different sections from the top to bottom of the head. The image (<b>b</b>) shows the mask results of different tissues of section (<b>3</b>). The image (<b>c</b>) shows the comparison of tissue thickness between different individuals at the same section of the head.</p> Full article ">Figure 3
<p>The methodology framework of the study</p> Full article ">Figure 4
<p>Comparison of simulated results of the model used in this study with the published experimental data [<a href="#B16-atmosphere-11-00218" class="html-bibr">16</a>], and simulated results of American University of Beirut model (AUB model) [<a href="#B14-atmosphere-11-00218" class="html-bibr">14</a>] for skin temperature and core temperature at constant ambient conditions of 28.5 °C /31% RH.</p> Full article ">Figure 5
<p>Comparison of the simulated results with the measured data [<a href="#B16-atmosphere-11-00218" class="html-bibr">16</a>], and Kaynakli and Kilic [<a href="#B25-atmosphere-11-00218" class="html-bibr">25</a>] as the ambient temperature step change.</p> Full article ">Figure 6
<p>Comparison of the temperatures influenced by different compositions and structures of different layers of the head.</p> Full article ">Figure 7
<p>(<b>a</b>) Temperature variation by muscle thickness step change; (<b>b</b>) Tissues temperature distribution under different muscle thickness.</p> Full article ">Figure 8
<p>(<b>a</b>) Temperature variation by fat thickness step change; (<b>b</b>) Tissues temperature distribution under different fat thickness.</p> Full article ">Figure 9
<p>(<b>a</b>) Temperature variation by skin thickness step change; (<b>b</b>) Tissues temperature distribution under different skin thickness.</p> Full article ">Figure 10
<p>(<b>a</b>) The variation of the thermal sensation index with tissue thickness change; (<b>b</b>) the sensitivity of the thermal sensation index to tissue thickness change.</p> Full article ">
19 pages, 5800 KiB  
Article
Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials
by Jennifer Kreklow, Björn Tetzlaff, Benjamin Burkhard and Gerald Kuhnt
Atmosphere 2020, 11(2), 217; https://doi.org/10.3390/atmos11020217 - 21 Feb 2020
Cited by 26 | Viewed by 5981
Abstract
Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties [...] Read more.
Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalyzed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets were statistically analyzed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums were examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation. Full article
(This article belongs to the Special Issue Radar Hydrology and QPE Uncertainties)
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<p>Height above sea level (m) of Germany based on a SRTM Digital Elevation Model (<b>left map</b>) and German radar station network with 128 km radius as used for RADKLIM (<b>right map</b>).</p> Full article ">Figure 2
<p>Spatiotemporal distribution of outliers in the annual precipitation sums of RADOLAN and RADKLIM. Blue values represent high outliers (MAP &gt; cleaned MAP), yellow and red values represent low outliers (cleaned MAP &gt; MAP).</p> Full article ">Figure 3
<p>Mean annual precipitation sums for the period 2006 to 2017 derived from (<b>a</b>) RADKLIM, (<b>b</b>) RADOLAN and (<b>c</b>) gauge data.</p> Full article ">Figure 4
<p>Mean annual precipitation sums of Germany between 2006–2017.</p> Full article ">Figure 5
<p>Comparison of mean annual precipitation sums from the 997 rain gauges and the corresponding RADKLIM pixels (<b>left plot</b>) as well as from the 392529 RADOLAN-RADKLIM pixel pairs (<b>right plot</b>).</p> Full article ">Figure 6
<p>Ratio of mean annual and seasonal precipitation sums 2006–2017 between RADKLIM and RADOLAN.</p> Full article ">Figure 7
<p>Quadratic regression between seasonal precipitation sums and altitude for gauges (<b>left plot</b>), RADKLIM (<b>centre</b>) and RADOLAN (<b>right</b>).</p> Full article ">Figure 8
<p>Linear regression between the distance from the next radar and the RADKLIM/gauge MAP ratio (<b>left plot</b>) as well as the RADOLAN/gauge MAP ratio (<b>right plot</b>).</p> Full article ">Figure 9
<p>Number of days with precipitation amounts &gt;1 mm (<b>left plot</b>) and &gt;20 mm (<b>right plot</b>) in summer and winter half-years for gauges, RADKLIM and RADOLAN data. The numbers above the bars indicate the average daily precipitation sum of all days exceeding the respective threshold.</p> Full article ">Figure 10
<p>Difference of intensity class frequency between all point-pixel data pairs (n = 997) of the period 2006–2017 of gauge data (<math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>G</mi> </msub> </mrow> </semantics></math>) and RADKLIM RW product (<math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>R</mi> <msub> <mi>W</mi> <mi>G</mi> </msub> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">
15 pages, 3879 KiB  
Article
Estimating Turbulent Fluxes in the Tropical Andes
by Mario Córdova, Linda Bogerd, Paul Smeets and Galo Carrillo-Rojas
Atmosphere 2020, 11(2), 213; https://doi.org/10.3390/atmos11020213 - 21 Feb 2020
Cited by 1 | Viewed by 3672
Abstract
The correct estimation of Sensible Heat Flux (H) and Latent Heat Flux (LE) (i.e., turbulent fluxes) is vital in the understanding of exchange of energy and mass among hydrosphere, atmosphere, and biosphere in an ecosystem. One of the most popular methods to measure [...] Read more.
The correct estimation of Sensible Heat Flux (H) and Latent Heat Flux (LE) (i.e., turbulent fluxes) is vital in the understanding of exchange of energy and mass among hydrosphere, atmosphere, and biosphere in an ecosystem. One of the most popular methods to measure these fluxes is the Eddy Covariance (EC) technique; however, there are a number of setbacks to its application, especially in remote and topographically complex terrain such as the higher altitudes of the Andes. Efforts have been made by the scientific community to parameterise these fluxes based on other more commonly measured variables. One of the most widespread methods is the so-called bulk method, which relates average temperature, humidity, and wind vertical profiles to the turbulent fluxes. Another approach to estimate LE is the Penman-Monteith (PM) equation which uses meteorological measurements at a single level. The objective of this study was to validate these methods for the first time in the Tropical Andes in Southern Ecuador (in the páramo ecosystem at 3780 m a.s.l.) using EC and meteorological measurements. It was determined that the bulk method was the best to estimate H, although some adjustments had to be made to the typical assumptions used to estimate surface meteorological values. On the other hand, the PM equation yielded the best LE estimations. For both fluxes, the error in the estimations was within the uncertainty range of the EC measurements. It can be concluded that it is possible to accurately estimate H and LE using the methods described in this paper in this ecosystem when no direct measurements are available. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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<p>Study area. (<b>A</b>) Aerial view of the surroundings of the Eddy covariance tower in the Zhurucay Ecohydrological Observatory. It is observed that the main vegetation cover is tussock grass. (<b>B</b>) Eddy covariance tower installed at 3780 m a.s.l.</p> Full article ">Figure 2
<p>Turbulent fluxes estimated by the bulk method compared to observations: (<b>a</b>) Sensible Heat Flux (H) and (<b>b</b>) Latent Heat Flux (LE). The correlation for both H and LE was significant (<span class="html-italic">p</span>-value &lt; 0.01).</p> Full article ">Figure 3
<p>Turbulent fluxes estimated using the best possible estimation with the bulk method compared to observations (ε = 0.95, ΔRH<sub>max</sub> = 5%, and C<sub>1</sub> = 0.7): (<b>a</b>) Sensible Heat Flux, (<b>b</b>) Latent Heat Flux (<span class="html-italic">p</span> &lt; 0.01 for both correlations).</p> Full article ">Figure 4
<p>Latent Heat Flux estimated via the Penman-Monteith equation compared to observations (correlation is found to be significant with <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 5
<p>(<b>a</b>) Total monthly precipitation, (<b>b</b>) monthly average soil moisture, and (<b>c</b>) monthly average solar radiation for the study period. <a href="#atmosphere-11-00213-f006" class="html-fig">Figure 6</a> shows the time series for the first 12 days of March and November. In the case of H (<a href="#atmosphere-11-00213-f006" class="html-fig">Figure 6</a>a,b), it was observed that the fluxes are larger in November, primarily as a result of the increased solar radiation in the study area during this month [<a href="#B43-atmosphere-11-00213" class="html-bibr">43</a>]. The main errors in the H calculations are made when large negative values occur (i.e., when H &lt; 0), as observed in <a href="#atmosphere-11-00213-f006" class="html-fig">Figure 6</a>a,b. This causes the MAB to be higher in November than in March (<a href="#atmosphere-11-00213-t002" class="html-table">Table 2</a>). LE is shown in <a href="#atmosphere-11-00213-f006" class="html-fig">Figure 6</a>c,d for March and November respectively. In <a href="#atmosphere-11-00213-f006" class="html-fig">Figure 6</a>c,d, we observe the characteristic overestimation of LE calculations; nonetheless, this overestimation is more pronounced during November (<a href="#atmosphere-11-00213-f006" class="html-fig">Figure 6</a>d), and this is why MAB is so much higher in this month compared to the March value.</p> Full article ">Figure 6
<p>Time series of observed and estimated Sensible and Latent Heat Flux for a period for when estimations are the best (<b>a</b> and <b>c</b>) and for a period when estimations are worst (<b>b</b> and <b>d</b>). A t-test was performed for the four pairs of time series shown in the figure; the <span class="html-italic">p</span>-values were smaller than 0.05 for all of them. Therefore, it can be concluded that the measurements are significantly different from the calculations for all the figures.</p> Full article ">Figure 7
<p>Latent Heat Flux estimated via the Penman-Monteith equation compared to observations. Brown points correspond to dry conditions and green points to wet conditions.</p> Full article ">
28 pages, 13046 KiB  
Article
Uncertainties in the Annual Cycle of Rainfall Characteristics over West Africa in CMIP5 Models
by Magatte Sow, Moussa Diakhaté, Ross D. Dixon, Françoise Guichard, Diarra Dieng and Amadou T. Gaye
Atmosphere 2020, 11(2), 216; https://doi.org/10.3390/atmos11020216 - 20 Feb 2020
Cited by 9 | Viewed by 4526
Abstract
We analyse uncertainties associated with the main features of the annual cycle of West African rainfall (amplitude, timing, duration) in 15 CMIP5 simulations over the Sahelian and Guinean regions with satellite daily precipitation estimates. The annual cycle of indices based on daily rainfall [...] Read more.
We analyse uncertainties associated with the main features of the annual cycle of West African rainfall (amplitude, timing, duration) in 15 CMIP5 simulations over the Sahelian and Guinean regions with satellite daily precipitation estimates. The annual cycle of indices based on daily rainfall such as the frequency and the intensity of wet days, the consecutive dry (CDD) and wet (CWD) days, the 95th percentile of daily rainfall (R95), have been assessed. Over both regions, satellite datasets provide more consistent results on the annual cycle of monthly precipitation than on higher-frequency rainfall indices, especially over the Guinean region. By contrast, CMIP5 simulations display much higher uncertainties in both the mean precipitation climatology and higher-frequency indices. Over both regions, most of them overestimate the frequency of wet days. Over the Guinean region, the difficulty of models to represent the bimodality of the annual cycle of precipitation involves systematic biases in the frequency of wet days. Likewise, we found strong uncertainties in the simulation of the CWD and the CDD over both areas. Finally, models generally provide too early (late) onset dates over the Sahel (the Guinean region) and overestimate rainfall during the early and late monsoon phases. These errors are strongly coupled with errors in the latitudinal position of the ITCZ and do not compensate at the annual scale or when considering West Africa as a whole. Full article
(This article belongs to the Special Issue West African Monsoon Climate Dynamics and Impacts: Past, Present and Future)
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Figure 1

Figure 1
<p>Climatological annual-mean rainfall (mm/year) averaged over 1985–2004 over West Africa (using CHIRPS data). The black boxes highlight the two-considered subregions (Sahel and Guinean region).</p> Full article ">Figure 2
<p>Annual cycle of rainfall (<b>a</b>,<b>b</b>), frequency (<b>c</b>,<b>d</b>) and intensity of wet days (<b>e</b>,<b>f</b>) over the Sahel (left) and Guinean region (right) from CHIRPS (black), TRMM (blue), GPCP (green) and 15 CMIP5 models and their ensemble mean (cyan). The shaded area indicate the spread between the three satellite estimates.</p> Full article ">Figure 3
<p>Taylor diagrams of the annual cycle of daily mean rainfall (mm/day), intensity and frequency of wet days from 1985 to 2004 over the Sahel (<b>a</b>) and the Guinean region (<b>b</b>) for TRMM, GPCP and 15 CMIP5 models compared to CHIRPS. The values have been computed using the monthly-mean of each index averaged over the period 1985–2004.</p> Full article ">Figure 4
<p>Annual cycle of wet spells (<b>a</b>,<b>b</b>), dry spells (<b>c</b>,<b>d</b>), 95th percentile (<b>e</b>,<b>f</b>) and the fraction of precipitation accounted for by the very wet days (<b>g</b>,<b>h</b>) over the Sahel (Left) and the Guinean region (right) from 1985 to 2004 in CHIRPS (black), Tropical Rainfall Measuring Mission (TRMM) (blue), GPCP (green) and 15 models CMIP5 and their ensemble mean (cyan).</p> Full article ">Figure 5
<p>Taylor diagrams of wet spells (cyan), dry spells (purple), 95th percentile (orange) and the fraction of precipitation accounted for by the very wet days (brown) from 1985 to 2004 in the Sahel (<b>a</b>,<b>c</b>) and the Guinean region (<b>b</b>,<b>d</b>) on TRMM, GPCP and 15 CMIP5 models compared to CHIRPS. The values have been computed using the monthly-mean of each index averaged over the period 1985–2004.</p> Full article ">Figure 6
<p>Daily accumulated-anomalies of precipitation (left panel) from observations (bold lines) and CMIP5 models simulations and their multi-model mean (MMM) over the Sahel (<b>a</b>) and the Guinean region (<b>b</b>); averaged over 1985–2004 for CHIRPS and models and over 2000–2010 for TRMM and GPCP. The box plots (right panel) show the onset (minimum), the cessation (maximum), the date at which half of the total cumulative precipitation is recorded (median), as well as the first and third quartiles.</p> Full article ">Figure 7
<p>Mean onset and cessation dates in CHIRPS (<b>a</b>,<b>b</b>) and MMM (<b>d</b>,<b>e</b>) as well as the associated standard deviation in models (<b>c</b>,<b>f</b>).</p> Full article ">Figure 8
<p>Monthly mean of the latitude of the centre of the Inter-Tropical Convergence Zone (ITCZ) band over West Africa (<b>a</b>) and the JAS mean of the latitude of the centre and the width of the ITCZ (<b>b</b>). r in legend indicate the correlation between the monthly mean precipitation and latitude of the centre of the ITCZ. For each box plot, the distance from minimum to maximum represent the width and the median is the ITCZ position.</p> Full article ">Figure A1
<p>Same as <a href="#atmosphere-11-00216-f002" class="html-fig">Figure 2</a> but with a finer grid resolution: 0.5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> × 0.5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>.</p> Full article ">Figure A2
<p>Same as <a href="#atmosphere-11-00216-f004" class="html-fig">Figure 4</a> but with a finer grid resolution: 0.5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> × 0.5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>.</p> Full article ">Figure A3
<p>Hovmuller diagrams of monthly precipitation averaged over 20W-25E for CHIRPS, TRMM and GPCP (first line) and 15 CMIP5 outputs.</p> Full article ">Figure A4
<p>CMIP5 models bias with CHIRPS on Hovmuller diagrams.</p> Full article ">Figure A5
<p>Mean onset dates over West Africa in CHIRPS, TRMM, GPCP and CMIP5 models outputs. The grey shaded area represent ignored domain.</p> Full article ">Figure A6
<p>Mean cessation dates over West Africa in CHIRPS, TRMM, GPCP and CMIP5 models outputs. The grey shaded area represent ignored domain.</p> Full article ">Figure A7
<p>Relation between the bias of July-September-mean precipitation and the bias of July-September-mean ITCZ position.</p> Full article ">
14 pages, 5546 KiB  
Article
A Synergistic Effect of Blockings on a Persistent Strong Cold Surge in East Asia in January 2018
by Wei Dong, Liang Zhao, Shunwu Zhou and Xinyong Shen
Atmosphere 2020, 11(2), 215; https://doi.org/10.3390/atmos11020215 - 20 Feb 2020
Cited by 12 | Viewed by 4316
Abstract
A persistent strong cold surge occurred in East Asia in late January 2018, causing mean near-surface air temperature in China to hit the second lowest since 1984. Moreover, the daily mean air temperature remained persistently negative for more than 20 days. Here, we [...] Read more.
A persistent strong cold surge occurred in East Asia in late January 2018, causing mean near-surface air temperature in China to hit the second lowest since 1984. Moreover, the daily mean air temperature remained persistently negative for more than 20 days. Here, we find that a synergistic effect of double blockings in Western Europe and North America plays an important accelerating role in the rapid phase transition of Arctic Oscillation and an amplifying role in the strength of cold air preceding to the cold surge outbreaks by the use of an isentropic potential vorticity analysis. In mid-January, an Atlantic mid-latitude anticyclone merged with Western Europe blocking, which led to a strengthening of the blocking. Simultaneously, the Pacific-North American blocking was also significantly strengthened. The two blockings synchronously deeply stretched towards the Arctic, which resulted in, on the one hand, warm and moist air of the Pacific and the Atlantic being excessively transported into the Arctic, and on the other hand, the polar vortex being split and cold air being squeezed southwards and accumulating extensively on the West Siberian Plain. After the breakdown of the double blocking pattern, which lasted for about 10 days, the record-breaking cold surge broke out in East Asia. It was discovered that the synergistic effect of double blockings extending into the Arctic, which is conducive to extreme cold events, has been rapidly increasing in recent years. Full article
(This article belongs to the Section Climatology)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Time series of the near-surface air temperature anomaly averaged over China for January from 1959 to 2018 using gauged data (red curve with hollow circle; units: °C; the green and black straight lines are the trend lines of recent 18 years (−0.012 °C /year) and 60 years (0.023 °C /year), respectively; the horizontal dashed line is −1.42 °C, corresponding to a −1.25 standard deviation threshold). (<b>b</b>) Same as (a) but for late (21–31) January. The trends in the last 18 and 60 years are −0.032 °C /year and 0.012 °C /year, respectively, and the horizontal dashed line is −2.35 °C. (<b>c</b>) Probability density functions (PDFs) of surface temperature anomalies in late January 1959–2018 (the red and blue solid curves are a normal and a generalized extreme value (GEV) fits, respectively). The anomalies are relative to the 1959–2018 mean.</p> Full article ">Figure 2
<p>The maps of the near-surface air temperature anomalies (colored shading; units: °C) over China in early (<b>a</b>), mid (<b>b</b>), late (<b>c</b>) January 2018, relative to 1959–2018. The thick purple lines are 0 °C in climatology (for the period 1959–2018) and the thick green lines are 0 °C in 2018.</p> Full article ">Figure 3
<p>(<b>a</b>) The leading correlation coefficient map when 345 K isentropic PV is 11 days ahead of China’s surface temperature for the period from 1 January to 10 February in 2018 (the contour outlines the significant correlation regions with a 95% confidence level). (<b>b</b>) Leading correlation coefficients between the daily isentropic PV series averaged over the northwestern region of Lake Baikal (80–120° E, 50–70° N) as shown in the box in (<b>a</b>) at 315 K (blue), 330 K (red) and 345 K (green) and the daily mean China near-surface air temperature series for the period from 1 January to 10 February (the thick horizontal black line indicates the 95% confidence level).</p> Full article ">Figure 4
<p>(<b>a</b>) Isentropic surface–time profile of area (60–70° N, 80–100° E) in the high PV source (located on the northwestern side of Lake Baikal (80–120° E, 50–70° N)) in January 2018, overlapped lead and lag correlations. The dot (negative correlation) and the mesh (positive correlation) areas represent significant correlations above a 0.10 significance level. Lead01–27 represents that the PV series lead surface temperature series of China (from 1 January to 9 February) by 1 to 27 days, respectively; lag0–03 represents that the PV lag temperature by 0 to 3 days, respectively. The lowest temperature date, 28 January, is the base date, indicated by the thick red dashed straight line. The thick solid contours in (<b>a</b>) and (<b>b</b>) are 2PVU. (<b>b</b>) Same as (<b>a</b>), but for the 305 K 90° E latitude-time profile. <b>(c)</b> Daily series of surface temperature averaged over China (T) (red curve, units: °C), 305 K PV averaged over the high PV source (80–120° E, 50–70° N) (isentropic PV) (black dotted curve, units: PVU), 1000hPa temperature at the point (60° N, 100° E) (T_Baikal) (purple dotted curve, units: °C) and AO index (blue curve) in January.</p> Full article ">Figure 5
<p>315 K isentropic PV (contour, units: PVU) and wind (arrow, units: m s<sup>−1</sup>) fields on some given dates in January 2018. The purple and blue arrows represent southerly and northerly, respectively, and wind speed larger than 15 m s<sup>−1</sup>. PV in the blue shade is greater than 4 PVU; PV less than 2 PVU in the north of 50° N is marked in red. The thick solid contours are 2 PVU.</p> Full article ">Figure 6
<p>(<b>a</b>) Thirteen and (<b>b</b>) 14 January 2018, longitude-height profiles of PV (contours), wind (vectors) along latitudes (50–55° N) and diabatic heating along latitudes (50–60° N) (shading, units: K day<sup>−1</sup>). The red thick solid contours are 0.8 PVU. The black thick solid contours are 2 PVU. The arrows are vector wind field from composites of the zonal wind (units: m s<sup>−1</sup>) and the vertical velocity of p-coordinate system (units: −100×Pa s<sup>−1</sup>): blue indicates easterly and red indicates westerly.</p> Full article ">Figure 7
<p>On January 2018 (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">V</mi> <mo>−</mo> <mi>θ</mi> </mrow> </semantics></math> blocking index (left panel) and AO index (right panel); (<b>b</b>) diabatic heating in the Arctic (67–90° N) (shading, units: K day<sup>−1</sup>) and the PV in Eurasian polar region (0–70° E, 70–90° N) (contours, units: PVU).</p> Full article ">Figure 8
<p>Different items of the temperature diagnostic equation, AO index and mean temperature anomaly between 1000 and 300hPa averaged over the Arctic (67°–90° N). The blue curve shows the AO index, red is the Arctic temperature anomaly, purple is the diabatic heating (<math display="inline"><semantics> <mrow> <mfrac> <mrow> <msub> <mi>Q</mi> <mn>1</mn> </msub> </mrow> <mrow> <msub> <mi>C</mi> <mi>p</mi> </msub> </mrow> </mfrac> </mrow> </semantics></math>), black is the convection item (<math display="inline"><semantics> <mrow> <mi>ω</mi> <mi>σ</mi> </mrow> </semantics></math>), yellow is the advection term (<math display="inline"><semantics> <mrow> <mo>−</mo> <mi>v</mi> <mo>×</mo> <mo>∇</mo> <mi>T</mi> </mrow> </semantics></math>), and green is the local variation of temperature (<math display="inline"><semantics> <mrow> <mfrac> <mrow> <mo>∂</mo> <mi>T</mi> </mrow> <mrow> <mo>∂</mo> <mi>t</mi> </mrow> </mfrac> </mrow> </semantics></math>).</p> Full article ">Figure 9
<p>The three-dimensional map of PV (contours, thickened black contours of 2 PVU represent the dynamic tropopause), diabatic heating (red-yellow-green shading in the top two panels) and wind field (the purple and blue arrows indicate southerly and northerly, respectively) between North America and Western Europe on (<b>a</b>) 15 and (<b>b</b>) 18 January 2018: (bottom) 300 K isentropic surface (area with &gt;2 PVU PV is filled with blue shading); (top) latitudinal-isentropic height profiles along 135° W and 10° E.</p> Full article ">Figure 10
<p>Time series of the standardized synergy index of blockings stretching into the Arctic (in the north of 66° N) for mid-January from 1979 to 2018. The red straight line is the trend line of 40 years, 0.018/year; the blue straight line is the trend line of recent 20 years, 0.083/year. See text for the details.</p> Full article ">
26 pages, 15760 KiB  
Review
Ambient Air Quality in the Czech Republic: Past and Present
by Iva Hůnová
Atmosphere 2020, 11(2), 214; https://doi.org/10.3390/atmos11020214 - 20 Feb 2020
Cited by 54 | Viewed by 10880
Abstract
Based on an analysis of related core papers and reports, this review presents a historical perspective on ambient air pollution and ambient air quality development in the modern-day Czech Republic (CR) over the past seven decades, i.e., from the 1950s to the present. [...] Read more.
Based on an analysis of related core papers and reports, this review presents a historical perspective on ambient air pollution and ambient air quality development in the modern-day Czech Republic (CR) over the past seven decades, i.e., from the 1950s to the present. It offers insights into major air pollution problems, reveals the main hot spots and problematic regions and indicates the principal air pollutants in the CR. Air pollution is not presented as a stand-alone problem, but in the wider context of air pollution impacts both on human health and the environment in the CR. The review is arranged into three main parts: (1) the time period until the Velvet Revolution of 1989, (2) the transition period of the 1990s and (3) the modern period after 2000. Obviously, a major improvement in ambient air quality has been achieved since the 1970s and 1980s, when air pollution in the former Czechoslovakia culminated. Nevertheless, new challenges including fine aerosol, benzo[a]pyrene and ground-level ozone, of which the limit values are still vastly exceeded, have emerged. Furthermore, in spite of a significant reduction in overall emissions, the atmospheric deposition of nitrogen, in particular, remains high in some regions. Full article
(This article belongs to the Special Issue Ambient Air Quality in the Czech Republic)
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Figure 1

Figure 1
<p>Prunerov coal power plant (photo by the author).</p> Full article ">Figure 2
<p>Map showing the areas and places mentioned in this review.</p> Full article ">Figure 3
<p>Map of Northwest Bohemia (NWR in <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), showing the SO<sub>2</sub> annual means in µg m<sup>−3</sup> in 1971–1980, the lines represent the isoconcentrations (redrawn according to [<a href="#B20-atmosphere-11-00214" class="html-bibr">20</a>]).</p> Full article ">Figure 4
<p>Map of Northwest Bohemia (NWR in <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), showing the SO<sub>2</sub> daily mean in µg m<sup>−3</sup> on 16 January 1982, the lines represent the isoconcentrations (redrawn according to [<a href="#B20-atmosphere-11-00214" class="html-bibr">20</a>]).</p> Full article ">Figure 5
<p>Map indicating the formerly impacted regions (in the 1970s and 1980s) with respect to air pollution delimited by annual SO<sub>2</sub> mean concentration above 30 µg m<sup>−3</sup> (redrawn according to [<a href="#B41-atmosphere-11-00214" class="html-bibr">41</a>]).</p> Full article ">Figure 6
<p>Overall emissions of SO<sub>2</sub>, NO<sub>x</sub>, NH<sub>3</sub>, NM VOC and TSP in the Czech Republic (CR), according to European Monitoring and Evaluation Programme (EMEP) data [<a href="#B97-atmosphere-11-00214" class="html-bibr">97</a>].</p> Full article ">Figure 7
<p>Overall emissions of TSP, PM<sub>10</sub> and PM<sub>2.5</sub> in the CR, according to EMEP data [<a href="#B97-atmosphere-11-00214" class="html-bibr">97</a>].</p> Full article ">Figure 8
<p>Trends in ambient annual mean SO<sub>2</sub> concentrations at selected sites (see <a href="#atmosphere-11-00214-t001" class="html-table">Table 1</a>, <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), 1993–2019, based on Czech Hydrometeorological Institute (CHMI) data.</p> Full article ">Figure 9
<p>Trends in ambient annual mean NO<sub>x</sub> concentrations at selected sites (see <a href="#atmosphere-11-00214-t001" class="html-table">Table 1</a>, <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), 1993–2019, based on CHMI data.</p> Full article ">Figure 10
<p>Trends in ambient annual mean PM<sub>10</sub> concentrations at selected sites (see <a href="#atmosphere-11-00214-t001" class="html-table">Table 1</a>, <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), 1993–2019, based on CHMI data.</p> Full article ">Figure 11
<p>Trends in ambient annual mean O<sub>3</sub> concentrations at selected sites (see <a href="#atmosphere-11-00214-t001" class="html-table">Table 1</a>, <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), 1993–2018, based on CHMI data.</p> Full article ">Figure 12
<p>Trends in ambient annual mean BaP concentrations at selected sites (see <a href="#atmosphere-11-00214-t001" class="html-table">Table 1</a>, <a href="#atmosphere-11-00214-f002" class="html-fig">Figure 2</a>), 2004–2018, based on CHMI data.</p> Full article ">
17 pages, 6782 KiB  
Article
Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions
by Hongyong Li, Yujiao Zhu, Yong Zhao, Tianshu Chen, Ying Jiang, Ye Shan, Yuhong Liu, Jiangshan Mu, Xiangkun Yin, Di Wu, Cheng Zhang, Shuchun Si, Xinfeng Wang, Wenxing Wang and Likun Xue
Atmosphere 2020, 11(2), 212; https://doi.org/10.3390/atmos11020212 - 19 Feb 2020
Cited by 17 | Viewed by 4476
Abstract
Low-cost sensors have become an increasingly important supplement to air quality monitoring networks at the ground level, yet their performances have not been evaluated at high-elevation areas, where the weather conditions are complex and characterized by low air pressure, low temperatures, and high [...] Read more.
Low-cost sensors have become an increasingly important supplement to air quality monitoring networks at the ground level, yet their performances have not been evaluated at high-elevation areas, where the weather conditions are complex and characterized by low air pressure, low temperatures, and high wind speed. To address this research gap, a seven-month-long inter-comparison campaign was carried out at Mt. Tai (1534 m a.s.l.) from 20 April to 30 November 2018, covering a wide range of air temperatures, relative humidities (RHs), and wind speeds. The performance of three commonly used sensors for carbon monoxide (CO), ozone (O3), and particulate matter (PM2.5) was evaluated against the reference instruments. Strong positive linear relationships between sensors and the reference data were found for CO (r = 0.83) and O3 (r = 0.79), while the PM2.5 sensor tended to overestimate PM2.5 under high RH conditions. When the data at RH >95% were removed, a strong non-linear relationship could be well fitted for PM2.5 between the sensor and reference data (r = 0.91). The impacts of temperature, RH, wind speed, and pressure on the sensor measurements were comprehensively assessed. Temperature showed a positive effect on the CO and O3 sensors, RH showed a positive effect on the PM sensor, and the influence of wind speed and air pressure on all three sensors was relatively minor. Two methods, namely a multiple linear regression model and a random forest model, were adopted to minimize the influence of meteorological factors on the sensor data. The multi-linear regression (MLR) model showed a better performance than the random forest (RF) model in correcting the sensors’ data, especially for O3 and PM2.5. Our results demonstrate the capability and potential of the low-cost sensors for the measurement of trace gases and aerosols at high mountain sites with complex weather conditions. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Figure 1

Figure 1
<p>(<b>a</b>) Map showing the location of Mt. Tai. The emission data of fine particulate matter (PM<sub>2.5</sub>) were downloaded from <a href="http://meicmodel.org/about.html" target="_blank">http://meicmodel.org/about.html</a>. (<b>b</b>) Photo of the sampling inlets of the air quality sensor and benchmark instruments on the rooftop of Mt. Tai station.</p> Full article ">Figure 2
<p>Schematic diagrams of the sensors evaluated in this work: (<b>a</b>) CO and O<sub>3</sub>, and (<b>b</b>) PM<sub>2.5</sub>. PT: Photodetector.</p> Full article ">Figure 3
<p>Time series of the measured metrological and air quality data at Mt. Tai from 20 April to 30 November 2018: (<b>a</b>) Wind speed (WS) and pressure, (<b>b</b>) temperature (T) and relative humidity (RH), (<b>c</b>) CO, (<b>d</b>) O<sub>3</sub>, and (<b>e</b>) PM<sub>2.5</sub>.</p> Full article ">Figure 4
<p>The difference between the sensors’ data and the reference data, (a) CO, (b) O<sub>3</sub>, and (c) PM<sub>2.5</sub>. MLR: multi-linear regression, RF: random forest.</p> Full article ">Figure 5
<p>Scatter plots of (<b>a</b>) CO, (<b>b</b>) O<sub>3</sub>, and (<b>c</b>) PM<sub>2.5</sub> between low-cost air quality sensor and the benchmark instruments (blue dots were at RH &gt;95%). RMA: reduced major axis.</p> Full article ">Figure 6
<p>Scatter plots of sensor data and reference data, color-coded with meteorological factors: (<b>a</b>–<b>c</b>) temperature, (<b>d</b>–<b>f</b>) relative humidity, (<b>g</b>–<b>i</b>) wind speed, and (<b>j</b>–<b>l</b>) pressure.</p> Full article ">Figure 7
<p>Scatter plots of sensor/reference ratios against meteorological factors: (<b>a</b>–<b>c</b>) temperature, (<b>d</b>–<b>f</b>) relative humidity, (<b>g</b>–<b>i</b>) wind speed, and (<b>j</b>–<b>l</b>) pressure.</p> Full article ">Figure 8
<p>Comparison of scatterplots between the reference and sensor data for the test dataset of the original out-of-sensor data (<b>a</b>,<b>d</b>,<b>g</b>), after correction using multiple linear regression (<b>b</b>,<b>e</b>,<b>h</b>), and after correction using a random forest model (<b>c</b>,<b>f</b>,<b>i</b>).</p> Full article ">
16 pages, 1847 KiB  
Article
Characterization of the Gaseous and Odour Emissions from the Composting of Conventional Sewage Sludge
by Daniel González, Nagore Guerra, Joan Colón, David Gabriel, Sergio Ponsá and Antoni Sánchez
Atmosphere 2020, 11(2), 211; https://doi.org/10.3390/atmos11020211 - 19 Feb 2020
Cited by 25 | Viewed by 5120
Abstract
Many different alternatives exist to manage and treat sewage sludge, all with the common drawback of causing environmental and odour impacts. The main objective of this work is to present a full inventory of the gaseous and odorous emissions generated during the bench-scale [...] Read more.
Many different alternatives exist to manage and treat sewage sludge, all with the common drawback of causing environmental and odour impacts. The main objective of this work is to present a full inventory of the gaseous and odorous emissions generated during the bench-scale composting of conventional sewage sludge, aiming at assessing the process performance and providing global valuable information of the different gaseous emission patterns and emission factors found for greenhouse gases (GHG) and odorant pollutants during the conventional sewage sludge composting process. The main process parameters evaluated were the temperature of the material, specific airflow, average oxygen uptake rate (OUR), and final dynamic respiration index (DRI), resulting in a proper performance of the sewage sludge composting process and obtaining the expected final product. The obtained material was properly stabilized, presenting a final DRI of 1.2 ± 0.2 g O2·h−1·kg−1 Volatile Solids (VS). GHGs emission factor, in terms of kg CO2eq·Mg−1 dry matter of sewage sludge (DM–SS), was found to be 2.30 × 102. On the other hand, the sewage sludge composting odour emission factor (OEF) was 2.68 × 107ou·Mg−1 DM–SS. Finally, the most abundant volatile organic compounds (VOC) species found in the composting gaseous emissions were terpenes, sulphur compounds, ketones, and aromatic hydrocarbons, whereas the major odour contributors identified were dimethyldisulphide, eucalyptol, and α-pinene. Full article
(This article belongs to the Special Issue Environmental Odour: Emission, Dispersion, and the Assessment of Annoyance)
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<p>Schematic of the bench-scale composting reactor.</p> Full article ">Figure 2
<p>Performance of the composting process. Black dots represent the evolution of the average temperature of the reactor (°C); green crosses represent de average oxygen uptake rate (OUR) value for 24 h (g O<sub>2</sub>·h<sup>−1</sup>·kg<sup>−1</sup> VS); solid line represents the specific airflow passed through the reactor (L·min<sup>−1</sup>·kg<sup>−1</sup> VS of SS).</p> Full article ">Figure 3
<p>CH<sub>4</sub> and N<sub>2</sub>O emission rate profiles during the composting process. Black dots represent the average temperature of the reactor (°C); white diamonds represent the specific airflow passed through the reactor (L·min<sup>−1</sup>·kg<sup>−1</sup> VS of SS); blue squares represent the emission rate of CH<sub>4</sub> (mg CH<sub>4</sub>·d<sup>−1</sup>); green triangles represents the emission rates of N<sub>2</sub>O (mg N<sub>2</sub>O·d<sup>−1</sup>).</p> Full article ">Figure 4
<p>Odour emission rate (OER) profile during the composting process. Black dots represent the average temperature of the reactor (°C); white diamonds represent the specific airflow passed through the reactor (L·min<sup>−1</sup>·kg<sup>−1</sup> VS of SS); cyan squares represent the odour emission rate (ou·d<sup>−1</sup>).</p> Full article ">Figure 5
<p>Distribution of the different volatile organic compound (VOC) categories found in the off gases of the composting reactor, expressed in relative abundance with respect to the whole sample.</p> Full article ">Figure 6
<p>Odour concentration and individual odour activity value (OAV<sub>i</sub>) &gt;1 determined for the gaseous samples obtained during the thermophilic phase (day 2) and the mesophilic phase (day 11) of the conventional sewage sludge composting process.</p> Full article ">
14 pages, 6863 KiB  
Article
The Influences of Tropical Volcanic Eruptions with Different Magnitudes on Persistent Droughts over Eastern China
by Kefan Chen, Liang Ning, Zhengyu Liu, Jian Liu, Weiyi Sun, Mi Yan, Bin Liu, Yanmin Qin and Jiao Xue
Atmosphere 2020, 11(2), 210; https://doi.org/10.3390/atmos11020210 - 18 Feb 2020
Cited by 5 | Viewed by 3193
Abstract
In this study, the influences on persistent droughts over Eastern China from tropical volcanic eruptions with three categories of magnitudes, i.e., 25 Tg, 50 Tg, and 100 Tg, were investigated through three groups of volcanic sensitivity experiments based on the Community Earth System [...] Read more.
In this study, the influences on persistent droughts over Eastern China from tropical volcanic eruptions with three categories of magnitudes, i.e., 25 Tg, 50 Tg, and 100 Tg, were investigated through three groups of volcanic sensitivity experiments based on the Community Earth System Model (CESM). The results showed that, the 25 Tg tropical volcanic eruptions are too weak to significantly influence the regional precipitation changes over Eastern China, while the 50 Tg tropical volcanic eruptions can strongly intensify droughts and prolong the drought conditions for about five years. Both the extension and intensification of the drought conditions induced by 100 Tg tropical volcanic eruption are the largest among the three sensitivity experiments. These drought conditions are mainly caused by the weakened East Asia Summer Monsoon (EASM), and their extension and intensification depend on the strength of the volcanic eruptions. The intensities of weakened EASMs after volcanic eruptions are associated with the distinct ocean–land thermal contrast after eruptions. The ocean–land thermal contrast is the largest after the 100 Tg tropical volcanic eruptions, while it is much weaker after the 25 Tg volcanic eruptions. The durations of drought extensions are determined by the recovery rates of the West Pacific Subtropical High (WPSH), which are associated with the magnitudes of the volcanic eruptions. Full article
(This article belongs to the Special Issue Atmospheric Hazards)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The superposed epoch analysis (SEA) of the annual mean precipitation anomalies (unit: mm/day) from the control experiments (<b>a</b>) and volcanic sensitivity experiments of the 25 Tg (<b>b</b>), 50 Tg (<b>c</b>), and 100 Tg (<b>d</b>) volcanic eruptions. The symbol “+” indicates that the anomalies are significant at 95% level based on the Student t-test. The orange and green dash lines indicate the ±1 and ±2 standard deviations, respectively.</p> Full article ">Figure 1 Cont.
<p>The superposed epoch analysis (SEA) of the annual mean precipitation anomalies (unit: mm/day) from the control experiments (<b>a</b>) and volcanic sensitivity experiments of the 25 Tg (<b>b</b>), 50 Tg (<b>c</b>), and 100 Tg (<b>d</b>) volcanic eruptions. The symbol “+” indicates that the anomalies are significant at 95% level based on the Student t-test. The orange and green dash lines indicate the ±1 and ±2 standard deviations, respectively.</p> Full article ">Figure 2
<p>(<b>a</b>) The scatter plots of the changes of volcano-induced drought intensity and the magnitudes of volcanic eruptions in the three groups of sensitivity experiments (purple dots), and corresponding ensemble means of drought intensity after 25 Tg, 50 Tg, and 100 Tg volcanic eruptions (black dots). The X-axis represents the magnitudes of volcano eruptions (Tg), and the Y-axis represents the average precipitation anomalies (unit: mm/day) after volcanic eruptions (years 0–9). The red line is the linear regression between the volcano-induced drought intensity and magnitudes of volcanic eruptions. (<b>b</b>) is similar to (<b>a</b>), but for the results of the reconstructed PDSI after volcanic eruptions.</p> Full article ">Figure 3
<p>The ensemble means of the reconstructed Palmer Drought Severity Index (PDSI) coinciding with volcanic eruptions during the period 1300–2005 with magnitudes less than 25 Tg (<b>a</b>), between 25 and 50 Tg (<b>b</b>), between 50 and 100 Tg (<b>c</b>), and larger than 100 Tg (<b>d</b>). The green and orange dash lines indicate the ±1 and ±2 standard deviations, respectively. The yellow bars indicate the years of drought after volcanic eruptions.</p> Full article ">Figure 4
<p>Changes of the Eastern Asian summer monsoon index (EASMI) before and after the years of volcanic eruptions with magnitudes of 25 Tg (red), 50 Tg (blue), and 100 Tg (green), respectively.</p> Full article ">Figure 5
<p>(<b>a</b>)–(<b>c</b>) The summer (May–September) surface temperature (unit: °C) in the year of volcanic eruptions with magnitudes of 25 Tg, 50 Tg and 100 Tg, respectively. (<b>d</b>)–(<b>f</b>) The regional mean surface temperature (SAT; green dashed line; unit: °C) over Eastern China (20–50° N, 90–120° E), the surface temperature (SST; green solid line; unit: °C) over China’s eastern coastal area (20–50° N, 120–150° E), the sea level pressure (SLP) anomalies over Eastern China (blue solid line; unit: hPa) and surrounding oceans (blue dashed line; unit: hPa) in the volcanic sensitivity experiment with magnitudes of 25 Tg, 50 Tg and 100 Tg, respectively. The pink shaded area covers the time period when land-SLP is higher than the ocean-SLP after a volcanic eruption. The yellow shaded area covers the time period when decreases of the SAT are larger than decreases of the SST; * indicates that the result is significant based on the Student t-test.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>)–(<b>c</b>) The summer (May–September) surface temperature (unit: °C) in the year of volcanic eruptions with magnitudes of 25 Tg, 50 Tg and 100 Tg, respectively. (<b>d</b>)–(<b>f</b>) The regional mean surface temperature (SAT; green dashed line; unit: °C) over Eastern China (20–50° N, 90–120° E), the surface temperature (SST; green solid line; unit: °C) over China’s eastern coastal area (20–50° N, 120–150° E), the sea level pressure (SLP) anomalies over Eastern China (blue solid line; unit: hPa) and surrounding oceans (blue dashed line; unit: hPa) in the volcanic sensitivity experiment with magnitudes of 25 Tg, 50 Tg and 100 Tg, respectively. The pink shaded area covers the time period when land-SLP is higher than the ocean-SLP after a volcanic eruption. The yellow shaded area covers the time period when decreases of the SAT are larger than decreases of the SST; * indicates that the result is significant based on the Student t-test.</p> Full article ">Figure 6
<p>(<b>a</b>)–(<b>c</b>) The superposed echo analyses of precipitation anomalies (bar, left y-axis, unit: mm/day) in Eastern China, summer SST anomalies over the northwestern Pacific key region (123° E–150° E, 15° N–30° N; black line, right y-axis, unit: °C; <a href="#app1-atmosphere-11-00210" class="html-app">Figure S8</a>), variations of WPSH’s west ridge point (pink asterisk), grid number anomaly that is covered by WPSH (yellow line) from the volcanic forcing sensitivity experiments with volcanic eruptions of 25 Tg, 50 Tg and 100 Tg, respectively.</p> Full article ">
24 pages, 1654 KiB  
Article
Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland
by John T. Braggio, Eric S. Hall, Stephanie A. Weber and Amy K. Huff
Atmosphere 2020, 11(2), 209; https://doi.org/10.3390/atmos11020209 - 18 Feb 2020
Cited by 6 | Viewed by 4935
Abstract
The fine particulate matter baseline (PMB), which includes PM2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was [...] Read more.
The fine particulate matter baseline (PMB), which includes PM2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km2 CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17. Full article
(This article belongs to the Special Issue The Effect of Ambient Particulate Matter on Respiratory and Cardiovascular Health)
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<p>Map shows Maryland’s Counties and Baltimore City in the study area. The extent of the study area is defined by the 1–11 (south–north row) by 1–9 (west–east column) Community Multiscale Air Quality (CMAQ) 12 km<sup>2</sup> grids (blue circles). The 17 Federal Reference Method (FRM) PM<sub>2.5</sub> ambient air monitors are shown as red triangles. Baltimore City and Maryland Counties within the CMAQ grid boundaries provided the 2004–2006 respiratory-cardiovascular chronic disease hospital events included in this data analysis study.</p> Full article ">Figure 2
<p>Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for the four experimental aerosol optical depth (AOD)-particulate matter (PM)<sub>2.5</sub> concentration surfaces and particulate matter baseline (PMB) under both grid conditions (Both), grids with monitors (Yes) and grids without monitors (No) at lag day 0: (<b>A</b>) ED asthma (top left panel), (<b>B</b>) IP asthma (top right panel), (<b>C</b>) IP MI (bottom left panel), and (<b>D</b>) IP HF (bottom right panel).</p> Full article ">Figure 3
<p>Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for the four experimental AOD-PM<sub>2.5</sub> concentration surfaces and PMB under both grid conditions (Both), grids with monitors (Yes) and grids without monitors (No) at lag day 1: (<b>A</b>) ED asthma (top left panel), (<b>B</b>) IP asthma (top right panel), (<b>C</b>) IP MI (bottom left panel) and (<b>D</b>) IP HF (bottom right panel).</p> Full article ">Figure 4
<p>Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for the four experimental AOD-PM<sub>2.5</sub> concentration surfaces and PMB under both grid conditions (Both), grids with monitors (Yes) and grids without monitors (No) at lag days 01: (<b>A</b>) ED asthma (top left panel), (<b>B</b>) IP asthma (top right panel), (<b>C</b>) IP MI (bottom left panel), and (<b>D</b>) IP HF (bottom right panel).</p> Full article ">Figure 5
<p>Percent change between no monitor and monitor Odds Ratios (ORs) for the four experimental AOD-PM<sub>2.5</sub> concentration surfaces and PMB at lag days of 0, 1 and 01: (<b>A</b>) ED asthma (top left panel), (<b>B</b>) IP asthma (top right panel), (<b>C</b>) IP MI (bottom left panel), and (<b>D</b>) IP HF (bottom right panel).</p> Full article ">Figure 6
<p>Percent change between warm and cold season Odds Ratios (ORs) for PMB and the four experimental aerosol optical depth (AOD)-PM<sub>2.5</sub> concentration surfaces at lag days of 0, 1 and 01: (<b>A</b>) ED asthma (top left panel), (<b>B</b>) IP asthma (top right panel), (<b>C</b>) IP MI (bottom left panel) and (<b>D</b>) IP HF (bottom right panel).</p> Full article ">
15 pages, 3915 KiB  
Article
The Rise of Climate-Driven Sediment Discharge in the Amazonian River Basin
by Nazzareno Diodato, Naziano Filizola, Pasquale Borrelli, Panos Panagos and Gianni Bellocchi
Atmosphere 2020, 11(2), 208; https://doi.org/10.3390/atmos11020208 - 18 Feb 2020
Cited by 13 | Viewed by 5213
Abstract
The occurrence of hydrological extremes in the Amazon region and the associated sediment loss during rainfall events are key features in the global climate system. Climate extremes alter the sediment and carbon balance but the ecological consequences of such changes are poorly understood [...] Read more.
The occurrence of hydrological extremes in the Amazon region and the associated sediment loss during rainfall events are key features in the global climate system. Climate extremes alter the sediment and carbon balance but the ecological consequences of such changes are poorly understood in this region. With the aim of examining the interactions between precipitation and landscape-scale controls of sediment export from the Amazon basin, we developed a parsimonious hydro-climatological model on a multi-year series (1997–2014) of sediment discharge data taken at the outlet of Óbidos (Brazil) watershed (the narrowest and swiftest part of the Amazon River). The calibrated model (correlation coefficient equal to 0.84) captured the sediment load variability of an independent dataset from a different watershed (the Magdalena River basin), and performed better than three alternative approaches. Our model captured the interdecadal variability and the long-term patterns of sediment export. In our reconstruction of yearly sediment discharge over 1859–2014, we observed that landscape erosion changes are mostly induced by single storm events, and result from coupled effects of droughts and storms over long time scales. By quantifying temporal variations in the sediment produced by weathering, this analysis enables a new understanding of the linkage between climate forcing and river response, which drives sediment dynamics in the Amazon basin. Full article
(This article belongs to the Special Issue 10th Anniversary of Atmosphere: Climatology and Meteorology)
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<p>(<b>a</b>) Amazon River Basin with the drainage network and station of Óbidos where river sediment discharge is measured. (<b>b</b>) Erosivity map of South America (annual mean of the period 2002–2011), based on the Revised Universal Soil Loss Equation as arranged from Panagos et al. [<a href="#B9-atmosphere-11-00208" class="html-bibr">9</a>] (map view created from a basic Environmental Systems Research Institute (ESRI) ArcGIS configuration [<a href="#B55-atmosphere-11-00208" class="html-bibr">55</a>]).</p> Full article ">Figure 2
<p>Perspective view of a nested scheme of hydrological processes modelling for Amazonia drainage basin (arranged from OpenStreet Map [<a href="#B68-atmosphere-11-00208" class="html-bibr">68</a>]). BGE, Basin Gross Erosion; CGE, Catchment Gross Erosion.</p> Full article ">Figure 3
<p>Performance of the RSD model for the Amazonia River basin (Equation (1)) in the period 1997–2014. (<b>a</b>) Scatterplot of observed and predicted river sediment discharge (Mg km<sup>−2</sup> year<sup>−1</sup>), with their respective 1:1 line, the inner bounds showing 90% confidence limits for the mean y of many observations at given values of x, and the outer bounds showing 95% prediction limits for new observations. (<b>b</b>) Histogram of residuals. (<b>c</b>) Coevolution of RSDA model estimates (blue curve) and observed sediment load (brown histogram) for the validation stage at the Magdalena River basin (Colombia, from Walling [<a href="#B78-atmosphere-11-00208" class="html-bibr">78</a>]), both expressed in Mg km<sup>−2</sup> year<sup>−1</sup>.</p> Full article ">Figure 4
<p>Scatterplots between observed and predicted sediment rates (Mg km<sup>−2</sup> year<sup>−1</sup>). (<b>a</b>) Water discharge model, Equation (6). (<b>b</b>) Fournier Index-based model, Equation (7). (<b>c</b>) Antecedent rainfall-based model, Equation (8), with their respective 1:1 line, the inner bounds showing 90% confidence limits for the mean y of many observations at given values of x, and the outer bounds showing 95% prediction limits for new observations.</p> Full article ">Figure 5
<p>(<b>a</b>) Reconstructed river sediment discharge rate (orange curve) in the Amazonia River basin over 1858–2014 by means of the RSDA model (Equation (1); <a href="#app1-atmosphere-11-00208" class="html-app">Table S1</a>) with over-imposed mean values (orange dotted lines) before and after the first change point of series in 1931 (bold red vertical line) as found by cumulative deviation-Buishand test, and the annual evolution of Niño-4 (grey curve, from [<a href="#B90-atmosphere-11-00208" class="html-bibr">90</a>] with its smoothed long-term trend (black curve). (<b>b</b>) the rainfall rate change occurred during the wet season (1979–2015) across Southern America as derived from CRU dataset (arranged from [<a href="#B91-atmosphere-11-00208" class="html-bibr">91</a>]).</p> Full article ">
13 pages, 7994 KiB  
Article
Parameterization of Wave-Induced Mixing Using the Large Eddy Simulation (LES) (I)
by Haili Wang, Changming Dong, Yongzeng Yang and Xiaoqian Gao
Atmosphere 2020, 11(2), 207; https://doi.org/10.3390/atmos11020207 - 15 Feb 2020
Cited by 4 | Viewed by 6101
Abstract
Turbulent motions in the thin ocean surface boundary layer control exchanges of momentum, heat and trace gases between the atmosphere and ocean. However, present parametric equations of turbulent motions that are applied to global climate models result in systematic or substantial errors in [...] Read more.
Turbulent motions in the thin ocean surface boundary layer control exchanges of momentum, heat and trace gases between the atmosphere and ocean. However, present parametric equations of turbulent motions that are applied to global climate models result in systematic or substantial errors in the ocean surface boundary layer. Significant mixing caused by surface wave processes is missed in most parametric equations. A Large Eddy Simulation model is applied to investigate the wave-induced mixed layer structure. In the wave-averaged equations, wave effects are calculated as Stokes forces and breaking waves. To examine the effects of wave parameters on mixing, a series of wave conditions with varying wavelengths and heights are used to drive the model, resulting in a variety of Langmuir turbulence and wave breaking outcomes. These experiments suggest that wave-induced mixing is more sensitive to wave heights than to the wavelength. A series of numerical experiments with different wind intensities-induced Stokes drifts are also conducted to investigate wave-induced mixing. As the wind speed increases, the influence depth of Langmuir circulation deepens. Additionally, it is observed that breaking waves could destroy Langmuir cells mainly at the sea surface, rather than at deeper layers. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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<p>Distributions of three-dimensional vertical velocity (m/s) without Langmuir circulation (LC) and breaking waves (BW) (<b>a</b>); with only BW (<b>c</b>); with only LC (<b>e</b>) and with LC and BW (<b>g</b>). Horizontal cross-section of vertical velocity (z = 51 m) without LC and BW (<b>b</b>); with BW (<b>d</b>); with LC (<b>f</b>); with LC and BW (<b>h</b>).</p> Full article ">Figure 1 Cont.
<p>Distributions of three-dimensional vertical velocity (m/s) without Langmuir circulation (LC) and breaking waves (BW) (<b>a</b>); with only BW (<b>c</b>); with only LC (<b>e</b>) and with LC and BW (<b>g</b>). Horizontal cross-section of vertical velocity (z = 51 m) without LC and BW (<b>b</b>); with BW (<b>d</b>); with LC (<b>f</b>); with LC and BW (<b>h</b>).</p> Full article ">Figure 2
<p>Horizontal cross-section of vertical velocity (m/s) (z = 8 m) without LC and BW (<b>a</b>); with only BW (<b>b</b>), with only LC (<b>c</b>); with LC and BW (<b>d</b>).</p> Full article ">Figure 3
<p>Horizontal cross-section of vertical velocity (m/s) (z = 8 m). The EXPLB with different values of <math display="inline"><semantics> <mrow> <mi>L</mi> <msub> <mi>a</mi> <mi>t</mi> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>L</mi> <msub> <mi>a</mi> <mi>t</mi> </msub> </mrow> </semantics></math> = 0.30, 0.26, 0.23, 0.37, 0.32, 0.29, 0.44, 0.38, 0.34).</p> Full article ">Figure 4
<p>Horizontal cross-section of vertical velocity (m/s) (z = 1, 8, 18, and 42 m) for EXP WIND L and EXP WIND LB.</p> Full article ">Figure 5
<p>Horizontal cross-section of vertical velocity (m/s) at 6 m (z56), 23 m (z39) and 43 m (z18) for EXP WIND L under different wind speed conditions (U = 8, 10 and 12 m/s).</p> Full article ">Figure 6
<p>Vertical profile of the eddy viscosity <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>m</mi> </msub> </mrow> </semantics></math> for simulations with no BW and LC (blue); BW only (green); LC only (black); with LC and BW (red). Eddy viscosity is based on Stokes velocities calculated using Equation (3) (<b>a</b>) and Equation (5) (<b>b</b>).</p> Full article ">
17 pages, 4803 KiB  
Article
Near-Surface Ozone Variations in East Asia during Boreal Summer
by Jieun Wie, Hyo-Jin Park, Hyomee Lee and Byung-Kwon Moon
Atmosphere 2020, 11(2), 206; https://doi.org/10.3390/atmos11020206 - 15 Feb 2020
Cited by 4 | Viewed by 3407
Abstract
This study examined the variability of near-surface (850 hPa) ozone during summer in East Asia using simulations from 12 models participating in the Chemistry–Climate Model Initiative (CCMI). The empirical orthogonal function (EOF) analysis of non-detrended ozone shows that the first (second) EOF mode [...] Read more.
This study examined the variability of near-surface (850 hPa) ozone during summer in East Asia using simulations from 12 models participating in the Chemistry–Climate Model Initiative (CCMI). The empirical orthogonal function (EOF) analysis of non-detrended ozone shows that the first (second) EOF mode is characterized by a monopole (dipole) structure that describe 83.3% (7.1%) of total variance. The corresponding the first principle component (PC1) time series exhibits a gradually increasing trend due to the rising anthropogenic emission, whereas PC2 shows interannual variation. To understand the drivers of this interannual variability, the detrended ozone is also analyzed. The two leading EOF patterns of detrended ozone, EOF1 and EOF2, explain 37.0% and 29.2% of the total variance, respectively. The regression results indicate that the positive ozone anomaly in East Asia associated with EOF1 is caused by the combination of net ozone production and transport from the upper atmosphere. In contrast, EOF2 is associated with the weakened western Pacific subtropical high during the La Niña decaying summer, which tends to decrease monsoon precipitation, thus increasing ozone concentration in China. Our results suggest that the El Niño-Southern Oscillation (ENSO) plays a key role in driving interannual variability in tropospheric ozone in East Asia. Full article
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<p>Multi-model mean (MMM) surface (850 hPa) ozone concentration (ppbv) during summer from 1979 to 2010. Rectangular box denotes the East Asian region (20°–50° N and 110°–145° E).</p> Full article ">Figure 2
<p>(<b>a</b>) The first and (<b>b</b>) second empirical orthogonal function (EOF) patterns of summer (June–July–August) near-surface (850 hPa) ozone MMM anomaly over the East Asian region (20° N–50° N, 110° E–145° E). (<b>c</b>) The corresponding principal component (PC) time series.</p> Full article ">Figure 3
<p>The regression fields between PC1 and (<b>a</b>) geopotential height (shaded; m) and horizontal wind (vector; m s<sup>−1</sup>), (<b>b</b>) temperature (K), (<b>c</b>) precipitation (mm day<sup>−1</sup>), (<b>d</b>) surface downwelling shortwave flux (W m<sup>−2</sup>), (<b>e</b>) nitrogen oxides (ppbv), (<b>f</b>) carbon monoxide (ppbv), (<b>g</b>) net ozone production rate (×10<sup>−12</sup> mole m<sup>−3</sup> s<sup>−1</sup>), and (<b>h</b>) 500 hPa omega (hPa day<sup>−1</sup>) during summer. Note that all variables except (<b>c</b>–<b>h</b>) are for an 850 hPa level. Black dots indicate statistical significance at 90% confidence level using a Student’s t-test.</p> Full article ">Figure 4
<p>The regressed fields of (<b>a</b>) ozone (ppbv) and (<b>b</b>) net ozone production (shaded; ×10<sup>−12</sup> mole m<sup>−3</sup> s<sup>−1</sup>) overlaid with meridional–vertical circulations (vector; v, -omega) averaged over 110° E–120° E against PC1 (blue line in <a href="#atmosphere-11-00206-f002" class="html-fig">Figure 2</a>c) during summer. Black dots indicate statistical significance at the 90% confidence level based on a Student’s t-test.</p> Full article ">Figure 5
<p>Same as <a href="#atmosphere-11-00206-f002" class="html-fig">Figure 2</a>, except for the detrended ozone.</p> Full article ">Figure 6
<p>The regression fields between PC1 from the EOF analysis of detrended ozone and (<b>a</b>) geopotential height (shaded; m) and horizontal wind (vector; m s<sup>−1</sup>), (<b>b</b>) temperature (K), (<b>c</b>) precipitation (mm day<sup>−1</sup>), (<b>d</b>) surface downwelling shortwave flux (W m<sup>−2</sup>), (<b>e</b>) nitrogen oxides (ppbv), (<b>f</b>) carbon monoxide (ppbv), (<b>g</b>) net ozone production rate (×10<sup>−12</sup> mole m<sup>−3</sup> s<sup>−1</sup>), and (<b>h</b>) 500 hPa omega (hPa day<sup>−1</sup>) during summer. Note that all variables except (<b>c</b>,<b>d</b>,<b>h</b>) are for an 850 hPa level. Black dots indicate statistical significance at 90% confidence level using a Student’s t-test.</p> Full article ">Figure 7
<p>The regressed fields of (<b>a</b>) ozone (ppbv) and (<b>b</b>) net ozone production (shaded; ×10<sup>−12</sup> mole m<sup>−3</sup> s<sup>−1</sup>) overlaid with meridional–vertical circulations (vector; v, -omega) averaged over 120°E–130°E during summer against PC1 from EOF analysis of the detrended ozone (blue line in <a href="#atmosphere-11-00206-f005" class="html-fig">Figure 5</a>). Black dots indicate statistical significance at the 90% confidence level based on a Student’s t-test.</p> Full article ">Figure 8
<p>Same as <a href="#atmosphere-11-00206-f006" class="html-fig">Figure 6</a>, except for PC2 time series.</p> Full article ">Figure 9
<p>Same as <a href="#atmosphere-11-00206-f007" class="html-fig">Figure 7</a>, except for PC2 time series and longitude from 110° E to 120° E.</p> Full article ">Figure 10
<p>The regressed fields of (<b>a</b>) DJF, (<b>b</b>) MAM, and (<b>c</b>) JJA sea surface temperature (SST) anomalies (shaded; °C) and 850 hPa geopotential height anomalies (contour; m). Black dots indicate statistical significance at the 90% confidence level for SST anomalies based on a Student’s t-test.</p> Full article ">Figure 11
<p>The regressed fields of (<b>a</b>–<b>c</b>) 850 hPa geopotential height (shaded; m) and wind (vector; m s<sup>−1</sup>), (<b>d</b>–<b>f</b>) precipitation (mm day<sup>−1</sup>), (<b>g</b>–<b>i</b>) surface downwelling shortwave flux (W m<sup>−2</sup>) against PC2 time series. The left panel shows the MMM for the six models showing the best performance (high correlation), the center panel demonstrates the MMM for another six models with the worst performance, and the right panel shows the difference between the left and center panels.</p> Full article ">Figure 12
<p>Same as <a href="#atmosphere-11-00206-f011" class="html-fig">Figure 11</a>, except for the regressed JJA 850 hPa ozone.</p> Full article ">
27 pages, 597 KiB  
Article
Sensitivity of Radiative Fluxes to Aerosols in the ALADIN-HIRLAM Numerical Weather Prediction System
by Laura Rontu, Emily Gleeson, Daniel Martin Perez, Kristian Pagh Nielsen and Velle Toll
Atmosphere 2020, 11(2), 205; https://doi.org/10.3390/atmos11020205 - 14 Feb 2020
Cited by 9 | Viewed by 4470
Abstract
The direct radiative effect of aerosols is taken into account in many limited-area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We studied the impact of aerosol distribution and optical properties on radiative transfer, based on [...] Read more.
The direct radiative effect of aerosols is taken into account in many limited-area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We studied the impact of aerosol distribution and optical properties on radiative transfer, based on climatological and more realistic near real-time aerosol data. Sensitivity tests were carried out using the single-column version of the ALADIN-HIRLAM numerical weather prediction system, set up to use the HLRADIA simple broadband radiation scheme. The tests were restricted to clear-sky cases to avoid the complication of cloud–radiation–aerosol interactions. The largest differences in radiative fluxes and heating rates were found to be due to different aerosol loads. When the loads are large, the radiative fluxes and heating rates are sensitive to the aerosol inherent optical properties and the vertical distribution of the aerosol species. In such cases, regional weather models should use external real-time aerosol data for radiation parametrizations. Impacts of aerosols on shortwave radiation dominate longwave impacts. Sensitivity experiments indicated the important effects of highly absorbing black carbon aerosols and strongly scattering desert dust. Full article
(This article belongs to the Special Issue Aerosol Radiative Effects)
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Figure 1

Figure 1
<p>Temperature tendencies due to radiation (K/day) at Badajoz (left column) and Ladoga (right column): SW tendency (<b>a</b>,<b>b</b>), LW tendency (<b>c</b>,<b>d</b>), total tendency (<b>e</b>,<b>f</b>). <span class="html-italic">y</span>-axis shows the pressure in hPa. Colored curves correspond to the experiments and are labeled in the legends according to <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>. The last letter in the label name denotes the radiation scheme with i for IFSRADIA, h for HLRADIA and a for ACRANEB.</p> Full article ">Figure 2
<p>Net HLRADIA radiative fluxes (Wm<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>) for Badajoz (left) and Ladoga (right): SWNET (<b>a</b>,<b>b</b>) and LWNET (<b>c</b>,<b>d</b>). The curves correspond to the experiments in <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 3
<p>MMR (<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>g/kg) profiles for the (<b>a</b>,<b>b</b>) NMMR3 experiments, (<b>c</b>,<b>d</b>) NMMR2 and (<b>e</b>,<b>f</b>) CMMR2 over Badajoz (left) and Ladoga (right). <span class="html-italic">y</span>-axis shows pressure in hPa. Note the logarithmic scale on both axes.</p> Full article ">Figure 4
<p>Profiles of the aerosol SW optical properties for Badajoz (left) and Ladoga (right): TAU-SW (<b>a</b>,<b>b</b>), TAUA-SW (<b>c</b>,<b>d</b>), SSA-SW (<b>e</b>,<b>f</b>), ASY-SW (<b>g</b>,<b>h</b>). TAUA-SW denotes aerosol absorption optical depth, the rest of acronyms are explained in <a href="#atmosphere-11-00205-t004" class="html-table">Table 4</a>. Names in the curve legends correspond to the experiments in <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 5
<p>Profiles of the aerosol LW optical properties for Badajoz (left) and Ladoga (right): TAU-LW (<b>a</b>,<b>b</b>), SSA-LW (<b>c</b>,<b>d</b>), ASY-LW (<b>e</b>,<b>f</b>). The acronyms are explained in <a href="#atmosphere-11-00205-t004" class="html-table">Table 4</a>. Names in the curve legends correspond to the experiments in <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 6
<p>Temperature tendencies due to SW radiation (K/day) at Badajoz: (<b>a</b>) all species included (<b>b</b>) black carbon excluded. <span class="html-italic">y</span>-axis shows pressure in hPa. Figure legends correspond to the experiments as given in <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 7
<p>Net radiative fluxes (Wm<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>) for the MMR (left) and AOD550 (right) series of experiments: (<b>a</b>,<b>b</b>) SWNET and (<b>c</b>,<b>d</b>) LWNET. Figure legends correspond to the experiments as given in <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>. SWD TOA = 779 Wm<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math> (conditions over Lake Ladoga).</p> Full article ">Figure 8
<p>Profiles of aerosol SW (left) and LW (right) optical properties for the five aerosol species in the MMR series of experiments: (<b>a</b>) TAU-SW, (<b>b</b>) TAU-LW, (<b>c</b>) SSA-SW, (<b>d</b>) SSA-LW, (<b>e</b>) ASY-SW and (<b>f</b>) ASY-LW. Acronyms are explained in <a href="#atmosphere-11-00205-t003" class="html-table">Table 3</a> and <a href="#atmosphere-11-00205-t004" class="html-table">Table 4</a>. The figure legends correspond to the experiments according to <a href="#atmosphere-11-00205-t001" class="html-table">Table 1</a>.</p> Full article ">
18 pages, 2787 KiB  
Article
Regulated and Non-Regulated Emissions from Euro 6 Diesel, Gasoline and CNG Vehicles under Real-World Driving Conditions
by Ricardo Suarez-Bertoa, Martin Pechout, Michal Vojtíšek and Covadonga Astorga
Atmosphere 2020, 11(2), 204; https://doi.org/10.3390/atmos11020204 - 14 Feb 2020
Cited by 73 | Viewed by 9758
Abstract
The transport sector is one of the main sources air pollutants. Different exhaust after-treatment systems have been implemented over the years to control the emissions of criteria pollutants. However, while reducing the emissions of the target compounds these systems can lead to the [...] Read more.
The transport sector is one of the main sources air pollutants. Different exhaust after-treatment systems have been implemented over the years to control the emissions of criteria pollutants. However, while reducing the emissions of the target compounds these systems can lead to the emissions of other pollutants and/or greenhouse gases such as NH3 or N2O. Following the implementation of the Real Driving Emissions (RDE) test procedure in the EU, vehicles have been equipped with more complex after-treatment configurations. The impact that these technologies may have on the emissions of non-regulated pollutants during real-world driving have not been evaluated until now. In the current study we present the on-road emissions of a series of non-regulated pollutants, including NH3, N2O, CH4 and HCHO, measured with a portable FTIR from a series of Euro 6d, Euro 6c and Euro 6d-TEMP, gasoline diesel and compressed natural gas (CNG) vehicles during real-world testing. The obtained results show that it is possible to measure N2O, NH3, CH4 and HCHO during on-road operation. The results also highlight the importance of the measurement of the emissions of these pollutants during real-world driving, as the emissions of NH3 (a particulate matter precursor) and those of N2O and CH4 (green-house gases) can be high from some vehicle technologies. NH3 emissions were up to 49 mg/km for gasoline passenger cars, up to 69 mg/km for the CNG light-commercial vehicle and up to 17 mg/km a diesel passenger car equipped with a selective catalytic reduction system (SCR). On the other hand, N2O and CH4 emissions accounted for up to 9.8 g CO2 eqv/km for a diesel passenger car equipped with a combination of diesel oxidation catalysts (DOC), lean NOx traps (LNT), SCR and possibly an ammonia slip catalyst ASC. Full article
(This article belongs to the Special Issue 10th Anniversary of Atmosphere: Air Quality)
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Figure 1
<p>Correlation between the measurements of (<b>a</b>) NO, (<b>b</b>) CO, and (<b>c</b>) CO<sub>2</sub> carried out with the portable FTIR and the AVL MOVE PEMS during on-road testing of the DV1.</p> Full article ">Figure 2
<p>Correlation of (<b>a</b>) N<sub>2</sub>O, (<b>b</b>) NH<sub>3</sub>, and (<b>c</b>) CH<sub>4</sub> measured using the portable FTIR and the laboratory-grade instrument. The measurements NH<sub>3</sub>, N<sub>2</sub>O and CH<sub>4</sub> were compared to those of the MKS Multigas analyzer 2030-HS during a WLTP test of DV1.</p> Full article ">Figure 3
<p>NH<sub>3</sub>, N<sub>2</sub>O, NO, NO<sub>2</sub>, CO, CH<sub>4</sub>, and HCHO on-road emissions profiles registered for the GV1 during the dynamic test on Route-2 (left panels) and for the GV2 during the RDE test on Route-1 (right panels).</p> Full article ">Figure 4
<p>NH<sub>3</sub>, N<sub>2</sub>O, NO, NO<sub>2</sub>, CO, CH<sub>4</sub>, and HCHO on-road emissions profiles registered during the RDE test on route 1 for the DV1 (left panels) and for the DV2 (right panels).</p> Full article ">Figure 5
<p>NH<sub>3</sub>, N<sub>2</sub>O, NO, NO<sub>2</sub>, CO, CH<sub>4</sub>, and HCHO on-road emissions profiles registered during the RDE test on Route-3 for the CNG-LCV.</p> Full article ">
23 pages, 2535 KiB  
Article
Coupling Large Eddies and Waves in Turbulence: Case Study of Magnetic Helicity at the Ion Inertial Scale
by Annick Pouquet, Julia E. Stawarz and Duane Rosenberg
Atmosphere 2020, 11(2), 203; https://doi.org/10.3390/atmos11020203 - 14 Feb 2020
Cited by 7 | Viewed by 3157
Abstract
In turbulence, for neutral or conducting fluids, a large ratio of scales is excited because of the possible occurrence of inverse cascades to large, global scales together with direct cascades to small, dissipative scales, as observed in the atmosphere and oceans, or in [...] Read more.
In turbulence, for neutral or conducting fluids, a large ratio of scales is excited because of the possible occurrence of inverse cascades to large, global scales together with direct cascades to small, dissipative scales, as observed in the atmosphere and oceans, or in the solar environment. In this context, using direct numerical simulations with forcing, we analyze scale dynamics in the presence of magnetic fields with a generalized Ohm’s law including a Hall current. The ion inertial length ϵ H serves as the control parameter at fixed Reynolds number. Both the magnetic and generalized helicity—invariants in the ideal case—grow linearly with time, as expected from classical arguments. The cross-correlation between the velocity and magnetic field grows as well, more so in relative terms for a stronger Hall current. We find that the helical growth rates vary exponentially with ϵ H , provided the ion inertial scale resides within the inverse cascade range. These exponential variations are recovered phenomenologically using simple scaling arguments. They are directly linked to the wavenumber power-law dependence of generalized and magnetic helicity, ∼ k − 2 , in their inverse ranges. This illustrates and confirms the important role of the interplay between large and small scales in the dynamics of turbulent flows. Full article
(This article belongs to the Special Issue Turbulence, Waves and Transport in Stratified, Rotating Fluid and Plasma Flows)
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Figure 1

Figure 1
<p>For runs of <a href="#atmosphere-11-00203-t001" class="html-table">Table 1</a>, as a function of time in units of turn-over time <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <mi>N</mi> <mi>L</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>L</mi> <mn>0</mn> </msub> <mo>/</mo> <msub> <mi>U</mi> <mn>0</mn> </msub> </mrow> </semantics></math>: Left: Total energy <math display="inline"><semantics> <msub> <mi>E</mi> <mi>T</mi> </msub> </semantics></math> (<b>a</b>), and ratio of magnetic to kinetic energy, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>M</mi> </msub> <mo>/</mo> <msub> <mi>E</mi> <mi>V</mi> </msub> </mrow> </semantics></math> (<b>d</b>). Middle: Total dissipation (<b>b</b>), and ratio of <math display="inline"><semantics> <msub> <mi>L</mi> <mn>2</mn> </msub> </semantics></math> norms of current and vorticity (<b>e</b>). Right: Integral scales built on the kinetic energy (<b>c</b>) and on the magnetic energy (<b>f</b>). Note the different scaling on the vertical axes. Dotted lines indicate linear fits for growth rates.</p> Full article ">Figure 2
<p>Temporal data for the runs of <a href="#atmosphere-11-00203-t001" class="html-table">Table 1</a>. Left column: Generalized helicity <math display="inline"><semantics> <msub> <mi>H</mi> <mi>G</mi> </msub> </semantics></math> (<b>a</b>) and its relative counterpart <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>G</mi> </msub> </semantics></math> (<b>d</b>). Middle: Total magnetic helicity <math display="inline"><semantics> <msub> <mi>H</mi> <mi>M</mi> </msub> </semantics></math> (<b>b</b>), and its relative counterpart <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>M</mi> </msub> </semantics></math> (<b>e</b>). Right: cross-helicity <math display="inline"><semantics> <msub> <mi>H</mi> <mi>C</mi> </msub> </semantics></math> (<b>c</b>), and its relative counterpart <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>C</mi> </msub> </semantics></math> (<b>f</b>).</p> Full article ">Figure 3
<p>Horizontal cut of the pointwise relative rate of magnetic helicity <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>M</mi> </msub> <mrow> <mo>(</mo> <mi mathvariant="bold">x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (<b>a</b>) and at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math> (<b>b</b>) for run AH5 of <a href="#atmosphere-11-00203-t001" class="html-table">Table 1</a>, with <math display="inline"><semantics> <mrow> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mspace width="4pt"/> <msub> <mi>σ</mi> <mi>M</mi> </msub> <mo>=</mo> <mn>0.65</mn> </mrow> </semantics></math>. The signature of the forcing, at <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>F</mi> </msub> <mo>≈</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mn>20</mn> <mo>≈</mo> <mn>0.16</mn> </mrow> </semantics></math> in units of the size of the box, is visible on both plots, as well as the formation of large-scale structures at long times.</p> Full article ">Figure 4
<p>For the runs of <a href="#atmosphere-11-00203-t001" class="html-table">Table 1</a>, different scaling laws given as a function of <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> </semantics></math>, in lin-log coordinates. (<b>a</b>) temporal growth rate of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>C</mi> </msub> <mo>+</mo> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> <msub> <mi>H</mi> <mi>V</mi> </msub> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> (see Equation (<a href="#FD4-atmosphere-11-00203" class="html-disp-formula">4</a>)). (<b>b</b>) temporal mean of the magnetic integral scale <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>〈</mo> <msub> <mi>L</mi> <mi>M</mi> </msub> <mo>〉</mo> </mrow> <mi>t</mi> </msub> </mrow> </semantics></math>. (<b>c</b>) growth rate of <math display="inline"><semantics> <msub> <mi>H</mi> <mi>G</mi> </msub> </semantics></math>. (<b>d</b>) growth rate of <math display="inline"><semantics> <msub> <mi>H</mi> <mi>M</mi> </msub> </semantics></math>. When appropriate, least-square fits are done as indicated with dash lines (see insets). In black is an exponential form <math display="inline"><semantics> <mrow> <mi>a</mi> <mspace width="4pt"/> <msup> <mi>e</mi> <mrow> <mo>−</mo> <mi>b</mi> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> </mrow> </msup> </mrow> </semantics></math>, for which a simple argument is given in <a href="#sec5-atmosphere-11-00203" class="html-sec">Section 5</a>, and in red, a fit to <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>/</mo> <msup> <mrow> <mo>(</mo> <mi>β</mi> <mo>+</mo> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> <mo>)</mo> </mrow> <mi>γ</mi> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Top: Spectra of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>G</mi> </msub> <mo>−</mo> <msub> <mi>H</mi> <mi>M</mi> </msub> <mo>=</mo> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> <msub> <mi>H</mi> <mi>X</mi> </msub> <mo>=</mo> <mn>2</mn> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> <msub> <mi>H</mi> <mi>C</mi> </msub> <mo>+</mo> <msubsup> <mi>ϵ</mi> <mi>H</mi> <mn>2</mn> </msubsup> <msub> <mi>H</mi> <mi>V</mi> </msub> </mrow> </semantics></math> (<b>b</b>) Bottom: Spectra of <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>M</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>c</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>M</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>E</mi> <mi>V</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>d</b>) All data is for run AH5 of <a href="#atmosphere-11-00203-t001" class="html-table">Table 1</a> at different times, in units of turn-over times. Reference power laws are also provided.</p> Full article ">Figure 6
<p>(<b>a</b>) Total magnetic helicity as a function of time measured in turn-over times for a subset of the runs of <a href="#atmosphere-11-00203-t002" class="html-table">Table 2</a> with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>F</mi> </msub> <mo>≈</mo> <mn>8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> <mo>≤</mo> <mn>0.2</mn> </mrow> </semantics></math>. (<b>b</b>) The same with values of <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> </semantics></math> extended to <math display="inline"><semantics> <mrow> <mi mathvariant="script">O</mi> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> (see insets); dotted lines indicate temporal fits.</p> Full article ">Figure 7
<p>Variation of the rate of temporal growth of <math display="inline"><semantics> <msub> <mi>H</mi> <mi>M</mi> </msub> </semantics></math> with the Hall control parameter for the same runs as in <a href="#atmosphere-11-00203-f006" class="html-fig">Figure 6</a>a, in lin-log coordinates. Note the three dynamical regimes, with again an exponential decay for intermediate values of <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>H</mi> </msub> </semantics></math>.</p> Full article ">
14 pages, 4781 KiB  
Article
Characteristics of Black Carbon Particle-Bound Polycyclic Aromatic Hydrocarbons in Two Sites of Nanjing and Shanghai, China
by Shijie Cui, Ruoyuan Lei, Yangzhou Wu, Dandan Huang, Fuzhen Shen, Junfeng Wang, Liping Qiao, Min Zhou, Shuhui Zhu, Yingge Ma and Xinlei Ge
Atmosphere 2020, 11(2), 202; https://doi.org/10.3390/atmos11020202 - 14 Feb 2020
Cited by 17 | Viewed by 3892
Abstract
Airborne polycyclic aromatic hydrocarbons (PAHs) are of great concern to human health due to their potential high toxicity. Understanding the characteristics and sources of PAHs, as well as the governing factors, is therefore critical. PAHs and refractory black carbon (rBC) are [...] Read more.
Airborne polycyclic aromatic hydrocarbons (PAHs) are of great concern to human health due to their potential high toxicity. Understanding the characteristics and sources of PAHs, as well as the governing factors, is therefore critical. PAHs and refractory black carbon (rBC) are both from combustion sources. This work, for the first time, investigated exclusively the rBC-bound PAH properties by using a laser-only Aerodyne soot-particle aerosol mass spectrometer (SP-AMS). This technique offers highly time-resolved PAH results that a traditional offline measurement is unable to provide. We analyzed two datasets conducted in urban Shanghai during the fall of 2018 and in suburban Nanjing during the winter of 2017, respectively. Results show that the average concentration of PAHs in Nanjing was much higher than that in Shanghai. Nanjing PAHs contained more low molecular weight components while Shanghai PAHs contained more high molecular weight ones. PAHs in Shanghai presented two peaks in early morning and evening, while Nanjing PAHs had only one significant morning peak, but remained high throughout the nighttime. A multi-linear regression algorithm combined with positive matrix factorization (PMF) analyses on sources of PAHs reveals that the industry emissions contributed the majority of PAHs in Nanjing (~80%), while traffic emissions dominated PAHs in Shanghai (~70%). We further investigated the relationships between PAHs with various factors. PAHs in both sites tended to positively correlate with primary pollutants, including primary organic aerosol (OA) factors, and gaseous pollutants of CO, NO2 and SO2, but negatively correlated with secondary OA factors and O3. This result highlights the enhancement of rBC-bound PAHs level due to primary emissions and their oxidation loss upon atmospheric aging reactions. High concentration of PAHs seemed to frequently appear under low temperature and high relative humidity conditions, especially in Shanghai. Full article
(This article belongs to the Special Issue Sources and Composition of Ambient Particulate Matter)
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Figure 1
<p>Field measurement sites (Nanjing University of Information Science and Technology—NUIST; Shanghai Academy of Environmental Sciences—SAES) and surrounding areas.</p> Full article ">Figure 2
<p>Time series of refractory black carbon (<span class="html-italic">r</span>BC) and <span class="html-italic">r</span>BC-bound polycyclic aromatic hydrocarbons (PAHs) in Nanjing University of Information Science and Technology (NUIST) (<b>a</b>) and in Shanghai Academy of Environmental Sciences (SAES) (<b>c</b>), and normalized average mass spectrum of PAHs in NUIST (<b>b</b>) and in SAES (<b>d</b>) (PAH fragments are classified into three groups; pie charts show mass contributions of the three groups of PAHs).</p> Full article ">Figure 3
<p>Diurnal patterns of <span class="html-italic">r</span>BC, <span class="html-italic">r</span>BC-bound PAHs, and the mass ratios of PAHs to <span class="html-italic">r</span>BC in NUIST (<b>a</b>) and SAES (<b>b</b>).</p> Full article ">Figure 4
<p>High-resolution mass spectra of the positive matrix factorization (PMF)-resolved factor profiles (classified by six different ion families; elemental ratios were calculated based on Canagaratna et al. [<a href="#B51-atmosphere-11-00202" class="html-bibr">51</a>]) (<b>a</b>), and their corresponding time series in SAES (<b>b</b>).</p> Full article ">Figure 5
<p>Scatter plots of measured and re-constructed <span class="html-italic">r</span>BC (<b>a</b>) and PAHs concentrations (<b>b</b>), and pie charts of mass contributions of different factors to the total primary organic aerosol (POA) (<b>c</b>), and relative contributions of these factors to <span class="html-italic">r</span>BC (<b>d</b>) and PAHs (<b>e</b>) in NUIST.</p> Full article ">Figure 6
<p>Scatter plots of measured and re-constructed <span class="html-italic">r</span>BC (<b>a</b>) and PAHs concentrations (<b>b</b>), and pie charts of mass contributions of different factors to the total primary organic aerosol (POA) (<b>c</b>), and relative contributions of these factors to <span class="html-italic">r</span>BC (<b>d</b>) and PAHs (<b>e</b>) in SAES.</p> Full article ">Figure 7
<p>Variations of PAH concentrations versus the oxidation states (OS<sub>c</sub>), POA, semi-volatile oxygenated OA (SV-OOA) or less oxidized oxygenated OA (LO-OOA), and low-volatility oxygenated OA (LV-OOA) or more oxidized oxygenated OA (MO-OOA) concentrations: (<b>a</b>–<b>d</b>) NUIST; (<b>e</b>–<b>h</b>) SAES.</p> Full article ">Figure 8
<p>Variations of PAH concentrations versus CO, O<sub>3</sub>, NO<sub>2</sub>, and SO<sub>2</sub> concentrations: (<b>a</b>–<b>d</b>) NUIST; (<b>e</b>–<b>h</b>) SAES.</p> Full article ">Figure 9
<p>Variations of PAHs concentrations versus relative humidity (RH), temperature (T), wind speed (WS), and wind direction (WD): (<b>a</b>–<b>d</b>) NUIST; (<b>e</b>–<b>h</b>) SAES.</p> Full article ">
41 pages, 6460 KiB  
Article
Study of Realistic Urban Boundary Layer Turbulence with High-Resolution Large-Eddy Simulation
by Mikko Auvinen, Simone Boi, Antti Hellsten, Topi Tanhuanpää and Leena Järvi
Atmosphere 2020, 11(2), 201; https://doi.org/10.3390/atmos11020201 - 13 Feb 2020
Cited by 34 | Viewed by 5865
Abstract
This study examines the statistical predictability of local wind conditions in a real urban environment under realistic atmospheric boundary layer conditions by means of Large-Eddy Simulation (LES). The computational domain features a highly detailed description of a densely built coastal downtown area, which [...] Read more.
This study examines the statistical predictability of local wind conditions in a real urban environment under realistic atmospheric boundary layer conditions by means of Large-Eddy Simulation (LES). The computational domain features a highly detailed description of a densely built coastal downtown area, which includes vegetation. A multi-scale nested LES modelling approach is utilized to achieve a setup where a fully developed boundary layer flow, which is also allowed to form and evolve very large-scale turbulent motions, becomes incident with the urban surface. Under these nonideal conditions, the local scale predictability and result sensitivity to central modelling choices are scrutinized via comparative techniques. Joint time–frequency analysis with wavelets is exploited to aid targeted filtering of the problematic large-scale motions, while concepts of information entropy and divergence are exploited to perform a deep probing comparison of local urban canopy turbulence signals. The study demonstrates the utility of wavelet analysis and information theory in urban turbulence research while emphasizing the importance of grid resolution when local scale predictability, particularly close to the pedestrian level, is sought. In densely built urban environments, the level of detail of vegetation drag modelling description is deemed most significant in the immediate vicinity of the trees. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
►▼ Show Figures

Figure 1

Figure 1
<p>A two-dimensional visualization of the urban LES domain used in the study: The parent domain <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </semantics></math>, domain <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math>, and <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </msup> </semantics></math> are marked with *, **, and ***, respectively, in the upper left corner of the domains’ highlighted borders. The urban turbulence data is primarily extracted from within the <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </msup> </semantics></math> domain, which is discretized with uniform 1 m resolution (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <mspace width="-0.166667em"/> <mo>=</mo> <mspace width="-0.166667em"/> <mi>H</mi> <mo>/</mo> <mn>20</mn> </mrow> </semantics></math>). The horizontal extent of the precursor simulation domain, used to initialize the urban LES, is also shown in the bottom left corner of the parent domain.</p> Full article ">Figure 2
<p>Normalized distributions of (<b>a</b>,<b>b</b>) building and (<b>c</b>,<b>d</b>) tree heights within <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>3</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math> domains respectively.</p> Full article ">Figure 3
<p>Double-averaged vertical profiles characterizing the precursor flow solution: (<b>a</b>) nondimensional <span class="html-italic">u</span>-component profile (solid blue line) juxtaposed with a reference logarithmic profile (dashed blue line); (<b>b</b>) nondimensional variance profile of <span class="html-italic">u</span>; and (<b>c</b>) vertical momentum flux profile.</p> Full article ">Figure 4
<p>(<b>a</b>) A mock-up illustration of the two different leaf area density (LAD) distributions used in the study: In the low level of detail approach, every grid cell within a tree contains a fixed mean <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>A</mi> <mi>D</mi> </mrow> </semantics></math> value. In the higher level of detail model, each grid column respects the prescribed vertical profile processed from a point cloud dataset (<b>b</b>). The starting height of the tree crown <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> m originates from a city policy that dictates that all branches below 3–4 m are trimmed. The two approaches always yield the same <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>A</mi> <mi>I</mi> <mo>=</mo> <msubsup> <mo>∫</mo> <msub> <mi>h</mi> <mn>0</mn> </msub> <msub> <mi>h</mi> <mi>v</mi> </msub> </msubsup> <mi>L</mi> <mi>A</mi> <mi>D</mi> <mspace width="0.166667em"/> <mi>d</mi> <mi>z</mi> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Locations of the flow data extraction sites (indicated by red dots next to circled identification number 1-11) within the <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="sans-serif">Ω</mi> </mrow> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </msup> </semantics></math> domain: In this document, the data sites are denoted by M1, <tt>M2</tt>, ... , <tt>M11</tt>. At each location, vertical profiles of prognostic variables are gathered at frequency <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mn>80</mn> <mo>/</mo> <mi>T</mi> </mrow> </semantics></math>. For conciseness, the main document only includes results from selected sites.</p> Full article ">Figure 6
<p>Horizontal distribution of <math display="inline"><semantics> <msubsup> <mover> <mi>u</mi> <mo>¯</mo> </mover> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> from case [<tt>R</tt>] across the urban area within the <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math> domain featuring <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mspace width="-0.166667em"/> <mo>=</mo> <mspace width="-0.166667em"/> <mi>H</mi> <mo>/</mo> <mn>10</mn> </mrow> </semantics></math> resolution.</p> Full article ">Figure 7
<p>(<b>a</b>) Horizontal distribution of <math display="inline"><semantics> <msubsup> <mover> <mi>u</mi> <mo>¯</mo> </mover> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics></math>. (<b>b</b>–<b>d</b>) Relative difference distributions between cases [<tt>P</tt>], [<tt>B</tt>], and [<tt>D</tt>]. (<b>e</b>) Vertical variation of root-normalized-mean-square difference <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>N</mi> <mi>M</mi> <mi>S</mi> <mi>D</mi> <mo>(</mo> <msubsup> <mover> <mi>u</mi> <mo>¯</mo> </mover> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> <mo>)</mo> </mrow> </semantics></math> and (<b>f</b>) fractional bias <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>B</mi> <mo>(</mo> <msubsup> <mover> <mi>u</mi> <mo>¯</mo> </mover> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> <mo>)</mo> </mrow> </semantics></math> evaluated over 12 horizontal planes across <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math> for heights <math display="inline"><semantics> <mrow> <mn>0.35</mn> <mo>&lt;</mo> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>&lt;</mo> <mn>7.2</mn> </mrow> </semantics></math> and over 4 planes across <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </msup> </semantics></math> for heights <math display="inline"><semantics> <mrow> <mn>0.35</mn> <mo>&lt;</mo> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>&lt;</mo> <mn>1.6</mn> </mrow> </semantics></math>. Recommended criteria suggested by Chang and Hanna [<a href="#B69-atmosphere-11-00201" class="html-bibr">69</a>] are <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>N</mi> <mi>M</mi> <mi>S</mi> <mi>D</mi> <mo>&lt;</mo> <msub> <mi>C</mi> <mrow> <mi>R</mi> <mi>N</mi> <mi>M</mi> <mi>S</mi> </mrow> </msub> <mspace width="0.166667em"/> <mrow> <mo>(</mo> <mo>=</mo> <mn>1.22</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>F</mi> <mi>B</mi> <mo>|</mo> </mrow> <mo>≤</mo> <msub> <mi>C</mi> <mrow> <mi>F</mi> <mi>B</mi> </mrow> </msub> <mspace width="0.166667em"/> <mrow> <mo>(</mo> <mo>=</mo> <mn>0.3</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, whereas zero is the ideal value for both.</p> Full article ">Figure 8
<p>(<b>a</b>) Horizontal distribution of <math display="inline"><semantics> <msup> <mover> <mi>w</mi> <mo>¯</mo> </mover> <mo>+</mo> </msup> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0.35</mn> <mi>H</mi> </mrow> </semantics></math>. (<b>b</b>–<b>d</b>) Relative difference distributions between cases [<tt>P</tt>], [<tt>B</tt>], and [<tt>D</tt>]. (<b>e</b>) Vertical variation of conditional root-normalized-mean-square difference <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>N</mi> <mi>M</mi> <mi>S</mi> <mi>D</mi> <mo>(</mo> <msup> <mover> <mi>w</mi> <mo>¯</mo> </mover> <mo>+</mo> </msup> <mo>|</mo> <msup> <mover> <mi>w</mi> <mo>¯</mo> </mover> <mo>+</mo> </msup> <mspace width="-0.166667em"/> <mo>&gt;</mo> <mspace width="-0.166667em"/> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> and (<b>f</b>) conditional fractional bias <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>B</mi> <mo>(</mo> <msup> <mover> <mi>w</mi> <mo>¯</mo> </mover> <mo>+</mo> </msup> <mo>|</mo> <msup> <mover> <mi>w</mi> <mo>¯</mo> </mover> <mo>+</mo> </msup> <mspace width="-0.166667em"/> <mo>&gt;</mo> <mspace width="-0.166667em"/> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> evaluated over 12 horizontal planes across <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math> for heights <math display="inline"><semantics> <mrow> <mn>0.35</mn> <mo>&lt;</mo> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>&lt;</mo> <mn>7.2</mn> </mrow> </semantics></math> and over 4 planes across <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </msup> </semantics></math> for heights <math display="inline"><semantics> <mrow> <mn>0.35</mn> <mo>&lt;</mo> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>&lt;</mo> <mn>1.6</mn> </mrow> </semantics></math>. Recommended criteria suggested by Chang and Hanna [<a href="#B69-atmosphere-11-00201" class="html-bibr">69</a>] are <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>N</mi> <mi>M</mi> <mi>S</mi> <mi>D</mi> <mo>&lt;</mo> <msub> <mi>C</mi> <mrow> <mi>R</mi> <mi>N</mi> <mi>M</mi> <mi>S</mi> </mrow> </msub> <mspace width="0.166667em"/> <mrow> <mo>(</mo> <mo>=</mo> <mn>1.22</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>F</mi> <mi>B</mi> <mo>|</mo> </mrow> <mo>≤</mo> <msub> <mi>C</mi> <mrow> <mi>F</mi> <mi>B</mi> </mrow> </msub> <mspace width="0.166667em"/> <mrow> <mo>(</mo> <mo>=</mo> <mn>0.3</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, whereas zero is the ideal value for both.</p> Full article ">Figure 9
<p>Normalized streamwise-rotated velocity profiles in the vertical direction obtained from each modified model simulation and all 11 virtual tower locations: A double-averaged profile is included in the bottom-right-hand corner for reference.</p> Full article ">Figure 10
<p>Normalized vertical velocity profiles obtained from each modified model simulation at selected virtual tower locations.</p> Full article ">Figure 11
<p>Local vertical plots of streamwise velocity variance <math display="inline"><semantics> <mrow> <msubsup> <mi>σ</mi> <mrow> <mi>u</mi> <mn>1</mn> </mrow> <mn>2</mn> </msubsup> <mo>/</mo> <msubsup> <mi>u</mi> <mrow> <mo>*</mo> </mrow> <mn>2</mn> </msubsup> </mrow> </semantics></math> from selected measurement sites: The contribution of statistically insufficiently described very-large-scale turbulence motions is distinctly visible above the urban canopy.</p> Full article ">Figure 12
<p>Example power scalograms of streamwise velocity time series from station <tt>M3</tt> at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>1.7</mn> </mrow> </semantics></math> (<b>right</b>), collected from the reference simulation [<tt>R</tt>].</p> Full article ">Figure 13
<p>Temporally averaged power scalograms of <math display="inline"><semantics> <msubsup> <mi>u</mi> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> </semantics></math> time series obtained from stations <tt>M1</tt>, <tt>M3</tt>, and <tt>M6</tt>, collected from (<b>a</b>) case [<tt>B</tt>] with the largest ABL eddies removed, (<b>b</b>) reference case [<tt>R</tt>], and (<b>c</b>) coarser resolution reference case [<tt>R2</tt>], where the data is coincidentally sampled from domain <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math>. Note the vertical extent of the <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msup> </semantics></math> results is <math display="inline"><semantics> <mrow> <mo>∼</mo> <mspace width="-0.166667em"/> <mn>12</mn> <mi>H</mi> </mrow> </semantics></math>, whereas <math display="inline"><semantics> <msup> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </msup> </semantics></math> results are limited to <math display="inline"><semantics> <mrow> <mo>∼</mo> <mspace width="-0.166667em"/> <mn>6</mn> <mi>H</mi> </mrow> </semantics></math>. White dashed line highlights <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> for convenience.</p> Full article ">Figure 14
<p>Example spectral energy densities (<math display="inline"><semantics> <mi mathvariant="script">S</mi> </semantics></math>) of the streamwise-oriented velocity <math display="inline"><semantics> <msub> <mi>u</mi> <mn>1</mn> </msub> </semantics></math> signal extracted at two heights from simulation [<tt>R</tt>] and location <tt>M6</tt> which cuts through a park with densely packed trees: (<b>a</b>) Spectral energy density of the original velocity signal and (<b>b</b>) spectral energy density of the filtered <math display="inline"><semantics> <msub> <mover accent="true"> <mi>u</mi> <mo>˜</mo> </mover> <mn>1</mn> </msub> </semantics></math> signal (cutoff frequency <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>0.011</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> or scale <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>2.3</mn> <mi>T</mi> </mrow> </semantics></math>).</p> Full article ">Figure 15
<p>A juxtaposition of original (<b>a</b>) and filtered (<b>b</b>) dimensionless streamwise-aligned velocity variance profiles at selected measurement locations: The range on (<b>b</b>) axes has been modified to allow better differentiation of the differences. Black dashed lines have been added to (<b>a</b>) to indicate the variance range of (<b>b</b>) graphs.</p> Full article ">Figure 16
<p>A juxtaposition of original (<b>a</b>) and filtered (<b>b</b>) dimensionless vertical velocity variance profiles at selected measurement locations.</p> Full article ">Figure 17
<p>Example discrete sample frequency density distributions <math display="inline"><semantics> <mrow> <msup> <mi>ρ</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <mo>•</mo> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>f</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <mo>•</mo> <mo>)</mo> </mrow> <mo>/</mo> <msup> <mo>Δ</mo> <mo>*</mo> </msup> </mrow> </semantics></math> used to construct the entropy and divergence distributions on <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>s</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo> </mrow> </semantics></math> plane as presented in <a href="#atmosphere-11-00201-f018" class="html-fig">Figure 18</a>: White (colorless) bars are from [<tt>R</tt>] reference simulation, while the yellow bars are from one of the modified cases. (<b>a</b>) Nearly equivalent histograms, both resulting in relatively high entropy values while yielding very low divergence. (<b>b</b>) Moderately deviating histrograms which give rise to relatively low divergence. (<b>c</b>) Clearly deviating histograms that result in very high divergence. Note the merging of bins at the tail end of the distributions.</p> Full article ">Figure 18
<p>Distributions of Shannon entropy <math display="inline"><semantics> <msub> <mi mathvariant="script">H</mi> <mn>1</mn> </msub> </semantics></math> and divergence <math display="inline"><semantics> <msub> <mi mathvariant="script">D</mi> <mn>1</mn> </msub> </semantics></math> on <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>s</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo> </mrow> </semantics></math>-plane evaluated from power scalograms <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>W</mi> <msubsup> <mi>u</mi> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> </msub> <msup> <mrow> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> obtained from selected measurement stations (<b>a</b>) <tt>M4</tt>, (<b>b</b>) <tt>M5</tt>, and (<b>c</b>) <tt>M6</tt>. In each subfigure, the <math display="inline"><semantics> <msub> <mi mathvariant="script">H</mi> <mn>1</mn> </msub> </semantics></math> distribution for the reference case [<tt>R</tt>] is shown on the right, while the distributions for the modified [<tt>P</tt>], [<tt>B</tt>], [<tt>D</tt>], and [<tt>R2</tt>] cases are aligned on the top row. Each Shannon entropy value is computed from discrete sample frequency distributions <math display="inline"><semantics> <msup> <mi>f</mi> <mo>*</mo> </msup> </semantics></math> of which subscript <span class="html-italic">A</span> signifies [<tt>R</tt>] and <span class="html-italic">B</span> one of the modified cases. The divergence distributions <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">D</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msubsup> <mi>f</mi> <mi>A</mi> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>f</mi> <mi>B</mi> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> </mrow> </semantics></math> are positioned at the <span class="html-italic">B</span> vs. <span class="html-italic">A</span> cross sections.</p> Full article ">Figure 18 Cont.
<p>Distributions of Shannon entropy <math display="inline"><semantics> <msub> <mi mathvariant="script">H</mi> <mn>1</mn> </msub> </semantics></math> and divergence <math display="inline"><semantics> <msub> <mi mathvariant="script">D</mi> <mn>1</mn> </msub> </semantics></math> on <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>s</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo> </mrow> </semantics></math>-plane evaluated from power scalograms <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>W</mi> <msubsup> <mi>u</mi> <mrow> <mn>1</mn> </mrow> <mo>+</mo> </msubsup> </msub> <msup> <mrow> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> obtained from selected measurement stations (<b>a</b>) <tt>M4</tt>, (<b>b</b>) <tt>M5</tt>, and (<b>c</b>) <tt>M6</tt>. In each subfigure, the <math display="inline"><semantics> <msub> <mi mathvariant="script">H</mi> <mn>1</mn> </msub> </semantics></math> distribution for the reference case [<tt>R</tt>] is shown on the right, while the distributions for the modified [<tt>P</tt>], [<tt>B</tt>], [<tt>D</tt>], and [<tt>R2</tt>] cases are aligned on the top row. Each Shannon entropy value is computed from discrete sample frequency distributions <math display="inline"><semantics> <msup> <mi>f</mi> <mo>*</mo> </msup> </semantics></math> of which subscript <span class="html-italic">A</span> signifies [<tt>R</tt>] and <span class="html-italic">B</span> one of the modified cases. The divergence distributions <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">D</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msubsup> <mi>f</mi> <mi>A</mi> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>f</mi> <mi>B</mi> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> </mrow> </semantics></math> are positioned at the <span class="html-italic">B</span> vs. <span class="html-italic">A</span> cross sections.</p> Full article ">
22 pages, 11397 KiB  
Article
Operational Modelling of Umbrella Cloud Growth in a Lagrangian Volcanic Ash Transport and Dispersion Model
by Helen N. Webster, Benjamin J. Devenish, Larry G. Mastin, David J. Thomson and Alexa R. Van Eaton
Atmosphere 2020, 11(2), 200; https://doi.org/10.3390/atmos11020200 - 13 Feb 2020
Cited by 18 | Viewed by 5099
Abstract
Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport [...] Read more.
Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available. Full article
(This article belongs to the Special Issue Forecasting the Transport of Volcanic Ash in the Atmosphere)
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Figure 1

Figure 1
<p>Observed (<b>a</b>) and modelled (<b>b</b>–<b>i</b>) growth of the Pinatubo umbrella cloud at hourly intervals from 05:40 to 14:40 UTC on 15 June 1991. Simulations (<b>b</b>,<b>c</b>) do not use the umbrella cloud parameterisation and are described in <a href="#sec5-atmosphere-11-00200" class="html-sec">Section 5</a>. Simulations (<b>d</b>–<b>i</b>) use the umbrella parameterisation with different estimates of the volume flow rate, <span class="html-italic">Q</span>. The modelled ash contour denotes ash column loads of 0.5 g m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math> (approximately the ash detection level of satellite instrumentation) and indicates an outline of the predicted umbrella cloud. Satellite observations are digitised from Holasek et al. [<a href="#B12-atmosphere-11-00200" class="html-bibr">12</a>]. The volcano location is marked by a green triangle.</p> Full article ">Figure 2
<p>Modelled ash column loads (filled contours in g m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>) and 11 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m brightness temperature satellite observations (orange open contour: 210 K) of the Kelud umbrella cloud at 18:19 UTC on 13 February 2014 (2 h, 10 min after the eruption start). Model simulations in (<b>a</b>,<b>b</b>) do not use the umbrella cloud parameterisation and are described in <a href="#sec5-atmosphere-11-00200" class="html-sec">Section 5</a>. Simulations in (<b>c</b>–<b>h</b>) use the umbrella parameterisation with different estimates of the volume flow rate, <span class="html-italic">Q</span>. The volcano location is marked by a green triangle. Satellite data are from MTSAT [<a href="#B36-atmosphere-11-00200" class="html-bibr">36</a>].</p> Full article ">Figure 3
<p>Modelled ash column loads (filled contours in g m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>) and GOES-13 brightness temperatures in the 11 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m channel (open contours: yellow (225 K) and red (255 K)) of the Calbuco ash cloud at 07:08 UTC on 23 April 2015 (3 h 8 min after the start of Phase 2). Model simulations in (<b>a</b>,<b>b</b>) do not use the umbrella cloud parameterisation and are described in <a href="#sec5-atmosphere-11-00200" class="html-sec">Section 5</a>. Simulations in (<b>c</b>–<b>g</b>) use the umbrella parameterisation with different estimates of the volume flow rate, <span class="html-italic">Q</span>. The volcano location is marked by a green triangle.</p> Full article ">Figure 4
<p>Modelled (filled contours in g m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>) and SEVIRI satellite observations (orange open contour corresponding to ash retrievals of 0.5 g m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>) of the Eyjafjallajökull ash cloud at 13:00 UTC on 6 May 2010. Model simulations in (<b>a</b>,<b>b</b>) do not use the umbrella cloud parameterisation and are described in <a href="#sec5-atmosphere-11-00200" class="html-sec">Section 5</a>. Simulations in (<b>c</b>–<b>g</b>) use the umbrella parameterisation with different estimates of the volume flow rate, <span class="html-italic">Q</span>. The volcano location is marked by a green triangle.</p> Full article ">Figure A1
<p>Modelled ash column loads within the Pinatubo ash cloud at 09:40 UTC on 15 June 1991 showing issues with particle accumulation on the leading edge of the theoretical umbrella cloud radius predicted by Equation (<a href="#FD4-atmosphere-11-00200" class="html-disp-formula">4</a>). The volcano location is marked by a green triangle.</p> Full article ">
22 pages, 1048 KiB  
Article
Comparison of Different Techniques to Calculate Properties of Atmospheric Turbulence from Low-Resolution Data
by Marta Wacławczyk, Amoussou S. Gozingan, Jackson Nzotungishaka, Moein Mohammadi and Szymon P. Malinowski
Atmosphere 2020, 11(2), 199; https://doi.org/10.3390/atmos11020199 - 13 Feb 2020
Cited by 7 | Viewed by 4931
Abstract
In this work we study different techniques to estimate basic properties of turbulence, that is its characteristic velocity and length scale from low-resolution data. The methods are based on statistics of the signals like the velocity spectra, second-order structure function, number of signal’s [...] Read more.
In this work we study different techniques to estimate basic properties of turbulence, that is its characteristic velocity and length scale from low-resolution data. The methods are based on statistics of the signals like the velocity spectra, second-order structure function, number of signal’s zero-crossings and the variance of velocity derivative. First, in depth analysis of estimates from artificial velocity time series is performed. Errors due to finite averaging window, finite cut-off frequencies and different fitting ranges are discussed. Next, real atmospheric measurement data are studied. It is demonstrated that differences between results of the methods can indicate deviations from the Kolmogorov’s theory or the presence of external intermittency, that is the existence of alternating laminar/turbulent flow patches. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Figure 1

Figure 1
<p><b>Left</b>: Exemplary frequency spectra of the transverse velocity component from experimental POST data [<a href="#B15-atmosphere-11-00199" class="html-bibr">15</a>], <b>Right</b>: Exemplary second-order structure function. Vertical lines indicate bounds of the fitting range, magenta dashed line is a curve-fit within this range.</p> Full article ">Figure 2
<p><b>Left</b>: Exemplary number of zero-crossing scaling calculated from experimental POST data, for cut-off within the inertial range, see Equation (<a href="#FD9-atmosphere-11-00199" class="html-disp-formula">9</a>), dashed magenta line indicates the best fit-line. <b>Right</b>: Power spectra of a signal with spectral cut-off, magenta line indicates reconstructed part of the spectrum.</p> Full article ">Figure 3
<p>Top plots: Part of an artificial signal, middle plots: EDR estimates from the power spectrum <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>P</mi> <mi>S</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD6-atmosphere-11-00199" class="html-disp-formula">6</a>), structure function <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>S</mi> <mi>F</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD7-atmosphere-11-00199" class="html-disp-formula">7</a>) and number of crossings scaling in the inertial range <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>N</mi> <mi>C</mi> <mi>F</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD9-atmosphere-11-00199" class="html-disp-formula">9</a>), bottom plots: EDR estimates from the iterative method based on the velocity variance <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>V</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD10-atmosphere-11-00199" class="html-disp-formula">10</a>) and the number of zero-crossings per unit length, Equation (<a href="#FD11-atmosphere-11-00199" class="html-disp-formula">12</a>). Left column: averaging window <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>c</mi> <mi>h</mi> </mrow> </msub> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>, right column: averaging window <math display="inline"><semantics> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>c</mi> <mi>h</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 4
<p>Statistics of EDR estimates for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>10</mn> <mo>÷</mo> <mn>20</mn> <mo>]</mo> </mrow> </semantics></math> Hz, or <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>c</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> Hz from artificial signals with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates. EDR estimates based on the power spectrum <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>P</mi> <mi>S</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD6-atmosphere-11-00199" class="html-disp-formula">6</a>), structure function <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>S</mi> <mi>F</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD7-atmosphere-11-00199" class="html-disp-formula">7</a>), number of zero-crossings scaling in the inertial range <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>N</mi> <mi>C</mi> <mi>F</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD9-atmosphere-11-00199" class="html-disp-formula">9</a>), iterative method based on the velocity variance <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>V</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </semantics></math>, Equation (<a href="#FD10-atmosphere-11-00199" class="html-disp-formula">10</a>) and the number of zero-crossings per unit length, Equation (<a href="#FD11-atmosphere-11-00199" class="html-disp-formula">12</a>).</p> Full article ">Figure 5
<p>As in <a href="#atmosphere-11-00199-f004" class="html-fig">Figure 4</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>5</mn> <mo>÷</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 6
<p>As in <a href="#atmosphere-11-00199-f004" class="html-fig">Figure 4</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 7
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>P</mi> <mi>S</mi> </mrow> </msub> </semantics></math> estimates based on the power spectra, Equation (<a href="#FD6-atmosphere-11-00199" class="html-disp-formula">6</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>10</mn> <mo>÷</mo> <mn>20</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from artificial signals with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> Hz, 100 Hz and 50 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 8
<p>As in <a href="#atmosphere-11-00199-f007" class="html-fig">Figure 7</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>5</mn> <mo>÷</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 9
<p>As in <a href="#atmosphere-11-00199-f007" class="html-fig">Figure 7</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 10
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>S</mi> <mi>F</mi> </mrow> </msub> </semantics></math> estimates based on the second-order structure function, Equation (<a href="#FD7-atmosphere-11-00199" class="html-disp-formula">7</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>10</mn> <mo>÷</mo> <mn>20</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from artificial signals with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> Hz, 100 Hz and 50 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 11
<p>As in <a href="#atmosphere-11-00199-f010" class="html-fig">Figure 10</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>5</mn> <mo>÷</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 12
<p>As in <a href="#atmosphere-11-00199-f010" class="html-fig">Figure 10</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 13
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>N</mi> <mi>C</mi> <mi>F</mi> </mrow> </msub> </semantics></math> estimates based on the number of zero-crossing scaling, Equation (<a href="#FD9-atmosphere-11-00199" class="html-disp-formula">9</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>10</mn> <mo>÷</mo> <mn>20</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from artificial signals with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> Hz, 100 Hz and 50 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 14
<p>As in <a href="#atmosphere-11-00199-f013" class="html-fig">Figure 13</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>5</mn> <mo>÷</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 15
<p>As in <a href="#atmosphere-11-00199-f013" class="html-fig">Figure 13</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 16
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>V</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </semantics></math> estimates based on the variance of velocity derivative, Equation (<a href="#FD10-atmosphere-11-00199" class="html-disp-formula">10</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>10</mn> <mo>÷</mo> <mn>20</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from artificial signals with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> Hz, 100 Hz and 50 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 17
<p>As in <a href="#atmosphere-11-00199-f016" class="html-fig">Figure 16</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>5</mn> <mo>÷</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 18
<p>As in <a href="#atmosphere-11-00199-f016" class="html-fig">Figure 16</a>, but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 19
<p>Mean of EDR estimates for artificial signals which deviate from the <math display="inline"><semantics> <mrow> <mo>-</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> scaling (left panel) or with deviations from the standard value of the Kolmogorov’s constant <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>K</mi> </msub> <mo>≈</mo> <mn>0</mn> <mo>.</mo> <mn>49</mn> </mrow> </semantics></math>.</p> Full article ">Figure 20
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>P</mi> <mi>S</mi> </mrow> </msub> </semantics></math> estimates based on the power spectra, Equation (<a href="#FD6-atmosphere-11-00199" class="html-disp-formula">6</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from POST signals [<a href="#B15-atmosphere-11-00199" class="html-bibr">15</a>] with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>40</mn> </mrow> </semantics></math> Hz, 20 Hz and 10 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 21
<p>As in <a href="#atmosphere-11-00199-f020" class="html-fig">Figure 20</a> but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>0</mn> <mo>.</mo> <mn>2</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 22
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>S</mi> <mi>F</mi> </mrow> </msub> </semantics></math> estimates based on the structure function, Equation (<a href="#FD7-atmosphere-11-00199" class="html-disp-formula">7</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from POST signals [<a href="#B15-atmosphere-11-00199" class="html-bibr">15</a>] with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>40</mn> </mrow> </semantics></math> Hz, 20 Hz and 10 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 23
<p>As in <a href="#atmosphere-11-00199-f022" class="html-fig">Figure 22</a> but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>0</mn> <mo>.</mo> <mn>2</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 24
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>N</mi> <mi>C</mi> <mi>F</mi> </mrow> </msub> </semantics></math> estimates based on the number of zero-crossings, Equation (<a href="#FD9-atmosphere-11-00199" class="html-disp-formula">9</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from POST signals [<a href="#B15-atmosphere-11-00199" class="html-bibr">15</a>] with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>40</mn> </mrow> </semantics></math> Hz, 20 Hz and 10 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 25
<p>As in <a href="#atmosphere-11-00199-f024" class="html-fig">Figure 24</a> but for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>0</mn> <mo>.</mo> <mn>2</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz.</p> Full article ">Figure 26
<p>Statistics of <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>V</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </semantics></math> estimates based on the iterative method, Equation (<a href="#FD10-atmosphere-11-00199" class="html-disp-formula">10</a>), for the fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>1</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz, from POST signals [<a href="#B15-atmosphere-11-00199" class="html-bibr">15</a>] with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>40</mn> </mrow> </semantics></math> Hz, 20 Hz and 10 Hz. <b>Left panel</b>: EDR averaged along the signal, <b>right panel</b>: standard deviation of the estimates.</p> Full article ">Figure 27
<p>Top plots: Part of an artificial signal, middle plots: <math display="inline"><semantics> <mo>ϵ</mo> </semantics></math> estimates, bottom plots: Taylor-to Liepmann scale ratio. Left column: signal with the intermittency parameter 0.69, right column: signal with the intermittency parameter 0.84.</p> Full article ">Figure 28
<p>Top plots: Part of the POST signals, middle plots: <math display="inline"><semantics> <mo>ϵ</mo> </semantics></math> estimates, bottom plots: Taylor-to Liepmann scale ratio. Left panel: horizontal segment of the flight 13, right panel: flight 3, signal recorded during vertical cloud penetrations.</p> Full article ">Figure 29
<p>EDR estimates for signals measured during POST flight 3 [<a href="#B15-atmosphere-11-00199" class="html-bibr">15</a>] with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> Hz. <b>Left panel</b>: fitting range <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>0</mn> <mo>.</mo> <mn>5</mn> <mo>÷</mo> <mn>3</mn> <mo>.</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>c</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics></math> Hz, <b>right panel</b>: fitting range corresponding to <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>2</mn> <mo>÷</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz for <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>S</mi> <mi>F</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mo>[</mo> <mn>0</mn> <mo>.</mo> <mn>5</mn> <mo>÷</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> <mo>]</mo> </mrow> </semantics></math> Hz for <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>P</mi> <mi>S</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>N</mi> <mi>C</mi> <mi>F</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>c</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics></math> Hz for <math display="inline"><semantics> <msub> <mo>ϵ</mo> <mrow> <mi>V</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </semantics></math>.</p> Full article ">
20 pages, 4446 KiB  
Article
Inter-Comparison of Ensemble Forecasts for Low Level Wind Shear against Local Analyses Data over Jeju Area
by Young-Gon Lee, Sang-Boom Ryoo, Keunhee Han, Hee Wook Choi and Chansoo Kim
Atmosphere 2020, 11(2), 198; https://doi.org/10.3390/atmos11020198 - 13 Feb 2020
Cited by 5 | Viewed by 2639
Abstract
Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operations and management. In this study, we conducted an accuracy and reliability analysis using a rank histogram, Brier score, and reliability diagram to verify LLWS ensemble member forecasts based [...] Read more.
Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operations and management. In this study, we conducted an accuracy and reliability analysis using a rank histogram, Brier score, and reliability diagram to verify LLWS ensemble member forecasts based on grid points over the Jeju area of the Republic of Korea. Thirteen LLWS ensemble member forecasts derived from a limited area ensemble prediction system (LENS) were obtained between 1 July 2016 and 30 May 2018, and 3-h LLWS forecasts for lead times up to 72 h (three days) were issued twice a day at 0000 UTC (9 am local time) and 1200 UTC (9 pm local time). We found that LLWS ensemble forecasts have a weak negative bias in summer and autumn and a positive bias in the spring and winter; the forecasts also have under-dispersion for all seasons, which implies that the ensemble spread of an ensemble is smaller than that of the corresponding observations. Additionally, the reliability curve in the associated reliability diagram indicates an over-forecasting of LLWS events bias. The selection of a forecast probability threshold from the LLWS ensemble forecast was confirmed to be one of the most important factors for issuing a severe LLWS warning. A simple method to select a forecast probability threshold without economic factors was conducted. The results showed that the selection of threshold is more useful for issuing a severe LLWS warning than none being selected. Full article
(This article belongs to the Section Meteorology)
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Figure 1

Figure 1
<p>The schematic flow chart of the Limited area ENsemble prediction System (LENS) operated in conjunction with the Global Data Assimilation and Prediction System (GDAPS) and Ensemble Prediction System for Global (EPSG) at the Korea Meteorological Administration (KMA) (Sourced from [<a href="#B25-atmosphere-11-00198" class="html-bibr">25</a>]).</p> Full article ">Figure 2
<p>(<b>a</b>) Horizontal domain boundaries of LENS (solid lines) and LDAPS (dashed lines) and (<b>b</b>) the verification (shaded) area determined from the overlapping regions of both model forecasts around the Korean peninsula.</p> Full article ">Figure 3
<p>The orography of the 102 km × 84 km region around the Jeju International Airport (JIA). The thick rectangular area indicates the verification area of 34 × 28 grids with 3-km resolution.</p> Full article ">Figure 4
<p>Verification rank histograms of LLWS events for each season. (<b>a</b>) 2016 JA; (<b>b</b>) 2016 SON; (<b>c</b>) 2016-17 DJF; (<b>d</b>) 2017 MAM; (<b>e</b>) 2017 JJA; (<b>f</b>) 2017 SON; (<b>g</b>) 2017-18 DJF; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 4 Cont.
<p>Verification rank histograms of LLWS events for each season. (<b>a</b>) 2016 JA; (<b>b</b>) 2016 SON; (<b>c</b>) 2016-17 DJF; (<b>d</b>) 2017 MAM; (<b>e</b>) 2017 JJA; (<b>f</b>) 2017 SON; (<b>g</b>) 2017-18 DJF; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 5
<p>LLWS mean (black lines) and spread (filled colors) distribution over Jeju Island (yellow green line) at (<b>a</b>) 69 h (18 December 2016) and (<b>b</b>) 60 h (8 May 2017).</p> Full article ">Figure 6
<p>Prediction skills of the LLWS ensemble mean for each season.</p> Full article ">Figure 7
<p>Box plot for the deviation between the LDAPS analyses and ensemble mean and the average mean absolute error (MAE) and biases for each season.</p> Full article ">Figure 8
<p>Box plots for LDAPS analyses and the average ensemble forecasts when a severe LLWS event did not occur. The red dot and horizontal line for each box denote the mean and median of the data, respectively. (<b>a</b>) 2016 JA; (<b>b</b>) 2016 SON; (<b>c</b>) 2016-17 DJF; (<b>d</b>) 2017 MAM; (<b>e</b>) 2017 JJA; (<b>f</b>) 2017 SON; (<b>g</b>) 2017-18 DJF; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 8 Cont.
<p>Box plots for LDAPS analyses and the average ensemble forecasts when a severe LLWS event did not occur. The red dot and horizontal line for each box denote the mean and median of the data, respectively. (<b>a</b>) 2016 JA; (<b>b</b>) 2016 SON; (<b>c</b>) 2016-17 DJF; (<b>d</b>) 2017 MAM; (<b>e</b>) 2017 JJA; (<b>f</b>) 2017 SON; (<b>g</b>) 2017-18 DJF; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 9
<p>Box plots for LDAPS analyses and the average ensemble forecasts when a severe LLWS event was observed. (<b>a</b>) 2016 JA; (<b>b</b>) 2016 SON; (<b>c</b>) 2016-17 DJF; (<b>d</b>) 2017 MAM; (<b>e</b>) 2017 JJA; (<b>f</b>) 2017 SON; (<b>g</b>) 2017-18 DJF; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 9 Cont.
<p>Box plots for LDAPS analyses and the average ensemble forecasts when a severe LLWS event was observed. (<b>a</b>) 2016 JA; (<b>b</b>) 2016 SON; (<b>c</b>) 2016-17 DJF; (<b>d</b>) 2017 MAM; (<b>e</b>) 2017 JJA; (<b>f</b>) 2017 SON; (<b>g</b>) 2017-18 DJF; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 10
<p>Reliability, resolution, uncertainty and Brier score of the LLWS for each season.</p> Full article ">Figure 11
<p>Reliability diagram of LLWS for each season. The numbers on the panel are the sample frequency for each bin, which are obtained as the number of forecast probabilities included in the bins are divided by the total number of forecast probabilities. (<b>a</b>) 2016 JA; (<b>b</b>) 2017 JJA; (<b>c</b>) 2016 SON; (<b>d</b>) 2017 SON; (<b>e</b>) 2016-17 DJF; (<b>f</b>) 2017-18 DJF; (<b>g</b>) 2017 MAM; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 11 Cont.
<p>Reliability diagram of LLWS for each season. The numbers on the panel are the sample frequency for each bin, which are obtained as the number of forecast probabilities included in the bins are divided by the total number of forecast probabilities. (<b>a</b>) 2016 JA; (<b>b</b>) 2017 JJA; (<b>c</b>) 2016 SON; (<b>d</b>) 2017 SON; (<b>e</b>) 2016-17 DJF; (<b>f</b>) 2017-18 DJF; (<b>g</b>) 2017 MAM; (<b>h</b>) 2018 MAM.</p> Full article ">Figure 12
<p>Forecast probability distribution at 24 h, 9 May 2017.</p> Full article ">Figure 13
<p>The best probability threshold for issuing a severe LLWS warning (2017 MAM). (<b>a</b>) projection time = 6 h; (<b>b</b>)projection time = 15 h; (<b>c</b>) projection time = 24 h; (<b>d</b>) projection time = 33 h; (<b>e</b>) projection time = 42 h; (<b>f</b>) projection time = 51 h; (<b>g</b>) projection time = 60; (<b>h</b>) projection time = 72 h.</p> Full article ">Figure 14
<p>Forecast probability distribution of LDAPS analyses and forecasts using probability threshold. (<b>a</b>–<b>c</b>) May 8, 2017 projection time 60 h; (<b>d</b>–<b>f</b>) May 9, 2017 projection time 24 h.</p> Full article ">Figure 14 Cont.
<p>Forecast probability distribution of LDAPS analyses and forecasts using probability threshold. (<b>a</b>–<b>c</b>) May 8, 2017 projection time 60 h; (<b>d</b>–<b>f</b>) May 9, 2017 projection time 24 h.</p> Full article ">
17 pages, 7120 KiB  
Article
Dust Dry Deposition over Israel
by Pavel Kishcha, Evgeni Volpov, Boris Starobinets, Pinhas Alpert and Slobodan Nickovic
Atmosphere 2020, 11(2), 197; https://doi.org/10.3390/atmos11020197 - 13 Feb 2020
Cited by 14 | Viewed by 4111
Abstract
Similar quasiperiodic year-to-year variations of dust dry deposition (DDD) with a two–three-year period were found over Israel and north-east Africa. This phenomenon of quasiperiodic interannual variations of DDD has not been discussed in previous publications. Moreover, similar seasonal variations of DDD were found [...] Read more.
Similar quasiperiodic year-to-year variations of dust dry deposition (DDD) with a two–three-year period were found over Israel and north-east Africa. This phenomenon of quasiperiodic interannual variations of DDD has not been discussed in previous publications. Moreover, similar seasonal variations of DDD were found over both Israel and north-east Africa, characterized by significant dust deposition in spring and a decrease in DDD from spring to autumn. These findings indicate the existence of the same causal factors for interannual and seasonal variations of DDD over the two regions, such as similar surface winds created by Mediterranean cyclones. Daily runs of the Dust REgional Atmospheric Model (DREAM) at Tel Aviv University from 2006 to 2019 were used to investigate the main features of the spatio-temporal distribution of dust dry deposition in the eastern Mediterranean, with a focus on Israel. DREAM showed that, on average, during the 14-year study period, in the winter, spring, and summer months, the spatial distribution of monthly-accumulated DDD over Israel was non-uniform with the maximum of DDD over southern Israel. In the autumn months, DREAM showed an increase in DDD over northern Israel, resulting in an almost uniform DDD pattern. The knowledge of DDD spatio-temporal distribution is helpful for understanding the negative effects of DDD on the performance of solar panels and on insulator flashover in the Israel power electric network. Full article
(This article belongs to the Special Issue Desert-Dust Aerosols in the Earth System)
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Figure 1
<p>Maps of (<b>A</b>) the whole Dust REgional Atmospheric Model (DREAM) domain (15° N–50° N; 20° W–45° E) including (<b>B</b>) north-east Africa (29° N–31.5° N; 28° E–33.5° E) and (<b>C</b>) the study region (Israel and surrounding areas) (29° N–34° N; 33.5° E–36° E). The colors designate the distribution of the potential dust source parameter α, which is specified by the global vegetation data [<a href="#B13-atmosphere-11-00197" class="html-bibr">13</a>]. α ranges from 0 (no dust emissions) to 1.</p> Full article ">Figure 2
<p>Flowchart of the Tel-Aviv University (TAU)/DREAM dust model system used for daily operational dust forecasts over the Mediterranean region from 2006 to 2019.</p> Full article ">Figure 3
<p>(<b>a</b>) Year-to-year variations of DREAM-based annually-accumulated dust dry deposition at the specified sites, located in the northern, central, and southern parts of Israel, and over north-east Africa (29° N–31.5° N; 28° E–33.5° E), located in close proximity to the region under study. (<b>b</b>) Geographic locations of the specified sites: Haifa (32.79° N; 34.99° E), Tel-Aviv (32.085° N; 34.781° E), and Sede-Boker (30.855° N, 34.782° E).</p> Full article ">Figure 4
<p>Year-to-year variations of the 14-year mean surface wind speed over the southern part of the study region (29.5° N–31.5° N; 34.5° E–35.5° E) and north-east Africa (29° N–31.5° N; 28° E–33.5° E), based on NASA MERRA reanalysis (2006–2019) [<a href="#B15-atmosphere-11-00197" class="html-bibr">15</a>].</p> Full article ">Figure 5
<p>Spatial distribution of annually-accumulated dust dry deposition over Israel and surrounding areas, averaged over the 14-year study period.</p> Full article ">Figure 6
<p>(<b>a</b>) Spatial distribution of measured annually-accumulated dust dry deposition over Israel and surrounding areas (adapted from Ganor and Foner [<a href="#B9-atmosphere-11-00197" class="html-bibr">9</a>]). The measured annually-accumulated dust dry deposition data were normalized on the annually-accumulated dust deposition in Sede-Boker (30.855° N, 34.782° E). (<b>b</b>) Comparison between modeled and measured annually-accumulated dust dry deposition at the specified sites, located in the south–north direction along Israel: Sede-Boker (30.855° N, 34.782° E), Beer-Sheba (31.25° N; 34.80° E), Ashkelon (31.639° N; 34.521° E), Ashdod (31.834° N; 34.637° E), Tel-Aviv (32.085° N; 34.781° E), Hadera (32.47° N; 34.881° E), Haifa (32.79° N; 34.99° E), and Naharia (33.002° N; 35.091° E).</p> Full article ">Figure 7
<p>Spatial distribution of seasonally-accumulated dust dry deposition over Israel and surrounding areas in (a) winter, (b) spring, (c) summer, and (d) autumn, averaged over the 14-year study period.</p> Full article ">Figure 8
<p>(<b>a</b>) Fourteen-year mean seasonal variations of DREAM dust dry deposition at the specified sites located in the southern, central, and northern parts of Israel. (<b>b</b>) Geographic locations of the specified sites: Haifa (32.79° N; 34.99° E), Tel-Aviv (32.085° N; 34.781° E), Hafez-Hayim (31.79° N; 34.81° E), Beer-Sheba (31.25° N; 34.80° E), and Sede-Boker (30.855° N, 34.782° E).</p> Full article ">Figure 9
<p>Fourteen-year mean seasonal variations of DREAM dust dry deposition over north-east Africa (29° N–31.5° N; 28° E–33.5° E).</p> Full article ">Figure 10
<p>Month-to-month variations of the 14-year mean surface wind speed over (<b>a</b>) the southern part of the study region (29.5° N–31.5° N; 34.5° E–35.5° E) and (<b>b</b>) north-east Africa (29° N–31.5° N; 28° E–33.5° E), based on NASA MERRA reanalysis (2006–2019) [<a href="#B15-atmosphere-11-00197" class="html-bibr">15</a>,<a href="#B16-atmosphere-11-00197" class="html-bibr">16</a>]. The vertical lines designate standard deviation.</p> Full article ">Figure 11
<p>Maps of 14-year mean monthly-accumulated dust dry deposition in (<b>a</b>) September, (<b>b</b>) October, and (<b>c</b>) November.</p> Full article ">Figure 12
<p>Maps of 14-year mean monthly-accumulated dust dry deposition in (<b>a</b>) January, (<b>b</b>). March, and (<b>c</b>) July.</p> Full article ">Figure 13
<p>Month-to-month variations of DREAM monthly-accumulated dust dry deposition at the specified sites located in the southern, central, and northern parts of Israel: (a) and (b) – Haifa; (c) and (d) – Tel-Aviv; (e) and (f) – Beer-Sheba; (g) and (h) – Sede-Boker. The left column represents original monthly-accumulated dust dry deposition at the specified sites, while the right column represents their associated deseasonalized monthly anomalies. The straight red lines designate linear fits characterized by the slope a [µg cm<sup>−2</sup> year<sup>−1</sup>] and <span class="html-italic">p</span> values. One can see that all <span class="html-italic">p</span> values were essentially higher than 0.05. Consequently, there were no statistically significant trends (at the 95% confidence level) in dust dry deposition at any of the specified sites during the study period.</p> Full article ">Figure 14
<p>Year-to-year variations of DREAM monthly-accumulated dust dry deposition at the specified sites located in the southern, central, and northern parts of Israel in March (the left panel) and November (the right panel): (a) and (b) – Haifa; (c) and (d) – Tel-Aviv; (e) and (f) – Beer-Sheba; (g) and (h) – Sede-Boker. The straight lines designate linear fits, characterized by the slope a [mg cm<sup>−2</sup> year<sup>−1</sup>] and <span class="html-italic">p</span> values. In November, the <span class="html-italic">p</span>-values lower than 0.05 designate statistically significant trends in DDD at the 95% confidence level in Haifa, Tel-Aviv, and Beer-Sheba.</p> Full article ">Figure 15
<p>Comparison between monthly-accumulated dust dry deposition over the study region in November in 2006, 2007, 2008, 2009 (top panel) and that in 2013, 2014, 2015, 2016 (bottom panel).</p> Full article ">
16 pages, 3067 KiB  
Article
Measurements of Ozone Vertical Profiles in the Upper Troposphere–Stratosphere over Western Siberia by DIAL, MLS, and IASI
by Sergey Dolgii, Alexey A. Nevzorov, Alexey V. Nevzorov, Yurii Gridnev and Olga Kharchenko
Atmosphere 2020, 11(2), 196; https://doi.org/10.3390/atmos11020196 - 12 Feb 2020
Cited by 12 | Viewed by 3581
Abstract
The purpose of this work is to measure the ozone vertical distribution (OVD) in the upper troposphere–stratosphere by differential absorption lidar (DIAL) at 299/341 nm and 308/353 nm and to compare and analyze the results against satellite data. А lidar complex for measuring [...] Read more.
The purpose of this work is to measure the ozone vertical distribution (OVD) in the upper troposphere–stratosphere by differential absorption lidar (DIAL) at 299/341 nm and 308/353 nm and to compare and analyze the results against satellite data. А lidar complex for measuring the OVD in the altitude range ≈(5–45) km has been created. Here we analyze the results of ozone lidar measurements at wavelengths of 299/341 nm and 308/353 nm in 2018 at Siberian Lidar Station (SLS) and compare them with satellite (MLS/Aura and IASI/MetOp) measurements of OVD. The retrieved lidar OVD profiles in the upper troposphere–stratosphere in comparison with MLS/Aura and IASI/MetOp profiles, as well as the stitched OVD profile in comparison with the mid-latitude Krueger model, confirm the prospects of using the pairs of ozone sounding wavelengths 299/341 and 308/353 nm. Full article
(This article belongs to the Special Issue Atmospheric and Ocean Optics: Atmospheric Physics)
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Figure 1
<p>Block diagram of the ozone lidar complex: field stop (FS), cuvette of spectral selection with a photomultiplier (CSS), interference filter (IF), dichroic mirror (DM), amplifiers–discriminators (AD), high-voltage supply units (HSU), rotary mirrors (RM), lenses (L), photomultiplying tube (PMT), photodiodе (PD).</p> Full article ">Figure 2
<p>Average measurement errors of ozone vertical distribution (OVD) for 2018: error in the (<b>a</b>) stratosphere and (<b>b</b>) upper troposphere–lower stratosphere.</p> Full article ">Figure 3
<p>Average profile retrieved in the upper troposphere–stratosphere in 2018.</p> Full article ">Figure 4
<p>Average OVDs and their differences: (<b>a</b>) average profiles; (<b>b</b>) differences between the lidar and Aura data, in absolute units; (<b>c</b>) relative differences 100 × (lidar – Aura)/lidar.</p> Full article ">Figure 5
<p>Average OVDs and their differences: (<b>a</b>) average profiles; (<b>b</b>) differences between the lidar and MetOp data, in absolute units; (<b>c</b>) relative differences 100 × (lidar − MetOp)/lidar.</p> Full article ">Figure 6
<p>Average OVDs for season of winter–spring and their differences: (<b>a</b>) average profiles; (<b>b</b>) differences between the lidar and Aura data, in absolute units; and (<b>c</b>) relative differences 100 × (lidar – Aura)/lidar.</p> Full article ">Figure 7
<p>Average OVDs for season of summer–fall and their differences: (<b>a</b>) average profiles; (<b>b</b>) differences between the lidar and Aura data, in absolute units; and (<b>c</b>) relative differences 100 × (lidar – Aura)/lidar.</p> Full article ">Figure 8
<p>Average OVDs for season of winter–spring and their differences: (<b>a</b>) average profiles; (<b>b</b>) differences between the lidar and MetOp data, in absolute units; and (<b>c</b>) relative differences 100 × (lidar – MetOp)/lidar.</p> Full article ">Figure 9
<p>Average OVDs for season of summer–fall and their differences: (<b>a</b>) average profiles; (<b>b</b>) differences between the lidar and MetOp data, in absolute units; and (<b>c</b>) relative differences 100 × (lidar – MetOp)/lidar.</p> Full article ">Figure 10
<p>Comparison of ozone vertical profiles in the upper troposphere–stratosphere with Aura and MetOp satellite data.</p> Full article ">
14 pages, 8069 KiB  
Article
Decadal-to-Multidecadal Variability of Seasonal Land Precipitation in Northern Hemisphere in Observation and CMIP6 Historical Simulations
by Hua Chen and Zhenchen Xu
Atmosphere 2020, 11(2), 195; https://doi.org/10.3390/atmos11020195 - 12 Feb 2020
Cited by 10 | Viewed by 3341
Abstract
Based on the centennial-scale observations and CMIP6 historical simulations, this paper employs the ensemble empirical mode decomposition to extract the decadal-to-multidecadal variability of land precipitation (DMVLP) in the northern hemisphere. The spatial distributions of the dominant mode from the empirical orthogonal function are [...] Read more.
Based on the centennial-scale observations and CMIP6 historical simulations, this paper employs the ensemble empirical mode decomposition to extract the decadal-to-multidecadal variability of land precipitation (DMVLP) in the northern hemisphere. The spatial distributions of the dominant mode from the empirical orthogonal function are different in four seasons. Regions with the same sign of precipitation anomalies are likely to be teleconnected through oceanic forcing. The temporal evolutions of the leading modes are similar in winter and spring, with an amplitude increasing after the late 1970s, probably related to the overlap of oceanic multidecadal signals. In winter and spring, the Interdecadal Pacific Oscillation (IPO) and the Atlantic Multidecadal Oscillation (AMO) play a joint role. They were in phase before late 1970s and out of phase after then, weakening/strengthening the impacts of the North Pacific and North Atlantic on the DMVLP before/after late 1970s. In summer and autumn, AMO alone plays a part and the amplitude of time series does not vary as in winter and spring. The ability of the coupled models from CMIP6 historical simulations is also evaluated. The good-models average largely captures the spatial structure in four seasons and the associated oceanic signals. The poor-models average is hardly or weakly correlated with observation. Full article
(This article belongs to the Section Climatology)
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<p>Spatial distributions of the leading empirical orthogonal function (EOF) of the decadal-to-multidecadal variability of land precipitation from the Global Precipitation Climatology Centre (GPCC) in the northern hemisphere in (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn. Dotted regions indicate exceeding 95% significance <span class="html-italic">t</span>-test. Unit: mm/day.</p> Full article ">Figure 2
<p>The normalized first principle component of the decadal-to-multidecadal variability of land precipitation from GPCC in the northern hemisphere in (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn.</p> Full article ">Figure 3
<p>Spectrum of the first principle component of the decadal-to-multidecadal variability of land precipitation from GPCC in the northern hemisphere in (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn (black curves). The red solid curves are corresponding red noise spectrum, and the blue dashed curves are 5% confidence upper limit of red noise spectrum. The y-axis is power. The x-axis is period, unit: year.</p> Full article ">Figure 4
<p>Regressions of the sea surface temperature (SST) anomalies onto the first principle component of the decadal-to-multidecadal variability of land precipitation from GPCC in the northern hemisphere in (<b>a</b>) winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn. Regions exceeding the 95% significance <span class="html-italic">t</span>-test are dotted. Unit: degrees Celsius.</p> Full article ">Figure 5
<p>Scatter plot of the spatial and temporal correlation coefficients between the leading EOF mode of GPCC and the leading EOF mode of each ensemble member from the coupled models in CMIP6 historical simulations. (<b>a</b>) Winter, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) autumn. Different models are indicated by different marks. The numeral beside the marks indicates the ordinal of member in the ensemble. Good models are located inside red boxes, and poor models are inside blue boxes.</p> Full article ">Figure 6
<p>Averages of the spatial distributions of the leading EOF from (left) good models and (right) poor models in (<b>a</b>,<b>b</b>) winter, (<b>c</b>,<b>d</b>) spring, (<b>e</b>,<b>f</b>) summer, and (<b>g</b>,<b>h</b>) autumn. Unit: mm/day.</p> Full article ">Figure 7
<p>Averages of regressions of SST anomalies onto the first principle component from (left) good models and (right) poor models in (<b>a</b>,<b>b</b>) winter, (<b>c</b>,<b>d</b>) spring, (<b>e</b>,<b>f</b>) summer, and (<b>g</b>,<b>h</b>) autumn. Unit: degrees Celsius.</p> Full article ">Figure 8
<p>Spatial distributions of the leading EOF of the externally forced wintertime land precipitation in northern hemisphere from (<b>a</b>) GPCC, (<b>b</b>) the first member of the CanESM5 model ensemble in CMIP6 historical all-forcings simulation, and (<b>c</b>) the first member of the CanESM5 model ensemble in CMIP6 historical aerosol forcing experiment. Regions exceeding 95% significance <span class="html-italic">t</span>-test are dotted. Unit: mm/day. (<b>d</b>) The normalized first principle component. Note that the externally forced component of all ensemble members of CanESM5 had a similar feature in a single experiment, thus the result of the first member is displayed.</p> Full article ">
11 pages, 6168 KiB  
Article
On Aerosol Liquid Water and Sulfate Associations: The Potential for Fine Particulate Matter Biases
by Jonathon E. Babila, Annmarie G. Carlton, Christopher J. Hennigan and Virendra P. Ghate
Atmosphere 2020, 11(2), 194; https://doi.org/10.3390/atmos11020194 - 12 Feb 2020
Cited by 11 | Viewed by 4312
Abstract
In humid locations of the Eastern U.S., sulfate is a surrogate for aerosol liquid water (ALW), a poorly measured particle constituent. Regional and seasonal variation in ALW–sulfate relationships offers a potential explanation to reconcile epidemiology and toxicology studies regarding particulate sulfur and health [...] Read more.
In humid locations of the Eastern U.S., sulfate is a surrogate for aerosol liquid water (ALW), a poorly measured particle constituent. Regional and seasonal variation in ALW–sulfate relationships offers a potential explanation to reconcile epidemiology and toxicology studies regarding particulate sulfur and health endpoints. ALW facilitates transfer of polar species from the gas phase to the particle phase and affects particle pH and metal oxidation state. Though abundant and a potential indicator of adverse health endpoints, ALW is largely removed in most particulate matter measurement techniques, including in routine particulate matter (PM2.5) networks that use federal reference method (FRM) monitors, which are used in epidemiology studies. We find that in 2004, a typical year in the available record, ambient ALW mass is removed during sampling and filter equilibration to standard laboratory conditions at most (94%) sites, up to 85% of the ambient water mass. The removal of ALW can induce the evaporation of other semi-volatile compounds present in PM2.5, such as ammonium nitrate and numerous organics. This produces an artifact in the PM mass measurements that is, importantly, not uniform in space or time. This suggests that PM2.5 epidemiology studies that exclude ALW are biased. This work provides a plausible explanation to resolve multi-decade discrepancies regarding ambient sulfate and health impacts in some epidemiological and toxicological studies. Full article
(This article belongs to the Special Issue Atmospheric Aqueous-Phase Chemistry)
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<p>Yearly average in 2004 at Interagency Monitoring of PROtected Visual Environments (IMPROVE) monitoring sites of (<b>a</b>) ratio of aerosol liquid water (ALW) mass per measured PM<sub>2.5</sub> ‘dry’ mass (<b>b</b>) ambient ALW mass concentrations and (<b>c</b>) equivalent concentration loss upon filter equilibration to standard laboratory conditions. Point color and diameter are relative to ALW per dry mass ratio (orange), ALW mass (blue), and ALW mass difference (green, red). Green indicates ALW loss during equilibration and red indicates ALW gain.</p> Full article ">Figure 2
<p>Differences between ambient and laboratory-equilibrated PM<sub>2.5</sub> absolute mass and constituent fractional contributions. Idealized particles are drawn to scale by mass for ambient and laboratory conditions for each city, but not between cities.</p> Full article ">Figure 3
<p>Mass concentrations of aerosol liquid water and particulate sulfate are abundant and positively associated in Washington D.C., and not Phoenix, AZ in 2004. 2004 monthly mean values of ambient water content, relative humidity, and sulfate (<b>a</b>) (left) Phoenix, Arizona, and (<b>b</b>) (right) Washington D.C. Each variable is presented as the mean (solid line) and 95% confidence interval (shaded area and dotted lines). Pie charts illustrate the monthly average dry mass (brown) and water fractions (blue) for each month in each city at ambient and lab conditions. Pie chart diameter is relative to the largest monthly average mass concentration of each city.</p> Full article ">
23 pages, 3041 KiB  
Review
Long-Term Variations of Air Quality Influenced by Surface Ozone in a Coastal Site in India: Association with Synoptic Meteorological Conditions with Model Simulations
by Resmi C T, Nishanth T, Satheesh Kumar M K, Balachandramohan M and Valsaraj K T
Atmosphere 2020, 11(2), 193; https://doi.org/10.3390/atmos11020193 - 12 Feb 2020
Cited by 13 | Viewed by 3782
Abstract
Atmospheric ozone (O3) in the surface level plays a central role in determining air quality and atmospheric oxidizing capacity. In this paper, we review our comprehensive results of simultaneous measurements of surface ozone (O3) and its precursor gas (NOx) [...] Read more.
Atmospheric ozone (O3) in the surface level plays a central role in determining air quality and atmospheric oxidizing capacity. In this paper, we review our comprehensive results of simultaneous measurements of surface ozone (O3) and its precursor gas (NOx) and weather parameters that were carried out continuously for a span of six years (January 2013–December 2018) at a typical rural coastal site, Kannur (11.9° N, 75.4° E) in South India. Surface O3 concentration reached its maximum during daytime hours and minimum during the night time. The influence of solar radiation and water content on variations of O3 are discussed. A Multi-Layer Perceptron (MLP) artificial neural network technique has been used to understand the effect of atmospheric temperature on the increase in O3 over the past six years. This has been found that temperature has been a major contributor to the increase in O3 levels over the years. The National Centre for Atmospheric Research- Master Mechanism (NCAR-MM) Photochemical box model study was conducted to validate the variations of O3 in different seasons and years, and the results were shown to be in good agreement with observed trends. Full article
(This article belongs to the Special Issue 10th Anniversary of Atmosphere: Air Quality)
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<p>Geographical location of observational site at Kannur.</p> Full article ">Figure 2
<p>Diurnal variation of O<sub>3</sub> and solar radiation.</p> Full article ">Figure 3
<p>(<b>a</b>) Diurnal, (<b>b</b>) monthly variation of O<sub>3</sub>, NOx, and water content for the period January 2013–December 2018.</p> Full article ">Figure 4
<p>Neural network output for O<sub>3</sub> concentration with respect to the variation of temperature, solar radiation, and relative humidity.</p> Full article ">Figure 5
<p>Scatter plot showing the correlations among (<b>a</b>) observed temperature, (<b>b</b>) solar radiation, and (<b>c</b>) relative humidity with observed and modelled ozone.</p> Full article ">Figure 6
<p>Correlation between predicted daytime O<sub>3</sub> concentration using neural model and observed daytime maximum O<sub>3</sub> concentration.</p> Full article ">Figure 7
<p>Annual mean 24-h variations of (<b>a</b>) O<sub>3</sub> and <b>(b)</b> surface air temperature for the years 2013 and 2018.</p> Full article ">Figure 8
<p>Diurnal variation of measured (averaged over the period 2013–2018) and modeled O<sub>3</sub> for (<b>a</b>) winter, (<b>b</b>) summer, (<b>c</b>) scatter plot showing the correlation between model O<sub>3</sub> and measured O<sub>3</sub> for winter, (<b>d</b>) summer.</p> Full article ">Figure 9
<p>Diurnal variation of measured (averaged over the period 2013–2018) and modeled O<sub>3</sub> for (<b>a</b>) monsoon, (<b>b</b>) post monsoon, and (<b>c</b>) scatter plot showing the correlation between model O<sub>3</sub> and measured O<sub>3</sub> for monsoon, (<b>d</b>) post monsoon.</p> Full article ">Figure 10
<p>Yearly average (over the period 2013–2018) (<b>a)</b> diurnal profile of measured and modeled O<sub>3,</sub> (<b>b</b>) correlation between modeled O<sub>3</sub> and measured O<sub>3</sub>.</p> Full article ">
12 pages, 1779 KiB  
Article
An Air Quality Health Index (AQHI) with Different Health Outcomes Based on the Air Pollution Concentrations in Stockholm during the Period of 2015–2017
by Henrik Olstrup
Atmosphere 2020, 11(2), 192; https://doi.org/10.3390/atmos11020192 - 12 Feb 2020
Cited by 10 | Viewed by 5511
Abstract
The Air Quality Health Index (AQHI) is a tool that has been developed in order to address the health effects caused by simultaneous exposure to several different air pollutants. Short-term health effects in terms of mortality or morbidity are used in order to [...] Read more.
The Air Quality Health Index (AQHI) is a tool that has been developed in order to address the health effects caused by simultaneous exposure to several different air pollutants. Short-term health effects in terms of mortality or morbidity are used in order to construct an index. In this study, different indexes for different health outcomes, based on the concentrations of NO2, O3, and PM10 at an urban background measuring station in Stockholm during the period of 2015–2017, are calculated by using different risk-coefficients obtained from a meta-analysis. An AQHI based on local risk-coefficients for asthma emergency department visits (AEDV) in Stockholm is also included in the analysis. Correlation coefficients between different pairs of AQHIs, where the additive effects associated with exposure to NO2, O3, and PM10 during 2015–2017 are used, exhibit R-values as in 12 out of 15 cases exceed 0.80. However, the average risk increase for different AQHIs are very different, where indexes based on hospital admissions for asthma are larger than those based on mortality outcomes. An overall conclusion is that different AQHIs for different population groups are not needed, but the index may need to be weighted differently for different population groups. Full article
(This article belongs to the Special Issue Health Impact Assessment of Air Pollution)
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<p>The calculated average monthly percentage risk increase associated with NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub> for all-cause mortality in all ages in Stockholm during 2015–2017 based on meta-coefficients [<a href="#B8-atmosphere-11-00192" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>The calculated average monthly percentage risk increase associated with NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub> for all-cause mortality in elderly in Stockholm during 2015–2017 based on meta-coefficients [<a href="#B8-atmosphere-11-00192" class="html-bibr">8</a>].</p> Full article ">Figure 3
<p>The calculated average monthly percentage risk increase associated with NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub> for cardiovascular mortality in all ages in Stockholm during 2015–2017 based on meta-coefficients [<a href="#B8-atmosphere-11-00192" class="html-bibr">8</a>].</p> Full article ">Figure 4
<p>The calculated average monthly percentage risk increase associated with NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub> for hospital admissions for asthma in all ages in Stockholm during 2015–2017 based on meta-coefficients [<a href="#B8-atmosphere-11-00192" class="html-bibr">8</a>].</p> Full article ">Figure 5
<p>The calculated average monthly percentage risk increase associated with NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub> for hospital admissions for asthma in children in Stockholm during 2015–2017 based on meta-coefficients [<a href="#B8-atmosphere-11-00192" class="html-bibr">8</a>].</p> Full article ">Figure A1
<p>Daily mean concentrations of NO<sub>2</sub> at an urban background station in Stockholm during the period of 2015–2017.</p> Full article ">Figure A2
<p>Daily maximum 8-h mean concentrations of O<sub>3</sub> at an urban background station in Stockholm during the period of 2015–2017.</p> Full article ">Figure A3
<p>Daily mean concentrations of PM<sub>10</sub> at an urban background station in Stockholm during the period of 2015–2017.</p> Full article ">
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