[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Issue
Volume 9, July
Previous Issue
Volume 9, May
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Atmosphere
Volume 9
Issue 6
share announcement

Atmosphere, Volume 9, Issue 6 (June 2018) – 34 articles

Cover Story (view full-size image): The Aullene fire (23 July 2009) burned more than 3000 ha of forest, of which 2000 ha during the first afternoon. This study explores by simulation the strong fire–atmosphere interactions leading to intense updrafts extending above the boundary layer and horizontal wind speeds feeding the fire. Simulation of such scenarios involves reproducing processes that scale from the flames to the larger regional smoke transport, requiring the use of a coupled fire–atmosphere model. View this paper.
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
12 pages, 12643 KiB  
Article
An Ensemble Mean and Evaluation of Third Generation Global Climate Reanalysis Models
by Jeffrey D. Auger, Sean D. Birkel, Kirk A. Maasch, Paul A. Mayewski and Keah C. Schuenemann
Atmosphere 2018, 9(6), 236; https://doi.org/10.3390/atmos9060236 - 19 Jun 2018
Cited by 13 | Viewed by 5040
Abstract
We have produced a global ensemble mean of the four third-generation climate reanalysis models for the years 1981–2010. The reanalysis system models used in this study are National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather [...] Read more.
We have produced a global ensemble mean of the four third-generation climate reanalysis models for the years 1981–2010. The reanalysis system models used in this study are National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Interim (ERA-I), Japan Meteorological Agency (JMA) 55-year Reanalysis (JRA-55), and National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA). Two gridded datasets are used as a baseline, for temperature the Global Historical Climatology Network (GHCN), and for precipitation the Global Precipitation Climatology Centre (GPCC). The reanalysis ensemble mean is used here as a comparison tool of the four reanalysis members. Meteorological fields investigated within the reanalysis models include 2-m air temperature, precipitation, and 500-hPa geopotential heights. Comparing the individual reanalysis models to the ensemble mean, we find that each perform similarly over large domains but exhibit significant differences over particular regions. Full article
(This article belongs to the Special Issue Precipitation: Measurement and Modeling)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>–<b>d</b>) Gridded differences in 2-m air temperature for each reanalysis model subtract Global Historical Climatology Network (GHCN) over the 1981–2010 period. Panel (<b>e</b>) shows the same but for the ensemble mean.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>d</b>) Gridded differences in 2-m air temperature for each reanalysis model subtract (<b>e</b>) the ensemble mean 2-m air temperature over the 1981–2010 period. Black boxes show locations for annual average time series in <a href="#atmosphere-09-00236-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 3
<p>Time series of each member and ensemble mean (Ens) of annual 2-m air temperature averaged over the 1981–2010 period. Locations shown as black boxes in <a href="#atmosphere-09-00236-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Global annual 2-m air temperature for each member and the ensemble mean (Ens) during 1981–2010. Correlation coefficients (r) for each time series shown in upper, left table.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>d</b>) Gridded differences in precipitation for each reanalysis model subtract Global Precipitation Climatology Centre (GPCC) over the 1981–2010 period. Panel (<b>e</b>) shows the same but for the ensemble mean.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>d</b>) Gridded differences in precipitation for each reanalysis model subtract (<b>e</b>) the ensemble mean annual precipitation over the 1981–2010 period.</p> Full article ">Figure 7
<p>(<b>a</b>) The ensemble mean precipitation over the 1981–2010 average for the western hemispheric tropics. The black curve in (<b>b</b>) is the ensemble zonal average of precipitation from (<b>a</b>). The black curves in (<b>c</b>–<b>f</b>) are the respective precipitation zonal averages over the same region as (<b>a</b>) for each reanalysis model. The gray curves in (<b>c</b>–<b>f</b>) show the differences of the ensemble subtracted from the reanalysis model. The thin black line in (<b>c</b>–<b>f</b>) marks zero difference.</p> Full article ">Figure 8
<p>A comparison between CFSR (<b>top</b>) and ERA-I (<b>bottom</b>) 1981–2010 average annual precipitation totals. CFSR contains unrealistic, harmonic spherical artifacts on short and long-term averages whereas ERA-I (as well as JRA-55 and Modern-Era Retrospective Analysis for Research and Applications (MERRA)) displays a more spatially consistent output.</p> Full article ">Figure 9
<p>(<b>a</b>–<b>d</b>) The difference of 500-hPa geopotential heights between the reanalysis and (<b>e</b>) the ensemble, reanalysis subtract the ensemble over the 1981–2010 period. Horizontal-line artifacts in maps (<b>a</b>–<b>d</b>) are inherited from regridding within MERRA, which was averaged into the ensemble.</p> Full article ">
17 pages, 8697 KiB  
Article
East Asian Summer Monsoon Representation in Re-Analysis Datasets
by Bo Huang, Ulrich Cubasch and Yan Li
Atmosphere 2018, 9(6), 235; https://doi.org/10.3390/atmos9060235 - 16 Jun 2018
Cited by 6 | Viewed by 5413
Abstract
Eight current re-analyses—NCEP/NCAR Re-analysis (NCEPI), NCEP/DOE Re-analysis (NCEPII), NCEP Climate Forecast System Re-analysis (CFSR), ECMWF Interim Re-analysis (ERA-Interim), Japanese 55-year Re-analysis (JRA-55), NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA), NOAA Twentieth Century Re-analysis (20CR), and ECMWF’s first atmospheric re-analysis of the [...] Read more.
Eight current re-analyses—NCEP/NCAR Re-analysis (NCEPI), NCEP/DOE Re-analysis (NCEPII), NCEP Climate Forecast System Re-analysis (CFSR), ECMWF Interim Re-analysis (ERA-Interim), Japanese 55-year Re-analysis (JRA-55), NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA), NOAA Twentieth Century Re-analysis (20CR), and ECMWF’s first atmospheric re-analysis of the 20th century (ERA-20C)—are assessed to clarify their quality in capturing the East Asian summer monsoon (EASM) rainfall structure and its associated general circulation. They are found to present similar rainfall structures in East Asia, whereas they illustrate some differences in rainfall intensity, especially at lower latitudes. The third generation of re-analysis shows a better estimate of rainfall structure than that in the first and extended generation of re-analysis. Given the fact that the rainfall is ingested by the data assimilation system, the re-analysis cannot improve its production of rainfall quality. The mean sea level pressure is generated by re-analysis, showing a significant uncertainty over the Tibetan Plateau and its surrounding area. In that region, the JRA-55 and MERRA have a negative bias (BIAS), while the other six re-analyses present a positive BIAS to the observed mean sea level pressure. The 20CR and the ERA-20C are ancillary datasets to analyse the EASM due to the fact that they only apply limit observations into the data assimilation system. These two re-analyses demonstrate a prominent difference from the observed winds in the upper-air. Although the upper level winds exhibit difference, the EASM index is consistent in the eight re-analyses, which are based upon the zonal wind over 850 hPa. Full article
(This article belongs to the Special Issue Precipitation: Measurement and Modeling)
►▼ Show Figures

Figure 1

Figure 1
<p>Precipitation latitude-time cross section of observation ((<b>a</b>) Global Precipitation Climatology Project; GPCP) and different re-analysis datasets (<b>b–i</b>) in the East Asian summer monsoon region (0–50° N, 100–140° E) from 1979 to 2010. The number in the upper-left corner of each panel indicates the pattern correlation coefficient (<b>left</b>) and the root-mean-square error (<b>right</b>) skill of the observed precipitation.</p> Full article ">Figure 2
<p>Temporal statistics describing inter-annual variability of the re-analysis datasets and the multi-datasets ensemble mean in terms of June–July–August (JJA) mean precipitation (black), zonal winds (blue) and meridional winds (green) at 850 hPa, and mean sea level pressure (red) over the East Asian summer monsoon (EASM) region (0–50° N, 100–140° E) (<b>a</b>), the EASM land only (<b>b</b>), and the EASM ocean only (<b>c</b>) from 1979 to 2010. The Global Precipitation Climatology Project (GPCP) was employed as the reference data for precipitation, while the mean sea level pressure was compared by extended the Hadley Centre’s monthly historical mean sea level pressure dataset (HadSLPr2), and the wind fields were evaluated by the Integrated Global Radiosonde Archive (IGRA).</p> Full article ">Figure 3
<p>Spatial distribution of the multi-reanalysis ensemble deviation for June–July–August mean precipitation (pr), mean sea level pressure (SLP), and winds at 850 hPa (u850 and v850) from 1979 to 2010. The red box represents the East Asian summer monsoon region (0–50° N, 100–140° E).</p> Full article ">Figure 4
<p>Summer (JJA) precipitation of the Global Precipitation Climatology Project (GPCP) and the precipitation anomalies ‘re-analysis minus GPCP’ in 1979–2010. The presented anomalies of precipitation pass Student’s <span class="html-italic">t</span>-test at 0.05 level. The green box represents the East Asian summer monsoon region (0–50° N, 100–140° E).</p> Full article ">Figure 5
<p>Summer (JJA) mean sea level pressure (observation shaded) of the extended Hadley Centre monthly historical mean sea level pressure dataset (HadSLPr2) and wind fields at 850 hPa (Observation vector) of the Integrated Global Radiosonde Archive (IGRA) and the mean sea level pressure anomalies ‘re-analysis minus HadSLPr2; shaded’, and the wind anomalies ‘re-analysis minus IGRA; vector’ in 1979–2010. The presented anomalies of precipitation pass Student’s <span class="html-italic">t</span>-test at 0.05 level. The green box represents the East Asian summer monsoon region (0–50° N, 100–140° E).</p> Full article ">Figure 6
<p>Root-mean-square error skill score for summer (June–July–August) rainfall during 1979–2010. The Global Precipitation Climatology Project (GPCP) is employed as the reference data for rainfall. The green box is the East Asian summer monsoon region (0–50° N, 100–140° E).</p> Full article ">Figure 7
<p>As in <a href="#atmosphere-09-00235-f006" class="html-fig">Figure 6</a>, but for mean sea level pressure (SLP; shaded) and lower level winds (at 850 hPa; vector). The reference data for mean sea level pressure is the extended Hadley Centre monthly historical mean sea level pressure dataset (HadSLPr2). The Integrated Global Radiosonde Archive (IGRA) is selected to evaluate the winds. The green box is the East Asian summer monsoon region (0–50° N, 100–140° E).</p> Full article ">Figure 8
<p>East Asian summer monsoon index of observation (IGRA) and multi-reanalysis ensemble mean (<b>a</b>), individual re-analysis dataset (<b>b</b>), and the 20CR and ERA-20C in 1900–1957 (<b>c</b>). The number following the re-analysis presents the correlation coefficient between the East Asian summer monsoon index produced by re-analysis and the observed one during 1958–2010 (left), during 1958–1978 (middle), and during 1979–2010 (right).</p> Full article ">
16 pages, 3104 KiB  
Article
A Velocity Dealiasing Algorithm on Frequency Diversity Pulse-Pair for Future Geostationary Spaceborne Doppler Weather Radar
by Xuehua Li, Chuanzhi Wang, Zhengxia Qin, Jianxin He, Fang Liu and Qing Sun
Atmosphere 2018, 9(6), 234; https://doi.org/10.3390/atmos9060234 - 15 Jun 2018
Cited by 3 | Viewed by 4428
Abstract
Velocity ambiguity is one of the main challenges in accurately measuring velocity for the future Geostationary Spaceborne Doppler Weather Radar (GSDWR) due to its short wavelength. The aim of this work was to provide a novel velocity dealiasing method for frequency diversity for [...] Read more.
Velocity ambiguity is one of the main challenges in accurately measuring velocity for the future Geostationary Spaceborne Doppler Weather Radar (GSDWR) due to its short wavelength. The aim of this work was to provide a novel velocity dealiasing method for frequency diversity for the future implementation of GSDWR. Two different carrier frequencies were transmitted on the adjacent pulse-pair and the order of the pulse-pair was exchanged during the transmission of the next pulse-pair. The Doppler phase shift between these two adjacent pulses was estimated based on the technique of the frequency diversity pulse-pair (FDPP), and Doppler velocity was estimated on the sum of the Doppler phase within the adjacent pulse repetition time (PRT). From the theoretical result, the maximum unambiguous velocity estimated by FDPP is only decided by the interval time of the two adjacent pulses and radar wavelength. An echo signal model on frequency diversity was established to simulate echo signals of the GSDWR to verify the extension of the maximum unambiguous velocity and the accuracy of the velocity estimation for FDPP used on GSDWR. The study demonstrates that the FDPP algorithm can extend the maximum unambiguous velocity greater than the Stagger PRT method and the unambiguous range and velocity are no longer limited by the chosen value of pulse repetition frequency (PRF). In the Ka band, the maximum unambiguous velocity can be extended to 105 m/s when the interval time is 10 μs and most velocity estimation biases are less than 0.5 m/s. Full article
(This article belongs to the Special Issue Radar and Satellite Observations of Precipitation Systems: Climate Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>The geometric relationship between Geostationary Spaceborne Doppler Weather Radar (GSDWR) and the earth. The altitude of the geostationary orbit <span class="html-italic">H<sub>Orb</sub></span> is 36,000 km and the scan angle <span class="html-italic">θ</span> is from 0° to 4°. (<b>a</b>) Spiral scan model; GSDWR performs the spiral scan from nadir to 4° and its horizontal resolution is from 12 km (nadir) to 14 km (4°); (<b>b</b>) Model of relationship between satellite and ground angle, describing the relationship between the scan angle <span class="html-italic">θ</span>, incidence angle <span class="html-italic">α</span>, and the grazing angle <span class="html-italic">φ<sub>Gra</sub></span>; (<b>c</b>) Antenna scanning strategy, mainly used to describe the relationship between maximum ambiguous distance and PRF.</p> Full article ">Figure 2
<p>The maximum radial velocity at different pointing angles when observing Saffir–Simpson Hurricane Wind Scale (SSHS)-5 by GSDWR when the pointing angles <span class="html-italic">α</span> and <span class="html-italic">β</span> are +4° or −4°, with a maximum radial velocity of 40.7 m/s.</p> Full article ">Figure 3
<p>Illustration of a frequency diversity pulse-pair (FDPP) pulse transmission mode. Two pulses at different central frequencies of <span class="html-italic">f</span><sub>1</sub> and <span class="html-italic">f</span><sub>2</sub> are transmitted with a time interval Δ<span class="html-italic">T</span>. Generally speaking, Δ<span class="html-italic">T</span> is relatively small, only taking a few μs or dozens of μs. The combination of four pulses is a complete pulse repetition time. The difference between <span class="html-italic">f</span><sub>1</sub> and <span class="html-italic">f</span><sub>2</sub> is usually a few MHz or the value of radar band. PRT is the complete pulse repetition time.</p> Full article ">Figure 4
<p>FDPP algorithm phase estimation and velocity estimation. (<b>a</b>) Phase estimation of ΔΦ<span class="html-italic"><sub>R</sub></span><sub>1</sub> and ΔΦ<span class="html-italic"><sub>R</sub></span><sub>2</sub>; the solid line indicates ΔΦ<span class="html-italic"><sub>R</sub></span><sub>1</sub> and dotted line indicates ΔΦ<span class="html-italic"><sub>R</sub></span><sub>2</sub>; (<b>b</b>) the phase sum of ΔΦ<span class="html-italic"><sub>R</sub></span><sub>1</sub> and ΔΦ<span class="html-italic"><sub>R</sub></span><sub>2</sub> and (<b>c</b>) the velocity estimation; a dotted line indicates the unmodified velocity and solid line indicates the modified result of velocity estimation.</p> Full article ">Figure 5
<p>The influence of PRF, the signal to noise ratio (SNR), and Δ<span class="html-italic">T</span> on the velocity estimation error. Velocity estimation deviation was determined with Monte-Carlo statistical tests for 1000 repetitions. (<b>a</b>) Velocity estimation error under different SNR; (<b>b</b>) velocity estimation error with different PRF and (<b>c</b>) velocity estimation error with different Δ<span class="html-italic">T</span> and SNR.</p> Full article ">Figure 6
<p>(<b>a</b>) The spatial distribution of the precipitation particles model and (<b>b</b>) the antenna weighted and distance weighted schemes.</p> Full article ">Figure 7
<p>(<b>a</b>) Flowchart of the simulation used to verify the FDPP algorithm; (<b>b</b>) reflectivity and velocity data of APR-2. The red rectangle <b>A</b> is one radial data region of GSDWR.</p> Full article ">Figure 8
<p>FDPP velocity estimation and dealiasing results. (<b>a</b>) Comparison between FDPP and PPP velocity estimation results when PRF is 4000 Hz: the maximum unambiguous velocity is 8.5 m/s with PPP and 105.6 m/s with FDPP. The black line represents the estimation results; (<b>b</b>) Comparison between FDPP and PPP velocity estimation results when PRF is 1000 Hz.</p> Full article ">Figure 9
<p>Comparison of the results of two velocity estimation methods. (<b>a</b>) Velocity echo with PPP method and the real velocity of the elliptical region is greater than 8.5 m/s; the white part of the ellipse indicates the ambiguous region; (<b>b</b>) velocity echo with FDPP method and the velocity in the ellipse close to the virtual velocity.</p> Full article ">Figure 10
<p>Velocity estimation bias and probability distribution of FDPP at different SNR. The red line in the scatter plots represents the zero-deviation standard, and the histogram indicates the probability of velocity bias being less than 0.5 m/s.</p> Full article ">Figure 11
<p>Velocity estimation bias and probability distribution of FDPP at different Δ<span class="html-italic">T</span>. The red line in the scatter plots represents the zero-deviation standard, and the histogram indicates the probability of velocity bias being less than 0.5 m/s.</p> Full article ">Figure 12
<p>Velocity estimation bias and probability distribution of FDPP at different PRF. The red line in the scatter plots represents the zero-deviation standard, and the histogram indicates the probability of velocity bias being less than 0.5 m/s.</p> Full article ">Figure 13
<p>(<b>a</b>) Velocity and spectrum width with FDPP estimation: the maximum spectral width is 4 m/s and most of the spectral width values are within the range of 0–2 m/s. Where the marginal part and the speed value change are obvious, and the velocity data and the velocity spectrum are relatively large; (<b>b</b>) Distribution characteristics of velocity spectral width. The <span class="html-italic">X</span>-axis represents the normal distribution characteristics of the theory, and the <span class="html-italic">Y</span>-axis represents the velocity spectrum width data. The red line represents the ideal distribution; the more data scattered closer to the red line, the closer the spectral width data is to the normal distribution.</p> Full article ">
13 pages, 64372 KiB  
Communication
Snow Level Characteristics and Impacts of a Spring Typhoon-Originating Atmospheric River in the Sierra Nevada, USA
by Benjamin J. Hatchett
Atmosphere 2018, 9(6), 233; https://doi.org/10.3390/atmos9060233 - 15 Jun 2018
Cited by 14 | Viewed by 6080
Abstract
On 5–7 April 2018, a landfalling atmospheric river resulted in widespread heavy precipitation in the Sierra Nevada of California and Nevada. Observed snow levels during this event were among the highest snow levels recorded since observations began in 2002 and exceeded 2.75 km [...] Read more.
On 5–7 April 2018, a landfalling atmospheric river resulted in widespread heavy precipitation in the Sierra Nevada of California and Nevada. Observed snow levels during this event were among the highest snow levels recorded since observations began in 2002 and exceeded 2.75 km for 31 h in the northern Sierra Nevada and 3.75 km for 12 h in the southern Sierra Nevada. The anomalously high snow levels and over 80 mm of precipitation caused flooding, debris flows, and wet snow avalanches in the upper elevations of the Sierra Nevada. The origin of this atmospheric river was super typhoon Jelawat, whose moisture remnants were entrained and maintained by an extratropical cyclone in the northeast Pacific. This event was notable due to its April occurrence, as six other typhoon remnants that caused heavy precipitation with high snow levels (mean = 2.92 km) in the northern Sierra Nevada all occurred during October. Full article
(This article belongs to the Special Issue Tropical Cyclones and Their Impacts)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of study area. Abbreviations include: Shasta Dam (STD), Bucks Lake (BKL), Chico (CCO), Middle Fork of the Feather River at Merrimac (MFF), Colfax (CFC), Merced River at Pohono Bridge (MRP), Half Moon Bay (OXMT), Mammoth Pass (MHP); New Exchequer (NEW), and Pine Flat Dam (PFD).</p> Full article ">Figure 2
<p>Non-interpolated outgoing longwave radiation anomalies (W m<sup>2</sup>) for (<b>a</b>) 25 March 2018; (<b>b</b>) 27 March 2018; (<b>c</b>) 29 March 2018; (<b>d</b>) 31 March 2018; (<b>e</b>) 2 April 2018; (<b>f</b>) 4 April 2018; (<b>g</b>) 6 April 2018; (<b>h</b>) 7 April 2018. The green star denotes the approximate center of the typhoon based on Japanese Meteorological Agency Regional Specialized Meteorological Center (RSMC) Tokyo-Typhoon Center best track data [<a href="#B17-atmosphere-09-00233" class="html-bibr">17</a>] for panels (<b>a</b>–<b>d</b>). The typhoon center is estimated for panel (<b>e</b>) based on analysis of MERRA 1000 hPa geopotential height and sea level pressure [<a href="#B20-atmosphere-09-00233" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>Morphed Integrated Microwave Imagery at CIMMS (Cooperative Institute for Meteorological Satellite Studies)-Total Precipitable Water version 2 (MIMIC-TPW2; [<a href="#B18-atmosphere-09-00233" class="html-bibr">18</a>]) total precipitable water (mm; filled contours) observations and MERRA (Modern-Era Retrospective analysis for Research and Applications) integrated vapor transport (IVT; kg m<sup>−1</sup> s<sup>−1</sup>; black vectors) for: (<b>a</b>) 25 March 2018; (<b>b</b>) 27 March 2018; (<b>c</b>) 29 March 2018; (<b>d</b>) 31 March 2018; (<b>e</b>) 2 April 2018; (<b>f</b>) 4 April 2018; (<b>g</b>) 6 April 2018; (<b>h</b>) 7 April 2018. Only IVT vectors exceeding 500 kg m<sup>−1</sup> s<sup>−1</sup> are plotted with the largest vector equal to 1500 kg m<sup>−1</sup> s<sup>−1</sup>. For clarity, only every fifth IVT vector is shown.</p> Full article ">Figure 4
<p>(<b>a</b>) Time series of precipitable water (mm) at the California (CA) coast near Half Moon Bay; (<b>b</b>) precipitation observations (mm) at Bucks Lake, California; (<b>c</b>) brightband-derived snow levels at five snow level radars upstream of the Sierra Nevada crest (km; ordered from north to south) and (<b>d</b>) observed streamflow (cms) on the Middle Fork of the Feather River. Vertical blue bars denote the two periods of intense precipitation.</p> Full article ">Figure 5
<p>Histogram of hours of observed snow levels (km) at Chico, California for all cool seasons (October–April; blue bars), five other typhoon remnant-related precipitation (PPT) events (red bars; composed of six typhoon remnants), and the 5–7 April 2018 event (black bars).</p> Full article ">Figure 6
<p>High elevation hydrogeomorphological impacts of the 5–7 April atmospheric river. (<b>a</b>) Deadman Creek near Sonora Pass (2698 m); (<b>b</b>) Fresno Bowl avalanche near Mammoth, California (3000 m); (<b>c</b>) Deposition of debris onto Lower Pine Lake (3032 m); (<b>d</b>) Deposition of debris onto Upper Pine Lake (3109 m); (<b>e</b>) Flooding of Dana Meadow (2962 m); (<b>f</b>) Flooding of the Tuolumne River in Tuolumne Meadows (2626 m); (<b>g</b>) Pine Creek mining road cut by debris chute (~2700 m).</p> Full article ">
15 pages, 5233 KiB  
Article
PM1 Chemical Characterization during the ACU15 Campaign, South of Mexico City
by Dara Salcedo, Harry Alvarez-Ospina, Oscar Peralta and Telma Castro
Atmosphere 2018, 9(6), 232; https://doi.org/10.3390/atmos9060232 - 15 Jun 2018
Cited by 12 | Viewed by 4859
Abstract
The “Aerosoles en Ciudad Universitaria 2015” (ACU15) campaign was an intensive experiment measuring chemical and optical properties of aerosols in the winter of 2015, from 19 January to 19 March on a site in the south of Mexico City. The mass concentration and [...] Read more.
The “Aerosoles en Ciudad Universitaria 2015” (ACU15) campaign was an intensive experiment measuring chemical and optical properties of aerosols in the winter of 2015, from 19 January to 19 March on a site in the south of Mexico City. The mass concentration and chemical composition of the non-refractory submicron particulate matter (NR-PM1) was determined using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). The total NR-PM1 mass concentration measured was lower than reported in previous campaigns that took place north and east of the city. This difference might be explained by the natural variability of the atmospheric conditions, as well as the different sources impacting each site. However, the composition of the aerosol indicates that the aerosol is more aged (a larger fraction of the mass corresponds to sulfate and to low-volatility organic aerosol (LV-OOA)) in the south than the north and east areas; this is consistent with the location of the sources of PM and their precursors in the city, as well as the meteorological patterns usually observed in the metropolitan area. Full article
(This article belongs to the Special Issue Aerosol Mass Spectrometry)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the Mexico City Metropolitan Area, showing the sites mentioned in the text (red stars). Thick black lines represent state boundaries, and thin grey contour lines represent topographical features. Urban areas are colored in grey. LAA: Laboratorio de Análisis Ambiental; CCA: Centro de Ciencias de la Atmósfera.</p> Full article ">Figure 2
<p>Time series of the mass concentration and fraction of the non-refractory submicron particulate matter (NR-PM<sub>1</sub>) components (organics, sulfate, nitrate, ammonium, and chloride), meteorological variables (temperature (T), relative humidity (RH), wind speed (WS) and direction (WD), and rain), black carbon (BC), and other pollutants (CO, SO<sub>2</sub>, O<sub>3</sub>, and NOx) during ACU15. The gray area highlights the acidic periods mentioned in <a href="#sec3dot3-atmosphere-09-00232" class="html-sec">Section 3.3</a>. ACU15: Aerosoles en Ciudad Universitaria 2015.</p> Full article ">Figure 3
<p>Average mass fraction diurnal profile and average percent composition of NR-PM<sub>1</sub> as determined by the Aerosol Chemical Speciation Monitor (ACSM) during ACU15.</p> Full article ">Figure 4
<p>Average diurnal profiles of the mass concentration of the total NR-PM<sub>1</sub> and its component during ACU15 and previous studies in Mexico City involving an AMS [<a href="#B13-atmosphere-09-00232" class="html-bibr">13</a>,<a href="#B18-atmosphere-09-00232" class="html-bibr">18</a>,<a href="#B19-atmosphere-09-00232" class="html-bibr">19</a>]. AMS: Aerosol Mass Spectrometer.</p> Full article ">Figure 5
<p>(<b>a</b>) Scatter plot of the predicted ammonium assuming neutrality (ammonium_pred) vs. the measured ammonium. (<b>b</b>) Time series of the extra ammonium needed to neutralize the aerosol (ammonium_mis). Data points with ammonium_mis larger than the average plus one standard deviation are colored in black for visualization purposes.</p> Full article ">Figure 6
<p>Wind speed (WS) and wind direction (WD) vs. hour of the day. Black circles represent the median WS during the campaign, and black horizontal bars in both panels represent the median WD during the campaign. Colored small circles, are 15 min wind data for the whole campaign (<b>a</b>) and during the first acidic period (<b>b</b>).</p> Full article ">Figure 7
<p>Mass spectra of the Positive Matrix Factorization (PMF) organic factors during ACU15 and MILAGRO (Megacity Initiative: Local And Global Research Observations) [<a href="#B13-atmosphere-09-00232" class="html-bibr">13</a>]. HOA: hydrocarbon-like organic aerosol; SV-OOA: semi-volatile oxygenated organic aerosol; LV-OOA: low-volatility oxygenated organic aerosol.</p> Full article ">Figure 8
<p>Average diurnal profiles of the mass concentration of the PMF-AMS organic factors during ACU15. Each factor is compared with other atmospheric species as markers of primary emissions (<b>a</b>), photochemistry (<b>b</b>), and regional transport (<b>c</b>).</p> Full article ">Figure 9
<p>(<b>a</b>) Time series of the signal of ion <span class="html-italic">m</span>/<span class="html-italic">z</span> 60 during ACU15. (<b>b</b>) Times series of the signal of ion <span class="html-italic">m</span>/<span class="html-italic">z</span> 60, organics in NR-PM<sub>1</sub>, and hydrocarbon-like organic aerosol (HOA) and semi-volatile oxygenated organic aerosol (SV-OOA) PMF factors from 27 February to 5 March. All signals are scaled arbitrarily.</p> Full article ">Figure 10
<p>Time series, diurnal profiles, and percent of total organic mass of PMF organic factors.</p> Full article ">Figure 11
<p>f44 vs. f43 of the Organics mass spectra. Grey points correspond to all data points during ACU15. Black symbols correspond to average f44 vs. f43 during the AMS campaigns [<a href="#B13-atmosphere-09-00232" class="html-bibr">13</a>,<a href="#B18-atmosphere-09-00232" class="html-bibr">18</a>,<a href="#B19-atmosphere-09-00232" class="html-bibr">19</a>]. Red symbols correspond to the PMF OOA factors during ACU15 and MILAGRO [<a href="#B13-atmosphere-09-00232" class="html-bibr">13</a>].</p> Full article ">
3 pages, 153 KiB  
Editorial
Climate Change and Human Health—The Links to the UN Landmark Agreement on Disaster Risk Reduction
by Virginia Murray and Thomas David Waite
Atmosphere 2018, 9(6), 231; https://doi.org/10.3390/atmos9060231 - 15 Jun 2018
Cited by 3 | Viewed by 4822
Abstract
n/a Full article
(This article belongs to the Special Issue Impacts of Climate Change on Human Health)
14 pages, 1759 KiB  
Article
Thermochemical Properties of PM2.5 as Indicator of Combustion Phase of Fires
by Yuch-Ping Hsieh, Glynnis Bugna and Kevin Robertson
Atmosphere 2018, 9(6), 230; https://doi.org/10.3390/atmos9060230 - 14 Jun 2018
Cited by 3 | Viewed by 4024
Abstract
Past studies suggest that certain properties of fire emitted particulate matter (PM) relate to the combustion phase (flaming, smoldering) of biomass burning, but to date there has been little consideration of such properties for use as combustion phase indicators. We studied the thermochemical [...] Read more.
Past studies suggest that certain properties of fire emitted particulate matter (PM) relate to the combustion phase (flaming, smoldering) of biomass burning, but to date there has been little consideration of such properties for use as combustion phase indicators. We studied the thermochemical properties of PM2.5 emitted from experimental and prescribed fires using multi-element scanning thermal analysis (MESTA). Resulting thermograms show that the carbon from PM2.5 generally can be grouped into three temperature categories: low (peak ~180 °C), medium (peak between 180–420 °C), and high (peak > 420 °C) temperature carbons. PM2.5 from smoldering phase combustion is composed of much more low-temperature carbon (fraction of total carbon = 0.342 ± 0.067, n = 9) than PM2.5 from the flaming phase (fraction of total carbon = 0.065 ± 0.018, n = 9). The fraction of low-temperature carbon of the PM2.5 correlates well with modified combustion efficiency (MCE; r2 = 0.76). Therefore, this MESTA thermogram method can potentially be used as a combustion phase indicator solely based on the property of PM2.5. Since the MESTA thermogram of PM2.5 can be determined independently of MCE, we have a second parameter to describe the combustion condition of a fire, which may refine our understanding of fire behavior and improve the accuracy of emission factor determinations. This PM2.5 indicator should be useful for discerning differential diffusion between PM2.5 and gases and providing insight into the impact of PM emission on atmospheric environment and the public health. Full article
(This article belongs to the Special Issue Fire and the Atmosphere)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The typical C and N thermograms of flaming PM<sub>2.5</sub> ((<b>A</b>) MCE = 0.98–0.99), smoldering PM<sub>2.5</sub> ((<b>B</b>) MCE = 0.83–0.84), and the PM<sub>2.5</sub> produced from the flaming drum after smoldering PM was introduced through the base of the drum ((<b>C</b>) MCE = 0.93–0.94). The C and N are plotted to their relative atomic unit scales; N is presented ×50. Dashed black lines indicate the partitioning of the C fraction into low, medium, and high temperature categories as described in the text.</p> Full article ">Figure 2
<p>Some examples of C and N thermograms of the field experiments, including flaming dominated combustion (<b>A</b>,<b>B</b>), smoldering dominated combustion (<b>C</b>,<b>D</b>), and mixed combustion phase (<b>E</b>,<b>F</b>). The C and N are plotted to their relative atomic unit scales; N is presented ×50. Dashed black lines indicate the partitioning of the C fraction into low, medium, and high temperature categories as described in the text.</p> Full article ">Figure 3
<p>Relationship between MCE of gases and the fraction of low-temperature carbon determined by MESTA. Round symbols = field measurements; square symbols = outdoor controlled experiment. Open circle = flaming combustion; gray fill = flaming with added smoldering PM in controlled experiments or flaming with intermediate MCE (0.90–0.95) in field measurements; black fill = smoldering combustion.</p> Full article ">Figure 4
<p>The relationship between the fractions of low-temperature carbon (peak at 180 °C) and the high-temperature carbon (&gt;420 °C) of the PM<sub>2.5</sub> collected in the field experiments. Round symbols = field measurements; square symbols = outdoor controlled experiment. Open circle = flaming combustion; gray fill = flaming with added smoldering PM in controlled experiments or flaming with intermediate MCE (0.90–0.95) in field measurements; black fill = smoldering combustion.</p> Full article ">Figure 5
<p>Examples of the various C and N thermograms of PM<sub>2.5</sub> samples with MCE = 0.95. The C and N are plotted to their relative atomic unit scales; N is presented ×10. Dashed black lines indicate the partitioning of the C fraction into low, medium, and high temperature categories as described in the text.</p> Full article ">
15 pages, 4512 KiB  
Article
Influences of the North Pacific Victoria Mode on the South China Sea Summer Monsoon
by Ruiqiang Ding, Jianping Li, Yu-heng Tseng, Lijuan Li, Cheng Sun and Fei Xie
Atmosphere 2018, 9(6), 229; https://doi.org/10.3390/atmos9060229 - 13 Jun 2018
Cited by 25 | Viewed by 7373
Abstract
Using the reanalysis data and the numerical experiments of a coupled general circulation model (CGCM), we illustrated that perturbations in the second dominant mode (EOF2) of springtime North Pacific sea surface temperature (SST) variability, referred to as the Victoria mode (VM), are closely [...] Read more.
Using the reanalysis data and the numerical experiments of a coupled general circulation model (CGCM), we illustrated that perturbations in the second dominant mode (EOF2) of springtime North Pacific sea surface temperature (SST) variability, referred to as the Victoria mode (VM), are closely linked to variations in the intensity of the South China Sea summer monsoon (SCSSM). The underlying physical mechanism through which the VM affects the SCSSM is similar to the seasonal footprinting mechanism (SFM). Thermodynamic ocean–atmosphere coupling helps the springtime SST anomalies in the subtropics associated with the VM to persist into summer and to develop gradually toward the equator, leading to a weakened zonal SST gradient across the western North Pacific (WNP) to central equatorial Pacific, which in turn induces an anomalous cyclonic flow over the WNP and westerly anomalies in the western equatorial Pacific that tend to strengthen the WNP summer monsoon (WNPSM) as well as the SCSSM. The VM influence on both the WNPSM and SCSSM is intimately tied to its influence on ENSO through westerly anomalies in the western equatorial Pacific. Full article
(This article belongs to the Special Issue Monsoons)
►▼ Show Figures

Figure 1

Figure 1
<p>Correlations of summer (June–September (JJAS)) precipitation (shaded) and wind vectors at 850 hPa (vector) with the concurrent South China Sea summer monsoon (SCSSM) index (SCSSMI). Positive (red) and negative (blue) precipitation anomalies, with correlation significant at the 90% confidence level, are stippled. Only wind vectors at 850 hPa with correlation significant at the 90% confidence level are shown.</p> Full article ">Figure 2
<p>Spatial patterns and corresponding principal components (PCs) of the first two leading EOF (empirical orthogonal function) modes of FMA (February–April)-averaged sea surface temperature (SST) anomalies over the North Pacific poleward of 20° N (after removing the global mean SST anomaly).</p> Full article ">Figure 3
<p>(<b>a</b>) Correlations of the February–April (FMA) VM index (VMI) with monthly SCSSMI from May to September. The horizontal dashed line shows the 90% confidence level; (<b>b</b>) Composite differences in the monthly evolution of the SCSSMI between strongly positive and negative VM cases. Red closed circles indicate the values significant at the 90% confidence level; (<b>c</b>) Time series of the FMA VMI (red line) and the following June–September (JAS) SCSSMI (green line).</p> Full article ">Figure 4
<p>Spatial properties of the leading singular value decomposition (SVD) mode for (<b>a</b>) the FMA SST anomalies in the North Pacific (20° N–65° N, 125° E–100° W) and (<b>b</b>) the following JAS precipitation anomalies in the SCS (South China Sea) monsoon region (0°–22° N, 110° E–120° E), shown as correlation maps of the respective heterogeneous SST and precipitation fields with the SVD leading normalized expansion coefficients. Areas with the correlation coefficients significant at the 90% confidence level are shaded. (<b>c</b>) The SVD leading normalized expansion coefficients of the FMA SST field (red line) and the following JAS precipitation field (green line).</p> Full article ">Figure 5
<p>Regressions of the 3-month averaged SST (shading), wind vectors at 850 hPa (vector), and precipitation (stippled) anomalies onto the FMA VMI for FMA (<b>a</b>); MAM (<b>b</b>); AMJ (<b>c</b>); MJJ (<b>d</b>); JJA (<b>e</b>); and JAS (<b>f</b>). Positive (red) and negative (blue) SST anomalies, significant at the 90% confidence level, are shaded. Positive (green) and negative (red) precipitation anomalies significant at the 80% confidence level are stippled. Only 850 hPa wind vectors significant at the 90% confidence level are shown.</p> Full article ">Figure 6
<p>(<b>a</b>) Distribution of the prescribed ideal heating and cooling for the Matsuno-Gill model. The location of the prescribed ideal heating (cooling) source is consistent with the distribution of maximum positive (negative) precipitation anomalies during JAS, with strength decreasing from the center to the surroundings. (<b>b</b>) Analytical solutions for horizontal winds forced by the heating and cooling in (<b>a</b>).</p> Full article ">Figure 7
<p>Composite differences in the seasonal evolution of SST (shading), wind vectors at 850 hPa (vector), and precipitation (stippled) anomalies between the forcing and control experiments for FMA (<b>a</b>); MAM (<b>b</b>); AMJ (<b>c</b>); MJJ (<b>d</b>); JJA (<b>e</b>); and JAS (<b>f</b>). Positive (red) and negative (blue) SST anomalies, significant at the 90% confidence level, are shaded. Positive (green) and negative (red) precipitation anomalies significant at the 90% confidence level are stippled. Only 850 hPa wind vectors significant at the 90% confidence level are shown.</p> Full article ">Figure 8
<p>Seasonal evolution of SST (shaded) and 850 hPa wind (vectors) anomalies in 2015 for FMA (<b>a</b>); MAM (<b>b</b>); AMJ (<b>c</b>); MJJ (<b>d</b>); JJA (<b>e</b>); and JAS (<b>f</b>).</p> Full article ">Figure 9
<p>(<b>a</b>) Scatterplot of the FMA VMI versus the following JAS SCSSMI, plotted only for those years in which the FMA VMI has the same sign as the following DJF (December–February) Niño3.4 index; (<b>b</b>) As for (<b>a</b>) but for only those years in which the FMA VMI has the opposite sign as the following DJF Niño3.4 index. In (<b>a</b>) and (<b>b</b>), the correlation coefficient of the FMA VMI with the following JAS SCSSMI is given in the upper left corner.</p> Full article ">Figure 10
<p>Correlation maps of the summer (JAS) SCSSMI with the previous spring (FMA) SST anomalies. Areas with correlation significant at the 90% confidence level are stippled.</p> Full article ">Figure 11
<p>Schematic figure illustrating how anomalous SST in the tropical Pacific associated with the VM causes an anomalous cyclonic flow over the WNP (western North Pacific) and westerly anomalies in the western equatorial Pacific that tend to intensify the WNP summer monsoon (WNPSM) as well as the SCSSM.</p> Full article ">
19 pages, 3151 KiB  
Article
A Closure Study of Total Scattering Using Airborne In Situ Measurements from the Winter Phase of TCAP
by Evgueni Kassianov, Larry K. Berg, Mikhail Pekour, James Barnard, Duli Chand, Jennifer Comstock, Connor Flynn, Arthur Sedlacek, John Shilling, Hagen Telg, Jason Tomlinson, Alla Zelenyuk and Jerome Fast
Atmosphere 2018, 9(6), 228; https://doi.org/10.3390/atmos9060228 - 12 Jun 2018
Cited by 3 | Viewed by 5106
Abstract
We examine the performance of our approach for calculating the total scattering coefficient of both non-absorbing and absorbing aerosol at ambient conditions from aircraft data. Our extended examination involves airborne in situ data collected by the U.S. Department of Energy’s (DOE) Gulf Stream [...] Read more.
We examine the performance of our approach for calculating the total scattering coefficient of both non-absorbing and absorbing aerosol at ambient conditions from aircraft data. Our extended examination involves airborne in situ data collected by the U.S. Department of Energy’s (DOE) Gulf Stream 1 aircraft during winter over Cape Cod and the western North Atlantic Ocean as part of the Two-Column Aerosol Project (TCAP). The particle population represented by the winter dataset, in contrast with its summer counterpart, contains more hygroscopic particles and particles with an enhanced ability to absorb sunlight due to the larger fraction of black carbon. Moreover, the winter observations are characterized by more frequent clouds and a larger fraction of super-micron particles. We calculate model total scattering coefficient at ambient conditions using size spectra measured by optical particle counters (OPCs) and ambient complex refractive index (RI) estimated from measured chemical composition and relative humidity (RH). We demonstrate that reasonable agreement (~20% on average) between the observed and calculated scattering can be obtained under subsaturated ambient conditions (RH < 80%) by applying both screening for clouds and chemical composition data for the RI-based correction of the OPC-derived size spectra. Full article
(This article belongs to the Section Aerosols)
►▼ Show Figures

Figure 1

Figure 1
<p>Example of combined size distributions generated for each FL during a given day (25 February 2013). Here and in the following plots, aerosol characteristics represent FL-averaged values. Elevation and time of each FL are shown in <a href="#atmosphere-09-00228-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 2
<p>Example of FL-dependent chemical compositions (colored lines) and rBC (thick black lines) mass measured by the AMS and SP2, respectively (25 February 2013). Additionally, altitude (dotted black line) as a function of FL is included. FLs are labeled with numbers 1 through 16 on top of the dashed black altitude line.</p> Full article ">Figure 3
<p>(<b>a</b>) Example of the FL-dependent number fraction of particles with different compositions (note the log scale) characterized by the miniSPLAT during 25 February 2013. Different types of particles are represented by different colors indicated in the legend. The length of each color bar represents the number fraction. The log scale is used to illustrate small (&lt;3%) number fraction of sea salt particles characterized on ‘shallow’ FLs (altitude &lt; 1 km). (<b>b</b>) The corresponding mass fraction of different chemical components (note the linear scale) characterized by the AMS and SP2.</p> Full article ">Figure 4
<p>The same as <a href="#atmosphere-09-00228-f002" class="html-fig">Figure 2</a>, except for the dry scattering coefficient measured by nephelometer (red lines), ambient scattering coefficient obtained with measured f(RH) (blue lines) at 0.55 μm wavelength and ambient RH (green lines). FLs are labeled with numbers 1 through 16 on top of green lines.</p> Full article ">Figure 5
<p>Example of ambient RH and RH-dependent HGF<sub>mix</sub> calculated for each FL on 25 February 2013 (<b>a</b>); scatterplot of RH-dependent HGF<sub>mix</sub> (blue dots) for all TCAP FLs used in this study with polynomial fit (red line) (<b>b</b>).</p> Full article ">Figure 6
<p>Example of RH-dependent dry and ambient values of the real and imaginary parts of the complex RI calculated for each FL on 25 February 2013 (<b>a</b>,<b>b</b>); the corresponding histograms obtained for all winter TCAP FLs (<b>c</b>,<b>d</b>). The real (n<sub>OPC</sub> = 1.588) and imaginary (k<sub>OPC</sub> = 0) parts of the complex RI set for OPC calibration are shown in (<b>a</b>) (magenta) and (<b>b</b>) (magenta), respectively. The real RI of water (n<sub>water</sub> = 1.33) is also shown (a, cyan). The imaginary RI of water (k<sub>water</sub> = 0) is equal to the imaginary RI used for OPC calibration (k<sub>OPC</sub> = 0).</p> Full article ">Figure 7
<p>Example of size distributions obtained for two FLs on 25 February 2013 with large (<b>a</b>,<b>b</b>) and small (<b>c</b>,<b>d</b>) values of ambient RH, respectively. Measured dry size distributions (red) are converted into their wet counterparts (blue) without (<b>a</b>,<b>c</b>) and with (<b>b</b>,<b>d</b>) the size-dependent scaling factor.</p> Full article ">Figure 8
<p>Ambient RH (<b>a</b>) and spectral values (<b>b</b>–<b>d</b>) of the total scattering coefficient measured (blue) and calculated for the original (green) and RI-based adjusted (red) size distributions for FLs on 25 February 2013 at three wavelengths: (<b>b</b>) 0.45, (<b>c</b>) 0.55, and (<b>d</b>) 0.70 μm. Error bars represent uncertainties of measured scattering coefficients. Vertical columns (magenta) identify shallow FLs with low (&lt;0.1 km) altitude (<a href="#atmosphere-09-00228-f002" class="html-fig">Figure 2</a>).</p> Full article ">Figure 9
<p>Comparison of the ambient total scattering coefficient observed (σ<sub>obs</sub>) with ambient total scattering calculated (σ<sub>mod</sub>) for the original (<b>a</b>) and RI-based adjusted (<b>b</b>) size distributions at 0.55 μm wavelength for all TCAP FLs. Here <span class="html-italic">b</span> is the slope of the linear regression fits to the data (straight orange lines). Error bars represent uncertainties of measured (<a href="#sec2-atmosphere-09-00228" class="html-sec">Section 2</a>) and calculated (<a href="#sec3dot4-atmosphere-09-00228" class="html-sec">Section 3.4</a>) scattering coefficients.</p> Full article ">Figure A1
<p>The normalized scattering coefficient (<b>a</b>) and its derivative (<b>b</b>) as a function of particle size cut-off. The blue line shows the mean for all TCAP flight legs and the red line represents the corresponding standard deviations.</p> Full article ">
13 pages, 8590 KiB  
Article
Statistical Analysis of Tropical Cyclones in the Solomon Islands
by Edward Maru, Taiga Shibata and Kosuke Ito
Atmosphere 2018, 9(6), 227; https://doi.org/10.3390/atmos9060227 - 12 Jun 2018
Cited by 12 | Viewed by 6400
Abstract
This study examines tropical cyclone (TC) activity around the Solomon Islands (SIs) using best track data from the Tropical Cyclone Warning Centre, Brisbane, and the Regional Specialized Meteorological Centre, Nadi. Analysis of long-term trends showed that the frequency of TCs has decreased in [...] Read more.
This study examines tropical cyclone (TC) activity around the Solomon Islands (SIs) using best track data from the Tropical Cyclone Warning Centre, Brisbane, and the Regional Specialized Meteorological Centre, Nadi. Analysis of long-term trends showed that the frequency of TCs has decreased in this region, while the average TC intensity has increased. Datasets were classified according to the phase of Madden–Julian Oscillation (MJO) and the index of El Niño Southern Oscillation (ENSO), provided by Bureau of Meteorology. The MJO significantly influenced TC activity in the SIs, with TC genesis occurring most frequently in phases 6–8. In contrast, TC genesis occurred less frequently in phase 5. ENSO also influenced TC genesis; more TCs were generated in El Niño periods. The TC genesis locations during El Niño (La Niña) periods were significantly displaced to the north (south) over the SIs. TCs generated during El Niño conditions tended to be strong. This study also explores the modulation of TCs in terms of the seasonal climatic variability of large-scale environmental variables such as sea surface temperature (SST), low-level relative vorticity, vertical wind shear, and upper level divergence. Full article
(This article belongs to the Special Issue Tropical Cyclones and Their Impacts)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Tracks (black lines) for all 126 TCs (tropical cyclones) analysed in this study and (<b>b</b>) the genesis locations (black dots). Red lines in (<b>a</b>) indicate the track for a cyclone that does not satisfy the criteria of TC (tropical cyclone).</p> Full article ">Figure 2
<p>(<b>a</b>) The number of TCs from 1986 through 2016 and (<b>b</b>) the average wind speed (kt) per year over the same interval. Regressions for each dataset are indicated by a thin line.</p> Full article ">Figure 3
<p>Air temperature change (K) in 2001/2002–2015/2016 TC seasons with respect to 1986/1987–2000/2001 TC seasons at (<b>a</b>) 850 hPa and (<b>b</b>) 300 hPa.</p> Full article ">Figure 4
<p>(<b>a</b>) Average SSTs (sea surface temperatures) from TC seasons 2001/2002–2015/2016 (K) and (<b>b</b>) SST change (K) between the recent 15 seasons (2001/2002–2015/2016) and earlier 15 seasons (1986/1987–2000/2001).</p> Full article ">Figure 5
<p>(<b>a</b>) The averaged vertical shear of horizontal wind (m s<sup>−1</sup>) from TC seasons 2001/2002–2015/2016, and (<b>b</b>) change in averaged vertical shear of horizontal wind (m s<sup>−1</sup>) between 2001/2002–2015/2016 and 1986/1987–2000/2001.</p> Full article ">Figure 6
<p>(<b>a</b>) NTC (number of tropical cyclone) for each MJO (Madden–Julian Oscillation) phase. The NTC divided by the number of days in each MJO phase is shown in (<b>b</b>).</p> Full article ">Figure 7
<p>(<b>a</b>) Number of TCs categorized by phase of MJO and intensity class. The NTC divided by the number of days in each MJO phase is shown in (<b>b</b>).</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) Number of TCs categorized by phase of MJO and intensity class. The NTC divided by the number of days in each MJO phase is shown in (<b>b</b>).</p> Full article ">Figure 8
<p>Composite of OLR (shade; W m<sup>−2</sup>) and wind vector at 850 hPa (arrows) for (<b>a</b>) phase 6 and (<b>b</b>) phase 5.</p> Full article ">Figure 9
<p>TC genesis locations (black dots) for (<b>a</b>) El Niño, (<b>b</b>) La Niña, and (<b>c</b>) neutral periods, respectively.</p> Full article ">Figure 10
<p>(<b>a</b>) NTC at different ENSO conditions, broken down by each intensity category (<b>b</b>) The NTC divided by the number of days in each category is shown in (<b>b</b>).</p> Full article ">Figure 11
<p>Anomalies in large-scale environmental variables for El Niño periods: (<b>a</b>) SST (°C), (<b>b</b>) 850 hPa relative vorticity (10<sup>−6</sup> s<sup>−1</sup>), (<b>c</b>) vertical wind shear (m s<sup>−1</sup>), and (<b>d</b>) 200 hPa divergence (10<sup>−6</sup> s<sup>−1</sup>). Panels (<b>e</b>–<b>h</b>) are the same as (<b>a</b>–<b>d</b>) but for La Niña periods.</p> Full article ">Figure 12
<p>(<b>a</b>) Number of TCs formed during each ENSO condition for each MJO phase. The NTC divided by the number of days in each category is shown in (<b>b</b>).</p> Full article ">
23 pages, 15920 KiB  
Article
A Survey of Regional-Scale Blocking Patterns and Effects on Air Quality in Ontario, Canada
by Frank Dempsey
Atmosphere 2018, 9(6), 226; https://doi.org/10.3390/atmos9060226 - 12 Jun 2018
Cited by 2 | Viewed by 4030
Abstract
Blocking weather patterns cause persistent weather situations that alter typical wind and circulation patterns which may result in stagnant weather conditions at the surface and potentially adverse conditions that affect society, such as extended warmth, drought, precipitation or fog. One problem that may [...] Read more.
Blocking weather patterns cause persistent weather situations that alter typical wind and circulation patterns which may result in stagnant weather conditions at the surface and potentially adverse conditions that affect society, such as extended warmth, drought, precipitation or fog. One problem that may develop is adverse concentrations of air pollutants in populated regions that may persist for several days or longer. This study looks for possible correlation between blocking patterns and air quality episodes in southern Ontario, Canada. The method used was examination of various cases of air quality episodes. The meteorological details of these examples were examined to determine possible correlations with blocking patterns. Results of the comparisons revealed that various types of blocking patterns correlated with worsening air quality episodes in various regions of southern Ontario. The conclusion is that some large-scale as well as regional-scale blocking patterns may cause adverse air quality in different cities or regions of the province during any month, and forecasters need to be vigilant for these patterns. Full article
(This article belongs to the Special Issue Air Quality Prediction)
►▼ Show Figures

Figure 1

Figure 1
<p>Locations of the MOECC air quality monitoring stations.</p> Full article ">Figure 2
<p>Daily 500 hPa GPH from June 20 (<b>upper left</b>) to 25 (<b>lower right</b>), 2003. Credit: NOAA ARL (website: <a href="http://www.arl.noaa.gov/index.php" target="_blank">http://www.arl.noaa.gov/index.php</a>).</p> Full article ">Figure 3
<p>O<sub>3</sub> hourly concentrations, 22–26 June 2003. Dates are shown at EDT (local time) 12:00.</p> Full article ">Figure 4
<p>PM2.5 hourly concentrations at Windsor and Toronto during 22–26 June 2003. Dates are shown at EDT (local time) 12:00.</p> Full article ">Figure 5
<p>NO<sub>2</sub> hourly concentrations at Windsor and Toronto during 22–26 June 2003. Dates are shown at EDT (local time) 12:00.</p> Full article ">Figure 6
<p>Composite 500 hPa GPH, 1–5 February 2005. Credit: NOAA ESRL PSD (website: <a href="http://www.esrl.noaa.gov/psd/" target="_blank">http://www.esrl.noaa.gov/psd/</a>).</p> Full article ">Figure 7
<p>MSLP, 5 February 2005. Credit: NOAA ESRL PSD.</p> Full article ">Figure 8
<p>Daily maximum 1-h PM2.5 concentrations, 20 January–20 February 2005.</p> Full article ">Figure 9
<p>Daily maximum 1-h NO<sub>2</sub> concentrations, 20 January–20 February 2005.</p> Full article ">Figure 10
<p>Pattern of 500 hPa GPH from 8 November (<b>upper left</b>) to 13 November (<b>lower right</b>), 2010. Credit: NOAA ARL.</p> Full article ">Figure 11
<p>Pattern of MSLP, 8 November (<b>upper left</b>) to 13 November (<b>lower right</b>), 2010. Credit: NOAA ARL.</p> Full article ">Figure 12
<p>PM2.5 hourly concentrations at various locations, 11–13 November 2010.</p> Full article ">Figure 13
<p>NO<sub>2</sub> hourly concentrations at various locations, 11–13 November 2010.</p> Full article ">Figure 14
<p>Daily 500 hPa GPH from 28 June (<b>upper left</b>) to 3 July (<b>lower right</b>), 2013. Credit: NOAA ARL.</p> Full article ">Figure 15
<p>PM2.5 hourly concentrations, 29 June–4 July 2013. Dates are shown at Local Time 12:00.</p> Full article ">Figure 16
<p>500 hPa GPH pattern, 11 July (<b>upper left</b>) to 16 July (<b>lower right</b>), 2013. Credit: NOAA ARL.</p> Full article ">Figure 17
<p>MSLP (shown at 0.2 hPa intervals), 15 July 2013, EDT 14:00, with the arrow marking Hamilton, Ontario. Credit: NOAA ARL.</p> Full article ">Figure 18
<p>Streamlines at 850 hPa level, 15 July 2013, EDT 14:00. Credit: NOAA ARL.</p> Full article ">Figure 19
<p>One-hour O<sub>3</sub> concentrations at local time 18:00 15 July (<b>left</b>) and local time 19:00 16 July (<b>right</b>), 2013. Credit: Ontario Ministry of Environment and Climate Change, website: <a href="http://www.airqualityontario.com/" target="_blank">http://www.airqualityontario.com/</a> (last accessed 9 January 2017).</p> Full article ">
6 pages, 513 KiB  
Communication
The Similarity of the Action of Franklin and ESE Lightning Rods under Natural Conditions
by Vernon Cooray
Atmosphere 2018, 9(6), 225; https://doi.org/10.3390/atmos9060225 - 11 Jun 2018
Cited by 6 | Viewed by 4787
Abstract
In the lightning rods categorized as Early Streamer Emission (ESE) types, an intermittent voltage impulse is applied to the lightning rod to modulate the electric field at its tip in an attempt to speed up the initiation of a connecting leader from the [...] Read more.
In the lightning rods categorized as Early Streamer Emission (ESE) types, an intermittent voltage impulse is applied to the lightning rod to modulate the electric field at its tip in an attempt to speed up the initiation of a connecting leader from the lightning rod when it is under the influence of a stepped leader moving down from the cloud. In this paper, it is shown that, due to the stepping nature of the stepped leader, there is a natural modulation of the electric field at the tip of any lightning rod exposed to the lightning stepped leaders and this modulation is much more intense than any artificial modulation that is possible under practical conditions. Based on the results, it is concluded that artificial modulation of the electric field at the tip of lightning rods by applying voltage pulses is an unnecessary endeavor because the nature itself has endowed the tip of the lightning rod with a modulating electric field. Therefore, as far as the effectiveness of artificial modulation of the tip electric field is concerned, there could be no difference in the lightning attachment efficiency between ESE and Franklin lightning rods. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Electric field at ground level produced by a stepped leader as it moves towards the ground. The solid line (red) depicts the electric field corresponding to a uniformly moving leader and the dashed line (black) corresponding to a leader moving down in 20 m long steps. (<b>b</b>) Section of the expanded electric field change where the solid line (red) shows the electric field due to a continuously moving leader and the data points show the individual jumps in the electric field as the leader extends in steps of 20 m. The prospective return stroke current associated with the stepped leader is 30 kA.</p> Full article ">Figure 2
<p>Amplitude of the voltage pulses that are needed to be applied to the tip of a lightning rod to simulate the electric field changes caused by the stepping process of the stepped leader. (<b>a</b>) 50 m step length. (<b>b</b>) 20 m step length. The prospective return stroke current associated with the stepped leader is 30 kA. The length of the lightning rod is 10 m.</p> Full article ">
20 pages, 3183 KiB  
Article
Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index
by Ming Li and Kefeng Liu
Atmosphere 2018, 9(6), 224; https://doi.org/10.3390/atmos9060224 - 11 Jun 2018
Cited by 10 | Viewed by 3700
Abstract
The effective prediction of storm track (ST) is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII), a new probabilistic prediction model was proposed based on dynamic Bayesian network [...] Read more.
The effective prediction of storm track (ST) is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII), a new probabilistic prediction model was proposed based on dynamic Bayesian network (DBN) and wavelet analysis (WA). We introduced probability theory and graph theory for the first time to quantitatively describe the nonlinear relationship and uncertain interaction of the ST system. Then a casual prediction network (i.e., DBN) was constructed through wavelet decomposition, structural learning, parameter learning, and probabilistic inference, which was used for expression of relation among predictors and probabilistic prediction of STII. The intensity prediction of the North Pacific ST with data from 1961–2010 showed that the new model was able to give more comprehensive prediction information and higher prediction accuracy and had strong generalization ability and good stability. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Technical structure of WA-DBN probabilistic prediction model.</p> Full article ">Figure 2
<p>DBN structure between two adjacent time slices of STII prediction.</p> Full article ">Figure 3
<p>Modes of wavelet decomposition for STII time-series (First 240 months).</p> Full article ">Figure 3 Cont.
<p>Modes of wavelet decomposition for STII time-series (First 240 months).</p> Full article ">Figure 4
<p>DBN structure of STII prediction under MATLAB.</p> Full article ">Figure 5
<p>Monthly prediction of STII in the test period.</p> Full article ">Figure 6
<p>Comparative results of STII prediction between WA-DBN and P-regression.</p> Full article ">Figure 7
<p>STII Prediction with WA-DBN model learned by different training data.</p> Full article ">Figure 8
<p>Comparative fitting results between WA-DBN and NN.</p> Full article ">Figure 8 Cont.
<p>Comparative fitting results between WA-DBN and NN.</p> Full article ">Figure 9
<p>Test experiment of predictable time with different inputting data. (The effective predictions are highlighted with dotted line).</p> Full article ">Figure 9 Cont.
<p>Test experiment of predictable time with different inputting data. (The effective predictions are highlighted with dotted line).</p> Full article ">Figure 10
<p>DBN structure between two adjacent time slices of NAST prediction.</p> Full article ">Figure 11
<p>Monthly Prediction of NAST intensity index in the test period.</p> Full article ">
18 pages, 4788 KiB  
Article
Estimation of the Impact of Ozone on Four Economically Important Crops in the City Belt of Central Mexico
by Luis Gerardo Ruiz-Suárez, Bertha Eugenia Mar-Morales, José Agustín García-Reynoso, Gema Luz Andraca-Ayala, Ricardo Torres-Jardón, José Santos García-Yee, Hugo Alberto Barrera-Huertas, Arturo Gavilán-García and Roberto Basaldud Cruz
Atmosphere 2018, 9(6), 223; https://doi.org/10.3390/atmos9060223 - 11 Jun 2018
Cited by 5 | Viewed by 5400
Abstract
In this work, we report the economic impact of exposure to high ozone concentrations on four important crops in the area of influence of the Mexico City Megalopolis. Estimated yield losses were as follows: maize: 3%; oats: 26%; beans: 14%; sorghum: 15%. The [...] Read more.
In this work, we report the economic impact of exposure to high ozone concentrations on four important crops in the area of influence of the Mexico City Megalopolis. Estimated yield losses were as follows: maize: 3%; oats: 26%; beans: 14%; sorghum: 15%. The information needed to estimate the impact of air pollution in Mexico is decidedly deficient. Regarding ozone, the coverage provided by the monitoring networks is strongly focused on urban monitoring and its consistency over time is highly irregular. Apart from the Mexico City Metropolitan Area (MCMA) and less than a handful of other cities, the quality of the data is poor. Ozone in rural areas can be estimated with air quality models. However, these models depend on a high-resolution emissions inventory, which has only been done through validation processes in the MCMA. With these limitations, we set out to estimate the economic impact of exposure to ozone in these crops with a varying degree of sensitivity to ozone in the city belt of Central Mexico. To this end, we developed a procedure that makes optimal use of the sparse information available for construction of AOT40 (accumulated exposure over the threshold of 40 ppb) exceedance maps for the 2011 growing season. We believe that, due to the way in which we dealt with the sparse information and the uncertainty regarding the available data, our findings lie on the safe side of having little knowledge such that they may be useful to decision-makers. We believe that this procedure can be extended to the rest of the country, and that it may be useful to developing countries with similar monitoring and modeling capacities. In addition, these impacts are not evenly distributed in the region and sometimes they were greater in municipalities that have a higher index of poverty. Air pollution arriving from urban areas increases the social inequalities to which these already vulnerable populations are exposed. Full article
(This article belongs to the Special Issue Tropospheric Ozone and Its Precursors)
►▼ Show Figures

Figure 1

Figure 1
<p>Study area with orography and boundaries between federal states and Mexico City. Four data point categories are shown.</p> Full article ">Figure 2
<p>Conceptual model of the process used to obtain the AOT40 exceedance maps.</p> Full article ">Figure 3
<p>The average passive monitoring values at any site (Co) and the Mesoscale Climate and Chemistry Model (MCCM) average (Cm), which is obtained by averaging the profile with the hourly means from MCCM calculated over the two-month passive sampling period.</p> Full article ">Figure 4
<p>Evolution of the hybrid exceedance maps as observational data were gradually added to correct the maps obtained only with the air quality model. (<b>a</b>) Using only modeled and corrected regular grid points. (<b>b</b>) Panel (a) + RAMA + REMA. (<b>c</b>) Panel (b) + García-Yee + Barrera-Huertas. (<b>d</b>) Panel (c) + MILAGRO + CARIEM + Tula.</p> Full article ">Figure 5
<p>Maize: (<b>a</b>) Yield (t/ha). (<b>b</b>) Relative yield loss (%). (<b>c</b>) Production (t). (<b>d</b>) Crop production loss (t). (<b>e</b>) Production value (MXN). (<b>f</b>) Economic loss (MXN), for 2011.</p> Full article ">
16 pages, 3074 KiB  
Article
Changes in Cold Surge Occurrence over East Asia in the Future: Role of Thermal Structure
by Jin-Woo Heo, Chang-Hoi Ho, Tae-Won Park, Woosuk Choi, Jee-Hoon Jeong and Jinwon Kim
Atmosphere 2018, 9(6), 222; https://doi.org/10.3390/atmos9060222 - 10 Jun 2018
Cited by 20 | Viewed by 8683
Abstract
The occurrence of wintertime cold surges (CSs) over East Asia is largely controlled by the surface air temperature (SAT) distribution at high latitudes and thermal advection in the lower troposphere. The thermodynamic background state over northeastern Asia is associated with the strength of [...] Read more.
The occurrence of wintertime cold surges (CSs) over East Asia is largely controlled by the surface air temperature (SAT) distribution at high latitudes and thermal advection in the lower troposphere. The thermodynamic background state over northeastern Asia is associated with the strength of the East Asian winter monsoon and the variation of Arctic Oscillation. This study assesses the importance of the SAT structure with thermal advection in determining the frequency of CS occurrences over East Asia through the analysis of nine atmosphere–ocean coupled global climate models participating in the Coupled Model Intercomparison Project Phase 5. The historical simulations can reproduce the observed typical characteristics of CS development. On the basis of this model performance, ensemble-averaged future simulations under the representative concentration pathway 8.5 project a reduction in CS frequency by 1.1 yr−1 in the late 21st century (2065–2095) compared to the present-day period (1975–2005). The major reason for less frequent CSs in the future is the weakened cold advection, caused by notable SAT warming over the northern part of East Asia. These results suggest that changes in the meridional SAT structure and the associated changes in thermal advection would play a more substantial role than local warming in determining future changes in the frequency of CS occurrences over East Asia. Full article
(This article belongs to the Special Issue Monsoons)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The SAT domains of East Asia (two dashed boxes; 35° N–40° N, 125° E–130° E and 40° N–45° N, 120° E–125° E) and the domain of the Siberian High (solid box; 35° N–55° N, 90° E–115° E) used to define cold surges. The climatology of observed mean SLP (contour interval of 2 hPa) and SAT (shading) during the winter season (November to March). (<b>b</b>) Times series of daily SAT (solid line) and its climatology (dash line) averaged over 35° N–40° N, 125° E–130° E during January in 2009. Red (blue) dots indicate the occurrence (termination) date of CSs. (<b>c</b>) Time series of daily SLP averaged over the domain of Siberian High during the same period mentioned in <a href="#atmosphere-09-00222-f001" class="html-fig">Figure 1</a>b.</p> Full article ">Figure 2
<p>Composite of geopotential height anomalies at 250 hPa (contour interval of 30 m; values significant at the 99% confidence level are represented by thick lines) and at 850 hPa (shading; values significant at the 99% confidence level are represented by black dots) for cold surge occurrence dates identified in the ERA-Interim data, the output of the nine CGCMs for the historical run, and the ensemble mean of the CGCMs for the historical run. The number of CS occurrences for composite are represented on the top-right of each panel.</p> Full article ">Figure 3
<p>Composite of SLP anomalies (contour interval of 2 hPa; values significant at the 99% confidence level are represented by thick lines) and SAT anomalies (shading; values significant at the 99% confidence level are represented by black dots) for cold surge occurrence dates identified in the ERA-Interim data, the output of the nine CGCMs for the historical run, and the ensemble mean of the CGCMs for the historical run.</p> Full article ">Figure 4
<p>Composites for cold surge occurrence dates of the ERA-Interim (<b>a</b>) temperature anomalies (°C), (<b>b</b>) advection of climatological temperature by anomalous wind (°C day<sup>−1</sup>), (<b>c</b>) advection of anomalous temperature by climatological wind, and (<b>d</b>) advection of anomalous temperature by anomalous wind at 850 hPa. (<b>e</b>–<b>h</b>) are same as (<b>a</b>–<b>d</b>) but for the ensemble mean of the nine CGCMs in the historical run. The spatial correlation coefficients between each advection term and temperature anomalies are shown in the top-right of each panel (values significant at 99% confidence level are represented by black stars).</p> Full article ">Figure 5
<p>Changes in the frequency of cold surge occurrences (yr<sup>−1</sup>) with changes in the criteria for the identification of cold surges (the temperature drop criterion is shown in the white bar and the temperature anomaly criterion is shown in the black bar). The horizontal axis is divided by the standard deviation of the winter daily SAT anomaly.</p> Full article ">Figure 6
<p>The interannual correlation coefficient between the occurrence of cold surges and the boreal winter SAT for (<b>a</b>) 1979–2016 in ERA-Interim (values significant at the 95% confidence level are represented by black dots), (<b>b</b>) 1975–2005 in the historical run, and (<b>c</b>) 2065–2095 in ensemble mean of nine CGCMs based on RCP8.5 scenario. Regions with same sign in at least 6 CGCMs are represented by black dots.</p> Full article ">Figure 7
<p>(<b>a</b>) SAT anomalies (°C) during 1988–1994 from climatology in winter season and composite of daily wind anomalies at 850 hPa for cold surge occurrence dates, (<b>b</b>) time series of the number of winter cold surge occurrences. The dashed lines show the mean number of cold surge occurrences during 1979–1987, 1988–1994, and 1995–2016.</p> Full article ">Figure 8
<p>The number of cold surge occurrences (yr<sup>−1</sup>) in winter from 1979–2016 in observations (horizontal black line) and the historical (1975–2005; white bar), and future (RCP8.5; 2065–2095; black bar) from 9 CGCMs and their ensemble means (significant changes at 95% confidence level are represented by red stars).</p> Full article ">Figure 9
<p>The SAT climatology for the historical run (1975–2005; shown as a contour with intervals of 5 °C). Changes in SAT between 2065–2095 and 1975–2005 (shading; left) from nine CGCMs and their ensemble mean. The climatology of zonal mean SAT along 80° E to 160° E (black lines) and its change (red lines) in winter from nine CGCMs and their ensemble mean.</p> Full article ">Figure 10
<p>The climatology of zonal wind at 250 hPa (contour: intervals of 10 m s<sup>−1</sup>) for 1975–2005 and the changes in zonal wind at 250 hPa (shading) and wind vector at 850 hPa (m s<sup>−1</sup>) in winter from nine CGCMs and their ensemble mean.</p> Full article ">Figure 11
<p>Regression coefficient maps for SAT anomalies based on the AO index for 1975–2005. The values in the top-right of each panel show the projection values between the AO pattern and changes in the mean SLP between 2065–2095 and 1975–2005 from nine CGCMs and their ensemble mean.</p> Full article ">
19 pages, 768 KiB  
Article
Making Administrative Systems Adaptive to Emerging Climate Change-Related Health Effects: Case of Estonia
by Kati Orru, Mari Tillmann, Kristie L. Ebi and Hans Orru
Atmosphere 2018, 9(6), 221; https://doi.org/10.3390/atmos9060221 - 9 Jun 2018
Cited by 9 | Viewed by 5587
Abstract
To facilitate resilience to a changing climate, it is necessary to go beyond quantitative studies and take an in-depth look at the functioning of health systems and the variety of drivers shaping its effectiveness. We clarify the factors determining the effectiveness of the [...] Read more.
To facilitate resilience to a changing climate, it is necessary to go beyond quantitative studies and take an in-depth look at the functioning of health systems and the variety of drivers shaping its effectiveness. We clarify the factors determining the effectiveness of the Estonian health system in assessing and managing the health risks of climate change. Document analyses, expert interviews with key informants from health systems whose responsibilities are relevant to climate change, and analysis of a population-based survey conducted in 2015, indicate that the health effects of climate change have not been mainstreamed into policy. Therefore, many of the potential synergistic effects of combining information on health systems, environment, and vulnerable populations remain unexploited. The limited uptake of the issue of climate change-related health risks may be attributed to the lack of experience with managing extreme weather events; limited understanding of how to incorporate projections of longer-term health risks into policies and plans; unclear divisions of responsibility; and market liberal state approaches. Minority groups and urban dwellers are placing strong pressure on the health system to address climate change-related risks, likely due to their lower levels of perceived control over their physical wellbeing. The results have implications for national, community, and individual resilience in upper-middle income countries in Eastern Europe. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Human Health)
►▼ Show Figures

Figure 1

Figure 1
<p>Determinants of climate adaptation of health systems.</p> Full article ">
20 pages, 5836 KiB  
Article
Forecasting the Impacts of Prescribed Fires for Dynamic Air Quality Management
by M. Talat Odman, Ran Huang, Aditya A. Pophale, Rushabh D. Sakhpara, Yongtao Hu, Armistead G. Russell and Michael E. Chang
Atmosphere 2018, 9(6), 220; https://doi.org/10.3390/atmos9060220 - 8 Jun 2018
Cited by 9 | Viewed by 5306
Abstract
Prescribed burning (PB) is practiced throughout the USA, most extensively in the southeast, for the purpose of maintaining and improving the ecosystem and reducing wildfire risk. However, PB emissions contribute significantly to trace gas and particulate matter loads in the atmosphere. In places [...] Read more.
Prescribed burning (PB) is practiced throughout the USA, most extensively in the southeast, for the purpose of maintaining and improving the ecosystem and reducing wildfire risk. However, PB emissions contribute significantly to trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, PB can lead to major health and environmental problems. We developed a PB impact forecasting system to facilitate the dynamic management of air quality by modulating PB activity. In our system, a new decision tree model predicts burn activity based on the weather forecast and historic burning patterns. Emission estimates for the forecast burn activity are input into an air quality model, and simulations are performed to forecast the air quality impacts of the burns on trace gas and particulate matter concentrations. An evaluation of the forecasts for two consecutive burn seasons (2015 and 2016) showed that the modeling system has promising forecasting skills that can be further improved with refinements in burn area and plume rise estimates. Since 2017, air quality and burn impact forecasts are being produced daily with the ultimate goal of incorporating them into the management of PB operations. Full article
(This article belongs to the Special Issue Fire and the Atmosphere)
►▼ Show Figures

Figure 1

Figure 1
<p>Illustration of HiRes2 daily forecast products: (<b>a</b>) 24-h average PM<sub>2.5</sub> (particulate matter with an aerodynamic diameter smaller than 2.5 μm) concentrations (μg m<sup>−3</sup>) on 9 March 2016 and; (<b>b</b>) on a different scale, the portion of PM<sub>2.5</sub> associated with prescribed burns in Georgia USA.</p> Full article ">Figure 2
<p>January–April 2015 burn day forecast <span class="html-italic">F</span>1 scores by county for the (<b>a</b>) statewide and (<b>b</b>) county-specific decision tree models.</p> Full article ">Figure 3
<p>(<b>a</b>) NOAA’s Hazard Mapping System Fire and Smoke Analysis; (<b>b</b>) forecast of burn impacts on PM<sub>2.5</sub> levels; and (<b>c</b>) cloud cover for 3 March 2016. The largest impacts on PM<sub>2.5</sub> (shown in purple) can be used as a proxy for burn locations.</p> Full article ">Figure 4
<p>Forecast versus permitted daily total burn areas (ha) in Georgia for the January–April 2015 period: (<b>a</b>) statewide model; and (<b>b</b>) county-specific models.</p> Full article ">Figure 5
<p>Forecast versus permitted county total burn areas (in ha) in Georgia for the January–April 2015 period: (<b>a</b>) statewide model; and (<b>b</b>) county-specific models.</p> Full article ">Figure 6
<p>Forecast from county-specific models versus permitted (<b>a</b>) daily total burn areas in Georgia; and (<b>b</b>) county total burn areas for the January–April 2016 period.</p> Full article ">Figure 7
<p>The 2016 burn impact forecast statistics. The thresholds for burn impacts are drawn at 32 μg m<sup>−3</sup>, which is the 95th percentile of all observed maximum afternoon PM<sub>2.5</sub> concentrations throughout Georgia between 2 January and 1 May 2016.</p> Full article ">Figure 8
<p>Normalized bias in PM<sub>2.5</sub> versus normalized bias in burn areas in upwind counties. The burn areas were generally underestimated. Independent from the bias in burn areas, the PM<sub>2.5</sub> impacts were mostly overestimated.</p> Full article ">Figure A1
<p>An example decision tree to generate the forecasting probabilities of each class.</p> Full article ">Figure A2
<p>Decision boundaries created by the Classification and Regression Tree (CART) algorithm to classify the data using variable 1 and variable 2.</p> Full article ">Figure A3
<p>Information gain calculations.</p> Full article ">Figure A4
<p>(<b>a</b>) The type of burners for each county in Georgia as single dominant (Category 1), multiple large (Category 2) and various small burners (Category 3). (<b>b</b>) The average daily burn areas (acres) normalized by the areas of the counties (km<sup>2</sup>).</p> Full article ">
24 pages, 5411 KiB  
Article
Dynamic and Thermodynamic Factors Associated with Different Precipitation Regimes over South China during Pre-Monsoon Season
by Wenqian Ma, Wenyu Huang, Zifan Yang, Bin Wang, Daiyu Lin and Xinsheng He
Atmosphere 2018, 9(6), 219; https://doi.org/10.3390/atmos9060219 - 7 Jun 2018
Cited by 8 | Viewed by 4240
Abstract
Nine precipitation regimes over South China are obtained by applying the Self-Organizing Map (SOM) technique to the sub-daily precipitation during the pre-monsoon season (April to June) of 1979–2015. These nine regimes are distinct from each other in terms of precipitation amount and spatial [...] Read more.
Nine precipitation regimes over South China are obtained by applying the Self-Organizing Map (SOM) technique to the sub-daily precipitation during the pre-monsoon season (April to June) of 1979–2015. These nine regimes are distinct from each other in terms of precipitation amount and spatial pattern. The relationships between precipitation and different atmospheric dynamic and thermodynamic factors (large-scale divergence, water vapor flux, low-level jet, precipitable water, convective available potential energy (CAPE), and K index) are explored under the nine regimes. The upper-level divergence performs best in indicating the geographic positions of precipitation centers, which are also modulated by the orientations of low-level jets. The estimation of water vapor transport reveals that there are two major moisture sources for the precipitation during the pre-monsoon season, i.e., the Bay of Bengal (for all the nine regimes) and the South China Sea and West Pacific Ocean (for five regimes). Furthermore, the occurrence probability of more precipitation increases with the water vapor transported from the South China Sea and West Pacific Ocean. Compared to CAPE, K index performs better in indicating the precipitation centers and has a tighter relationship with area-average precipitation. The precipitable water exhibits complicated relationships with spatial patterns and amounts of precipitation, indicating that it may be not a good indicator for precipitation during pre-monsoon season over South China. Estimation of the persistence and transformation probabilities for precipitation regimes reveals that the persistence probabilities basically decrease with the precipitation amounts, and the transformations between different precipitation regimes are inclined to be associated with the southward shifts of precipitation centers. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>Climatological mean of April, May and June precipitation (units: mm month<sup>−1</sup>) during 1979–2015: (<b>a</b>) April; (<b>b</b>) May; and (<b>c</b>) June based on GPCC; and (<b>d</b>–<b>f</b>) the same as (<b>a</b>–<b>c</b>) but based on ERA-Interim. South China is defined as the continental areas within the red box (21.5–32° N, 105–123° E).</p> Full article ">Figure 2
<p>Time evolutions of area-averaged precipitation anomalies (units: mm month<sup>−1</sup>) over South China for April, May and June during 1979–2015: (<b>a</b>–<b>c</b>) low-pass-filtered area-averaged precipitation anomalies for April, May and June based on GPCC (red lines) and ERA-Interim (blue lines); and (<b>d</b>–<b>f</b>) the same as (<b>a</b>–<b>c</b>) but for high-pass-filtered area-averaged precipitation anomalies. The correlation coefficients between the GPCC and ERA-Interim filtered area-averaged precipitation anomalies are given at the top right of each panel. Note that the area-averaged precipitation anomalies are obtained by removing the long-term mean of the area-averaged precipitation during all the corresponding calendar months of 1979–2015.</p> Full article ">Figure 3
<p>WGDs (the red dotted line; units: mm day<sup>−1</sup>), BGDs (the blue dotted line; units: mm day<sup>−1</sup>) and RIs (the purple bars; units:%) when the number of regimes X is varied. The black dashed line denotes that RI equals 1%.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>i</b>) Composite means of precipitation (units: mm day<sup>−1</sup>) for the nine regimes during AMJ 1979–2015. The number of samples belonging to each regime is shown at the top right of each panel. Only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples (i.e., the samples not belonging to a specified regime) are plotted.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>i</b>) Composite means of divergence (units: 10<sup>−5</sup> s<sup>−1</sup>) at 850 hPa for the nine regimes during AMJ 1979–2015. Only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>i</b>) Composite means of divergence (units: 10<sup>−5</sup> s<sup>−1</sup>) at 200 hPa for the nine regimes during AMJ 1979–2015. Only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>i</b>) Composite means of water vapor flux (shading; units: g (s·hPa·cm)<sup>−1</sup>) and its vectors (vectors; units: g (s·hPa·cm)<sup>−1</sup>) for the nine regimes during AMJ 1979–2015 and (<b>j</b>) the magnitudes of water vapor transport (units: 10<sup>10</sup> g s<sup>−1</sup>) from the Bay of Bengal, and the South China Sea and West Pacific Ocean for the nine regimes. In (<b>a</b>–<b>i</b>), only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted, and the vectors are plotted if only water vapor flux is significant in one direction (zonal or meridional). The red lines and blue AB line in (<b>a</b>) denote the region of South China (21.5–32° N, 105–123° E). The blue lines AB (21.5° N, 105–123° E) and AC (10.0–21.5° N, 105° E) are used to estimate the magnitudes of water vapor transport.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>i</b>) Composite means of winds (vectors; units: m s<sup>−1</sup>) at 850 hPa and wind speeds (shading; units: m s<sup>−1</sup>) at 850 hPa for the nine regimes during AMJ 1979–2015. For the wind speeds, only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted. The winds are plotted if only winds are significant in one direction (zonal or meridional).</p> Full article ">Figure 9
<p>(<b>a</b>–<b>i</b>) Composite means of precipitable water (units: kg m<sup>2</sup>) for the nine regimes during AMJ 1979–2015. Only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted.</p> Full article ">Figure 10
<p>(<b>a</b>–<b>i</b>) Composite means of CAPE (units: J kg<sup>−1</sup>) for the nine regimes during AMJ 1979–2015. Only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted.</p> Full article ">Figure 11
<p>(<b>a</b>–<b>i</b>) Composite means of K index for the nine regimes during AMJ 1979–2015. The black line in each panel is the contour of 30. Only composite means that pass the 95% confidence level based on two-sided Student’s <span class="html-italic">t</span> test against the complementary set of samples are plotted.</p> Full article ">Figure 12
<p>(<b>a</b>–<b>i</b>) Relationships between regional average precipitation (units: mm day<sup>−1</sup>) and CAPE (units: J kg<sup>−1</sup>) and K index over Guangdong Province (21.5–25° N, 109–117° E) under the nine regimes. The left part of each panel denotes the relationships between regional average precipitation and CAPE, and the right part of each panel denotes the relationships between regional average precipitation and K index. The correlation coefficients between regional average precipitation and CAPE are given at the top right of left part of each panel, while the correlation coefficients between regional average precipitation and K index are given at the top right of right part of each panel.</p> Full article ">Figure 13
<p>The occurrence frequencies of transformations (PR; units: %) from each of the nine regimes (ordinate) to each of the nine regimes (abscissa). Note that PRs along the diagonal denote the frequencies of persistence for the nine regimes. Only PRs with statistical significance are shown and the statistical test can be referred to in <a href="#sec3dot4-atmosphere-09-00219" class="html-sec">Section 3.4</a>.</p> Full article ">Figure 14
<p>A diagram for the major findings of our study.</p> Full article ">Figure 15
<p>(<b>a</b>–<b>i</b>) Composite means of precipitation (units: mm day<sup>−1</sup>) for the nine regimes during AMJ 1979–2014 based on precipitation data from surface stations. Note that the end year of the available precipitation data from the surface stations is 2014. Thus, we only composite precipitation regimes during 1979–2014.</p> Full article ">Figure 16
<p>Composite means of vertical profiles of area-averaged relative humidity (<b>a</b>) and relative humidity anomalies (<b>b</b>) over South China for the nine regimes during AMJ 1979–2015. The units for both relative humidity and relative humidity anomalies are %.</p> Full article ">
19 pages, 7903 KiB  
Article
Simulation of a Large Wildfire in a Coupled Fire-Atmosphere Model
by Jean-Baptiste Filippi, Frédéric Bosseur, Céline Mari and Christine Lac
Atmosphere 2018, 9(6), 218; https://doi.org/10.3390/atmos9060218 - 7 Jun 2018
Cited by 28 | Viewed by 6964
Abstract
The Aullene fire devastated more than 3000 ha of Mediterranean maquis and pine forest in July 2009. The simulation of combustion processes, as well as atmospheric dynamics represents a challenge for such scenarios because of the various involved scales, from the scale of [...] Read more.
The Aullene fire devastated more than 3000 ha of Mediterranean maquis and pine forest in July 2009. The simulation of combustion processes, as well as atmospheric dynamics represents a challenge for such scenarios because of the various involved scales, from the scale of the individual flames to the larger regional scale. A coupled approach between the Meso-NH (Meso-scale Non-Hydrostatic) atmospheric model running in LES (Large Eddy Simulation) mode and the ForeFire fire spread model is proposed for predicting fine- to large-scale effects of this extreme wildfire, showing that such simulation is possible in a reasonable time using current supercomputers. The coupling involves the surface wind to drive the fire, while heat from combustion and water vapor fluxes are injected into the atmosphere at each atmospheric time step. To be representative of the phenomenon, a sub-meter resolution was used for the simulation of the fire front, while atmospheric simulations were performed with nested grids from 2400-m to 50-m resolution. Simulations were run with or without feedback from the fire to the atmospheric model, or without coupling from the atmosphere to the fire. In the two-way mode, the burnt area was reproduced with a good degree of realism at the local scale, where an acceleration in the valley wind and over sloping terrain pushed the fire line to locations in accordance with fire passing point observations. At the regional scale, the simulated fire plume compares well with the satellite image. The study explores the strong fire-atmosphere interactions leading to intense convective updrafts extending above the boundary layer, significant downdrafts behind the fire line in the upper plume, and horizontal wind speeds feeding strong inflow into the base of the convective updrafts. The fire-induced dynamics is induced by strong near-surface sensible heat fluxes reaching maximum values of 240 kW m − 2 . The dynamical production of turbulent kinetic energy in the plume fire is larger in magnitude than the buoyancy contribution, partly due to the sheared initial environment, which promotes larger shear generation and to the shear induced by the updraft itself. The turbulence associated with the fire front is characterized by a quasi-isotropic behavior. The most active part of the Aullene fire lasted 10 h, while 9 h of computation time were required for the 24 million grid points on 900 computer cores. Full article
(This article belongs to the Special Issue Fire and the Atmosphere)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sartene weather station records on 23 July 2009 (in hours UTC): temporal evolution of 10-m wind speed (in red, in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>), 2-m temperature (in blue, in deg C) and relative humidity (in black, in %).</p> Full article ">Figure 2
<p>Overview of the nested Meso-NH domains.</p> Full article ">Figure 3
<p>Aullene’s fire, markers (blue) and heat fluxes above 30 <math display="inline"><semantics> <mrow> <mi>kW</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math> at fire resolution (5 m) (<b>A</b>) used to force the atmospheric surface fields (resolution 50 m) (<b>B</b>).</p> Full article ">Figure 4
<p>Overview of the fuel distributions with the four main fuel classes over the final observed fire area over elevation in gray shades.</p> Full article ">Figure 5
<p>View of the “coupled” fire simulation (grey scale) 27 min, 1 h, 2 h and 3 h 30 after the beginning of the fire, with corresponding observed fire passing points (yellow marks), superimposed on the real burnt area including fire fighting actions (dashed red area). The horizontal extension at the base of the figure is 5 km.</p> Full article ">Figure 6
<p>Simulated smoke tracer on 23 July 2009 (<b>a</b>) in the 50-m resolution domain compared to the plume’s photograph (at the top left) at 14:50 UTC and (<b>b</b>) in the 600-m resolution domain highlighted in red (A) at 15:00 UTC compared to the MODIS image (B) of Corsica at 14:50 UTC.</p> Full article ">Figure 7
<p>Vertical cross-section of the wind speed (in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, between blue and red) and streamlines initiated at the ground and colored according to the vorticity (in <math display="inline"><semantics> <mrow> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, between green and pink) at 1500 UTC.</p> Full article ">Figure 8
<p>Horizontal cross-sections on 23 July 2009 at 1500 UTC: (<b>a</b>) envelop of the fire front (in black) at the ground superimposed on the 10-m wind vectors (arrows) of the “coupled” simulation and on the orography (color, in m) with the axis of the vertical cross-sections; (<b>b</b>,<b>c</b>) 10-m wind speed (in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) with wind vectors superimposed for the (<b>b</b>) “uncoupled” and (<b>c</b>) difference “coupled minus uncoupled” simulations; (<b>d</b>) Vertical velocity at a 300-m height (in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) for the difference “coupled minus uncoupled” simulations. Orography isocontours are superimposed in (<b>b</b>).</p> Full article ">Figure 9
<p>Vertical cross-sections on 23 July 2009 at 1500 UTC: (<b>a</b>,<b>b</b>) horizontal wind (in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) with wind vectors superimposed for (<b>a</b>) the “uncoupled” simulation and (<b>b</b>) the “coupled” one along the direction of propagation (axis (A) in <a href="#atmosphere-09-00218-f008" class="html-fig">Figure 8</a>a); (<b>c</b>,<b>d</b>) vertical velocity difference between “coupled” and “uncoupled” simulations (in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) with the wind vectors (arrows) (<b>c</b>) across the fire front (axis (B)) and (<b>d</b>) along the direction of propagation (axis (A)).</p> Full article ">Figure 10
<p>Horizontal cross-sections on 23 July 2009 at 1500 UTC: 2-m temperature (in deg Celsius) for the (<b>a</b>) “uncoupled”; (<b>b</b>) “coupled” and (<b>c</b>) the difference “coupled minus uncoupled”; (<b>d</b>) Sensible heat flux at a 20-m height for the difference “coupled minus uncoupled” (in <math display="inline"><semantics> <mrow> <mi>kW</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>). Isolines of orography are superimposed in (<b>a</b>).</p> Full article ">Figure 11
<p>Difference between “coupled” and “uncoupled” simulations on 23 July 2009 at 1500 UTC: Horizontal cross-sections of (<b>a</b>) 10-m horizontal divergence (in <math display="inline"><semantics> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) with the fire tracer superimposed (in black); (<b>b</b>) 10-m pressure (in Pa).</p> Full article ">Figure 12
<p>Vertical cross-sections along the direction of propagation (axis(A) in <a href="#atmosphere-09-00218-f008" class="html-fig">Figure 8</a>a) on 23 July 2009 at 1500 UTC of Turbulent Kinetic Energy (TKE) (in <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>) for the (<b>a</b>) “uncoupled” and (<b>b</b>) the difference “coupled minus uncoupled” (with a ratio of 10 between isocontours); (<b>c</b>) dynamical production of TKE (in <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>) and (<b>d</b>) thermal production of TKE (in <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>) for the difference “coupled minus uncoupled”.</p> Full article ">Figure 13
<p>Mean kinetic energy spectra for vertical wind on 23 July 2009 at 1500 UTC for the “Coupled” (continuous line) and “Uncoupled” (dashed line) simulations according to the wavelength (in m). The dashed line indicates the power law with an exponent of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> (the Kolmogorov spectrum).</p> Full article ">Figure 14
<p>“Coupled” simulation on 23 July 2009 at 1500 UTC: vertical cross-sections along the direction of propagation (axis in <a href="#atmosphere-09-00218-f008" class="html-fig">Figure 8</a>a) of (<b>a</b>) zonal wind variance <math display="inline"><semantics> <msup> <mi>u</mi> <mrow> <mo>′</mo> <mn>2</mn> </mrow> </msup> </semantics></math>, (<b>b</b>) meridional wind variance <math display="inline"><semantics> <msup> <mi>v</mi> <mrow> <mo>′</mo> <mn>2</mn> </mrow> </msup> </semantics></math> and (<b>c</b>) vertical wind variance <math display="inline"><semantics> <msup> <mi>w</mi> <mrow> <mo>′</mo> <mn>2</mn> </mrow> </msup> </semantics></math> (in <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>).</p> Full article ">Figure 15
<p>“Fire-to-atm” simulation on 23 July 2009 at 1500 UTC: horizontal cross-sections of (<b>a</b>) the fire front (in black) at the ground superimposed on the wind vectors (arrows) and on the orography (color, in m); (<b>b</b>) the difference between “fire-to-atm” and “uncoupled” simulations of 10-m wind intensity with wind vectors superimposed (in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>).</p> Full article ">
16 pages, 4478 KiB  
Article
Coupled Simulations of Indoor-Outdoor Flow Fields for Cross-Ventilation of a Building in a Simplified Urban Array
by Yuki Murakami, Naoki Ikegaya, Aya Hagishima and Jun Tanimoto
Atmosphere 2018, 9(6), 217; https://doi.org/10.3390/atmos9060217 - 4 Jun 2018
Cited by 18 | Viewed by 3752
Abstract
Computational fluid dynamics simulations with a Reynolds-averaged Navier-Stokes model were performed for flow fields over a building array and inside a building in the array with different building opening positions. Ten combinations of opening locations were selected to investigate the effect of the [...] Read more.
Computational fluid dynamics simulations with a Reynolds-averaged Navier-Stokes model were performed for flow fields over a building array and inside a building in the array with different building opening positions. Ten combinations of opening locations were selected to investigate the effect of the locations on indoor cross-ventilation rates. The results of these simulations show that the exterior distributions of mean wind speed and turbulence kinetic energy hardly differ even though building openings exist. Although similar patterns of outdoor flow fields were observed, the opening positions produced two different types of ventilations: one-way and two-way. In one-way ventilation, the wind flows through the opening are unidirectional: diagonally downward at the windward wall. In two-way ventilation, both inflow and outflow simultaneously occur through the same opening. Determination of ventilation rates showed that the ventilation types can explain what type of ventilation rate may be significant for each opening location. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic of the numerical simulation target: (<b>a</b>) simulation domain with numerical grids; (<b>b</b>) side view of the mesh arrangement for the block with openings (case A12C12); and (<b>c</b>) the definition of the opening positions.</p> Full article ">Figure 2
<p>Combinations of windward and leeward openings: A and C indicate the windward and leeward walls, respectively, and the subsequent numbers indicate the vertical and spanwise positions of the openings, respectively.</p> Full article ">Figure 3
<p>Vertical profiles of (<b>a</b>) streamwise wind speed; (<b>b</b>) turbulence kinetic energy; and (<b>c</b>) vertical Reynolds stress for Solid, A12C12, A22C22, A32C32, A12C32, and A32C12 with reference from Coceal et al. [<a href="#B28-atmosphere-09-00217" class="html-bibr">28</a>]. Present simulation data, except for the Solid case, are plotted every four vertical grid points for clear presentation.</p> Full article ">Figure 4
<p>Sectional views of flow fields at the spanwise centre of the block: <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> below <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math> for (<b>a</b>) Solid; (<b>b</b>) A12C12; and (<b>c</b>) A32C32.</p> Full article ">Figure 5
<p>Plan view of flow field at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> for the Solid case.</p> Full article ">Figure 6
<p>Wall pressure distribution on solid block on the (<b>a</b>) windward wall and (<b>b</b>) leeward wall. Black rectangles indicate positions of openings.</p> Full article ">Figure 7
<p>Sectional view of flow fields at spanwise centre of windward opening for (<b>a</b>) A12C12; (<b>b</b>) A32C32; and (<b>c</b>) A31C31.<math display="inline"><semantics> <mrow> <mtext> </mtext> <msub> <mi>x</mi> <mi>w</mi> </msub> </mrow> </semantics></math> is coordinate of <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>w</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> at windward wall. <math display="inline"><semantics> <mi>D</mi> </semantics></math> represents the wall thickness.</p> Full article ">Figure 8
<p>Sectional view of flow fields at spanwise centre of leeward openings for (<b>a</b>) A12C12 and (<b>b</b>) A31C31. <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>l</mi> </msub> </mrow> </semantics></math> is coordinate of <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>l</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> at leeward wall. <math display="inline"><semantics> <mi>D</mi> </semantics></math> represents the wall thickness.</p> Full article ">Figure 9
<p>Ventilation rates for each opening position. <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>n</mi> <mi>v</mi> </mrow> </msub> </mrow> </semantics></math>: determined by conventional method by Equation (1), <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> : net ventilation rate by Equation (3), <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> : gross ventilation rate by Equation (4), and <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> : estimated cumulative and average instantaneous ventilation rate. The error bar in <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> indicates the rate with a 30% increase of TKE by reflecting the underestimation of TKE in the present simulation as compared with those of LES (Xie and Castro [<a href="#B30-atmosphere-09-00217" class="html-bibr">30</a>]).</p> Full article ">
32 pages, 3552 KiB  
Article
Does Marine Surface Tension Have Global Biogeography? Addition for the OCEANFILMS Package
by Scott Elliott, Susannah Burrows, Philip Cameron-Smith, Forrest Hoffman, Elizabeth Hunke, Nicole Jeffery, Yina Liu, Mathew Maltrud, Zachary Menzo, Oluwaseun Ogunro, Luke Van Roekel, Shanlin Wang, Michael Brunke, Meibing Jin, Robert Letscher, Nicholas Meskhidze, Lynn Russell, Isla Simpson, Dale Stokes and Oliver Wingenter
Atmosphere 2018, 9(6), 216; https://doi.org/10.3390/atmos9060216 - 4 Jun 2018
Cited by 10 | Viewed by 6817
Abstract
We apply principles of Gibbs phase plane chemistry across the entire ocean-atmosphere interface to investigate aerosol generation and geophysical transfer issues. Marine surface tension differences comprise a tangential pressure field controlling trace gas fluxes, primary organic inputs, and sea spray salt injections, in [...] Read more.
We apply principles of Gibbs phase plane chemistry across the entire ocean-atmosphere interface to investigate aerosol generation and geophysical transfer issues. Marine surface tension differences comprise a tangential pressure field controlling trace gas fluxes, primary organic inputs, and sea spray salt injections, in addition to heat and momentum fluxes. Mapping follows from the organic microlayer composition, now represented in ocean system models. Organic functional variations drive the microforcing, leading to (1) reduced turbulence and (by extension) laminar gas-energy diffusion; plus (2) altered bubble film mass emission into the boundary layer. Interfacial chemical behaviors are, therefore, closely reviewed as the background. We focus on phase transitions among two dimensional “solid, liquid, and gaseous” states serving as elasticity indicators. From the pool of dissolved organic carbon (DOC) only proteins and lipids appear to occupy significant atmospheric interfacial areas. The literature suggests albumin and stearic acid as the best proxies, and we distribute them through ecodynamic simulation. Consensus bulk distributions are obtained to control their adsorptive equilibria. We devise parameterizations for both the planar free energy and equation of state, relating excess coverage to the surface pressure and its modulus. Constant settings for the molecular surrogates are drawn from laboratory study and successfully reproduce surfactant solid-to-gas occurrence in compression experiments. Since DOC functionality measurements are rare, we group them into super-ecological province tables to verify aqueous concentration estimates. Outputs are then fed into a coverage, tension, elasticity code. The resulting two dimensional pressure contours cross a critical range for the regulation of precursor piston velocity, bubble breakage, and primary aerosol sources plus ripple damping. Concepts extend the water-air adsorption theory currently embodied in our OCEANFILMS aerosol emissions package, and the two approaches could be inserted into Earth System Models together. Uncertainties in the logic include kinetic and thermochemical factors operating at multiple scales. Full article
(This article belongs to the Special Issue Ocean Contributions to the Marine Boundary Layer Aerosol Budget)
►▼ Show Figures

Figure 1

Figure 1
<p>Log surface pressure maps (Δtension) assembled using the baseline Ogunro et al. model output [<a href="#B33-atmosphere-09-00216" class="html-bibr">33</a>] plus marine 2D equations of state described in <a href="#app1-atmosphere-09-00216" class="html-app">Appendix A</a>. The color bar has been set so a reference range of 0.3–3 mN/m is central (−0.5 to 0.5 in log units). February and August monthly averages are shown for a typical year near the turn of the millennium.</p> Full article ">Figure 2
<p>Log surface pressure maps (Δtension) constructed as in <a href="#atmosphere-09-00216-f001" class="html-fig">Figure 1</a>, but with protein (or lipid) levels lowered (or raised) by a factor of three. The color bar has been set so that a reference range of 0.3–3 mN/m is central (−0.5 to 0.5 in log units). February and August monthly averages are shown for a typical year near the turn of the millennium.</p> Full article ">Figure 3
<p>Log surface pressure maps (Δtension) constructed as in <a href="#atmosphere-09-00216-f002" class="html-fig">Figure 2</a>, but with lipid levels returned to baseline. The color bar has been set so that a reference range of 0.3–3 mN/m is central (−0.5 to 0.5 in log units). May and November monthly averages are shown for a typical year near the turn of the millennium.</p> Full article ">Figure 4
<p>Log surface pressure maps (Δtension) constructed as in <a href="#atmosphere-09-00216-f002" class="html-fig">Figure 2</a> so that albumin is cut by three relative to the baseline, but with lipid levels zeroed. The color bar has been set so that a reference range of 0.3–3 mN/m is central (−0.5 to 0.5 in log units). February and August monthly averages are shown for a typical year near the turn of the millennium.</p> Full article ">Figure 5
<p>Log surface pressure maps (Δtension) with protein levels decremented 10×, while lipids are returned to baseline. The color bar has been set so that a reference range of 0.3–3 mN/m is central (−0.5 to 0.5 in log units). February and August monthly averages are shown for a typical year near the turn of the millennium.</p> Full article ">Figure A1
<p>Comparison of film pressure versus area isotherms as calculated using the appendix (power Langmuir) equations of state for (<b>A</b>) the main proxy compounds along with a sample mixture containing 3% fatty acid, as judged by carbon atom solute concentration, and (<b>B</b>) oleic acid substituted for stearic acid. The potential for loss of 2D condensed behaviors is clear. “Long” and “short” refer to the dashed curves.</p> Full article ">
17 pages, 3354 KiB  
Article
Assessment of Air Thermal Conditions in the Lowland Part of South-Western Poland for Agriculture Development Purposes
by Robert Kalbarczyk, Eliza Kalbarczyk, Monika Ziemiańska and Beata Raszka
Atmosphere 2018, 9(6), 215; https://doi.org/10.3390/atmos9060215 - 3 Jun 2018
Cited by 9 | Viewed by 3899
Abstract
The recognition of changes in the course of agricultural thermal periods is vital when it comes to determining appropriate measures for adapting agriculture to climate change. The present study examined changes in air temperature between 1951 and 2014 in the area of south-western [...] Read more.
The recognition of changes in the course of agricultural thermal periods is vital when it comes to determining appropriate measures for adapting agriculture to climate change. The present study examined changes in air temperature between 1951 and 2014 in the area of south-western Poland. A statistically significant, positive linear trend was confirmed for the annual average temperature, seasonal averages, and monthly averages in the periods spanning February–May and July–August. From the beginning of the 21st century, the period of winter dormancy of plants started increasingly later; farming and plant vegetation periods started increasingly earlier, and the period of active plant growth was prolonged. Among the considered agricultural periods, the growing season was the most prolonged. The duration of the farming period was also significantly longer, but the winter dormancy period was shortened. The negative linear trend of days when the temperature stood at <0 °C was statistically confirmed for temperature in the entire region and most of the stations. In terms of predicting the consequences of the changes that were observed today over the next decades, this is not an easy task. However, the nature of these changes suggests that further cultivation of winter crops may require far-reaching adaptation measures. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of meteorological stations in south-western Poland.</p> Full article ">Figure 2
<p>Monthly time distribution of air temperature (Ta) in the studied region, 1951–2014.</p> Full article ">Figure 3
<p>Monthly variation in the standard deviation of daily mean temperature (Ta) in the studied region, 1951–2014.</p> Full article ">Figure 4
<p>Quantile-based classification of monthly, seasonal and annual mean temperature in the studied region, 1951–2014.</p> Full article ">Figure 5
<p>Frequency of occurrence of daily air temperature values (Ta) in 2 °C intervals by seasons and annually for the studied region (<span class="html-italic">n</span> = 23344), 1951–2014; n-sample size.</p> Full article ">Figure 6
<p>Time distribution of the number of days with air temperature (Ta) characterizing thermal agricultural periods: winter dormancy of plants (&lt;0 °C), farming (&gt;3 °C), vegetation (&gt;5 °C), active plant growth (&gt;10 °C), and maturation of plants (&gt;15 °C) in the studied region, 1951–2014.</p> Full article ">Figure 7
<p>Difference in the number of days with air temperature in different agricultural classes between the decades 2005–2014 and 1951–1960.</p> Full article ">
21 pages, 13947 KiB  
Article
Impact of Biomass Home Heating, Cooking Styles, and Bread Toasting on the Indoor Air Quality at Portuguese Dwellings: A Case Study
by Nuno Canha, Joana Lage, Catarina Galinha, Susana Coentro, Célia Alves and Susana Marta Almeida
Atmosphere 2018, 9(6), 214; https://doi.org/10.3390/atmos9060214 - 1 Jun 2018
Cited by 24 | Viewed by 5820
Abstract
This study evaluated the emissions of specific indoor sources usually present in Portuguese dwellings in order to understand their impact on the indoor air quality. With this aim, three typical activities were studied including home heating using two types of fireplaces (open and [...] Read more.
This study evaluated the emissions of specific indoor sources usually present in Portuguese dwellings in order to understand their impact on the indoor air quality. With this aim, three typical activities were studied including home heating using two types of fireplaces (open and closed) and biofuels (pinewood and briquettes), cooking styles (frying and boiling) in different types of kitchen appliances, and several levels of bread toasting. The levels of specific pollutants were found to be above the established Portuguese limit values including VOCs, formaldehyde, and particulate matter (PM2.5 and PM10). Although these emissions are transient and short in duration, the resulting concentrations are high and can severely impact the occupants’ daily exposure. Besides promoting good ventilation, the choice of residential appliances with low emissions should be taken into account. In addition, it is important that occupants perform specific activities following the best practices so that their exposure to pollutants is minimized. Full article
(This article belongs to the Special Issue Indoor Air Pollution)
►▼ Show Figures

Figure 1

Figure 1
<p>Floor plans of the studied dwellings with specification of the rooms where the monitoring was conducted: A—kitchen and B—living room.</p> Full article ">Figure 2
<p>Appliances used for biomass burning include (<b>left</b>) an open fireplace located in the living room of dwelling 2 and (<b>right</b>) a closed fireplace (insert) located in the living room of dwelling 3.</p> Full article ">Figure 3
<p>Type of meals prepared and cooking utilities and appliances used.</p> Full article ">Figure 4
<p>Levels of loaf bread toasting studied (<b>right</b>: slightly, medium, and heavily toasted) using a Philips toaster (<b>left</b>).</p> Full article ">Figure 5
<p>Indoor air pollutants and comfort parameters during the four combustion scenarios (OP—Open fireplace + Pine, OB—Open fireplace + Briquettes, CP—Closed fireplace (insert) + Pine, CB—Closed fireplace (insert) + Briquettes). The box plot represents mean (square) and the quartiles (5%, 25%, median, 75% and 95%).</p> Full article ">Figure 5 Cont.
<p>Indoor air pollutants and comfort parameters during the four combustion scenarios (OP—Open fireplace + Pine, OB—Open fireplace + Briquettes, CP—Closed fireplace (insert) + Pine, CB—Closed fireplace (insert) + Briquettes). The box plot represents mean (square) and the quartiles (5%, 25%, median, 75% and 95%).</p> Full article ">Figure 6
<p>Temporal variability of CO<sub>2</sub> (line—mean; light colored area—standard deviation) during the combustion period for the four studied conditions: (<b>top</b>, <b>left</b>) OP—open fireplace and pinewood, (<b>top</b>, <b>right</b>) OB—open fireplace and briquettes, (<b>bottom</b>, <b>left</b>) CP—closed fireplace (insert) and pinewood, and (<b>bottom</b>, <b>right</b>) CB—closed fireplace (insert) and briquettes. Red line represents the CO<sub>2</sub> limit value of 2250 mg·m<sup>−3</sup> defined by the Portuguese legislation [<a href="#B32-atmosphere-09-00214" class="html-bibr">32</a>].</p> Full article ">Figure 7
<p>Temporal variability of CO (line—mean; light coloured area—standard deviation) during the combustion period for the four studied conditions: (<b>top</b>, <b>left</b>) OP—open fireplace and pinewood, (<b>top</b>, <b>right</b>) OB—open fireplace and briquettes, (<b>bottom</b>, <b>left</b>) CP—closed fireplace (insert) and pinewood, and (<b>bottom</b>, <b>right</b>) CB—closed fireplace (insert) and briquettes. The red line represents the CO limit value of 10 mg·m<sup>−3</sup> defined by the Portuguese legislation [<a href="#B32-atmosphere-09-00214" class="html-bibr">32</a>].</p> Full article ">Figure 8
<p>Temporal variability of VOCs (line—mean; light coloured area—standard deviation) during the combustion period for the four studied conditions: (<b>top</b>, <b>left</b>) OP—open fireplace and pinewood, (<b>top</b>, <b>right</b>) OB—open fireplace and briquettes, (<b>bottom</b>, <b>left</b>) CP—closed fireplace (insert) and pinewood, and (<b>bottom</b>, <b>right</b>) CB—closed fireplace (insert) and briquettes. The red line represents the VOC limit value of 0.6 mg·m<sup>−3</sup> defined by the Portuguese legislation [<a href="#B32-atmosphere-09-00214" class="html-bibr">32</a>].</p> Full article ">Figure 9
<p>Temporal variability of CH<sub>2</sub>O (line—mean; light colored area—standard deviation) during the combustion period for the four studied conditions: (<b>top</b>, <b>left</b>) OP—open fireplace and pinewood, (<b>top</b>, <b>right</b>) OB—open fireplace and briquettes, (<b>bottom</b>, <b>left</b>) CP—closed (insert) fireplace and pinewood, and (<b>bottom</b>, <b>right</b>) CB—closed fireplace (insert) and briquettes. The red line represents the CH<sub>2</sub>O limit value of 0.1 mg·m<sup>−3</sup> defined by the Portuguese legislation [<a href="#B32-atmosphere-09-00214" class="html-bibr">32</a>].</p> Full article ">Figure 10
<p>Temporal variability of PM<sub>2.5</sub> (line—mean; light coloured area—standard deviation) during the combustion period for the four studied conditions: (<b>top</b>, <b>left</b>) OP—open fireplace and pinewood, (<b>top</b>, <b>right</b>) OB—open fireplace and briquettes, (<b>bottom</b>, <b>left</b>) CP—closed fireplace and pinewood, and (<b>bottom</b>, <b>right</b>) CB—closed fireplace and briquettes. The red line represents the PM<sub>25</sub> limit value of 25 µg·m<sup>−3</sup> defined by the Portuguese legislation [<a href="#B2-atmosphere-09-00214" class="html-bibr">2</a>].</p> Full article ">Figure 11
<p>Selected indoor air pollutants for the studied cooking scenarios: 1GF—House 1 | Gas | Frying, 1EF—House 1 | Electric | Frying, 1GB—House 1 | Gas | Boiling, 1EB—House 1 | Electric | Boiling, 2GF—House 2 | Gas | Frying, 2EF—House 2 | Electric | Frying, 2GB—House 2 | Gas | Boiling, 2EB—House 2| Electric | Boiling, combustion scenarios. The red line represents the limit values of the Portuguese legislation [<a href="#B32-atmosphere-09-00214" class="html-bibr">32</a>]. The box plot represents mean (black square), the quartiles (5%, 25%, median, 75% and 95%), and lower circle the minimum value while the upper circle represents the maximum value.</p> Full article ">Figure 12
<p>Indoor air pollutants for the studied toasting scenarios at two dwellings (1 and 2): S—slightly toasted, M—medium toasted; H—heavily toasted. The red line represents the limit values of the Portuguese legislation [<a href="#B32-atmosphere-09-00214" class="html-bibr">32</a>]. The box plot represents mean (black square), the quartiles (5%, 25%, median, 75% and 95%) and lower circle the minimum value while the upper circle represents the maximum value.</p> Full article ">
23 pages, 1308 KiB  
Article
Cluster Sampling Filters for Non-Gaussian Data Assimilation
by Ahmed Attia, Azam Moosavi and Adrian Sandu
Atmosphere 2018, 9(6), 213; https://doi.org/10.3390/atmos9060213 - 31 May 2018
Cited by 10 | Viewed by 4698
Abstract
This paper presents a fully non-Gaussian filter for sequential data assimilation. The filter is named the “cluster sampling filter”, and works by directly sampling the posterior distribution following a Markov Chain Monte-Carlo (MCMC) approach, while the prior distribution is approximated using [...] Read more.
This paper presents a fully non-Gaussian filter for sequential data assimilation. The filter is named the “cluster sampling filter”, and works by directly sampling the posterior distribution following a Markov Chain Monte-Carlo (MCMC) approach, while the prior distribution is approximated using a Gaussian Mixture Model (GMM). Specifically, a clustering step is introduced after the forecast phase of the filter, and the prior density function is estimated by fitting a GMM to the prior ensemble. Using the data likelihood function, the posterior density is then formulated as a mixture density, and is sampled following an MCMC approach. Four versions of the proposed filter, namely C ℓ MCMC , C ℓ HMC , MC- C ℓ HMC , and MC- C ℓ HMC are presented. C ℓ MCMC uses a Gaussian proposal density to sample the posterior, and C ℓ HMC is an extension to the Hamiltonian Monte-Carlo (HMC) sampling filter. MC- C ℓ MCMC and MC- C ℓ HMC are multi-chain versions of the cluster sampling filters C ℓ MCMC and C ℓ HMC respectively. The multi-chain versions are proposed to guarantee that samples are taken from the vicinities of all probability modes of the formulated posterior. The new methodologies are tested using a simple one-dimensional example, and a quasi-geostrophic (QG) model with double-gyre wind forcing and bi-harmonic friction. Numerical results demonstrate the usefulness of using GMMs to relax the Gaussian prior assumption especially in the HMC filtering paradigm. Full article
(This article belongs to the Special Issue Efficient Formulation and Implementation of Data Assimilation Methods)
►▼ Show Figures

Figure 1

Figure 1
<p>The one-dimensional example. A random sample of size <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">N</mi> <mi>ens</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> generated from the “true” GMM prior with parameters given by (<a href="#FD26-atmosphere-09-00213" class="html-disp-formula">26</a>), and a GMM constructed by EM algorithm with AIC model selection criterion.</p> Full article ">Figure 2
<p>The one-dimensional example. A GMM prior, a Gaussian likelihood, and the resulting posterior, along with histograms of 1000 sample points generated by the <math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>M</mi> <mi>C</mi> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math> (<b>a</b>), MC-<math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>M</mi> <mi>C</mi> <mi>M</mi> <mi>C</mi> </mrow> </semantics></math> (<b>b</b>), <math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math> (<b>c</b>), and the MC-<math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math> (<b>d</b>) sampling algorithms. The symplectic integrator used for HMC filters is Verlet with pseudo-time stepping parameters <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mi>m</mi> <mi>h</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>0.045</mn> </mrow> </semantics></math>. The number of burn-in steps is zero, and the number of mixing steps is 20.</p> Full article ">Figure 3
<p>The QG-1.5 model. The red dots in (<b>a</b>) indicate the location of observations for one of the test cases employed.</p> Full article ">Figure 4
<p>Data assimilation results with the linear observation operator. RMSE of the (<a href="#FD31-atmosphere-09-00213" class="html-disp-formula">31</a>) analyses obtained by EnKF, HMC, <math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math>, and MC-<math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math> filters. Forecast results here refer to the RMSE obtained from a free run of the dynamical model, with initial condition set to the forecast state at the initial time.</p> Full article ">Figure 5
<p>Data assimilation results with the linear observation operator. The rank histograms of where the truth ranks among posterior ensemble members. The ranks are evaluated for every 16th variable in the state vector (past the correlation bound) at 100 assimilation times.</p> Full article ">Figure 6
<p>Data assimilation results using a linear observation operator. Rank histograms of where the truth ranks among posterior ensemble members. The ranks are evaluated for every 16th variable in the state vector (past the correlation bound). Rank histograms of <math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math> results obtained at the first two, five, and 10 assimilation cycles, respectively, are shown. The model selection criterion used is AIC.</p> Full article ">Figure 7
<p>Data assimilation with a linear observation operator. Chi-square Q-Q plots for the forecast ensembles obtained from propagating analyses of EnKF, HMC, and MC-<math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math> filtering systems to times t = 300, 775, 1000, and 1200 provide a strong indication of non-Gaussianity. The filtering methodology, and the assimilation time are given under each panel. Localization is applied to the ensemble covariance matrix to avoid singularity while evaluating the Mahalanobis distances of the ensemble members.</p> Full article ">Figure 8
<p>Data assimilation results with the nonlinear observation operator (<a href="#FD30-atmosphere-09-00213" class="html-disp-formula">30</a>). RMSE of the analyses obtained by HMC, <math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math>, and MC-<math display="inline"><semantics> <mrow> <mi mathvariant="script">C</mi> <mi>ℓ</mi> <mi>HMC</mi> </mrow> </semantics></math> filtering schemes. The Forecast RMSE results are obtained from a free run of the dynamical model.</p> Full article ">Figure 9
<p>Data assimilation results using the nonlinear observation operator (<a href="#FD30-atmosphere-09-00213" class="html-disp-formula">30</a>). The rank histograms of where the truth ranks among posterior ensemble members. The ranks are evaluated for every 16th variable in the state vector (past the correlation bound) at 100 assimilation times. The filtering scheme used is indicated under each panel.</p> Full article ">
18 pages, 1461 KiB  
Article
Impact of Management Practices on Methane Emissions from Paddy Grown on Mineral Soil over Peat in Central Hokkaido, Japan
by Habib Mohammad Naser, Osamu Nagata, Sarmin Sultana and Ryusuke Hatano
Atmosphere 2018, 9(6), 212; https://doi.org/10.3390/atmos9060212 - 31 May 2018
Cited by 5 | Viewed by 5679
Abstract
This study was carried out at Kita-mura near Bibai located in central Hokkaido, Japan, with the intention of investigating the effects of different agronomical managements on CH4 emissions from paddy fields on mineral soil over peat under farmers’ actual management conditions in [...] Read more.
This study was carried out at Kita-mura near Bibai located in central Hokkaido, Japan, with the intention of investigating the effects of different agronomical managements on CH4 emissions from paddy fields on mineral soil over peat under farmers’ actual management conditions in the snowy temperate region. Four fields were studied, including two fields with twice drainage (D1-M and D2-M) and also a single-drainage field (D3-S) under annual single-cropping and a paddy-fallow-paddy crop rotation as their systems. The other field was under single cropping annual with continuous flooding (CF-R) in the pattern of soybean (upland crop)-fallow-paddy. The mineral-soil thickness of these soil-dressed peatland fields varied from 20 to 47 cm. The amount of crop residues leftover in the fields ranged from 277 to 751 g dry matter m−2. Total CH4 emissions ranged from 25.3 to 116 g CH4-C m−2 per growing season. There was a significant relationship between crop-residue carbon (C) and total CH4 emissions during the rice-growing season. Methane fluxes from paddy soils had a strong interaction between readily available C source for methanogens and anaerobic conditions created by water management. Despite the differences in water regime and soil type, the average values of straw’s efficiency on CH4 production in this study were significantly higher than those of southern Japan and statistically identical with central Hokkaido. Our results suggest that the environmental conditions of central Hokkaido in association with crop-residue management had a significant influence on CH4 emission from paddy fields on mineral soil over peat. Rotation soybean (upland)-to-paddy followed by drainage-twice practices also largely reduces CH4 emission. However, mineral-soil dressing on peat could have a significant impact on suppression of CH4 emissions from beneath the peat reservoir. Full article
(This article belongs to the Special Issue C and N Cycling and Greenhouse Gases Emission in Agroecosystem)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Investigated sites.</p> Full article ">Figure 2
<p>Mineral soil dressing on peatland in Kita-mura, near Bibai during 1960s.</p> Full article ">Figure 3
<p>Climatic conditions of investigated area during winter-fallow (<b>a</b>) and rice growing period (<b>b</b>).</p> Full article ">Figure 4
<p>The CH<sub>4</sub> emissions over time and Eh measured from paddy fields during the growing season. Error bar indicating standard deviation. ↔ = Drainage period. → = Final drainage for harvest.</p> Full article ">Figure 5
<p>Relationship between the amount of organic residue C and total CH<sub>4</sub> emissions measured during rice growing season.</p> Full article ">Figure 6
<p>Straw C loss during the winter-fallow period (October to April).</p> Full article ">
2 pages, 152 KiB  
Correction
Correction: Recent Advances in Atmospheric Chemistry of Mercury
by Lin Si, Parisa A. Ariya and Atmosphere Editorial Office
Atmosphere 2018, 9(6), 211; https://doi.org/10.3390/atmos9060211 - 31 May 2018
Cited by 1 | Viewed by 4582
Abstract
The published paper [1] has been updated to remove instances of copied text from other publications [2–6].[...] Full article
(This article belongs to the Special Issue Atmospheric Metal Pollution)
16 pages, 3211 KiB  
Article
Development of a Distributed Modeling Framework to Estimate Thermal Comfort along 2020 Tokyo Olympic Marathon Course
by Satoshi Hirabayashi, Tsutomu Abe, Fumiko Imamura and Chie Morioka
Atmosphere 2018, 9(6), 210; https://doi.org/10.3390/atmos9060210 - 30 May 2018
Cited by 8 | Viewed by 5429
Abstract
Heat stress is an issue for marathon races in the summer, such as the one planned for the 2020 Tokyo Summer Olympic games. The Tokyo Metropolitan Government is planning to grow existing street trees’ canopies to enlarge their shade to reduce air temperature [...] Read more.
Heat stress is an issue for marathon races in the summer, such as the one planned for the 2020 Tokyo Summer Olympic games. The Tokyo Metropolitan Government is planning to grow existing street trees’ canopies to enlarge their shade to reduce air temperature and solar radiation. To formulate a baseline to assess the effect of street trees and buildings on human thermal comfort, Distributed-COMfort FormulA (D-COMFA), a prototype of a distributed computer model using a geographic information system (GIS) was developed. D-COMFA calculates the energy budget of a human body on a 1 m cell basis, using readily available datasets such as weather measurements and polygon data for street structures. D-COMFA was applied to a street segment along the marathon course in Tokyo on an hourly-basis on 9 August 2016, the hottest day in Tokyo in 2016. Our case study showed that the energy budget was positively related to the sky view factor, air temperature, and solar radiation. The energy budget was reduced on average by 26–62% in the shade throughout the day. Full article
(This article belongs to the Special Issue Urban Design and City Microclimates)
►▼ Show Figures

Figure 1

Figure 1
<p>Energy components of <span class="html-italic">R<sub>abs</sub></span> that stem from (<b>a</b>) solar radiation and (<b>b</b>) radiant heat.</p> Full article ">Figure 2
<p>(<b>a</b>) Illustration of the algorithm to estimate <span class="html-italic">SVF</span>; (<b>b</b>) fisheye photograph showing a building and a tree overlapped.</p> Full article ">Figure 3
<p>Study site along 2020 Tokyo Olympic marathon course in Tokyo, Japan; photograph taken northward from southern end of site, showing Yurinoki—tulip tree (Liriodendron tulipifera).</p> Full article ">Figure 4
<p>(<b>a</b>) <span class="html-italic">SVF</span> measurement location (<b>b</b>) fisheye measurement and estimate <span class="html-italic">SVF</span> comparisons.</p> Full article ">Figure 5
<p><span class="html-italic">EB</span> variations across time and space as a function of <span class="html-italic">SVF</span> and shade on August 9, 2016 at (<b>a</b>) 7 a.m.; (<b>b</b>) 9 a.m.; and (<b>c</b>) 1 p.m.</p> Full article ">Figure 6
<p><span class="html-italic">R<sub>abs</sub></span>’s component (<b>a</b>) <span class="html-italic">D</span>; (<b>b</b>) <span class="html-italic">V</span>; (<b>c</b>) <span class="html-italic">S</span>; and (<b>d</b>) <span class="html-italic">F</span> as a function of <span class="html-italic">SVF</span> and shade on 9 August 2016 at 1 p.m.</p> Full article ">Figure 7
<p>D-COMFA results for study site street at 10 a.m. 9 August 2016. (<b>a</b>) shade and sunlit areas and (<b>b</b>) <span class="html-italic">EB</span>.</p> Full article ">Figure 8
<p>(<b>a</b>) Sunlit, building- and tree-shaded area fraction on street; (<b>b</b>) average <span class="html-italic">EB</span> for sunlit, building- and tree-shaded area on street.</p> Full article ">Figure 9
<p>Results due to temperature adjustment for sunlit/shaded ground cover and surface objects, and under tree canopy at 1 p.m. for (<b>a</b>) <span class="html-italic">M</span>; (<b>b</b>) <span class="html-italic">Conv</span>; (<b>c</b>) <span class="html-italic">Evap</span>; (<b>d</b>) <span class="html-italic">TR<sub>emit</sub></span>; and (<b>e</b>) <span class="html-italic">G</span>.</p> Full article ">
14 pages, 22139 KiB  
Article
Spatial Estimation of Thermal Indices in Urban Areas—Basics of the SkyHelios Model
by Dominik Fröhlich and Andreas Matzarakis
Atmosphere 2018, 9(6), 209; https://doi.org/10.3390/atmos9060209 - 29 May 2018
Cited by 34 | Viewed by 7098
Abstract
Thermal perception and stress for humans can be best estimated based on appropriate indices. Sophisticated thermal indices, e.g., the Perceived Temperature (PT), the Universal Thermal Climate Index (UTCI), or the Physiologically Equivalent Temperature (PET) do require the meteorological input parameters air temperature ( [...] Read more.
Thermal perception and stress for humans can be best estimated based on appropriate indices. Sophisticated thermal indices, e.g., the Perceived Temperature (PT), the Universal Thermal Climate Index (UTCI), or the Physiologically Equivalent Temperature (PET) do require the meteorological input parameters air temperature ( T a ), vapour pressure ( V P ), wind speed (v), as well as the different short- and longtime radiation fluxes summarized as the mean radiant temperature ( T m r t ). However, in complex urban environments, especially v and T m r t are highly volatile in space. They can, thus, only be estimated by micro-scale models. One easy way to apply the model for the determination of thermal indices within urban environments is the advanced SkyHelios model. It is designed to estimate sky view factor ( S V F ), sunshine duration, global radiation, wind speed, wind direction, T m r t considering reflections, as well as the three thermal indices PT, UTCI, and PET spatially and temporarily resolved with low computation time. Full article
(This article belongs to the Special Issue Atmospheric Effects on Humans—EMS 2017 Session)
►▼ Show Figures

Figure 1

Figure 1
<p>Fisheye image showing the upper hemisphere with trees and buildings as generated by the SkyHelios model in production mode. The colors and opacity correspond to different shortwave albedo, longwave emissivity, direct radiation factor of the surfaces (including direct shortwave reflections). The checkerboard background was added to visualize the objects opacity.</p> Full article ">Figure 2
<p>Screenshot of SkyHelios main window showing a combined model domain consisting of two areas of interest in Freiburg, Southwest Germany: the “Institutes Quarter” and the “Place of the Old Synagogue”.</p> Full article ">Figure 3
<p>Physiologically Equivalent Temperature (PET) on 1 July 2008 at 1:30 p.m. in a height of 1.1 m above ground level. The calculations consider both the “Institutes Quarter” (<b>upper right</b>) and the “Place of the Old Synagogue” (<b>lower left</b>) together in one large area of interest.</p> Full article ">
13 pages, 7660 KiB  
Article
Cold Waves in Poznań (Poland) and Thermal Conditions in the City during Selected Cold Waves
by Arkadiusz M. Tomczyk, Marek Półrolniczak and Leszek Kolendowicz
Atmosphere 2018, 9(6), 208; https://doi.org/10.3390/atmos9060208 - 28 May 2018
Cited by 13 | Viewed by 3892
Abstract
The objective of the paper was to characterize the occurrence of cold days and cold waves in Poznań in the years 1966/67–2015/16, as well as to characterize thermal conditions in the city during selected cold waves in the years 2008/09–2015/16. The study was [...] Read more.
The objective of the paper was to characterize the occurrence of cold days and cold waves in Poznań in the years 1966/67–2015/16, as well as to characterize thermal conditions in the city during selected cold waves in the years 2008/09–2015/16. The study was based on daily data on maximum and minimum air temperature for station Poznań-Ławica from the years 1966/67–2015/16 and daily air temperature values from eight measurement points located in the territory of the city in different types of land use from the years 2008/08–2015/16. In addition, to characterize thermal conditions during selected days forming cold waves, satellite images were used, on the basis of which the land surface temperature (LST) was calculated. A cold day was defined as a day with daily maximum temperature (Tmax) below the value of 5th annual percentile of Tmax, and a cold wave was defined as at least five consecutive cold days. The study showed an increase in Tmax in winter, which translated to a decrease in the number of cold days over the last 50 years, although the changes were not statistically significant. Thermal conditions in the city showed high variability in the winter season and during the analyzed cold waves. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Human Health)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of Poznań (<b>a</b>) as well as Landsat 5 (thermal) 8-bit greyscale image of the study area (<b>b</b>) and image composite of three infrared bands of the ETM+ radiometer RGB = 654 (<b>c</b>) (acquisition date: 9 January 2010) and location of measurement points against type of land use (<b>d</b>) (Urban Atlas 2012).</p> Full article ">Figure 2
<p>Course of the average Tmax in the winter season with long-term average value in Poznań-Ławica in the years 1966–2015.</p> Full article ">Figure 3
<p>Number of cold days in the period 1966/67–2015/16 and the frequency of occurrence of cold days in months.</p> Full article ">Figure 4
<p>Average Tmax (black solid line) in the winter season and number of cold days (grey bars) in 2008/09–2015/16 in Poznań.</p> Full article ">Figure 5
<p>Tmax during the cold wave of 7–11 February 2010 (<b>a</b>) and 14–26 February 2011 (<b>b</b>).</p> Full article ">Figure 6
<p>Daily course of air temperature in Poznań on 9 February 2010 (<b>a</b>) and 21 February 2011 (<b>b</b>).</p> Full article ">Figure 7
<p>Land surface temperature pattern in Poznań ((<b>a</b>)—9 February 2010, (<b>b</b>)—21 February 2011).</p> Full article ">Figure 8
<p>Statistic of land surface temperature in Poznań ((<b>a</b>)—9 February 2010, (<b>b</b>)—21 February 2011) based on Landsat images according to Urban Atlas 2012 types (colors and order of types according to legend in <a href="#atmosphere-09-00208-f001" class="html-fig">Figure 1</a>). On the boxplot, the middle values denote medians, the box extends to the Q1 (first quartile) and Q3 (third quartile), and the whiskers show the range (99.3%): the upper whisker shows Q3 + 1.5 × IQR (the interquartile range), the lower shows Q1 − 1.5 × IQR. The notches extend to ±1.58 IQR/sqrt(n) and the dots represent outlier.</p> Full article ">Figure 9
<p>Synoptic maps on 9 February 2010 (<b>a</b>) and 21 February 2011 (<b>b</b>). Source: Met Office.</p> Full article ">
14 pages, 1016 KiB  
Article
Contemporary Pyrogeography and Wildfire-Climate Relationships of South Dakota, USA
by Darren R. Clabo
Atmosphere 2018, 9(6), 207; https://doi.org/10.3390/atmos9060207 - 25 May 2018
Cited by 1 | Viewed by 4040
Abstract
A recent wildland fire history and climate database was compiled for South Dakota, USA (SD). Wildfires are generally a warm season phenomenon across central and western SD while eastern SD exhibits a spring peak in annual wildfire activity. It is hypothesized that regional [...] Read more.
A recent wildland fire history and climate database was compiled for South Dakota, USA (SD). Wildfires are generally a warm season phenomenon across central and western SD while eastern SD exhibits a spring peak in annual wildfire activity. It is hypothesized that regional climate and land use are the two primary drivers of the spatiotemporal wildfire distribution across the state. To assess the relative impacts of climate to wildfire activity, Spearman’s rank order correlation coefficients were calculated for monthly values of temperature, precipitation, and the Palmer Drought Modified Index (PMDI) as compared to both monthly area burned and numbers of fire starts data for each of the nine climate divisions in South Dakota. Results show statewide variations in significant correlations but positive temperature anomalies, negative precipitation anomalies, and negative values of the PMDI were most frequently associated with months showing substantial area burned and large numbers of wildfire starts. Time-lagged significant correlations were also seen implying month(s)-ahead predictive capabilities. Positive PMDI values were most significantly correlated to warm season wildfire activity suggesting that the influence of drought on wildfires within SD may be limited to the summer months. Full article
(This article belongs to the Special Issue Fire and the Atmosphere)
►▼ Show Figures

Figure 1

Figure 1
<p>The nine climate divisions of South Dakota with underlying counties named and outlined. Figure adapted from the Climate Prediction Center, National Oceanic and Atmospheric Administration.</p> Full article ">Figure 2
<p>Monthly average precipitation and temperature for South Dakota: (<b>a</b>) Climate Division 1 (Northwest); (<b>b</b>) Climate Division 2 (Northcentral); (<b>c</b>) Climate Division 3 (Northeast); (<b>d</b>) Climate Division 4 (Black Hills); (<b>e</b>) Climate Division 6 (Central); (<b>f</b>) Climate Division 7 (Eastcentral); (<b>g</b>) Climate Division 5 (Southwest); (<b>h</b>) Climate Division 8 (Southcentral); (<b>i</b>) Climate Division 9 (Southeast).</p> Full article ">Figure 3
<p>Monthly total hectares burned and number of fire starts for South Dakota: (<b>a</b>) Climate Division 1 (Northwest); (<b>b</b>) Climate Division 2 (Northcentral); (<b>c</b>) Climate Division 3 (Northeast); (<b>d</b>) Climate Division 4 (Black Hills); (<b>e</b>) Climate Division 6 (Central); (<b>f</b>) Climate Division 7 (Eastcentral); (<b>g</b>) Climate Division 5 (Southwest); (<b>h</b>) Climate Division 8 (Southcentral); (<b>i</b>) Climate Division 9 (Southeast). Note: the scale of the vertical axes differs between panels.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-34
Previous Issue
Next Issue
Back to TopTop