[go: up one dir, main page]
Next Issue
Volume 11, January
Previous Issue
Volume 10, November
 
 
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 10
Issue 12
share announcement

Atmosphere, Volume 10, Issue 12 (December 2019) – 92 articles

Cover Story (view full-size image): Late-spring severe blizzards are crucially important due to agricultural damages and economic loss, with long-term consequences. The predictability of their occurrence, intensity, and location are challenging issues. Recent analysis emphasizes a development mechanism based on the coupled contribution of tropospheric ageostrophic circulations associated to jet streaks. These circulations: (1) interact under local and regional forcing (sea surface temperature, topography, and latent heat) and (2) feedback on enhancing an upper-level jet’s secondary streak, leading to a persistent, severe event. The enhanced secondary jet streak appears for severe events, as shown by 40 years of knowledge of late-spring severe blizzards over the area. Understanding the preconditioning indicated by this analysis could be useful in operational forecast analysis. 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, 7823 KiB  
Article
Effects of Modified Surface Roughness Length over Shallow Waters in a Regional Model Simulation
by So-Young Kim, Song-You Hong, Young Cheol Kwon, Yong Hee Lee and Da-Eun Kim
Atmosphere 2019, 10(12), 818; https://doi.org/10.3390/atmos10120818 - 16 Dec 2019
Cited by 4 | Viewed by 3003
Abstract
The effects of modified sea-surface roughness length over shallow waters are examined in a regional climate simulation over East Asia centered on the Korean Peninsula, using the Advanced Research Weather Research and Forecasting model (WRF-ARW). The control experiment calculates the sea-surface roughness length [...] Read more.
The effects of modified sea-surface roughness length over shallow waters are examined in a regional climate simulation over East Asia centered on the Korean Peninsula, using the Advanced Research Weather Research and Forecasting model (WRF-ARW). The control experiment calculates the sea-surface roughness length as a function of friction velocity based on the Charnock relationship. The experiment considering water depth in the sea-surface roughness length over shallow waters is compared with the control experiment. In the experiment considering water depth, the excessive near-surface wind speed over shallow waters is reduced compared to that of the control experiment. Wind speed is reduced also in the lower troposphere. The effects of modified surface roughness over shallow waters are not localized to the lower troposphere but extended into the upper troposphere. Through the vertical interaction between the lower and upper levels, upper tropospheric wind—which is underestimated in the control experiment—is enhanced in the experiment with modified sea-surface roughness length, not only over the shallow waters, but also over the entire domain. As a result, the vertical shear of zonal wind increases, leading to the enhancement of the negative meridional temperature gradient in the mid troposphere. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>Sea-surface roughness length (cm) with respect to water depth (m) calculated using original formula (Equation (1), dashed); formula suggested by Jiménez and Dudhia (2018) [<a href="#B9-atmosphere-10-00818" class="html-bibr">9</a>] (Equation (2), gray), and a combination of both formulas using a weighting factor that depends on water depth (black solid). Friction velocity is assumed to be 0.3 m s<sup>−1</sup>.</p> Full article ">Figure 2
<p>Model domains and ocean bathymetry (m, shaded).</p> Full article ">Figure 3
<p>Sea-surface roughness length (cm) over shallow waters with a depth less than 100 m as a function of wind speed at 10 m in the (<b>a</b>) control (CTL) and (<b>b</b>) sea-surface roughness length considering water depth (Z0MOD) experiments.</p> Full article ">Figure 4
<p>(<b>a</b>) Bathymetry (m) and wind speed at 10 m (m s<sup>-1</sup>) in the (<b>b</b>) final analysis (FNL) data, (<b>c</b>) ECMWF reanalysis (ERA5) data, (<b>d</b>) CTL experiment, (<b>e</b>) difference between the CTL experiment and the FNL data (CTL-FNL), (<b>f</b>) difference between the CTL experiment and the ERA5 data (CTL-ERA5), and (<b>g</b>) difference between the Z0MOD and CTL experiments (Z0MOD-CTL). The dashed box in (<b>g</b>) indicates the area where the wind speed is averaged for profile in Figure 6.</p> Full article ">Figure 5
<p>Wind speed at 10 m (m s<sup>−1</sup>) on 10 July 2017 at observational points in the (<b>a</b>) CTL experiment, and the (<b>b</b>) surface observational (OBS) data; (<b>c</b>) difference between the CTL experiment and the OBS data (CTL-OBS); (<b>d</b>) difference between the Z0MOD and CTL experiments (Z0MOD-CTL).</p> Full article ">Figure 6
<p>Wind speed profile averaged over the area indicated by dashed box in <a href="#atmosphere-10-00818-f004" class="html-fig">Figure 4</a>g in the CTL (blue solid line) and Z0MOD (red solid line) experiments and FNL (black solid line) and ERA5 (black dashed line) data.</p> Full article ">Figure 7
<p>Horizontal wind vector and speed (shaded) at (<b>a</b>–<b>c</b>) 200 hPa and (<b>d</b>–<b>f</b>) 850 hPa in (<b>a</b>,<b>d</b>) the CTL experiment, (<b>b</b>,<b>e</b>) difference between the CTL experiment and FNL data (CTL–FNL), and (<b>c</b>,<b>f</b>) difference between the Z0MOD and CTL experiments (Z0MOD–CTL).</p> Full article ">Figure 8
<p>Zonal wind at (<b>a</b>–<b>c</b>) 200 hPa, (<b>d</b>–<b>f</b>) 850 hPa, and (<b>g</b>–<b>i</b>) vertical difference between 200 hPa and 850 hPa (200 hPa–850 hPa) in the (<b>a</b>,<b>d</b>,<b>g</b>) CTL and (<b>b</b>,<b>e</b>,<b>h</b>) Z0MOD experiments, and (<b>c</b>,<b>f</b>,<b>i</b>) difference between the Z0MOD and CTL experiments (Z0MOD–CTL).</p> Full article ">Figure 9
<p>Temperature at (<b>a</b>,<b>b</b>) 500 hPa, (<b>c</b>,<b>d</b>) 850 hPa, and (<b>e</b>,<b>h</b>) 2m in (left) the CTL experiment and (right) difference between the Z0MOD and CTL experiments (Z0MOD–CTL).4. Summary and Conclusions.</p> Full article ">
15 pages, 5037 KiB  
Article
Observed Exposure of Population and Gross Domestic Product to Extreme Precipitation Events in the Poyang Lake Basin, China
by Mingjin Zhan, Jianqing Zhai, Hemin Sun, Xiucang Li and Lingjun Xia
Atmosphere 2019, 10(12), 817; https://doi.org/10.3390/atmos10120817 - 16 Dec 2019
Cited by 11 | Viewed by 2414
Abstract
Based on the observation data from the Poyang Lake Basin (China), an extreme precipitation event (EPE) is defined as that for which daily precipitation exceeded a threshold of 50 mm over a continuous area for a given time scale. By considering the spatiotemporal [...] Read more.
Based on the observation data from the Poyang Lake Basin (China), an extreme precipitation event (EPE) is defined as that for which daily precipitation exceeded a threshold of 50 mm over a continuous area for a given time scale. By considering the spatiotemporal continuity of EPEs, the intensity–area–duration method is applied to study both the characteristics of EPEs and the population and gross domestic product (GDP) exposures. The main results are as follows. (1) During 1961–2014, the frequencies and the intensities of the EPEs are found to be increasing. (2) The annual area impacted by EPEs is determined as 7.4 × 104 km2 with a general upward trend of 400 km2/year. (3) The annually exposed population is estimated as 19% of the entire population of the Basin, increasing by 1.37 × 105/year. The annual exposure of GDP is 8.5% of the entire GDP of the Basin, increasing by 3.8 billion Yuan/year. The Poyang Lake Basin experiences serious extreme precipitation with increasing trends in frequency, intensity, and exposure (for both GDP and population). It is imperative that effective disaster prevention and reduction measures be adopted in this area to mitigate the effects of extreme precipitation. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>The spatial distribution of meteorological stations and the grids in the Poyang Lake Basin.</p> Full article ">Figure 2
<p>Population time-series of the Poyang Lake Basin for 1984–2014.</p> Full article ">Figure 3
<p>Gross domestic product (GDP) time-series of the Poyang Lake Basin for 1984–2014.</p> Full article ">Figure 4
<p>Average population distribution of Poyang Lake Basin, from 1961 to 2014.</p> Full article ">Figure 5
<p>Average GDP distribution of Poyang Lake Basin, from 1961 to 2014.</p> Full article ">Figure 6
<p>Construction of intensity-area-duration (I-A-D) curve. ((<b>a</b>–<b>e</b>): Determination of the range of extreme events; (<b>e</b>): The extreme events in Poyang Lake Basin; (<b>f</b>): The IAD curve of all the events.).</p> Full article ">Figure 7
<p>The frequency of 1-day extreme precipitation event (EPEs) in the Poyang Lake Basin, from 1961 to 2014.</p> Full article ">Figure 8
<p>The frequency of ≥2-day EPEs in the Poyang Lake Basin, from 1961 to 2014.</p> Full article ">Figure 9
<p>The intensity of 1-day events in the Poyang Lake Basin, from 1961 to 2014.</p> Full article ">Figure 10
<p>The intensity of ≥2-day events in the Poyang Lake Basin, from 1961 to 2014.</p> Full article ">Figure 11
<p>Impacted area of extreme precipitation events in the Poyang Lake Basin from 1961 to 2014.</p> Full article ">Figure 12
<p>Identification of the most severe and normal 1-day extreme precipitation events, from 1961 to 2014.</p> Full article ">Figure 13
<p>The severest and normal ≥2-day extreme precipitation events, from 1961 to 2014.</p> Full article ">Figure 14
<p>Population exposure and proportion of population, from 1961 to 2014.</p> Full article ">Figure 15
<p>GDP exposure and proportion of GDP, from 1961 to 2014.</p> Full article ">
14 pages, 816 KiB  
Article
Application of DPPH Assay for Assessment of Particulate Matter Reducing Properties
by Maria Agostina Frezzini, Federica Castellani, Nayma De Francesco, Martina Ristorini and Silvia Canepari
Atmosphere 2019, 10(12), 816; https://doi.org/10.3390/atmos10120816 - 16 Dec 2019
Cited by 20 | Viewed by 5538
Abstract
Different acellular assays were developed to measure particulate matter’s (PM) oxidative potential (OP), a metric used to predict the ability of PM in generating oxidative stress in living organisms. However, there are still fundamental open issues regarding the complex redox equilibria among the [...] Read more.
Different acellular assays were developed to measure particulate matter’s (PM) oxidative potential (OP), a metric used to predict the ability of PM in generating oxidative stress in living organisms. However, there are still fundamental open issues regarding the complex redox equilibria among the involved species which could include reducing compounds. The aim of this study was the pilot application of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay to PM in order to evaluate the presence of reducing species. The assay, commonly applied to biological matrices, was adapted to PM and showed good analytical performances. It allowed the analysis of conventional 24 h airborne PM samples with suitable sensitivity and good repeatability of the measurements. The assay was applied to seven samples representing possible PM contributes (certified urban dust NIST1648a; brake dust; Saharan dust; coke dust; calcitic soil dust; incinerator dust; and diesel particulate matter certified material NIST1650b) and to PM2.5 field filters. The same samples were also analyzed for elements. Preliminary results indicated that the assay gave a linear response and that detectable amounts of reducing species were present in PM samples. The combined application of DPPH and conventional OP assays could then permit, in the future, to gain more knowledge about the reaction and/or competition between oxidative and reducing processes. Full article
(This article belongs to the Special Issue Oxidative Potential of Atmospheric Aerosols)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Reducing potential (%DPPHm) of the seven selected types of PM with the four experimental procedures obtained through a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay performed on EtOH-extracted samples (DPPH<sub>EtOH</sub>), H<sub>2</sub>O-extracted samples (DPPH<sub>H2O</sub>), H<sub>2</sub>O-extracted samples with added EtOH (DPPH<sub>H2O/EtOH</sub>), and whole dust samples (DPPH<sub>TOT</sub>). The mean ± SD of three replicates are reported. Values below limits of detection (LODs) are not reported. UD: certified urban dust (NIST168a); BD: brake dust; SD: Saharan dust; C: coke; CSD: calcitic soil dust; ID: incinerator dust; D: certified Diesel particulate matter (NIST1650b).</p> Full article ">Figure 2
<p>(<b>a</b>) Time patterns of natural radioactivity, expressed in counts per min, and (<b>b</b>) PM<sub>2.5</sub> mass concentration (µg/m³) from 29th to 17th March-April at Cassana (FE), Italy.</p> Full article ">Figure 3
<p>Reducing potential (%DPPHv) obtained through DPPH assay on 24 h PM<sub>2.5</sub> filters collected in Cassana (FE), Italy, from 29th to 17th March-April 2019. <sup>*</sup> The value was set at LOD/2.</p> Full article ">
19 pages, 13151 KiB  
Article
Observed Changes in Temperature and Precipitation Extremes Over the Yarlung Tsangpo River Basin during 1970–2017
by Chunyu Liu, Yungang Li, Xuan Ji, Xian Luo and Mengtao Zhu
Atmosphere 2019, 10(12), 815; https://doi.org/10.3390/atmos10120815 - 15 Dec 2019
Cited by 13 | Viewed by 3450
Abstract
Twenty-five climate indices based on daily maximum and minimum temperature and precipitation at 15 meteorological stations were examined to investigate changes in temperature and precipitation extremes over the Yarlung Tsangpo River Basin (1970–2017). The trend-free prewhitening (TFPW) Mann–Kendall test and Pettitt’s test were [...] Read more.
Twenty-five climate indices based on daily maximum and minimum temperature and precipitation at 15 meteorological stations were examined to investigate changes in temperature and precipitation extremes over the Yarlung Tsangpo River Basin (1970–2017). The trend-free prewhitening (TFPW) Mann–Kendall test and Pettitt’s test were used to identify trends and abrupt changes in the time series, respectively. The results showed widespread significant changes in extreme temperature indices associated with warming, most of which experienced abrupt changes in the 1990s. Increases in daily minimum and maximum temperature were detected, and the magnitude of daily minimum temperature change was greater than that of the daily maximum temperature, revealing an obvious decrease in the diurnal temperature range. Warm days and nights became more frequent, whereas fewer cold days and nights occurred. The frequency of frost and icing days decreased, while summer days and growing season length increased. Moreover, cold spell length shortened, whereas warm spell length increased. Additionally, changes in the precipitation extreme indices exhibited much less spatial coherence than the temperature indices. Spatially, mixed patterns of stations with positive and negative trends were found, and few trends in the precipitation extreme indices at individual stations were statistically significant. Generally, precipitation extreme indices showed a tendency toward wetter conditions, and the contribution of extreme precipitation to total precipitation has increased. However, no significant regional trends and abrupt changes were detected in total precipitation or in the frequency and duration of precipitation extremes. Full article
(This article belongs to the Special Issue Climates of the Himalayas: Present, Past and Future)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area and the meteorological stations.</p> Full article ">Figure 2
<p>Regionally averaged anomaly series of (<b>a</b>) TNn, (<b>b</b>) TNx, (<b>c</b>) TXn, (<b>d</b>) TXx, and (<b>e</b>) DTR during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope.</p> Full article ">Figure 3
<p>Spatial distribution of change trends for (<b>a</b>) TNn, (<b>b</b>) TNx, (<b>c</b>) TXn, (<b>d</b>) TXx, and (<b>e</b>) DTR during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box.</p> Full article ">Figure 4
<p>Regionally averaged anomaly series of (<b>a</b>) TN10p, (<b>b</b>) TX10p, (<b>c</b>) FD, (<b>d</b>) ID, and (<b>e</b>) CSDI during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope.</p> Full article ">Figure 5
<p>Spatial distribution of change trends for (<b>a</b>) TN10p, (<b>b</b>) TX10p, (<b>c</b>) FD, (<b>d</b>) ID, and (<b>e</b>) CSDI during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box.</p> Full article ">Figure 6
<p>Regionally averaged anomaly series of (<b>a</b>) TN90p, (<b>b</b>) TX90p, (<b>c</b>) GSL, (<b>d</b>) SU25, and (<b>e</b>) WSDI during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope.</p> Full article ">Figure 7
<p>Spatial distribution of change trends for (<b>a</b>) TN90p, (<b>b</b>) TX90p, (<b>c</b>) GSL, (<b>d</b>) SU25, and (<b>e</b>) WSDI during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box.</p> Full article ">Figure 8
<p>Regionally averaged anomaly series of (<b>a</b>) PRCPTOT, (<b>b</b>) SDII, (<b>c</b>) RX1d, (<b>d</b>) RX5d, (<b>e</b>) R95p, and (<b>f</b>) R99p during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope.</p> Full article ">Figure 9
<p>Spatial distribution of change trends for (<b>a</b>) PRCPTOT, (<b>b</b>) SDII, (<b>c</b>) RX1d, (<b>d</b>) RX5d, (<b>e</b>) R95p, and (<b>f</b>) R99p during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box.</p> Full article ">Figure 10
<p>Regionally averaged anomaly series of (<b>a</b>) R10mm, (<b>b</b>) R20mm, (<b>c</b>) CWD, and (<b>d</b>) CDD during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope.</p> Full article ">Figure 11
<p>Spatial distribution of change trends for (<b>a</b>) R10mm, (<b>b</b>) R20mm, (<b>c</b>) CWD, and (<b>d</b>) CDD during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box.</p> Full article ">Figure 12
<p>(<b>a</b>) Ratio of very wet day precipitation (R95p) to total precipitation; (<b>b</b>) ratio of extremely wet day precipitation (R99p) to total precipitation. Green dashed line is the linear trend during 1970–1999, and the red dashed line is the linear trend during 2000–2017.</p> Full article ">Figure 13
<p>Pearson correlation analysis of (<b>a</b>) temperature and (<b>b</b>) precipitation indices.</p> Full article ">
2 pages, 156 KiB  
Editorial
Remote Sensing of Clouds
by Filomena Romano
Atmosphere 2019, 10(12), 814; https://doi.org/10.3390/atmos10120814 - 15 Dec 2019
Viewed by 2012
Abstract
This special issue collects four original and review articles dealing with different cloud aspects, from microphysical properties to macrophysical features [...] Full article
(This article belongs to the Special Issue Remote Sensing of Clouds)
20 pages, 7084 KiB  
Article
The Relationship between the Wintertime Cold Extremes over East Asia with Large-Scale Atmospheric and Oceanic Teleconnections
by Ye Yang, Naru Xie and Meng Gao
Atmosphere 2019, 10(12), 813; https://doi.org/10.3390/atmos10120813 - 14 Dec 2019
Cited by 6 | Viewed by 2965
Abstract
The influence of large-scale teleconnection patterns, Western Pacific (WP), Arctic Oscillation (AO) and El Niño-Southern Oscillation (ENSO), on the minimum surface air temperature (Tmin) anomalies and extremes over East Asia during the boreal winter from 1979 to 2017 were investigated by the composite [...] Read more.
The influence of large-scale teleconnection patterns, Western Pacific (WP), Arctic Oscillation (AO) and El Niño-Southern Oscillation (ENSO), on the minimum surface air temperature (Tmin) anomalies and extremes over East Asia during the boreal winter from 1979 to 2017 were investigated by the composite analysis in terms of atmospheric and oceanic processes. The relationship between the Tmin and the geopotential height at 500 hPa (Z500) as well as sea surface temperature (SST) were first examined. Then we explored and estimated the contribution of the teleconnection patterns to the occurrence of extremely cold days and months quantitatively, and discussed other key factors in relation to the cold extremes. The WP and AO patterns play an important part in the prevalence of significant Tmin variability, whereas the effect of ENSO is relatively weak. Most of the cold extremes tend to appear in the negative phase of teleconnections, while there some extremes that occur in the opposite phase. In addition, the extreme months are more related to the preferred phase of the dominant pattern when compared to days. We conclude that the daily extremes are primarily triggered by the local-synoptic atmospheric circulations embedded in the large-scale teleconnection patterns, while the monthly extremes have a closer relationship with these low-frequency patterns. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>Minimum surface air temperature (Tmin) anomalies (red and blue shading) over East Asia during the winter months from 1979 to 2017 in dependence of the strong positive or negative phase of teleconnections: (<b>a</b>,<b>b</b>) for Western Pacific pattern (WP) patterns; (<b>c</b>,<b>d</b>) for Arctic Oscillation (AO) patterns; (<b>e</b>,<b>f</b>) for El Niño-Southern Oscillation (ENSO) patterns. The grids with dots indicate the values lower than the 0.2 quantile or higher than the 0.8 quantile.</p> Full article ">Figure 2
<p>The percentage of daily cold extremes in dependence of the strong positive or negative phase of teleconnections: (<b>a</b>,<b>b</b>) for WP patterns; (<b>c</b>,<b>d</b>) for AO patterns; (<b>e</b>,<b>f</b>) for ENSO patterns. Shaded grids with blue are significantly higher than expected chance (25%) at a 95% confidence level for percentages. The grids with dots indicate the values higher than 35%.</p> Full article ">Figure 3
<p>Same as <a href="#atmosphere-10-00813-f002" class="html-fig">Figure 2</a>, but for monthly cold extremes.</p> Full article ">Figure 4
<p>Geopotential height anomalies at the 500 hPa level (red and blue shading) around East Asia in winter months from 1979 to 2017 in dependence of the strong positive or negative phase of teleconnections: (<b>a</b>,<b>b</b>) for WP patterns; (<b>c</b>,<b>d</b>) for AO patterns; (<b>e</b>,<b>f</b>) for ENSO patterns. The boundary and location of the cyclones and anticyclones are visualized by the zonal and meridional wind anomalies (green vectors) at 500 hPa level.</p> Full article ">Figure 5
<p>Same as <a href="#atmosphere-10-00813-f004" class="html-fig">Figure 4</a>, but for sea surface temperature (SST) anomalies in winter months.</p> Full article ">Figure 6
<p>Scatterplots between the daily Tmin anomalies and teleconnection indices values for the individual cases: (<b>a</b>,<b>e</b>,<b>i</b>) for Shikoku during the negative phase of WP, AO and ENSO, respectively; (<b>b</b>,<b>f</b>,<b>j</b>) same as a, e, j, but for Orkhon; (<b>c</b>,<b>g</b>,<b>k</b>) same as a, e, j, but for Taiwan; (<b>d</b>,<b>h</b>,<b>l</b>) same as a, e, j, but for Shanxi. The scatterplots on the left side of the vertical red line represent the values at the lower 10% of the Tmin anomalies distribution. The horizontal blue lines present the upper and lower quartile of the teleconnection indices, respectively. The top-right-corner values from left to right represent separately the percentage of extreme days in the positive, normal and negative phases of modes.</p> Full article ">Figure 7
<p>Same as <a href="#atmosphere-10-00813-f006" class="html-fig">Figure 6</a>, but for monthly Tmin anomalies.</p> Full article ">Figure 8
<p>Time series of yearly teleconnection indices during winter of 1979–2017: (<b>a</b>) for WP; (<b>b</b>) for AO; (<b>c</b>) for ENSO. The blue and red lines present the raw sequence and its nine-year smooth result respectively.</p> Full article ">Figure 9
<p>Time series of year-average extreme cold days during winter of 1979–2017: (<b>a</b>) for Shikoku; (<b>b</b>) for Orkhon; (<b>c</b>) for Taiwan; (<b>d</b>) for Shanxi. The blue and red lines present the raw sequence and its nine-year smooth result respectively. The top-right-corner values from left to right display the <span class="html-italic">p</span>-values on correlations between the number of extreme days and WP, AO and ENSO teleconnection indices on the interannual (the first row) and interdecadal (the second row) timescale, respectively.</p> Full article ">Figure 10
<p>Same as <a href="#atmosphere-10-00813-f009" class="html-fig">Figure 9</a>, but for extreme cold months.</p> Full article ">Figure 11
<p>Composite maps of winter geopotential height anomalies at the 500 hPa level for the extreme day (the left column), and 15 days including 7 days before and after the extreme day (the right column) in the four locations (labeled by magenta stars): (<b>a</b>,<b>b</b>) for Shikoku; (<b>c</b>,<b>d</b>) for Orkhon; (<b>e</b>,<b>f</b>) for Taiwan; (<b>g</b>,<b>h</b>) for Shanxi.</p> Full article ">Figure 12
<p>Same as <a href="#atmosphere-10-00813-f011" class="html-fig">Figure 11</a>, but for SST anomalies with the extreme day and 15 days including 7 days before and after the extreme day.</p> Full article ">Figure 13
<p>Same as <a href="#atmosphere-10-00813-f011" class="html-fig">Figure 11</a>, but for Z500 anomalies with the extreme month.</p> Full article ">Figure 14
<p>Same as <a href="#atmosphere-10-00813-f012" class="html-fig">Figure 12</a>, but for SST anomalies with the extreme month.</p> Full article ">
16 pages, 12095 KiB  
Article
Discontinuities in the Ozone Concentration Time Series from MERRA 2 Reanalysis
by Peter Krizan, Michal Kozubek and Jan Lastovicka
Atmosphere 2019, 10(12), 812; https://doi.org/10.3390/atmos10120812 - 14 Dec 2019
Cited by 6 | Viewed by 4588
Abstract
Artificial discontinuities in time series are a great problem for trend analysis because they influence the values of the trend and its significance. The aim of this paper is to investigate their occurrence in the Modern-Era Retrospective analysis for Research and Applications, version [...] Read more.
Artificial discontinuities in time series are a great problem for trend analysis because they influence the values of the trend and its significance. The aim of this paper is to investigate their occurrence in the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA 2) ozone concentration data. It is the first step toward the utilization of the MERRA 2 ozone data for trend analysis. We use the Pettitt homogeneity test to search for discontinuities in the ozone time series. We showed the data above 4 hPa are not suitable for trend analyses due to the unrealistic patterns in an average ozone concentration and due to the frequent occurrence of significant discontinuities. Below this layer in the stratosphere, their number is much smaller, and mostly, they are insignificant, and the patterns of the average ozone concentration are explainable. In the troposphere, the number of discontinuities increases, but they are insignificant. The transition from Solar Backscatter Ultraviolet Radiometer (SBUV) to Earth Observing System (EOS) Aura data in 2004 is visible only above 1 hPa, where the data are not suitable for trend analyses due to other reasons. We can conclude the MERRA 2 ozone concentration data can be used in trend analysis with caution only below 4 hPa. Full article
(This article belongs to the Special Issue Ozone Evolution in the Past and Future)
►▼ Show Figures

Figure 1

Figure 1
<p>Average ozone concentration (kg/kg) in January at 0.1 hPa (<b>upper panel</b>) and at 0.5 hPa (<b>lower panel</b>).</p> Full article ">Figure 2
<p>The same as <a href="#atmosphere-10-00812-f001" class="html-fig">Figure 1</a>, but for January at 1 hPa (<b>upper panel</b>) and for July at 1 hPa (<b>lower panel</b>).</p> Full article ">Figure 3
<p>Average ozone concentration (kg/kg) in January at 10 hPa (<b>upper panel</b>) and at 50 hPa (<b>lower panel</b>).</p> Full article ">Figure 4
<p>Average ozone concentration (kg/kg) in January at 300 hPa (<b>upper panel</b>) and at 500 hPa (<b>lower panel</b>).</p> Full article ">Figure 5
<p>Temporal distribution of all discontinuities in January at 0.1 hPa (<b>upper panel</b>) and at 1 hPa (<b>lower panel</b>).</p> Full article ">Figure 6
<p>The same as <a href="#atmosphere-10-00812-f005" class="html-fig">Figure 5</a>, but for 50 hPa (<b>upper panel</b>) and for 300 hPa (<b>lower panel</b>).</p> Full article ">Figure 7
<p>Temporal distribution at 0.5 hPa of all (<b>upper panel</b>) and significant (<b>lower panel</b>) discontinuities.</p> Full article ">Figure 8
<p>The same as <a href="#atmosphere-10-00812-f007" class="html-fig">Figure 7</a>, but for 300 hPa.</p> Full article ">Figure 9
<p>Vertical profiles of percentage occurrence of all (<b>upper panel</b>) and significant discontinuities (<b>lower panel</b>) in all months.</p> Full article ">Figure 10
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities for January at 0.1 hPa (red—discontinuities, yellow—no discontinuities; upper panel all points have discontinuities.).</p> Full article ">Figure 11
<p>The same as <a href="#atmosphere-10-00812-f010" class="html-fig">Figure 10</a>, but for 3 hPa.</p> Full article ">Figure 12
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities for January at 10 hPa.</p> Full article ">Figure 13
<p>The same as <a href="#atmosphere-10-00812-f012" class="html-fig">Figure 12</a>, but for 250 hPa.</p> Full article ">Figure 14
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities for January at 500 hPa.</p> Full article ">Figure 15
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities over the globe at 0.3 hPa.</p> Full article ">Figure 16
<p>The same as <a href="#atmosphere-10-00812-f015" class="html-fig">Figure 15</a>, but for 0.4 hPa.</p> Full article ">Figure 17
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities over the globe at 1 hPa.</p> Full article ">Figure 18
<p>The same as <a href="#atmosphere-10-00812-f017" class="html-fig">Figure 17</a>, but for 5 hPa.</p> Full article ">Figure 19
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities over the globe at 30 hPa.</p> Full article ">Figure 20
<p>The same as <a href="#atmosphere-10-00812-f019" class="html-fig">Figure 19</a>, but for 300 hPa.</p> Full article ">Figure 21
<p>Geographical distribution of all (<b>upper panel</b>) and the significant (<b>lower panel</b>) discontinuities over the globe at 500 hPa.</p> Full article ">
16 pages, 2688 KiB  
Article
Sodar Observation of the ABL Structure and Waves over the Black Sea Offshore Site
by Vasily Lyulyukin, Margarita Kallistratova, Daria Zaitseva, Dmitry Kuznetsov, Arseniy Artamonov, Irina Repina, Igor Petenko, Rostislav Kouznetsov and Artem Pashkin
Atmosphere 2019, 10(12), 811; https://doi.org/10.3390/atmos10120811 - 14 Dec 2019
Cited by 10 | Viewed by 3864
Abstract
Sodar investigations of the breeze circulation and vertical structure of the atmospheric boundary layer (ABL) were carried out in the coastal zone of the Black Sea for ten days in June 2015. The measurements were preformed at a stationary oceanographic platform located 450 [...] Read more.
Sodar investigations of the breeze circulation and vertical structure of the atmospheric boundary layer (ABL) were carried out in the coastal zone of the Black Sea for ten days in June 2015. The measurements were preformed at a stationary oceanographic platform located 450 m from the southern coast of the Crimean Peninsula. Complex measurements of the ABL vertical structure were performed using the three-axis Doppler minisodar Latan-3m. Auxiliary measurements were provided by a temperature profiler and two automatic weather stations. During the campaign, the weather was mostly fair with a pronounced daily cycle. Characteristic features of breeze circulation in the studied area, primarily determined by the adjacent mountains, were revealed. Wave structures with amplitudes of up to 100 m were regularly observed by sodar over the sea surface. Various forms of Kelvin–Helmholtz billows, observed at the interface between the sea breeze and the return flow aloft, are described. Full article
(This article belongs to the Special Issue Vertical Structure of the Atmospheric Boundary Layer in Coastal Zone)
►▼ Show Figures

Figure 1

Figure 1
<p>Experimental site location: (<b>a</b>) Location of the platform and the coastline topography at the area. The coastline and the edge of the plateau on the map are outlined with black lines. (<b>b</b>) Topographic profile of the coast extending north from the platform.</p> Full article ">Figure 2
<p>General view of the platform (<b>a</b>) and sodar during installation (<b>b</b>).</p> Full article ">Figure 3
<p>Time series of radiative fluxes (<b>a</b>), air and sea surface temperature (<b>b</b>), and wind speed and direction (<b>c</b>,<b>d</b>). The yellow bars indicate local daytime. Periods of cloudy weather are shaded.</p> Full article ">Figure 4
<p>Diurnal behavior of the probability density of the wind speed (<b>a</b>) and wind direction (<b>b</b>) from the sodar at 50 m a.s.l. for days with fair weather. The red dots indicate the mean wind speed calculated for each time interval. Yellow lines indicate sunrise and sunset.</p> Full article ">Figure 5
<p>Wind roses near the surface for days with fair weather. (<b>a</b>–<b>c</b>) Wind from platform mast (15 m a.s.l.); (<b>d</b>–<b>f</b>) Wind from onshore mast (10 m a.g.l.). Left panels correspond to roses over the entire time; middle, for day hours (07:00–19:00 local time); and right, for night hours (19:00–07:00).</p> Full article ">Figure 6
<p>Windroses from sodar measurements for days with fair weather. (<b>a</b>–<b>c</b>) Wind at 300 m a.s.l. (<b>d</b>–<b>f</b>) Wind at 200 m a.s.l. (<b>g</b>–<b>i</b>) Wind at 100 m a.s.l. (<b>j</b>–<b>l</b>) Wind at 50 m a.s.l.</p> Full article ">Figure 7
<p>Half-hourly vertical profiles of wind speed and direction during steady winds from the north (<b>a</b>) and west (<b>b</b>). Diamonds indicate wind speed and direction measured at the platform mast at 15 m a.s.l.</p> Full article ">Figure 8
<p>Vertical profiles of the wind speed and direction during transition periods.</p> Full article ">Figure 9
<p>Wind speed probability density distribution with height (<b>a</b>) and a histogram of wind speed distribution at 50 m a.s.l. (<b>b</b>). The green line shows the Weibull distribution fitting.</p> Full article ">Figure 10
<p>Two episodes of Kelvin–Helmholtz billows (KHB) observation in the nocturnal boundary layer under the north wind (offshore) near the sea surface and a return flow aloft (onshore) on the 13th (<b>a</b>) and 14th (<b>b</b>) of June. Panels (<b>a1</b>,<b>a2</b>) and (<b>b1</b>,<b>b2</b>) present half hourly vertical profiles of wind speed and direction, and panels (<b>a3</b>,<b>b3</b>) present the sodar return signal in height–time coordinates (echograms). The colors show the relative intensity of the return signal. Note the opposite orientation of the billows in the lower and upper parts of the echogram in episode (<b>a</b>).</p> Full article ">Figure 11
<p>Examples of KHB episodes with vertical motion in the observed ABL. (<b>a</b>) Two different wave layers in the case of wind direction changing from west to east with height in the daytime. (<b>b</b>) A complex wave structure in several layers under the north wind (offshore) near the sea surface and a return flow aloft. Panels (<b>a4</b>,<b>b4</b>) show the temperature stratification by MTP-5 profiler with a one-hour averaging period. Panels (<b>a5</b>,<b>b5</b>) show the vertical velocity fields obtained by sodar.</p> Full article ">Figure 11 Cont.
<p>Examples of KHB episodes with vertical motion in the observed ABL. (<b>a</b>) Two different wave layers in the case of wind direction changing from west to east with height in the daytime. (<b>b</b>) A complex wave structure in several layers under the north wind (offshore) near the sea surface and a return flow aloft. Panels (<b>a4</b>,<b>b4</b>) show the temperature stratification by MTP-5 profiler with a one-hour averaging period. Panels (<b>a5</b>,<b>b5</b>) show the vertical velocity fields obtained by sodar.</p> Full article ">Figure 12
<p>Episodes representing strongly stable temperature stratification with the wind from the sea (<b>a</b>) and convection under unstable temperature stratification with the wind from the shore (<b>b</b>).</p> Full article ">Figure 12 Cont.
<p>Episodes representing strongly stable temperature stratification with the wind from the sea (<b>a</b>) and convection under unstable temperature stratification with the wind from the shore (<b>b</b>).</p> Full article ">
15 pages, 5970 KiB  
Article
Convective Shower Characteristics Simulated with the Convection-Permitting Climate Model COSMO-CLM
by Christopher Purr, Erwan Brisson and Bodo Ahrens
Atmosphere 2019, 10(12), 810; https://doi.org/10.3390/atmos10120810 - 13 Dec 2019
Cited by 18 | Viewed by 8105
Abstract
This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and [...] Read more.
This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and total precipitation are compared to characteristics observed by weather radar. For this purpose, a tracking algorithm was applied to simulated and observed precipitation with 5-min temporal resolution. The total amount of convective precipitation is well simulated, with a small overestimation of 2%. However, the simulation underestimates convective activity, represented by the number of convective objects, by 33%. This underestimation is especially pronounced in the lowlands of Northern Germany, whereas the simulation matches observations well in the mountainous areas of Southern Germany. The underestimation of activity is compensated by an overestimation of the simulated lifetime of convective objects. The observed mean intensity, maximum intensity, and area of precipitation objects increase with their lifetime showing the spectrum of convective storms ranging from short-living single-cell storms to long-living organized convection like supercells or squall lines. The CPM is capable of reproducing the lifetime dependence of these characteristics but shows a weaker increase in mean intensity with lifetime resulting in an especially pronounced underestimation (up to 25%) of mean precipitation intensity of long-living, extreme events. This limitation of the CPM is not identifiable by classical evaluation techniques using rain gauges. The simulation can reproduce the general increase of the highest percentiles of cell area, total precipitation, and mean intensity with temperature but fails to reproduce the increase of lifetime. The scaling rates of mean intensity and total precipitation resemble observed rates only in parts of the temperature range. The results suggest that the evaluation of coarse-grained (e.g., hourly) precipitation fields is insufficient for revealing challenges in convection-permitting simulations. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>Model domain and model orography.</p> Full article ">Figure 2
<p>Visualization of the tracking algorithm: (<b>a</b>) detection probabilities for a cone with <span class="html-italic">X<sub>max</sub></span> = 8 and <span class="html-italic">Y<sub>cent</sub></span> = 0 (assuming a grid size of 1 km × 1 km and a time step of 5 min, this is equal to a westward wind of ca. 13.3 m/s), and (<b>b</b>) radar snapshot of a cell (shown is the 5-min precipitation intensity on 30 May 2008 at 21:40 (UTC) in colors and the detected cell track as red line).</p> Full article ">Figure 3
<p>Mean precipitation intensities and differences in the period 2001–2015; (<b>a</b>–<b>c</b>) full year; (<b>d</b>–<b>f</b>) summer half-year (April–September).</p> Full article ">Figure 4
<p>Frequency distribution of (<b>a</b>) hourly and of (<b>b</b>) 5-min precipitation intensities from radar observations (black) and CCLM simulation (red).</p> Full article ">Figure 5
<p>Frequency distributions of the cell characteristics (<b>a</b>) lifetime, (<b>b</b>) total precipitation, (<b>c</b>) maximum area, and (<b>d</b>) mean cell intensity as observed by the radar (black), by radar remapped to the model grid (blue) and simulated (red).</p> Full article ">Figure 6
<p>Dependence of (<b>a</b>) cell mean intensity and (<b>b</b>) cell maximum area on cell lifetime for radar observation and CCLM simulation. The boxes denote the 25th, 50th, and 75th percentiles. The whiskers denote the 5th and 95th percentile.</p> Full article ">Figure 7
<p>Spatial distribution of the number of convective cells; (<b>a</b>) observation, (<b>b</b>) simulation, (<b>c</b>) relative difference CCLM—radar.</p> Full article ">Figure 8
<p>Diurnal cycle of convection; (<b>a</b>) cell number at cell initiation (every cell is counted once), (<b>b</b>) cell number at each individual time step (cells are counted multiple times, according to their lifetime), and (<b>c</b>) mean intensity of all cells at a certain point in time.</p> Full article ">Figure 9
<p>Dependence of the diurnal cycle of cell initiation. (<b>a</b>) Cells originating over terrain with an elevation &lt;400 m. (<b>b</b>) Cells originating over terrain with an elevation &gt;400 m.</p> Full article ">Figure 10
<p>Temperature scaling of cell characteristics. (<b>a</b>) Spatial and temporal mean intensity of cells, (<b>b</b>) total precipitation, (<b>c</b>) lifetime, and (<b>d</b>) maximum area. Shaded areas denote the uncertainty range estimated by repeatedly calculating the respective quantile using bootstrapping. Note the logarithmic y-axis in all panels.</p> Full article ">
20 pages, 7268 KiB  
Article
Rainfall and Flooding in Coastal Tourist Areas of the Canary Islands (Spain)
by Abel López Díez, Pablo Máyer Suárez, Jaime Díaz Pacheco and Pedro Dorta Antequera
Atmosphere 2019, 10(12), 809; https://doi.org/10.3390/atmos10120809 - 13 Dec 2019
Cited by 14 | Viewed by 5949
Abstract
Coastal spaces exploited for tourism tend to be developed rapidly and with a desire to maximise profit, leading to diverse environmental problems, including flooding. As the origin of flood events is usually associated with intense precipitation episodes, this study considers the general rainfall [...] Read more.
Coastal spaces exploited for tourism tend to be developed rapidly and with a desire to maximise profit, leading to diverse environmental problems, including flooding. As the origin of flood events is usually associated with intense precipitation episodes, this study considers the general rainfall characteristics of tourist resorts in two islands of the Canary Archipelago (Spain). Days of intense rainfall were determined using the 99th percentile (99p) of 8 daily precipitation data series. In addition, the weather types that generated these episodes were identified, the best-fitting distribution functions were determined to allow calculation of probable maximum daily precipitation for different return periods, and the territorial and economic consequences of flood events were analysed. The results show highly irregular rainfall, with 99p values ranging 50–80 mm. The weather types associated with 49 days of flooding events were predominantly cyclonic and hybrid cyclonic. The Log Pearson III distribution function best fitted the data series, with a strong likelihood in a 100-year return period of rainfall exceeding 100 mm in a 24 h period. However, values below 30 mm have already resulted in significant flood damage, while intense rainfall events in the period 1998–2016 saw over 11.5 million euros paid out in damages for insured goods. Such flood-induced damages were found to be caused more by inadequate urban planning than by rainfall intensity. Full article
(This article belongs to the Special Issue Tourism Climatology: Past, Present and Future)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study areas in the islands of Tenerife and Gran Canaria, and location of the weather stations employed. (<b>a</b>). South Tenerife; (<b>b</b>). South Gran Canaria; (<b>c</b>). Southwest Gran Canaria.</p> Full article ">Figure 2
<p>Equations for the calculation of circulation weather types according to Jenkinson and Collinson [<a href="#B28-atmosphere-10-00809" class="html-bibr">28</a>] and location of grid points centred around the Canary Archipelago for the calculation of flow (SF, WF) and vorticity (ZS, ZW). Legend: WF, westerly flow; SF, southerly flow; F, total flow; ZW, westerly shear vorticity; ZS, southerly shear vorticity; Z, total shear vorticity.</p> Full article ">Figure 3
<p>Frequency of the duration of dry spells (<b>a</b>) and rainfall episodes (<b>b</b>). Source: State Meteorological Agency (Spanish initials: AEMET).</p> Full article ">Figure 4
<p>Total annual precipitation in Reina Sofía airport in the south of Tenerife (1981–2018). Q<sub>1</sub>, Q<sub>2</sub>, Q<sub>3</sub> and Q<sub>4</sub> are the values of the percentiles which divide these series into 5 equal parts and allow differentiation between very dry years (≤ Q<sub>1</sub>), dry years (&gt; Q<sub>1</sub> and ≤ Q<sub>2</sub>), normal years (&gt; Q<sub>2</sub> and ≤ Q<sub>3</sub>), rainy years (&gt; Q<sub>3</sub> and ≤ Q<sub>4</sub>) and very rainy years (&gt; Q<sub>4</sub>). Source: AEMET.</p> Full article ">Figure 5
<p>Hourly rainfall frequency in Reina Sofía airport in the south of Tenerife (1997–2018). Source: AEMET.</p> Full article ">Figure 6
<p>Graphical representation with the different functions which best fit the 24 h maximum precipitation values in the 153-Berriel (<b>a</b>) and C429I-Tenerife Sur (<b>b</b>) series.</p> Full article ">Figure 7
<p>(<b>a</b>) Map showing the position of flooded areas in the tourist resorts of Playa de Las Américas and Los Cristianos situated in the municipalities of Arona and Adeje (Tenerife) between 1980 and 2018 and (<b>b</b>) compensations paid by the Insurance Compensation Consortium (Spanish initials: CCS) for the various losses that took place between 1996 and 2016.</p> Full article ">Figure 8
<p>(<b>a</b>) Map showing the position of flooded areas in the tourist resorts of San Agustín, Playa del Inglés, and Maspalomas in the municipality of San Bartolomé de Tirajana (Gran Canaria) between 1980 and 2018 and (<b>b</b>) compensation payments made by the CCS for the different incidents that took place between 1996 and 2016.</p> Full article ">Figure 9
<p>(<b>a</b>) Map showing the position of flooded areas in the tourist resorts of Puerto Rico and Amadores in the municipality of Mogán (Gran Canaria) between 1980 and 2018 and (<b>b</b>) compensation payments made by the CCS for the different losses that took place between 1996 and 2016.</p> Full article ">Figure 10
<p>Location of El Veril ravine. Images are shown of the water rerouting system that has been carried out and the proposed construction of “Siam Park” in the natural floodplain and course of the ravine.</p> Full article ">
17 pages, 47895 KiB  
Article
Cloud Occurrence Frequency at Puy de Dôme (France) Deduced from an Automatic Camera Image Analysis: Method, Validation, and Comparisons with Larger Scale Parameters
by Jean-Luc Baray, Asmaou Bah, Philippe Cacault, Karine Sellegri, Jean-Marc Pichon, Laurent Deguillaume, Nadège Montoux, Vincent Noel, Geneviève Seze, Franck Gabarrot, Guillaume Payen and Valentin Duflot
Atmosphere 2019, 10(12), 808; https://doi.org/10.3390/atmos10120808 - 13 Dec 2019
Cited by 8 | Viewed by 3585
Abstract
We present a simple algorithm that calculates the cloud occurrence frequency at an altitude site using automatic camera image analysis. This algorithm was applied at the puy de Dôme station (PUY, 1465 m. a.s.l., France) over 2013–2018. Cloud detection thresholds were determined by [...] Read more.
We present a simple algorithm that calculates the cloud occurrence frequency at an altitude site using automatic camera image analysis. This algorithm was applied at the puy de Dôme station (PUY, 1465 m. a.s.l., France) over 2013–2018. Cloud detection thresholds were determined by direct comparison with simultaneous in situ cloud probe measurements (particulate volume monitor (PVM) Gerber). The cloud occurrence frequency has a seasonal cycle, with higher values in winter (60%) compared to summer (24%). A cloud diurnal cycle is observed only in summer. Comparisons with the larger scale products from satellites and global model reanalysis are also presented. The NASA cloud-aerosol transport system (CATS) cloud fraction shows the same seasonal and diurnal variations and is, on average, 11% higher. Monthly variations of the ECMWF ERA-5 fraction of cloud cover are also highly correlated with the camera cloud occurrence frequency, but the values are 19% lower and up to 40% for some winter months. The METEOSAT-SEVIRI cloud occurrence frequency also follows the same seasonal cycle but with a much smaller decrease in summer. The all-sky imager cloud fraction (CF) presents larger variability than the camera cloud occurrence but also follows similar seasonal variations (67% in winter and 44% in summer). This automatic low-cost detection of cloud occurrence is of interest in characterizing altitude observation sites, especially those that are not yet equipped with microphysical instruments and can be deployed to other high-altitude sites equipped with cameras. Full article
(This article belongs to the Special Issue Atmospheric Composition and Cloud Cover Observations)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geographical configuration and fields of view of the network cameras in operation at Cézeaux and puy de Dôme station (PUY) sites superimposed to a ©Google Map image (<b>top</b>) and photographs of the PUY platform hosting the network camera (<b>bottom left</b>), PVM Gerber (<b>bottom center</b>), and the EKO all-sky imager on the Cézeaux site (<b>bottom right</b>). A camera similar to the one in operation at PUY is also in operation of the Cézeaux site (not used in this work) but is not visible in the photo.</p> Full article ">Figure 2
<p>Examples of images and MATLAB processing outputs for different situations, from top to bottom: night time clear sky, day-time clear sky, night-time sea of clouds, and day-time cloud. The two zones are indicated with black rectangles, and the days and hours and standard deviation values in the two areas of Clermont (bottom large rectangle) and Livradois-Forez (top little rectangle) are given on the top of each image.</p> Full article ">Figure 3
<p>(<b>a</b>) Percentage of the agreement between cloud occurrence from the particulate volume monitor (PVM) and camera depending on the threshold values of the camera data processing (Clermont on the X axis, Livradois Forez on the y axis). (<b>b</b>) The same plot reduced for daytime images (9 to 16 UT). (<b>c</b>) Same plot reduced for nighttime images (23 to 4 UT).</p> Full article ">Figure 4
<p>(<b>a</b>) Cloud occurrence frequency (COF) monthly time series calculated from camera images in black, with PVM Gerber in red. The lower limit of the camera curve is obtained with the threshold value 5.5, and the upper limit is obtained by adding the cases classified as “uncertain” with threshold values between 5.5 and 6.5. The PVM Gerber points with less than 30% of validated available data have been removed, and the PVM Gerber points with more than 30% and 70% are in red and blue, respectively. (<b>b</b>) A scatterplot of the monthly cloud occurrence from the camera images (threshold value at 6) as a function of PVM Gerber. The color is the percentage of validated PVM Gerber data per month, and the line y = x is in black.</p> Full article ">Figure 5
<p>(<b>a</b>) Network camera DJF 2013-2018; (<b>b</b>) Network camera MAM 2013-2018; (<b>c</b>) Network camera JJA 2013-2018; (<b>d</b>) Network camera SON 2013-2018. COF obtained from the camera image processing separated by seasons and years as a function of the hours of the day (UTC). For each year and season, the uncertainty interval has been estimated similarly to <a href="#atmosphere-10-00808-f004" class="html-fig">Figure 4</a>a. The correspondence between the colors and years is given in the top-right corner.</p> Full article ">Figure 6
<p>CF as function of time of the day (UT), extracted from the cloud-aerosol transport system (CATS) in summer 2015–2017 (<b>a</b>) and winter 2015–2017 (<b>b</b>) for three vertical ranges above sea level, 0–1 km in blue, 1–2 km in green, and 2–3 km in red, compared to the COF deduced from the PUY camera in black. The sea cloud camera situations have been added to the cloud situation for this comparison. The lower limit of the camera cloud occurrence (black curve) is obtained with a threshold value of 5.5, and the upper limit is obtained by adding the cases classified as “uncertain” (with a threshold value between 5.5 and 6.5).</p> Full article ">Figure 7
<p>Total, high-topped, mid-topped, and low-topped COF (in blue, red, cyan, and green, respectively), extracted from METEOSAT-SEVIRI on the nearest pixel over PUY (continuous lines), as well as on a larger region between 44.6 and 46.9° N and between 2 and 4° E (dashed lines), compared to the monthly mean of the camera COF in black.</p> Full article ">Figure 8
<p>Daily (red crosses) and monthly +/− one sigma (red lines) means of the CF calculated with the algorithm ELIFAN applied to the all-sky camera images taken at the Cézeaux site from December 2015 to June 2019, compared to the monthly mean of the COF from the camera analysis in black.</p> Full article ">Figure 9
<p>Time series of the monthly mean and standard deviation of the ERA 5 ECMWF fraction of the cloud cover interpolated on the point 45.77° N, 2.95° E on the 850 hPa pressure level in red, compared to the monthly mean of the camera COF in black.</p> Full article ">Figure 10
<p>Examples of images of a camera in operation at Maïdo station (Réunion Island, 2160 m above the sea level, towards the south-east—clear sky and cloudy (top), and towards the south west during night and day (bottom), where the algorithm of detection of the CF could be applied in the future.</p> Full article ">
12 pages, 3165 KiB  
Article
On the Constellation Design of Multi-GNSS Reflectometry Mission Using the Particle Swarm Optimization Algorithm
by Yi Han, Jia Luo and Xiaohua Xu
Atmosphere 2019, 10(12), 807; https://doi.org/10.3390/atmos10120807 - 13 Dec 2019
Cited by 8 | Viewed by 3166
Abstract
Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global [...] Read more.
Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions. Full article
(This article belongs to the Special Issue GNSS Meteorology and Climatology)
►▼ Show Figures

Figure 1

Figure 1
<p>Geometry of the specular reflection points.</p> Full article ">Figure 2
<p>Coverage and revisited coverage as a function of time during one day obtained by the optimal 8-, 24-, 60-, and 120-satellite 2D-LFCs, respectively. Black solid lines, blue dotted lines, and dashed lines represent the coverage, the revisited coverage, and 90% coverage (revisited coverage), respectively.</p> Full article ">Figure 3
<p>Average numbers of the GNSS-R specular reflection points in the 1° × 1° grid cells at (<b>a</b>) different latitudes and (<b>b</b>) different longitudes obtained by the optimal 8-, 24-, 60-, and 120-satellite 2D-LFCs, respectively, during one day.</p> Full article ">Figure 4
<p>The spatial distribution of the mean revisit time for the 1° × 1° grid cells of the optimal (<b>a</b>) 8-, (<b>b</b>) 24-, (<b>c</b>) 60-, and (<b>d</b>) 120-satellite 2D-LFCs, respectively, during one day.</p> Full article ">Figure 5
<p>Coverage as a function of time during one day for different cell sizes obtained by the optimal 8-, 24-, 60-, and 120-satellite 2D-LFCs, respectively.</p> Full article ">
14 pages, 4841 KiB  
Article
Relating Moisture Transport to Stable Water Vapor Isotopic Variations of Ambient Wintertime along the Western Coast of Korea
by Songyi Kim, Yeongcheol Han, Soon Do Hur, Kei Yoshimura and Jeonghoon Lee
Atmosphere 2019, 10(12), 806; https://doi.org/10.3390/atmos10120806 - 12 Dec 2019
Cited by 7 | Viewed by 3693
Abstract
Atmospheric water vapor transfers energy, causes meteorological phenomena and can be modified by climate change in the western coast region of Korea. In Korea, previous studies have utilized precipitation isotopic compositions in the water cycle for correlations with climate variables, but there are [...] Read more.
Atmospheric water vapor transfers energy, causes meteorological phenomena and can be modified by climate change in the western coast region of Korea. In Korea, previous studies have utilized precipitation isotopic compositions in the water cycle for correlations with climate variables, but there are few studies using water vapor isotopes. In this study, water vapor was directly collected by a cryogenic method, analyzed for its isotopic compositions, and used to trace the origin and history of water vapor in the western coastal region of Korea during the winter of 2015/2016. Our analysis of paired mixing ratios with water vapor isotopes can explain the mechanism of water vapor isotopic fractionation and the extent of the mixing of two different air masses. We confirm the correlation between water vapor isotopes and meteorological parameters such as temperature, relative humidity, and specific humidity. The main water vapor in winter was derived from the continental polar region of northern Asia and showed an enrichment of 10 per mil (δ18O) through the evaporation of the Yellow Sea. Our results demonstrate the utility of using ground-based isotope observations as a complementary resource for constraining isotope-enabled Global Circulation Model in future investigations of atmospheric water cycles. These measurements are expected to support climate studies (speleothem) in the west coast region of Korea. Full article
(This article belongs to the Special Issue Stable Isotopes in Atmospheric Research)
►▼ Show Figures

Figure 1

Figure 1
<p>The study area around the Korean peninsula.</p> Full article ">Figure 2
<p>Time series of (<b>a</b>) air temperature, (<b>b</b>) relative humidity and mixing ratio, (<b>c</b>) stable oxygen and hydrogen isotopic compositions of water vapor, and (<b>d</b>) <span class="html-italic">d</span>-excess.</p> Full article ">Figure 3
<p>Scatter plot and linear and log-scale regressions of isotopic values and meteorological parameters: δ<sup>18</sup>O and δD with (<b>a</b>) temperature, (<b>b</b>) relative humidity, and (<b>c</b>) mixing ratio for log-scale, and <span class="html-italic">d</span> values with (<b>d</b>) temperature, (<b>e</b>) relative humidity, and (<b>f</b>) mixing ratio for log-scale.</p> Full article ">Figure 4
<p>(<b>a</b>) δD versus water mixing ratio (q) and (<b>b</b>) q×δD versus q. The purple diamonds are measurements. The curves are for Rayleigh processes (red solid lines), which assume a source at 100% humidity with a sea-surface temperature of 25 °C, and the mixing model (except for the red solid lines), which reasonably assumes two end members. The conditions for each end member are described in the legend. The slope and intercept of the q×δD/q linear relationship in observation are −95.2 and −123.3, respectively.</p> Full article ">Figure 5
<p>(<b>a</b>) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back trajectories showing the locations of wintertime (DJF) back-calculated air parcels 120 h prior to their arrival at the study sites, (<b>b</b>) HYSPLIT back trajectories under sub-zero temperature conditions, as shown in <a href="#atmosphere-10-00806-f004" class="html-fig">Figure 4</a>a (these 40 back trajectories follow a roughly parallel transect across central China), and (<b>c</b>) 120-h air-mass back trajectories from the sampling locations with air parcels along the trajectories under lower temperatures (roughly 40 samples).</p> Full article ">Figure 6
<p>Comparison between observed water vapor for (<b>a</b>) δ<sup>18</sup>O and (<b>b</b>) δD with Iso-GSM data during the sampling period. The isotope-enabled global spectral model (Iso-GSM) data were calculated by interpolating four different areas near the study area.</p> Full article ">
20 pages, 8042 KiB  
Article
Impact of Effective Roughness Length on Mesoscale Meteorological Simulations over Heterogeneous Land Surfaces in Taiwan
by Fang-Yi Cheng, Chin-Fang Lin, Yu-Tzu Wang, Jeng-Lin Tsai, Ben-Jei Tsuang and Ching-Ho Lin
Atmosphere 2019, 10(12), 805; https://doi.org/10.3390/atmos10120805 - 12 Dec 2019
Cited by 5 | Viewed by 4274
Abstract
The Weather Research and Forecasting (WRF) modeling system obtains the aerodynamic roughness length (z0) from a land use (LU) lookup table. The effective aerodynamic roughness length (z0eff) was estimated for the island of Taiwan by considering the [...] Read more.
The Weather Research and Forecasting (WRF) modeling system obtains the aerodynamic roughness length (z0) from a land use (LU) lookup table. The effective aerodynamic roughness length (z0eff) was estimated for the island of Taiwan by considering the individual roughness lengths (z0i) of the underlying LU types within a modeling grid box. Two z0eff datasets were prepared: one using the z0i from the default LU lookup table and the other using the observed z0i for three LU types (urban, dry cropland and pasture, and irrigated cropland and pasture). The spatial variability of the z0eff distribution was higher than that of the LU table-based z0 distribution. Three WRF sensitivity experiments were performed: (1) dominant LU table-based z0 (namely, S1), (2) z0eff estimated from the default z0i (namely, S2), and (3) z0eff estimated from the observed z0i (namely, S3). Comparisons of the thermal field, temperature, and surface sensible and latent heat fluxes revealed no significant differences among the three simulations. The wind field overestimation and surface momentum flux underestimation in S1 were reduced in S2 and S3, and these improvements were more prominent over areas with highly heterogeneous land surface conditions. Full article
(This article belongs to the Special Issue Meteorological Phenomena Driving Extreme Air Pollution)
►▼ Show Figures

Figure 1

Figure 1
<p>Shaded elevation map where the colors represent the terrain height (unit: m). The names of major metropolitan cities are identified. The blue circles mark the locations of Central Weather Bureau (CWB) surface weather stations. The red circle marks the location of Chaoliao.</p> Full article ">Figure 2
<p>Comparison between the land use (LU) type distributions: default U.S. Geological Survey (USGS) 25-category distribution (<b>left</b>) and reclassified distribution from the 2009 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (<b>right</b>).</p> Full article ">Figure 3
<p>(<b>a</b>) <span class="html-italic">z</span><sub>0</sub> values from the lookup LU table, (<b>b</b>) <span class="html-italic">z</span><sub>0eff</sub> values estimated using the default <span class="html-italic">z</span><sub>0<span class="html-italic">i</span></sub>, (<b>c</b>) <span class="html-italic">z</span><sub>0eff</sub> values estimated using the updated <span class="html-italic">z</span><sub>0<span class="html-italic">i</span></sub>, (<b>d</b>) difference between (<b>a</b>) and (<b>b</b>), and (<b>e</b>) difference between (<b>b</b>) and (<b>c</b>) (unit: m).</p> Full article ">Figure 4
<p>Sea level pressure (SLP) (hPa) and surface wind fields (m/s) at 08:00 local standard time (LST) from (<b>a</b>) 22, (<b>b</b>) 23, (<b>c</b>) 24 and (<b>d</b>) 25 October, 2011.</p> Full article ">Figure 5
<p>Observed O<sub>3</sub> concentrations and wind fields from surface air quality monitoring stations at 13:00 LST on 24 and 25 October, 2011.</p> Full article ">Figure 6
<p>Model simulation domains: the outermost domain (<b>D01</b>) is with 81 km grid spacing; the nested domains, (<b>D02</b>), (<b>D03</b>), and (<b>D04</b>), are with 27, 9, and 3 km grid spacing, respectively.</p> Full article ">Figure 7
<p>Near-surface sensible heat flux (SHF) distribution (units: W/m<sup>2</sup>) from the S1 simulation, the difference between the S2 and S1 simulations, and the difference between the S3 and S2 simulations. (<b>a</b>) and (<b>b</b>) is produced at 00:00 and 12:00 LST, respectively on 24 October. (<b>c</b>) and (<b>d</b>) is produced at 00:00 and 12:00 LST, respectively on 25 October. The circle marks the location of Chaoliao.</p> Full article ">Figure 8
<p>Near-surface air temperature (TA) distribution (unit: degrees Celsius) from the S1 simulation, the difference between the S2 and S1 simulations, and the difference between the S3 and S2 simulations. (<b>a</b>) and (<b>b</b>) is produced at 00:00 and 12:00 LST, respectively on 24 October. (<b>c</b>) and (<b>d</b>) is produced at 00:00 and 12:00 LST, respectively on 25 October. The circle marks the location of Chaoliao.</p> Full article ">Figure 9
<p>Near-surface wind field distribution (units: m s<sup>−1</sup>).from the S1 simulation, the difference between the S2 and S1 simulations, and the difference between the S3 and S2 simulations. (<b>a</b>) and (<b>b</b>) is produced at 00:00 and 12:00 LST, respectively on 24 October. (<b>c</b>) and (<b>d</b>) is produced at 00:00 and 12:00 LST, respectively on 25 October. The circle marks the location of Chaoliao.</p> Full article ">Figure 10
<p>Time series comparison of the temperature (upper panel), wind speed (middle panel), and wind direction (bottom panel) among the observations (black) and the S1 (<b>green</b>), S2 (<b>blue</b>), and S3 (<b>red</b>) sensitivity simulations.</p> Full article ">Figure 11
<p>Similar to <a href="#atmosphere-10-00805-f009" class="html-fig">Figure 9</a> but for the SHF (<b>upper panel</b>), latent heat flux (LHF) (<b>middle panel</b>) and frictional velocity (UST) (<b>bottom panel</b>).</p> Full article ">Figure 12
<p>Observed potential temperature profiles at the Chaoliao site on 24 and 25 October, 2011.</p> Full article ">Figure 13
<p>Comparison among the vertical potential temperature profiles (unit: degrees Celsius) based on the observations and the S1, S2, and S3 simulations on 24 and 25 October, 2011 at the Chaoliao site.</p> Full article ">Figure 14
<p>Comparison between the mean bias (MB) and root mean square error (RMSE) for (<b>a</b>), (<b>c</b>) TA (unit: degrees Celsius) and (<b>b</b>), (<b>d</b>) wind speed (unit: m/s) for the CWB surface stations in Taiwan. The yellow bars are for the S1 simulation, the blue bars are for the S2 simulation, and the red bars are for the S3 simulation.</p> Full article ">
17 pages, 2925 KiB  
Article
Evaluation of Near-Surface Wind Speed Changes during 1979 to 2011 over China Based on Five Reanalysis Datasets
by Jiang Yu, Tianjun Zhou, Zhihong Jiang and Liwei Zou
Atmosphere 2019, 10(12), 804; https://doi.org/10.3390/atmos10120804 - 12 Dec 2019
Cited by 30 | Viewed by 3983
Abstract
Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, [...] Read more.
Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, including National Centers for Environmental Predictions (NCEP)-U.S. Department of Energy (DOE) Reanalysis 2 (NCEP-2), Modern-ERA Retrospective Analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis Project (JRA-55), Interim ECMWF Re-Analysis product (ERA-Interim), and 20th Century Reanalysis (20CR) in reproducing the climatology, interannual variation, and long-term trend of near-surface (10 m above ground) wind speed, for the period of 1979–2011 over continental China are comprehensively evaluated. Compared to the gridded data compiled from meteorological stations, all five reanalysis datasets reasonably reproduce the spatial distribution of the climatology of near-surface wind speed, but underestimate the intensity of the near-surface wind speed in most regions except for Tibetan Plateau where the wind speed is overestimated. All five reanalysis datasets show large weaknesses in reproducing the annual cycle of near-surface wind speed averaged over the continental China. The near-surface wind speed derived from the observations exhibit significant decreasing trends over most parts of continental China during 1979 to 2011. Although the spatial patterns of the linear trends reproduced by reanalysis datasets are close to the observation, the magnitudes are weaker in annual, spring, summer and autumn season. The qualities of all reanalysis datasets are limited in winter. For the interannual variability, except for winter, all five reanalysis datasets reasonably reproduce the interannual standard deviation but with larger amplitude. Quantitative comparison indicates that among the five reanalysis datasets, the MERRA (JRA-55) shows the relatively highest (lowest) skill in terms of the climatology and linear trend. These results call for emergent needs for developing high quality reanalysis data that can be used in wind resource assessment and planning. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>The distribution of nine regions: <b>A</b> (Xinjiang), <b>B</b> (Inner Mongolia), <b>C</b> (Notheast China), <b>D</b> (North China), <b>E</b> (Sichuan Basin), <b>F</b> (Southwest China), <b>G</b> (Central China), <b>H</b> (coastal area), and <b>I</b> (Tibetan Plateau).</p> Full article ">Figure 2
<p>Spatial patterns of the long-term (1979–2011) mean annual near-surface wind speeds (unit: m·s<sup>−1</sup>) over China: (<b>a</b>) CN05.1, (<b>b</b>) NCEP-2, (<b>c</b>) MERRA, (<b>d</b>) JRA-55, (<b>e</b>) ERA-Interim, and (<b>f</b>) 20CR.</p> Full article ">Figure 3
<p>Taylor diagram for displaying pattern statistics during annual (<b>a</b>), spring (<b>b</b>), summer (<b>c</b>), autumn (<b>d</b>), and winter (<b>e</b>) from 1979 to 2011. The numbers represent the China and nine regions, 1–10 represent China-I region respectively. The colors represent the different reanalysis datasets: NCEP-2 (red), MERRA (blue), JRA-55 (green), ERA-Interim (purple), and 20CR (yellow). The axis means the standardized deviations (Normalized) between reanalysis and observation. The angular coordinate means the pattern correlation coefficient between reanalysis and observation.</p> Full article ">Figure 4
<p>Annual cycle of near-surface wind speed (unit: m·s<sup>−1</sup>) for CN05.1 (<b>a</b>), NCEP-2 (<b>b</b>), MERRA (<b>c</b>), JRA-55(<b>d</b>), ERA-Interim (<b>e</b>), and 20CR (<b>f</b>) over China (black), A (red), B (green), C (blue), D (yellow), E (purple), F (orange), G (pink), H (mauve), and I (tan) region. A–I represent different subregions shown in <a href="#atmosphere-10-00804-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 5
<p>Linear trends (unit: m·s<sup>−1</sup> per decades) of annual, spring, summer, autumn, and winter mean near-surface wind speeds for the observation (column) and reanalysis datasets (star) NCEP-2 (red), MERRA (blue), JRA-55(green), ERA-Interim (purple), and 20CR (yellow), respectively, over China (<b>a</b>) and nine subregions (<b>b</b>–<b>j</b>).</p> Full article ">Figure 6
<p>Spatial distributions of linear trends for mean annual near-surface wind speeds (unit: m·s<sup>−1</sup>·10a<sup>−1</sup>) from 1979 to 2011 for (<b>a</b>) CN05.1, (<b>b</b>) NCEP-2, (<b>c</b>) MERRA, (<b>d</b>) JRA-55, (<b>e</b>) ERA-Interim, and (<b>f</b>) 20CR. Areas with above 0.05 significance level using the Student’ s <span class="html-italic">t</span>-test are dotted.</p> Full article ">Figure 7
<p>Time series of anomalous near-surface wind speed (unit: m·s<sup>−1</sup>) in annual (<b>a</b>), spring (<b>b</b>), summer (<b>c</b>), autumn (<b>d</b>), and winter (<b>e</b>) averaged over the continental China derived from CN05.1 (black), NCEP-2 (red), MERRA (green), JRA-55 (blue), ERA-Interim (purple), and 20CR (yellow).</p> Full article ">Figure 8
<p>Interannual standard deviations of mean annual near-surface wind speed (units: m·s<sup>−1</sup>) during 1979 to 2011 over continental China: (<b>a</b>) observation, (<b>b</b>) NCEP-2, (<b>c</b>) MERRA, (<b>d</b>) JRA-55, (<b>e</b>) ERA-Interim, and (<b>f</b>) 20CR.</p> Full article ">
21 pages, 9179 KiB  
Review
Hierarchical Modeling of Solar System Planets with Isca
by Stephen I. Thomson and Geoffrey K. Vallis
Atmosphere 2019, 10(12), 803; https://doi.org/10.3390/atmos10120803 - 12 Dec 2019
Cited by 17 | Viewed by 6366
Abstract
We describe the use of Isca for the hierarchical modeling of Solar System planets, with particular attention paid to Earth, Mars, and Jupiter. Isca is a modeling framework for the construction and use of models of planetary atmospheres at varying degrees of complexity, [...] Read more.
We describe the use of Isca for the hierarchical modeling of Solar System planets, with particular attention paid to Earth, Mars, and Jupiter. Isca is a modeling framework for the construction and use of models of planetary atmospheres at varying degrees of complexity, from featureless model planets with an atmosphere forced by a thermal relaxation back to a specified temperature, through aquaplanets with no continents (or no ocean) with a simple radiation scheme, to near-comprehensive models with a multi-band radiation scheme, a convection scheme, and configurable continents and topography. By a judicious choice of parameters and parameterization schemes, the model may be configured for fairly arbitrary planets, with stellar radiation input determined by astronomical parameters, taking into account the planet’s obliquity and eccentricity. In this paper, we describe the construction and use of models at varying levels of complexity for Earth, Mars and Jupiter using the primitive equations and/or the shallow water equations. Full article
(This article belongs to the Special Issue Modeling and Simulation of Planetary Atmospheres)
►▼ Show Figures

Figure 1

Figure 1
<p>Zonal-mean zonal wind averaged over three winter months (March, April, and May (<b>a</b>,<b>b</b>) and December, January, and February (<b>c</b>,<b>d</b>)): (<b>a</b>) sample model of Earth; (<b>b</b>) intermediate-complexity model of Earth; (<b>c</b>) high-complexity model of Earth; and (<b>d</b>) JRA-55 reanalysis for 1958–2018.</p> Full article ">Figure 2
<p>Zonal winds at 250 hPa in our most realistic Earth model (<b>a</b>,<b>b</b>) and JRA-55 (<b>c</b>,<b>d</b>). The left column is averaged over December, January, and February, and the right column is averaged over June, July, and August.</p> Full article ">Figure 3
<p>Meridional overturning streamfunction in our most realistic Earth model (<b>a</b>,<b>b</b>) and from JRA-55 (<b>c</b>,<b>d</b>). The left column is averaged over December, January, and February, and the right column is averaged over June, July, and August.</p> Full article ">Figure 4
<p>Zonal-mean zonal wind for Mars averaged over the solar longitude range <span class="html-italic">L<sub>S</sub></span> = 225°–315°, corresponding to a seasonal average centered on northern-hemisphere winter solstice at <span class="html-italic">L<sub>S</sub></span> = 270°: (<b>a</b>) Newtonian-cooling model of Mars, with radiative-equilibrium relaxation temperatures; (<b>b</b>) grey radiation model of Mars; (<b>c</b>) SOCRATES-radiation model of Mars with Martian topography included; and (<b>d</b>) MACDA reanalysis from MY24-27 [<a href="#B40-atmosphere-10-00803" class="html-bibr">40</a>].</p> Full article ">Figure 5
<p>Zonal mean atmospheric temperatures in our most realistic Mars model (<b>a</b>,<b>b</b>) and MACDA (<b>c</b>,<b>d</b>). The left column is averaged over <span class="html-italic">L<sub>S</sub></span> = 225°–315°, which is a seasonal average centered on northern-hemisphere winter solstice at <span class="html-italic">L<sub>S</sub></span> = 270°, and the right column is averaged over <span class="html-italic">L<sub>S</sub></span> = 45°–135°, which is a seasonal average centered on northern-hemisphere summer solstice at <span class="html-italic">L<sub>S</sub></span> = 90°.</p> Full article ">Figure 6
<p>Surface pressure in our most realistic Mars model (<b>a</b>,<b>b</b>) and MACDA (<b>c</b>,<b>d</b>). The left column is averaged over <span class="html-italic">L<sub>S</sub></span> = 225°–315° which is a seasonal average centered on northern-hemisphere winter solstice at <span class="html-italic">L<sub>S</sub></span> = 270°, and the right column is averaged over <span class="html-italic">L<sub>S</sub></span> = 45°–35°, which is a seasonal average centered on northern-hemisphere summer solstice at <span class="html-italic">L<sub>S</sub></span> = 90°.</p> Full article ">Figure 7
<p>Zonal-mean zonal wind for Jupiter with prescribed deep-jet profiles: (<b>a</b>) 1.5-layer shallow-water model of Jupiter, with prescribed deep jets (<span class="html-italic">u</span><sub>2</sub>) with meridional wavenumber <span class="html-italic">n</span> = 26; (<b>b</b>) grey radiation model of Jupiter, with prescribed jets at 14.5 bars (<span class="html-italic">u</span><sub>2</sub>), with profiles in blue shown at the same time as those in (<b>a</b>), where the deep jets are shown with <span class="html-italic">n</span> = 12; and (<b>c</b>) zonal-mean zonal-winds plotted as a function of pressure as an average between 10,770 and 10,800 days of the same simulation shown in (<b>b</b>). Solid contours are positive values. Values of “days” in (<b>a</b>,<b>b</b>) are the time in Earth days in the simulations shown.</p> Full article ">Figure 8
<p>Zonal-mean zonal wind for Jupiter with prescribed deep-jet profiles. Data are from the same simulations as in <a href="#atmosphere-10-00803-f007" class="html-fig">Figure 7</a>, but showing Isca’s ability to well-simulate Jupiter’s well-known zonal symmetry in the presence of zonally-symmetric deep jets. (<b>a</b>) The 1.5-layer shallow-water model averaged between 10,770 and 10,800 days. (<b>b</b>) Grey radiation model of Jupiter, zonal-winds shown at 1 bar pressure level.</p> Full article ">
25 pages, 17732 KiB  
Article
Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics
by Gnim Tchalim Gnitou, Tinghuai Ma, Guirong Tan, Brian Ayugi, Isaac Kwesi Nooni, Alia Alabdulkarim and Yuan Tian
Atmosphere 2019, 10(12), 802; https://doi.org/10.3390/atmos10120802 - 12 Dec 2019
Cited by 13 | Viewed by 3728
Abstract
Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of [...] Read more.
Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties. Full article
(This article belongs to the Section Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>Topographic map of the study domain overlaid by the Coordinated Regional Downscaling Experiment (CORDEX) interpolated 0.5° × 0.5° grid setting used in this study.</p> Full article ">Figure 2
<p>The spatial pattern of temporal rainfall considering 1980–2005 period with the top panel illustrating the mean and the bottom panel showing the 95th percentile for CRU TS 4.02, UDEL v5.01, and GPCP v2.3 observational datasets.</p> Full article ">Figure 3
<p>The spatial pattern of temporal rainfall mean bias considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to CRU TS v4.02.</p> Full article ">Figure 4
<p>The spatial pattern of temporal rainfall 95th percentile bias considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to CRU TS v4.02.</p> Full article ">Figure 5
<p>The spatial pattern of temporal rainfall mean bias considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to GPCP v2.3.</p> Full article ">Figure 6
<p>The spatial pattern of temporal rainfall 95th percentile bias considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to GPCP v2.3.</p> Full article ">Figure 7
<p>The spatial pattern of temporal rainfall mean bias considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to UDEL v5.01.</p> Full article ">Figure 8
<p>The spatial pattern of temporal rainfall 95th percentile bias considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to UDEL v5.01.</p> Full article ">Figure 9
<p>The temporal pattern of spatial rainfall correlation coefficient considering the 1980–2005 period for GCMs and their corresponding RCA4 dynamically downscaled outputs with respect to all the three datasets.</p> Full article ">Figure 10
<p>Summary of GCMs and their corresponding RCA4 dynamically downscaled performances with respect to the three observational datasets for the mean bias.</p> Full article ">Figure 11
<p>Same as <a href="#atmosphere-10-00802-f010" class="html-fig">Figure 10</a> for the 95th percentile Bias.</p> Full article ">Figure 12
<p>Climatology of mean sea level pressure (shaded, hPa) and 200 hPa wind (vectors, m/s) during the June–September (JJAS).</p> Full article ">Figure 13
<p>Climatology of mean sea level pressure (shaded, hPa) and 500 hPa wind (vectors, m/s) during JJAS.</p> Full article ">Figure 14
<p>Climatology of mean sea level pressure (shaded, hPa) and 850 hPa wind (vectors, m/s) during JJAS.</p> Full article ">
17 pages, 628 KiB  
Article
An Assessment of the Suitability of Active Green Walls for NO2 Reduction in Green Buildings Using a Closed-Loop Flow Reactor
by Thomas Pettit, Peter J. Irga, Nicholas C. Surawski and Fraser R. Torpy
Atmosphere 2019, 10(12), 801; https://doi.org/10.3390/atmos10120801 - 11 Dec 2019
Cited by 25 | Viewed by 5202
Abstract
Nitrogen dioxide (NO2) is a common urban air pollutant that is associated with several adverse human health effects from both short and long term exposure. Additionally, NO2 is highly reactive and can influence the mixing ratios of nitrogen oxide (NO) [...] Read more.
Nitrogen dioxide (NO2) is a common urban air pollutant that is associated with several adverse human health effects from both short and long term exposure. Additionally, NO2 is highly reactive and can influence the mixing ratios of nitrogen oxide (NO) and ozone (O3). Active green walls can filter numerous air pollutants whilst using little energy, and are thus a candidate for inclusion in green buildings, however, the remediation of NO2 by active green walls remains untested. This work assessed the capacity of replicate active green walls to filter NO2 at both ambient and elevated concentrations within a closed-loop flow reactor, while the concentrations of NO and O3 were simultaneously monitored. Comparisons of each pollutant’s decay rate were made for green walls containing two plant species (Spathiphyllum wallisii and Syngonium podophyllum) and two lighting conditions (indoor and ultraviolet). Biofilter treatments for both plant species exhibited exponential decay for the biofiltration of all three pollutants at ambient concentrations. Furthermore, both treatments removed elevated concentrations of NO and NO2, (average NO2 clean air delivery rate of 661.32 and 550.8 m3∙h−1∙m−3 of biofilter substrate for the respective plant species), although plant species and lighting conditions influenced the degree of NOx removal. Elevated concentrations of NOx compromised the removal efficiency of O3. Whilst the current work provided evidence that effective filtration of NOx is possible with green wall technology, long-term experiments under in situ conditions are needed to establish practical removal rates and plant health effects from prolonged exposure to air pollution. Full article
(This article belongs to the Special Issue Green Buildings and Indoor Air Quality)
►▼ Show Figures

Figure 1

Figure 1
<p>The replicate biofilters used in this experiment. (<b>A</b>) A replicate biofilter with <span class="html-italic">Spathiphyllum wallisii</span>. (<b>B</b>) A replicate biofilter with <span class="html-italic">Syngonium podophyllum</span>.</p> Full article ">Figure 2
<p>The closed loop flow reactor used in this experiment.</p> Full article ">Figure 3
<p>The biofiltration of ambient concentrations of NO<sub>2</sub> by biofilters containing two different plant species. NO<sub>2</sub> concentrations were normalised by the starting ambient concentration of NO<sub>2</sub>. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the standard error of the mean (SEM).</p> Full article ">Figure 4
<p>The biofiltration of ambient concentrations of NO by biofilters containing two different plant species. NO concentrations were normalised by the starting ambient concentration of NO. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 5
<p>The biofiltration of ambient concentrations of O<sub>3</sub> by biofilters containing two different plant species. O<sub>3</sub> concentrations were normalised by the starting ambient concentration of O<sub>3</sub>. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 6
<p>The biofiltration of elevated concentrations of NO<sub>2</sub> by biofilters containing two different plant species at indoor light levels. NO<sub>2</sub> concentrations were normalised by the starting ambient concentration of NO<sub>2</sub>. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 7
<p>The biofiltration of elevated concentrations of NO by biofilters containing two different plant species at indoor light levels. NO concentrations were normalised by the starting ambient concentration of NO. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 8
<p>The biofiltration of elevated concentrations of O<sub>3</sub> by biofilters containing two different plant species at indoor light levels. O<sub>3</sub> concentrations were normalised by the starting ambient concentration of O<sub>3</sub>. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 9
<p>The biofiltration of elevated concentrations of NO<sub>2</sub> by biofilters containing two different plant species under UV light. NO<sub>2</sub> concentrations were normalised by the starting ambient concentration of NO<sub>2</sub>. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 10
<p>The biofiltration of elevated concentrations of NO by biofilters containing two different plant species under UV light. NO concentrations were normalised by the starting ambient concentration of NO. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">Figure 11
<p>The biofiltration of elevated concentrations of O<sub>3</sub> by biofilters containing two different plant species under UV light. O<sub>3</sub> concentrations were normalised by the starting ambient concentration of O<sub>3</sub>. <span class="html-italic">n</span> = 4 independent samples per treatment, error bars represent the SEM.</p> Full article ">
13 pages, 1726 KiB  
Article
June Temperature Trends in the Southwest Deserts of the USA (1950–2018) and Implications for Our Urban Areas
by Anthony Brazel
Atmosphere 2019, 10(12), 800; https://doi.org/10.3390/atmos10120800 - 11 Dec 2019
Cited by 6 | Viewed by 3084
Abstract
Within the United States, the Southwest USA deserts show the largest temperature changes (1901–2010) besides Alaska, according to the most recent USA National Climate Assessment report. The report does not discuss urban effects vs. regional effects that might be evident in trends. Twenty-five [...] Read more.
Within the United States, the Southwest USA deserts show the largest temperature changes (1901–2010) besides Alaska, according to the most recent USA National Climate Assessment report. The report does not discuss urban effects vs. regional effects that might be evident in trends. Twenty-five temperature stations with ca. 68-year records (1950 to 2018) have been accessed from US Global Historical Climate Network archives. Land cover data are accessed from a National Land Cover Database. June results considering both urban and rural sites show an astounding rate per year change among sites ranging from −0.01 to 0.05 °C for maximum temperatures and 0.01 to 0.11 °C for minimum temperatures (−0.8 to 3.2 °C, and 0.8 to 8.0 °C for the entire period). For maximum temperatures, almost half of the sites showed no significant trends at a stringent 0.01 level of statistical significance, but 20 of 25 were significant at the 0.05 level. For minimum temperatures, over 75% of sites were significant at the 0.01 level (92% at 0.05 level of significance). The urban-dominated stations in Las Vegas, Phoenix, Tucson, and Yuma show large minimum temperature trends, indicating emerging heat island effects. Rural sites, by comparison, show much smaller trends. Addressing heat in our urban areas by local actions, through collaborations with stakeholders and political resolve, will aid in meeting future urban challenges in this era of projected global climate change and continued warming. Full article
(This article belongs to the Special Issue Infrastructure Planning for Urban Climate Moderation)
►▼ Show Figures

Figure 1

Figure 1
<p>Five climate divisions designated by National Ocenaic and Atmospheric Administration (NOAA), individual stations, four major urban locations of Las Vegas, Phoenix, Tucson, and Yuma (see <a href="#atmosphere-10-00800-t001" class="html-table">Table 1</a>, <a href="#atmosphere-10-00800-t002" class="html-table">Table 2</a> and <a href="#atmosphere-10-00800-t003" class="html-table">Table 3</a>).</p> Full article ">Figure 2
<p>Four airport urban sites. Right panel shows urban extent and land cover; left panel is the zoomed-in view showing weather site placement and 500 m circle around each site. There have been some station moves locally within the airports over time. At times of T<sub>min</sub> and T<sub>max</sub>, prevailing wind regimes show SW for Las Vegas; E (night) to W (day) for Phoenix; SE (night) to SW (day) for Tucson; and NE (night) to SW (day) for Yuma.</p> Full article ">Figure 3
<p>Four urban airport sites and rural reference sites showing June maximum and minimum temperatures and urban-rural time series (see <a href="#atmosphere-10-00800-f002" class="html-fig">Figure 2</a>). Red lines refer to maximum temperatures; blue lines, minimum temperatures. Lower panel per site provides a measure of the day and night urban-rural estimates (UHI). All values in °C.</p> Full article ">
25 pages, 11890 KiB  
Article
On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin
by Lorenzo Sangelantoni, Barbara Tomassetti, Valentina Colaiuda, Annalina Lombardi, Marco Verdecchia, Rossella Ferretti and Gianluca Redaelli
Atmosphere 2019, 10(12), 799; https://doi.org/10.3390/atmos10120799 - 10 Dec 2019
Cited by 11 | Viewed by 3068
Abstract
The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are [...] Read more.
The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to −50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season. Full article
(This article belongs to the Special Issue Forecasting Heavy Weather in Mediterranean Region)
►▼ Show Figures

Figure 1

Figure 1
<p>Aterno-Pescara river basin with the Abruzzo region boarders (Central Italy).</p> Full article ">Figure 2
<p>Location of the Abruzzo region observational sites and a representative RCM grid. Magenta circles indicate sites providing observed time series of both temperature and precipitation.</p> Full article ">Figure 3
<p>Original (<b>a</b>,<b>c</b>) and bias corrected (<b>b</b>,<b>d</b>) annual precipitation (<b>a</b>,<b>b</b>) and temperature (<b>c</b>,<b>d</b>) statistical distributions (PDFs) over the 17 reference sites for the calibration (or reference) period 2002-2016. On the tables at the right of the figures, we report biases over three statistics: wet-day frequency (WDF), median (ρ50), and the 99th percentile (ρ99) for precipitation and 5th, 50th, and 95th percentiles for the temperature. Simulations shown belong to a representative RCM (CNRM-CM5-RCA4).</p> Full article ">Figure 3 Cont.
<p>Original (<b>a</b>,<b>c</b>) and bias corrected (<b>b</b>,<b>d</b>) annual precipitation (<b>a</b>,<b>b</b>) and temperature (<b>c</b>,<b>d</b>) statistical distributions (PDFs) over the 17 reference sites for the calibration (or reference) period 2002-2016. On the tables at the right of the figures, we report biases over three statistics: wet-day frequency (WDF), median (ρ50), and the 99th percentile (ρ99) for precipitation and 5th, 50th, and 95th percentiles for the temperature. Simulations shown belong to a representative RCM (CNRM-CM5-RCA4).</p> Full article ">Figure 4
<p>Original (ORG, blue box plots) and bias corrected (BC, red box plots) climate change signals (CCSs) for precipitation (<b>a</b>) and temperature (<b>b</b>). CCSs are reported on an annual (upper panels), winter season (central panels), and summer season (bottom panels). For each variable, CCS is defined over three statistics. For the precipitation, wet-day frequency (WDF), 50th and 99th percentiles. For the temperature, over 5th, 50th, and 95th percentiles. Box plots represent the statistical distribution built on the CCSs produced by the five RCMs over the 17 reference sites (5 RCMs * 17 reference sites = 85 values). On each box, quartiles (central mark and box edges), values within the 1.5 interquartile range from the box edges (whiskers), and outliers (plus signs) are shown. Results are shown for the RCP 8.5.</p> Full article ">Figure 4 Cont.
<p>Original (ORG, blue box plots) and bias corrected (BC, red box plots) climate change signals (CCSs) for precipitation (<b>a</b>) and temperature (<b>b</b>). CCSs are reported on an annual (upper panels), winter season (central panels), and summer season (bottom panels). For each variable, CCS is defined over three statistics. For the precipitation, wet-day frequency (WDF), 50th and 99th percentiles. For the temperature, over 5th, 50th, and 95th percentiles. Box plots represent the statistical distribution built on the CCSs produced by the five RCMs over the 17 reference sites (5 RCMs * 17 reference sites = 85 values). On each box, quartiles (central mark and box edges), values within the 1.5 interquartile range from the box edges (whiskers), and outliers (plus signs) are shown. Results are shown for the RCP 8.5.</p> Full article ">Figure 5
<p>Monthly mean discharge for the reference period (<b>a</b>) and future period (<b>b</b>) and the percentage difference between the two periods (<b>c</b>). In the left column hydrological simulations consider RCP 4.5 and in the right column RCP 8.5. In all the panels, dashed (solid) lines indicate CHyM simulations forced by original (bias-corrected) climate simulations. Black thick lines represent the CHyM simulation ensembles. In the upper panels, a red tick line represents the CHyM simulation driven by the reference period observed for the precipitation and temperature. Hydrological simulations have been extracted at the nearest grid node to the station of S. Teresa located in correspondence of the Pescara river mouth.</p> Full article ">Figure 6
<p>Spatial distribution of mean discharge flow percentage changes on an annual and a seasonal basis, according to the RCP 8.5 emission scenario. Original (ORG) and bias corrected (BC) climate inputs are considered.</p> Full article ">Figure 7
<p>Same as in <a href="#atmosphere-10-00799-f006" class="html-fig">Figure 6</a>, but according to the RCP 4.5.</p> Full article ">Figure 8
<p>Percentage annual MD-CS for the individual CHyM runs according to the RCP 8.5. Original climate inputs and bias-corrected climate inputs are reported in the upper and bottom panels, respectively.</p> Full article ">Figure 9
<p>Spatial distribution of the BDD index changes, on an annual and seasonal basis, defined as the difference between the number of events characterizing the future period and the reference period. Results considering the RCP 8.5 are shown. Original (ORG) and bias corrected (BC) climate inputs are considered.</p> Full article ">Figure 9 Cont.
<p>Spatial distribution of the BDD index changes, on an annual and seasonal basis, defined as the difference between the number of events characterizing the future period and the reference period. Results considering the RCP 8.5 are shown. Original (ORG) and bias corrected (BC) climate inputs are considered.</p> Full article ">Figure 10
<p>Similar to <a href="#atmosphere-10-00799-f008" class="html-fig">Figure 8</a> but for the BDD index representing the HS-CS.</p> Full article ">
45 pages, 10464 KiB  
Article
Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling
by Richard Ménard, Pierre Gauthier, Yves Rochon, Alain Robichaud, Jean de Grandpré, Yan Yang, Cécilien Charrette and Simon Chabrillat
Atmosphere 2019, 10(12), 798; https://doi.org/10.3390/atmos10120798 - 9 Dec 2019
Cited by 10 | Viewed by 3563
Abstract
We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed [...] Read more.
We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Additionally, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone–radiation interaction is shown to give comparable results on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere. Full article
(This article belongs to the Special Issue Air Quality Prediction)
►▼ Show Figures

Figure 1

Figure 1
<p>Observation component of the cost function for ozone assimilation as a function of iteration. Solid line is associated with the value of <span class="html-italic">J</span><sub>o</sub> of the first inner loop and the dashed line the value of <span class="html-italic">J</span><sub>o</sub> of the second inner loop.</p> Full article ">Figure 2
<p>Spatial autocovariance of innovation for MIPAS CH<sub>4</sub> at 63 hPa. The abscissa shows the lags between orbits (each orbits are separated by about 530 km along the satellite track). The red squares represent the sample autocovariance values, and the dashed curve is a piecewise linear interpolation between the sample points. Note that the sample covariance at zero distance separation is at the top of the graph (near 36 × 10<sup>−13</sup>), and the dashed line is extrapolated at lag 0.</p> Full article ">Figure 3
<p>Estimated error variance for CH<sub>4</sub>/MIPAS as a function of height (pressure in hPa). (<b>Left panel</b>) The estimated background error variance (blue with circles) and observation error variance (red with squares) using the HL method. (<b>Right panel</b>) Three different estimates of observation error variance. The blue curve with squares is the estimate given by the instrument team (i.e., the instrument error), the red curve with squares is the observation error variance obtained from the HL method (note it is identical to the red curve in the left panel), and the green curve with squares is the observation error variance estimated from the Desroziers method [<a href="#B81-atmosphere-10-00798" class="html-bibr">81</a>] (single iterate).</p> Full article ">Figure 4
<p>Zonal mean analysis increment for HNO<sub>3</sub> as function of height (in hPa). (<b>Left panel</b>) Using the first guess or old statistics. (<b>Right panel</b>) Using the new statistics consisting of CQC correlation and HL error variances. The value of the increment should be scaled by 10<sup>−9</sup> volume mixing ratio. Note that the contour interval on the left panel are much smaller than on the right panel, and only one shaded blue contour (values between −0.5 and −1.0) appears in the left panel.</p> Full article ">Figure 5
<p>Cross-correlation between ozone and temperature derived from six-hour differences (i.e., CQC method) for July 2003. (<b>Left panel</b>) The non-interactive ozone-radiation run of GEM-BACH and (<b>right panel</b>) for an interactive ozone-radiation run.</p> Full article ">Figure 6
<p>Balance operator between ozone and temperature for July 2003. (<b>Left panel</b>) <math display="inline"><semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>A</mi> </mstyle> <mrow> <mi>C</mi> <mi>Q</mi> <mi>C</mi> <mo>-</mo> <mi>N</mi> <mi>M</mi> <mi>C</mi> </mrow> </msup> </mrow> </semantics></math>, which uses CQC for correlations and the NMC method for temperature error variance, and (<b>right panel</b>) <math display="inline"><semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>A</mi> </mstyle> <mrow> <mi>L</mi> <mi>I</mi> <mi>N</mi> <mi>O</mi> <mi>Z</mi> </mrow> </msup> </mrow> </semantics></math>, as derived from the LINOZ scheme (derivation in <a href="#app3-atmosphere-10-00798" class="html-app">Appendix C</a>).</p> Full article ">Figure 7
<p>Mean (lower curves) and standard deviation (upper curves) of the AMSU-A radiance observations minus the forecast (6 h) for channels 11 to 14 (<b>upper panel to lower panel</b>). In blue are the results using the standard CMC bias correction scheme, which uses only the model in the stratosphere, and in red using only MIPAS temperature in the stratosphere.</p> Full article ">Figure 8
<p>Global verification (observation-minus-forecast) of temperatures as function of height (in hPa) for two assimilation runs. In blue is the assimilation of AMSU-A only, and in red the assimilation of MIPAS temperature and AMSU-A. All AMSU-A data are processed with the new bias correction. The left panel illustrates verification against MIPAS temperatures, and (<b>right panel</b>) verification against HALOE temperatures. The squares on the far right of the panels indicate a significant Student’s <span class="html-italic">t</span>-test with 95% (or higher) confidence interval, and the dots on the far right of the panels indicate a significant the Fisher test of variances with a 95% (or higher) confidence interval. These markers are red (blue) if the red (blue) experiment shows an improvement.</p> Full article ">Figure 9
<p>Global verification (observation-minus-forecast) against HALOE temperatures for two assimilation runs. In blue is the assimilation of AMSU-A, and in red is the assimilation of MIPAS temperatures only. Tests of significant differences are the same as in <a href="#atmosphere-10-00798-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 10
<p>Global verification (observation-minus-forecast) of temperature as for two assimilation runs. In red, is the assimilation of MIPAS temperature and AMSU-A but no stratospheric channels, and in blue is the assimilation of MIPAS temperatures with all the AMSU-A channels. (<b>Left panel</b>) The verification against MIPAS temperatures, and (<b>right panel</b>) the verification against HALOE temperatures. Tests of significant differences are the same as in <a href="#atmosphere-10-00798-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 11
<p>Same as <a href="#atmosphere-10-00798-f010" class="html-fig">Figure 10</a>, but for verification of ozone MIPAS on the left and ozone HALOE on the right.</p> Full article ">Figure 12
<p>Global verification of the impact of ozone radiation interaction. All runs with the assimilation of MIPAS temperatures and AMSU-A channels 1–8 only. (<b>Left panel</b>) The temperature impact, (<b>right panel</b>) and the ozone impact. Runs with no ozone–radiation interaction are in blue, and with ozone–radiation interaction in red. Test of significant differences of statistics are the same as in <a href="#atmosphere-10-00798-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 13
<p>Fifteen-day forecast of temperatures at 70 hPa verified against analyses over the South Pole region resulting from the assimilation of MIPAS temperature and ozone. The curves in blue are from using the BASCOE chemistry, in red using the LINOZ linearized ozone chemistry, and in green using a climatological ozone. Note that, here, the bias is depicted with dash lines, while the solid lines represent the root mean square (RMS) error (not the standard deviation).</p> Full article ">Figure 14
<p>Total column ozone (DU) (analysis) over the South Pole region on 3 October 2003 resulting from the assimilation of MIPAS temperature and ozone. (<b>Left panel</b>) Experiment using the BASCOE chemistry scheme. (<b>Right panel</b>) Experiment using the LINOZ linearized ozone chemistry scheme.</p> Full article ">Figure 15
<p>Time series of ozone at 70 hPa over the South Pole region resulting from the assimilation of MIPAS temperature and ozone. The blue curve is from using the BASCOE chemistry scheme, and the red curve using the LINOZ linearized ozone chemistry scheme.</p> Full article ">Figure 16
<p>Anomaly correlations at 10 (red), 50 (green) and 100 (purple) hPa in the southern hemisphere (20–90S) for ozone-radiation interactive (dashed lines) and non-interactive ozone (solid lines) experiments.</p> Full article ">Figure 17
<p>Multivariate temperature-ozone assimilation. Univariate ozone and temperature assimilation (blue), multivariate assimilation performed with the CQC-NMC balance <math display="inline"><semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>A</mi> </mstyle> <mrow> <mi>C</mi> <mi>Q</mi> <mi>C</mi> <mo>-</mo> <mi>N</mi> <mi>M</mi> <mi>C</mi> </mrow> </msup> </mrow> </semantics></math> (red) and with LINOZ balance <math display="inline"><semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>A</mi> </mstyle> <mrow> <mi>L</mi> <mi>I</mi> <mi>N</mi> <mi>O</mi> <mi>Z</mi> </mrow> </msup> </mrow> </semantics></math> (grey dots). The solid lines denote average differences (biases) and the dashed lines indicate the standard deviations (by ±σ), except for <math display="inline"><semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>A</mi> </mstyle> <mrow> <mi>L</mi> <mi>I</mi> <mi>N</mi> <mi>O</mi> <mi>Z</mi> </mrow> </msup> </mrow> </semantics></math> where we use only grey dots for both metrics. (<b>Left panel</b>) The temperature O-P (observation minus six-hour forecast) statistics from comparisons to MIPAS observations. (<b>Right panel</b>) The ozone O-P statistics. Note that to simplify we have not presented the significant tests of differences of statistics because it would have been cumbersome to illustrate with three pairs of experiments.</p> Full article ">Figure 18
<p>Wind analysis increments in response to MIPAS CH<sub>4</sub> observations obtained with (<b>a</b>) the first estimate of background-error statistics for chemical constituents, and (<b>b</b>) the new statistics estimated using the Hollingsworth–Lönnberg method. Contours are the wind amplitude in m/s and arrows indicate the wind direction. Results are shown here at the 100-hPa level.</p> Full article ">Figure 19
<p>Wind analysis increments at 10 hPa obtained by assimilating CH<sub>4</sub> (<b>top left</b>), O<sub>3</sub> (<b>top right</b>), N<sub>2</sub>O (<b>bottom left</b>), and all three species (<b>bottom right</b>). Contours are the wind amplitude in m/s and arrows indicate the wind direction.</p> Full article ">Figure 20
<p>Impact of assimilating stratospheric chemical tracers on tropospheric tropical zonal winds. Verification against radiosonde observations over the tropical region (20° S–20° N) of observation minus 6-h forecast for the period 15 August to 5 October 2003. The results in red correspond to a 4D-Var assimilation experiment with assimilation of ozone, methane, and nitrous oxide. Results in blue are 4D-Var experiments but without assimilation of the long-lived species. The squares on the far right of the figure indicate a significant Student t-test of means with 95% confidence interval, and the dots indicate a significant the Fisher test of variances with a 95% confidence interval. These markers are red, indicating an improvement.</p> Full article ">Figure 21
<p>Difference between the (analysis) wind magnitude (in m/s) obtained from two 4D-Var assimilation cycles executed with and without the assimilation of ozone, methane and nitrous oxide. The results are averaged over the period 15 August to 5 October 2003. The zonal mean average is shown here.</p> Full article ">Figure 22
<p>OmP temperature time series between the radiosondes and the 3D-Var (blue) and 4D-var (red) assimilation cycles at 20 hPa in the North Hemisphere.</p> Full article ">Figure A1
<p>Ratio <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>p</mi> <mo stretchy="false">)</mo> <mo>/</mo> <msub> <mi>c</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>p</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> for the month of September.</p> Full article ">Figure A2
<p>Point-by-point, <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi>λ</mi> <mo>,</mo> <mi>p</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, scatter of <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>p</mi> <mo stretchy="false">)</mo> <mo>/</mo> <msub> <mi>c</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>p</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mi mathvariant="normal">O</mi> <mo>¯</mo> </mover> </mrow> <mn>3</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure A3
<p>Vertical staggering of temperature and height.</p> Full article ">
29 pages, 4528 KiB  
Article
Intercomparison of Multiple UV-LIF Spectrometers Using the Aerosol Challenge Simulator
by Elizabeth Forde, Martin Gallagher, Maurice Walker, Virginia Foot, Alexis Attwood, Gary Granger, Roland Sarda-Estève, Warren Stanley, Paul Kaye and David Topping
Atmosphere 2019, 10(12), 797; https://doi.org/10.3390/atmos10120797 - 9 Dec 2019
Cited by 13 | Viewed by 4965
Abstract
Measurements of primary biological aerosol particles (PBAPs) have been conducted worldwide using ultraviolet light-induced fluorescence (UV-LIF) spectrometers. However, how these instruments detect and respond to known biological and non-biological particles, and how they compare, remains uncertain due to limited laboratory intercomparisons. Using the [...] Read more.
Measurements of primary biological aerosol particles (PBAPs) have been conducted worldwide using ultraviolet light-induced fluorescence (UV-LIF) spectrometers. However, how these instruments detect and respond to known biological and non-biological particles, and how they compare, remains uncertain due to limited laboratory intercomparisons. Using the Defence Science and Technology Laboratory, Aerosol Challenge Simulator (ACS), controlled concentrations of biological and non-biological aerosol particles, singly or as mixtures, were produced for testing and intercomparison of multiple versions of the Wideband Integrated Bioaerosol Spectrometer (WIBS) and Multiparameter Bioaerosol Spectrometer (MBS). Although the results suggest some challenges in discriminating biological particle types across different versions of the same UV-LIF instrument, a difference in fluorescence intensity between the non-biological and biological samples could be identified for most instruments. While lower concentrations of fluorescent particles were detected by the MBS, the MBS demonstrates the potential to discriminate between pollen and other biological particles. This study presents the first published technical summary and use of the ACS for instrument intercomparisons. Within this work a clear overview of the data pre-processing is also presented, and documentation of instrument version/model numbers is suggested to assess potential instrument variations between different versions of the same instrument. Further laboratory studies sampling different particle types are suggested before use in quantifying impact on ambient classification. Full article
(This article belongs to the Special Issue Detection and Monitoring of Bioaerosols)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of the Aerosol Challenge Simulator (ACS), UV-LIF (ultraviolet light-induced fluorescence) spectrometers were positioned on the highlighted yellow “working section”. (ULPA: Ultra Low Particulate Air).</p> Full article ">Figure 2
<p>WIBS-4D, WIBS-4M and WIBS-NEO (WIBS: Wideband Integrated Bioaerosol Spectrometer) particle size histograms, split into particle type groups (bacteria, fungal spores, etc.) and presenting the size distribution of each sample.</p> Full article ">Figure 3
<p>MBS-D and MBS-M (MBS: Multiparameter Bioaerosol Spectrometers) particle size histograms, split into particle type groups (bacteria, fungal spores, etc.) and presenting the size distribution of each sample.</p> Full article ">Figure 4
<p>SEM (SEM: Scanning Electron Microscopy) images taken of (<b>a</b>) <span class="html-italic">Cladosporium</span> material and (<b>b</b>,<b>c</b>) <span class="html-italic">Alternaria</span> material, from the back of the ACS.</p> Full article ">Figure 5
<p>SEM images taken of Nettle pollen from: (<b>a</b>) the sample pot; and (<b>b</b>) the back of the ACS.</p> Full article ">Figure 6
<p>Box and whisker plots displaying the difference in fluorescence profiles for the “best” case scenario (<b>Top</b>) (BT washed, <span class="html-italic">Alternaria</span>, and White Poplar) and “worst’ case scenario (<b>Bottom</b>) (E. coli, <span class="html-italic">Cladosporium</span>, and Olive), as detected by the MBS-D.</p> Full article ">Figure 7
<p>Ratio plot of fluorescence for each particle group type for: (<b>a</b>) WIBS-4M; (<b>b</b>) WIBS-4D; and (<b>c</b>) WIBS-NEO.</p> Full article ">Figure 8
<p>Ratio plot of channels XE2/XE3 (detection at 340–385 nm/390–435 nm) in relation to the sum of channels XE4–XE7, divided by the sum of XE1–XE3 (detection at 440–615 nm/300–435 nm) for (<b>a</b>) the MBS-M and (<b>b</b>) the MBS-D, following Kaye et al. (2013) [<a href="#B41-atmosphere-10-00797" class="html-bibr">41</a>], for each biological particle group type (bacteria, fungal spores and pollen).</p> Full article ">Figure 9
<p>SEM images taken of Sheep’s Sorrel pollen from: (<b>a</b>) the sample pot; and (<b>b</b>) the back of the ACS.</p> Full article ">Figure 10
<p>Fluorescence ratio of pollen sampled by the WIBS-NEO in relation to: (<b>a</b>) particle size; (<b>b</b>) particle shape, and (<b>c</b>) particle type.</p> Full article ">Figure 11
<p>The relationship between median particle size and median particle shape for: (<b>a</b>) WIBS; and (<b>b</b>) MBS. Different markers represent the different instruments, and are coloured according to particle type group.</p> Full article ">
36 pages, 7822 KiB  
Article
An Evaluation of Relationships between Radar-Inferred Kinematic and Microphysical Parameters and Lightning Flash Rates in Alabama Storms
by Lawrence D. Carey, Elise V. Schultz, Christopher J. Schultz, Wiebke Deierling, Walter A. Petersen, Anthony Lamont Bain and Kenneth E. Pickering
Atmosphere 2019, 10(12), 796; https://doi.org/10.3390/atmos10120796 - 9 Dec 2019
Cited by 35 | Viewed by 4851
Abstract
Lightning flash rate parameterizations based on polarimetric and multi-Doppler radar inferred microphysical (e.g., graupel volume, graupel mass, 35 dBZ volume) and kinematic (e.g., updraft volume, maximum updraft velocity) parameters have important applications in atmospheric science. Although past studies have established relations between flash [...] Read more.
Lightning flash rate parameterizations based on polarimetric and multi-Doppler radar inferred microphysical (e.g., graupel volume, graupel mass, 35 dBZ volume) and kinematic (e.g., updraft volume, maximum updraft velocity) parameters have important applications in atmospheric science. Although past studies have established relations between flash rate and storm parameters, their expected performance in a variety of storm and flash rate conditions is uncertain due to sample limitations. Radar network and lightning mapping array observations over Alabama of a large and diverse sample of 33 storms are input to hydrometeor identification, vertical velocity retrieval and flash rate algorithms to develop and test flash rate relations. When applied to this sample, prior flash rate linear relations result in larger errors overall, including often much larger bias (both over- and under-estimation) and root mean square errors compared to the new linear relations. At low flash rates, the new flash rate relations based on kinematic parameters have larger errors compared to those based on microphysical ones. Sensitivity of error to the functional form (e.g., zero or non-zero intercept) is also tested. When considering all factors (e.g., low errors including at low flash rate, consistency with past linear relations, and insensitivity to functional form), the flash rate parameterization based on graupel volume has the best overall performance. Full article
(This article belongs to the Special Issue 10th Anniversary of Atmosphere: Climatology and Meteorology)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the radar and lightning networks centered over Northern Alabama and used in this study. The solid red (blue) circle represents the ARMOR (KHTX) radar at KHSV (Hytop, AL, USA). The green triangles depict the sensor locations of the NALMA. The black, dashed circles represent the ARMOR-KHTX dual-Doppler regions as defined by the 30° beam crossing angle. A distance scale is shown in the upper-right and latitude and longitude values are provided along the outside. Adapted from [<a href="#B56-atmosphere-10-00796" class="html-bibr">56</a>]. © Copyright [2015] AMS.</p> Full article ">Figure 2
<p>Temporal evolution of graupel volume (km<sup>3</sup>), graupel mass (10<sup>6</sup> kg), 35 dBZ echo volume (km<sup>3</sup>), convective updraft volume &gt;5 m s<sup>−1</sup> and &gt;10 m s<sup>−1</sup> (km<sup>3</sup>), maximum updraft velocity (m s<sup>−1</sup>) and lightning flash rate (min<sup>−1</sup>) for a severe storm embedded in a QLCS traversing the analysis domain over Northern Alabama (<a href="#atmosphere-10-00796-f001" class="html-fig">Figure 1</a>) on 12 March 2010.</p> Full article ">Figure 3
<p>Pearson correlation coefficients between the time series of storm parameters shown in <a href="#atmosphere-10-00796-f002" class="html-fig">Figure 2</a> for a severe storm on 12 March 2010, including lightning flash rate (FPM, min<sup>−1</sup>) graupel volume (graupel, km<sup>3</sup>), graupel mass (10<sup>6</sup> kg), 35 dBZ echo volume (km<sup>3</sup>), convective updraft volume &gt;5 m s<sup>−1</sup> (w5, km<sup>3</sup>), convective updraft volume &gt;10 m s<sup>−1</sup> (w10, km<sup>3</sup>), and maximum updraft (m s<sup>−1</sup>).</p> Full article ">Figure 4
<p>Scatter plot of lightning flash rate versus radar-inferred storm parameters shown in <a href="#atmosphere-10-00796-f002" class="html-fig">Figure 2</a> for a severe storm on 12 March 2010, including (<b>a</b>) graupel volume (km<sup>3</sup>), (<b>b</b>) graupel mass (10<sup>6</sup> kg), (<b>c</b>) 35 dBZ echo volume (km<sup>3</sup>), (<b>d</b>) updraft volume &gt;5 m s<sup>−1</sup> (km<sup>3</sup>), (<b>e</b>) updraft volume &gt;10 m s<sup>−1</sup> (km<sup>3</sup>) and (<b>f</b>) maximum updraft (m s<sup>−1</sup>). The weighted least squares (WLS) linear regression is depicted as a solid red line and the resulting equation, where y = flash rate and x = radar parameter, is given for each scatterplot. The coefficient of determination (R<sup>2</sup>), the root mean square error (RMSE) and the normalized RMSE (NRMSE) are provided for each line.</p> Full article ">Figure 5
<p>Frequency and cumulative (%) histograms of storm (<b>a</b>) mean and (<b>b</b>) maximum flash rate (min<sup>−1</sup>) for all 33 storms in this study (<a href="#atmosphere-10-00796-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 6
<p>Same as <a href="#atmosphere-10-00796-f003" class="html-fig">Figure 3</a> except for all 33 storms in this study (<a href="#atmosphere-10-00796-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 7
<p>Same as <a href="#atmosphere-10-00796-f004" class="html-fig">Figure 4</a> except the flash rate versus radar parameter scatterplots are for data encompassing all 33 storms in this study (<a href="#atmosphere-10-00796-t001" class="html-table">Table 1</a>). For each parameter, the linear equation (<a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a>) that is forced through the origin (i.e., with zero y-intercept) is depicted as a solid red line. (<b>a</b>) graupel volume (km<sup>3</sup>), (<b>b</b>) graupel mass (10<sup>6</sup> kg), (<b>c</b>) 35 dBZ echo volume (km<sup>3</sup>), (<b>d</b>) updraft volume &gt;5 m s<sup>−1</sup> (km<sup>3</sup>), (<b>e</b>) updraft volume &gt;10 m s<sup>−1</sup> (km<sup>3</sup>) and (<b>f</b>) maximum updraft (m s<sup>−1</sup>).</p> Full article ">Figure 8
<p>Same as <a href="#atmosphere-10-00796-f007" class="html-fig">Figure 7</a> except flash rate parameterization equations derived in a recent study using lightning and radar data from Colorado thunderstorms [<a href="#B48-atmosphere-10-00796" class="html-bibr">48</a>] are also applied to the Alabama data in this study and plotted with red circles (B15) alongside the same results from this study using relations in <a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a> (blue triangles). Note that only (<b>a</b>) graupel volume (km<sup>3</sup>), (<b>b</b>) graupel mass (10<sup>6</sup> kg), (<b>c</b>) 35 dBZ echo volume (km<sup>3</sup>), and (<b>d</b>) updraft volume &gt; 5 m s<sup>−1</sup> (km<sup>3</sup>) are shown here. The other two parameters are shown in <a href="#atmosphere-10-00796-f009" class="html-fig">Figure 9</a> in order to highlight aspects not possible in a log plot.</p> Full article ">Figure 9
<p>Similar to <a href="#atmosphere-10-00796-f008" class="html-fig">Figure 8</a> except predicted flash rates based on (<b>a</b>) updraft volume &gt; 10 m s<sup>−1</sup> (km<sup>3</sup>) and (<b>b</b>) maximum updraft velocity (m s<sup>−1</sup>). Linear axes are utilized to emphasize the zero and negative flash rates predicted by the relations, which would not be possible on a log plot.</p> Full article ">Figure 10
<p>Flash rate parameterization equations based on radar-inferred microphysical or kinematic properties, including (<b>a</b>) graupel volume (km<sup>3</sup>), (<b>b</b>) graupel mass (10<sup>6</sup> kg), (<b>c</b>) 35 dBZ echo volume (km<sup>3</sup>), (<b>d</b>) updraft volume &gt; 5 m s<sup>−1</sup> (km<sup>3</sup>), (<b>e</b>) updraft volume &gt; 10 m s<sup>−1</sup> (km<sup>3</sup>), and (<b>f</b>) maximum updraft velocity (km<sup>3</sup>). Linear flash rate parameterization equations are shown for the Alabama storms in this study derived with a zero y-intercept (<a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a>, red lines) and a non-zero y-intercept (<a href="#atmosphere-10-00796-t005" class="html-table">Table 5</a>, blue lines), the recent study using Colorado-only storms [<a href="#B48-atmosphere-10-00796" class="html-bibr">48</a>] (green lines) and the Alabama (AL) only storm relations from an earlier series of related studies [<a href="#B13-atmosphere-10-00796" class="html-bibr">13</a>,<a href="#B14-atmosphere-10-00796" class="html-bibr">14</a>,<a href="#B86-atmosphere-10-00796" class="html-bibr">86</a>] (black line).</p> Full article ">Figure 11
<p>Error performance when applying the various flash rate parameterization equations with zero y-intercept (<a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a>) derived from the entire Alabama dataset to each of the 33 individual storms in this study (<a href="#atmosphere-10-00796-t001" class="html-table">Table 1</a>). Storm-level error statistics are plotted versus storm mean flash rate (min<sup>−1</sup>), including (<b>a</b>) MBE (min<sup>−1</sup>), (<b>b</b>) |NMBE| (%), (<b>c</b>) RMSE (min<sup>−1</sup>), and (<b>d</b>) NRMSE (%). Each type of relation has a different type and color marker, as shown in the legend in panel (<b>a</b>).</p> Full article ">Figure 12
<p>Same as <a href="#atmosphere-10-00796-f011" class="html-fig">Figure 11</a> except for the flash rate parameterization equations with non-zero y-intercept in <a href="#atmosphere-10-00796-t005" class="html-table">Table 5</a>. (<b>a</b>) MBE (min<sup>−1</sup>), (<b>b</b>) |NMBE| (%), (<b>c</b>) RMSE (min<sup>−1</sup>), and (<b>d</b>) NRMSE (%).</p> Full article ">Figure 13
<p>Difference in percentage errors (%) between the various sets of linear equations with zero y-intercept (<a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a>) and non-zero y-intercept (<a href="#atmosphere-10-00796-t005" class="html-table">Table 5</a>) versus storm mean flash rate (min<sup>−1</sup>) for (<b>a</b>) Δ|NBE| = (|NBE| <a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a> Equation) − (|NBE| <a href="#atmosphere-10-00796-t005" class="html-table">Table 5</a> Equation) and (<b>b</b>) Δ NRMSE = (NRMSE <a href="#atmosphere-10-00796-t002" class="html-table">Table 2</a> Equation) − (NRMSE <a href="#atmosphere-10-00796-t005" class="html-table">Table 5</a> Equation). The type of flash rate parameterization equation is provided in the figure legend.</p> Full article ">
14 pages, 2449 KiB  
Article
Aqueous Reactions of Sulfate Radical-Anions with Nitrophenols in Atmospheric Context
by Krzysztof J. Rudziński and Rafał Szmigielski
Atmosphere 2019, 10(12), 795; https://doi.org/10.3390/atmos10120795 - 9 Dec 2019
Cited by 11 | Viewed by 4030
Abstract
Nitrophenols, hazardous environmental pollutants, react promptly with atmospheric oxidants such as hydroxyl or nitrate radicals. This work aimed to estimate how fast nitrophenols are removed from the atmosphere by the aqueous-phase reactions with sulfate radical-anions. The reversed-rates method was applied to determine the [...] Read more.
Nitrophenols, hazardous environmental pollutants, react promptly with atmospheric oxidants such as hydroxyl or nitrate radicals. This work aimed to estimate how fast nitrophenols are removed from the atmosphere by the aqueous-phase reactions with sulfate radical-anions. The reversed-rates method was applied to determine the relative rate constants for reactions of 2-nitrophenol, 3-nitrophenol, 4-nitrophenol, 2,4-dinitrophenol, and 2,4,6-trinitrophenol with sulfate radical-anions generated by the autoxidation of sodium sulfite catalyzed by iron(III) cations at ~298 K. The constants determined were: 9.08 × 108, 1.72 × 109, 6.60 × 108, 2.86 × 108, and 7.10 × 107 M−1 s−1, respectively. These values correlated linearly with the sums of Brown substituent coefficients and with the relative strength of the O–H bond of the respective nitrophenols. Rough estimation showed that the gas-phase reactions of 2-nitrophenol with hydroxyl or nitrate radicals dominated over the aqueous-phase reaction with sulfate radical-anions in deliquescent aerosol and haze water. In clouds, rains, and haze water, the aqueous-phase reaction of 2-nitrophenol with sulfate radical-anions dominated, provided the concentration of the radical-anions was not smaller than that of the hydroxyl or nitrate radicals. The results presented may be also interesting for designers of advanced oxidation processes for the removal of nitrophenol. Full article
(This article belongs to the Special Issue Atmospheric Aqueous-Phase Chemistry)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Concentration of oxygen recorded during autoxidation of NaHSO<sub>3</sub> inhibited by 4-NP at various initial concentrations.</p> Full article ">Figure 2
<p>Rate of oxygen consumption during autoxidation of NaHSO<sub>3</sub> inhibited by 4-NP (initially 0.28 mM) evaluated from data in <a href="#atmosphere-10-00795-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>Linear plots of reciprocal quasi-stationary rates of the autoxidation of NaHSO<sub>3</sub> inhibited by 4-NP or by the reference ethanol versus initial concentrations of each inhibitor. The slope uncertainties are equal to standard errors of regression coefficients.</p> Full article ">Figure 4
<p>Correlation of rate constants for reactions of sulfate radical-anions with phenol (P), chlorophenols (green circles: 4-CP; 2,4-DCP; 2,5-DCP; 2,4,5-TCP; 2,4,6-TCP; 2,3,5,6-TTCP) [<a href="#B83-atmosphere-10-00795" class="html-bibr">83</a>] and nitrophenols (blue squares: 2-NP; 3-NP; 4-NP; 2,4-DNP; 2,4,6-TNP) [this work] against (<b>a</b>) sums of Brown substituent coefficients and (<b>b</b>) the relative strength of the O–H bond.</p> Full article ">Figure 5
<p>Total rate of 2-NP conversion in the atmosphere due the gas-phase and aqueous-phase reactions with OH radicals (<b>a</b>) or NO<sub>3</sub> radicals (<b>b</b>) compared to the rate of the aqueous-phase reaction of 2-NP with sulfate radical-anions for various ratios of radicals in the aqueous phases ([OH]<span class="html-italic"><sub>aq</sub></span>/[SO<sub>4</sub><sup>•−</sup>]<span class="html-italic"><sub>aq</sub></span>) and various liquid water contents ω (based on Equation (10)).</p> Full article ">Figure 6
<p>Rate of 2-NP conversion in the atmosphere due to the gas-phase reaction with OH radicals (<b>a</b>) or with NO<sub>3</sub> radicals (<b>b</b>) compared to the rate of the aqueous-phase reaction of 2-NP with sulfate radical-anions for various proportions of radicals in the aqueous phase [OH]<sub>aq</sub>/[SO<sub>4</sub><sup>•−</sup>]<sub>aq</sub> and varying liquid water contents (ω) (based on Equation (11)).</p> Full article ">Figure 7
<p>Comparison of the rates of aqueous-phase conversion of 2-NP due to reaction with SO<sub>4</sub><sup>•−</sup> radicals and OH radicals (blue line) or NO<sub>3</sub> radicals (red line).</p> Full article ">
18 pages, 2193 KiB  
Article
Implicit Definition of Flow Patterns in Street Canyons—Recirculation Zone—Using Exploratory Quantitative and Qualitative Methods
by Arsenios E. Chatzimichailidis, Christos D. Argyropoulos, Marc J. Assael and Konstantinos E. Kakosimos
Atmosphere 2019, 10(12), 794; https://doi.org/10.3390/atmos10120794 - 8 Dec 2019
Cited by 6 | Viewed by 3002
Abstract
Air pollution is a major health hazard for the population that increasingly lives in cities. Street-scale Air Quality Models (AQMs) are a cheap and efficient way to study air pollution and possibly provide solutions. Having to include all the complex phenomena of wind [...] Read more.
Air pollution is a major health hazard for the population that increasingly lives in cities. Street-scale Air Quality Models (AQMs) are a cheap and efficient way to study air pollution and possibly provide solutions. Having to include all the complex phenomena of wind flow between buildings, AQMs employ several parameterisations, one of which is the recirculation zone. Goal of this study is to derive an implicit or explicit definition for the recirculation zone from the flow in street canyons using computational fluid dynamics (CFD). Therefore, a CFD-Large Eddy Simulation model was employed to investigate street canyons with height to width ratio from 1 to 0.20 under perpendicular wind direction. The developed dataset was analyzed with traditional methods (vortex visualization criteria and pollutant dispersion fields), as well as clustering methods (machine learning). Combining the above analyses, it was possible to extract qualitative features that agree well with literature but most importantly to develop quantitative expressions that describe their topology. The extracted features’ topology depends strongly on the street canyon dimensions and not surprisingly is independent of the wind velocity. The developed expressions describe areas with common flow characteristics inside the canyon and thus they can be characterised as an implicit definition for the recirculation zone. Furthermore, the presented methodology can be further applied to cover more parameters such us oblique wind direction and heated-facades and more methods for data analysis. Full article
(This article belongs to the Special Issue CFD Modelling of Air Pollutant Dispersion and Inverse Source Reconstruction)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The quasi-2d street canyon geometry: (<b>a</b>) Physical geometry and boundary conditions; (<b>b</b>) x-z cross-section of the considered computational grid for canyon with <span class="html-italic">AR</span> = 0.50.</p> Full article ">Figure 2
<p>Numerical and experimental data for the unity street canyon: (<b>a</b>) average horizontal velocity (<span class="html-italic">U</span><sub>mean</sub>/<span class="html-italic">U</span><sub>ref</sub>); (<b>b</b>) average vertical velocity (<span class="html-italic">W</span><sub>mean</sub>/<span class="html-italic">U</span><sub>ref</sub>); (<b>c</b>) standard deviation of the horizontal velocity (<span class="html-italic">σ</span><sub>U</sub>/<span class="html-italic">U</span><sub>ref</sub>); (<b>d</b>) standard deviation of the vertical velocity (<span class="html-italic">σ</span><sub>W</sub>/<span class="html-italic">U</span><sub>ref</sub>).</p> Full article ">Figure 3
<p>Basic flow and dispersion statistics for the street canyons U5AR100, U5AR033 and U5AR020 (left to right): (<b>a</b>–<b>c</b>) the average dimensionless magnitude of velocity mag<span class="html-italic">U</span><sub>mean</sub>/<span class="html-italic">U</span><sub>ref</sub>; (<b>d</b>–<b>f</b>) the <span class="html-italic">λ</span><sub>2</sub> vortex identification criterion; (<b>g</b>–<b>i</b>) the normalised concentration <span class="html-italic">C</span><sub>mean</sub>/<span class="html-italic">C</span><sub>max</sub>; (<b>j</b>–<b>l</b>) the dimensionless vertical velocity <span class="html-italic">W</span><sub>mean</sub>/<span class="html-italic">U</span><sub>ref</sub>; the street canyon orientation is the same as <a href="#atmosphere-10-00794-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 4
<p>Results of the k-means clustering with four clusters, for <span class="html-italic">U</span><sub>mean</sub>/<span class="html-italic">U</span><sub>ref</sub> and <span class="html-italic">W</span><sub>mean</sub>/<span class="html-italic">U</span><sub>ref</sub>, for the street canyons U5AR100, U5AR033 and U5AR020 (left to right): (<b>a</b>–<b>c</b>) scatter plots; (<b>d</b>–<b>f</b>) contour plots; the street canyon orientation is the same as <a href="#atmosphere-10-00794-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 5
<p>The combined results of the k-means clustering for all 19 combinations of <span class="html-italic">AR</span> and reference velocity <span class="html-italic">U</span><sub>ref</sub>, using normalised lengths (<span class="html-italic">z/H</span> and <span class="html-italic">x/W</span>) with the four clusters denoted.</p> Full article ">Figure 6
<p>Selected relations between the area covered by the four regions and the <span class="html-italic">AR</span>: (<b>a</b>) <span class="html-italic">AR</span> and <span class="html-italic">REG3</span>; (<b>b</b>) <span class="html-italic">AR</span> and <span class="html-italic">REG3/REG2</span>; (<b>c</b>) <span class="html-italic">AR</span> and <span class="html-italic">REG1+REG2</span>.</p> Full article ">
15 pages, 1733 KiB  
Article
North American Winter Dipole: Observed and Simulated Changes in Circulations
by Yu-Tang Chien, S.-Y. Simon Wang, Yoshimitsu Chikamoto, Steve L. Voelker, Jonathan D. D. Meyer and Jin-Ho Yoon
Atmosphere 2019, 10(12), 793; https://doi.org/10.3390/atmos10120793 - 7 Dec 2019
Cited by 7 | Viewed by 4896
Abstract
In recent years, a pair of large-scale circulation patterns consisting of an anomalous ridge over northwestern North America and trough over northeastern North America was found to accompany extreme winter weather events such as the 2013–2015 California drought and eastern U.S. cold outbreaks. [...] Read more.
In recent years, a pair of large-scale circulation patterns consisting of an anomalous ridge over northwestern North America and trough over northeastern North America was found to accompany extreme winter weather events such as the 2013–2015 California drought and eastern U.S. cold outbreaks. Referred to as the North American winter dipole (NAWD), previous studies have found both a marked natural variability and a warming-induced amplification trend in the NAWD. In this study, we utilized multiple global reanalysis datasets and existing climate model simulations to examine the variability of the winter planetary wave patterns over North America and to better understand how it is likely to change in the future. We compared between pre- and post-1980 periods to identify changes to the circulation variations based on empirical analysis. It was found that the leading pattern of the winter planetary waves has changed, from the Pacific–North America (PNA) mode to a spatially shifted mode such as NAWD. Further, the potential influence of global warming on NAWD was examined using multiple climate model simulations. Full article
(This article belongs to the Special Issue The Impacts of Climate Change on Atmospheric Circulations)
►▼ Show Figures

Figure 1

Figure 1
<p>Empirical orthogonal function (EOF) analysis results of (<b>a</b>) 1948–1979 and (<b>b</b>) 1980–2017 with the PNA pattern superimposed and the dipole centers marked with × and + in North America. The correlation map between monthly SST anomalies and the first principle component (PC1) for (<b>c</b>) 1948–1979 and (<b>d</b>) 1980–2017.</p> Full article ">Figure 2
<p>Percent of variance explained for Δ<math display="inline"><semantics> <msub> <mi>Z</mi> <mi>E</mi> </msub> </semantics></math>250 during (<b>a</b>) 1948–1979 and (<b>b</b>) 1980–2017. Only the first four modes are presented.</p> Full article ">Figure 3
<p>First and second EOF modes of NDJF winter seasonal mean Δ<math display="inline"><semantics> <msub> <mi>Z</mi> <mi>E</mi> </msub> </semantics></math>250 for the early period (1948–1979) (<b>a</b>,<b>c</b>) and the late period (1980–2017) (<b>b</b>,<b>d</b>). The Dipole centers (marked with × and +) are superimposed with the PNA pattern (contour) as in <a href="#atmosphere-10-00793-f001" class="html-fig">Figure 1</a>. The percent of variance each EOF mode explains is listed as well.</p> Full article ">Figure 4
<p>Spatial correlation coefficients between PNA/Dipole pattern and (<b>a</b>) EOF1 (<b>b</b>) EOF2 results. EOF analysis and the spatial correlations are conducted via a five-year sliding-EOF window with three different datasets.</p> Full article ">Figure 5
<p>The regressed NDJF monthly 250-hPa Streamfunction (Gray lines for contour; interval (CI) = 1 × 10<sup>6</sup>) during (<b>a</b>) 1948–1979 and (<b>b</b>) 1980–2017. Vectors represent the associated horizontal component of the wave activity flux (m<sup>2</sup> s<sup>−2</sup>).</p> Full article ">Figure 6
<p>The 30-year running variance of the Dipole index (DPI) derived from Wang et al. (2014) by using multiplied reanalysis datasets. Gray shaded represents CESM 40-member ensemble spread, which represents two standard deviations from the ensemble mean.</p> Full article ">Figure 7
<p>The 100-year running variance of the NAWD index (DJF mean, 500 hPa; blue line) derived using paleoclimate and 20CR dataset. The green line represents the Northern Hemisphere temperature mean, with notable historical listed. The orange line represents continental reconstruction temperature derived from palynological record, with anomalies relative to the 1450–1950 A.D. mean.</p> Full article ">Figure 8
<p>The x-axis presents the middle era for: (<b>a</b>) The 30-year running correlation of the Dipole index and Climate indices. The solid lines represent that the index exceeds the significant level; the dotted lines show the index does not exceed the significant level. Gray lines indicate significant value (<span class="html-italic">p</span> &lt; 0.01). (<b>b</b>) The five-year running average for AMO and PDO index.</p> Full article ">
17 pages, 1853 KiB  
Review
The Impact of Green Roofs on the Parameters of the Environment in Urban Areas—Review
by Dariusz Suszanowicz and Alicja Kolasa Więcek
Atmosphere 2019, 10(12), 792; https://doi.org/10.3390/atmos10120792 - 7 Dec 2019
Cited by 33 | Viewed by 8695
Abstract
This study presents the results of a review of publications conducted by researchers in a variety of climates on the implementation of ‘green roofs’ and their impact on the urban environment. Features of green roofs in urban areas have been characterized by a [...] Read more.
This study presents the results of a review of publications conducted by researchers in a variety of climates on the implementation of ‘green roofs’ and their impact on the urban environment. Features of green roofs in urban areas have been characterized by a particular emphasis on: Filtration of air pollutants and oxygen production, reduction of rainwater volume discharged from roof surfaces, reduction of so-called ‘urban heat islands’, as well as improvements to roof surface insulation (including noise reduction properties). The review of the publications confirmed the necessity to conduct research to determine the coefficients of the impact of green roofs on the environment in the city centers of Central and Eastern Europe. The results presented by different authors (most often based on a single case study) differ significantly from each other, which does not allow us to choose universal coefficients for all the parameters of the green roof’s impact on the environment. The work also includes analysis of structural recommendations for the future model green roof study, which will enable pilot research into the influence of green roofs on the environment in urban agglomerations and proposes different kinds of plants for different kinds of roofs, respectively. Full article
(This article belongs to the Section Air Quality)
►▼ Show Figures

Figure 1

Figure 1
<p>Number of publications on green roofs indexed on the web of science.</p> Full article ">Figure 2
<p>Percentage of academic publications by country.</p> Full article ">Figure 3
<p>Green roof construction diagram.</p> Full article ">Figure 4
<p>An example image of a green roof model made with a thermographic camera (A—automatic temperature scale, E—emissivity).</p> Full article ">Figure 5
<p>Green roof models on a laboratory scale [<a href="#B74-atmosphere-10-00792" class="html-bibr">74</a>].</p> Full article ">
17 pages, 2502 KiB  
Article
Effect of Bulk Composition on the Heterogeneous Oxidation of Semi-Solid Atmospheric Aerosols
by Hanyu Fan and Fabien Goulay
Atmosphere 2019, 10(12), 791; https://doi.org/10.3390/atmos10120791 - 7 Dec 2019
Cited by 3 | Viewed by 3450
Abstract
The OH-initiated heterogeneous oxidation of semi-solid saccharide particles with varying bulk compositions was investigated in an atmospheric pressure flow tube at 30% relative humidity. Reactive uptake coefficients were determined from the rate loss of the saccharide reactants measured by mass spectrometry at different [...] Read more.
The OH-initiated heterogeneous oxidation of semi-solid saccharide particles with varying bulk compositions was investigated in an atmospheric pressure flow tube at 30% relative humidity. Reactive uptake coefficients were determined from the rate loss of the saccharide reactants measured by mass spectrometry at different monosaccharide (methyl-β-d-glucopyranoside, C7H14O6) and disaccharide (lactose, C12H22O11) molar ratios. The reactive uptake for the monosaccharide was found to decrease from 0.53 ± 0.10 to 0.05 ± 0.06 as the mono-to-disaccharide molar ratio changed from 8:1 to 1:1. A reaction–diffusion model was developed in order to determine the effect of chemical composition on the reactive uptake. The observed decays can be reproduced using a Vignes relationship to predict the composition dependence of the reactant diffusion coefficients. The experimental data and model results suggest that the addition of the disaccharide significantly increases the particle viscosity leading to slower mass transport phenomena from the bulk to the particle surface and to a decreased reactivity. These findings illustrate the impact of bulk composition on reactant bulk diffusivity which determines the rate-limiting step during the chemical transformation of semi-solid particles in the atmosphere. Full article
(This article belongs to the Special Issue Nanoparticles in the Atmosphere)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of the flow reactor used for particle collection and offline gas chromatography coupled to mass spectrometry (GC-MS) analysis. Saccharide particles were generated by a constant output atomizer and mixed with flows of humidified N<sub>2</sub>, O<sub>2</sub>, O<sub>3</sub>, and dry N<sub>2</sub>. A total of a 3.0 L min<sup>−1</sup> aerosol stream entered the atmospheric pressure flow tube to react with OH radicals. Hexane was injected from the bottom 1/5 of the flow tube. Upon exiting the flow tube, the OH concentration was measured by quantifying the loss of hexane tracer using gas chromatograph with flame ionization detection (GC-FID). The aerosol stream was analyzed by a scanning mobility particle sizer (SMPS) and collected by a Teflon filter.</p> Full article ">Figure 2
<p>Surface-weighted particle size distribution for unreacted equimolar saccharide particles. The mean surface-weighted diameter and the total concentration of number particle size were 218.2 nm and 3.05 × 10<sup>5</sup> cm<sup>−3</sup> for the VUV-AMS analysis sample (black, dashed line) and 366.4 nm and 2.58 × 10<sup>5</sup> cm<sup>−3</sup> for the GC-MS analysis sample (red, dashed line).</p> Full article ">Figure 3
<p>VUV-AMS spectrum of unreacted equimolar MGP–lactose aerosols recorded at 10.5 eV photoionization energy. The m/z 60, m/z 73, m/z 121, m/z 144, and m/z 163 were monitored during the kinetic measurements.</p> Full article ">Figure 4
<p>Relative abundance of the MGP reactant as a function of OH exposure in particles with MGP:lactose molar ratios of 1:1 (red, solid circles), 2:1 (black, solid squares), 4:1 (blue, solid triangles), and 8:1 (green, solid diamonds) at RH = 30%. Online analyses were performed using VUV-AMS. Also displayed is the MGP decay for MGP particles at RH = 30% (purple open triangles) [<a href="#B16-atmosphere-10-00791" class="html-bibr">16</a>]. The error bars are 2σ of the mean values. The solid lines are exponential fits to the data for OH exposures below 2 × 10<sup>12</sup> cm<sup>−3</sup> s and extrapolated to higher values (dashed lines).</p> Full article ">Figure 5
<p>Modeled (solid line) and experimental (markers) fractions of unreacted MGP remaining in the particle as a function of OH exposure for MGP:lactose molar ratios of 1:1 (red), 2:1 (black), 4:1 (blue), and 8:1 (green).</p> Full article ">Figure 6
<p>GC-MS chromatogram of silylated reacted (upper panel) and unreacted (lower panel) particles for an MGP:lactose molar ratio of 2:1 at 0.7 × 10<sup>12</sup> cm<sup>−3</sup> s OH exposure. The retention time for the internal standard xylose was 6.17 min and 6.83 min, 9.13 min for MGP, 12.79 min and 13.10 min for lactose, and 10.09 min for glucose. All the saccharides were identified with authentic samples.</p> Full article ">Figure 7
<p>Relative abundance of unreacted MGP (open circles) and lactose (filed squares) in semi-solid MGP–lactose particles at 30% RH as a function of OH exposure for MGP:lactose molar ratios of (<b>a</b>) 1:1 (red markers); (<b>b</b>) 2:1 (black markers); (<b>c</b>) 4:1 (blue markers). The offline analyses were performed using GC-MS. The error bars represent the maximum and minimum experimental values. The lines were modeled MGP (solid lines) and lactose (dashed lines) profiles using the parameters displayed in <a href="#atmosphere-10-00791-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 8
<p>Logarithm of the particle viscosity calculated using the Stock–Einstein equation for MGP:lactose molar ratios of (<b>a</b>) 1:1; (<b>b</b>) 2:1; and (<b>c</b>) 4:1 at a constant OH gas number density of 1.08 × 10<sup>10</sup> cm<sup>−3</sup>, corresponding to total OH exposure of 5 × 10<sup>11</sup> cm<sup>−3</sup> s. The top panels display the viscosity gradient near the particle surface.</p> Full article ">
11 pages, 3954 KiB  
Article
Influence of the Anthropogenic Fugitive, Combustion, and Industrial Dust on Winter Air Quality in East Asia
by Jaein I. Jeong and Rokjin J. Park
Atmosphere 2019, 10(12), 790; https://doi.org/10.3390/atmos10120790 - 7 Dec 2019
Cited by 5 | Viewed by 2990
Abstract
We estimate the effects of the anthropogenic fugitive, combustion, and industrial dust (AFCID) on winter air quality in China and South Korea for November 2015–March 2016 using the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (KU-CREATE) monthly anthropogenic emission inventory in conjunction [...] Read more.
We estimate the effects of the anthropogenic fugitive, combustion, and industrial dust (AFCID) on winter air quality in China and South Korea for November 2015–March 2016 using the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (KU-CREATE) monthly anthropogenic emission inventory in conjunction with a nested version of GEOS-Chem. Including AFCID emissions in models results in a better agreement with observations and a reduced normalized mean bias of −28% compared to −40% without AFCID. Furthermore, we find that AFCID amounts to winter PM10 concentrations of 17.9 μg m−3 (17%) in eastern China (30−40° N, 112−120° E) with the largest contribution of AFCID to winter PM10 concentrations of up to 45 μg m−3 occurring in eastern China causing a significant impact on air quality to downwind regions. Including AFCID in the model results in an increase of simulated winter PM10 concentrations in South Korea by 3.1 μg m−3 (9%), of which transboundary transport from China accounts for more than 70% of this increased PM10 concentration. Our results indicate that AFCID is an essential factor for winter PM10 concentrations over East Asia and its sources and physical characteristics need to be better quantified to improve PM air quality forecasts. Full article
(This article belongs to the Special Issue Recent Advances of Air Pollution Studies in South Korea)
►▼ Show Figures

Figure 1

Figure 1
<p>Spatial distributions of annual AFCID emissions from KU-CREATE for (<b>a</b>) 2015 and (<b>b</b>) 2016, and (<b>c</b>) time series of monthly AFCID emissions over East Asia (20−50° N, 100−140° E) for 2015 (blue line) and 2016 (red line).</p> Full article ">Figure 2
<p>Observed and simulated PM<sub>10</sub> concentrations in surface air for (<b>a</b>) without, and (<b>b</b>) with AFCID emissions over East Asia in the winter season (January−March 2016). Scatter plots of the observed and simulated surface PM<sub>10</sub> concentrations for (<b>c</b>) without, and (<b>d</b>) with AFCID emissions over China and South Korea in the winter season (January−March 2016). The observed (green) and simulated (red) mean values are shown in the upper right and left corner of each panel, respectively. The 1:1, 1:2, and 2:1 lines are inset. The correlation coefficient (R), slope, and normalized mean bias (NMB) are shown inset.</p> Full article ">Figure 3
<p>Spatial distributions of the enhancements of (<b>a</b>) concentration (μg m<sup>−3</sup>), and (<b>b</b>) percentage (%) of surface PM<sub>10</sub> due to AFCID in the winter season (November 2015−March 2016) over East Asia.</p> Full article ">Figure 4
<p>Enhancement (μg m<sup>−3</sup>) of PM<sub>10</sub> concentrations due to AFCID emissions from (<b>a</b>) all sources, (<b>b</b>) China, (<b>c</b>) the rest of the world (RoW), and (<b>d</b>) South Korea in wintertime (November 2015−March 2016) over South Korea.</p> Full article ">Figure 5
<p>Percentage enhancement (%) of PM<sub>10</sub> concentrations due to AFCID emissions from (<b>a</b>) all sources, (<b>b</b>) China, (<b>c</b>) the rest of the world (RoW), and (<b>d</b>) South Korea in wintertime (November 2015−March 2016) over South Korea.</p> Full article ">Figure 6
<p>Scatter plots of the observed and simulated surface PM<sub>2.5</sub> concentrations for (<b>a</b>) without, and (<b>b</b>) with AFCID emissions over China and South Korea in the winter season (January−March 2016). The observed (green) and simulated (red) mean values are shown in the upper right and left corner of each panel, respectively. The 1:1, 1:2, and 2:1 lines are inset. The correlation coefficient (R), slope and normalized mean bias (NMB) are shown in inset.</p> Full article ">Figure 7
<p>Scatter plots of the (<b>a</b>) observed and (<b>b</b>) simulated PM<sub>2.5</sub> versus PM<sub>10</sub> concentrations over China and South Korea in the winter season (January−March 2016). Simulated concentrations are sampled from model grids corresponding to the observation sites. (<b>c</b>) Scatter plots of the simulated PM<sub>2.5</sub> and PM<sub>10</sub> concentrations from the model with no AFCID emission. PM<sub>10</sub> (green) and PM<sub>2.5</sub> (red) mean values are shown in the upper right and left corner of each panel, respectively. The 1:1, 1:2, and 2:1 lines are inset. The correlation coefficient (R), slope and ratio between PM<sub>2.5</sub> and PM<sub>10</sub> are shown in inset.</p> Full article ">
19 pages, 6851 KiB  
Article
Aerosol from Biomass Combustion in Northern Europe: Influence of Meteorological Conditions and Air Mass History
by Jun Noda, Robert Bergström, Xiangrui Kong, Torbjörn L. Gustafsson, Borka Kovacevik, Maria Svane and Jan B. C. Pettersson
Atmosphere 2019, 10(12), 789; https://doi.org/10.3390/atmos10120789 - 6 Dec 2019
Cited by 4 | Viewed by 3850
Abstract
Alkali-containing submicron particles were measured continuously during three months, including late winter and spring seasons in Gothenburg, Sweden. The overall aims were to characterize the ambient concentrations of combustion-related aerosol particles and to address the importance of local emissions and long-range transport for [...] Read more.
Alkali-containing submicron particles were measured continuously during three months, including late winter and spring seasons in Gothenburg, Sweden. The overall aims were to characterize the ambient concentrations of combustion-related aerosol particles and to address the importance of local emissions and long-range transport for atmospheric concentrations in the urban background environment. K and Na concentrations in the particulate matter PM1 size range were measured by an Alkali aerosol mass spectrometer (Alkali-AMS) and a cluster analysis was conducted. Local meteorological conditions and trace gas and PM concentrations were also obtained for a nearby location. In addition, back trajectory analyses and chemical transport model (CTM) simulations were included for the evaluation. The Alkali-AMS cluster analysis indicated three major clusters: (1) biomass burning origin, (2) mixture of other combustion sources, and (3) marine origin. Low temperatures and low wind speed conditions correlated with high concentrations of K-containing particles, mainly owing to local and regional emissions from residential biomass combustion; transport of air masses from continental Europe also contribute to Cluster 1. The CTM results indicate that open biomass burning in the eastern parts of Europe may have contributed substantially to high PM2.5 concentrations (and to Cluster 1) during an episode in late March. According to the CTM results, the mixed cluster (2) is likely to include particles emitted from different source types and no single geographical source region seems to dominate for this cluster. The back trajectory analysis and meteorological conditions indicated that the marine origin cluster was correlated with westerly winds and high wind speed; this cluster had high concentrations of Na-containing particles, as expected for sea salt particles. Full article
(This article belongs to the Section Aerosols)
►▼ Show Figures

Figure 1

Figure 1
<p>Cluster boundaries in logarithmic scale, where the boundaries are defined based on clustering analysis of Na and K content of individual aerosol particles.</p> Full article ">Figure 2
<p>Concentrations of Na and K in submicron particles as a function of time measured by the Alkali aerosol mass spectrometer (AMS), together with temperature, and particulate matter PM<sub>10</sub>, and NOx concentrations measured at the Femman station [<a href="#B43-atmosphere-10-00789" class="html-bibr">43</a>]. Displayed values are averages over 6 min intervals.</p> Full article ">Figure 3
<p>(<b>a</b>) Na and (<b>b</b>) K concentrations as a function of wind speed for all data obtained during the measurement campaign. Error bars correspond to ±1 standard deviation.</p> Full article ">Figure 4
<p>(<b>a</b>) Na and (<b>b</b>) K concentrations as a function of wind direction (Femman station) for all data obtained during the measurement campaign.</p> Full article ">Figure 5
<p>(<b>a</b>) Na and (<b>b</b>) K concentrations as a function of temperature for all data obtained during the measurement campaign. Error bars correspond to ±1 standard deviation.</p> Full article ">Figure 6
<p>Trajectory group analysis for six air mass types reaching Gothenburg during the period from 16 February 00:00 to 21 May 00:00 mean trajectories (uppermost panel), and individual trajectories attributed to the six groupings: (1)C, (2)E, (3)N, (4)W, (5)A, and (6)P. The traces correspond to 72 h backward trajectories calculated using the HYSPLIT4 model [<a href="#B46-atmosphere-10-00789" class="html-bibr">46</a>]. New trajectories were started every 6 h and ended 500 m above ground level in Gothenburg.</p> Full article ">Figure 7
<p>Mean back trajectories based on the group analysis illustrated in <a href="#atmosphere-10-00789-f006" class="html-fig">Figure 6</a>. The six groups and percent of the total number of trajectories assigned to each group are indicated. See text for further details.</p> Full article ">Figure 8
<p>(<b>a</b>) Seventy-two hour backward trajectories for 24 March to 1 April 2007. A new trajectory was started every 12 h. (<b>b</b>) Distribution of fires from the same period. Each dot on the map indicates a fire detected with the moderate resolution imaging spectroradiometer (MODIS) aboard the Terra satellite [<a href="#B58-atmosphere-10-00789" class="html-bibr">58</a>].</p> Full article ">Figure 9
<p>Data from an episode from 24 March to 4 April; the vertical lines indicate midnight: (<b>a</b>) K and Na concentrations, (<b>b</b>) wind direction, and (<b>c</b>) wind speed.</p> Full article ">Figure 10
<p>Potassium (red lines) and sodium (blue) concentrations in Clusters 1 (upper panel), 2 (middle), and 3 (lower) during the measurement campaign.</p> Full article ">Figure 11
<p>K concentration in Clusters 1, 2, and 3 as a function of (<b>a</b>) temperature and (<b>b</b>) wind speed.</p> Full article ">Figure 12
<p>PM<sub>2.5</sub> concentrations at the urban background station Femman in Gothenburg (black line, right scale), during the period 17 Feb–20 May 2007 [<a href="#B43-atmosphere-10-00789" class="html-bibr">43</a>], and modeled concentrations of particulate carbonaceous matter (PCM) in PM<sub>2.5</sub> from the following: residential biomass combustion (grey, dashed line), open biomass burning fires (including wild fires and agricultural fires, orange line), and total biomass burning PCM (red line). Unit: μg m<sup>−3</sup>. The data were smoothed using moving 24 h mean concentrations.</p> Full article ">Figure 13
<p>Comparison of the modeled hourly concentrations of primary PM<sub>2.5</sub> from residential biomass combustion (BMC, blue line) and open fires (red line) and measured (hourly average) concentrations of K in Cluster 1 (black line). Units, model results: ng m<sup>−3</sup>; measurements: ng K m<sup>−3</sup>.</p> Full article ">Figure 14
<p>Comparison of the modeled hourly concentrations of primary PM<sub>2.5</sub> from fossil fuel combustion (red line) and measured (hourly average) concentrations of K in Cluster 2 (black line). Units, model results: µg m<sup>−3</sup>; measurements: ng K m<sup>−3</sup>.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-92
Previous Issue
Next Issue
Back to TopTop