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Volume 13
Issue 8
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Atmosphere, Volume 13, Issue 8 (August 2022) – 182 articles

Cover Story (view full-size image): The changes in the cut-off low (COL) activity are found to be driven by SST variations in the tropical Pacific. The enhanced COL activity is observed with a weakened jet stream, while COLs are suppressed with strengthened westerlies. The present-day simulations of AMIP-CMIP6 models reproduce realistic features of the ENSO–COL teleconnection, but biases exist in coupled models due to their inability to predict the mean zonal flow, which may be partly due to systematic biases in the predicted SST. The underestimation of warm SST anomalies over the eastern Pacific is a common problem in CMIP3 and CMIP5 models and remains a major uncertainty in CMIP6. The study suggests the potential for the seasonal prediction of COLs and the benefits that would result from using accurate initialization and consistent model coupling. View this paper
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21 pages, 15981 KiB  
Article
A Systematic Study of 7 MHz Greyline Propagation Using Amateur Radio Beacon Signals
by Sam Lo, Nikola Rankov, Cathryn Mitchell, Benjamin Axel Witvliet, Talini Pinto Jayawardena, Gary Bust, William Liles and Gwyn Griffiths
Atmosphere 2022, 13(8), 1340; https://doi.org/10.3390/atmos13081340 - 22 Aug 2022
Cited by 4 | Viewed by 3013
Abstract
This paper investigates 7 MHz ionospheric radio wave propagation between pairs of distant countries that simultaneously lie on the terminator. This is known as greyline propagation. Observations of amateur radio beacon transmitters recorded in the Weak Signal Propagation Reporter (WSPR) database are used [...] Read more.
This paper investigates 7 MHz ionospheric radio wave propagation between pairs of distant countries that simultaneously lie on the terminator. This is known as greyline propagation. Observations of amateur radio beacon transmitters recorded in the Weak Signal Propagation Reporter (WSPR) database are used to investigate the times of day that beacon signals were observed during the year 2017. The WSPR beacon network consists of thousands of automated beacon transmitters and observers distributed over the globe. The WSPR database is a very useful resource for radio science as it offers the date and time at which a propagation path was available between two radio stations, as well as their precise locations. This paper provides the first systematic study of grey-line propagation between New Zealand/Eastern Australia and UK/Europe. The study shows that communications were predominantly made from the United Kingdom (UK) to New Zealand at around both sunset and sunrise times, whereas from New Zealand to the UK, communication links occurred mainly during UK sunrise hours. The lack of observations at the UK sunset time was particularly evident during the UK summer. The same pattern was found in the observations of propagation from Eastern Australia to UK, and from New Zealand and Eastern Australia to Italy and the surrounding regions in Europe. The observed asymmetry in reception pattern could possibly be due to the increase in electromagnetic noise across Europe in the summer afternoon/evening from thunderstorms. Full article
(This article belongs to the Special Issue Recent Advances in Ionosphere Observation and Investigation)
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Figure 1

Figure 1
<p>Day and night region for an example from 08:00 UTC on 15th December 2017 with the sunrise terminator in yellow and the sunset terminator in red. The shaded region is the day region. The blue triangle indicates the location of the United Kingdom, and the black triangle indicates the location of New Zealand.</p> Full article ">Figure 2
<p>WSPR signal transmission example block diagram.</p> Full article ">Figure 3
<p>WSPR receiver block diagram example with SDR receiver.</p> Full article ">Figure 4
<p>Number of links received from the New Zealand transmitter ZL3TKI in 2017. The colours indicate the number of links made in each half-hour interval.</p> Full article ">Figure 5
<p>Number of global active receivers operating at 7 MHz in 2017. Only receivers with a proven capability to cover the distance from the station ZL3TKI to any radio stations were considered. The colours indicate the number of active transmitters received in each half-hour interval.</p> Full article ">Figure 6
<p>Number of active UK WSPR transmitters in 2017. Only transmitters with a proven capability to cover the distance from the UK to New Zealand and from the UK to Eastern Australia are shown. The colours indicate the number of transmitters available in each half-hour interval.</p> Full article ">Figure 7
<p>Number of active UK WSPR receivers in 2017. Only receivers with a proven capability to cover the distance from New Zealand to the UK are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 8
<p>Number of active New Zealand WSPR transmitters in 2017. Only transmitters with a proven capability to cover the distance from New Zealand to the UK are shown. The colours indicate the number of transmitters available in each half-hour interval.</p> Full article ">Figure 9
<p>Number of active New Zealand WSPR receivers in 2017. Only receivers with a proven capability to cover the distance from the UK to New Zealand are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 10
<p>Number of active Eastern Australia WSPR transmitters in 2017. Only transmitters with a proven capability to cover the distance from Eastern Australia to the UK are shown. The colours indicate the number of transmitters available in each half-hour interval.</p> Full article ">Figure 11
<p>Number of active Eastern Australia WSPR receivers in 2017. Only receivers with a proven capability to cover the distance from the UK to Eastern Australia are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 12
<p>(<b>a</b>) The 7 MHz radio links made from New Zealand to the UK in 2017. The blue shaded area is the New Zealand daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval. (<b>b</b>) Percentages of active New Zealand WSPR transmitters which have transmitted a link to the UK in 2017. Only transmitters with a proven capability to cover the distance from New Zealand to the UK were considered. The colours indicate the percentage of transmitters available in each half-hour interval. (<b>c</b>) Percentages of active UK WSPR receivers which have received a link from New Zealand in 2017. Only receivers with a proven capability to cover the distance from New Zealand to the UK were considered. The colours indicate the percentage of receivers available in each half-hour interval.</p> Full article ">Figure 12 Cont.
<p>(<b>a</b>) The 7 MHz radio links made from New Zealand to the UK in 2017. The blue shaded area is the New Zealand daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval. (<b>b</b>) Percentages of active New Zealand WSPR transmitters which have transmitted a link to the UK in 2017. Only transmitters with a proven capability to cover the distance from New Zealand to the UK were considered. The colours indicate the percentage of transmitters available in each half-hour interval. (<b>c</b>) Percentages of active UK WSPR receivers which have received a link from New Zealand in 2017. Only receivers with a proven capability to cover the distance from New Zealand to the UK were considered. The colours indicate the percentage of receivers available in each half-hour interval.</p> Full article ">Figure 13
<p>(<b>a</b>) The 7 MHz radio links made from Eastern Australia to the UK in 2017. The blue shaded area is the Australian daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval. (<b>b</b>) Percentages of active Eastern Australia WSPR transmitters which have transmitted a link to the UK in 2017. Only transmitters with a proven capability to cover the distance from Eastern Australia to the UK are shown. The colours indicate the number of receivers available in each half-hour interval. (<b>c</b>) Percentages of active UK WSPR receivers which have receivers a link from Eastern Australia in 2017. Only receivers with a proven capability to cover the distance from Eastern Australia to the UK are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 14
<p>(<b>a</b>) The 7 MHz radio links made from the UK to New Zealand in 2017. The blue shaded area is the New Zealand daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval. (<b>b</b>) Percentages of active UK WSPR transmitters which have transmitted a link to New Zealand in 2017. Only transmitters with a proven capability to cover the distance from the UK to New Zealand are shown. The colours indicate the number of receivers available in each half-hour interval. (<b>c</b>) Percentages of active New Zealand WSPR receivers which have received a link from the UK in 2017. Only receivers with a proven capability to cover the distance from the UK to New Zealand are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 15
<p>(<b>a</b>) The 7 MHz radio links made from the UK to Australia in 2017. The blue shaded area is the Australian daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval. (<b>b</b>) Percentages of active Australian WSPR receivers which have received a link from the UK in 2017. Only receivers with a proven capability to cover the distance from the UK to Australia are shown. The colours indicate the number of receivers available in each half-hour interval. (<b>c</b>) Percentages of active UK WSPR transmitters which have transmitted a link to Australia in 2017. Only transmitters with a proven capability to cover the distance from the UK to Australia are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 15 Cont.
<p>(<b>a</b>) The 7 MHz radio links made from the UK to Australia in 2017. The blue shaded area is the Australian daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval. (<b>b</b>) Percentages of active Australian WSPR receivers which have received a link from the UK in 2017. Only receivers with a proven capability to cover the distance from the UK to Australia are shown. The colours indicate the number of receivers available in each half-hour interval. (<b>c</b>) Percentages of active UK WSPR transmitters which have transmitted a link to Australia in 2017. Only transmitters with a proven capability to cover the distance from the UK to Australia are shown. The colours indicate the number of receivers available in each half-hour interval.</p> Full article ">Figure 16
<p>The 7 MHz radio links made from the Eastern Australia to Italy and the adjacent regions in 2017. The blue shaded area is the Australian daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval.</p> Full article ">Figure 17
<p>The 7 MHz radio links made from the Italy and the adjacent regions to Eastern Australia in 2017. The blue shaded area is the Australian daytime hours. The red area is the UK daytime hours. The yellow shaded area is the common daytime hours, and the white shaded area is the common night hours. The colours indicate the number of links available in each half-hour interval.</p> Full article ">Figure 18
<p>Noise level measurements from a 7 MHz radio receiver in Austria with a callsign OE9GHV. The colour bar indicates the radio noise level in dBm/Hz. The x-axis indicates the day of the year, and the y-axis indicates the time of day, from 17 October 2011 to 21 May 2022, [<a href="#B12-atmosphere-13-01340" class="html-bibr">12</a>].</p> Full article ">
15 pages, 1771 KiB  
Article
The Physical Experimental Modelling of the Formation Processes of Upward Discharges from Grounded Objects in the Artificial Thunderstorm Cell’s Electric Field
by Nikolay Lysov, Alexander Temnikov, Leonid Chernensky, Alexander Orlov, Olga Belova, Tatiana Kivshar, Dmitry Kovalev, Garry Mirzabekyan, Natalia Lebedeva and Vadim Voevodin
Atmosphere 2022, 13(8), 1339; https://doi.org/10.3390/atmos13081339 - 22 Aug 2022
Cited by 4 | Viewed by 1698
Abstract
The results of the physical modelling of the formation processes of upward discharges from grounded objects in the artificial thunderstorm cell’s electric field are presented. We established the considerable influence of the electrode tip’s radius on the pulse streamer corona stem’s parameters and, [...] Read more.
The results of the physical modelling of the formation processes of upward discharges from grounded objects in the artificial thunderstorm cell’s electric field are presented. We established the considerable influence of the electrode tip’s radius on the pulse streamer corona stem’s parameters and, subsequently, on the probability of the transformation of the impulse streamer corona first flash’s stem into a first stage of upward leader. We determined the diapason of the optimal tip radii for a lightning rod or lightning conductor, which allows for the most probable formation of the first impulse streamer corona, with the parameters providing the best conditions for the upward leader’s start, the purpose of which is the lowering of the probability of lightning striking the object under protection. A considerable difference between the electrical characteristics of the first impulse corona flash with and without the streamer–leader transition was established. It was shown that the amplitude of the streamer corona flash current impulse is considerable, but not the main defining factor of the streamer–leader transition. It is established that the charge value of the streamer corona first flash is not a threshold requirement for the formation of the upward leader from a ground object, but only defines the probability of the successful upward leader formation. Based on the analysis of the experimental data received, we suggest that there is a dependency between the probability of upward positive leader formation from the grounded objects and the charge value of the first pulse streamer corona flash for the rod (centered) and rope (elongated) lightning conductors and objects in the electric field of the thundercloud and downward lightning leader. The obtained results can be used for mathematical modelling of the formation processes of upward discharges from grounded objects in the artificial thunderstorm’s electric field, as in a natural thunderstorm situation. Full article
(This article belongs to the Special Issue Lightning Physics)
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Figure 1

Figure 1
<p>Basic scheme of the experimental measurement complex: 1—charged aerosol generator, 2—grounded electrostatic screens, 3—artificial thunderstorm cell, 4—rod or elongated model electrodes, 5–7—flat antennas, 8—spark discharges, 9—low inductive shunts, 10, 11—digital memory oscillographs, 12—a system of photoelectronic multipliers, 13—digital camera, 14—trigger generator, 15—photoelectron multiplier, 16—electron-optical camera.</p> Full article ">Figure 2
<p>Pulse streamer corona flash (<b>a</b>) without upward leader transition and (<b>b</b>) transitioning into an upward leader.</p> Full article ">Figure 3
<p>Oscillogram of the streamer corona current flowing through (<b>a</b>) the stem without transition into the upward leader (shunt 1.39 Ohm, 1 V/del, 4 µs/div) and (<b>b</b>) the stem with transition into the upward leader (shunt 1.39 Ohm, 5 V/del, 4 µs/div).</p> Full article ">Figure 4
<p>Streamer corona flash (<b>a</b>) without upward leader transition (frame duration 3.5 μs, pause between frames 0.1 μs, frame size 0.75 × 0.75 m) and (<b>b</b>) with upward leader transition (frame duration 2.5 μs, pause between frames 0.1 μs, frame size 0.75 × 0.75 m).</p> Full article ">Figure 5
<p>Probability of transition of a pulse streamer corona flash into an upward leader as a function of the tip radius of the rod electrode (radius of the tubular electrode).</p> Full article ">Figure 6
<p>Dependence of the probability of a successful streamer–leader transition (P) on the magnitude of the charge flowing through the base of a pulsed streamer corona flash Q<sub>cor</sub> (1—rod model electrodes; 2—elongated model electrodes).</p> Full article ">
18 pages, 9484 KiB  
Article
Temperature and Precipitation Bias Patterns in a Dynamical Downscaling Procedure over Europe during the Period 1951–2010
by Ioannis Stergiou, Efthimios Tagaris and Rafaella-Eleni P. Sotiropoulou
Atmosphere 2022, 13(8), 1338; https://doi.org/10.3390/atmos13081338 - 22 Aug 2022
Cited by 1 | Viewed by 1897
Abstract
The Weather Research and Forecasting (WRF) mesoscale meteorological model is used to dynamically downscale data from the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) CMIP5 version (Model E2-R) over Europe at a 0.25° grid size resolution, for the period [...] Read more.
The Weather Research and Forecasting (WRF) mesoscale meteorological model is used to dynamically downscale data from the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) CMIP5 version (Model E2-R) over Europe at a 0.25° grid size resolution, for the period of 1951 to 2010. The model configuration is single nested with grid resolutions of 0.75° to 0.25°. Two 30-year datasets are produced for the periods of 1951–1980 and 1981–2010, representing the historic and current periods, respectively. Simulated changes in climate normals are estimated and compared against the change derived from the E-OBS gridded dataset at 0.25° spatial analysis. Results indicate that the model consistently underpredicts the temperature fluctuations observed across all subregions, indicative of a colder model climatology. Winter has the strongest bias of all seasons, with the northeastern part of the domain having the highest. This is largely due to the land–atmosphere interactions. Conversely, spring and summer have the lowest regional biases, owing to a combination of low snow cover (relative to winter) and milder radiation effects (as opposed to summer). Precipitation has a negative bias in most cases, regardless of the subregion analyzed, due to the physical mechanism employed and the topographic features of each region. Both the change in the number of days when the temperature exceeds 25 °C and the change in the number of days when precipitation exceeds 5 mm/day are captured by the model reasonably well, exhibiting similar characteristics with their counterpart means. Full article
(This article belongs to the Special Issue Feature Papers in Atmosphere Science)
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Figure 1

Figure 1
<p>Domain setup (<b>a</b>) WRF model domains at 0.75° and 0.25° grid size resolutions (<b>b</b>) WRF domain map factor, (<b>c</b>) the subregions considered, where 1. BI, 2. IP, 3. FR, 4. ME, 5. SC, 6. AL, 7. MD, 8. EE.</p> Full article ">Figure 2
<p>Mean surface temperature change between current (1981–2010) and historic (1951–1980) periods (<b>a</b>) Observed (E-OBS) (<b>b</b>) Model bias (E-OBS—Model).</p> Full article ">Figure 3
<p>Seasonal mean surface temperature changes observed (E-OBS, left panels) and the related model biases (E-OBS—Model, right panels) between current and historic periods for (<b>a</b>) spring (MAM), (<b>b</b>) summer (JJA), (<b>c</b>) autumn (SON), and (<b>d</b>) winter (DJF).</p> Full article ">Figure 3 Cont.
<p>Seasonal mean surface temperature changes observed (E-OBS, left panels) and the related model biases (E-OBS—Model, right panels) between current and historic periods for (<b>a</b>) spring (MAM), (<b>b</b>) summer (JJA), (<b>c</b>) autumn (SON), and (<b>d</b>) winter (DJF).</p> Full article ">Figure 4
<p>(<b>a</b>) Change in the number of days/month with mean temperature over 25 °C observed (E-OBS) and (<b>b</b>) the related model bias (E-OBS—Model) between current and historic periods for summer (JJA).</p> Full article ">Figure 5
<p>Mean daily precipitation change between current (1981–2010) and historic (1951–1980) periods (<b>a</b>) Observed (E-OBS) (<b>b</b>) Model bias (E-OBS-model).</p> Full article ">Figure 6
<p>Seasonal mean precipitation changes observed (E-OBS, left panels) and the related model biases (E-OBS-model, right panels) between current and historic periods for (<b>a</b>) spring (MAM), (<b>b</b>) summer (JJA), (<b>c</b>) autumn (SON), and (<b>d</b>) winter (DJF).</p> Full article ">Figure 6 Cont.
<p>Seasonal mean precipitation changes observed (E-OBS, left panels) and the related model biases (E-OBS-model, right panels) between current and historic periods for (<b>a</b>) spring (MAM), (<b>b</b>) summer (JJA), (<b>c</b>) autumn (SON), and (<b>d</b>) winter (DJF).</p> Full article ">Figure 7
<p>Mean change in the number of days/month with mean precipitation over 5 mm observed (E-OBS, left panel) and the related model bias (E-OBS-model, right panel) between current (1981–2010) and historic (1951–1980) periods (<b>a</b>) Observed (E-OBS) (<b>b</b>) Model bias (ModelE2-WRF).</p> Full article ">Figure 8
<p>Mean change in the number of days/month with mean precipitation over 5 mm observed (E-OBS, left panels) and the related model biases (E-OBS-model, right panels) between current and historic periods for (<b>a</b>) spring (MAM), (<b>b</b>) summer (JJA), (<b>c</b>) autumn (SON), and (<b>d</b>) winter (DJF).</p> Full article ">
32 pages, 11435 KiB  
Article
Enhancing Capacity for Short-Term Climate Change Adaptations in Agriculture in Serbia: Development of Integrated Agrometeorological Prediction System
by Ana Vuković Vimić, Vladimir Djurdjević, Zorica Ranković-Vasić, Dragan Nikolić, Marija Ćosić, Aleksa Lipovac, Bojan Cvetković, Dunja Sotonica, Dijana Vojvodić and Mirjam Vujadinović Mandić
Atmosphere 2022, 13(8), 1337; https://doi.org/10.3390/atmos13081337 - 22 Aug 2022
Cited by 6 | Viewed by 2381
Abstract
The Integrated Agrometeorological Prediction System (IAPS) was a two-year project for the development of the long term forecast (LRF) for agricultural producers. Using LRF in decision-making, to reduce the risks and seize the opportunities, represents short-term adaptation to climate change. High-resolution ensemble forecasts [...] Read more.
The Integrated Agrometeorological Prediction System (IAPS) was a two-year project for the development of the long term forecast (LRF) for agricultural producers. Using LRF in decision-making, to reduce the risks and seize the opportunities, represents short-term adaptation to climate change. High-resolution ensemble forecasts (51 forecasts) were made for a period of 7 months and were initiated on the first day of each month. For the initial testing of the capacity of LRF to provide useful information for producers, 2017 was chosen as the test year as it had a very hot summer and severe drought, which caused significant impacts on agricultural production. LRF was very useful in predicting the variables which bear the memory of the longer period, such are growing degree days for the prediction of dates of the phenophases’ occurrences and the soil moisture of deeper soil layers as an indicator for the drought. Other project activities included field observations, communication with producers, web portal development, etc. Our results showed that the selected priority forecasting products were also identified by the producers as being the highest weather-related risks, the operational forecast implementation with the products designed for the use in agricultural production is proven to be urgent and necessary for decision-making, and required investments are affordable. The total cost of the full upgrade of agrometeorological climate services to meet current needs (including monitoring, seamless forecasting system development and the development of tools for information dissemination) was found to be about three orders of magnitude lower than the assessed losses in agricultural production in the two extreme years over the past decade. Full article
(This article belongs to the Special Issue Adaptation for Crop Production and Sustainable Agriculture in a Changing Climate (2nd Edition))
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Figure 1
<p>The location of the Republic of Serbia in Europe (marked in orange) and the domain for long range forecasts (marked with the blue line) (<b>a</b>) and the topography of the Serbian territory and locations with observed meteorological data (in blue) and phenology data (in red) (<b>b</b>).</p> Full article ">Figure 2
<p>A schematic diagram of the IAPS project activities.</p> Full article ">Figure 3
<p>Mean monthly values, averaged for the territory of Serbia, for temperature (<b>a</b>) and accumulated precipitation (<b>b</b>) for the period 2011–2020 (in orange) and for the reference period 1961–1990 (in grey).</p> Full article ">Figure 4
<p>The SPEI6a values for the period of March–August (<b>a</b>) and anomalies with respect to the values for the period of 1961–1990 for the average temperature (<b>b</b>) and the average accumulated precipitation (<b>c</b>) for the period March-August for each year in 1961–2020; in black are marked values for the years with drought in the reference period 1961–1990 and in red are marked values for years with drought in the period 2011–2020; blue dashed line is a linear trend; years with drought the ones with SPEI6a lower than −1.</p> Full article ">Figure 5
<p>Information from the questionnaire that was conducted among agricultural producers: the distribution of the farm size of the respondents (<b>a</b>); the distribution of the altitudes of the respondents’ farms (<b>b</b>); the distributions of the levels of risk of the different types of hazards for production, as assessed by the respondents (<b>c</b>).</p> Full article ">Figure 6
<p>The range of values lesser or equal to minimum value, between minimum and 25th percentile, between 25th and 75th percentile, between 75th percentile and maximum value, and higher or equal to maximum value) in which the observed value for the average summer temperature in 2017 fits: for: climate period 1961–1990 (x), climate period 1991–2020 (+), ensemble of long range forecast for 2017 initiated at 1 April 2017 (o); sign minus (−) imply that the forecasted anomaly was opposite than the observed, compared to the average values for the climate period 1961–1990; forecast skill is given for each station according to the criteria given in the text.</p> Full article ">Figure 7
<p>The observed (dots) and forecasted dates (ensemble spread: minimum, 25th percentile, median, 75th percentile and maximum) for budburst and flowering (<b>a</b>) and veraison and harvest (<b>b</b>) at the Plavinci winery in 2017; the forecasts were initiated in March (Mar) and April (Apr) 2017.</p> Full article ">Figure 8
<p>Soil moisture (m<sup>3</sup>m<sup>−3</sup>) 10-day running average, averaged for 8 locations (at lower altitudes), for 2017, from the forecast initiated on 1 April 2017, at 10 cm depth (<b>a</b>), 30 cm (<b>b</b>) and 60 cm (<b>c</b>); values are given for the ensemble forecast median values (darker colors) and for the 25th and 75th percentile (lighter colors); dashed lines represent threshold for dry soil for different soil types, as explained in the text and in <a href="#app4-atmosphere-13-01337" class="html-app">Appendix D</a>.</p> Full article ">Figure A1
<p>The results from the questionnaire: the number of respondents (%) who monitored meteorological parameters (<b>a</b>); the number of respondents (%) who had knowledge about the existence of long range forecasts (<b>b</b>); the number of respondents (%) who used different types of forecasts (<b>c</b>); the number of respondents (%) who were willing to use long range forecast information if available in an understandable form (<b>d</b>); the distribution by altitude of average grades for the damages from different types of weather hazards with total average grade of damage (<b>e</b>).</p> Full article ">Figure A2
<p>The average JJA values for the climate periods of 1961–1990 and 1991–2020, the average observed (obs) JJA value for 2017 and the median of the ensemble forecast (fcst) from April for the average JJA temperature for 2017 (red dots): the range of minimum to 25th percentile values is colored in light blue; range of 25th–75th percentile values is shaded in green; the range 75th percentile to maximum values is colored in light red; the values are given for Palic (<b>upper left</b>), Sremska Mitrovica (<b>upper right</b>), Smederevska Palanka (<b>lower left</b>) and Kraljevo (<b>lower right</b>).</p> Full article ">Figure A3
<p>Same as <a href="#atmosphere-13-01337-f0A2" class="html-fig">Figure A2</a> but for Zajecar (<b>upper left</b>), Valjevo (<b>upper right</b>), Nis (<b>lower left</b>) and Vranje (<b>lower right</b>).</p> Full article ">Figure A4
<p>Same as <a href="#atmosphere-13-01337-f0A2" class="html-fig">Figure A2</a> but for Zlatibor (<b>left</b>) and Sjenica (<b>right</b>).</p> Full article ">Figure A5
<p>The USDA soil texture classification (as in <a href="#atmosphere-13-01337-t0A3" class="html-table">Table A3</a>) showing the clay, silt and clay contents in each category; the meaning of the colors is the same as in <a href="#atmosphere-13-01337-t0A3" class="html-table">Table A3</a>. (Figure adapted from the original that was authored by Derek G. Groenendyk, Ty P.A. Ferré, Kelly R. Thorp and Amy K. Rice, named the “USDA Soil Texture Triangle”).</p> Full article ">Figure A6
<p>The ensemble medians of the 10-day running averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Palic (pal), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A7
<p>The ensemble medians of the 10-days moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Sremska Mitrovica (srm), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A8
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Smederevska Palanka (smp), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A9
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Kraljevo (kra), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A10
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Zajecar (zaj), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A11
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Valjevo (vlj), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A12
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Nis (nis), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A13
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Vranje (vra), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A14
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Zlatibor (zla), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">Figure A15
<p>The ensemble medians of the 10-day moving averages of soil moisture content (wg, in m<sup>3</sup>m<sup>−3</sup>) at different depths at Sjenica (sje), obtained from forecasts that were initiated in March (<b>a</b>), April (<b>b</b>) and May (<b>c</b>) 2017.</p> Full article ">
14 pages, 1813 KiB  
Article
Potential Yield of World Rice under Global Warming Based on the ARIMA-TR Model
by Chengzhi Cai, Hongyan Yang, Lin Zhang and Wenfang Cao
Atmosphere 2022, 13(8), 1336; https://doi.org/10.3390/atmos13081336 - 22 Aug 2022
Cited by 5 | Viewed by 1920
Abstract
As one of two most important cereals in the world, and with the continuous increase in population and demand for food consumption worldwide, rice has been attracting researchers’ attention for improving its potential yield in the future, particularly as it relates to climate [...] Read more.
As one of two most important cereals in the world, and with the continuous increase in population and demand for food consumption worldwide, rice has been attracting researchers’ attention for improving its potential yield in the future, particularly as it relates to climate change. However, what will be the potential limit of world rice yield in the future, and how does global warming affect the yield of world rice? Therefore, analyzing the potential yield of world rice affected by global warming is of great significance to direct crop production worldwide in the future. However, by far, most modeled estimations of rice yield are based on the principle of production function from static biological dimension and at local or regional levels, whereas few are based on a time series model from a dynamic evolutionary angle and on global scale. Thus, in this paper, both average and top (national) yields of world rice by 2030 are projected creatively using the Auto-regressive Integrated Moving Average and Trend Regression (ARIMA-TR) model and based on historic yields since 1961; in addition, the impact of global warming on the yields of world rice is analyzed using a binary regression model in which global mean temperature is treated as the independent variable whereas the yield is expressed as the dependent variable. Our study concludes that between 2021 and 2030, the average yield of world rice is projected to be from 4835 kg/ha to 5195 kg/ha, the top yield from 10,127 kg/ha to 10,269 kg/ha, or the average yield ranging from 47.74% to 50.59% of the top yield. From 1961 to 2020, through to2030, global warming will exert a negative impact on the average yield of world rice less than that of the top yield, which partly drives the gap between these two yields and gradually narrowed; for world rice by 2030, the opportunities for improving global production should be dependent on both high and low yield countries as the average yield is approaching the turning point of an S-shaped curve in the long-term trend. These insights provide the academic circle with innovative comprehension of world rice yield and its biological evolution for global food security relating to global warming in the future. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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<p>Global mean temperature (°C), average and top yields (kg/ha) of world rice from 1961 to 2020. Source: <a href="https://www.ncdc.noaa.gov/temp-and-precip/" target="_blank">https://www.ncdc.noaa.gov/temp-and-precip/</a> (accessed on 1 March 2022); <a href="http://www.fao.org/faostat/en/#data" target="_blank">http://www.fao.org/faostat/en/#data</a> (accessed on 1 March 2022).</p> Full article ">Figure 2
<p>Test results of series’ ACF and PACF for average and top yields of world rice and global mean temperature from 1961 to 2020.</p> Full article ">Figure 3
<p>Distribution of top (national) yields of world rice from 1961 to 2020.</p> Full article ">Figure 4
<p>Average and top yields of world rice from 1961 to 2020 and up to 2030.</p> Full article ">
21 pages, 8461 KiB  
Article
A Success Story in Controlling Sand and Dust Storms Hotspots in the Middle East
by Ali Al-Dousari, Ali Omar, Ali Al-Hemoud, Abdulaziz Aba, Majid Alrashedi, Mohamad Alrawi, Alireza Rashki, Peter Petrov, Modi Ahmed, Noor Al-Dousari, Omar Baloshi, Meshael Jarba, Ala Esmail, Abeer Alsaleh and Teena William
Atmosphere 2022, 13(8), 1335; https://doi.org/10.3390/atmos13081335 - 22 Aug 2022
Cited by 17 | Viewed by 3384
Abstract
Using 30 years of satellite observations, two sand and dust storms (SDS) source locations (hotspots) were detected on the southern side of the Mesopotamian Flood Plain. Around 40 million people in the region are affected by the two hotspots, including populations in Iraq, [...] Read more.
Using 30 years of satellite observations, two sand and dust storms (SDS) source locations (hotspots) were detected on the southern side of the Mesopotamian Flood Plain. Around 40 million people in the region are affected by the two hotspots, including populations in Iraq, Iran, Kuwait, Saudi Arabia, Qatar, Bahrain, and Emirates. Both hotspots encompass roughly 8212 km2 and contribute 11% to 85% in 2005 and 2021, respectively, of the total SDS in the region. Dust physical (particle surface area and size percentages) and chemical (mineralogy, major and trace elements, and radionuclides) properties show close similarities between source and downwind samples during SDS originated solely from the two hotspots. Deposited dust size particles show a finning trend towards the north in the Middle East compared to the south. A comprehensive assessment of the chemical and physical properties of soil and dust samples was conducted as an essential step in developing and implementing a mitigation plan in order to establish a success story in reducing SDS, improving air quality, and benefiting the gulf countries and neighboring regions. Full article
(This article belongs to the Special Issue Sand and Dust Storms: Impact and Mitigation Methods)
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<p>Recent two MODIS images showing the two sand and dust storms hotspots with wind rose from Kuwait City (2000–2020).</p> Full article ">Figure 2
<p>(DEM), SRTM 30meters, USGS (<b>a</b>) and soil map (<b>b</b>).</p> Full article ">Figure 3
<p>The hotspot area-A sediments are composed of silty sand and bare vegetation.</p> Full article ">Figure 4
<p>Deposited dust sampling locations in the Middle East.</p> Full article ">Figure 5
<p>Sorting record of SDS in reference to sand and dust storms width (3 main form types containing 12 sub-forms).</p> Full article ">Figure 6
<p>The mean volume depolarization for all dust layers detected below 8 km (<b>a</b>) from June 2006 to April 2019, night and day for the indicated regions (<b>b</b>,<b>c</b>).</p> Full article ">Figure 7
<p>CALIPSO observation on 8 July 2019, at night of a small arrow shape curved SDS type from the area (A) towards Kuwait and the northern Arabian Gulf (<b>a</b>) and the vertical feature mask shows that the dust extended from the surface to a height of ~6 km near Kuwait City (<b>b</b>).</p> Full article ">Figure 8
<p>Kuwait annual deposited dust amounts in (tons/km<sup>2</sup>) for two years from the 1st of September to the 31st of August (<b>a</b>,<b>b</b>).</p> Full article ">Figure 9
<p>The two SDS hotspots’ contribution percentages in the Middle East as a single source or combined with other sources (2003–2021).</p> Full article ">Figure 10
<p>Dust surface concentration image from the Barcelona Dust Forecast Center showing that the SDS on 23 August 2018 originated from the two hotspots affecting the Arabian Gulf and east Arabia.</p> Full article ">Figure 11
<p>The two studied hotspot areas using Corona Satellite imagery, on 5 May 1968, and Landsat Satellite imagery, on 1984, 1991, 2000, 2013, and 2020.</p> Full article ">Figure 12
<p>Changes series total area of the two SDS hotspots during 1968, 1984, 1991, 2000, 2013, and 2020.</p> Full article ">Figure 13
<p>Average major oxides (<b>a</b>) and trace elements (<b>b</b>) for the source area (mid area A) compared to dust collected from upwind (west and north area A) and far downwind (Kuwait and Ahwaz in Iran).</p> Full article ">Figure 14
<p>The average concentrations of the radionuclides in dust samples.</p> Full article ">Figure 15
<p>Multivariate analysis of the radionuclide concentrations (<b>a</b>) and their locations (<b>b</b>) in dust samples.</p> Full article ">Figure 16
<p>Average histogram and statistical parameters from surface sediments of the hotspot (A), with 32% composed of very fine sand and mud (silt and clay).</p> Full article ">Figure 17
<p>A trimodal AOT/Angstrom for sand and dust storms (SDS) of three combined sources, mostly from the Mesopotamian Flood Plain (<b>a</b>), from the two hotspots (<b>b</b>), characterized by a high peak at 0.25 μm, and the Western Desert of Iraq (<b>c</b>,<b>d</b>).</p> Full article ">Figure 18
<p>Temporal variations of grain size percentages of dust in Kuwait within the coastal (<b>a</b>) and the desert (<b>b</b>) areas downwind from the two SDS hotspots. (V.C.S.: Very Coarse sand; C.S.: Coarse sand; M.S.: Medium sand; F.S.: Fine sand; V.F.S: Very fine sand; V.C.Silt.: Very coarse silt; C. Silt: Coarse silt; M.Silt: Medium silt; F.Silt: Fine silt; V.F.Silt: Very fine silt).</p> Full article ">Figure 19
<p>The dust BET surface area in the hotspot (A) compared to up- and downwind collected dust samples.</p> Full article ">Figure 20
<p>The general plan for draining channels from main streams and rivers for SDS hotspot (A).</p> Full article ">
13 pages, 5930 KiB  
Article
Modeling PM2.5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm
by João Gregório, Carla Gouveia-Caridade and Pedro J. S. B. Caridade
Atmosphere 2022, 13(8), 1334; https://doi.org/10.3390/atmos13081334 - 22 Aug 2022
Cited by 14 | Viewed by 3764
Abstract
The machine learning algorithm based on multiple-input multiple-output linear regression models has been developed to describe PM2.5 and PM10 concentrations over time. The algorithm is fact-acting and allows for speedy forecasts without requiring demanding computational power. It is also simple enough that it [...] Read more.
The machine learning algorithm based on multiple-input multiple-output linear regression models has been developed to describe PM2.5 and PM10 concentrations over time. The algorithm is fact-acting and allows for speedy forecasts without requiring demanding computational power. It is also simple enough that it can self-update by introducing a recursive step that utilizes newly measured values and forecasts to continue to improve itself. Starting from raw data, pre-processing methods have been used to verify the stationary data by employing the Dickey–Fuller test. For comparison, weekly and monthly decompositions have been achieved by using Savitzky–Golay polynomial filters. The presented algorithm is shown to have accuracies of 30% for PM2.5 and 26% for PM10 for a forecasting horizon of 24 h with a quarter-hourly data acquisition resolution, matching other results obtained using more computationally demanding approaches, such as neural networks. We show the feasibility of using multivariate linear regression (together with the small real-time computational costs for the training and testing procedures) to forecast particulate matter air pollutants and avoid environmental threats in real conditions. Full article
(This article belongs to the Special Issue Air Quality Prediction and Modeling)
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<p>Data processing flowchart and algorithm application.</p> Full article ">Figure 2
<p>Dispersion, concentration, and error over time dependencies for the third test iteration of forecasting without seasonality decomposition. Dispersion plots: <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.933</mn> </mrow> </semantics></math> for PM10 and <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.920</mn> </mrow> </semantics></math> for PM2.5.</p> Full article ">Figure 3
<p>Dispersion, concentration, and errors over time dependencies for the third test iteration of forecasting without seasonality decomposition with weekly seasonality decomposition. Dispersion plots: <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.917</mn> </mrow> </semantics></math> for PM10 and <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.909</mn> </mrow> </semantics></math> PM2.5.</p> Full article ">Figure 4
<p>Dispersion, concentration, and errors over time dependencies for the third test iteration of forecasting without seasonality decomposition with monthly seasonality decomposition. Dispersion plots: <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.932</mn> </mrow> </semantics></math> for PM10 and <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.917</mn> </mrow> </semantics></math> PM2.5.</p> Full article ">
9 pages, 1631 KiB  
Communication
Network Theory to Reveal Ionospheric Anomalies over North America and Australia
by Ilya V. Zhivetiev and Yury V. Yasyukevich
Atmosphere 2022, 13(8), 1333; https://doi.org/10.3390/atmos13081333 - 22 Aug 2022
Cited by 1 | Viewed by 2415
Abstract
There are significant challenges to model the ionosphere due to different anomalies, especially under the increasing requirements for precision level. We used network theory to construct an ionospheric network analysis based on the data of global ionospheric maps for the period from 1998 [...] Read more.
There are significant challenges to model the ionosphere due to different anomalies, especially under the increasing requirements for precision level. We used network theory to construct an ionospheric network analysis based on the data of global ionospheric maps for the period from 1998 to 2015. The network approach revealed different domains in the ionosphere. Besides the well-known equatorial anomaly, we revealed two more essential areas with “anomalous” behavior in the total electron content (TEC). Both anomalies are located at mid-latitudes: the first over most of North America, and the second one over the southeast part of Australia and the adjacent part of the Indian Ocean. The revealed areas partly coincide with the winter anomaly regions. Our results demonstrate that complex ionosphere/magnetic field/neutral atmosphere interaction can result in atypical ionosphere dynamics in huge areas. Full article
(This article belongs to the Special Issue Feature Papers in Atmosphere Science)
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<p>Global ionospheric map of TEC based on CODE data for 1 January 2008, at 16:00 UT. Dashed line indicates geomagnetic equator and geomagnetic parallels, triangles are GPS/GLONASS stations used to create the map.</p> Full article ">Figure 2
<p>Distribution of maxima <span class="html-italic">r<sub>max</sub></span> of cross-correlation functions calculated for the series of deviations of mean daily TEC values from the mean annual TEC <span class="html-italic">dI</span> for the period from 1998 to 2015; the shaded area indicates the values where <span class="html-italic">r<sub>max</sub></span> is higher than the threshold <span class="html-italic">A</span> = 0.86.</p> Full article ">Figure 3
<p>Ionospheric network. Color indicates the degree—how many links each node υ of the network has. Dashed lines on both panels indicate the geomagnetic equator. A large white circle is an example of a node in the chosen region, and small black circles are the nodes directly linked with it (so-called neighbors). Panel (<b>a</b>) shows nodes (−75° E, −42.5° N) and (50° E, 52° N) with their neighbors in the regions of the North American and Australian anomalies. Panel (<b>b</b>) shows node (−155° E, 0° N) in the equatorial region, node (−85° E, 45° N) in the northern hemisphere and node (90° E, −45°N) in the southern hemisphere with their neighbors.</p> Full article ">Figure 4
<p>Comparison of ionospheric network based on CODE data (<b>a</b>) and the winter anomaly intensity in TEC (<b>b</b>). In panel (<b>a</b>), color shows the degree (<span class="html-italic">deg</span>, how many links the node has) for each node <span class="html-italic">v</span> of the network. In panel (<b>b</b>), the color shows the winter anomaly intensity (winter-to-summer TEC) at high solar activity (F10.7 = 200 s.f.u.) and moderate geomagnetic activity. Panel (<b>b</b>) was re-drawn from <a href="#atmosphere-13-01333-f004" class="html-fig">Figure 4</a>f in [<a href="#B31-atmosphere-13-01333" class="html-bibr">31</a>]. Dashed lines show the geomagnetic equator.</p> Full article ">
13 pages, 1583 KiB  
Article
Early Night Fog Prediction Using Liquid Water Content Measurement in the Monterey Bay Area
by Steven Kim, Conor Rickard, Julio Hernandez-Vazquez and Daniel Fernandez
Atmosphere 2022, 13(8), 1332; https://doi.org/10.3390/atmos13081332 - 22 Aug 2022
Cited by 2 | Viewed by 1939
Abstract
Fog is challenging to predict, and the accuracy of fog prediction may depend on location and time of day. Furthermore, accurate detection of fog is difficult, since, historically, it is often carried out based on visual observations which can be biased and are [...] Read more.
Fog is challenging to predict, and the accuracy of fog prediction may depend on location and time of day. Furthermore, accurate detection of fog is difficult, since, historically, it is often carried out based on visual observations which can be biased and are often not very frequent. Furthermore, visual observations are more challenging to make during the night. To overcome these limitations, we detected fog using FM-120 instruments, which continuously measured liquid water content in the air in the Monterey, California (USA), area. We used and compared the prediction performance of logistic regression (LR) and random forest (RF) models each evening between 5 pm and 9 pm, which is often the time when advection fog is generated in this coastal region. The relative performances of the models depended on the hours between 5 pm and 9 pm, and the two models often generated different predictions. In such cases, a consensus approach was considered by revisiting the past performance of each model and weighting more heavily the more trustworthy model for a given hour. The LR resulted in a higher sensitivity (hit rate) than the RF model early in the evening, but the overall performance of the RF was usually better than that of the LR. The consensus approach provided more robust prediction performance (closer to a better accuracy level between the two methods). It was difficult to conclude which of the LR and RF models was superior consistently, and the consensus approach provided robustness in 3 and 2 h forecasts. Full article
(This article belongs to the Special Issue Decision Support System for Fog)
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<p>Observed daily trends (gray) and smoothing splines for overall average (black): liquid water content (LWC), temperature (T), dew point depression (DPD), wind speed (WS), wind direction (WD), shortwave (SW), and longwave (LW), observed from 29 July to 6 November 2020. Solid curves are average trends; dotted curves are the first and third quartiles.</p> Full article ">Figure 2
<p>The daily trend of LWC (<b>left</b>) and the observed distribution of LWC(t) from t = 17 to t = 21 (<b>right</b>), observed from 29 July to 6 November 2020. The red line represents the threshold above which fog is defined to be present. This paper used a slightly lower threshold of log(LWC) = −2.5.</p> Full article ">Figure 3
<p>CSI of LR1, RF, and the consensus prediction; CSI = TP/(TP + FP + FN).</p> Full article ">
9 pages, 2175 KiB  
Article
Electric Field Variations Caused by Low, Middle and High-Altitude Clouds over the Negev Desert, Israel
by Roy Yaniv and Yoav Yair
Atmosphere 2022, 13(8), 1331; https://doi.org/10.3390/atmos13081331 - 21 Aug 2022
Cited by 4 | Viewed by 1820
Abstract
Ground-based measurements of the electric field from a station located in the arid Negev region of southern Israel have been conducted continuously since 2013. We present here results of observations of the electric field (Potential Gradient, PG) variability during 22 cloudy days, with [...] Read more.
Ground-based measurements of the electric field from a station located in the arid Negev region of southern Israel have been conducted continuously since 2013. We present here results of observations of the electric field (Potential Gradient, PG) variability during 22 cloudy days, with varying cloud types and cloud base heights, and compare the measured values with the mean fair-weather PG. The results show an increase of PG (~+10 to +70 V m−1) from mean fair weather values during times of low clouds. During times of mid-altitude (alto) clouds or during a superposition of low and high clouds, there were small departures in the PG values (~0 to −30 V m−1) compared to mean fair weather PG values. During times of high-altitude cirrus clouds there is a clear decrease of the PG (~−40 to −90 V m−1). The data was compared with the Israeli meteorological service cloud data and with MODIS 7 satellite cloud top height maps. In addition, AERONET aerosol optical depth values and wind speed magnitude from a local meteorological station were analyzed. Full article
(This article belongs to the Special Issue Atmospheric Electricity)
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<p>Data for two case studies: 4 January 2018 (<b>top</b>) and 6 February 2018 (<b>Bottom</b>). In each row: (<b>left</b>) Satellite image of clouds above Israel in visible light. (<b>center</b>) Measured cloud top height by Terra MODIS with colors indicating approximate height. (<b>right</b>) Observed PG values at ground level in Mitzpe Ramon.</p> Full article ">Figure 2
<p>The height of clouds and their effect on the measured ground level PG. Circles are single layer of clouds while the triangles are a superposition of two or three types of clouds and are the average height. (for example, a presence of a ~10 km high cirrus cloud together with a ~1500 m stratus layer is indicated as a single combined height of 5750 m).</p> Full article ">Figure 3
<p>The variability of PG for different types of clouds at Mitzpe Ramon. Variability is expressed in terms of the Inter-Quartile Range and is scaled to one standard deviation for a normal distribution.</p> Full article ">Figure 4
<p>Mean Aerosol Optical Depth values [nm] for 19 out of 22 days and the 0.17–0.3 range of AOD mean fair weather values from previous studies [<a href="#B10-atmosphere-13-01331" class="html-bibr">10</a>,<a href="#B20-atmosphere-13-01331" class="html-bibr">20</a>].</p> Full article ">Figure 5
<p>The vertical electric field (<b>top</b>) and wind speed results (<b>bottom</b>) for 3 events. 15 February 2014 (<b>left</b>), 16 February 2014 (<b>middle</b>) and 14 December 2016 (<b>right</b>). High values were recorded during short periods of light rain.</p> Full article ">Figure 5 Cont.
<p>The vertical electric field (<b>top</b>) and wind speed results (<b>bottom</b>) for 3 events. 15 February 2014 (<b>left</b>), 16 February 2014 (<b>middle</b>) and 14 December 2016 (<b>right</b>). High values were recorded during short periods of light rain.</p> Full article ">
12 pages, 642 KiB  
Article
Statistical Associations between Geomagnetic Activity, Solar Wind, Cosmic Ray Intensity, and Heart Rate Variability in Patients after Open-Heart Surgery
by Jone Vencloviene, Margarita Beresnevaite, Sonata Cerkauskaite, Nijole Ragaisyte, Rugile Pilviniene and Rimantas Benetis
Atmosphere 2022, 13(8), 1330; https://doi.org/10.3390/atmos13081330 - 21 Aug 2022
Cited by 1 | Viewed by 2890
Abstract
The aim of this study was to identify associations of the parameters of heart rate variability (HRV) with the variations in geomagnetic activity (GMA), solar wind, and cosmic ray intensity (CRI) in patients after coronary artery bypass grafting or valve surgery in Kaunas, [...] Read more.
The aim of this study was to identify associations of the parameters of heart rate variability (HRV) with the variations in geomagnetic activity (GMA), solar wind, and cosmic ray intensity (CRI) in patients after coronary artery bypass grafting or valve surgery in Kaunas, Lithuania, during 2008–2012. The data from 5-minute electrocardiograms (ECGs) in 220 patients were used. ECGs were carried out at 1.5 months, 1 year, and 2 years after the surgery (N = 495). A lower (higher) very-low-frequency-band (VLF) and a higher (lower) high-frequency band (HF) in normalised units (n.u.) were associated with a low maximal daily 3-hourly ap (the DST index > 1). A lower mean standard deviation of beat-to-beat intervals (SDNN) and VLF, LF, and HF powers were lower in patients when Ap < 8 occurred two days after the surgery, and a low solar wind speed (SWS) occurred two days before the ECG. The effect of CRI was non-significant if the linear trend was included in the model. Low GMA and a low SWS may effect some HRV variables in patients after open-heart surgery. The GMA during the surgery may affect the SDNN in short-term ECG during the longer period. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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<p>The distribution of GMA levels (<b>a</b>) and the mean values ± SE of solar wind speed (<b>b</b>).</p> Full article ">Figure 2
<p>The effects of Ap, local <span class="html-italic">A</span>, and DST on the previous day (<b>a</b>,<b>b</b>), and SWS on the day of the ECG (<b>a</b>,<b>c</b>) quartiles on HRV variables (β coefficients with 95% confidence interval adjusting for the linear trend and month).</p> Full article ">
18 pages, 7249 KiB  
Article
Microphysical and Kinematic Characteristics of Anomalous Charge Structure Thunderstorms in Cordoba, Argentina
by Bruno Medina, Lawrence Carey, Wiebke Deierling and Timothy Lang
Atmosphere 2022, 13(8), 1329; https://doi.org/10.3390/atmos13081329 - 21 Aug 2022
Cited by 1 | Viewed by 2239
Abstract
Some thunderstorms in Cordoba, Argentina, present a charge structure with an enhanced low-level positive charge layer, and practically nonexistent upper-level positive charge. Storms with these characteristics are uncommon in the United States, even when considering regions with a high frequency of anomalous charge [...] Read more.
Some thunderstorms in Cordoba, Argentina, present a charge structure with an enhanced low-level positive charge layer, and practically nonexistent upper-level positive charge. Storms with these characteristics are uncommon in the United States, even when considering regions with a high frequency of anomalous charge structure storms such as Colorado. In this study, we explored the microphysical and kinematic conditions inferred by radar that led to storms with this unique low-level anomalous charge structure in Argentina, and compared them to conditions conducive for anomalous and normal charge structures. As high liquid water contents in the mixed-phase layer lead to positive charging of graupel and anomalous storms through the non-inductive charging mechanism, we explored radar parameters hypothesized to be associated with large cloud supercooled liquid water contents in the mixed-phase layer and anomalous storms, such as mass and volume of hail and high-density graupel, large reflectivity associated with the growth of rimed precipitation to hail size, and parameters that are proxies for strong updrafts such as echo-top and Zdr column heights. We found that anomalous storms had higher values of mass and volume of hail in multiple sub-layers of the mixed-phase zone and higher frequency of high reflectivity values. Low-level anomalous events had higher hail mass in the lower portion of the mixed-phase zone when compared to normal events. Weaker updraft proxies were found for low-level anomalous events due to the shallow nature of these events while there was no distinction between the updraft proxies of normal and anomalous storms. Full article
(This article belongs to the Special Issue Advances in Atmospheric Electricity)
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<p>Charge layers estimated from lightning flashes using the Chargepol method for (<b>a</b>) a normal charge structure event on 17 December 2018 from 174503 to 175124 UTC, (<b>c</b>) an anomalous event on 14 December 2018 from 001504 to 002124 UTC, and (<b>e</b>) a low-level anomalous event on 5 December 2018 from 193003 to 193623 UTC. Each red (blue) vertical line represents a positive (negative) charge layer estimated from a lightning flash. (<b>b</b>,<b>d</b>,<b>f</b>) Histograms of probability density for positive (red) and negative (blue) charge layers detected from flashes for each storm. The 0 °C and the −10 °C isotherm heights are displayed in the histogram plots.</p> Full article ">Figure 2
<p>Mass of hail values in kg and box plots for low-level anomalous (green), anomalous (blue), and normal (red) storms for (<b>a</b>) 0 °C to −10 °C, (<b>b</b>) 0 °C to −20 °C, (<b>c</b>) above the height of the −20 °C isotherm, and (<b>d</b>) above the height of the 0 °C isotherm. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value.</p> Full article ">Figure 3
<p>Mass of high-density graupel and hail in kg and box plots for low-level anomalous (green), anomalous (blue), and normal (red) storms for (<b>a</b>) 0 °C to −10 °C, (<b>b</b>) 0 °C to −20 °C, (<b>c</b>) above the height of the −20 °C isotherm, and (<b>d</b>) above the height of the 0 °C isotherm. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value.</p> Full article ">Figure 4
<p>Fraction of mass of hail in relation to total rimer mass and box plots for low-level anomalous (green), anomalous (blue), and normal (red) storms for (<b>a</b>) 0 °C to −10 °C, (<b>b</b>) 0 °C to −20 °C, (<b>c</b>) above the height of the −20 °C isotherm, and (<b>d</b>) above the height of the 0 °C isotherm. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value.</p> Full article ">Figure 5
<p>Fraction of mass of high-density graupel and hail in relation to total rimer mass and box plots for low-level anomalous (green), anomalous (blue), and normal (red) storms for (<b>a</b>) 0 °C to −10 °C, (<b>b</b>) 0 °C to −20 °C, (<b>c</b>) above the height of the −20 °C isotherm, and (<b>d</b>) above the height of the 0 °C isotherm. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value.</p> Full article ">Figure 6
<p>Fraction of volume of hail in relation to total rimer volume and box plots for low-level anomalous (green), anomalous (blue), and normal (red) storms for (<b>a</b>) 0 °C to −10 °C, (<b>b</b>) 0 °C to −20 °C, (<b>c</b>) above the height of the −20 °C isotherm, and (<b>d</b>) above the height of the 0 °C isotherm. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value.</p> Full article ">Figure 7
<p>Fraction of volume of high-density graupel and hail in relation to total rimer volume and box plots for low-level anomalous (green), anomalous (blue), and normal (red) storms for (<b>a</b>) 0 °C to −10 °C, (<b>b</b>) 0 °C to −20 °C, (<b>c</b>) above the height of the −20 °C isotherm, and (<b>d</b>) above the height of the 0 °C isotherm. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value.</p> Full article ">Figure 8
<p>Average CFADs for (<b>a</b>) LLA, (<b>b</b>) anomalous, and (<b>c</b>) normal events. White contour lines show the difference between events equal to zero (compare it with features shown in <a href="#atmosphere-13-01329-f009" class="html-fig">Figure 9</a>).</p> Full article ">Figure 9
<p>Average CFAD differences between (<b>a</b>) LLA and anomalous, (<b>b</b>) anomalous and normal, and (<b>c</b>) normal and LLA.</p> Full article ">Figure 10
<p>Values and box plots for LLA (green), anomalous (blue), and normal (red) storms for (<b>a</b>) echo top height of 20 dBZ, (<b>b</b>) echo top height of 30 dBZ, and (<b>c</b>) maximum altitude of a Z<sub>dr</sub> column above 0 °C, all units in km. Mean values are shown as gray dashed lines, and median values as gray horizontal continuous lines along with its numerical value. Size of Xs is proportional to the number of observations, with the smallest size being one and largest size being eight observations.</p> Full article ">Figure 11
<p>Schematic figure with main regions of charge (+ and − signs), hydrometeors, isotherm heights, and updrafts (arrows) for normal, anomalous, and low-level anomalous charge structure storms.</p> Full article ">
17 pages, 4361 KiB  
Article
Relationship between Land Use and Spatial Variability of Atmospheric Brown Carbon and Black Carbon Aerosols in Amazonia
by Fernando G. Morais, Marco A. Franco, Rafael Palácios, Luiz A. T. Machado, Luciana V. Rizzo, Henrique M. J. Barbosa, Fabio Jorge, Joel S. Schafer, Brent N. Holben, Eduardo Landulfo and Paulo Artaxo
Atmosphere 2022, 13(8), 1328; https://doi.org/10.3390/atmos13081328 - 21 Aug 2022
Cited by 12 | Viewed by 3624
Abstract
The aerosol radiative effect is an important source of uncertainty in estimating the anthropogenic impact of global climate change. One of the main open questions is the role of radiation absorption by aerosols and its relation to land use worldwide, particularly in the [...] Read more.
The aerosol radiative effect is an important source of uncertainty in estimating the anthropogenic impact of global climate change. One of the main open questions is the role of radiation absorption by aerosols and its relation to land use worldwide, particularly in the Amazon Rainforest. Using AERONET (Aerosol Robotic Network) long-term measurements of aerosol optical depth (AOD) at a wavelength of 500 nm and absorption AOD (AAOD) at wavelengths of 440, 675, and 870 nm, we estimated the fraction and seasonality of the black carbon (BC) and brown carbon (BrC) contributions to absorption at 440 nm. This was conducted at six Amazonian sites, from central Amazon (Manaus and the Amazon Tall Tower Observatory—ATTO) to the deforestation arc (Rio Branco, Cuiabá, Ji-Paraná, and Alta Floresta). In addition, land use and cover data from the MapBiomas collection 6.0 was used to access the land transformation from forest to agricultural areas on each site. The results showed, for the first time, important geographical and seasonal variability in the aerosol optical properties, particularly the BC and BrC contributions. We observed a clear separation between dry and wet seasons, with BrC consistently accounting for an average of approximately 12% of the aerosol AAOD at 440 nm in the deforestation arc. In central Amazon, the contribution of BrC was approximately 25%. A direct relationship between the reduction in forests and the increase in the area dedicated to agriculture was detected. Moreover, places with lower fractions of forest had a smaller fraction of BrC, and regions with higher fractions of agricultural areas presented higher fractions of BC. Therefore, significant changes in AOD and AAOD are likely related to land-use transformations and biomass burning emissions, mainly during the dry season. The effects of land use change could introduce differences in the radiative balance in the different Amazonian regions. The analyses presented in this study allow a better understanding of the role of aerosol emissions from the Amazon Rainforest that could have global impacts. Full article
(This article belongs to the Special Issue Aerosols and Particulate Matters in the Southern Hemisphere)
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<p>Time series from 1999 to 2021 of aerosol optical depth (AOD) at 500 nm at the six Amazonian sites. The strong seasonality is evident from the wet to dry season for all sites.</p> Full article ">Figure 2
<p>The time series of the AAOD at 440 nm for BrC and BC in Alta Floresta.</p> Full article ">Figure 3
<p>Spatial distribution of total BC and BrC fractions (here, considering the whole time series for each site), where in yellow is the portion of total BC and in blue the percentage of total BrC, obtained from the AAOD at 440 nm.</p> Full article ">Figure 4
<p>Ångström matrices were obtained with the AERONET network photometer data for the sites along the deforestation arc.</p> Full article ">Figure 5
<p>Ångström matrices obtained with sun photometer data of the AERONET network for the two sites in central Amazon.</p> Full article ">Figure 6
<p>Land-use comparison between 1985 (<b>left</b>) and 2020 (<b>right</b>) for the legal Amazon. Deforestation and conversion of forest areas into agricultural and livestock regions is a strong mark—images obtained from the MapBiomas platform.</p> Full article ">Figure 7
<p>Time series of forest (green) and agriculture (magenta) areas for each municipal region of the sites investigated in this study. For the ATTO site, we used the urban region of São Sebastião do Uatumã, according to Andreae et al., 2015 [<a href="#B36-atmosphere-13-01328" class="html-bibr">36</a>]. Please note the different y-axis.</p> Full article ">Figure 8
<p>Bar plots of BC and BrC fractions compared to the forest and agricultural land fractions for the six Amazonian sites.</p> Full article ">
13 pages, 3813 KiB  
Article
Moisture Sources for the Precipitation of Tropical-like Cyclones in the Mediterranean Sea: A Case of Study
by Patricia Coll-Hidalgo, Albenis Pérez-Alarcón and Raquel Nieto
Atmosphere 2022, 13(8), 1327; https://doi.org/10.3390/atmos13081327 - 20 Aug 2022
Cited by 8 | Viewed by 2555
Abstract
Tropical-like cyclones (TLCs) are hybrid low-pressure systems formed over the Mediterranean Sea, showing the characteristics of tropical and extratropical cyclones. The literature review revealed that several studies have focused on determining the physical mechanisms that favour their formation; however, their rainfall has received [...] Read more.
Tropical-like cyclones (TLCs) are hybrid low-pressure systems formed over the Mediterranean Sea, showing the characteristics of tropical and extratropical cyclones. The literature review revealed that several studies have focused on determining the physical mechanisms that favour their formation; however, their rainfall has received little attention. In this study, we attempted to identify the origin of the precipitation produced by TLCs through a Lagrangian approach based on the analysis of moisture sources for the TLC Qendresa from 6 to 9 November 2014. For the Lagrangian analysis, we used the trajectories of air parcels from the global outputs of the FLEXPART model fed by the ERA-5 reanalysis provided by the European Centre for Medium-Range Weather Forecast and backtracked those parcels that precipitated within the outer radius of the storm up to 10 days. Our results showed that the moisture mainly came from the western Mediterranean Sea, Northern Africa, the central Mediterranean Sea, Western Europe, the eastern North Atlantic, and the eastern Mediterranean Sea with contributions of 35.09%, 27.6%, 18.62%, 10.40%, 6.79%, and 1.5%, respectively. The overall large-scale conditions for the genesis of Qendresa agreed with previous climatological studies. Therefore, our findings contribute to the understanding of precipitation associated with TLCs. Future studies will focus on a climatological analysis of the origin of rainfall produced by these hybrid cyclones. Full article
(This article belongs to the Section Meteorology)
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<p>Mediterranean Region (red box) limited from 9° W to 42° E and from 27° N to 48° N.</p> Full article ">Figure 2
<p>Trajectory of Qendresa from 6 November 2014 at 1800 UTC to 9 November 2014 at 0000 UTC. The squares denoted the 6-hourly position of the storm. Days and hours (UTC) of each Qendresa position are also plotted.</p> Full article ">Figure 3
<p>Potential vorticity (PVU, shaded) at 300 hPa, geopotential height (gpm, solid green line), and temperature (°C, dashed brown line) from ERA-5 reanalyses at 500 hPa during Qendresa’s lifetime. (<b>a</b>) 6 November 2014 at 1800 UTC, (<b>b</b>) 7 November 2014 at 0000 UTC, (<b>c</b>) 7 November 2014 at 0600 UTC, (<b>d</b>) 7 November 2014 at 1200 UTC, (<b>e</b>) 7 November 2014 at 1800 UTC, (<b>f</b>) 8 November 2014 at 0000 UTC, (<b>g</b>) 8 November 2014 at 0600 UTC, (<b>h</b>) 8 November 2014 at 1200 UTC, and (<b>i</b>) 8 November 2014 at 1800 UTC. The dashed red circles and the red squares represent the cyclone size and centre, respectively.</p> Full article ">Figure 4
<p>Integrated vertically water vapour transport (IVT, in kg/ms, shaded), vertically integrated moisture transport (VIMF, in kg/ms, arrows), and mean sea level pressure (MSLP, hPa, contour) from ERA-5 reanalysis during Qendresa’s lifetime. (<b>a</b>) 6 November 2014 at 1800 UTC, (<b>b</b>) 7 November 2014 at 0000 UTC, (<b>c</b>) 7 November 2014 at 0600 UTC, (<b>d</b>) 7 November 2014 at 1200 UTC, (<b>e</b>) 7 November 2014 at 1800 UTC, (<b>f</b>) 8 November 2014 at 0000 UTC, (<b>g</b>) 8 November 2014 at 0600 UTC, (<b>h</b>) 8 November 2014 at 1200 UTC, and (<b>i</b>) 8 November 2014 at 1800 UTC. The dashed red circles and the red squares represent the cyclone size and centre, respectively.</p> Full article ">Figure 5
<p>Precipitation rate from the Multi-Source Weighted-Ensemble Precipitation during Qendresa’s lifetime. (<b>a</b>) 6 November 2014 at 1800 UTC, (<b>b</b>) 7 November 2014 at 0000 UTC, (<b>c</b>) 7 November 2014 at 0600 UTC, (<b>d</b>) 7 November 2014 at 1200 UTC, (<b>e</b>) 7 November 2014 at 1800 UTC, (<b>f</b>) 8 November 2014 at 0000 UTC, (<b>g</b>) 8 November 2014 at 0600 UTC, (<b>h</b>) 8 November 2014 at 1200 UTC, and (<b>i</b>) 8 November 2014 at 1800 UTC. The red square denotes the TLC centre.</p> Full article ">Figure 6
<p>Trajectories of air parcels that produced the precipitation associated with Qendresa during its lifetime. (<b>a</b>) 6 November 2014 at 1800 UTC, (<b>b</b>) 7 November 2014 at 0000 UTC, (<b>c</b>) 7 November 2014 at 0600 UTC, (<b>d</b>) 7 November 2014 at 1200 UTC, (<b>e</b>) 7 November 2014 at 1800 UTC, (<b>f</b>) 8 November 2014 at 0000 UTC, (<b>g</b>) 8 November 2014 at 0600 UTC, (<b>h</b>) 8 November 2014 at 1200 UTC, and (<b>i</b>) 8 November 2014 at 1800 UTC. The mean sea level pressure (hPa) is plotted in contours; the 6 h changes in specific humidity (g/kg) are shown with coloured dots; the cyclone size is denoted by the red dashed line.</p> Full article ">Figure 7
<p>Accumulated moisture uptake (mm, shaded) and mean vertically integrated moisture flux (VIMF, in kg/ms, arrows) during the lifetime of Qendresa from 6 November 2014 at 1800 UTC to 9 November at 0000 UTC. The red line and squares denote the trajectory and the 6-hourly position of the storm, respectively.</p> Full article ">
18 pages, 4728 KiB  
Article
Climate and the Radial Growth of Conifers in Borderland Natural Areas of Texas and Northern Mexico
by José Villanueva-Díaz, David W. Stahle, Helen Mills Poulos, Matthew D. Therrell, Ian Howard, Aldo Rafael Martínez-Sifuentes, David Hermosillo-Rojas, Julián Cerano-Paredes and Juan Estrada-Ávalos
Atmosphere 2022, 13(8), 1326; https://doi.org/10.3390/atmos13081326 - 19 Aug 2022
Cited by 2 | Viewed by 2156
Abstract
The forests of northern Mexico and the southwestern United States have been subjected to warmer temperatures, persistent drought, and more intense and widespread wildfire. Tree-ring data from four conifer species native to these borderlands forests are compared with regional and large-scale precipitation and [...] Read more.
The forests of northern Mexico and the southwestern United States have been subjected to warmer temperatures, persistent drought, and more intense and widespread wildfire. Tree-ring data from four conifer species native to these borderlands forests are compared with regional and large-scale precipitation and temperature data. These species include Abies durangensis, Pinus arizonica, Pinus cembroides, and Pseudotsuga menziesii. Twelve detrended and standardized ring-width chronologies are derived for these four species, all are cross-correlated during their common interval of 1903–2000 (r = 0.567 to 0.738, p < 0.01), and all load positively on the first principal component of radial growth, which alone represents 56% of the variance in the correlation matrix. Correlation with monthly precipitation and temperature data for the study area indicates that all four species respond primarily to precipitation during the cool season of autumn and winter, October–May (r = 0.71, p < 0.01, 1931–2000), and to temperature primarily during the late spring and early summer, January–July (r 0 −0.67, p < 0.01, 1931–2000), in spite of differences in phylogeny and microsite conditions. The instrumental climate data for the region indicate that warmer conditions during the January–July season most relevant to radial growth are beginning to exceed the warmest episode of the 20th century in both intensity and duration. The strong negative correlation between temperature and tree growth indicates that these four conifer species may be challenged by the warmer temperatures forecast in the coming decades for the borderlands region due to anthropogenic forcing. This information could constitute a baseline to analyze the impact of climate change in other regions of Mexico and the USA, where conifer species are of great ecological and socioeconomical importance. Full article
(This article belongs to the Special Issue Development and Application of Indices for Assessing the Potential Impacts of Climate Change)
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<p>The locations of tree-ring chronologies from the borderland are numbered and plotted (red triangles). The three large protected natural areas in the Big Bend region of the Rio Brave are also mapped along with the international frontier and the municipal divisions in Chihuahua and Coahuila.</p> Full article ">Figure 2
<p>Regional average ring-width index chronologies based on two collection sites per species are plotted for their common intervals. The Abies average is based on ABCTXABDU and MDCMXABDU (site codes defined in <a href="#atmosphere-13-01326-t001" class="html-table">Table 1</a>), which are correlated at r = 0.58 for 1905–2000 (<b>a</b>). The <span class="html-italic">P. arizonica</span> average is based on MDCMXPIAZ and SANMXPIAZ, correlated at r = 0.47 for 1860–2000 (<b>b</b>). The <span class="html-italic">P. menziesii</span> average is based on BIGTXPSME and MDCMXPSME, correlated at r = 0.42 from 1811–2000 (<b>c</b>). The <span class="html-italic">P. cembroides</span> average is based on ELEMXPICE and NAMMXPICE, correlated at r = 0.62 for 1725–2014 (<b>d</b>). The correlation matrix for these four species chronologies is presented in <a href="#atmosphere-13-01326-t004" class="html-table">Table 4</a>.</p> Full article ">Figure 3
<p>The monthly climate response for the 12 conifer tree-ring chronologies from the borderlands region is plotted, based on the correlation between each chronology and the monthly precipitation totals (<b>a</b>) and mean temperature (<b>b</b>) for the study area from the prior August to the current August. The response of the <span class="html-italic">A. durangensis</span> chronologies is plotted in blue, <span class="html-italic">P. menziesii</span> in green, <span class="html-italic">P. arizonica</span> in black, and <span class="html-italic">P. cembroides</span> in red. The average monthly correlation for the 12 chronologies is also plotted (heavy black line) and with the significance thresholds (<span class="html-italic">p</span> &lt; 0.05). The chronologies are listed in <a href="#atmosphere-13-01326-t001" class="html-table">Table 1</a> and correlation coefficients are presented for all chronologies, climate variables, and months in <a href="#atmosphere-13-01326-t005" class="html-table">Table 5</a>.</p> Full article ">Figure 4
<p>PC1 of the Borderlands tree-ring chronologies correlated with monthly precipitation data, 1946–2000. The time series scores for the first principal component of tree growth for the 12 detrended and standardized ring-width chronologies are correlated with gridded monthly precipitation totals over North America. The precipitation data were obtained from the GPCC with a grid spacing of 0.5° [<a href="#B24-atmosphere-13-01326" class="html-bibr">24</a>], and the correlations were all based on the common period 1946–2000 when the tree ring and instrumental precipitation data are well replicated.</p> Full article ">Figure 5
<p>PC1 of the Borderlands tree-ring chronologies correlated with monthly temperature data, 1946–2000. Same as <a href="#atmosphere-13-01326-f003" class="html-fig">Figure 3</a>, except PC1 of tree growth is correlated with the 0.5 grid of monthly mean temperature from the University of Delaware [<a href="#B25-atmosphere-13-01326" class="html-bibr">25</a>].</p> Full article ">Figure 6
<p>The instrumental precipitation data were totaled monthly for each grid point and then averaged for the study area (28.5–30.5° N and 102–107° W) for 1901–2017. These regional precipitation data were then summed for the October–May cool season and correlated with the PC1 scores for the 12 conifer chronologies from the borderlands for the common period 1931–2000 ((<b>a</b>); PC1 in red). The correlation is r = 0.71 (<span class="html-italic">p</span> &lt; 0.001). The monthly temperature data were also averaged for the study area and for the months of January–July (plotted for 1901–2017; (<b>b</b>)). The PC1 time series of tree growth was inverted and is plotted along with the regional temperature series. The correlation is r = −0.67 (<span class="html-italic">p</span> &lt; 0.001), prior to inverting the PC1 time series. Regional precipitation for October–May has generally declined since the wet extremes in the 1980s and 1990s (<b>a</b>) while January–July temperatures have increased over the region (<b>b</b>). Some of the tree-ring chronologies unfortunately end in 2000, but the PC1 time series does follow the low-frequency changes in both seasonal precipitation and temperature over the borderlands region.</p> Full article ">
10 pages, 2053 KiB  
Article
Sensitivity of the 4–10-Day Planetary Wave Structures in the Middle Atmosphere to the Solar Activity Effects in the Thermosphere
by Andrey V. Koval, Nikolai M. Gavrilov, Ksenia A. Didenko, Tatiana S. Ermakova and Elena N. Savenkova
Atmosphere 2022, 13(8), 1325; https://doi.org/10.3390/atmos13081325 - 19 Aug 2022
Cited by 6 | Viewed by 2137
Abstract
Numerical simulation of the general atmospheric circulation was performed to estimate changes in amplitudes of the westward-travelling planetary waves (PWs) at altitudes from the Earth’s surface up to 300 km under different solar activity (SA) levels. The three-dimensional nonlinear mechanistic model of circulation [...] Read more.
Numerical simulation of the general atmospheric circulation was performed to estimate changes in amplitudes of the westward-travelling planetary waves (PWs) at altitudes from the Earth’s surface up to 300 km under different solar activity (SA) levels. The three-dimensional nonlinear mechanistic model of circulation of the middle and upper atmosphere “MUAM” was used. The atmospheric general circulation and PW amplitudes were calculated based on ensembles containing 16 model runs for conditions corresponding to low and high SA. PWs having periods of 4–10 days were considered. Comparison with the data of digital ionosondes showed that the MUAM model is capable of reproducing the considered PW modes at thermospheric heights. It is shown that under high SA conditions, PW amplitudes are significantly larger in the thermosphere and smaller in the middle atmosphere. The observed PW structures are influenced not only by changes in atmospheric refractive index and Eliassen–Palm flux but also by varying PW reflection in the lower thermosphere, which can change proportions of the wave energy transferred from the lower atmosphere to the upper layers and reflected downwards. Full article
(This article belongs to the Special Issue Links between Solar Activity and Atmospheric Circulation (2nd Volume))
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<p>The spectrum of the critical frequency f<sub>0</sub>F<sub>2</sub> in Peterhof in a sliding 60-day time interval, centered on 31 January 2019 (<b>left</b>), and the corresponding spectrum of temperature fluctuations at an altitude of 200 km according to the MUAM model data (<b>right</b>). The lower axis is the frequency (1/day), and the upper axis is the period in days.</p> Full article ">Figure 2
<p>Amplitudes of the geopotential height variations (in gp.m.) caused by the westward−travelling PWs having periods 4, 5, 7 and 10 days ((<b>a</b>–<b>d</b>), respectively) under high SA (<b>left</b>) and differences (<b>right</b>) due to SA changes in thermosphere, averaged over 16-member ensembles and from mid−December–February. Areas shaded with lines show statistically unconfident differences. Altitude range 0–300 km.</p> Full article ">Figure 3
<p>The same as <a href="#atmosphere-13-01325-f002" class="html-fig">Figure 2</a> but for altitude range 0–100 km.</p> Full article ">
20 pages, 5086 KiB  
Article
Offline Diagnostics of Skin Sea Surface Temperature from a Prognostic Scheme and Its Application in Typhoon Forecasting Using the CMA-TRAMS Model over South China
by Yanxia Zhang, Daosheng Xu, Zitong Chen and Weiguang Meng
Atmosphere 2022, 13(8), 1324; https://doi.org/10.3390/atmos13081324 - 19 Aug 2022
Cited by 7 | Viewed by 2116
Abstract
In the Tropical Regional Atmospherical Model System of South China of the China Meteorological Administration (CMA-TRAMS), the skin sea surface temperature (Ts) remains fixed during the forecast time. This limits the model’s performance in describing interactions between air and sea. [...] Read more.
In the Tropical Regional Atmospherical Model System of South China of the China Meteorological Administration (CMA-TRAMS), the skin sea surface temperature (Ts) remains fixed during the forecast time. This limits the model’s performance in describing interactions between air and sea. The offline diagnostics and online analysis coupled with the CMA-TRAMS of Ts prognostic scheme were discussed. The results of the offline diagnostics showed that the profile shape parameter, ν, and initial temperature, Tb, were sensitive to the forecasted Ts. Based on our observations, when ν was set to 0.2 and Tb was the averaged Ts without obvious diurnal variation, the forecasted Ts was relatively reasonable. The forecasted Ts of CMA-TRAMS after coupling with the Ts scheme had diurnal variations during the overall forecast time, which was different from the fixed Ts from the uncoupled model. There existed a certain difference of forecasted Ts between uncoupled and coupled models in those days influenced by typhoons. The biases and Root Mean Square Errors (RMSEs) for the temperature and moisture in the lower layer and those for the wind speed in most layers were reduced and, therefore, the accuracy of environmental field forecasting was improved from the coupled model. The typhoon track errors after 36-h decreased due to the improvement of steering flow on the west side of subtropical high from the coupled model. However, the difference of typhoon intensity errors was insignificant, which might mean that the differences of forecasted Ts and heat flux between uncoupled and coupled model are small. The reasons for the small difference need to be further investigated. Full article
(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))
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<p>The range of CMA-TRAMS operational model over South China.</p> Full article ">Figure 2
<p>Changes in the observation datasets with time at BoHe Base: (<b>a</b>) u* (black line), LH (red line), and SH (orange line); (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (black line) and RS (red line); and (<b>c</b>) Q (SH + LH + RL) (black line) and Q + RS (red line).</p> Full article ">Figure 3
<p>Variation in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">w</mi> </msub> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> (<b>b</b>) in the first set of experiments with time (black solid line: ts-v0.3-d3.0; red-dotted line: ts-v0.2-d3.0; and green-dashed line: ts-v0.1-d3.0).</p> Full article ">Figure 4
<p>Sensitivity experiments for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>b</mi> </msub> </mrow> </semantics></math> (black bold line: observed <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (tso); green-dashed line: ts-v0.2-d3.0; blue-dashed line: tsd-v0.2-d3.0; and dark-green-dashed line: tsm-v0.2-d3.0).</p> Full article ">Figure 5
<p>Sensitivity experiments on the warm layer depth, d (<b>a</b>), with variations in <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> with time (black solid line: tsm-v0.2-d3.0; red-dotted line: tsm-v0.2-d2.5; green-dashed line: tsm-v0.2-d3.5); (<b>b</b>) differences between the sensitivity experiments (red solid line: tsm-v0.2-d2.5 minus tsm-v0.2-d3.0; and blue-dashed line: tsm-v0.2-d3.5 minus tsm-v0.2-d3.0).</p> Full article ">Figure 5 Cont.
<p>Sensitivity experiments on the warm layer depth, d (<b>a</b>), with variations in <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> with time (black solid line: tsm-v0.2-d3.0; red-dotted line: tsm-v0.2-d2.5; green-dashed line: tsm-v0.2-d3.5); (<b>b</b>) differences between the sensitivity experiments (red solid line: tsm-v0.2-d2.5 minus tsm-v0.2-d3.0; and blue-dashed line: tsm-v0.2-d3.5 minus tsm-v0.2-d3.0).</p> Full article ">Figure 6
<p>The diagram of the uncoupling (<b>a</b>) and coupling (<b>b</b>) between CMA-TRAMS and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> prognostic scheme.</p> Full article ">Figure 6 Cont.
<p>The diagram of the uncoupling (<b>a</b>) and coupling (<b>b</b>) between CMA-TRAMS and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> prognostic scheme.</p> Full article ">Figure 7
<p>120-h forecasted <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> at a single point (140° E, 20° N) on 3 August 2019, in two experiments (solid line: COU; dotted line: NCOU).</p> Full article ">Figure 8
<p>The forecasted <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> of the two experiments along 114° E and 20° N from 1 August 31 August. (<span class="html-italic">a</span>–<span class="html-italic">e</span>) respectively show the forecasts from 24 h, 48 h, 72 h, 96 h and 120 h (dotted line: COU; dashed line: NCOU).</p> Full article ">Figure 8 Cont.
<p>The forecasted <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> of the two experiments along 114° E and 20° N from 1 August 31 August. (<span class="html-italic">a</span>–<span class="html-italic">e</span>) respectively show the forecasts from 24 h, 48 h, 72 h, 96 h and 120 h (dotted line: COU; dashed line: NCOU).</p> Full article ">Figure 9
<p>The forecasted <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> of the two experiments in the average region over 5° N to 25° N and 130° E to 150° E from 1 August 31 August. (<span class="html-italic">a</span>–<span class="html-italic">e</span>) respectively show the forecasts from 24 h, 48 h, 72 h, 96 h and 120 h (dotted line: COU; dashed line: NCOU).</p> Full article ">Figure 9 Cont.
<p>The forecasted <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </semantics></math> of the two experiments in the average region over 5° N to 25° N and 130° E to 150° E from 1 August 31 August. (<span class="html-italic">a</span>–<span class="html-italic">e</span>) respectively show the forecasts from 24 h, 48 h, 72 h, 96 h and 120 h (dotted line: COU; dashed line: NCOU).</p> Full article ">Figure 10
<p>Time-pressure cross-sections of the biases (shaded) and RMSEs (contour) for the temperature (K), specific humidity (g kg <sup>−1</sup>), and wind speed (m s <sup>−1</sup>) in (<b>a</b>,<b>c</b>,<b>e</b>), respectively. NCOU against the EC analysis data and (<b>b</b>,<b>d</b>,<b>f</b>) their differences between the NCOU and COU (i.e., COU minus NCOU), averaged over the model region during August 2019. Solid and dashed lines indicate positive and negative values, respectively.</p> Full article ">Figure 11
<p>Errors for the track (<b>a</b>) and intensity (<b>c</b>) for the 1616 “Malakas” typhoon and (<b>b</b>,<b>d</b>), for the 1622 “Haima” typhoon.</p> Full article ">Figure 12
<p>Average errors for the track and intensity from six typhoons: (<b>a</b>) track and (<b>b</b>) intensity errors.</p> Full article ">
23 pages, 6707 KiB  
Article
Spatio-Temporal Variation of Precipitation and Evaporation on the Tibetan Plateau and Their Influence on Regional Drought
by Yuanzhi Tang, Junjun Huo, Dejun Zhu, Tailai Gao and Xiaoxuan Jiang
Atmosphere 2022, 13(8), 1323; https://doi.org/10.3390/atmos13081323 - 19 Aug 2022
Cited by 1 | Viewed by 2431
Abstract
The Tibetan Plateau (TP) is an important water source in Asia, and precipitation and evaporation patterns at different geographical and temporal scales play a significant role in managing water resource distribution. Based on quality control data from 87 meteorological stations, this study analyzed [...] Read more.
The Tibetan Plateau (TP) is an important water source in Asia, and precipitation and evaporation patterns at different geographical and temporal scales play a significant role in managing water resource distribution. Based on quality control data from 87 meteorological stations, this study analyzed the spatial and temporal evolution patterns of precipitation and pan evaporation (Epan) on the TP in 1966–2016 using the Mann–Kendall test, the moving t-test, wavelet analysis, Sen’s slope method, and correlation analysis. The results revealed that the average mean temperature in the TP area increased by about 2.1 °C during the study period, and precipitation steadily increased at an average rate of 8.2 mm/10a, with summer and autumn precipitation making up about 80% of the year. In contrast, Epan showed an overall decreasing trend at a decline rate of 20.8 mm/10a, with spring and summer Epan values making up about 67% of the year. The time series of the precipitation and Epan within the TP region clearly exhibit nonstationary features. Precipitation is more concentrated in the southeast than in the northwest, while Epan is mostly concentrated in the southwest and northeast of the plateau around the Qaidam Basin. The “evaporation paradox” phenomenon was common in the TP region for about 40 years (1960s–1990s) and gradually faded in the 21st century. In addition, we introduced a standardized precipitation evaporation index (SPEI) to investigate the differences and relationships between precipitation and Epan time series over the past 50 years. The findings indicate that the southern Qinghai was dominated by an arid trend, while the central and southeast TP remained wet. Droughts and floods coexist in the eastern Qinghai and southern Tibet areas with high population concentrations, and the risk of both is rising as the inhomogeneity of precipitation distribution in the TP region will increase in the future. This study can be used as a reference for managing water resources and predicting regional drought and flood risk. Full article
(This article belongs to the Special Issue Hydrological Responses under Climate Changes)
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<p>Overview of the TP and the locations of meteorological stations.</p> Full article ">Figure 2
<p>Variation of precipitation in the TP region on different timescales in 1966–2016. (<b>a</b>) Annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter.</p> Full article ">Figure 3
<p>Spatial distribution of the multiyear average precipitation on the TP in 1966–2016. (<b>a</b>) Annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter.</p> Full article ">Figure 4
<p>Spatial distribution of precipitation trends at different timescales on the TP in 1966–2016. (<b>a</b>) Annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter.</p> Full article ">Figure 5
<p>Wavelet variance of the annual and seasonal precipitation series and a contour map of the real part of wavelet coefficients in the TP area in 1966–2016. (<b>a</b>) Annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter.</p> Full article ">Figure 6
<p>Variation of Epan on the TP on different timescales in 1966–2016. (<b>a</b>) Annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter.</p> Full article ">Figure 7
<p>Annual and seasonal spatial trends of Epan on the TP in 1966–2016. (<b>a</b>) Annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter.</p> Full article ">Figure 8
<p>The relationship between the Epan mutation time and the longitude (<b>a</b>) and latitude (<b>b</b>) for each site on the TP.</p> Full article ">Figure 9
<p>Correlation of the annual and seasonal mean Epan with longitude (<b>a</b>), latitude (<b>b</b>), and altitude (<b>c</b>) at each site on the TP.</p> Full article ">Figure 10
<p>Comparison of Epan and precipitation at different timescales of the TP. (<b>a</b>) Correlation between the annual mean precipitation and Epan at each site, (<b>b</b>) interannual variation of normalized precipitation and Epan in August.</p> Full article ">Figure 11
<p>Comparison of the trends of precipitation and Epan at different stations on the TP. (<b>a</b>) Trend’s z-value relationship, (<b>b</b>) relationship between precipitation and Epan’s Sen’s slope, (<b>c</b>) relationship between Epan and precipitation’s Sen’s slope.</p> Full article ">Figure 12
<p>Temporal evolution of the SPEI at 1-, 3-, 6-, and 12-month timescales on the TP in 1966–2016.</p> Full article ">Figure 13
<p>Spatial trend distribution of the SPEI at different timescales on the TP in 1966–2016. (<b>a</b>) SPEI-1, (<b>b</b>) SPEI-3, (<b>c</b>) SPEI-6, (<b>d</b>) SPEI-12.</p> Full article ">Figure 14
<p>Interannual trends of the average temperature (<b>a</b>) and the drought index (<b>b</b>) on the TP.</p> Full article ">
21 pages, 7752 KiB  
Article
Conversion Coefficient Analysis and Evaporation Dataset Reconstruction for Two Typical Evaporation Pan Types—A Study in the Yangtze River Basin, China
by Ziheng Li, Xuefeng Sang, Siqi Zhang, Yang Zheng and Qiming Lei
Atmosphere 2022, 13(8), 1322; https://doi.org/10.3390/atmos13081322 - 19 Aug 2022
Cited by 4 | Viewed by 1815
Abstract
For the day-by-day evaporation observation data in the Yangtze River Basin from 1951 to 2019, the effects of the gradual shift of observation instruments from 20 cm diameter evaporation pan (D20) to E601 evaporation pan after 1980 are discussed, including inconsistent data series, [...] Read more.
For the day-by-day evaporation observation data in the Yangtze River Basin from 1951 to 2019, the effects of the gradual shift of observation instruments from 20 cm diameter evaporation pan (D20) to E601 evaporation pan after 1980 are discussed, including inconsistent data series, and missing and anomalous data. This study proposes a governance and improvement method for dual-source evaporation data (GIME). The method can accomplish the homogenization of data from different observation series and solve the problem of inconsistent and missing data, and we applied it in practice on data of the Yangtze River Basin. Firstly, the primary and secondary periods of the data were obtained by wavelet periodicity analysis; secondly, we considered the first cycle of observations to be representative of the sample and calculated the conversion relationship between the primary and secondary periods; thirdly, the conversion coefficient between the dual-source observations was calculated, and the results were corrected for stations outside the main cycle; finally, the daily evaporation dataset of E601 pan was established through data fusion and interpolation technology. The study found that the annual average conversion coefficients of the D20 and E601 pans in the Yangtze River Basin are basically between 0.55 and 0.80, and there are obvious differences in different regions. The conversion coefficient is positively correlated with relative humidity, wind speed, minimum temperature and altitude; and negatively correlated with sunshine duration, average temperature and maximum temperature. Evaporation is high in the upper reaches of the basin and low in the middle and lower reaches; in particular, evaporation is highest in the southwest, which is associated with the drought hazards. In addition, the article presents the spatial distribution of the conversion coefficients of D20 and E601 pans in the Yangtze River Basin. The results can realize the rapid correction of the evaporation data of the local meteorological department, and can be extended to the processing of other types of data in similar areas. Full article
(This article belongs to the Special Issue Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology)
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<p>Two types of evaporation pans: 20 cm diameter evaporation pan (<b>a</b>); E601 evaporation pan (<b>b</b>).</p> Full article ">Figure 2
<p>Distribution of meteorological stations in the Yangtze River Basin.</p> Full article ">Figure 3
<p>Governance and improvement method process for dual-source evaporation data.</p> Full article ">Figure 4
<p>Practical application steps of governance and improvement method for dual-source evaporation data.</p> Full article ">Figure 5
<p>Wavelet variance of annual evaporation variation at 25 stations in the Yangtze River Basin.</p> Full article ">Figure 6
<p>Variance diagram of wavelet analysis of annual evaporation at Station 56093 in the Yangtze River Basin.</p> Full article ">Figure 7
<p>Frequency histograms of the first, second and third cycles of annual evaporation changes in the Yangtze River Basin, (<b>a</b>) first cycle, (<b>b</b>) second cycle and (<b>c</b>) third cycle.</p> Full article ">Figure 8
<p>Distribution of stations corresponding to evaporation data meeting the first, second and third cycles.</p> Full article ">Figure 9
<p>Conversion coefficients for two evaporation pans for the first cycle (series length of 23 years). The conversion coefficient is the observed value of E601 pan/D20 pan, and the formula used was Formula (<a href="#FD10-atmosphere-13-01322" class="html-disp-formula">10</a>).</p> Full article ">Figure 10
<p>Matrix of conversion coefficients for two pans for the second cycle (series length of 12 years.</p> Full article ">Figure 11
<p>Correction factor matrix for the conversion of the second sub-cycle (12 years) to the first main cycle (23 years).</p> Full article ">Figure 12
<p>Matrix of conversion coefficients for two pans for the third cycle (series length of 6 years).</p> Full article ">Figure 13
<p>Matrix of correction coefficients for the conversion of the third subcycle (6 years) to the first major cycle (23 years).</p> Full article ">Figure 14
<p>Spatial distribution of conversion coefficients for D20 and E601 pans in the Yangtze River Basin.</p> Full article ">Figure 15
<p>The spatial distribution of the reconstructed dataset for the E601 pan of the Yangtze River Basin.</p> Full article ">
16 pages, 1676 KiB  
Article
A Coverage Sampling Path Planning Method Suitable for UAV 3D Space Atmospheric Environment Detection
by Lunke Yang, Shurui Fan, Binggang Yu and Yingmiao Jia
Atmosphere 2022, 13(8), 1321; https://doi.org/10.3390/atmos13081321 - 19 Aug 2022
Cited by 5 | Viewed by 2029
Abstract
Air pollution affects people’s life and health, and controlling air pollution requires the collection of polluting gas information. Unmanned aerial vehicles (UAVs) have been used for environmental detection due to their characteristics. However, the limitation of onboard energy sources of UAVs will limit [...] Read more.
Air pollution affects people’s life and health, and controlling air pollution requires the collection of polluting gas information. Unmanned aerial vehicles (UAVs) have been used for environmental detection due to their characteristics. However, the limitation of onboard energy sources of UAVs will limit the coverage of the detection area and the number of gas samples collected, which will affect the assessment of pollution levels. In addition, to truly and completely reflect the distribution of atmospheric pollutants, it is necessary to sample the entire three-dimensional space. This paper proposes a three-dimensional space path planning method suitable for UAV atmospheric environment detection, which can generate a full-coverage path with optimal coverage density under energy constraints. Simulation results show that the proposed method can effectively improve the coverage density compared with other path generation methods. Field experiments show that the proposed method is reliable and accurate in the application of UAV atmospheric environment space detection. Full article
(This article belongs to the Special Issue Air Pollution Modelling)
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<p>Coverage sampling design. (<b>a</b>) Basic decomposition unit. (<b>b</b>) Distribution of sampling points in a subspace. The red solid line represents the outline of the study area A. The yellow dot is the sampling point. (<b>c</b>) Space decomposition.</p> Full article ">Figure 1 Cont.
<p>Coverage sampling design. (<b>a</b>) Basic decomposition unit. (<b>b</b>) Distribution of sampling points in a subspace. The red solid line represents the outline of the study area A. The yellow dot is the sampling point. (<b>c</b>) Space decomposition.</p> Full article ">Figure 2
<p>Steering angle.</p> Full article ">Figure 3
<p>Convergence curve.</p> Full article ">Figure 4
<p>Path planning results. (<b>a</b>) Result of GA with the sum of angles 114.14; (<b>b</b>) result of DF-GA with the sum of angles 92.15, whose sum of turn angles is smaller than the GA result, with more straight paths.</p> Full article ">Figure 5
<p>Supplementary waypoint interpolation. (<b>a</b>) Raw waypoint. (<b>b</b>) Supplementary Interpolated Post Waypoints. (<b>c</b>) Raw Waypoint Curve Fitting. (<b>d</b>) Waypoint curve fitting after supplementary interpolation.</p> Full article ">Figure 6
<p>(<b>a</b>) Subspace path fitting result; (<b>b</b>) 3D detection path.</p> Full article ">Figure 7
<p>AirSim simulation system.</p> Full article ">Figure 8
<p>(<b>a</b>) Field tests. The red area is the detection area. (<b>b</b>) 3D detection path. (<b>c</b>) UAV in field tests.</p> Full article ">Figure 9
<p>Comparison between the experimental measurement value and the national control station value. The yellow solid line is the station value, and the blue dotted line is the measured value.</p> Full article ">Figure 10
<p>PM2.5 data interpolation results. (<b>a</b>) Horizontal interpolation at 50 m height; (<b>b</b>) vertical interpolation at latitude 39.240467, longitude from 117.053912 to 117.054761.</p> Full article ">
16 pages, 3183 KiB  
Article
Assessment and Characterization of Alkylated PAHs in Selected Sites across Canada
by Andrzej Wnorowski, David Harnish, Ying Jiang, Valbona Celo, Ewa Dabek-Zlotorzynska and Jean-Pierre Charland
Atmosphere 2022, 13(8), 1320; https://doi.org/10.3390/atmos13081320 - 19 Aug 2022
Cited by 9 | Viewed by 2404
Abstract
Alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), dibenzothiophenes (DBTs), and unsubstituted polycyclic aromatic hydrocarbons (PAHs) are naturally present in fossil fuels. Thus, they can be considered as candidates for markers of pollution from petrogenic emissions such as those from traffic. Consequently, ambient air concentrations of [...] Read more.
Alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), dibenzothiophenes (DBTs), and unsubstituted polycyclic aromatic hydrocarbons (PAHs) are naturally present in fossil fuels. Thus, they can be considered as candidates for markers of pollution from petrogenic emissions such as those from traffic. Consequently, ambient air concentrations of alkyl-PAHs, DBTs, and PAHs at selected ambient air monitoring sites of various types (residential, near-road, urban-industrial, agricultural) in Montréal, Toronto, Hamilton, Edmonton, and Simcoe, were evaluated from 2015 to 2016 to study their profiles, trends, and assess potential primary emission source types. Alkyl-PAHs were the prevailing species at all sites and were most elevated at the high-traffic impacted near-road site in Toronto which was also accompanied by the highest unsubstituted PAH concentrations. Comparison of relative abundance ratios of alkyl-PAH and PAH groupings suggests that the profile differences amongst sites were small. Source attribution with cluster grouping suggested similar emission sources of alkyl-PAH and PAH at all sites, with the exception of Hamilton which was particularly impacted by additional emission sources of PAHs. The Principal Component Analysis further indicated distinct PAC profiles at HWY401 and HMT that have the same variability of “heavy PACs” but differ in “medium mass PAHs” sources. Seasonality affected the bulk species trends (alkylated naphthalenes, fluorenes, and phenanthrenes/anthracenes), especially at sites with lower concentrations of these species. This study findings confirm a notable contribution of traffic emissions to alkyl-PAH levels in urban ambient air at the studied Canadian sites, and show that enhanced speciation of alkyl-PAHs provides more data on ambient air quality and additional health risks, and can also help distinguish petrogenic-influenced sources from other sources. Full article
(This article belongs to the Special Issue Monitoring and Modelling Air Pollution and Thermal Environment through Applications in Urban Areas)
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<p>Location of the monitoring sites with reference to the map of Canada.</p> Full article ">Figure 2
<p>Median total concentrations of (<b>A</b>) alkyl-PAHs, (<b>B</b>) unsubstituted PAHs, and (<b>C</b>) DBTs (DBT + alkylated DBTs) in samples collected at the five NAPS sites. Note, median concentration of PACs collected in the Oil Sands Region (AMS11) is reported for comparison [<a href="#B16-atmosphere-13-01320" class="html-bibr">16</a>].</p> Full article ">Figure 3
<p>Ratios of (<b>A</b>) alkyl-PAHs/PAHs and (<b>B</b>) DBTs/alkyl-PAHs at the five NAPS sites and the industrial-petrogenic AMS11 site for comparison [<a href="#B16-atmosphere-13-01320" class="html-bibr">16</a>].</p> Full article ">Figure 4
<p>Seasonality effects of alkyl-PAH by site for 2015–2016 period; the data from the AMS11 site are reported for comparison [<a href="#B16-atmosphere-13-01320" class="html-bibr">16</a>].</p> Full article ">Figure 5
<p>Characteristic profiles of individual alkyl-PAH classes by site.</p> Full article ">Figure 6
<p>Unsubstituted PAH and alkylated PAH grouping clusters at the five NAPS sites and the industrial-petrogenic AMS11 site [<a href="#B16-atmosphere-13-01320" class="html-bibr">16</a>]. Circled grouped sites indicate similar relationship of volatility groupings amongst the sites (see <a href="#app1-atmosphere-13-01320" class="html-app">Table S2</a> for grouping description).</p> Full article ">Figure 7
<p>PCA projection of cases on the factor-plane for Factors 1 and 2 at the studied sites.</p> Full article ">
14 pages, 3017 KiB  
Article
A 3D Monte Carlo Simulation of Convective Diffusional Deposition of Ultrafine Particles on Fiber Surfaces
by Shixian Wu, Yongping Chen, Can Qi, Chunyu Liu, Gang Li and Hui Zhu
Atmosphere 2022, 13(8), 1319; https://doi.org/10.3390/atmos13081319 - 18 Aug 2022
Cited by 1 | Viewed by 2023
Abstract
The microscale simulation of ultrafine particle transport and deposition in fibrous filtration media was achieved with a novel particle tracking model using a 3D Monte Carlo model. The particle deposition process is governed by the convection–diffusion field. Simulations were performed by considering the [...] Read more.
The microscale simulation of ultrafine particle transport and deposition in fibrous filtration media was achieved with a novel particle tracking model using a 3D Monte Carlo model. The particle deposition process is governed by the convection–diffusion field. Simulations were performed by considering the fibrous filtration media as an array of identical parallel fibers, in which the flow field was accurately described by an analytical solution. The model of particle movement was described by the random probability distribution characterized by a dimensionless factor, the Peclet number (Pe), based on a convection–diffusive equation of particle transport in fluid. The influence of the particle Peclet number (Pe) on the particle deposition process and the resulting deposition morphology was investigated. The results were analyzed in terms of dust layer growth, particles’ trajectories and dust layer porosity for a vast range of Peclet numbers. The development of distinct deposition morphologies was found by varying the Peclet number (Pe). With a small Peclet number, diffusion dominated deposition and led to the formation of a more open and looser dust layer structure, while with larger Peclet numbers, convection dominated deposition and was found to form compact deposits. According to the change in the location of the packing densities along the dust layer height direction, the dust layer structure could be divided into three typical parts: the substructure, main profile and surface layer. In addition, the deposit morphologies observed for a high Pe were in good agreement with the experimental results found in the literature. Full article
(This article belongs to the Special Issue Developments and Applications of Scrubbers and Auxiliary Facilities for Air Pollution Control)
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<p>(<b>a</b>) Schematic of the simulation of a dust layer; (<b>b</b>) Kuwabara flow configuration.</p> Full article ">Figure 2
<p>Flow chart of the simulation procedure.</p> Full article ">Figure 3
<p>Comparisons of the deposit morphologies on a fiber between the simulations and the experimental observations. (<b>a</b>) Comparison of the simulation results in Case 1 with the experimental results of Kanaoka et al. [<a href="#B47-atmosphere-13-01319" class="html-bibr">47</a>]. (<b>b</b>) Comparison of the simulation results in Case 2 with the experimental results of Bhutra and Paratakes [<a href="#B48-atmosphere-13-01319" class="html-bibr">48</a>].</p> Full article ">Figure 4
<p>Typical simulated morphological structures of dust layers: (<b>a</b>) <span class="html-italic">Pe</span> = 0.5; (<b>b</b>) <span class="html-italic">Pe</span> = 5000.</p> Full article ">Figure 5
<p>Characteristics of particle trajectories: (<b>a</b>) <span class="html-italic">Pe</span> = 0.2; (<b>b</b>) <span class="html-italic">Pe</span> = 0.5; (<b>c</b>) <span class="html-italic">Pe</span> = 1; (<b>d</b>) <span class="html-italic">Pe</span> = 5000.</p> Full article ">Figure 6
<p>Dust layer density, <span class="html-italic">ρ</span><sub>c</sub>, vs. height, <span class="html-italic">H</span>: (<b>a</b>) <span class="html-italic">Pe</span> &gt; 1; (<b>b</b>) <span class="html-italic">Pe</span> ≤ 1.</p> Full article ">
12 pages, 3356 KiB  
Article
Effects of Horizontal Transport and Vertical Mixing on Nocturnal Ozone Pollution in the Pearl River Delta
by Honglong Yang, Chao Lu, Yuanyuan Hu, Pak-Wai Chan, Lei Li and Li Zhang
Atmosphere 2022, 13(8), 1318; https://doi.org/10.3390/atmos13081318 - 18 Aug 2022
Cited by 12 | Viewed by 2521
Abstract
Based on the meteorological, ozone (O3), and vertical observation data of 2020, this study sought to evaluate the daily variation in O3, particularly the characteristics of nocturnal ozone pollution. We also discuss the effect of local and mesoscale horizontal [...] Read more.
Based on the meteorological, ozone (O3), and vertical observation data of 2020, this study sought to evaluate the daily variation in O3, particularly the characteristics of nocturnal ozone pollution. We also discuss the effect of local and mesoscale horizontal transport and vertical mixing on the formation of nocturnal O3 pollution. Distinct seasonal characteristics of the daily O3 variation in Shenzhen were identified. In particular, significant nocturnal peaks were found to regularly occur in the winter and spring (November–December and January–April). The monthly average of daily variation had a clear bimodal distribution. During the period, O3 pollution frequently occurred at night, with the maximum hourly O3 concentration reaching 203.5 μg/m3. Nocturnal O3 pollution was closely associated with horizontal transport and vertical mixing. During the study period, the O3 maximum values were recorded on 68 nights, primarily between 23:00 and 03:00, with occasional observation of two peaks. The impact of horizontal transport and vertical mixing on the nocturnal secondary O3 maximum values was elaborated in two case studies, where vertical mixing was mainly associated with low-level jets, with strong wind shear enhancing turbulent mixing and transporting O3 from the upper layers to the surface. Full article
(This article belongs to the Special Issue Monitoring and Modelling Air Pollution and Thermal Environment through Applications in Urban Areas)
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<p>The geographical location of the Pearl River Delta region in China (<b>a</b>) and the research station in Shenzhen (<b>b</b>) (marked as yellow dot).</p> Full article ">Figure 2
<p>Daily variation in O<sub>3</sub> concentrations in individual months of 2020. Gray indicates nighttime.</p> Full article ">Figure 3
<p>Corresponding daily variation in O<sub>3</sub> concentrations during the occurrence of nighttime O<sub>3</sub> maximums.</p> Full article ">Figure 4
<p>Characteristics of the variation in the bulk Richardson number (Ri<sub>b</sub>) (<b>a</b>), wind direction (<b>b</b>), wind speed (<b>c</b>), and O<sub>3</sub> concentration (<b>d</b>) from 8 to 9 April 2020.</p> Full article ">Figure 5
<p>The 12 h backward trajectories of air mass calculated using the HYSPLIT model at 23:00, 8 April.</p> Full article ">Figure 6
<p>(<b>a</b>) Temporal change in hourly ozone concentration from 07:00 LT on 13 March to 06:00 LT on 14 March 2020; (<b>b</b>) Horizontal wind speed profiles from 00:00 to 06:00 LT on 14 March 2020, respectively; (<b>c</b>) Ozone concentration profiles at 00:00, 04:00, and 06:00 LT on 14 March 2020, respectively, which were observed by atmospheric ozone detection lidar on the Futian Archives building (indicated by black pentagram in <a href="#atmosphere-13-01318-f007" class="html-fig">Figure 7</a>); (<b>d</b>) Characteristic variations in TKE at 40 m from the meteorology tower from 13 to 14 March 2020.</p> Full article ">Figure 7
<p>Geographical distribution of the composites of 950 hPa O<sub>3</sub> concentration (units: μg/m<sup>3</sup>; shaded colours) and 950 hPa horizontal wind vectors at 00:00, 04:00, 07:00 on 14 March 2020, respectively (data from the ECMWF). The black pentagram is the location of the ozone profile.</p> Full article ">Figure 8
<p>Conceptual model of nocturnal O<sub>3</sub> pollution under the land breeze over Shenzhen.</p> Full article ">
20 pages, 1062 KiB  
Article
Life Cycle Assessment of a Prospective Technology for Building-Integrated Production of Broccoli Microgreens
by Michael G. Parkes, Julieth P. Cubillos Tovar, Filipe Dourado, Tiago Domingos and Ricardo F. M. Teixeira
Atmosphere 2022, 13(8), 1317; https://doi.org/10.3390/atmos13081317 - 18 Aug 2022
Cited by 13 | Viewed by 3705
Abstract
Indoor Vertical Farms (IVF) can contribute to urban circular food systems by reducing food waste and increasing resource use efficiency. They are also known for high energy consumption but could potentially be improved by integration with buildings. Here, we aim to quantify the [...] Read more.
Indoor Vertical Farms (IVF) can contribute to urban circular food systems by reducing food waste and increasing resource use efficiency. They are also known for high energy consumption but could potentially be improved by integration with buildings. Here, we aim to quantify the environmental performance of a prospective building-integrated urban farm. We performed a Life Cycle Assessment for a unit installed in a university campus in Portugal, producing broccoli microgreens for salads. This technology integrates IVF, product processing and Internet of Things with unused space. Its environmental performance was analyzed using two supply scenarios and a renewable energy variation was applied to each scenario. Results show that the IVF system produces 7.5 kg of microgreens daily with a global warming potential of 18.6 kg CO2e/kg in the case of supply direct on campus, or 22.2 kg CO2e/kg in the case of supply off campus to retailers within a 10-km radius. Consistently in both scenarios, electricity contributed the highest emission, with 10.03 kg CO2e/kg, followed by seeds, with 4.04 kg CO2e/kg. The additional use of photovoltaic electricity yields a reduction of emissions by 32%; an improvement of approximately 16% was found for most environmental categories. A shortened supply chain, coupled with renewable electricity production, can contribute significantly to the environmental performance of building-integrated IVF. Full article
(This article belongs to the Special Issue Urban Design, Microclimate and Environment)
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<p>System boundary for the LCA study of prospective IVF technology and processes (Blue) to service through circular supply on campus (Green) for comparison with linear supply (Orange) delivering to offsite retail food businesses.</p> Full article ">Figure 2
<p>Global Warming Potential results of the LCA model of the building-integrated IVF technology for Circular Supply (CS) and Linear Supply (LS) scenarios. Presented in carbon emissions CO<sub>2</sub> e/kg per kg of fresh weight broccoli microgreens for material inputs.</p> Full article ">Figure 3
<p>Photovoltaic system (PV) variation of the GWP results of for the building-integrated IVF technology for Circular Supply (CS) and Linear Supply (LS); add the inclusion of PV to infrastructure and PV replacing 70% electricity. Presented are results for global warming potential in carbon emissions (CO<sub>2</sub>e) per kg of fresh weight broccoli microgreens for material inputs.</p> Full article ">Figure 4
<p>Ecotoxicity indicator results per kg of fresh weight produced by the building-integrated IVF technology for Circular Supply (CS) and Linear Supply (LS) in relationship to each other. Presented in Global Warming (CO<sub>2</sub>e), Freshwater Ecotoxicity, Marine Ecotoxicity, Terrestrial Ecotoxicity and Human Toxicity (kg 1,4-DCB/kg) (<a href="#app1-atmosphere-13-01317" class="html-app">Table S2 and Figure S1</a>).</p> Full article ">
18 pages, 4856 KiB  
Article
Analysis of Spatial and Temporal Variations of the Near-Surface Wind Regime and Their Influencing Factors in the Badain Jaran Desert, China
by Ziying Hu, Guangpeng Wang, Yong Liu, Peijun Shi, Guoming Zhang, Jifu Liu, Yu Gu, Xichen Huang, Qingyan Zhang, Xu Han, Xueling Wang, Jiewen Du, Ruoxin Li and Lianyou Liu
Atmosphere 2022, 13(8), 1316; https://doi.org/10.3390/atmos13081316 - 18 Aug 2022
Cited by 2 | Viewed by 2142
Abstract
Wind regime is one of the main natural factors controlling the evolution and distribution of aeolian sand landforms, and sand drift potential (DP) is usually used to study the capacity of aeolian sand transport. The Badain Jaran Desert (BJD) is located where polar [...] Read more.
Wind regime is one of the main natural factors controlling the evolution and distribution of aeolian sand landforms, and sand drift potential (DP) is usually used to study the capacity of aeolian sand transport. The Badain Jaran Desert (BJD) is located where polar cold air frequently enters China. Based on wind data of eight nearby meteorological stations, this research is intended to explore the temporal variation and spatial distribution features of wind speed and DP using linear regression and cumulative anomaly method, and reveal the relationship between atmospheric circulation and wind speed with correlation analysis. We found that the wind speed and frequency of sand-blowing wind in the BJD decreased significantly during 1971–2016, and the wind speed obviously mutated in 1987. The regional wind speed change was affected by the Asian polar vortex, the northern hemisphere polar vortex and the Tibet Plateau circulation. The wind rose of the annual sand-blowing wind in this region was the “acute bimodal” type. Most of the annual wind directions clustered into the W-NW, and the prevailing wind direction was WNW. During 1971–2016, the annual DP, the resultant drift potential (RDP) and the directional variability (PDP/DP) in the desert showed an obvious downtrend, with a “cliff-like” decline in the 1980s and relative stable fluctuation thereafter. The BJD was under a low-energy wind environment with the acute bimodal wind regime. Wind speed, sand-blowing wind frequency and DP were high in the northeast and low in the southwest. Full article
(This article belongs to the Topic Wind, Wave and Tidal Energy Technologies in China)
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<p>The location of the Badain Jaran Desert (BJD) and wind regime in the BJD. The yellow, blue, green and purple bars represent wind speed, sand-blowing wind frequency, DP and RDP, respectively.</p> Full article ">Figure 2
<p>Change process of (<b>a</b>) annual, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) autumn, (<b>e</b>) winter wind speed and (<b>f</b>) cumulative anomaly curve of annual wind speed in the BJD. The black dots and line indicate the annual and seasonal wind speed during 1971–2016; the black solid line indicates the cumulative anomaly curve of annual wind speed during 1971 to 2016; the black dashed line means the turning point.</p> Full article ">Figure 3
<p>Annual sand-blowing wind speed and frequency in the BJD. The black line indicates annual sand-blowing wind speed during 1971–2016; the bar means the sand-blowing wind frequency.</p> Full article ">Figure 4
<p>Related coefficient between wind speed and atmospheric circulation indices in different periods in the BJD (* The significance level is 0.05, ** The significance level is 0.01).</p> Full article ">Figure 5
<p>Change process of wind speed and (<b>a</b>) AO, (<b>b</b>) NHSHII, (<b>c</b>) WPSHII, (<b>d</b>) APVAI, (<b>e</b>) NHPVAI, (<b>f</b>) EATII and (<b>g</b>) TPR2I in the BJD.</p> Full article ">Figure 6
<p>Wind roses of sand-blowing wind at (<b>a</b>) Ejina Qi, (<b>b</b>) Guaizihu, (<b>c</b>) Bayinnuoergong, (<b>d</b>) Dingxin, (<b>e</b>) Jinta, (<b>f</b>) Minqin, (<b>g</b>) Alxa you Qi, (<b>h</b>) Yabrai and (<b>i</b>) BJD.</p> Full article ">Figure 7
<p>Change process of wind regime (DP, drift potential; RDP, resultant drift potential; RDD, resultant drift direction; RDP/DP, directional variability) before and after the turning point in the BJD.</p> Full article ">Figure 8
<p>Wind regime of (<b>a</b>) Ejina Qi, (<b>b</b>) Guaizihu, (<b>c</b>) Bayinnuoergong, (<b>d</b>) Dingxin, (<b>e</b>) Jinta, (<b>f</b>) Minqin, (<b>g</b>) Alxa you Qi, (<b>h</b>) Yabrai and (<b>i</b>) BJD.</p> Full article ">
16 pages, 2799 KiB  
Article
Study of Absorbing CO2 from Emissions Using a Spray Tower
by Zhongcheng Wang, Xiaoyu Liu and Ke Li
Atmosphere 2022, 13(8), 1315; https://doi.org/10.3390/atmos13081315 - 18 Aug 2022
Cited by 4 | Viewed by 2613
Abstract
In order to reduce the environmental impact caused by CO2 emissions from ships and achieve the goal of green shipping, a spray tower using NaOH solution for the absorption of CO2 has been established in this paper. Using the characteristics of [...] Read more.
In order to reduce the environmental impact caused by CO2 emissions from ships and achieve the goal of green shipping, a spray tower using NaOH solution for the absorption of CO2 has been established in this paper. Using the characteristics of a 6135G128ZCa marine diesel engine, the CO2 absorption system was designed and mathematical models of CO2 absorption efficiency were developed. The effects of the variation in engine exhaust gas temperature, the concentration of NaOH solution, the exhaust gas velocity, different load conditions, and different nozzle types on the absorption efficiency of CO2 were thoroughly investigated experimentally. Moreover, the mechanism of CO2 absorption was analyzed. The developed model was verified by comparing the test results with the simulation results. The results of the study proved that using NaOH solution to absorb CO2 from ship exhausts could reduce the level of CO2 emissions from ships by more than 20%, which indicates that this technology could be used in the future to reduce the level of CO2 emissions from ships. Full article
(This article belongs to the Special Issue Advances of Materials and Processes in CO2 Capture and Utilization)
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<p>System for the recovery of ship’s CO<sub>2</sub>. 1—6135G128ZCa marine diesel engine; 2—spray tower; 3—mixing tank; 4—dosing tank; 5—dosing pump; 6—direct injection nozzle; 7—spiral nozzle system; 8—exhaust pipe; 9—bypass valve; 10—diesel exhaust out; 11—measurement pointing position; 12—demister; 13—cooler; 14—liquid flow meter; 15—shut-off valve; 16—cooling water inlet; 17—cooling water outlet.</p> Full article ">Figure 2
<p>The 6135-type spray tower.</p> Full article ">Figure 3
<p>Exhaust gas cooler.</p> Full article ">Figure 4
<p>Simulation flow chart of decarburization process.</p> Full article ">Figure 5
<p>CO<sub>2</sub> absorption rate of the simulation.</p> Full article ">Figure 6
<p>Reaction temperature effect on the CO<sub>2</sub> absorption rate.</p> Full article ">Figure 7
<p>Effect of concentration of NaOH solution on the CO<sub>2</sub> absorption rate.</p> Full article ">Figure 8
<p>Experimental results at different load conditions.</p> Full article ">Figure 9
<p>The CO<sub>2</sub> absorption rate of the experiment compared with the simulation.</p> Full article ">Figure 10
<p>Types and atomization effect of nozzles. (<b>A</b>)—Spiral nozzle. (<b>B</b>)—Direct nozzle.</p> Full article ">Figure 11
<p>Nozzle installation layout. 1—Spiral nozzle system. 2—Direct injection nozzle.</p> Full article ">Figure 12
<p>CO<sub>2</sub> absorption rate with different types of nozzles.</p> Full article ">
15 pages, 4346 KiB  
Article
Revealing the Effects of Water Imbibition on Gas Production in a Coalbed Matrix Using Affected Pore Pressure and Permeability
by Yi Lou, Yuliang Su, Ke Wang, Peng Xia, Wendong Wang, Wei Xiong, Linjie Shao and Fuqin Yang
Atmosphere 2022, 13(8), 1314; https://doi.org/10.3390/atmos13081314 - 18 Aug 2022
Cited by 2 | Viewed by 1833
Abstract
The effect of water imbibition on characteristics of coalbed methane reservoirs, such as permeability, gas occurrence state, and gas production, is controversial. According to the mechanism of imbibition, gas and water distribution in blind pores is reconfigured during the fracturing process. Therefore, a [...] Read more.
The effect of water imbibition on characteristics of coalbed methane reservoirs, such as permeability, gas occurrence state, and gas production, is controversial. According to the mechanism of imbibition, gas and water distribution in blind pores is reconfigured during the fracturing process. Therefore, a new comprehensive model of pore pressure and permeability, based on the perfect gas equation and the weighted superposition of viscous flow and Knudsen diffusion, was established for micro- and nanoscale blind pores during water drainage. Using the numerical simulation module in the Harmony software, the effects of imbibition on coal pore pressure, permeability, and gas production were analyzed. The results showed that (1) water imbibition can increase pore pressure and reduce permeability, and (2) water imbibition is not always deleterious to gas production and estimated ultimate reserve (EUR), when the imbibition is constant, the thicker water film is deleterious to coalbed methane wells; when the thickness of water film is constant, more imbibition is beneficial to gas production and EUR. This research is beneficial to optimize the operation of well shut-ins after fracturing and provides methods for optimizing key parameters of gas reservoirs and insights into understanding the production mechanism of coalbed methane wells. Full article
(This article belongs to the Special Issue CO2 Sequestration, Capture and Utilization)
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<p>High temperature and high-pressure NMR visualization experimental platform (MacroMR12-150H-l).</p> Full article ">Figure 2
<p>The influence of fracturing fluid imbibition on different types of pores (CA: compressed area, IA: imbibition area).</p> Full article ">Figure 3
<p>Change in the gas–water state in blind pores during the process of imbibition and drainage (according to Wang et al., [<a href="#B10-atmosphere-13-01314" class="html-bibr">10</a>]). (<b>a</b>) Change in the gas–water state in blind pores during the imbibition process. (<b>b</b>) Change in the gas–water state in blind pores during the drainage process.</p> Full article ">Figure 4
<p>Schematic of water drainage (according to Wang et al., [<a href="#B10-atmosphere-13-01314" class="html-bibr">10</a>]).</p> Full article ">Figure 5
<p>Effect of imbibition on water saturation.</p> Full article ">Figure 6
<p>Effect of imbibition on the maximum pore pressure after drainage.</p> Full article ">Figure 7
<p>Effect of imbibition on permeability.</p> Full article ">Figure 8
<p>Model of wet coal—numerical vertical in harmony-CBM. (x-y) Pressure, iteration no. 9 to 347.</p> Full article ">Figure 9
<p>Simulation results of coalbed methane well production after the impaction of different imbibition degrees, (<b>A</b>) is the magnification of the intersection with the ordinate, (<b>B</b>) is the magnification of the cumulative production between 1800 d and 1820 d.</p> Full article ">Figure 10
<p>Effect of imbibition length on gas well production when the thickness of the retained water film is constant, the subfigure is the magnification of the intersection with the ordinate.</p> Full article ">Figure 11
<p>Effect of water film thickness on gas well production, the subfigure is the magnification of the intersection with the ordinate.</p> Full article ">
9 pages, 826 KiB  
Article
Evaluating the Atmospheric Loss of H2 by NO3 Radicals: A Theoretical Study
by Manolis N. Romanias and Thanh Lam Nguyen
Atmosphere 2022, 13(8), 1313; https://doi.org/10.3390/atmos13081313 - 18 Aug 2022
Viewed by 1757
Abstract
Molecular hydrogen (H2) is now considered among the most prominent substitute for fossil fuels. The environmental impacts of a hydrogen economy have received more attention in the last years, but still, the knowledge is relatively poor. In this work, the reaction [...] Read more.
Molecular hydrogen (H2) is now considered among the most prominent substitute for fossil fuels. The environmental impacts of a hydrogen economy have received more attention in the last years, but still, the knowledge is relatively poor. In this work, the reaction of H2 with NO3 radical (the dominant night-time detergent of the atmosphere) is studied for the first time using high-level composite G3B3 and modification of high accuracy extrapolated ab initio thermochemistry (mHEAT) methods in combination with statistical kinetics analysis using non-separable semi-classical transition state theory (SCTST). The reaction mechanism is characterized, and it is found to proceed as a direct H-abstraction process to yield HNO3 plus H atom. The reaction enthalpy is calculated to be 12.8 kJ mol−1, in excellent agreement with a benchmark active thermochemical tables (ATcT) value of 12.2 ± 0.3 kJ mol−1. The energy barrier of the title reaction was calculated to be 74.6 and 76.7 kJ mol−1 with G3B3 and mHEAT methods, respectively. The kinetics calculations with the non-separable SCTST theory give a modified-Arrhenius expression of k(T) = 10−15 × T0.7 × exp(−6120/T) (cm3 s−1) for T = 200–400 K and provide an upper limit value of 10−22 cm3 s−1 at 298 K for the reaction rate coefficient. Therefore, as compared to the main consumption pathway of H2 by OH radicals, the title reaction plays an unimportant role in H2 loss in the Earth’s atmosphere and is a negligible source of HNO3. Full article
(This article belongs to the Special Issue Atmospheric Pollution Caused by Solid Fuels Combustion)
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<p>Schematic reaction energy profile for the reaction of NO<sub>3</sub> radical with H<sub>2</sub> calculated using the G3B3 (in black) and mHEAT (in red) methods. The conformation for the transition state calculated with the G3B3 is also presented in the graph. Bond lengths are given in Angstrom (Å) while angles are in degrees.</p> Full article ">Figure 2
<p>Calculated rate constants (cm<sup>3</sup> s<sup>−1</sup>) of the title reaction in the atmospheric temperature range of 200 to 400 K. The solid line represents the results from first principles while the dashed line shows the results obtained when lowering the barrier height by 4 kJ mol<sup>−1</sup>.</p> Full article ">
13 pages, 3889 KiB  
Article
Research on the Influence of Weather Patterns on Ozone Concentration: A Case Study in Tianjin
by Yuan Li, Jiguang Wang, Liwei Li, Yu Bai, Jingyun Gao, Lei He, Miao Tang and Ning Yang
Atmosphere 2022, 13(8), 1312; https://doi.org/10.3390/atmos13081312 - 18 Aug 2022
Cited by 2 | Viewed by 2099
Abstract
Ozone (O3) is an important secondary substance that plays a significant role in atmospheric chemistry and climate change. Although O3 is essential in the stratosphere, it is harmful to human health in the troposphere, where this study was conducted. In [...] Read more.
Ozone (O3) is an important secondary substance that plays a significant role in atmospheric chemistry and climate change. Although O3 is essential in the stratosphere, it is harmful to human health in the troposphere, where this study was conducted. In recent years, O3 pollution in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions has deteriorated, which has become an important environmental problem. The generation of O3 is closely related to meteorological factors. In this study, the weather classification method was adopted to study the effect of meteorological conditions on O3 concentration. In the BTH region, Tianjin was selected as the representative city for the research. The real-time pollutants data, meteorological re-analysis data, and meteorological data in 2019 were combined for the analysis. The subjective weather classification method was adopted to investigate the effects of different weather types on O3 concentration. The backward trajectory tracking model was used to explore the characteristics and changes of O3 pollution under two extreme weather types. The results indicate there is a good correlation between O3 concentration and ambient temperature. Under the control of low pressure on the ground and the influence of southwest airflow in the upper air for Tianjin, heavy O3 pollution occurred frequently. The addition of external transport and local generation will cause high O3 values when the weather system is weak. The O3 concentration is closely related to ambient temperature. Continuous high-temperature weather is conducive to the photochemical reaction. The multi-day O3 pollution process would occur when the weather system is robust. The first and second types of extreme weather are more likely to cause persistent O3 pollution processes. Under the premise of stable emission sources, the change in weather patterns was the main reason affecting the O3 concentration. This study aims to improve O3 pollution control and air quality prediction in the BTH region and large cities in China. Full article
(This article belongs to the Special Issue Advances in Understanding Ozone Pollution)
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<p>The national air quality monitoring station in Tianjin.</p> Full article ">Figure 2
<p>(<b>a</b>) Air quality classification during monitoring (<b>b</b>) Hourly-mean O<sub>3</sub> concentration of Tianjin (average of the hourly concentration from all monitors at each hour) and the number of exceedances over the secondary standard (160 μg/m<sup>3</sup>) for each hour during monitoring.</p> Full article ">Figure 3
<p>Correlation of O<sub>3</sub>-MDA8 with (<b>a</b>) maximum temperature, (<b>b</b>) average temperature, and (<b>c</b>) relative humidity.</p> Full article ">Figure 4
<p>Meteorological parameters during different weather types: (<b>a</b>) case 1, (<b>b</b>) case 2, (<b>c</b>) case 3, and (<b>d</b>) case 4.</p> Full article ">Figure 5
<p>(<b>a</b>) Surface weather map and (<b>b</b>) 500 hPa weather map at 08:00 on June 12. (<b>c</b>) Surface weather map and (<b>d</b>) 500 hPa weather map at 08:00 on June 24.</p> Full article ">Figure 6
<p>Diurnal variation of pollutant concentration and meteorological element from (<b>a</b>) June 12 to 14 and (<b>b</b>) June 22 to 29.</p> Full article ">Figure 7
<p>24-h backward air mass trajectory at 100 m, 500 m, and 1000 m altitudes during different research periods: (<b>a</b>) June 12th, (<b>b</b>) June 24th, and (<b>c</b>) June 16th.</p> Full article ">Figure 8
<p>(<b>a</b>) Surface and (<b>b</b>) 500 hPa weather map at 08:00 on 16 June 2019.</p> Full article ">Figure 9
<p>Diurnal variation of pollutant concentration and meteorological element from June 15 to 17.</p> Full article ">
14 pages, 37873 KiB  
Article
Numerical Simulation of a Typical Convective Precipitation and Its Cloud Microphysical Process in the Yushu Area, Based on the WRF Model
by Minghao He, Shaobo Zhang, Xianyu Yang and Shucheng Yin
Atmosphere 2022, 13(8), 1311; https://doi.org/10.3390/atmos13081311 - 17 Aug 2022
Cited by 2 | Viewed by 1957
Abstract
Cloud microphysical processes significantly impact the time variation and intensity of precipitation. However, due to the high altitude of the Tibetan Plateau (TP) and the lack of observational data, the understanding of cloud microphysical processes on the TP is relatively insufficient, affecting the [...] Read more.
Cloud microphysical processes significantly impact the time variation and intensity of precipitation. However, due to the high altitude of the Tibetan Plateau (TP) and the lack of observational data, the understanding of cloud microphysical processes on the TP is relatively insufficient, affecting the accuracy of precipitation simulations around the TP. To further reveal the characteristics of convective precipitation and cloud microphysical structure over the TP, the mesoscale numerical model, WRF, and various observational data were used to simulate and evaluate typical convective precipitation in the Yushu area, which was recorded from 11 to 12 August 2020. The results showed that the combination of the Lin scheme in the WRF model could effectively reproduce this case’s characteristics and evolution process. In the simulation process, the particles of each phase were distributed at different altitudes, and their mass and density over time reflected the characteristics of surface precipitation changes. Among the particles mentioned above, rainwater contributed the most to the initiation and growth of graupel particles. Further research established that the initiation of graupel was mainly affected by the freezing effect of rainwater and cloud ice, while the growth of graupel was influenced primarily by the collision of graupel particles and rainwater. On the whole, from the evolution characteristics of microphysical processes over time, it was found that the ice phase process plays an essential role in this typical convective precipitation. Full article
(This article belongs to the Special Issue Land-Atmosphere Interaction on the Tibetan Plateau)
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<p>Site geographic distribution information (the red five-pointed star represents Yushu, and the black origin represents Qumalai, Zhiduo, Zaduo, Nangqian, Qingshuihe, respectively) (<b>left</b>) and Ka-band millimeter-wave cloud radar system (photo from Yushu meteorological station) (<b>right</b>).</p> Full article ">Figure 2
<p>The model domain and topography (unit: m). The first nested area contains most of the TP area, the second nested area is located in the eastern part of the TP, and the third nested area focuses on the concentration of observation sites in the eastern part of Yushu Tibetan Autonomous Prefecture in southern Qinghai Province. The area average of the black dashed area in the figure is used for comparison with the radar profile.</p> Full article ">Figure 3
<p>(<b>a</b>) The blackbody brightness temperature (TBB) at 17:00 on 11 August 2020, (<b>b</b>) the reflectivity factor in Yushu, (<b>c</b>) the time variation of precipitation intensity in Yushu, and (<b>d</b>) photograph of the observed hail.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) The blackbody brightness temperature (TBB) at 17:00 on 11 August 2020, (<b>b</b>) the reflectivity factor in Yushu, (<b>c</b>) the time variation of precipitation intensity in Yushu, and (<b>d</b>) photograph of the observed hail.</p> Full article ">Figure 4
<p>The simulated spatial distribution of the 24h precipitation results from 08:00 on 11 August to 08:00 on 12 August; (<b>a</b>–<b>f</b>) show the Lin, WSM5, WSM6, New Thompson, Morrison, and Eta results, respectively (unit: mm).</p> Full article ">Figure 5
<p>The simulated reflectivity factors (<b>a</b>–<b>f</b>) were for Lin, WSM5, WSM6, New Thompson, Morrison, and Eta, respectively (unit: dbz). This figure is obtained by the regional averaging of the black dashed area (32.5–33.5° E, 96.5–97.5° N) in <a href="#atmosphere-13-01311-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 6
<p>The mixing ratios of particles in each phase: (<b>a</b>) ice, (<b>b</b>) snow, (<b>c</b>) graupel, (<b>d</b>) cloud water, (<b>e</b>) rainwater, (<b>f</b>) water vapor (unit: <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics></math> kg/kg). This figure is obtained via regional averaging of the black dashed area (32.5–33.5° E, 96.5–97.5° N) in <a href="#atmosphere-13-01311-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 7
<p>Conversion rate of the water source item to a horizontally averaged figure: (<b>a</b>) rainwater source item, (<b>b</b>) snow source item, (<b>c</b>) graupel primary source item, (<b>d</b>) graupel growth source item. This figure is obtained by the regional averaging of the black dashed area (32.5–33.5° E, 96.5–97.5° N) in <a href="#atmosphere-13-01311-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 8
<p>Microphysical process timing characteristics. (<b>a</b>) Sublimation and Bergeron, (<b>b</b>) ice phase particle aggregation, (<b>c</b>) coagulation, and (<b>d</b>) melt (unit:<math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mi mathvariant="normal">g</mi> <mo>·</mo> <msup> <mrow> <mi>kg</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>·</mo> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>). This figure is obtained by regional averaging of the black dashed area (32.5–33.5° E, 96.5–97.5° N) in <a href="#atmosphere-13-01311-f002" class="html-fig">Figure 2</a>.</p> Full article ">
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