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Atmosphere
Volume 14
Issue 9
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Atmosphere, Volume 14, Issue 9 (September 2023) – 147 articles

Cover Story (view full-size image): The Great American Solar Eclipse on 21 August 2017 was a stunning celestial event that traversed across the continental United States from coast to coast. This paper revisits this unique event to conduct a dedicated study of the 3D ionospheric electron density variation during the eclipse, using the powerful Millstone Hill incoherent scatter radar data and a new TEC-based ionospheric data assimilation system (TIDAS). The results effectively captured the altitude-resolved features of eclipse-induced electron density reduction and post-eclipse enhancement in the 3D domain with unprecedented fine-scale details. The 3D ionospheric electron density results reconstructed by TIDAS data assimilation help advance the current understanding of eclipse-induced changes in the ionosphere and the underlying mechanisms. View this paper
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9 pages, 3080 KiB  
Communication
Investigating the Characteristics of Tropical Cyclone Size in the Western North Pacific from 1981 to 2009
by Qing Cao, Xiaoqin Lu and Guomin Chen
Atmosphere 2023, 14(9), 1468; https://doi.org/10.3390/atmos14091468 - 21 Sep 2023
Viewed by 1130
Abstract
Tropical cyclone (TC) size is an important parameter for estimating TC risks, such as precipitation distribution, gale-force wind damage, and storm surge. This paper uses the TC size dataset compiled by the Shanghai Typhoon Institute of China Meteorological Administration (STI/CMA) to investigate the [...] Read more.
Tropical cyclone (TC) size is an important parameter for estimating TC risks, such as precipitation distribution, gale-force wind damage, and storm surge. This paper uses the TC size dataset compiled by the Shanghai Typhoon Institute of China Meteorological Administration (STI/CMA) to investigate the interannual, monthly variation in TC size, and the relationships between TC size and intensity in the WNP basin from 1981 to 2009. The results show that the annual mean TC size oscillated within the range of 175–210 km from 1981 to 2002, then decreased following 2003. For the monthly average TC size, there are two peaks in September and October. The TC size, overall, becomes larger with increasing intensity; the samples with an unusually large size are mainly concentrated near a 40 m s−1 intensity. After the TC intensity exceeds 40 m s−1, the number of unusually large size samples gradually decreases. About 60% of the TCs reach their maximum size after reaching the peak intensity, and the average lag time is 8.3 h. Full article
(This article belongs to the Special Issue Advances in Tropical Cyclone Prediction: Observation, Simulation, and Verification)
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<p>Evolution of the annual mean TC size (blue solid circle) and the 95% confidence interval (bars at upper and lower ends of the blue solid circle) of all the samples of each year’s TC size from 1981 to 2009. The red dashed line in the figure indicates the average TC size from 1981 to 2009. The numbers above abscissa are total samples of TC size and number of TCs for each year.</p> Full article ">Figure 2
<p>Monthly variation in mean TC size (green solid circle) and the 95% confidence interval (bars at upper and lower ends of the green solid circle) of all the monthly samples.</p> Full article ">Figure 3
<p>Relationship between TC intensity and size: (<b>a</b>) scatterplot of intensity and size for all TC samples, (<b>b</b>) scatterplot of intensity and size for a TC at peak intensity.</p> Full article ">Figure 4
<p>Geographical distribution of the (<b>a</b>) large size, (<b>b</b>) small size and (<b>c</b>) normal size of TC cases.</p> Full article ">Figure 5
<p>Geographic location of maximum size and peak intensity of each TC: (<b>a</b>) maximum size reaches after peak intensity, (<b>b</b>) maximum size reaches before peak intensity, and (<b>c</b>) maximum size coincides with peak intensity. The black solid lines indicate the TC best tracks, red circles indicate the location of maximum size, and blue dots indicate the location of peak intensity.</p> Full article ">Figure 6
<p>Scatterplot of TC intensity and size variation.</p> Full article ">
12 pages, 10189 KiB  
Article
Research on the CO2 Emission Characteristics of a Light-Vehicle Real Driving Emission Experiment Based on Vehicle-Specific Power Distribution
by Hualong Xu, Yi Lei, Ming Liu, Yunshan Ge, Lijun Hao, Xin Wang and Jianwei Tan
Atmosphere 2023, 14(9), 1467; https://doi.org/10.3390/atmos14091467 - 21 Sep 2023
Cited by 1 | Viewed by 1132
Abstract
China implemented the China VI emission standard in 2020. The China VI emission standard has added requirements for the RDE (real-world driving emission) test. To evaluate vehicle CO2 emission for different vehicles, 10 conventional gasoline vehicles were tested under the RDE procedure [...] Read more.
China implemented the China VI emission standard in 2020. The China VI emission standard has added requirements for the RDE (real-world driving emission) test. To evaluate vehicle CO2 emission for different vehicles, 10 conventional gasoline vehicles were tested under the RDE procedure using the PEMS (portable emission testing system) method. All vehicles tested meet the sixth emission regulation with a displacement of 1.4 L~2.0 L. Among the vehicles tested, the highest CO2 emission factor was 281 g/km and the lowest was 189 g/km, while the acceleration of RDE gets a wider distribution, varying from −2.5 m/s2 to 2.5 m/s2. The instantaneous mass emission rate could reach around 16 g/s. The amounts of total CO2 emission in the positive region and the negative region make up 82~89% and 11~18% of the overall CO2 emission during the entire RDE driving period, respectively. The same vehicle has a wide range of CO2 emission factors at different VSP (vehicle specific power) intervals. Different RDE test conditions can lead to large differences in CO2 emissions. Full article
(This article belongs to the Section Air Pollution Control)
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<p>Schematic diagram of PEMS installation. ① PEMS analysis module. ② PN counting module. ③ Exhaust gas flow meter. ④ Control computer. ⑤ Emergency stop switch. ⑥ OBD communication connection. ⑦ GPS. ⑧ Weather station. ⑨ External battery.</p> Full article ">Figure 2
<p>PEMS equipment on the vehicle.</p> Full article ">Figure 3
<p>A tracing of the test route.</p> Full article ">Figure 4
<p>Acceleration vs. velocity for test cycles.</p> Full article ">Figure 5
<p>CO<sub>2</sub> emission factor.</p> Full article ">Figure 6
<p>CO<sub>2</sub> emission factors for different displacement vehicles.</p> Full article ">Figure 7
<p>CO<sub>2</sub> emission rate for different displacement vehicles.</p> Full article ">Figure 8
<p>CO<sub>2</sub> emission frequencies of light-duty vehicles under RDE cycle.</p> Full article ">Figure 9
<p>CO<sub>2</sub> emission factors based on VSP interval.</p> Full article ">
13 pages, 1570 KiB  
Article
Modeling Turbulent Fluctuations in High-Latitude Ionospheric Plasma Using Electric Field CSES-01 Observations
by Simone Benella, Virgilio Quattrociocchi, Emanuele Papini, Mirko Stumpo, Tommaso Alberti, Maria Federica Marcucci, Paola De Michelis, Mirko Piersanti and Giuseppe Consolini
Atmosphere 2023, 14(9), 1466; https://doi.org/10.3390/atmos14091466 - 21 Sep 2023
Viewed by 1091
Abstract
High-latitude ionospheric plasma constitutes a very complex environment, which is characterized by turbulent dynamics in the presence of different ion species. The turbulent plasma motion produces statistical features of both electromagnetic and velocity fields, which have been broadly studied over the years. In [...] Read more.
High-latitude ionospheric plasma constitutes a very complex environment, which is characterized by turbulent dynamics in the presence of different ion species. The turbulent plasma motion produces statistical features of both electromagnetic and velocity fields, which have been broadly studied over the years. In this work, we use electric field high-resolution observations provided by the China-Seismo Electromagnetic Satellite-01 in order to investigate the properties of plasma turbulence within the Earth’s polar cap. We adopt a model of turbulence in which the fluctuations of the electric field are assimilated to a stochastic process evolving throughout the scales, and we show that such a process (i) satisfies the Markov condition (ii) can be modeled as a continuous diffusion process. These observations enable us to use a Fokker–Planck equation to model the changes in the statistics of turbulent fluctuations throughout the scales. In this context, we discuss the advantages and limitations of the proposed approach in modeling plasma electric field fluctuations. Full article
(This article belongs to the Special Issue The Role of Solar Wind-Magnetosphere Coupling in the Ionospheric Dynamics)
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<p>Electric field data of the southern polar cap crossing on 2018 August 10 gathered by EFD onboard the CSES-01 satellite.</p> Full article ">Figure 2
<p>Power spectral density of the electric field components. Vertical dashed lines indicate the frequency interval used in the data analysis. The black solid line shows the <math display="inline"><semantics> <msup> <mi>f</mi> <mrow> <mo>−</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </semantics></math> trend as a reference.</p> Full article ">Figure 3
<p>CK test on the fluctuations of the <span class="html-italic">y</span>-component of the electric field at three different scale separations: 0.005 (<b>a</b>), 0.05 (<b>b</b>) and 0.5 (<b>c</b>) s. The empirical joint PDF (red) is reported along with the PDF estimated through the CK relation (blue), and they are compared by superimposing PDF level curves.</p> Full article ">Figure 4
<p>First-, second-, and fourth-order KM coefficients (circles) evaluated at different time scales. Black solid lines indicate the parameterizations of Equations (<a href="#FD14-atmosphere-14-01466" class="html-disp-formula">14</a>) and (<a href="#FD15-atmosphere-14-01466" class="html-disp-formula">15</a>).</p> Full article ">Figure 5
<p>Comparison between the empirical PDFs (circles) and those obtained through the numerical solution of the FPE (solid lines). The dashed line marks the initial condition, whereas the dotted lines refer to the instantaneously steady-state solution of FPE.</p> Full article ">Figure 6
<p><b>Left panel</b>: PDFs of the noise at different scales. The dashed line refers to the Gaussian PDF expected from the theory. <b>Right panel</b>: the autocorrelation function of the noise term at the different timescales. The time lag <span class="html-italic">n</span> is given in units of 0.05 s.</p> Full article ">
17 pages, 3323 KiB  
Article
Concentration Gradients of Ammonia, Methane, and Carbon Dioxide at the Outlet of a Naturally Ventilated Dairy Building
by Harsh Sahu, Sabrina Hempel, Thomas Amon, Jürgen Zentek, Anke Römer and David Janke
Atmosphere 2023, 14(9), 1465; https://doi.org/10.3390/atmos14091465 - 21 Sep 2023
Cited by 1 | Viewed by 1112
Abstract
In natural ventilation system-enabled dairy buildings (NVDB), achieving accurate gas emission values is highly complicated. The external weather affects measurements of the gas concentration of pollutants (cP) and volume flow rate (Q) due to the open-sided design. Previous [...] Read more.
In natural ventilation system-enabled dairy buildings (NVDB), achieving accurate gas emission values is highly complicated. The external weather affects measurements of the gas concentration of pollutants (cP) and volume flow rate (Q) due to the open-sided design. Previous research shows that increasing the number of sensors at the side opening is not cost-effective. However, accurate measurements can be achieved with fewer sensors if an optimal sampling position is identified. Therefore, this study attempted to calibrate the outlet of an NVDB for the direct emission measurement method. Our objective was to investigate the cP gradients, in particular, for ammonia (cNH3), carbon dioxide (cCO2), and methane (cCH4) considering the wind speed (v) and their mixing ratios ([cCH4/cNH3¯]) at the outlet, and assess the effect of sampling height (H). The deviations in each cP at six vertical sampling points were recorded using a Fourier-transform infrared (FTIR) spectrometer. Additionally, wind direction and speed were recorded at the gable height (10 m) by an ultrasonic anemometer. The results indicated that, at varied heights, the average cNH3 (p < 0.001), cCO2 (p < 0.001), and (p < 0.001) were significantly different and mostly concentrated at the top (H = 2.7). Wind flow speed information revealed drastic deviations in cP, for example up to +105.1% higher cNH3 at the top (H = 2.7) compared to the baseline (H = 0.6), especially during low wind speed (v < 3 m s−1) events. Furthermore, [cCH4/cNH3¯] exhibited significant variation with height, demonstrating instability below 1.5 m, which aligns with the average height of a cow. In conclusion, the average cCO2, cCH4, and cNH3 measured at the barn’s outlet are spatially dispersed vertically which indicates a possibility of systematic error due to the sensor positioning effect. The outcomes of this study will be advantageous to locate a representative gas sampling position when measurements are limited to one constant height, for example using open-path lasers or low-cost devices. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution)
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<p>Sectional view of the investigated NVDB showing the gas sampling setups (SS1 and SS2), sampling position (SP), and the southwest inflow wind direction (WD).</p> Full article ">Figure 2
<p>Floor plan of the investigated dairy barn depicting the laying cubicles, free walkways, feeding table, and location of gas sampling setups (SS1) and (SS2).</p> Full article ">Figure 3
<p>Q-Q plot showing the normality of the data distribution.</p> Full article ">Figure 4
<p>Line plots showing mean values of (<b>a</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>C</mi> <msub> <mi>H</mi> <mn>4</mn> </msub> </mrow> </msub> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>N</mi> <msub> <mi>H</mi> <mn>3</mn> </msub> </mrow> </msub> </semantics></math> at different heights and sampling setups.</p> Full article ">Figure 5
<p>Line plots showing mean values of (<b>a</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>C</mi> <msub> <mi>H</mi> <mn>4</mn> </msub> </mrow> </msub> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>N</mi> <msub> <mi>H</mi> <mn>3</mn> </msub> </mrow> </msub> </semantics></math> at different heights and sampling setups, including the two levels of wind speed, i.e., high (<span class="html-italic">v</span> &gt; 3 m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) and low (<span class="html-italic">v</span> &lt; 3 m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) in green and blue color, respectively.</p> Full article ">Figure 6
<p>Line plots showing mean values of mixing ratio [<math display="inline"><semantics> <mover> <mrow> <msub> <mi mathvariant="normal">c</mi> <msub> <mi>CH</mi> <mn>4</mn> </msub> </msub> <mo>/</mo> <msub> <mi mathvariant="normal">c</mi> <msub> <mi>NH</mi> <mn>3</mn> </msub> </msub> </mrow> <mo>¯</mo> </mover> </semantics></math>] at each height and sampling setup: (<b>a</b>) without adding wind speed effect and (<b>b</b>) adding wind speed effect as two levels i.e., high and low in brown and violet color, respectively.</p> Full article ">
15 pages, 1444 KiB  
Article
Standardized Precipitation and Evapotranspiration Index Approach for Drought Assessment in Slovakia—Statistical Evaluation of Different Calculations
by Jaroslava Slavková, Martin Gera, Nina Nikolova and Cyril Siman
Atmosphere 2023, 14(9), 1464; https://doi.org/10.3390/atmos14091464 - 21 Sep 2023
Cited by 1 | Viewed by 1161
Abstract
In the conditions of rising air temperature and changing precipitation regimes in Central Europe and Slovakia over the last two decades, it is necessary to analyse drought, develop high-quality tools for drought detection, and understand its reactions to the emerging drought situation. One [...] Read more.
In the conditions of rising air temperature and changing precipitation regimes in Central Europe and Slovakia over the last two decades, it is necessary to analyse drought, develop high-quality tools for drought detection, and understand its reactions to the emerging drought situation. One of the frequently used meteorological drought indices is the Standardized Precipitation and Evapotranspiration Index (SPEI). Several parameters can be modified in different steps of the calculation process of SPEI. In the article, we analyse the influence of selected adjustable parameters on the index results. Our research has shown that the choice of a statistical distribution (Log-logistic, Pearson III, or Generalized Extreme Value) for fitting water balance can affect the feasibility of calculating distribution parameters (and thus the index) from the provided input data, as well as lead to either underestimation or overestimation of the index. The normality test of SPEI can be used as a tool for the detection and elimination of highly skewed indices and cases when the indices were not well determined by the distribution function. This study demonstrated improved results when using the GEV distribution, despite the common use of the Log-logistic distribution. With the Pearson III distribution, unusually high or low SPEI values (|SPEI| > 6) were detected. Full article
(This article belongs to the Special Issue Air Temperature and Precipitation and Relationship to Atmospheric Circulation)
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<p>Location of the study area in Europe (<b>top</b>) and stations included in the research (<b>bottom</b>). Stations lie between 112 m (station Veľký Meder) and 628 m (station Spišská Belá) above sea level.</p> Full article ">Figure 2
<p>(<b>a</b>) The average absolute difference between pairs of indices that differs in statistical distribution (see <a href="#atmosphere-14-01464-t003" class="html-table">Table 3</a>); black lines represent the average absolute difference for all three studied index lengths; (<b>b</b>) percentage of absolute differences greater than 2 (pink) and 0.5 (violet).</p> Full article ">Figure 3
<p>The number of no-solution cases for different modifications of SPEI calculation (type of SPEI; letters H and P after the name of distributions represent the used method for PET calculation: H = Hargreaves, P = Penman–Monteith) for all 13 used stations together (different stations are distinguished by colour). All shown indices are calculated based on a 39-year reference period.</p> Full article ">Figure 4
<p>The number of cases when SPEI is not normally distributed for different modifications of SPEI calculation (Type of SPEI; letters H and P after the name of distributions represents the used method for PET calculation: H = Hargreaves, P = Penman–Monteith) for all 13 used stations together (different stations are distinguished by colour). All shown indices were calculated based on a 39-year reference period.</p> Full article ">Figure 5
<p>Percentages of SPEI greater than 2 (blue) or lower than −2 (orange) for three studied statistical distributions.</p> Full article ">
17 pages, 1225 KiB  
Article
Exploring the Centennial-Scale Climate History of Southern Brazil with Ocotea porosa (Nees & Mart.) Barroso Tree-Rings
by Daniela Oliveira Silva Muraja, Virginia Klausner, Alan Prestes, Tuomas Aakala, Humberto Gimenes Macedo and Iuri Rojahn da Silva
Atmosphere 2023, 14(9), 1463; https://doi.org/10.3390/atmos14091463 - 20 Sep 2023
Cited by 1 | Viewed by 1600
Abstract
This article explores the dendrochronological potential of Ocotea porosa (Nees & Mart) Barroso (Imbuia) for reconstructing past climate conditions in the General Carneiro region, Southern Brazil, utilizing well-established dendroclimatic techniques. A total of 41 samples of Imbuia were subjected [...] Read more.
This article explores the dendrochronological potential of Ocotea porosa (Nees & Mart) Barroso (Imbuia) for reconstructing past climate conditions in the General Carneiro region, Southern Brazil, utilizing well-established dendroclimatic techniques. A total of 41 samples of Imbuia were subjected to dendroclimatic analysis to reconstruct precipitation and temperature patterns over the period from 1446 to 2011. Notably, we achieved the longest reconstructions of spring precipitation and temperature for the Brazilian southern region, spanning an impressive 566-year timeframe, by employing a mean chronology approach. To achieve our objectives, we conducted a Pearson’s correlation analysis between the mean chronology and the climatic time series, with a monthly temporal resolution employed for model calibration. Impressively, our findings reveal significant correlations with coefficients as high as |rx,P| = 0.32 for precipitation and |rx,T| = 0.45 for temperature during the spring season. Importantly, our climate reconstructions may elucidate a direct influence of the El Niño—South Oscillation phenomenon on precipitation and temperature patterns, which, in turn, are intricately linked to the natural growth patterns of the Imbuia trees. These results shed valuable light on the historical climate variability in the Southern Brazil region and provide insights into the climatic drivers affecting the growth dynamics of Ocotea porosa (Nees & Mart) Barroso. Full article
(This article belongs to the Special Issue Paleoclimate Reconstruction (2nd Edition))
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<p>Map of (<b>a</b>) annual average temperature, and (<b>b</b>) annual average rainfall for the southern region of Brazil, during the years 1931–1990. Source: [<a href="#B26-atmosphere-14-01463" class="html-bibr">26</a>].</p> Full article ">Figure 2
<p>Map of South America with Brazil and Paraná’s state demarcated. Source: [<a href="#B28-atmosphere-14-01463" class="html-bibr">28</a>].</p> Full article ">Figure 3
<p>Collection locations in the Southern Brazil region. General Carneiro city, Paraná, where tree ring samples were collected, is marked with a black asterisk. The city of Porto União (blue dot) provided the climatic data used in this study. Additionally, climatic data from nearby cities, including Lages, Castro, Curitiba, Campos Novos, Irineópolis, and Ivaí, were incorporated (red dots) to enhance the accuracy and completeness of the climate dataset, particularly by filling gaps and addressing missing data from Porto União.</p> Full article ">Figure 4
<p>Annual climate data. Annual precipitation time series (blue line) and the annual temperature time series (red line) for the study region of General Carneiro.</p> Full article ">Figure 5
<p>Climographs of monthly average precipitation (<b>top panel</b>) and temperature (<b>bottom panel</b>). The horizontal axis is labeled with the letters corresponding to months, starting from January and proceeding sequentially. In each graph, a box represents the median and interquartile range (IQR) of the data, while whiskers extend to 1.5 times the IQR.</p> Full article ">Figure 6
<p>The mean chronology of 41 <span class="html-italic">Imbuia</span> samples without the variance trend correction (light gray line) and with the variance trend correction (black line) based on the coefficient of variation, referred to here as the Index. The change in number of samples in the chronology is represented on the right y-axis (blue line).</p> Full article ">Figure 7
<p>Time variation and frequency distribution of mean chronology, referred to here as the Index. (<b>A</b>) Mean chronology from 1962 to 2011. (<b>B</b>) Histogram and fitted normal probability density function. Results of Lilliefors test for normality (Conover, 1980 [<a href="#B36-atmosphere-14-01463" class="html-bibr">36</a>]) are annotated at the upper left of (<b>B</b>).</p> Full article ">Figure 8
<p>Correlations (<b>top panel</b>) and partial correlations (<b>bottom panel</b>) of the mean chronology with seasonalized climate variables, namely precipitation (P) and temperature (T). In each top panel, the simple correlations with the primary climate variable are depicted, while in each bottom panel, the partial correlations of the mean chronology with the secondary climate variable are presented. Significance levels of <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.05 and <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.01 are color-coded. The notation <math display="inline"><semantics> <msub> <mi>r</mi> <mrow> <mi>x</mi> <mo>,</mo> <mi>P</mi> </mrow> </msub> </semantics></math> (<math display="inline"><semantics> <msub> <mi>r</mi> <mrow> <mi>x</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>) signifies the correlation of <span class="html-italic">x</span> (mean chronology) with <span class="html-italic">P</span> (<span class="html-italic">T</span>), while <math display="inline"><semantics> <msub> <mi>r</mi> <mrow> <mi>x</mi> <mo>,</mo> <mi>T</mi> <mo>.</mo> <mi>P</mi> </mrow> </msub> </semantics></math> (<math display="inline"><semantics> <msub> <mi>r</mi> <mrow> <mi>x</mi> <mo>,</mo> <mi>P</mi> <mo>.</mo> <mi>T</mi> </mrow> </msub> </semantics></math>) denotes the partial correlation of <span class="html-italic">x</span> with <span class="html-italic">T</span> (<span class="html-italic">P</span>), controlling for the influence of <span class="html-italic">P</span> (<span class="html-italic">T</span>). * stands for the previous year. (<b>a</b>) Top: Simple correlation with precipitation. Bottom: Partial correlation with temperature. (<b>b</b>) Top: Simple correlation with temperature. Bottom: Partial correlation with precipitation.</p> Full article ">Figure 9
<p>Scatterplots of mean chronology versus spring precipitation and temperature time series. The least-squares-fit straight line and correlation coefficient are illustrated on each plot.</p> Full article ">Figure 10
<p>Reconstruction of precipitation using the mean chronology by the linear regression method. The blue line represents the reconstruction, and the red line is the measured precipitation time series. The black line is the smoothing of the curve.</p> Full article ">
20 pages, 8251 KiB  
Article
A Convolutional Neural Network for Steady-State Flow Approximation Trained on a Small Sample Size
by Guodong Zhong, Xuesong Xu, Jintao Feng and Lei Yuan
Atmosphere 2023, 14(9), 1462; https://doi.org/10.3390/atmos14091462 - 20 Sep 2023
Cited by 1 | Viewed by 1292
Abstract
The wind microclimate plays an important role in architectural design, and computational fluid dynamics is a method commonly used for analyzing the issue. However, due to its high technical difficulty and time-consuming nature, it limits the interaction and exploration between designers and environment [...] Read more.
The wind microclimate plays an important role in architectural design, and computational fluid dynamics is a method commonly used for analyzing the issue. However, due to its high technical difficulty and time-consuming nature, it limits the interaction and exploration between designers and environment performance analyses. To address the issue, scholars have proposed a series of approximation models based on machine learning that have partially improved computational efficiency. However, these methods face challenges in terms of balancing applicability, prediction accuracy, and sample size. In this paper, we propose a method based on the classic Vggnet deep convolutional neural network as the backbone to construct an approximate model for predicting steady-state flow fields in urban areas. The method is trained on a small amount of sample data and can be extended to calculate the wind environment performance. Furthermore, we investigated the differences between geometric representation methods, such as the Boolean network representation and signed distance function, as well as different structure models, such as Vgg-CFD-11, Vgg-CFD-13, Vgg-CFD-16, and Vgg-CFD-19. The results indicate that the model can be trained using a small amount of sample data, and all models generally possess the ability to predict the wind environment. The best performance on the validation set and test set was achieved with an RMSE (Root Mean Square Error) of 0.7966 m/s and 2.2345 m/s, respectively, and an R-Squared score of 0.9776 and 0.8455. Finally, we embedded the best-performing model into an architect-friendly urban comprehensive analysis platform, URBAN NEURAL-CFD. Full article
(This article belongs to the Special Issue Urban Microclimate: Thermal Comfort, Air Quality and Green Building as Drivers of Healthy Urban Design)
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<p>Workflow.</p> Full article ">Figure 2
<p>(<b>a</b>) Velocity map and (<b>b</b>) scatter diagram of wind speed in each direction.</p> Full article ">Figure 3
<p>Spatial matrix arranged according to the wind speed component magnitude.</p> Full article ">Figure 4
<p>Illustration of generated street block forms.</p> Full article ">Figure 5
<p>CFD calculation grid for a case.</p> Full article ">Figure 6
<p>Wind speed maps for the case set.</p> Full article ">Figure 7
<p>BNR expression of 25 cases in the training set.</p> Full article ">Figure 8
<p>SDF expression of 25 cases in the training set.</p> Full article ">Figure 9
<p>Sampling of the training case dataset: (<b>a</b>) sampling slices of the BNR representation cases; (<b>b</b>) sampling slices of the SDF representation cases.</p> Full article ">Figure 10
<p>Vgg-CFD model structure diagram.</p> Full article ">Figure 11
<p>The data flow of Vgg-CFD-11.</p> Full article ">Figure 12
<p>Training and validation results for each model.</p> Full article ">Figure 13
<p>OpenFOAM calculation results of the test case.</p> Full article ">Figure 14
<p>Prediction and error analysis of each model group.</p> Full article ">Figure 15
<p>Scatter plots of the prediction errors of each model group on the test case.</p> Full article ">Figure 16
<p>Prediction of the vertical wind speed and comparison of the calculated values at different locations.</p> Full article ">Figure 17
<p>URBAN NEURAL workflow.</p> Full article ">
17 pages, 3678 KiB  
Article
Simulation of Storm Surge Heights Based on Reconstructed Historical Typhoon Best Tracks Using Expanded Wind Field Information
by Seung-Won Suh
Atmosphere 2023, 14(9), 1461; https://doi.org/10.3390/atmos14091461 - 20 Sep 2023
Viewed by 1118
Abstract
A numerical model integrating tides, waves, and surges can accurately evaluate the surge height (SH) risks of tropical cyclones. Furthermore, incorporating the external forces exerted by the storm’s wind field can help to accurately reproduce the SH. However, the lack of long-term typhoon [...] Read more.
A numerical model integrating tides, waves, and surges can accurately evaluate the surge height (SH) risks of tropical cyclones. Furthermore, incorporating the external forces exerted by the storm’s wind field can help to accurately reproduce the SH. However, the lack of long-term typhoon best track (BT) data degrades the SH evaluations of past events. Moreover, archived BT data (BTD) for older typhoons contain less information than recent typhoon BTD. Thus, herein, the wind field structure, specifically its relationship with the central air pressure, maximum wind speed, and wind radius, are augmented. Wind formulae are formulated with empirically adjusted radii and the maximum gradient wind speed is correlated with the central pressure. Furthermore, the process is expanded to four quadrants through regression analyses using historical asymmetric typhoon advisory data. The final old typhoon BTs are converted to a pseudo automated tropical cyclone forecasting format for consistency. Validation tests of the SH employing recent BT and reconstructed BT (rBT) indicate the importance of the nonlinear interactions of tides, waves, and surges for the macrotidal west and microtidal south coasts of Korea. The expanded wind fields—rBT—based on the historical old BT successfully assess the return periods of the SH. The proposed process effectively increases typhoon population data by incorporating actual storm tracks. Full article
(This article belongs to the Special Issue Sea-Level Rise and Associated Potential Storm Surge Vulnerability)
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<p>Typhoon tracks in the northwestern Pacific are represented by gray lines, whereas those affecting Korea are indicated in red. These tracks are derived from (<b>a</b>) historical data by Regional Specialized Meteorological Center (RSMC) from 1952 and (<b>b</b>) synthetic data by TCRM. The dark blue polygon shows the percentage of typhoon tracks as shown in (<b>c</b>) that entered the Yellow Sea and East China Sea and impacted the west and south coast of Korea. In the historical data (<b>a</b>), these tracks frequently resurface; however, in the synthetic data (<b>b</b>), such tracks are rare and when present, they exhibit a different direction of approach.</p> Full article ">Figure 2
<p>Central pressure distribution with respect to latitude is illustrated using gray dots, representing historical typhoon tracks by RSMC. Conversely, the red dots depict synthetic tracks generated by TCRM.</p> Full article ">Figure 3
<p>Empirical relationships between <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> for the historical typhoons [<a href="#B15-atmosphere-14-01461" class="html-bibr">15</a>,<a href="#B25-atmosphere-14-01461" class="html-bibr">25</a>].</p> Full article ">Figure 4
<p>Simulation cases based on synthetically generated wind fields.</p> Full article ">Figure 5
<p>Comparisons of SH for typhoons Bolaven in 2012 and Chaba in 2016 based on several wind structures using BT, rBT with or without tidal conditions at Incheon, Gunsan, Yeosu, and Busan tidal stations.</p> Full article ">
21 pages, 3607 KiB  
Article
Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil
by Erick Vinicius Ramos Vieira, Nilton Evora do Rosario, Marcia Akemi Yamasoe, Fernando Gonçalves Morais, Pedro José Perez Martinez, Eduardo Landulfo and Regina Maura de Miranda
Atmosphere 2023, 14(9), 1460; https://doi.org/10.3390/atmos14091460 - 20 Sep 2023
Cited by 2 | Viewed by 2131
Abstract
Air pollution in the Metropolitan Area of São Paulo (MASP), Brazil, is a serious problem and is strongly affected by local sources. However, atmosphere column composition in MASP is also affected by biomass burning aerosol (BB). Understanding the impacts of aerosol particles, from [...] Read more.
Air pollution in the Metropolitan Area of São Paulo (MASP), Brazil, is a serious problem and is strongly affected by local sources. However, atmosphere column composition in MASP is also affected by biomass burning aerosol (BB). Understanding the impacts of aerosol particles, from both vehicles and BB, on the air quality and climate depends on in-depth research with knowledge of some parameters such as the optical properties of particles and their chemical composition. This study characterized fine particulate matter (PM2.5) from July 2019 to August 2020 in the eastern part of the MASP, relating the chemical composition data obtained at the surface and columnar optical parameters, such as aerosol optical depth (AOD), Ångström Exponent (AE), and single-scattering albedo (SSA). According to the analyzed data, the mean PM2.5 concentration was 18.0 ± 12.5 µg/m3; however, daily events exceeded 75 times the air quality standard of the World Health Organization (15 µg/m3). The mean black carbon concentration was 1.8 ± 1.5 µg/m3 in the sampling period. Positive matrix factorization (PMF) identified four main sources of aerosol: heavy vehicles (42%), followed by soil dust plus local sources (38.7%), light vehicles (9.9%), and local sources (8.6%). AOD and AE presented the highest values in the dry period, during which biomass burning events are more frequent, suggesting smaller particles in the atmosphere. SSA values at 440 nm were between 0.86 and 0.94, with lower values in the winter months, indicating the presence of more absorbing aerosol. Full article
(This article belongs to the Section Aerosols)
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<p>Aerial view of the sampling site and surroundings with a wind rose and indications of the main land uses. The inset shows a map of the Metropolitan Area of São Paulo highlighted in green, and the study site highlighted in yellow. Source: Google Earth.</p> Full article ">Figure 2
<p>Daily concentrations for PM<sub>2.5</sub> and BC, highlighting PM<sub>2.5</sub> exceedances of the WHO air quality guideline (red line).</p> Full article ">Figure 3
<p>Monthly mean variability for PM<sub>2.5</sub>, black carbon (BC), and organic carbon (OC) during the sampling period.</p> Full article ">Figure 4
<p>Variation in the mean concentration of major elements during the months of the study (note the different scale used for sulfur).</p> Full article ">Figure 5
<p>Seasonal contribution by factor resulting from the PMF.</p> Full article ">Figure 6
<p>Time series of monthly mean values of AOD (500 nm), SSA (440 nm), and AE (400–870 nm) during the sampling period. Vertical bars represent the standard deviation.</p> Full article ">Figure 7
<p>Absorption Ångström exponent (AAE) vs. scattering Ångström exponent (SAE) for the AERONET data as a function of months (August 2016 to August 2020).</p> Full article ">Figure 8
<p>Relationships between AE (440–870 nm) and SSA (440 nm) (<b>a</b>), and between AE (440–870 nm) and AOD (500 nm) (<b>b</b>) as a function of months.</p> Full article ">Figure 9
<p>PM<sub>2.5</sub>, BC, and OC concentrations in September 2019 (the 15 to the 20), including AOD and AE values. The red squares indicate the highlighted days.</p> Full article ">
18 pages, 4992 KiB  
Article
Evaluation of the Performance of CMIP6 Climate Models in Simulating Rainfall over the Philippines
by Shelly Jo Igpuara Ignacio-Reardon and Jing-jia Luo
Atmosphere 2023, 14(9), 1459; https://doi.org/10.3390/atmos14091459 - 20 Sep 2023
Cited by 1 | Viewed by 1734
Abstract
The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to [...] Read more.
The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to simulate the observed rainfall using the latest state-of-the-art model is essential for reliable forecasting. Based on this background, this paper objectively aims at assessing and ranking the capabilities of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed rainfall over the Philippines. The Global Precipitation Climatology Project (GPCP) v2.3 was used as a proxy to gauge the performance of 11 CMIP6 models in simulating the annual and rainy-season rainfall during 1980–2014. Several statistical metrics (mean, standard deviation, normalized root means square error, percentage bias, Pearson correlation coefficient, Mann–Kendall test, Theil–Sen slope estimator, and skill score) and geospatial measures were assessed. The results show that that CMIP6 historical simulations exhibit satisfactory effectiveness in simulating the annual cycle, though some models display wet/dry biases. The CMIP6 models generally underestimate rainfall on the land but overestimate it over the ocean. The trend analysis shows that rainfall over the country is insignificantly increasing both annually and during the rainy seasons. Notably, most of the models could correctly simulate the trend sign but over/underestimate the magnitude. The CMIP6 historical rainfall simulating models significantly agree on simulating the mean annual cycle but diverge in temporal ability simulation. The performance of the models remarkably differs from one metric to another and among different time scales. Nevertheless, the models may be ranked from the best to the least best at simulating the Philippines’ rainfall in the order GFDL, NOR, ACCESS, ENS, MRI, CMCC, NESM, FIO, MIROC, CESM, TAI, and CAN. The findings of this study form a good basis for the selection of models to be used in robust future climate projection and impact studies regarding the Philippines. The climate model developers may use the documented shortcoming of these models and improve their physical parametrization for better performance in the future. Full article
(This article belongs to the Special Issue Climate and Weather Extremes and Their Impacts on Water Resources and Agriculture in Asia)
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<p>The geographical location of the Philippine highlands. The background color depicts the elevation of the region from the mean sea level (m).</p> Full article ">Figure 2
<p>The annual rainfall cycle over the Philippines based on the simulations of the CMIP6 individual models and multi-model ensemble mean (thick blue color), GPCP (thick red color), and observed rainfall (thick black color).</p> Full article ">Figure 3
<p>Taylor diagram depicting the ability of the CMIP6 models in simulating the observed annual rainfall cycle over the Philippines. The letters indicate the CMIP6 models as follows; A: ACCESS, B: CAN, C: CESM, D: CMCC, E: ENS, F: FIO, G: GFDL, H: MIROC, I: MRI, J: NESM, K: NOR, and L: TAI.</p> Full article ">Figure 4
<p>Hovmöller diagram representing longitudinal average along 115°–130° N of monthly mean rainfall over the Philippines in mm per month.</p> Full article ">Figure 5
<p>Spatial distribution of the rainy-season rains (mm) during May–November based on the GPCP dataset (first panel) and the corresponding bias of the CMIP6 models relative to GPCP.</p> Full article ">Figure 6
<p>Spatial distribution of annual mean rainfall (mm) based on the GPCP dataset (first panel) and the bias of CMIP6 models relative to GPCP.</p> Full article ">Figure 7
<p>The Taylor diagram representing the multiple statistical relationships between the observed rains and CMIP6 models during May–November. The letters stand for the CMIP6 models, i.e., A: ACCESS, B: CAN, C: CESM, D: CMCC, E: ENS, F: FIO, G: GFDL, H: MIROC, I: MRI, J: NESM, K: NOR, and L: TAI.</p> Full article ">Figure 8
<p>Taylor diagram representing the relationship between CMIP6 models in simulating the observed annual rainfall (Ref). The letters denote the CMIP6 models: A: ACCESS, B: CAN, C: CESM, D: CMCC, E: ENS, F: FIO, G: GFDL, H: MIROC, I: MRI, J: NESM, K: NOR, and L: TAI.</p> Full article ">Figure 9
<p>Skill score ranking of the CMIP6 models in simulating mean observed rainfall over the Philippines annually (red), during May–November (black), and overall (blue). The letters denote A: ACCESS, B: CAN, C: CESM, D: CMCC, E: ENS, F: FIO, G: GFDL, H: MIROC, I: MRI, J: NESM, K: NOR, and L: TAI.</p> Full article ">
20 pages, 8591 KiB  
Article
Reliability Analysis Based on Air Quality Characteristics in East Asia Using Primary Data from the Test Operation of Geostationary Environment Monitoring Spectrometer (GEMS)
by Won Jun Choi, Kyung-Jung Moon, Goo Kim and Dongwon Lee
Atmosphere 2023, 14(9), 1458; https://doi.org/10.3390/atmos14091458 - 20 Sep 2023
Cited by 1 | Viewed by 1459
Abstract
Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations [...] Read more.
Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations have been released since July 2022. The reliability of GEMS must be analyzed because it is the first payload on the geostationary Earth orbit satellite to observe trace gases. This study analyzed the initial results of GEMS observations such as the aerosol optical depth and vertical column densities (VCD) of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO), and compared them with previous studies. The correlation coefficient of O3 ranged from 0.90 (Ozone Monitoring Instrument, OMI) to 0.97 (TROPOspheric Monitoring Instrument, TROPOMI), whereas that of NO2 ranged from 0.47 (winter, OMI and OMPS) to 0.83 (summer, TROPOMI). GEMS yielded a higher VCD of NO2 than that of OMI and TROPOMI. Based on the sources of O3 and NO2, GEMS observed the maximum VCD at a different time (3–4 h) to that of the ground observations. Overall, GEMS can make observations several times a day and is a potential tool for atmospheric environmental analysis. Full article
(This article belongs to the Section Air Quality)
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<p>Classification of observation areas of the Geostationary Environment Monitoring Spectrometer (GEMS). The purple box in (<b>a</b>) indicates half east (HE), the yellow box in (<b>b</b>) indicates half Korea (HK), the green box in (<b>c</b>) indicates full central (FC), and the blue box in (<b>d</b>) indicates full west (FW). The arrows indicate the scanning direction.</p> Full article ">Figure 2
<p>Solar irradiance observed using GEMS. For Seoul (black lines) and the equator (red lines) at (<b>a</b>) vernal equinox, (<b>b</b>) summer solstice, (<b>c</b>) autumn equinox, and (<b>d</b>) winter solstice.</p> Full article ">Figure 3
<p>Level 1c ratios of the 350 nm and 455 nm normalized radiance observed in (<b>a</b>) vernal equinox, (<b>b</b>) summer solstice, (<b>c</b>) autumn equinox, and (<b>d</b>) winter solstice at 03:45–04:15 (UTC) in 2021. The region in blue indicates that the change width of the short wavelength (350 nm) was larger than that of the long wavelength (455 nm) in the red region.</p> Full article ">Figure 4
<p>Spatial distribution of the average seasonal total column O<sub>3</sub> and O<sub>3</sub> between the cloud optical centroid pressure and the surface is shown. The panels depict O<sub>3</sub> concentrations for (<b>a</b>,<b>e</b>) spring (1 March 2021–31 May 2021), (<b>b</b>,<b>f</b>) summer (1 June 2021–31 August 2021), (<b>c</b>,<b>g</b>) autumn (1 September 2021–30 November 2021), and (<b>d</b>,<b>h</b>) winter (1 December 2021–28 February 2022). Panels (<b>a</b>–<b>d</b>) represent the total column O<sub>3</sub>, while panels (<b>e</b>–<b>h</b>) represent O<sub>3</sub> between the cloud optical centroid pressure and the surface.</p> Full article ">Figure 5
<p>Spatial distribution using the median O<sub>3</sub> concentrations observed via GEMS, Ozone Monitoring Instrument (OMI), and TROPOspheric Monitoring Instrument (TROPOMI) for one year (1 March 2021 to 28 February 2022): (<b>a</b>) (GEMS-OMI)/GEMS and (<b>b</b>) (GEMS-TROPOMI)/GEMS. The distributions between the two results: (<b>c</b>) GEMS vs. OMI and (<b>d</b>) GEMS vs. TROPOMI.</p> Full article ">Figure 6
<p>Comparison of vertical column density (VCD, molecular number /cm<sup>3</sup>) of O<sub>3</sub> between GEMS and Dobson from Yonsei University in Seoul from 1 March 2021, to 28 February 2022 (<b>a</b>), the temporal variations of VCD of O<sub>3</sub> from GEMS and Dobson, and ground station O<sub>3</sub> concentration (ppb) (<b>b</b>). Same as (<b>b</b>), but for NO<sub>2</sub> from GEMS and the ground station (<b>c</b>).</p> Full article ">Figure 7
<p>Seasonal average of Level 2 NO<sub>2</sub> total concentration observed in 2021 at 12:45–13:15 (KST): (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter.</p> Full article ">Figure 8
<p>Correlation diagram for the seasonal analysis of the median NO<sub>2</sub> VCD observed by GEMS, OMI, TROPOMI, and Ozone Mapping and Profiler Suite (OMPS). (<b>a</b>) GEMS vs. OMI at Beijing; (<b>b</b>) GEMS vs. OMPS at Shanghai; (<b>c</b>) GEMS vs. TROPOMI at Seoul.</p> Full article ">Figure 9
<p>Total SO<sub>2</sub> concentration observed during the eruption of Taal Volcano in the Philippines in June 2022. (<b>a</b>) 5 June, 15:45–16:15, (<b>b</b>) 7 June, 08:45–09:15, (<b>c</b>) 8 June, 08:45–09:15, (<b>d</b>) 8 June, 10:45–11:15, (<b>e</b>) 9 June, 10:45–11:15, (<b>f</b>) 12 June, 08:45–09:15, (<b>g</b>) 12 June, 10:45–11:15, (<b>h</b>) 13 June, 10:45–11:15, and (<b>i</b>) 14 June, 10:45–11:15. Time is expressed in KST.</p> Full article ">Figure 10
<p>Temporal variations in the SO<sub>2</sub> concentration at the Bulusan and Taal Volcano sites as observed by GEMS.</p> Full article ">Figure 11
<p>Full-layer concentration average of the Level 2 aerosol optical depth (AOD) was calculated using spectral information for six wavelengths (354, 388, 412, 443, 477, and 490 nm) observed at 12:45–13:15 (KST): (<b>a</b>) spring (1 March 2021–31 May 2021), (<b>b</b>) summer (1 June 2021–31 August 2021), (<b>c</b>) fall (1 September 2021–30 November 2021), and (<b>d</b>) winter (1 December 2021–28 February 2022).</p> Full article ">Figure 12
<p>Spatial distribution of the monthly median vertical column density (VCD) of the HCHO concentrations observed at 12:45–13:15 in (<b>a</b>) April 2021, (<b>b</b>) July 2021, (<b>c</b>) October 2021, and (<b>d</b>) January 2022.</p> Full article ">
18 pages, 5623 KiB  
Article
Diagnosing Hurricane Barry Track Errors and Evaluating Physics Scalability in the UFS Short-Range Weather Application
by Nicholas D. Lybarger, Kathryn M. Newman and Evan A. Kalina
Atmosphere 2023, 14(9), 1457; https://doi.org/10.3390/atmos14091457 - 19 Sep 2023
Cited by 1 | Viewed by 913
Abstract
To assess the performance and scalability of the Unified Forecast System (UFS) Short-Range Weather (SRW) application, case studies are chosen to cover a wide variety of forecast applications. Here, model forecasts of Hurricane Barry (July 2019) are examined and analyzed. Several versions of [...] Read more.
To assess the performance and scalability of the Unified Forecast System (UFS) Short-Range Weather (SRW) application, case studies are chosen to cover a wide variety of forecast applications. Here, model forecasts of Hurricane Barry (July 2019) are examined and analyzed. Several versions of the Global Forecast System (GFS) and Rapid Refresh Forecast System (RRFS) physics suites are run in the UFS-SRW at grid spacings of 25 km, 13 km, and 3 km. All model configurations produce significant track errors of up to 350 km at landfall. The track errors are investigated, and several commonalities are seen between model configurations. A westerly bias in the environmental steering flow surrounding the tropical cyclone (TC) is seen across forecasts, and this bias is coincident with a warm sea surface temperature (SST) bias and overactive convection on the eastern side of the forecasted TC. Positive feedback between the surface winds, latent heating, moisture, convection, and TC intensification is initiated by this SST bias. The asymmetric divergent flow induced by the excess convection results in all model TC tracks being diverted to the east as compared to the track derived from reanalysis. The large differences between runs using the same physics packages at different grid spacing suggest a deficiency in the scalability of these packages with respect to hurricane forecasting in vertical wind shear. Full article
(This article belongs to the Special Issue Advances in Tropical Cyclone Prediction: Observation, Simulation, and Verification)
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<p>The domain used in the UFS-SRW runs. Lakes and ocean points are colored blue while land points show elevation in meters corresponding to the color axis on the right. White areas are not defined.</p> Full article ">Figure 2
<p>TC track forecasts from each GFS physics suite tested at each grid spacing: 25 km (blue), 13 km (orange), and 3 km (green). The ERA5 track is shown in black. The violet track in the GFSv16beta plot represents the 3 km sensitivity experiment with the cumulus convection scheme disabled. Scatter points denote each hour of the run, and hour 30 of each forecast is denoted by an especially large point.</p> Full article ">Figure 3
<p>Time series of the environmental steering flow around the TC. A 3 h rolling time average is applied to the hourly data to reduce noise. The figures on the left show the meridional (dashed lines) and zonal (solid lines) flow determined by the OSL method described in <a href="#sec2dot2-atmosphere-14-01457" class="html-sec">Section 2.2</a>. On the right is the magnitude of the steering flow. The 25 km (blue), 13 km (orange), and 3 km (green) runs for each physics suite are compared with ERA5 (black) for verification. The GFSv16beta NoConv sensitivity run is shown in violet.</p> Full article ">Figure 4
<p>TC-centered composite reflectivity (dB) at 30 h for the GFS physics suite runs with IMERG and Stage IV derived precipitation (mm/hr) for verification. The white cross indicates the TC center, and each concentric white circle indicates a 50, 100, and 150 km radius about the center. The whited out area in the Stage IV data are not defined due to being beyond the range of the radar from which these data are estimated.</p> Full article ">Figure 5
<p>TC-centered surface latent heating (W/m<sup>2</sup>) into the atmosphere at 30 h for the GFS physics suite runs, with ERA5 for verification. The white cross indicates the TC center and each concentric white circle indicates a 50, 100, and 150 km radius about the center. The color bar shown for GFDL-MP 13 km applies to all results shown in this figure.</p> Full article ">Figure 6
<p>TC-centered sea-surface temperature (°C) at 0 h (top) and 18 h (bottom) for the GFSv16beta 3 km run compared with DOISST, MURSST, and ERA5 reanalysis for verification. Although only one run is shown here, the SST field is virtually identical for all of the model forecasts considered in this study, regardless of physics suite or grid spacing.</p> Full article ">Figure 7
<p>As with <a href="#atmosphere-14-01457-f002" class="html-fig">Figure 2</a>, but for the GSD physics suites. The bolded scatter point here designates forecast hour 15 h, as several runs diverge from ERA5 around this time.</p> Full article ">Figure 8
<p>As with <a href="#atmosphere-14-01457-f003" class="html-fig">Figure 3</a>, but for the GSD physics suite runs.</p> Full article ">Figure 9
<p>As with <a href="#atmosphere-14-01457-f004" class="html-fig">Figure 4</a>, except for the GSD physics suite runs and at forecast hour 15 h.</p> Full article ">Figure 10
<p>As with <a href="#atmosphere-14-01457-f005" class="html-fig">Figure 5</a>, except for the GSD physics suite runs and at forecast hour 15 h.</p> Full article ">Figure 11
<p>850 hPa geopotential height difference at 12 h between GFDL-MP 13 km (<b>top left</b>), GFSv16beta 25 km (<b>bottom left</b>), GSD_noMY 25 km (<b>top right</b>), GSD_Noah 3 km (<b>bottom right</b>) and ERA5. These cases are chosen as demonstrative examples of features common to forecasts with similar track errors.</p> Full article ">
2 pages, 162 KiB  
Editorial
Mesosphere and Lower Thermosphere
by Chen Zhou and Zhibin Yu
Atmosphere 2023, 14(9), 1456; https://doi.org/10.3390/atmos14091456 - 19 Sep 2023
Viewed by 936
Abstract
The mesosphere and low thermosphere (MLT) region is defined as the region of the atmosphere between approximately 60 and 110 km in height [...] Full article
(This article belongs to the Special Issue Mesosphere and Lower Thermosphere)
18 pages, 4074 KiB  
Article
Simulation Analysis of Methane Exhaust Reforming Mechanism Based on Marine LNG Engine
by Jie Shi, Haoyu Yan, Yuanqing Zhu, Yongming Feng, Zhifan Mao, Xiaodong Ran and Chong Xia
Atmosphere 2023, 14(9), 1455; https://doi.org/10.3390/atmos14091455 - 19 Sep 2023
Viewed by 1064
Abstract
LNG is a potential alternative fuel for ships. Generating H2 through exhaust reforming is an effective method to improve the performance of the LNG engine and reduce its pollutant emissions. It is necessary to study the mechanism of methane exhaust reforming to [...] Read more.
LNG is a potential alternative fuel for ships. Generating H2 through exhaust reforming is an effective method to improve the performance of the LNG engine and reduce its pollutant emissions. It is necessary to study the mechanism of methane exhaust reforming to guide the design of the reformer. Based on the detailed mechanism, the characteristics of methane reforming reaction were studied for a marine LNG engine. Firstly, the reforming characteristics of exhaust were studied. The results show that methane reforming requires a lean oxygen environment, and the hydrogen production reaction will not occur when the O2 concentration is too high. Then, the effects of the O2/CH4 ratio (0.2–1) and H2O/CH4 ratio (0–2) on the reforming reaction were studied. The results show that under O2/CH4 = 0.4, the molar fraction of hydrogen at the outlet of the reactor decreases with the increase in the H2O/CH4 ratios. Finally, a mechanism analysis was conducted. The results show that an oxidation reaction occurs first and then the steam reforming reaction occurs on palladium-based catalysts. Full article
(This article belongs to the Section Air Quality)
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<p>The concentration distribution of the simulated (Sim.) and experimental (Exp.) values of each component along the axis.</p> Full article ">Figure 2
<p><math display="inline"><semantics> <mi>λ</mi> </semantics></math> = 1.59, axial reaction characteristic curves of each species along the reactor axis: (<b>a</b>) axial concentration distribution of each component; (<b>b</b>) axial distribution of key species coverage.</p> Full article ">Figure 3
<p><math display="inline"><semantics> <mi>λ</mi> </semantics></math> = 0.95, axial reaction characteristic curves of each species along the reactor axis: (<b>a</b>) axial concentration distribution of each component; (<b>b</b>) axial distribution of key species coverage.</p> Full article ">Figure 4
<p>Variation trends of molar fraction of H<sub>2</sub> in the outlet and methane conversion under different O<sub>2</sub>/CH<sub>4</sub> ratios.</p> Full article ">Figure 5
<p>Variation trends of H<sub>2</sub>/CO ratio and outlet temperature under different O<sub>2</sub>/CH<sub>4</sub> ratios.</p> Full article ">Figure 6
<p>Hydrogen production rate under different O<sub>2</sub>/CH<sub>4</sub> ratios.</p> Full article ">Figure 7
<p>Variation trends of molar fraction of H<sub>2</sub> in the outlet and methane conversion under different H<sub>2</sub>O/CH<sub>4</sub> ratios.</p> Full article ">Figure 8
<p>Variation trends of molar fraction of H<sub>2</sub>/CO ratio and reactor outlet temperature under different H<sub>2</sub>O/CH<sub>4</sub> ratios.</p> Full article ">Figure 9
<p>Hydrogen production rate under different H<sub>2</sub>O/CH<sub>4</sub> ratios.</p> Full article ">Figure 10
<p>Temperature sensitivity analysis.</p> Full article ">Figure 11
<p>Sensitivity analysis for H<sub>2</sub> generation.</p> Full article ">Figure 12
<p>Sensitivity analysis for CO generation.</p> Full article ">Figure 13
<p>The concentration distribution of each component along the axis when the oxygen-to-carbon ratio is 0.4.</p> Full article ">Figure 14
<p>Reaction path analysis of methane reforming at x = 0.5 mm.</p> Full article ">Figure 15
<p>Reaction path analysis of methane reforming at x = 10mm.</p> Full article ">
18 pages, 1731 KiB  
Article
New Observations of the Meteorological Conditions Associated with Particulate Matter Air Pollution Episodes in Santiago, Chile
by Ricardo C. Muñoz, René Garreaud, José A. Rutllant, Rodrigo Seguel and Marcelo Corral
Atmosphere 2023, 14(9), 1454; https://doi.org/10.3390/atmos14091454 - 19 Sep 2023
Cited by 3 | Viewed by 1319
Abstract
The meteorological factors of the severe wintertime particulate matter (PM) air pollution problem of the city of Santiago, Chile, are investigated with newly available observations, including a 30 m tower measuring near-surface stability, winds and turbulence, as well as lower-tropospheric vertical profiles of [...] Read more.
The meteorological factors of the severe wintertime particulate matter (PM) air pollution problem of the city of Santiago, Chile, are investigated with newly available observations, including a 30 m tower measuring near-surface stability, winds and turbulence, as well as lower-tropospheric vertical profiles of temperature and winds measured by commercial airplanes operating from the Santiago airport (AMDAR database). Focusing on the cold season of the years 2017–2019, high-PM days are defined using an index of evening concentrations measured in the western part of the city. The diurnal cycles of the different meteorological variables computed over 25 PM episodes are compared against the overall diurnal cycles. PM episodes are associated with enhanced surface stability and weaker surface winds and turbulence during the evening and night. AMDAR vertical profiles of temperature and winds during episodes reveal a substantial lower-tropospheric warming attributed to enhanced regional subsidence, which is consistent with the shallower daytime boundary layer depth and the increased surface thermal amplitude observed during these days. An explanation for the weak surface winds during PM episodes was not evident, considering that these are clear days that would strengthen the local valley wind system. Two possible mechanisms are put forward to resolve this issue, which can be tested in the future using high-resolution numerical modeling validated with the new data described here. Full article
(This article belongs to the Special Issue Atmospheric Particle Pollution and Interactions with Meteorological Factors)
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<p>(<b>a</b>) Topographic map of the Santiago valley and surroundings. Contour lines from 0 to 800 m ASL every 100 m. Shaded contours from 1000 to 5000 m ASL every 500 m. Black rectangle is zoomed in panel (<b>b</b>). (<b>b</b>) Zoom over the Santiago urban area (shaded). (<b>c</b>) Location of the study area (red square) in southern South America. Shading marks elevations greater than 3000 m ASL. In (<b>a</b>,<b>b</b>), red circles (1–11) mark sites of air quality monitoring stations and green circles mark sites with meteorological observations used in this work: 12: Santiago airport (SCEL), 13: DASA tower, 14: Quinta Normal weather station, 15: Santo Domingo aerological station.</p> Full article ">Figure 2
<p>(<b>a</b>) Three-day diurnal cycles of PM<math display="inline"><semantics> <msub> <mrow/> <mn>10</mn> </msub> </semantics></math> index for the period May–August 2017–2019. Shaded region shows 10–90 percentile regions of the data. Fine black line shows overall mean value and bold line the mean values of PM<math display="inline"><semantics> <msub> <mrow/> <mn>10</mn> </msub> </semantics></math> episodes. Fine gray lines show PM<math display="inline"><semantics> <msub> <mrow/> <mn>10</mn> </msub> </semantics></math> concentrations for the individual episode days corresponding to the central day of the 3-day window (day D0). Vertical dashed lines at hours 20, 22 of D0 mark the period defining the episodes. Vertical dashed lines at hours 20, 22 of day D−1 mark the same hours of the previous day. (<b>b</b>) Frequency distributions of daily PM<math display="inline"><semantics> <msub> <mrow/> <mn>10</mn> </msub> </semantics></math> concentrations for the 11 air quality stations shown in <a href="#atmosphere-14-01454-f001" class="html-fig">Figure 1</a>. Left boxes show distributions of all May–August days and right boxes are restricted over PM<math display="inline"><semantics> <msub> <mrow/> <mn>10</mn> </msub> </semantics></math> episode days. Box limits correspond to the upper and lower quartiles of the distributions, the red line marks the median and red crosses mark outlier values.</p> Full article ">Figure 3
<p>ERA5 fields averaged over PM episodes: (<b>a</b>) Sea level pressure minus 1000 hPa (colors; hPa) and 500 hPa heights (contours; m), (<b>b</b>) 24 h sea level pressure change (hPa), (<b>c</b>) 700 hPa vertical velocity (cm s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>). Anomalies with respect to May–August averages: (<b>d</b>) Sea level pressure (colors; hPa) and 500 hPa heights (contours; m), (<b>e</b>) temperature (°C) at 900 hPa (colors) and 700 hPa (contours) and (<b>f</b>) 700 hPa vertical velocity (cm s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>).</p> Full article ">Figure 4
<p>(<b>a</b>) Height–time diurnal cycles of temperature (°C) averaged over the full May–August period. (<b>b</b>) Three-day anomalies of temperature (°C) of PM episodes around day D0. (<b>c</b>) As (<b>a</b>) but for zonal wind speed (m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>). (<b>d</b>) As (<b>b</b>) but for zonal wind speed (m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>). (<b>e</b>) As (<b>a</b>) but for meridional wind speed (m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>). (<b>f</b>) As (<b>b</b>) but for meridional wind speed (m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>). White lines mark the zero values. In (<b>d</b>), the black contour surrounds the region with negative mean zonal winds.</p> Full article ">Figure 5
<p>Vertical profiles of meteorological variables at 20 LT: (<b>a</b>) temperature, (<b>b</b>) zonal wind and (<b>c</b>) meridional wind. Fine lines mark AMDAR averages for May–August. Bold lines are the average of the 25 days defined as PM episodes. Shading marks the 10 and 90 percentile regions of 1000 randomly generated 25-day sets. Fine and bold dotted lines show Santo Domingo averages for overall conditions and PM episodes, respectively.</p> Full article ">Figure 6
<p>DASA tower observations: (<b>a</b>) surface temperature; (<b>b</b>) temperature difference between 30 m and 2 m levels. Fine lines mark averages for May–August. Bold lines are the average of the 25 days defined as PM episodes. Shading marks the 10 and 90 percentile regions of 1000 randomly generated 25-day sets.</p> Full article ">Figure 7
<p>(<b>a</b>) Ten meter wind speed at DASA tower: (<b>b</b>) Sensible heat flux at DASA tower (black) and solar radiation at Quinta Normal station (orange). For better joint appreciation, the solar radiation is scaled by two.Fine lines mark averages for May–August. Bold lines are the average of the 25 days defined as PM episodes. Shading marks the 10 and 90 percentile regions of 1000 randomly generated 25-day sets.</p> Full article ">Figure 8
<p>(<b>a</b>) Vertical velocity variance at DASA tower. (<b>b</b>) Boundary layer height derived from AMDAR observations. Fine lines mark averages for May–August. Bold lines are the average of the 25 days defined as PM episodes. Shading marks the 10 and 90 percentile regions of 1000 randomly generated 25-day sets.</p> Full article ">Figure 9
<p>(<b>a</b>,<b>d</b>): height–time diurnal cycle of vertical velocity (cm s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) averaged over May–August. (<b>b</b>,<b>e</b>): 3-day variation in vertical velocity averaged over PM episodes. White contours mark zero values. (<b>c</b>,<b>f</b>): vertical profiles of 24 h mean vertical velocity averaged over May–August (fine line) and over PM episodes (bold line). Shading marks the 10 and 90 percentile regions of 1000 randomly generated 25-day sets. In the upper (lower) panels, vertical velocity is inferred from AMDAR data (ERA5 reanalysis).</p> Full article ">
19 pages, 4524 KiB  
Article
Spatial and Temporal Evolution Characteristics of Water Conservation in the Three-Rivers Headwater Region and the Driving Factors over the Past 30 Years
by Yao Pan and Yunhe Yin
Atmosphere 2023, 14(9), 1453; https://doi.org/10.3390/atmos14091453 - 18 Sep 2023
Cited by 1 | Viewed by 1073
Abstract
The Three-Rivers Headwater Region (TRHR), located in the hinterland of the Tibetan Plateau, serves as the “Water Tower of China”, providing vital water conservation (WC) services. Understanding the variations in WC is crucial for locally tailored efforts to adapt to climate change. This [...] Read more.
The Three-Rivers Headwater Region (TRHR), located in the hinterland of the Tibetan Plateau, serves as the “Water Tower of China”, providing vital water conservation (WC) services. Understanding the variations in WC is crucial for locally tailored efforts to adapt to climate change. This study improves the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) water yield model by integrating long-term time series of vegetation data, emphasizing the role of interannual vegetation variation. This study also analyzes the influences of various factors on WC variations. The results show a significant increase in WC from 1991 to 2020 (1.4 mm/yr, p < 0.05), with 78.17% of the TRHR showing improvement. Precipitation is the primary factor driving the interannual variations in WC. Moreover, distinct interactions play dominant roles in WC across different eco-geographical regions. In the north-central and western areas, the interaction between annual precipitation and potential evapotranspiration has the highest influence. Conversely, the interaction between annual precipitation and vegetation has the greatest impact in the eastern and central-southern areas. This study provides valuable insights into the complex interactions between the land and atmosphere of the TRHR, which are crucial for enhancing the stability of the ecosystem. Full article
(This article belongs to the Special Issue Land-Atmosphere Interactions over the Tibetan Plateau)
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<p>Study area location and natural environment in the Three−Rivers Headwater Region ((<b>a</b>) The study area location and land use types. (<b>b</b>) Annual average precipitation. (<b>c</b>) Annual average potential evapotranspiration. (<b>d</b>) Annual average leaf area index. (<b>e</b>) Slope, (<b>f</b>) Ecosystem zones: sub−cold sub−humid Guoluo−Naqu Plateau mountain alpine shrub–meadow region (HIB1), sub−cold semi−arid Qiangtang Plateau lake basin alpine steppe region (HIC2), sub−cold semi−arid southern Qinghai alpine meadow–steppe (HIC1), temperate semi−arid eastern Qinghai−Qilian montane steppe zone (HIIC2), sub−cold arid Kunlun high mountain and plateau alpine desert region (HID1), temperate arid North Kunlun mountain desert region (HIID2), temperate arid Ngali mountain desert region (HIID3), temperate humid/sub−humid Western Sihuan and Eastern Xizang high mountain and deep valley coniferous forest region (HIIAB1)).</p> Full article ">Figure 2
<p>Conceptual diagram of the water balance model used in the InVEST water yield model [<a href="#B31-atmosphere-14-01453" class="html-bibr">31</a>]. Only parameters shown in color are included, and parameters shown in grey are ignored.</p> Full article ">Figure 3
<p>Evaluation of simulated total water yield (TWY) versus observed TWY (TWY was simulated by the InVEST model using (<b>a</b>) calibration Kc values with interannual variation in vegetation, (<b>b</b>) Kc values obtained using the relationship Kc = LAI/3, or (<b>c</b>) Kc coefficients estimated at Kc = 0.65. Shadow represents a 95% confidence interval).</p> Full article ">Figure 4
<p>Implementation flow chart of this study (Formula a is derived from Zhao, et al. [<a href="#B37-atmosphere-14-01453" class="html-bibr">37</a>]. Formula b is derived from Allen, et al. [<a href="#B35-atmosphere-14-01453" class="html-bibr">35</a>]. Formula c is derived from Sharp, et al. [<a href="#B31-atmosphere-14-01453" class="html-bibr">31</a>]).</p> Full article ">Figure 5
<p>Regional corrected Kc coefficient of grassland in the Three-Rivers Headwater Region from 1990 to 2020.</p> Full article ">Figure 6
<p>Temporal variation in water conservation in the Three-Rivers Headwater Region from 1991 to 2020 (relative to the 1991–2020 anomaly).</p> Full article ">Figure 7
<p>The spatial distribution (<b>a</b>) and trend (<b>b</b>) of water conservation in the Three-Rivers Headwater Region from 1991 to 2020 (the inset panels in the bottom right of (<b>a</b>) display the water conservation values in different ecosystem zones using a violin diagram. The inset panels in the bottom right of (<b>b</b>) indicate the significance level (<span class="html-italic">p</span> &lt; 0.05). The percentages of increasing (I) and decreasing (D) trends (percentage of significant trends in parentheses) are shown at the bottom of (<b>b</b>)).</p> Full article ">Figure 8
<p>Interannual variability of climatic and vegetation elements from 1991 to 2000 ((<b>a</b>) Average annual precipitation. (<b>b</b>) Average annual potential evapotranspiration. (<b>c</b>) Annual average leaf area index).</p> Full article ">Figure 9
<p>Explanatory power of interactive influencing factors on water conservation in the Three-Rivers Headwater Region and its sub-regions ((<b>a</b>–<b>d</b>): LAI represents vegetation factor, (<b>e</b>–<b>h</b>): NDVI represents vegetation factor).</p> Full article ">
18 pages, 6626 KiB  
Article
Composition Characteristics of VOCs in the Atmosphere of the Beibei Urban District of Chongqing: Insights from Long-Term Monitoring
by Shixu Luo, Qingju Hao, Zhongjun Xu, Guosheng Zhang, Zhenghao Liang, Yongxiang Gou, Xunli Wang, Fanghui Chen, Yangjian He and Changsheng Jiang
Atmosphere 2023, 14(9), 1452; https://doi.org/10.3390/atmos14091452 - 18 Sep 2023
Cited by 1 | Viewed by 1302
Abstract
Reducing anthropogenic volatile organic compounds (VOCs) is the most effective way to mitigate O3 pollution, which has increased over the past decades in China. From 2012 to 2017, special stainless-steel cylinders were used to collect ambient air samples from the urban area [...] Read more.
Reducing anthropogenic volatile organic compounds (VOCs) is the most effective way to mitigate O3 pollution, which has increased over the past decades in China. From 2012 to 2017, special stainless-steel cylinders were used to collect ambient air samples from the urban area of Beibei district, Chongqing. Three-step pre-concentration gas chromatography–mass spectrometry was used to detect the collected air samples. The composition, concentration, photochemical reactivity, and sources of VOCs in Beibei were analyzed. During the observation period, the annual average VOC concentration was 31.3 ppbv, which was at an intermediate range compared to other cities in China. Alkanes (36.8%) and aromatics (35.6%) were the most abundant VOC groups, followed by halo-hydrocarbons (14.4%) and alkenes (12.6%). The overall trend of seasonal distribution of VOC concentration was high in summer and autumn, and low in winter and spring, with a statistically significant difference between summer and winter concentrations. The ozone formation potential (OFP) showed that alkenes were the most active species, followed by aromatics and alkanes, and summer was the season with the highest OFP (131.6 ppbv). Three major emission sources were identified through principal component analysis (PCA), i.e., vehicle exhaust emissions (66.2%), fuel oil evaporation (24.8%), and industrial sources (9.0%). To ameliorate the air quality within the study area, concerted efforts should be directed towards curtailing traffic emissions and mitigating the release of alkenes, particularly emphasizing more stringent interventions during the summer season. Full article
(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)
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Graphical abstract
Full article ">Figure 1
<p>Temporal fluctuations in TVOC concentrations in the Beibei urban area of Chongqing from 2012 to 2017.</p> Full article ">Figure 2
<p>The seasonal distribution of TVOC concentrations in each year during the observation period.</p> Full article ">Figure 3
<p>Changes in different VOC species throughout the observation period.</p> Full article ">Figure 4
<p>Concentration ratios of different VOC species in different seasons in Beibei urban area, Chongqing, during the observation period.</p> Full article ">Figure 5
<p>Concentration proportions of different VOCs species and their contribution ratios of OFP (the ozone formation potential) in the urban area of Beibei, Chongqing, in each year of the observation period.</p> Full article ">Figure 6
<p>The 10 VOC species with the highest contributions to OFP in each year from 2012 to 2017.</p> Full article ">Figure 7
<p>Seasonal variations in OFP for different VOCs species in the Beibei urban area, Chongqing. Different lowercase letters indicate significant differences between different seasons for each species (<span class="html-italic">p</span> &lt; 0.05). Differences with one of the same labelled letters are considered non-significant, while differences with different labelled letters are considered significant.</p> Full article ">
12 pages, 538 KiB  
Review
Vectorial EM Propagation Governed by the 3D Stochastic Maxwell Vector Wave Equation in Stratified Layers
by Bryce M. Barclay, Eric J. Kostelich and Alex Mahalov
Atmosphere 2023, 14(9), 1451; https://doi.org/10.3390/atmos14091451 - 18 Sep 2023
Viewed by 1110
Abstract
The modeling and processing of vectorial electromagnetic (EM) waves in inhomogeneous media are important problems in physics and engineering, and new methods need to be developed to incorporate novel vector sensor technology. Vectorial phenomena of EM waves in stratified atmospheric layers can be [...] Read more.
The modeling and processing of vectorial electromagnetic (EM) waves in inhomogeneous media are important problems in physics and engineering, and new methods need to be developed to incorporate novel vector sensor technology. Vectorial phenomena of EM waves in stratified atmospheric layers can be incorporated into governing equations by retaining the gradient of the refractive index when deriving the Maxwell Vector Wave Equation (MVWE) from Maxwell’s equations. The MVWE, as opposed to the scalar wave, Helmholtz, and paraxial equations, couples the EM field components in inhomogeneous media and thus captures important physics phenomena such as depolarization. Here, recent developments are reviewed on using sensor time series data to reconstruct electromagnetic waves that propagate through stratified media. In modern applications, often many sensors can be deployed simultaneously to observe electromagnetic waves. These networks of sensors can be used to improve the quality of the reconstructed EM wave fields and cross-validate the observed sensor time series. Lastly, the effects of noisy current densities on sensor time series are considered. Generally, as the sensor observes for longer periods of time, the variance of estimates of the wave field obtained from sensor time series data increases. As a result, longer sensor observation times do not always result in better estimates of the EM wave fields, and an optimal observation time can be obtained. Full article
(This article belongs to the Special Issue Turbulence from Earth to Planets, Stars and Galaxies—Commemorative Issue Dedicated to the Memory of Jackson Rea Herring)
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<p>Time series data from a network of seven sensors are used to approximate the wave field <span class="html-italic">u</span> by solving the systems (<a href="#FD25-atmosphere-14-01451" class="html-disp-formula">25</a>). (<b>a</b>) The locations of an eighth and ninth sensor in red plotted on the initial state of the system (<a href="#FD15-atmosphere-14-01451" class="html-disp-formula">15</a>). (<b>b</b>,<b>c</b>) Comparisons of time series of the eighth and ninth sensors to the reconstructions of the time series at those locations using the network of seven sensors.</p> Full article ">Figure 2
<p>The variance of the least squares estimate (28) of a coefficient <math display="inline"><semantics> <msub> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> <mi>i</mi> </msub> </semantics></math> plotted against the number of sensors used. Each line displays the variance for a different assumption for how the data <math display="inline"><semantics> <mrow> <msub> <mo>Φ</mo> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math> are obtained.</p> Full article ">Figure 3
<p>The <math display="inline"><semantics> <mrow> <msup> <mi>L</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mi>τ</mi> <mo>]</mo> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> relative error in approximating the time series of a sensor plotted against the sensor noise level <math display="inline"><semantics> <mi>σ</mi> </semantics></math> and the number of sensor time series used for approximation. The final time is <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>The decomposition of the relative mean squared error (<a href="#FD41-atmosphere-14-01451" class="html-disp-formula">41</a>) of a coefficient <math display="inline"><semantics> <msub> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> <mi>j</mi> </msub> </semantics></math> into squared-bias and variance terms. (<b>a</b>) The mean squared error (<a href="#FD41-atmosphere-14-01451" class="html-disp-formula">41</a>) versus sensor observation time <math display="inline"><semantics> <mi>τ</mi> </semantics></math>. (<b>b</b>) The squared-bias and variance terms versus <math display="inline"><semantics> <mi>τ</mi> </semantics></math>.</p> Full article ">
10 pages, 1223 KiB  
Communication
A New SLF/ELF Algorithm of Fields Excited by a Radiator in a Soil Foundation in the Earth–Ionosphere Cavity
by Yuanxin Wang, Jutao Yang, Shuji Hao, Jing Chen, Yonggan Liang and Yanshuai Zheng
Atmosphere 2023, 14(9), 1450; https://doi.org/10.3390/atmos14091450 - 18 Sep 2023
Viewed by 819
Abstract
Abnormal electromagnetic radiation associated with seismic activity has been reported across a wide range of frequencies, but its primary energy is concentrated in the super-low-frequency (SLF) and extremely low-frequency (ELF) bands. To estimate the effect of the seismic radiation source, a radiator in [...] Read more.
Abnormal electromagnetic radiation associated with seismic activity has been reported across a wide range of frequencies, but its primary energy is concentrated in the super-low-frequency (SLF) and extremely low-frequency (ELF) bands. To estimate the effect of the seismic radiation source, a radiator in a soil foundation was modeled as a horizontal electric dipole (HED), and the propagation characteristics of the electromagnetic fields were studied in the Earth–ionosphere cavity. The expressions of the electromagnetic fields could be obtained according to the reciprocity theorem. Therefore, a new algorithm named the numerical integral algorithm was proposed, which is suitable for both the SLF and ELF bands. The new algorithm was compared with the asymptotic approximation algorithm when the receiving point was not close to the field source and the antipode. The two algorithms were found to be in excellent agreement, confirming the validity of the new algorithm for SLF and ELF bands. Full article
(This article belongs to the Special Issue Electromagnetic Observations and Their Applications in Earthquake Research)
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<p>Reciprocal geometry relation of transmitting and receiving antennas.</p> Full article ">Figure 2
<p>Variation in the electric field component <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>r</mi> </msub> </mrow> </semantics></math> along the propagation distance.</p> Full article ">Figure 3
<p>Variation in the electromagnetic field components along the distance from antipode. (<b>a</b>) electric field component; (<b>b</b>) magnetic field component.</p> Full article ">Figure 4
<p>Variation in the electric field component <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>r</mi> </msub> </mrow> </semantics></math> along the propagation distance with different frequency.</p> Full article ">
32 pages, 15617 KiB  
Article
Trends and Variability in Temperature and Related Extreme Indices in Rwanda during the Past Four Decades
by Bonfils Safari and Joseph Ndakize Sebaziga
Atmosphere 2023, 14(9), 1449; https://doi.org/10.3390/atmos14091449 - 17 Sep 2023
Cited by 2 | Viewed by 1733
Abstract
Analysis of the trends and variability of climate variables and extreme climate events is important for climate change detection in space and time. In this study, the trends and variabilities of minimum, maximum, and mean temperatures, as well as five extreme temperature indices, [...] Read more.
Analysis of the trends and variability of climate variables and extreme climate events is important for climate change detection in space and time. In this study, the trends and variabilities of minimum, maximum, and mean temperatures, as well as five extreme temperature indices, are analyzed over Rwanda for the period of 1983 to 2022. The Modified Mann–Kendall test and the Theil–Sen estimator are used for the analysis of, respectively, the trend and the slope. The standard deviation is used for the analysis of the temporal variability. It is found, on average, over the country, a statistically significant (α = 0.05) positive trend of 0.17 °C/decade and 0.20 °C/decade in minimum temperature, respectively, for the long dry season and short rain season. Statistically significant (α = 0.05) positive trends are observed for spatially averaged cold days (0.84 days/decade), warm nights (0.62 days/decade), and warm days (1.28 days/decade). In general, maximum temperature represents higher variability compared to the minimum temperature. In all seasons except the long dry season, statistically significant (α = 0.05) high standard deviations (1.4–1.6 °C) are observed over the eastern and north-western highlands for the maximum temperature. Cold nights show more variability, with a standard deviation ranging between 5 and 7 days, than the cold days, warm nights, and warm days, having, respectively, standard deviations ranging between 2 and 3, 4 and 5 days, and 3 and 4, and, especially in the area covering the central, south-western, south-central, and northwestern parts of Rwanda. Temperature increase and its variability have an impact on agriculture, health, water resources, infrastructure, and energy. The results obtained from this study are important since they can serve as the baseline for future projections. These can help policy decision making take objective measures for mitigation and adaptation to climate change impacts. Full article
(This article belongs to the Special Issue Climate Change and Climate Variability, and Their Impact on Extreme Events)
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<p>The elevation map of Rwanda (<b>a</b>) and the grid covering the study area (<b>b</b>).</p> Full article ">Figure 2
<p>Spatial distribution of long-term mean of Tx, Tn, and T over Rwanda during the period of 1983–2022 expressed in °C. Tx for JF (<b>a1</b>), Tx for MAM (<b>a2</b>), Tx for JJA (<b>a3</b>), Tx for SOND (<b>a4</b>), Tx for mean annual (<b>a5</b>),Tn for JF (<b>b1</b>), Tn for MAM (<b>b2</b>), Tn for JJA (<b>b3</b>), Tn for SOND (<b>b4</b>), Tn for mean annual (<b>b5</b>), T for JF (<b>c1</b>), T for MAM (<b>c2</b>), T for JJA (<b>c3</b>), T for SOND (<b>c4</b>), T for mean annual (<b>c5</b>), (Computed from the data used in this study, obtained from Rwanda Meteorology Agency).</p> Full article ">Figure 3
<p>Spatial distribution of trends for Tx, Tn, and T over Rwanda during the period of 1983–2022 expressed in °C/decade. Tx for JF (<b>a1</b>), Tn for JF (<b>a2</b>), T for JF (<b>a3</b>), Tx for MAM (<b>b1</b>), Tn for MAM (<b>b2</b>), T for MAM (<b>b3</b>), Tx for JJA (<b>c1</b>), Tn for JJA (<b>c2</b>), T for JJA (<b>c3</b>), Tx for SOND (<b>d1</b>), Tn for SOND (<b>d2</b>), T for SOND (<b>d3</b>), mean annual Tx (<b>e1</b>), mean annual Tn (<b>e2</b>), and mean annual T (<b>e3</b>). Trend slope in °C/decade (obtained by multiplying by 10 the computed trend slope in °C/year using the MMK method). Areas with statistically significant positive trends are indicated with + sign, and areas with statistically significant negative trends are indicated with − sign.</p> Full article ">Figure 4
<p>Decadal differences of Tx for JF, MAM, JJA, SOND, and the annual mean with reference to the period of 1983–1992. Tx for JF (1993–2002) (<b>a1</b>), Tx for JF (2003–2012) (<b>a2</b>), Tx for JF (2013–2022) (<b>a3</b>), Tx for MAM (1993-2002) (<b>b1</b>), Tx for MAM (2003–2012) (<b>b2</b>), Tx for MAM (2013–2022) (<b>b3</b>), Tx for JJA (1993–2002) (<b>c1</b>), Tx for JJA (2003–2012) (<b>c2</b>), Tx for JJA (2013–2022) (<b>c3</b>), Tx for SOND (1993–2002) (<b>d1</b>), Tx for SOND (2003–2012) (<b>d2</b>), Tx for SOND (2013–2022) (<b>d3</b>), annual Tx (1993–2002) (<b>e1</b>), annual Tx (2003–2012) (<b>e2</b>), and for annual Tx (2013–2022) (<b>e3</b>).</p> Full article ">Figure 5
<p>Decadal differences of Tn for JF, MAM, JJA, SOND, and the annual mean with reference to the period of 1983–1992. Tn for JF (1993–2002) (<b>a1</b>), Tn for JF (2003–2012) (<b>a2</b>), Tn for JF (2013–2022) (<b>a3</b>), Tn for MAM (1993–2002) (<b>b1</b>), Tn for MAM (2003–2012) (<b>b2</b>), Tn for MAM (2013–2022) (<b>b3</b>), Tn for JJA (1993–2002) (<b>c1</b>), Tn for JJA (2003–2012) (<b>c2</b>), Tn for JJA (2013–2022) (<b>c3</b>), Tn for SOND (1993–2002) (<b>d1</b>), Tn for SOND (2003–2012) (<b>d2</b>), Tn for SOND (2013–2022) (<b>d3</b>), annual Tn (1993–2002) (<b>e1</b>), annual Tn (2003–2012) (<b>e2</b>), and annual Tn (2013–2022) (<b>e3</b>).</p> Full article ">Figure 6
<p>Decadal differences of T for JF, MAM, JJA, SOND, and the annual mean with reference to the period of 1983–1992. T for JF (1993–2002) (<b>a1</b>), T for JF (2003–2012) (<b>a2</b>), T for JF (2013–2022) (<b>a3</b>), T for MAM (1993–2002) (<b>b1</b>), T for MAM (2003–2012) (<b>b2</b>), T for MAM (2013–2022) (<b>b3</b>), T for JJA (1993–2002) (<b>c1</b>), T for JJA (2003–2012) (<b>c2</b>), T for JJA (2013–2022) (<b>c3</b>), T for SOND (1993–2002) (<b>d1</b>), T for SOND (2003–2012) (<b>d2</b>), T for SOND (2013–2022) (<b>d3</b>), annual T (1993–2002) (<b>e1</b>), annual T (2003–2012) (<b>e2</b>), and annual T (2013–2022) (<b>e3</b>).</p> Full article ">Figure 7
<p>Spatial distribution of long-term mean of DTR (<b>a</b>) expressed in °C, Tn10p (<b>b</b>); Tx10p (<b>c</b>); Tn90p (<b>d</b>); and Tx90p (<b>e</b>) expressed in days over Rwanda for the period 1983–2022. Legend is common for Tn10p, Tn90p, Tx10p, and Tx90p.</p> Full article ">Figure 8
<p>Spatial distribution of trends of DTR (<b>a</b>) expressed in °C/decade, Tn10p (<b>b</b>); Tx10p (<b>c</b>); Tn90p (<b>d</b>); and Tx90p (<b>e</b>) expressed in days/decade over Rwanda during the period of 1983–2022. “Trend slope in °C/decade (obtained by multiplying by 10 the computed trend slope in °C/year by the MMK method) for DRT” and “Trend slope in Days/decade (obtained by multiplying by 10 the computed trend slope in Days/year by the MMK method) for Tn10p, Tn90p, Tx10p, and Tx90p”. Areas with statistically significant positive trends are indicated with + sign, and areas with statistically significant negative trends are indicated with − sign. Legend is common for Tn10p, Tn90p, Tx10p, and Tx90p.</p> Full article ">Figure 9
<p>Decadal spatial distribution of DTR expressed in °C over Rwanda for the period of 1983–2022. The first decade (1983–1992) (<b>a</b>), the second decade (1993–2002) (<b>b</b>), the third decade (2003–2012) (<b>c</b>), and the fourth decade (2013–2022) (<b>d</b>).</p> Full article ">Figure 10
<p>Decadal spatial distribution of Tn10p expressed in day/year over Rwanda for the period of 1983–2022. The first decade (1983–1992) (<b>a</b>), the second decade (1993–2002) (<b>b</b>), the third decade (2003–2012) (<b>c</b>), and the fourth decade (2013–2022) (<b>d</b>).</p> Full article ">Figure 11
<p>Decadal spatial distribution of Tx10p expressed in day/year over Rwanda for the period of 1983–2022. The first decade (1983–1992) (<b>a</b>), the second decade (1993–2002) (<b>b</b>), the third decade (2003–2012) (<b>c</b>), and the fourth decade (2013–2022) (<b>d</b>).</p> Full article ">Figure 12
<p>Decadal spatial distribution of Tn90p expressed in day/year over Rwanda for the period of 1983–2022. The first decade (1983–1992) (<b>a</b>), the second decade (1993–2002) (<b>b</b>), the third decade (2003–2012) (<b>c</b>), and the fourth decade (2013–2022) (<b>d</b>).</p> Full article ">Figure 13
<p>Decadal spatial distribution of Tx90p expressed in day/year over Rwanda for the period of 1983–2022. The first decade (1983–1992) (<b>a</b>), the second decade (1993–2002) (<b>b</b>), the third decade (2003–2012) (<b>c</b>), and the fourth decade (2013–2022) (<b>d</b>).</p> Full article ">Figure 14
<p>Spatial distribution of decadal differences of DTR, Tn10p, Tx10p, Tn90p and Tx90p. DTR for the second decade (1993–2002) (<b>a1</b>), DTR for the third decade (2003–2012) (<b>a2</b>), DTR for the fourth decade (2013–2022) (<b>a3</b>). Tn10p for the second decade (1993–2002) (<b>b1</b>), Tn10p for the third decade (2003–2012) (<b>b2</b>), Tn10p for the fourth decade (2013–2022) (<b>b3</b>). Tn90p for the second decade (1993–2002) (<b>c1</b>), Tn90p for the third decade (2003–2012) (<b>c2</b>), and Tn90p for the fourth decade (2013–2022) (<b>c3</b>). Tx10p for the second decade (1993–2002) (<b>d1</b>), Tx10p for the third decade (2003–2012) (<b>d2</b>), and Tx10p for the fourth decade (2013–2022) (<b>d3</b>). Tx90p for the second decade (1993–2002) (<b>e1</b>), Tx90p for the third decade (2003–2012) (<b>e2</b>), and Tx90p for the fourth decade (2013–2022) (<b>e3</b>). Legend is common for each extreme temperature index for all decades.</p> Full article ">Figure 15
<p>Spatial variability of Tx, Tn, and T over Rwanda during the period of 1983–2022 expressed in terms of standard deviation in °C. Tx for JF (<b>a1</b>), Tn for JF (<b>a2</b>), T for JF (<b>a3</b>), Tx for MAM (<b>b1</b>), Tn for MAM (<b>b2</b>), T for MAM (<b>b3</b>), Tx for JJA (<b>c1</b>), Tn for JJA (<b>c2</b>), T for JJA (<b>c3</b>), Tx for SOND (d1), Tn for SOND (<b>d2</b>), T for SOND (<b>d3</b>), mean annual Tx (<b>e1</b>), mean annual Tn (<b>e2</b>), and mean annual T (<b>e3</b>).</p> Full article ">Figure 16
<p>Spatial variability expressed in terms of standard deviation for DTR (<b>a</b>) in °C/year, Tn10p (<b>b</b>); Tx10p (<b>c</b>); Tn90p (<b>d</b>); and Tx90p (<b>e</b>) in days/year over Rwanda during the period of 1983–2022. Legend is common for Tn10p, Tn90p, Tx10p, and Tx90p.</p> Full article ">
21 pages, 12799 KiB  
Article
A Case Study of Drought during Summer 2022: A Large-Scale Analyzed Comparison of Dry and Moist Summers in the Midwest USA
by Sarah M. Weaver, Patrick E. Guinan, Inna G. Semenova, Noel Aloysius, Anthony R. Lupo and Sherry Hunt
Atmosphere 2023, 14(9), 1448; https://doi.org/10.3390/atmos14091448 - 17 Sep 2023
Cited by 3 | Viewed by 3462
Abstract
The summer of 2022 was very dry across Missouri and the surrounding regions including much of the Great Lakes, Midwest, and southern plains of the USA. A comparison of this summer to the dry summer of 2012 and the relatively wet summers of [...] Read more.
The summer of 2022 was very dry across Missouri and the surrounding regions including much of the Great Lakes, Midwest, and southern plains of the USA. A comparison of this summer to the dry summer of 2012 and the relatively wet summers of 2018 and 2021 was carried out using the National Centers for Environmental Prediction/National Centers for Atmospheric Research reanalysis, the Climate Prediction Center teleconnection indexes, and the blocking archive at the University of Missouri. The summer of 2022 was like that of 2012 which was characterized by a strong 500 hPa height anomaly centered over the western US and plains as well as very little blocking in the East Pacific. The summers of 2018 and 2021 were characterized by more zonal flow over the USA and more blocking in the East Pacific, similarly to the results of an earlier study. The teleconnection indexes for the prior spring and summer were largely similar for the two drier years and opposite for the wetter years. The surface conditions for the drier years were more similar while these were opposite for the wetter years. The integrated enstrophy (IE) used in earlier studies identified a change in the large-scale flow regime in early June 2022, which coincided with a decrease in the precipitation over the study region. However, one key difference was that the spring of 2022 was characterized by blocking more consistent with a wetter summer. This would have made the predictability of the drought of summer 2022 less certain. Full article
(This article belongs to the Section Climatology)
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<p>The study region in the US as defined by [<a href="#B3-atmosphere-14-01448" class="html-bibr">3</a>] and adapted from them. The three dots across the State of Missouri correspond to the National Weather Service Office locations for Kansas City (MCI), Columbia (COU), Saint Louis (STL) from west to east, and Springfield (SGF) in the Southwest.</p> Full article ">Figure 2
<p>The sections of the US that include the West (deep orange color), Midwest (green color), South (light orange color), Southwest (yellow color) and Northeast (blue color) as defined by [<a href="#B39-atmosphere-14-01448" class="html-bibr">39</a>].</p> Full article ">Figure 3
<p>Statewide temperature and precipitation ranks for the United States during summer 2012 (Source: [<a href="#B40-atmosphere-14-01448" class="html-bibr">40</a>,<a href="#B41-atmosphere-14-01448" class="html-bibr">41</a>]). In (<b>a</b>)<b>,</b> the dark red, orange, beige, white, light blue, medium blue, and dark blue indicate temperatures that were record warmest, much above normal, above normal, near normal, below normal, much below normal, and record coldest, respectively. In (<b>b</b>), the dark green, medium green, light green, white, yellow/light brown, medium brown, and dark brown indicate precipitation that was wettest, much above normal, above normal, near normal, below normal, much below normal, and driest, respectively. Temperature is on the left and precipitation, on the right. Ranks are such that the highest number indicates the warmest or wettest months, respectively.</p> Full article ">Figure 3 Cont.
<p>Statewide temperature and precipitation ranks for the United States during summer 2012 (Source: [<a href="#B40-atmosphere-14-01448" class="html-bibr">40</a>,<a href="#B41-atmosphere-14-01448" class="html-bibr">41</a>]). In (<b>a</b>)<b>,</b> the dark red, orange, beige, white, light blue, medium blue, and dark blue indicate temperatures that were record warmest, much above normal, above normal, near normal, below normal, much below normal, and record coldest, respectively. In (<b>b</b>), the dark green, medium green, light green, white, yellow/light brown, medium brown, and dark brown indicate precipitation that was wettest, much above normal, above normal, near normal, below normal, much below normal, and driest, respectively. Temperature is on the left and precipitation, on the right. Ranks are such that the highest number indicates the warmest or wettest months, respectively.</p> Full article ">Figure 4
<p>Statewide ranks for the United States for the entire year of 2012 showing both temperature and precipitation that occurred for the year [<a href="#B43-atmosphere-14-01448" class="html-bibr">43</a>,<a href="#B44-atmosphere-14-01448" class="html-bibr">44</a>]. Color schemes follow <a href="#atmosphere-14-01448-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>As in <a href="#atmosphere-14-01448-f004" class="html-fig">Figure 4</a>, except for 2018. Source: [<a href="#B45-atmosphere-14-01448" class="html-bibr">45</a>]. Color schemes follow <a href="#atmosphere-14-01448-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 6
<p>As in <a href="#atmosphere-14-01448-f004" class="html-fig">Figure 4</a>, except for the year 2021. Source [<a href="#B46-atmosphere-14-01448" class="html-bibr">46</a>]. Color schemes follow <a href="#atmosphere-14-01448-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 7
<p>As in <a href="#atmosphere-14-01448-f004" class="html-fig">Figure 4</a>, except for the year 2022. Source: [<a href="#B47-atmosphere-14-01448" class="html-bibr">47</a>]. Color schemes follow <a href="#atmosphere-14-01448-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 8
<p>The US Drought Monitor maps for (<b>a</b>) 5 July 2022 and (<b>b</b>) 25 October 2022. Yellow (D0), beige (D1), orange (D2), red (D3), and dark brown (D4) correspond to abnormally dry, moderate drought, severe drought, extreme drought, and exceptional drought, respectively. Source: [<a href="#B48-atmosphere-14-01448" class="html-bibr">48</a>].</p> Full article ">Figure 9
<p>The daily teleconnection index values for the AO (blue), PNA (orange), and NAO (gray) from 1 April to 30 June, for (<b>a</b>) 2012, (<b>b</b>) 2022.</p> Full article ">Figure 10
<p>500 hPa geopotential height (m) for the years (<b>a</b>) 2012, (<b>b</b>) 2018, (<b>c</b>) 2021, and (<b>d</b>) 2022; focused on the United States [<a href="#B31-atmosphere-14-01448" class="html-bibr">31</a>].</p> Full article ">Figure 10 Cont.
<p>500 hPa geopotential height (m) for the years (<b>a</b>) 2012, (<b>b</b>) 2018, (<b>c</b>) 2021, and (<b>d</b>) 2022; focused on the United States [<a href="#B31-atmosphere-14-01448" class="html-bibr">31</a>].</p> Full article ">Figure 11
<p>Calculated integrated enstrophy (IE—km<sup>2</sup> s<sup>−2</sup>) for 1 April to 30 June for the years (<b>a</b>) 2012 and (<b>b</b>) 2022. The red, blue, and yellow lines are the IE, 90-day mean IE, and one standard deviation from the mean, respectively.</p> Full article ">Figure 12
<p>The spring (1 March to 31 May) season precipitation (mm day<sup>−1</sup>) (<b>left</b>) and surface potential evaporation (W m<sup>−2</sup>) (<b>right</b>) for (<b>a</b>,<b>b</b>) 2012, (<b>c</b>,<b>d</b>) 2018, (<b>e</b>,<b>f</b>) 2021, and (<b>g</b>,<b>h</b>) 2022.</p> Full article ">Figure 12 Cont.
<p>The spring (1 March to 31 May) season precipitation (mm day<sup>−1</sup>) (<b>left</b>) and surface potential evaporation (W m<sup>−2</sup>) (<b>right</b>) for (<b>a</b>,<b>b</b>) 2012, (<b>c</b>,<b>d</b>) 2018, (<b>e</b>,<b>f</b>) 2021, and (<b>g</b>,<b>h</b>) 2022.</p> Full article ">Figure 13
<p>The summer (1 April to 30 June) season precipitation (mm day<sup>−1</sup>) (<b>left</b>) and surface potential evaporation (W m<sup>−2</sup>) (<b>right</b>) for (<b>a</b>,<b>b</b>) 2012, (<b>c</b>,<b>d</b>) 2018, (<b>e</b>,<b>f</b>) 2021, and (<b>g</b>,<b>h</b>) 2022. The color palette is the same as in <a href="#atmosphere-14-01448-f012" class="html-fig">Figure 12</a>.</p> Full article ">Figure 13 Cont.
<p>The summer (1 April to 30 June) season precipitation (mm day<sup>−1</sup>) (<b>left</b>) and surface potential evaporation (W m<sup>−2</sup>) (<b>right</b>) for (<b>a</b>,<b>b</b>) 2012, (<b>c</b>,<b>d</b>) 2018, (<b>e</b>,<b>f</b>) 2021, and (<b>g</b>,<b>h</b>) 2022. The color palette is the same as in <a href="#atmosphere-14-01448-f012" class="html-fig">Figure 12</a>.</p> Full article ">Figure 14
<p>The precipitation anomalies for (<b>a</b>) spring (1 March–31 May 2022, inches) and (<b>b</b>) summer (1 June–31 August 2022, inches). Source: [<a href="#B56-atmosphere-14-01448" class="html-bibr">56</a>].</p> Full article ">
23 pages, 6963 KiB  
Article
Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria
by Mohammed Achite, Okan Mert Katipoğlu, Muhammad Jehanzaib, Nehal Elshaboury, Veysi Kartal and Shoaib Ali
Atmosphere 2023, 14(9), 1447; https://doi.org/10.3390/atmos14091447 - 17 Sep 2023
Cited by 9 | Viewed by 1669
Abstract
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging [...] Read more.
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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<p>Map of the study area along with located hydro-meteorological stations.</p> Full article ">Figure 2
<p>Variability of the meteorological data from the Matemore station (1977–2010).</p> Full article ">Figure 3
<p>Ombrothermic diagram of the study area (1977–2010).</p> Full article ">Figure 4
<p>PACF plots of droughts at station HS1.</p> Full article ">Figure 5
<p>PACF plots of droughts at station HS2.</p> Full article ">Figure 6
<p>PACF plots of droughts at station HS3.</p> Full article ">Figure 7
<p>PACF plots of droughts at station HS4.</p> Full article ">Figure 8
<p>PACF plots of droughts at station HS5.</p> Full article ">Figure 9
<p>SRI values’ subcomponents by discrete wavelet transform at the HS1 station.</p> Full article ">Figure 10
<p>Scatter diagrams of SRI values at the HS1 station.</p> Full article ">Figure 11
<p>Scatter diagrams of SRI values at the HS2 station.</p> Full article ">Figure 12
<p>Scatter diagrams of SRI values at the HS3 station.</p> Full article ">Figure 13
<p>Scatter diagrams of SRI values at the HS4 station.</p> Full article ">Figure 14
<p>Scatter diagrams of SRI values at the HS5 station.</p> Full article ">Figure 15
<p>Radar plot graphs of MSE values.</p> Full article ">
14 pages, 4077 KiB  
Article
Long-Term Tropospheric Ozone Data Analysis 1997–2019 at Giordan Lighthouse, Gozo, Malta
by Brunislav Matasović, Martin Saliba, Rebecca Muscat, Marvic Grima and Raymond Ellul
Atmosphere 2023, 14(9), 1446; https://doi.org/10.3390/atmos14091446 - 17 Sep 2023
Viewed by 1070
Abstract
Long-term data analysis of the hourly ozone volume fractions in the middle of the Mediterranean Seawas carried out covering a period of 22 years. It was noticed that the amount of ozone during this period very rarely exceeded the recommended upper limit value [...] Read more.
Long-term data analysis of the hourly ozone volume fractions in the middle of the Mediterranean Seawas carried out covering a period of 22 years. It was noticed that the amount of ozone during this period very rarely exceeded the recommended upper limit value of 80 ppb and that the amount of tropospheric ozone in the area is rather low. Fourier data analysis shows the presence of only a seasonal cycle in ozone concentrations. Statistical analysis of the data is showing a slightly negative trend in ozone concentrations of −0.46 ± 0.08 ppb/year for average values and a slightly higher negative trend of −0.54 ± 0.11 ppb/year for the 95th percentile values. These results obtained through simple linear regression were confirmed using the more appropriate Mann–Kendall test. The possible quadratic trend was not observed for the whole series of data. Air mass trajectories were calculated for those days in the year with the highest pollution, indicating that during those days horizontal air transfer, in most cases, brings the air mass from the North and from Sicily in Southern Italy. Full article
(This article belongs to the Special Issue Airborne Measurements and Analyses of Trace Gases)
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<p>Location of the monitoring station at Giordan Lighthouse in Gozo, Malta (marked with a drop point).</p> Full article ">Figure 2
<p>Wind rose based on the whole year data for the Giordan Lighthouse station showing wind direction frequencies.</p> Full article ">Figure 3
<p>Distribution of the hourly average ozone volume fractions from 1997 to 2019. Hourly averages of the ozone volume fraction are distributed in sets with the range of 5 ppb and shown with vertical columns. The red line shows the percentage of hourly averages of ozone volume fractions considered until the given set.</p> Full article ">Figure 4
<p>Box and whiskers plot of the hourly ozone volume fractions with statistical values from the whole dataset. Maxima and minima (upper and lower extremes) are shown with a dot, the upper whisker shows the 90th percentile, the upper box line shows the 75th percentile, the inner box line shows the median, the lower box line shows the 25the percentile and the lower whisker line shows the 10the percentile. <span class="html-italic">φ</span>(O<sub>3</sub>) stands for hourly ozone volume fractions. Values on the x-axis are the hours in the day when the data were obtained.</p> Full article ">Figure 5
<p>Monthly averages of ozone volume fractions <span class="html-italic">φ</span>(O<sub>3</sub>) for the whole period of observation. <span class="html-italic">φ</span>(O<sub>3</sub>) stands for ozone volume fractions. The black line represents the linear regression trend in ozone volume fractions. The dotted line represents quadratic polynomial regression with the corresponding equation. Units given in the equation are ppb/month on the appropriate polynomial order. Intercept points represent the value for the year 1997 as the first year of measurements.</p> Full article ">Figure 6
<p>Fourier transform periodicity analysis of O<sub>3</sub> during the whole observed period.</p> Full article ">Figure 7
<p>Sine fit of O<sub>3</sub> during the whole observed period. A curve shows the least square regression of the first harmonic shown in <a href="#atmosphere-14-01446-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 8
<p>Linear regression based on the annual average of ozone volume fractions, marked with dots, for the whole year (blue) and for the summer season (grey) and based on the 95th percentile of the hourly ozone volume fractions for the whole year (red) and for the summer season (orange). Values on the y-axis are <span class="html-italic">φ</span>(O<sub>3</sub>), which stands for ozone volume fractions. Values on the x-axis are the year for which an annual average was calculated. A confidence interval of 95% is applied.</p> Full article ">Figure 9
<p>Monthly averages of ozone volume fractions <span class="html-italic">φ</span>(O<sub>3</sub>) for the period from 1997 to 2008 in which maximum values of ozone volume fractions occurred. The dotted line represents quadratic polynomial regression with the corresponding equation. Units given in the equation are ppb/month on the appropriate polynomial order. Intercept points represent the value for the year 1997 as the first year of measurements.</p> Full article ">Figure 10
<p>Air trajectories calculated for the Giordan Lighthouse observation site using METEX software and kinematic model [<a href="#B46-atmosphere-14-01446" class="html-bibr">46</a>] on the most polluted day during the observations—14th of August 2003. Trajectories were calculated for 5 days backwards from the date mentioned above. Trajectories for the most polluted days per year can be found in the <a href="#app1-atmosphere-14-01446" class="html-app">Supplemental Material</a>.</p> Full article ">Figure 11
<p>Distribution of ozone volume fractions by wind direction.</p> Full article ">
17 pages, 17259 KiB  
Article
Coastal Flooding Associated with Hurricane Irma in Central Cuba (Ciego de Ávila Province)
by Felipe Matos-Pupo, Matthew C. Peros, Roberto González-De Zayas, Alexey Valero-Jorge, Osvaldo E. Pérez-López, Flor Álvarez-Taboada and Rogert Sorí
Atmosphere 2023, 14(9), 1445; https://doi.org/10.3390/atmos14091445 - 16 Sep 2023
Viewed by 1308
Abstract
Irma was a major hurricane that developed during the 2017 season. It was a category 5 on the Saffir–Simpson Hurricane wind scale. This hurricane caused severe damage in the Caribbean area and the Florida Keys. The social, economic, and environmental impacts, mainly related [...] Read more.
Irma was a major hurricane that developed during the 2017 season. It was a category 5 on the Saffir–Simpson Hurricane wind scale. This hurricane caused severe damage in the Caribbean area and the Florida Keys. The social, economic, and environmental impacts, mainly related to coastal flooding, were also significant in Cuba. The maximum limits of coastal flooding caused by this hurricane were determined in this research. Field trips and the use of the GPS supported our work, which focused on both the northern and southern coasts of the Ciego de Ávila province. This work has been critical for improving coastal flooding scenarios related to a strong hurricane, as it has been the first experience according to hurricane data since 1851. Results showed that the Punta Alegre and Júcaro towns were the most affected coastal towns. The locals had never seen similar flooding in these places before. The differences between flood areas associated with Hurricane Irma and previous modeled hazard scenarios were evident (the flooded areas associated with Hurricane Irma were smaller than those modeled for categories 1, 3, and 5 hurricanes). The effects of this hurricane on the most vulnerable coastal settlements, including the impacts on the archeological site “Los Buchillones”, were also assessed. Full article
(This article belongs to the Special Issue Recent and Future Cyclonic Activity and Associated Weather Extremes)
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<p>Map of the study area.</p> Full article ">Figure 2
<p>Digital Elevation Model map of the province Ciego de Ávila, including the four meteorological stations, source: Geocuba Agency.</p> Full article ">Figure 3
<p>Images of Hurricane Irma over central Cuba. (<b>a</b>) Path of Hurricane Irma over Cuba. (<b>b</b>) Infrared image of Hurricane Irma over central Cuba (from <a href="http://www.noaa.gov" target="_blank">http://www.noaa.gov</a>). Accessed on 14 November 2017.</p> Full article ">Figure 4
<p>Image of water level (approximately 0.9 m from the ground level) measured at a house in the town of Punta Alegre.</p> Full article ">Figure 5
<p>Coastal floodplain associated with Hurricane Irma on both coasts of Ciego de Ávila province.</p> Full article ">Figure 6
<p>Floodplain associated with Hurricane Irma (2017) and studies of hazard, vulnerability, and risks (HVR studies) for a category 1 hurricane in the southern coast of Ciego de Ávila.</p> Full article ">Figure 7
<p>Floodplain associated with Hurricane Irma (2017) and HVR studies for two intense hurricanes (categories 3 and 5) on the northern coast.</p> Full article ">Figure 8
<p>Hurricane-Irma-related floodplain in the coastal town of Júcaro, Source: Open Access Hub (copernicus.eu) Accessed on 28 November 2018.</p> Full article ">Figure 9
<p>Seagrass <span class="html-italic">Thalassia testudinum</span> pushed inland by sea (due to storm surge) in the coastal town of Júcaro.</p> Full article ">Figure 10
<p>Floodplain associated with Hurricane Irma in the towns of Punta Alegre and Máximo Gómez, respectively. Source: Open Access Hub (copernicus.eu).</p> Full article ">Figure 11
<p>Boats pushed inland by Hurricane-Irma-related surge in Punta Alegre.</p> Full article ">Figure 12
<p>Line of <span class="html-italic">Thalassia testudinum</span> moved by Hurricane Irma in the coastal sector of Punta Alegre. The yellow line was used as a reference to estimate the limits of coastal flooding.</p> Full article ">Figure 13
<p>Floodplain lagoon associated with Hurricane Irma at Los Buchillones archeological site (2017).</p> Full article ">
15 pages, 1611 KiB  
Article
Air Quality Mapping in Bandung City
by Resa Septiani Pontoh, Leivina Saliaputri, Audrey Nayla Nashwa, Nadhira Khairina, Bertho Tantular, Toni Toharudin and Farhat Gumelar
Atmosphere 2023, 14(9), 1444; https://doi.org/10.3390/atmos14091444 - 16 Sep 2023
Cited by 1 | Viewed by 1722
Abstract
One of the most commonly encountered issues in large cities is air pollution. As a major city, Bandung also experiences the same problem. This issue arises due to the increasing levels of human activity. This contributes to elevated levels of pollutants in the [...] Read more.
One of the most commonly encountered issues in large cities is air pollution. As a major city, Bandung also experiences the same problem. This issue arises due to the increasing levels of human activity. This contributes to elevated levels of pollutants in the atmosphere, which can impact human life and ecosystems. This research is intended to map the regions in Bandung based on their air quality. This study used ambient air quality measurement results from Bandung, which included PM10, PM2.5, dust, SO2, CO, and NO2. This ambient air quality measurement was conducted by the Department of Environment and Hygiene in Bandung. The research methodology utilized in this study was multidimensional scaling analysis. The outcomes of the examination carried out utilizing the multidimensional scaling technique reveal a clustering of regions in Bandung, West Java, based on their air quality. According to the research findings, the locations were grouped into four quadrants, each with different air quality characteristics. Some locations showed high similarity, while others did not exhibit similarity with other groups. These findings can be used for policy-making and improving air quality in Bandung. Conclusions were drawn from the formed groups, where each group had high similarity among its members, but differed from the members of other groups. Among all observed locations in Bandung City, there were areas that were most similar when viewed based on the distance between objects, namely Punclut St. and KPAD Sarijadi; Soekarno Hatta St. (in front of Astra Bizz) and Elang St.; and Buah Batu St. (in front of STSI/ISBI) and Bunderan Cibiru. Full article
(This article belongs to the Section Air Quality)
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<p>Research Object: Regions in Bandung City.</p> Full article ">Figure 2
<p>Metric Multidimensional Scaling Plot.</p> Full article ">Figure 3
<p>Air Quality Mapping in Bandung City.</p> Full article ">
20 pages, 11618 KiB  
Article
Dynamic and Thermodynamic Drivers of Severe Sub-Hourly Precipitation Events in Mainland Portugal
by José Cruz, Margarida Belo-Pereira, André Fonseca and João A. Santos
Atmosphere 2023, 14(9), 1443; https://doi.org/10.3390/atmos14091443 - 16 Sep 2023
Cited by 1 | Viewed by 1276
Abstract
Sub-hourly heavy precipitation events (SHHPs) associated with regional low-pressure (RegL) systems in Portugal are a natural hazard that may have significant socioeconomic implications, namely in agriculture. Therefore, in this paper, their dynamic and thermodynamic drivers are analysed. Three weather stations were used to [...] Read more.
Sub-hourly heavy precipitation events (SHHPs) associated with regional low-pressure (RegL) systems in Portugal are a natural hazard that may have significant socioeconomic implications, namely in agriculture. Therefore, in this paper, their dynamic and thermodynamic drivers are analysed. Three weather stations were used to isolate SHHPs from 2000 to 2022. Higher precipitation variability is found in southern Portugal, with a higher ratio of extreme events on fewer rainy days. This study shows that these SHHP events are associated with low-pressure systems located just to the west of the Iberian Peninsula. These systems exhibit a cold core, particularly strong at mid-levels, and a positive vorticity anomaly, which is stronger in the upper troposphere, extending downward to low levels. These conditions drive differential positive vorticity advection and, therefore, rising motion to the east of the low-pressure systems. Moreover, at low levels, these systems promote moisture advection over western Iberia, also generating instability conditions, which are assessed by instability indices (Convective available potential energy, the Total-Totals index, and the K-index). The combination of these conditions drives heavy precipitation events. Lastly, the total column cloud ice water revealed higher values for the heavier precipitation events, suggesting that it may be a useful predictor of such events. Full article
(This article belongs to the Special Issue High-Impact Weather Events: Dynamics, Variability and Predictability)
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<p>(<b>a</b>) Hypsometric map of mainland Portugal (elevation in m), with three weather stations (VC, CB, and F) with sub-hourly data (timescale of 10 min, 2000–2022) used in the calculation of the different classes of precipitation (C0–3). (<b>b</b>) Bar chart of the number of events recorded in each station and by precipitation class. (<b>c</b>) Bar chart of the percentage of events over the total number of precipitation records in each station and by precipitation class. (<b>d</b>) Spatial distribution of the Pearson correlation coefficients between daily precipitation in the three main weather stations (VC, CB, and F), excluding days without precipitation, and the remaining weather stations (see legend).</p> Full article ">Figure 2
<p>The 50th and 99th percentile (p50 and p99) of hourly ERA5: (<b>a</b>–<b>c</b>) total precipitation for p50, (<b>d</b>–<b>f</b>) convective precipitation for p50, (<b>g</b>–<b>i</b>) total precipitation for p99, (<b>j</b>–<b>l</b>) convective precipitation for p99, for the SHHP events (C3 class) in each WS (VC, CB, and F), and (<b>m</b>–<b>o</b>) quantile–quantile plot for all events of the precipitation (hourly) in each WS (VC, CB, and F); black line: reference line; red dotted line: tendency; blue dots: precipitation data (ERA5 vs. OBS).</p> Full article ">Figure 3
<p>Hourly composites of MSLP (in hPa) (shading) and Z500 (in gpm) (contour lines represented in blue, with a spacing of 50 gpm) for (<b>a</b>–<b>c</b>) C0, (<b>d</b>–<b>f</b>) C1, (<b>g</b>–<b>i</b>) C2 (<b>j</b>–<b>l</b>), and C3.</p> Full article ">Figure 4
<p>Composites of hourly anomalies of (<b>a</b>–<b>c</b>) MSLP (in hPa), (<b>d</b>–<b>f</b>) temperature 500 hPa (in °C), (<b>g</b>–<b>i</b>) 2 m temperature (in °C), (<b>j</b>–<b>l</b>) 2 m dew point temperature (shading in °C), and 10 m wind vector (black arrows in m s<sup>−1</sup>) for the SHHP events (C3 precipitation class) in the three selected WSs (VC, CB, and F).</p> Full article ">Figure 5
<p>Composites of hourly anomalies of geopotential anomalies (in gpm) for precipitation class C3 (SHHP events), at different vertical pressure levels, and for (<b>a</b>) VC, (<b>b</b>) CB, and (<b>c</b>) F.</p> Full article ">Figure 6
<p>Composites of hourly anomalies of the potential temperature anomaly (in °C) for precipitation class C3 (SHHP events), at different vertical levels, and for (<b>a</b>) VC, (<b>b</b>) CB, and (<b>c</b>) F.</p> Full article ">Figure 7
<p>(<b>a</b>) Meridional and (<b>b</b>) zonal cross-sections of the relative vorticity composites of hourly anomalies (shading in s<sup>−1</sup>) and omega vertical velocity (contours in Pa s<sup>−1</sup>, positive/negative values in solid/dashed lines, respectively) for class C3 and VC.</p> Full article ">Figure 8
<p>Composites of hourly atmospheric instability indices: (<b>a</b>–<b>c</b>) CAPE (shading in J kg<sup>−1</sup>) and the total totals index (blue contours in °C, 2 °C spacing), (<b>d</b>–<b>f</b>) the K-index (in °C). and (<b>g</b>–<b>i</b>) CIN (in J kg<sup>−1</sup>) for SHHP events (C3) in each WS (VC, CB, and F).</p> Full article ">Figure 9
<p>Hourly composites of total column cloud ice water, TCCIW (shading in g m<sup>−2</sup>) for precipitation classes (<b>a</b>–<b>c</b>) C1, (<b>d</b>–<b>f</b>) C2, and (<b>g</b>–<b>i</b>) C3 in each WS (VC, CB, and F).</p> Full article ">
18 pages, 3298 KiB  
Article
Validation and Selection of a Representative Subset from the Ensemble of EURO-CORDEX EUR11 Regional Climate Model Outputs for the Czech Republic
by Jan Meitner, Petr Štěpánek, Petr Skalák, Martin Dubrovský, Ondřej Lhotka, Radka Penčevová, Pavel Zahradníček, Aleš Farda and Miroslav Trnka
Atmosphere 2023, 14(9), 1442; https://doi.org/10.3390/atmos14091442 - 15 Sep 2023
Cited by 1 | Viewed by 1186
Abstract
To better understand the impact of climate change at a given location, it is crucial to consider a wide range of climate models that are representative of the area. In this study, we emphasize the importance of the careful validation and selection of [...] Read more.
To better understand the impact of climate change at a given location, it is crucial to consider a wide range of climate models that are representative of the area. In this study, we emphasize the importance of the careful validation and selection of climate models most suitable for a particular region. This step is critical to enhance the relevance of climate change impact studies and consequently design appropriate and robust adaptation measures, particularly in agriculture, forestry and water resources management. We propose validation and selection methods for regional climate models that can help identify a smaller group of well-performing models using the Central European area and Czech Republic as examples. In the validation process, 7 out of 19 regional climate models performed poorly. Of the 12 well-performing models, a subset of 7 models was selected to represent the uncertainty in the entire ensemble, which could be used in subsequent studies. The methodology is sufficiently general and may be applied to other climate model ensembles. Full article
(This article belongs to the Section Climatology)
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<p>The Czech Republic is located in the central part of Europe.</p> Full article ">Figure 2
<p>Temporal correlation of the annual cycle (AC) and spatial correlation (SC) as validation characteristics of the global and regional climate model (GCM-RCM) pairs compared to the station measurements (technical series) for the global radiation, air temperature (mean, minimum and maximum values), precipitation, relative humidity and 10 m wind speed. The abbreviations of the GCM-RCM pairs are given in <a href="#atmosphere-14-01442-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 3
<p>Taylor diagrams of the spatial variability of the global and regional climate model (GCM-RCM) pairs: Pearson correlation coefficient, normalized standard deviation and centered root mean square difference (cRMSD) for the precipitation and minimum air temperature in summer and winter, the global radiation in summer and the annual relative humidity, in which problematic validation characteristics are observed. The same symbols are used for the same RCMs, and the same color is used for the same driving GCM. The abbreviations of the GCM-RCM pairs are given in <a href="#atmosphere-14-01442-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 4
<p>Distances of the models from the center of the entire ensemble of 12 well-performing global and regional climate model (GCM-RCM) pairs for the mean air temperature, precipitation, global radiation and relative humidity. The abbreviations of the GCM-RCM pairs are given in <a href="#atmosphere-14-01442-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 5
<p>Estimated climate change as the average differences over 20-year periods from the average reference period (1981–2005) for the mean air temperature in degrees Celsius, the number of tropical days and the number of days with precipitation amounts equal to or greater than 50 mm and as precipitation and global radiation ratios to the reference period values. The global climate models (GCMs) are plotted in gray, while the global and regional climate model (GCM-RCM) pairs are marked in different colors. The green points indicate the observed climate changes in the Czech Republic represented by the station measurements (technical series). The GCM-RCM pairs are numbered based on their membership in the climate change envelope (CliChE); details are given in the text and listed in <a href="#atmosphere-14-01442-t002" class="html-table">Table 2</a>.</p> Full article ">
25 pages, 5083 KiB  
Review
Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions
by Elena Mitreska Jovanovska, Victoria Batz, Petre Lameski, Eftim Zdravevski, Michael A. Herzog and Vladimir Trajkovik
Atmosphere 2023, 14(9), 1441; https://doi.org/10.3390/atmos14091441 - 15 Sep 2023
Cited by 9 | Viewed by 3256
Abstract
In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how [...] Read more.
In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia’s capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution. Full article
(This article belongs to the Section Air Quality)
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<p>Flow diagram from initial search with NLP framework and PRISMA methodology.</p> Full article ">Figure 2
<p>Word cloud from selected publication abstracts.</p> Full article ">Figure 3
<p>The relationship between pollutants and number of studies.</p> Full article ">Figure 4
<p>The relationship between air pollution sensor types and number of studies.</p> Full article ">Figure 5
<p>The relationship between prediction models and number of studies.</p> Full article ">
9 pages, 2024 KiB  
Communication
Elevated Risk of Compound Extreme Precipitation Preceded by Extreme Heat Events in the Upper Midwestern United States
by Manas Khan, Rabin Bhattarai and Liang Chen
Atmosphere 2023, 14(9), 1440; https://doi.org/10.3390/atmos14091440 - 15 Sep 2023
Cited by 3 | Viewed by 1184
Abstract
Compound extreme events can potentially cause deadlier socio-economic consequences. Although several studies focused on individual extreme climate events, the occurrence of compound extreme events is still not well studied in the upper Midwestern United States. In this study, compound extreme precipitation preceded by [...] Read more.
Compound extreme events can potentially cause deadlier socio-economic consequences. Although several studies focused on individual extreme climate events, the occurrence of compound extreme events is still not well studied in the upper Midwestern United States. In this study, compound extreme precipitation preceded by extreme hot day events was investigated. Results showed a strong linkage between extreme precipitation events and extreme hot days. A significant increasing trend was noticed mainly in Iowa (10.1%), northern parts of Illinois (5.04%), and Michigan (5.04%). Results also showed a higher intensity of extreme precipitation events preceded by an extremely hot day compared to the intensity of extreme precipitation events not preceded by an extremely hot day, mostly in the central and lower parts of Minnesota, western and upper parts of Iowa, lower and upper parts of Illinois, parts of Ohio, Michigan, and Wisconsin for 1950–2010. In other words, extreme heat contributed to more extreme precipitation events. Our findings would provide important insights related to flood management under future climate change scenarios in the region. Full article
(This article belongs to the Section Meteorology)
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<p>Locations of 441 gage stations (blue circles) used in the study which are located in Illinois (IL), Missouri (MO), Kentucky (KY), Indiana (IN), Ohio (OH), Iowa (IA), Minnesota (MN), Wisconsin (WI), and Michigan (MI).</p> Full article ">Figure 2
<p>Spatial distribution of compound extreme precipitation events preceded by extreme hot days expressed as fraction of total extreme precipitation events from 1950–2010 in the upper Midwestern United States.</p> Full article ">Figure 3
<p>Trend of compound extreme precipitation events preceded by extreme hot day expressed as fraction of total extreme precipitation events from (<b>a</b>) 1950–2010; (<b>b</b>) 1960–2010; (<b>c</b>) 1970–2010; (<b>d</b>) 1980–2010 in the upper Midwestern United States. The significance of trend is determined at 95% significance level.</p> Full article ">Figure 4
<p>Difference between intensity of extreme precipitation events preceded by extremely hot day (ICEP) and the intensity of extreme precipitation events not preceded by extreme lyhot day (INCEP) from (<b>a</b>) 1950–1960; (<b>b</b>) 1961–1970; (<b>c</b>) 1971–1980; (<b>d</b>) 1981–1990; (<b>e</b>) 1991–2000 (<b>f</b>) 2001–2010; (<b>g</b>) 1950–2010 in the upper Midwestern United States. In (<b>g</b>), the circles with a dot in the center indicate the stations at which the difference between intensity of ICEP and INCEP is statistically significant from 1950–2010. The significance is determined at 95% significance level.</p> Full article ">Figure 5
<p>(<b>a</b>) Trend of compound extreme precipitation events preceded by extreme hot day expressed as fraction of total extreme precipitation events from 1980–2010 and (<b>b</b>) difference of convective inhibition (CIN) between compound events preceded by extreme hot day and not preceded by extremely hot day.</p> Full article ">
24 pages, 13409 KiB  
Article
A Study on Avalanche-Triggering Factors and Activity Characteristics in Aerxiangou, West Tianshan Mountains, China
by Jie Liu, Tianyi Zhang, Changtao Hu, Bin Wang, Zhiwei Yang, Xiliang Sun and Senmu Yao
Atmosphere 2023, 14(9), 1439; https://doi.org/10.3390/atmos14091439 - 15 Sep 2023
Cited by 1 | Viewed by 1514
Abstract
Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on [...] Read more.
Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height. Full article
(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment (2nd Edition))
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<p>Topographic map of the study area.</p> Full article ">Figure 2
<p>Technology roadmap.</p> Full article ">Figure 3
<p>Automatic weather station in Aerxiangou.</p> Full article ">Figure 4
<p>Monthly meteorological data for Aerxiangou for the last year (January 2022–April 2023) include (<b>a</b>) Average snow depth in Aerxiangou for the past year, month by month, (<b>b</b>) Average temperature in Aerxiangou for the last year, month by month, (<b>c</b>) Average precipitation in Aerxiangou for the last year, month by month, (<b>d</b>) Average wind speed in Aerxiangou for the last year, month by month.</p> Full article ">Figure 4 Cont.
<p>Monthly meteorological data for Aerxiangou for the last year (January 2022–April 2023) include (<b>a</b>) Average snow depth in Aerxiangou for the past year, month by month, (<b>b</b>) Average temperature in Aerxiangou for the last year, month by month, (<b>c</b>) Average precipitation in Aerxiangou for the last year, month by month, (<b>d</b>) Average wind speed in Aerxiangou for the last year, month by month.</p> Full article ">Figure 5
<p>Hazard-causing factor classification map: (<b>a</b>) Topographic factor grading chart; (<b>b</b>) Meteorological factor classification chart (February); (<b>c</b>) Meteorological factor classification chart (March); (<b>d</b>) Meteorological factor classification chart (April).</p> Full article ">Figure 6
<p>Degree of influence of the hazard-causing factors on avalanche.</p> Full article ">Figure 7
<p>Map of multiple avalanche points.</p> Full article ">Figure 8
<p>Avalanche disaster site 4#:(<b>a</b>) Disaster situation at the site; (<b>b</b>) Study area; (<b>c</b>) Cross-sectional view of the exploratory pit.</p> Full article ">Figure 9
<p>Illustration of the snow stress: (<b>a</b>) Full-layer avalanche; (<b>b</b>) Surface avalanche.</p> Full article ">Figure 10
<p>Characteristic values of the movement process at avalanche point 4# under the different periods: (<b>a</b>) Maximum height; (<b>b</b>) Maximum velocity; (<b>c</b>) Maximum pressure.</p> Full article ">Figure 10 Cont.
<p>Characteristic values of the movement process at avalanche point 4# under the different periods: (<b>a</b>) Maximum height; (<b>b</b>) Maximum velocity; (<b>c</b>) Maximum pressure.</p> Full article ">Figure 11
<p>Variation curves of the profile I–I motion eigenvalues under the different periods: (<b>a</b>) Flow height variation curve of profile I–I under the different periods; (<b>b</b>) Flow velocity curve of profile I-I flow under the different periods; (<b>c</b>) Pressure the curve of profile I–I under the different periods.</p> Full article ">Figure 11 Cont.
<p>Variation curves of the profile I–I motion eigenvalues under the different periods: (<b>a</b>) Flow height variation curve of profile I–I under the different periods; (<b>b</b>) Flow velocity curve of profile I-I flow under the different periods; (<b>c</b>) Pressure the curve of profile I–I under the different periods.</p> Full article ">
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