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A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 33109

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Prof. Dr. Anthony R. Lupo

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Department of Soil, Environmental, and Atmospheric Science, University of Missouri-Columbia, Columbia, MO 65211, USA
Interests: dynamic meteorology; synoptic meteorology; climate dynamics; climate variability
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Dear Colleagues,

Welcome to the 4th International Electronic Conference on Atmospheric Sciences to be held 16–30 July 2021. We look forward to seeing you at our event. A Special Issue will publish selected papers from the Proceedings Volume associated with our event on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. We also encourage submissions from outside.

In this edition, we welcome contributions from a variety of subject areas including aerosols, air quality, air quality and human health, climatology, meteorology, biometeorology, atmospheric techniques, instrumentation, numerical modeling, biosphere/hydrosphere/land–atmosphere interactions, the upper atmosphere, and planetary atmospheres. Given that the COVID-19 crisis has impacted many facets of society, contributions related to human health or environmental impacts from this pandemic would be especially welcome. Select papers that attract the most interest on the web or that provide an innovative contribution will be considered for publication. These papers will be subject to peer review and published with the aim of rapid and wide dissemination of research results, developments, and applications. It is our hope that this conference will present new and useful developments related to all areas of atmospheric sciences. The scientific committee cordially welcomes you all, and we look forward to your contributions.

Prof. Dr. Anthony R. Lupo
Guest Editor

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Keywords

aerosols
air quality and human heath
weather and human health/COVID-19
climatology
meteorology
biometeorology
atmospheric techniques, instrumentation, and modelling
biosphere/hydrosphere/land–atmosphere interactions
upper atmosphere
planetary atmospheres

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Published Papers (10 papers)

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20 pages, 3924 KiB

Open AccessArticle

Clustering and Regression-Based Analysis of PM_2.5 Sensitivity to Meteorology in Cincinnati, Ohio

by Madhumitaa Roy, Cole Brokamp and Sivaraman Balachandran

Atmosphere 2022, 13(4), 545; https://doi.org/10.3390/atmos13040545 - 29 Mar 2022

Cited by 2 | Viewed by 2150

Abstract

This study identified the meteorological parameters that influence PM

_{2.5}

concentrations in the Greater Cincinnati area by employing principal components analysis and multi-variable regression. Meteorological and PM

_{2.5}

data were collected over several years to derive statistical relationships about the seasonal variability of [...] Read more.

This study identified the meteorological parameters that influence PM

_{2.5}

concentrations in the Greater Cincinnati area by employing principal components analysis and multi-variable regression. Meteorological and PM

_{2.5}

data were collected over several years to derive statistical relationships about the seasonal variability of meteorological parameters and quantify their influence on PM

_{2.5}

. We studied the effect of meteorological parameters by seasons and by k-means clustering. The results show that outdoor temperature (OT), planetary boundary height (HPBL) and visibility (VIS) have the strongest effect on PM

_{2.5}

. The distribution of PM

_{2.5}

concentrations in each cluster and season was evaluated using the Kolmogorov–Smirnov test with data fitting using the lognormal and gamma distributions. To our observation, we found the PM

_{2.5}

concentration fits the gamma distribution marginally better than the lognormal distribution. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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17 pages, 8510 KiB

Open AccessArticle

Verification by Multiple Methods of Precipitation Forecast from HDRFFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

by Maibys Sierra-Lorenzo, Jose Medina, Juana Sille, Adrián Fuentes-Barrios, Shallys Alfonso-Águila and Tania Gascon

Atmosphere 2022, 13(3), 495; https://doi.org/10.3390/atmos13030495 - 19 Mar 2022

Cited by 4 | Viewed by 2166

Abstract

During 2020, the Dominican Republic received the impact of several tropical organisms. Among those that generated the greatest losses in the country, tropical storm Isaias stands out because of the significant precipitation (327.6 mm at Sabana del Mar during 29–31 July 2020) and flooding it caused. The study analyzes the behavior of the products of the Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the precipitation generated by Isaias on 30 July 2020 over the Dominican Republic. Traditional categorical verification and featured-based spatial verification methods are used in the study, taking as observation the quantitative precipitation estimation of GPM. The results show that both numerical weather prediction systems are powerful tools for QPF and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed. For the forecast of rain occurrence, the HIRESW-NMMB product of FFGS presented the highest ability with a CSI greater than 0.4. The HIRESW-ARW and SisPI products not only presented high rates of false alarms but also performed better in forecasting heavy rain values. The results of the verification based on objects with the MODE are consistent with those obtained in the verification by categories. The HIRESW-NMMB product underestimated the intense rainfall values by approximately 60 mm, while HIRESW-ARW and SisPI tools presented minor differences, the latter being the one with the greatest skill. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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11 pages, 1152 KiB

Open AccessArticle

Investigating Neutral and Stable Atmospheric Surface Layers Using Computational Fluid Dynamics

by Fraser Gemmell

Atmosphere 2022, 13(2), 221; https://doi.org/10.3390/atmos13020221 - 28 Jan 2022

Cited by 1 | Viewed by 2863

Abstract

Computational fluid dynamics (CFD) is an effective technique for investigating atmospheric processes at a local scale. For example, in near-source atmospheric dispersion applications, the effects of meteorology, air-pollutant sources, and buildings can be included. A prerequisite is to establish realistic atmospheric conditions throughout the computational domain. This work investigates the modeling of the atmospheric surface layer under neutral and stable boundary-layer conditions, respectively. Steady-state numerical solutions of the Reynolds averaged Navier–Stokes equations were used, including the k-ε turbulence model. Atmospheric profiles derived from the Cooperative Atmosphere–Surface Exchange Study-99 (Kansas, USA) were used as reference data. The results indicate that the observed profiles of velocity and potential temperature can be reproduced with CFD, while turbulent kinetic energy showed less agreement with the observations. For the stable boundary layer, reasonable agreement of the numerical results with the observations was also obtained for surface layer depth, shear stress, and heat-flux profiles, respectively. The results are discussed in relation to the boundary conditions and sources, and the observation data. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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21 pages, 12230 KiB

Open AccessArticle

Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin

by Hamed Hafizi and Ali Arda Sorman

Atmosphere 2022, 13(1), 143; https://doi.org/10.3390/atmos13010143 - 16 Jan 2022

Cited by 18 | Viewed by 3562

Abstract

Precipitation measurement with high spatial and temporal resolution over highly elevated and complex terrain in the eastern part of Turkey is an essential task to manage the water structures in an optimum manner. The objective of this study is to evaluate the consistency and hydrologic utility of 13 Gridded Precipitation Datasets (GPDs) (CPCv1, MSWEPv2.8, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3B42v7, TMPA-3B42RTv7, PERSIANN-CDR, PERSIANN-CCS, and PERSIANN) over a mountainous test basin (Karasu) at a daily time step. The Kling-Gupta Efficiency (KGE), including its three components (correlation, bias, and variability ratio), and the Nash-Sutcliffe Efficiency (NSE) are used for GPD evaluation. Moreover, the Hanssen-Kuiper (HK) score is considered to evaluate the detectability strength of selected GPDs for different precipitation events. Precipitation frequencies are evaluated considering the Probability Density Function (PDF). Daily precipitation data from 23 meteorological stations are provided as a reference for the period of 2015–2019. The TUW model is used for hydrological simulations regarding observed discharge located at the outlet of the basin. The model is calibrated in two ways, with observed precipitation only and by each GPD individually. Overall, CPCv1 shows the highest performance (median KGE; 0.46) over time and space. MSWEPv2.8 and CHIRPSv2.0 deliver the best performance among multi-source merging datasets, followed by CHIRPv2.0, whereas IMERGHHFv06, PERSIANN-CDR, and TMPA-3B42v7 show poor performance. IMERGHHLv06 is able to present the best performance (median KGE; 0.17) compared to other satellite-based GPDs (PERSIANN-CCS, PERSIANN, IMERGHHEv06, and TMPA-3B42RTv7). ERA5 performs well both in spatial and temporal validation compared to satellite-based GPDs, though it shows low performance in producing a streamflow simulation. Overall, all gridded precipitation datasets show better performance in generating streamflow when the model is calibrated by each GPD separately. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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17 pages, 17734 KiB

Open AccessArticle

Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations

by Roberta Valentina Gagliardi and Claudio Andenna

Atmosphere 2022, 13(1), 64; https://doi.org/10.3390/atmos13010064 - 30 Dec 2021

Cited by 1 | Viewed by 2136

Abstract

Identifying changes in ambient air pollution levels and establishing causation is a research area of strategic importance to assess the effectiveness of air quality interventions. A major challenge in pursuing these objectives is represented by the confounding effects of the meteorological conditions which easily mask or emphasize changes in pollutants concentrations. In this study, a methodological procedure to analyze changes in pollutants concentrations levels after accounting for changes in meteorology over time was developed. The procedure integrated several statistical tools, such as the change points detection and trend analysis that are applied to the pollutants concentrations meteorologically normalized using a machine learning model. Data of air pollutants and meteorological parameters, collected over the period 2013–2019 in a rural area affected by anthropic emissive sources, were used to test the procedure. The joint analysis of the obtained results with the available metadata allowed providing plausible explanations of the observed air pollutants behavior. Consequently, the procedure appears promising in elucidating those changes in the air pollutant levels not easily identifiable in the original data, supplying valuable information to identify an atmospheric response after an intervention or an unplanned event. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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22 pages, 4559 KiB

Open AccessArticle

Land Surface Temperature Retrieval Using High-Resolution Vertical Profiles Simulated by WRF Model

by Lucas Ribeiro Diaz, Daniel Caetano Santos, Pâmela Suélen Käfer, Nájila Souza da Rocha, Savannah Tâmara Lemos da Costa, Eduardo Andre Kaiser and Silvia Beatriz Alves Rolim

Atmosphere 2021, 12(11), 1436; https://doi.org/10.3390/atmos12111436 - 30 Oct 2021

Cited by 8 | Viewed by 3585

Abstract

This work gives a first insight into the potential of the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing. WRF numerical simulations were conducted to downscale NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the utility of these profiles for the atmospheric correction of TIR data and LST estimation, using the moderate resolution atmospheric transmission (MODTRAN) model and the Landsat 8 TIRS10 band. The accuracy evaluation was performed using 27 clear-sky cases over a radiosonde station in Southern Brazil. We included in the comparative analysis NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles obtained directly from the NCEP CFSv2 reanalysis. The atmospheric parameters from ACPC, followed by those from CFSv2, were in better agreement with parameters calculated using in situ radiosondes. When applied into the radiative transfer equation (RTE) to retrieve LST, the best results (RMSE) were, in descending order: CFSv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). Our findings suggest that there is no special need to increase the horizontal resolution of reanalysis profiles aiming at RTE-based LST retrieval. However, the WRF results were still satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and recommend the NCEP CFSv2 profiles for TIR atmospheric correction and LST single-channel retrieval. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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25 pages, 27795 KiB

Open AccessArticle

Leaf-Scale Study of Biogenic Volatile Organic Compound Emissions from Willow (Salix spp.) Short Rotation Coppices Covering Two Growing Seasons

by Tomas Karlsson, Leif Klemedtsson, Riikka Rinnan and Thomas Holst

Atmosphere 2021, 12(11), 1427; https://doi.org/10.3390/atmos12111427 - 29 Oct 2021

Cited by 1 | Viewed by 2290

Abstract

In Europe, willow (Salix spp.) trees have been used commercially since the 1980s at a large scale to produce renewable energy. While reducing fossil fuel needs, growing short rotation coppices (SRCs), such as poplar or willow, may have a high impact on local air quality as these species are known to produce high amounts of isoprene, which can lead to the production of tropospheric ozone (O₃). Here, we present a long-term leaf-scale study of biogenic volatile organic compound (BVOC) emissions from a Swedish managed willow site with the aim of providing information on the seasonal variability in BVOC emissions during two growing seasons, 2015–2016. Total BVOC emissions during these two seasons were dominated by isoprene (>96% by mass) and the monoterpene (MT) ocimene. The average standardized (STD, temperature of 30 °C and photosynthetically active radiation of 1000 µmol m⁻² s⁻¹) emission rate for isoprene was 45.2 (±42.9, standard deviation (SD)) μg g_dw⁻¹ h⁻¹. Isoprene varied through the season, mainly depending on the prevailing temperature and light, where the measured emissions peaked in July 2015 and August 2016. The average STD emission for MTs was 0.301 (±0.201) μg g_dw⁻¹ h⁻¹ and the MT emissions decreased from spring to autumn. The average STD emission for sesquiterpenes (SQTs) was 0.103 (±0.249) μg g_dw⁻¹ h⁻¹, where caryophyllene was the most abundant SQT. The measured emissions of SQTs peaked in August both in 2015 and 2016. Non-terpenoid compounds were grouped as other VOCs (0.751 ± 0.159 μg g_dw⁻¹ h⁻¹), containing alkanes, aldehydes, ketones, and other compounds. Emissions from all the BVOC groups decreased towards the end of the growing season. The more sun-adapted leaves in the upper part of the plantation canopy emitted higher rates of isoprene, MTs, and SQTs compared with more shade-adapted leaves in the lower canopy. On the other hand, emissions of other VOCs were lower from the upper part of the canopy compared with the lower part. Light response curves showed that ocimene and α-farnesene increased with light but only for the sun-adapted leaves, since the shade-adapted leaves did not emit ocimene and α-farnesene. An infestation with Melampsora spp. likely induced high emissions of, e.g., hexanal and nonanal in August 2015. The results from this study imply that upscaling BVOC emissions with model approaches should account for seasonality and also include the canopy position of leaves as a parameter to allow for better estimates for the regional and global budgets of ecosystem emissions. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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13 pages, 3247 KiB

Open AccessArticle

Carbon Dioxide and Methane Emissions during the Composting and Vermicomposting of Sewage Sludge under the Effect of Different Proportions of Straw Pellets

by Bayu Dume, Ales Hanc, Pavel Svehla, Pavel Míchal, Abraham Demelash Chane and Abebe Nigussie

Atmosphere 2021, 12(11), 1380; https://doi.org/10.3390/atmos12111380 - 22 Oct 2021

Cited by 8 | Viewed by 4456

Abstract

Owing to rapid population growth, sewage sludge poses a serious environmental threat across the world. Composting and vermicomposting are biological technologies commonly used to stabilize sewage sludge. The objective of this study was to assess the carbon dioxide (CO₂) and methane (CH₄) emissions from sewage sludge composting and vermicomposting under the influence of different proportions of straw pellets. Four treatments were designed, by mixing the initial sewage sludge with varying ratio of pelletized wheat straw (0, 25%, 50%, and 75% (w/w)). The experiment was conducted for 60 days, and Eisenia andrei was used for vermicomposting. The results revealed that the mixing ratio influenced CO₂ (F = 36.1, p = 0.000) and CH₄ (F= 73.9, p = 0.000) emissions during composting and CO₂ (F= 13.8, p = 0.000) and CH₄ (F= 4.5, p= 0.004) vermicomposting. Vermicomposting significantly reduced CH₄ emissions by 18–38%, while increasing CO₂ emissions by 64–89%. The mixing agent (pelletized wheat straw) decreased CO₂ emission by 60–70% and CH₄ emission by 30–80% compared to control (0%). The mass balance indicated that 5.5–10.4% of carbon was loss during composting, while methane release accounted for 0.34–1.69%, and CO₂ release accounted for 2.3–8.65%. However, vermicomposting lost 8.98–13.7% of its carbon, with a methane release of 0.1–0.6% and CO₂ release of 5.0–11.6% of carbon. The carbon loss was 3.3–3.5% more under vermicomposting than composting. This study demonstrated that depending on the target gas to be reduced, composting and vermicomposting, as well as a mixing agent (pelletized wheat straw), could be an option for reducing greenhouse gas emissions (i.e. CH₄, CO₂). Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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16 pages, 1713 KiB

Open AccessArticle

Sources of PM_2.5-Associated PAHs and n-alkanes in Changzhou China

by Ning Sun, Xudong Li, Ye Ji, Hongying Huang, Zhaolian Ye and Zhuzi Zhao

Atmosphere 2021, 12(9), 1127; https://doi.org/10.3390/atmos12091127 - 31 Aug 2021

Cited by 8 | Viewed by 2500

Abstract

Polycyclic aromatic hydrocarbons (PAHs) and n-alkanes are important specific organic constituents in fine particulate matter (PM_2.5). Seventy-five PM_2.5 samples were collected in Spring Changzhou, to investigate the concentrations and sources of n-alkanes (C₉–C₄₀) and PAHs. The average concentrations of total PAHs (∑PAHs) and n-alkanes (∑n-alkanes) were 4.37 ± 4.95 ng/m³ and 252.37 ± 184.02 ng/m³, ranging from 0.43 to 22.22 ng/m³ and 57.37 to 972.17 ng/m³, respectively. The average concentrations of ∑n-alkanes and ∑PAHs were higher in severely polluted days (PM_2.5 ≥ 150 μg/m³) in comparison to other days. Up to 85% of PAHs were four- and five-ring compounds, and the middle-chain-length n-alkanes (C₂₅–C₃₅) were the most abundant species (80.9%). The molecular distribution of n-alkanes was characterized by odd-number carbon predominance (carbon preference index, CPI > 1), with a maximum centered at C₂₇, C₂₉, and C₃₁ revealing a significant role of biogenic sources. Principal component analysis suggested that the biogenic sources that contributed the most to n-alkanes and PAHs were from coal combustion (46.3%), followed by biomass burning (16.0%), and vehicular exhaust (10.3%). The variation in the concentration of n-alkanes and PAHs from different air mass transports was not agreement with the change in PM_2.5 mass, indicating that regional transport had important impacts on the characterization of PM_2.5. The results of our study can provide useful information for evaluating the influence of anthropogenic and biogenic activities on organic matters (n-alkanes and PAHs). Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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Review

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28 pages, 9789 KiB

Open AccessReview

Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis

by Babak Mohammadi and Roozbeh Moazenzadeh

Atmosphere 2021, 12(3), 389; https://doi.org/10.3390/atmos12030389 - 17 Mar 2021

Cited by 20 | Viewed by 3803

Abstract

Solar radiation (Rs) is one of the main parameters controlling the energy balance at the Earth’s surface and plays a major role in evapotranspiration and plant growth, snow melting, and environmental studies. This work aimed at evaluating the performance of seven empirical models in estimating daily solar radiation over 1990–2004 (calibration) and 2004–2010 (validation) at 13 Peruvian meteorological stations. With the same variables used in empirical models (temperature) as well as two other parameters, namely precipitation and relative humidity, new models were developed by multiple linear regression analysis (proposed models). In calibration of empirical models with the same variables, the lowest estimation errors were 227.1 and 236.3 J·cm⁻²·day⁻¹ at Tacna and Puno stations, and the highest errors were 3958.4 and 3005.7 at San Ramon and Junin stations, respectively. The poorest-performing empirical models greatly overestimated Rs at most stations. The best performance of a proposed model (in terms of percentage of error reduction) was 73% compared to the average of all empirical models and 93% relative to the poorest result of empirical models, both at San Ramon station. According to root mean square errors (RMSEs) of proposed models, the worst and the best results are achieved at San Martin station (RMSE = 508.8 J·cm⁻²·day⁻¹) and Tacna station (RMSE = 223.2 J·cm⁻²·day⁻¹), respectively. Full article

(This article belongs to the Special Issue Advances in Atmospheric Sciences)

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