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Atmosphere
Special Issues
Meteorological and Air Quality Modelling
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Meteorological and Air Quality Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (15 April 2021) | Viewed by 7848

Special Issue Editors

Dr. Rafaella Eleni P. Sotiropoulou

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Western Macedonia, Ikaron 3, 501 00 Kozani, Greece
Interests: air pollution; air quality modelling; biogenic emissions; pollutant emissions; meteorology; climate change and feedbacks; aerosol indirect effect; earth energy balance; global climate models; Earth system models; numerical methods
Special Issues, Collections and Topics in MDPI journals
Dr. Efthimios Tagaris

E-Mail Website
Guest Editor
Department of Chemical Engineering, University of Western Macedonia, 501 00 Kozani, Greece
Interests: air pollution; air quality modelling; atmospheric chemistry; meteorological modelling; climate change; air quality meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Models are currently the primary components for analysis in most meteorological and air quality assessments and the only tools available for future projections, allowing alternative scenarios to be investigated. Moreover, in contrast to the limitations in the spatial coverage of field measurements, models allow assessments over large regions, even the globe. Despite their advantages, modelling outputs are subject to significant uncertainties due to deficiencies in our knowledge and limitations owed to the various spatial and temporal resolutions involved in the processes. These shortfalls can to some extent be offset by the validations of models with the help of measurements that can be used in a complementary manner, or the development of modelling ensembles that advance our knowledge on the impact of the various alternative parameterizations on the modelling outputs.

This Special Issue of Atmosphere is oriented towards numerical weather prediction and air quality modelling communities and aims to present a collection of studies that advance our knowledge on all aspects of this field. We invite authors to submit original articles that focus on meteorological modelling and air quality modelling on local, regional, or global scales. Topics of interest include, but are not limited to, model development, applications, evaluation, sensitivity studies, optimization and best practices that can be applied, impact studies, climate and air quality interactions, and extreme events. Research papers, analytical reviews, case studies, and policy-relevant articles are encouraged.

Dr. Rafaella Eleni P. Sotiropoulou
Prof. Efthimios Tagaris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

19 pages, 531 KiB  
Article
Forecasting Daily of Surface Ozone Concentration in the Grand Casablanca Region Using Parametric and Nonparametric Statistical Models
by Halima Oufdou, Lise Bellanger, Amal Bergam and Kenza Khomsi
Atmosphere 2021, 12(6), 666; https://doi.org/10.3390/atmos12060666 - 23 May 2021
Cited by 9 | Viewed by 2447
Abstract
Forecasting concentration levels is important for planning atmospheric protection strategies. In this paper, we focus on the daily average surface ozone (O3) concentration with a short-time resolution (one day ahead) in the Grand Casablanca Region of Morocco. The database includes previous [...] Read more.
Forecasting concentration levels is important for planning atmospheric protection strategies. In this paper, we focus on the daily average surface ozone (O3) concentration with a short-time resolution (one day ahead) in the Grand Casablanca Region of Morocco. The database includes previous day O3 concentrations measured at Jahid station and various meteorological explanatory variables for 3 years (2013 to 2015). Taking into account the multicollinearity problem in the data, adapted statistical models based on parametric (SPLS and Lasso) and nonparametric (CART, Bagging, and RF) models were built and compared using the coefficient of determination and the root mean square error. We conclude that the parametric models predict better than nonparametric ones. Finally, from the explanatory variables stored by the SPLS and Lasso parametric models, we deduce that a very simple linear regression with five variables remains the most appropriate for the available data at Jahid station (R2 = 0.86 and RMSE = 9.60). This resulting model, with few explanatory variables to prevent missing data, has good predictive quality and is easily implementable. It is the first to be built to predict ozone pollution in the Grand Casablanca region of Morocco. Full article
(This article belongs to the Special Issue Meteorological and Air Quality Modelling)
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13 pages, 3132 KiB  
Article
Evaluation and Bias Correction of the Secondary Inorganic Aerosol Modeling over North China Plain in Autumn and Winter
by Qian Wu, Xiao Tang, Lei Kong, Xu Dao, Miaomiao Lu, Zirui Liu, Wei Wang, Qian Wang, Duohong Chen, Lin Wu, Xiaole Pan, Jie Li, Jiang Zhu and Zifa Wang
Atmosphere 2021, 12(5), 578; https://doi.org/10.3390/atmos12050578 - 30 Apr 2021
Cited by 4 | Viewed by 1692
Abstract
Secondary inorganic aerosol (SIA) is the key driving factor of fine-particle explosive growth (FPEG) events, which are frequently observed in North China Plain. However, the SIA simulations remain highly uncertain over East Asia. To further investigate this issue, SIA modeling over North China [...] Read more.
Secondary inorganic aerosol (SIA) is the key driving factor of fine-particle explosive growth (FPEG) events, which are frequently observed in North China Plain. However, the SIA simulations remain highly uncertain over East Asia. To further investigate this issue, SIA modeling over North China Plain with the 15 km resolution Nested Air Quality Prediction Model System (NAQPMS) was performed from October 2017 to March 2018. Surface observations of SIA at 28 sites were obtained to evaluate the model, which confirmed the biases in the SIA modeling. To identify the source of these biases and reduce them, uncertainty analysis was performed by evaluating the heterogeneous chemical reactions in the model and conducting sensitivity tests on the different reactions. The results suggest that the omission of the SO2 heterogeneous chemical reaction involving anthropogenic aerosols in the model is probably the key reason for the systematic underestimation of sulfate during the winter season. The uptake coefficient of the “renoxification” reaction is a key source of uncertainty in nitrate simulations, and it is likely to be overestimated by the NAQPMS. Consideration of the SO2 heterogeneous reaction involving anthropogenic aerosols and optimization of the uptake coefficient of the “renoxification” reaction in the model suitably reproduced the temporal and spatial variations in sulfate, nitrate and ammonium over North China Plain. The biases in the simulations of sulfate, nitrate, ammonium, and particulate matter smaller than 2.5 μm (PM2.5) were reduced by 84.2%, 54.8%, 81.8%, and 80.9%, respectively. The results of this study provide a reference for the reduction in the model bias of SIA and PM2.5 and improvement of the simulation of heterogeneous chemical processes. Full article
(This article belongs to the Special Issue Meteorological and Air Quality Modelling)
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17 pages, 3751 KiB  
Article
Investigating the WRF Temperature and Precipitation Performance Sensitivity to Spatial Resolution over Central Europe
by Ioannis Stergiou, Efthimios Tagaris and Rafaella-Eleni P. Sotiropoulou
Atmosphere 2021, 12(2), 278; https://doi.org/10.3390/atmos12020278 - 19 Feb 2021
Cited by 6 | Viewed by 2623
Abstract
The grid size resolution effect on the annual and seasonal simulated mean, maximum and minimum daily temperatures and precipitation is assessed using the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) that dynamically downscales the National Centers for Environmental Prediction’s final [...] Read more.
The grid size resolution effect on the annual and seasonal simulated mean, maximum and minimum daily temperatures and precipitation is assessed using the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) that dynamically downscales the National Centers for Environmental Prediction’s final (NCEP FNL) Operational Global Analysis data. Simulations were conducted over central Europe for the year 2015 using 36, 12 and 4 km grid resolutions. Evaluation is done using daily E-OBS data. Several performance metrics and the bias adjusted equitable threat score (BAETS) for precipitation are used. Results show that model performance for mean, maximum and minimum temperature improves when increasing the spatial resolution from 36 to 12 km, with no significant added value when further increasing it to 4 km. Model performance for precipitation is slightly worsened when increasing the spatial resolution from 36 to 12 km while further increasing it to 4 km has minor effect. However, simulated and observed precipitation data are in quite good agreement in areas with precipitation rates below 3 mm/day for all three grid resolutions. The annual mean fraction of observed and/or forecast events that were correctly predicted (BAETS), when increasing the grid size resolution from 36 to 12 and 4 km, suggests a slight modification on average over the domain. During summer the model presents significantly lower BAETS skill score compared to the rest of the seasons. Full article
(This article belongs to the Special Issue Meteorological and Air Quality Modelling)
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