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Atmosphere
Special Issues
Numerical Analysis in Atmospheric Research
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Special Issue "Numerical Analysis in Atmospheric Research"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (21 February 2023) | Viewed by 882

Special Issue Editor

Dr. Andrea Mastellone

E-Mail Website
Guest Editor
Centro Italiano Ricerche Aerospaziali (CIRA), Via Maiorise, 81043 Capua, CE, Italy
Interests: turbulence; computational fluid dynamics; fluid mechanics; CFD simulation; numerical simulation; computational fluid mechanics; numerical modeling; CFD coding; modeling and simulation; numerics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the importance of atmospheric science has been increasing, considering in particular the critical impact of meteorology and climate changes on human activities. Atmospheric physics is a very complex discipline which has benefited from the exponential increase in the power of computers. As a result, we are able to adopt increasingly complex models with better predictive accuracy.

The main aim of this Special Issue is to give scientists in atmospheric disciplines the opportunity to share their valuable results with the scientific community. Potential topics of this Special Issue include, but are not limited to:

  • Algorithms for the solution of hydrodynamic governing equations in numerical models of the atmosphere (e.g., weather models, air quality models);
  • Performance of the Numerical Weather Prediction/Climate codes on cluster, with a special regard to scaling and parallel I/O issues;
  • High-level profiling in parallel programming paradigms, especially in Open-MP and MPI environments;
  • Reproducibility of results in different environments and clusters;
  • Artificial intelligence for atmospheric research.

Dr. Andrea Mastellone
Guest Editor

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 2000 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.

Keywords

  • atmospheric models
  • model performances
  • parallel programming
  • artificial intelligence

Published Papers (1 paper)

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Research

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Article
Research on the Spatiotemporal Characteristics and Concentration Prediction Model of PM2.5 during Winter in Jiangbei New District, Nanjing, China
by
Yuanxi Li
,
Zhongzheng Zhu
,
Chengrui Xin
,
Zhilong Chen
,
Sunyuan Wang
,
Zhenyu Liang
and
Xiuguo Zou
Atmosphere 2022, 13(10), 1542; https://doi.org/10.3390/atmos13101542 - 21 Sep 2022
Viewed by 622
Abstract
Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5 [...] Read more.
Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies. Full article
(This article belongs to the Special Issue Numerical Analysis in Atmospheric Research)
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