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Atmosphere
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Numerical Analysis in Atmospheric Research
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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 3895

Special Issue Editor

Dr. Andrea Mastellone

E-Mail Website
Guest Editor
Meteorology Laboratory, CIRA Italian Aerospace Research Center, 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 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.

Keywords

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

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Published Papers (2 papers)

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Research

23 pages, 4172 KiB  
Article
A Metamodel-Based Optimization of Physical Parameters of High Resolution NWP ICON-LAM over Southern Italy
by Davide Cinquegrana, Alessandra Lucia Zollo, Myriam Montesarchio and Edoardo Bucchignani
Atmosphere 2023, 14(5), 788; https://doi.org/10.3390/atmos14050788 - 26 Apr 2023
Cited by 4 | Viewed by 1669
Abstract
This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 [...] Read more.
This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window. Full article
(This article belongs to the Special Issue Numerical Analysis in Atmospheric Research)
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13 pages, 2974 KiB  
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
Cited by 1 | Viewed by 1714
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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