Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Atmosphere
Special Issues
Atmospheric Chemical Data Assimilation and Forecasting
share announcement

Atmospheric Chemical Data Assimilation and Forecasting

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 (9 December 2021)

Special Issue Editor

Dr. Zengliang Zang

E-Mail Website
Guest Editor
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Interests: aerosol data assimilation; atmospheric chemistry simulation; AOD inversion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aerosol and gas pollutants have fundamental impacts on the Earth’s environment, human health, and climate change. They also represent a great challenge in terms of forecasting their high concentrations using numerical models. Improving forecasting skills depends on several factors, including initial fields, emissions, atmospheric chemical models, and so on.

Data assimilation (DA) is generally used to improve the chemical initial fields (ICs) and to optimally estimate emission sources. With the development of air quality models, multiple DA methods, such as optimal interpolation (OI), extended/ensemble Kalman filter (EKF/EnKF), and three-dimensional/four-dimensional variational (3D/4D VAR), have been extensively applied in atmospheric chemical data assimilation. Additionally, multi-observations are integrated into DA systems to optimize chemical ICs and emission sources, including surface mass concentration data, satellite remote sensing observations, and light detection and ranging (lidar) data. This Special Issue invites papers that will help to disseminate knowledge around air quality modeling fields and emission inventories, as well as to reinforce the importance of using the DA method to improve the accuracy of atmospheric chemical forecasting.

We encourage the submission of articles that focus on atmospheric chemical data assimilation and forecasting. Solicited contributions include but are not limited to:

  • Aerosol data assimilation and forecasting;
  • Data assimilation with multi-observations;
  • Estimation and optimization of emission sources;
  • Development of atmospheric chemical models or air quality models.

Articles on chemical analysis and air quality forecasting with DA performance and evaluation are also encouraged.

Dr. Zengliang Zang
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 chemical model
  • data assimilation
  • air quality forecasting
  • numerical modeling
  • multi-observation data assimilation
  • emission sources

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 3736 KiB  
Article
Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation
by Soon-Young Park, Uzzal Kumar Dash and Jinhyeok Yu
Atmosphere 2022, 13(2), 160; https://doi.org/10.3390/atmos13020160 - 19 Jan 2022
Cited by 1 | Viewed by 2055
Abstract
Data assimilation (DA) combines incomplete background values obtained via chemical transport model predictions with observational information. Several 3-Dimensional variational (3DVAR) and sequential methods (e.g., ensemble Kalman filter (EnKF)) are used to define model errors and build a background error covariance (BEC) and are [...] Read more.
Data assimilation (DA) combines incomplete background values obtained via chemical transport model predictions with observational information. Several 3-Dimensional variational (3DVAR) and sequential methods (e.g., ensemble Kalman filter (EnKF)) are used to define model errors and build a background error covariance (BEC) and are important factors affecting the prediction performance of DA. The BEC determines the spatial range, where observation concentration is reflected in the model when DA is applied to an air pollution transport model. However, studies investigating the characteristics of BEC using air quality models remain lacking. In this study, horizontal length scale (HLS) and vertical length scale (VLS) analyses of a BEC were applied to EnKF and ensemble square root filter (EnSRF), respectively, and two ensemble-based DA methods were performed; the characteristics were compared with those of a BEC applied to 3DVAR. The results of 6 h PM2.5 predictions performed for 42 days were evaluated for a control run without DA (CTR), 3DVAR, EnKF, and EnSRF. HLS and VLS respectively exhibited a high correlation with the ground wind speed and with the planetary boundary layer height for diurnal and daily variations; EnKF and EnSRF exhibited superior performances among all the methods. The root mean square errors were 11.9 μg m−3 and 11.7 μg m−3 for EnKF and EnSRF, respectively, while those for 3DVAR and CTR were 12.6 μg m−3 and 18.3 μg m−3, respectively. Thus, we proposed a simple method to find a Gaussian function that best described the error correlation of the BEC based on the physical distance. Full article
(This article belongs to the Special Issue Atmospheric Chemical Data Assimilation and Forecasting)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop