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Atmosphere
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Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition)
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Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition)

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 525

Special Issue Editor

Dr. Stephan Havemann

E-Mail Website
Guest Editor
Met Office, Foundation and Weather Science, Exeter EX1 3PB, UK
Interests: atmospheric radiative transfer; satellite; airborne and ground-based remote sensing; retrieval of atmospheric and surface properties; electromagnetic scattering theory; cirrus; operational satellite data assimilation; numerical methods; big data; machine learning techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Section Atmospheric Techniques, Instruments, and Modeling is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our journal and outstanding scholars in this research field. We welcome contributions and recommendations from the EBMs.

This Special Issue is the second edition in a series of publications dedicated to “Feature Papers in Atmospheric Techniques, Instruments, and Modeling” (https://www.mdpi.com/journal/atmosphere/special_issues/DW0ZS0D1H2).

The purpose of this Special Issue is to publish a set of papers that typify the most exceptional, insightful, influential, and original research articles or reviews. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collated into a printed edition book after the deadline and will be well promoted.

We would also like to take this opportunity to call on more scholars to join the journal Section Atmospheric Techniques, Instruments, and Modeling so that we can work together to further develop this exciting field of research.

Dr. Stephan Havemann
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

  • remote sensing
  • instruments
  • laboratory measurement techniques
  • artificial intelligence
  • machine learning
  • data science
  • model development
  • algorithm
  • satellite
  • carbon balance/carbon cycle
  • infrared spectroscopy
  • lidar
  • radar
  • unmanned aerial vehicles/drone
  • point cloud
  • GNSS
  • microwave radiometry

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

Published Papers (2 papers)

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Research

16 pages, 1939 KiB  
Article
Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports
by Elizabeth Tirone, William A. Gallus, Jr. and Alexander J. Hamilton
Atmosphere 2025, 16(7), 881; https://doi.org/10.3390/atmos16070881 - 18 Jul 2025
Viewed by 113
Abstract
Output from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. [...] Read more.
Output from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. Meteorological data for these cases was compared to that for valid cases where the machine learning probability seemed consistent with the observed severity of the winds. The comparison revealed that the cases with high winds but low probabilities occurred in less conducive environments for severe wind production (less instability, greater low-level relative humidity, weaker lapse rates) than in the cases where high winds occurred with high probabilities. Cases with a low speed but a high probability had environmental characteristics that were more conducive to producing severe wind. These results suggest that the machine learning model is assigning probabilities based on storm modes that more often have measured severe wind speeds (i.e., clusters of cells and bow echoes), and counterintuitive values may reflect events where storm interactions or other smaller-scale features play a bigger role. In addition, some evidence suggests improper reporting may be common for some of these counterintuitive cases. Full article
(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))
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25 pages, 3014 KiB  
Article
Performance Assessment of Low- and Medium-Cost PM2.5 Sensors in Real-World Conditions in Central Europe
by Bushra Atfeh, Zoltán Barcza, Veronika Groma, Ágoston Vilmos Tordai and Róbert Mészáros
Atmosphere 2025, 16(7), 796; https://doi.org/10.3390/atmos16070796 - 30 Jun 2025
Viewed by 275
Abstract
In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically [...] Read more.
In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R2 < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R2 values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R2 = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM2.5. Full article
(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))
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