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

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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 (28 February 2026) | Viewed by 12783

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Dr. Stephan Havemann

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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
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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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (9 papers)

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Research

28 pages, 5459 KB

Open AccessArticle

A Hybrid Offline–Online Kalman–RBF Framework for Accurate Relative Humidity Forecasting

by Athanasios Donas, George Galanis, Ioannis Pytharoulis and Ioannis Th. Famelis

Atmosphere 2026, 17(2), 162; https://doi.org/10.3390/atmos17020162 - 31 Jan 2026

Viewed by 476

Abstract

Accurate humidity forecasts are crucial for environmental and operational applications, yet Numerical Weather Prediction systems frequently exhibit systematic and random errors. To address this problem, this study introduces a modified hybrid post-processing approach that extends a previously developed methodology, enabling a direct comparison of computational efficiency and predictive capacity. The proposed framework integrates a quadratic Kalman Filter with a Radial Basis Function Neural Network trained via the Orthogonal Least Squares algorithm and updated online through Recursive Least Squares. This modified method was evaluated via a time-window process, using forecasts from the Weather Research and Forecasting model and recorded observations from stations in northern Greece. The results show substantial improvements in forecast accuracy, as the Bias was reduced by over 85%, and the MAE and RMSE decreased by approximately 65% and 58%, respectively, compared with the baseline model. Furthermore, the proposed framework also demonstrates enhanced computational efficiency, reducing processing time by more than 95% relative to the initial methodology. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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22 pages, 8085 KB

Open AccessArticle

Estimation of High-Temporal-Resolution PM_2.5 Concentration from 2019 to 2023 Using an Interpretable Deep Learning Model

by Bo Li, Xiaoyang Chen, Wenhao Zhang, Tong Li, Meiling Xing, Jinyu Yang and Zhihua Han

Atmosphere 2025, 16(12), 1385; https://doi.org/10.3390/atmos16121385 - 8 Dec 2025

Viewed by 602

Abstract

The FY-4A satellite represents a new generation of geostationary platforms, providing high-temporal-resolution observations over China. However, challenges remain in effectively leveraging the FY-4A satellite data for high-temporal-resolution PM_2.5 concentration estimation, particularly regarding the unclear key parameters required for accurate estimation and the limited interpretability of models. This study utilizes an interpretable deep learning framework that integrates FY-4A Top-of-Atmosphere (TOA) reflectance data, meteorological variables, and auxiliary data to estimate surface high-temporal-resolution PM_2.5 concentrations from 2019 to 2023. A multicollinearity test was applied to optimize feature selection, while the SHapley Additive exPlanations (SHAP) method was used to enhance model interpretability. The results indicate that parameters such as TOA02, TOA03, TOA04, and boundary layer height (BLH) significantly influence model performance across years. The model demonstrates strong predictive ability in the Beijing–Tianjin–Hebei (BTH) region, achieving an average R² of 0.83. Root mean square error (RMSE) values remained below 15 µg/m³, aligning well with ground-based monitoring data. These findings demonstrate that combining high temporal satellite data with interpretable deep learning provides a reliable approach for long-term, high-temporal-resolution PM_2.5 monitoring in regions. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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22 pages, 3247 KB

Open AccessArticle

Simplifying Air Quality Forecasting: Logistic Regression for Predicting Particulate Matter in Chile

by Andrés M. Vélez-Pereira, Nicole Núñez-Magaña, Danay Barreau, Karim Bremer and David J. O’Connor

Atmosphere 2025, 16(12), 1377; https://doi.org/10.3390/atmos16121377 - 5 Dec 2025

Viewed by 890

Abstract

Widespread residential wood burning in southern Chile combined with cold climate conditions cause severe episodes of particulate matter (PM_2.5 and PM₁₀) pollution. In this study, we used logistic regression to predict daily exceedances of fine (PM_2.5) and coarse (PM₁₀) particulate levels at multiple urban sites, assessing model performance under different air quality standards. Results showed a clear latitudinal gradient in air pollution, with communities further south experiencing significantly higher PM levels and more frequent threshold exceedances, likely due to higher per capita firewood use and cooler temperatures. The logistic models achieved their best predictive accuracy under the strictest European (ESP) air quality standards (F1-scores up to ~0.72 for PM₁₀ and ~0.59 for PM_2.5), while Chile’s national (NCh) thresholds significantly underestimated pollution events. Additionally, annual per capita wood energy consumption in the far south was several times higher than in central Chile, contributing to disproportionately high emissions. These findings highlight the need to adopt more protective air quality standards and reduce wood-fueled emissions to improve early warning systems and decrease particulate exposure in southern Chile. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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12 pages, 1550 KB

Open AccessArticle

Deflection of Electric Streamer Channels in an Applied Electric Field

by Vernon Cooray, Gerald Cooray, Hasupama Jayasinghe, Farhad Rachidi and Marcos Rubinstein

Atmosphere 2025, 16(11), 1293; https://doi.org/10.3390/atmos16111293 - 14 Nov 2025

Cited by 1 | Viewed by 694

Abstract

Understanding how the path of streamers is influenced by background electric fields is crucial in leader progression models and electrical breakdown models in long gaps. While numerous advanced models of streamers exist, applying them to leader progression models to track streamer movement remains computationally intensive and impractical. In this study, we employ one of the simplest streamer models available in the literature to investigate how streamers are deflected in the presence of background electric fields. Our analysis identifies the key parameters that govern this interaction. Additionally, we estimate the time and length scales over which streamers are diverted by a background electric field of a specified strength. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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18 pages, 5250 KB

Open AccessArticle

Assessment of Accuracy of COSMIC and KOMPSAT GNSS Radio Occultation Temperature and Pressure Measurements over the Philippines

by Karl Philippe A. Descalzo and Ernest P. Macalalad

Atmosphere 2025, 16(11), 1285; https://doi.org/10.3390/atmos16111285 - 11 Nov 2025

Viewed by 1118

Abstract

Radio occultation (RO) is a technique used for measuring planetary atmosphere properties by orbiting satellites, like temperature, pressure, and water vapor. Typically using Global Navigation Satellite System (GNSS) signals, this technique is often assessed with atmospheric properties measured by radiosonde (RS) stations around the world. The aim of this study is to assess the radio occultation temperature and pressure profiles from the Constellation Observing System for Meteorology, Ionosphere and Climate 2 (COSMIC-2) and Korean Multi-purpose Satellite 5 (KOMPSAT-5) satellites using data from collocated radiosonde stations over the Philippines. Their deviations are analyzed using their mean and standard deviations. COSMIC-2 and KOMPSAT-5 temperature and pressure from the atmPrf product are in good agreement with radiosondes above 5–10 km, where moisture is negligible. COSMIC-2 has good agreement with radiosonde stations in 2020. KOMPSAT-5 has good agreement with radiosonde stations in 2019–2020. For both satellites, the deviations are larger within the lower troposphere, compared to heights above ~5–10 km. For both years, KOMPSAT-5 deviations are higher during the summer season until 10 km. For COSMIC-2, deviations are higher during the summer and autumn seasons. The quality of these results shows COSMIC and KOMPSAT as possible high-quality applications for weather prediction. In addition to providing comparable high-precision data, radio occultation can provide more dense coverage of areas without radiosondes. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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18 pages, 13697 KB

Open AccessArticle

A New Anticyclone Identification Method Based on Mask R-CNN Model and Its Application

by Yang Kong, Hao Wu, Ping Xia and Yumin Zhang

Atmosphere 2025, 16(10), 1140; https://doi.org/10.3390/atmos16101140 - 28 Sep 2025

Viewed by 648

Abstract

In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method using the Mask region-based convolutional neural network (Mask R-CNN) model to detect synoptic-scale anticyclones by capturing their two-dimensional structural features and investigating their relationship with snow-ice disasters in Northeast China. It is found that compared with traditional objective identification methods, the new method better captures the overall structural characteristics of anticyclones, significantly improving the description of large-scale, strong anticyclones. Specifically, it incorporates 7.3% of small-scale anticyclones into larger-scale systems. Anticyclones are closely correlated with local cooling and cold air mass changes over Northeast China, with 60% of anticyclones accompanying regional cold air mass accumulation and temperature drops. Two case studies of the rare rain-snow and cold wave events revealed that these events were preceded by the generation and eastward expansion of an upstream anticyclone identified by the new method. This demonstrates that the proposed method can effectively track anticyclones and the evolution of cold high-pressure systems, providing insights into extreme cold events. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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11 pages, 2829 KB

Open AccessEditor’s ChoiceArticle

Low-Cost, LED-Based Photoacoustic Spectrophone Using Hemispherical Acoustic Resonant Cavity for Measurement of Hydrocarbon Gases

by Gaoxuan Wang, Lingxiao Hou, Fangjun Li, Lihui Wang, Chao Fei, Xiaojian Hong and Sailing He

Atmosphere 2025, 16(9), 1012; https://doi.org/10.3390/atmos16091012 - 28 Aug 2025

Viewed by 1089

Abstract

Spherical acoustic resonant cavities have been increasingly reported in photoacoustic spectroscopy due to their small volume and enhanced effective gas absorption path length. For further reducing the acoustic cavity volume and exploiting broadband LED as a light source, this paper reports a low-cost, LED-based photoacoustic gas-sensing system using a hemispherical acoustic resonant (HAR) cavity with a radius of 15 mm and a volume of 7.07 mL. The placement of both the excitation light source and transducer, as important elements in photoacoustic spectroscopy, was systematically optimized for improving the generation efficient of photoacoustic signal. The frequency response of the HAR cavity was thoroughly characterized for exploring an optimal operation frequency of the light source. Through positional and frequency optimization, the developed low-cost, LED-based photoacoustic spectrophone realized highly sensitive measurements of hydrocarbon gases with measurement sensitivities of 111.6 ppm (3σ) for isobutane, 140.1 ppm (3σ) for propane, and 866.4 ppm (3σ) for ethylene at an integration time of 1 s. These results demonstrate the strong potential of low-cost, LED-HAR-based PA-sensing systems in the field of gas sensing for widespread deployment in distributed sensor networks and atmospheric monitoring platforms. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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16 pages, 1939 KB

Open AccessArticle

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 1609

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. 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 KB

Open AccessArticle

Performance Assessment of Low- and Medium-Cost PM_2.5 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

Cited by 3 | Viewed by 5042

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 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5, 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 PM_2.5. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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