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Atmosphere
Special Issues
Machine Learning for Atmospheric and Remote Sensing Research
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Machine Learning for Atmospheric and Remote Sensing 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: 30 September 2026 | Viewed by 1822

Special Issue Editors

Dr. Amit Kumar Mishra

E-Mail Website
Guest Editor
1. Faculty of Geology, Geophysics and Environmental Protection, AGH University of Krakow, Krakow, Poland
2. Engineering Department, University West, 461 86 Trollhaten, Sweden
Interests: remote sensing; machine learning; sensors; calibration; radar
Special Issues, Collections and Topics in MDPI journals
Dr. Michał Lupa

E-Mail Website
Guest Editor
Faculty of Space Technologies, AGH University of Krakow, Krakow, Poland
Interests: remote sensing; big data; time series analysis; environment protection; crisis management

Special Issue Information

Dear Colleagues,

Over the past few years, artificial intelligence (AI) and machine learning (ML) have revolutionized the field of remote sensing and atmospheric sciences. The availability of high-resolution satellites, aerial, and ground-based sensors has led to an unprecedented volume and complexity of environmental data. Simultaneously, rapid advancements in ML/AI—deep learning, transfer learning, and data-driven modeling in particular—are unlocking new possibilities for extracting timely insights, automating complex analyses, and improving predictive capabilities for weather, climate, and atmospheric events.

This Special Issue is particularly relevant due to the following challenges:

  • Global Environmental Challenges: Climate change, extreme weather, pollution, and resource management are growing in urgency and require robust, scalable, and data-driven approaches for environmental monitoring and decision support.
  • Technological Convergence: Recent breakthroughs have taken place in computer vision, natural language processing, and sensor technology to enable the more accurate and comprehensive retrieval of atmospheric parameters and geospatial features.
  • Data Availability: The expansion of open-access satellite missions, Earth observation programs, and environmental datasets has empowered the community to develop, test, and deploy innovative ML/AI solutions.
  • Emergence of Trustworthy AI: The call for explainable, transparent, and reliable AI models has grown, especially in critical applications such as disaster response and air quality monitoring.

By addressing these trends, this Special Issue aims to bring together pioneering work leveraging ML and AI to meet current and future challenges in atmospheric and remote sensing sciences. Researchers are encouraged to submit contributions that not only demonstrate technical novelty but also advance practical impacts in monitoring, understanding, and managing our environment.

Topics of Interest:

  • ML/AI algorithms for satellite, airborne, UAV, or ground-based remote sensing data analysis;
  • Deep learning techniques for atmospheric parameter retrieval, weather forecasting, and event detection;
  • ML-empowered environmental monitoring, air quality analysis, climate change studies, and land cover classification;
  • Explainable, trustworthy, and robust AI methods for geospatial and atmospheric data;
  • Data fusion, transfer learning, and multi-modal data integration strategies;
  • ML/AI approaches for limited-data scenarios (few-shot, semi-supervised, and self-supervised learning);
  • The real-time or edge processing of remote sensing data for atmospheric applications;
  • Open datasets, benchmarks, and reproducible research in the domain;
  • Application case studies: disaster monitoring, extreme event forecasting, pollution tracking, and resource management.

Dr. Amit Kumar Mishra
Dr. Michał Lupa
Guest Editors

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

  • machine learning
  • artificial intelligence
  • deep learning
  • remote sensing
  • atmospheric applications
  • earth observation
  • environmental monitoring
  • data fusion
  • climate science
  • air quality
  • satellite data
  • explainable AI
  • weather forecasting
  • anomaly detection
  • edge computing
  • geospatial analysis

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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

31 pages, 5855 KB  
Article
Comparative Evaluation of Machine Learning and Deep Learning Models for Tropical Cyclone Track and Intensity Forecasting in the North Atlantic Basin
by Henry A. Ogu, Liping Liu and Yuh-Lang Lin
Atmosphere 2026, 17(4), 418; https://doi.org/10.3390/atmos17040418 - 20 Apr 2026
Viewed by 416
Abstract
Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, [...] Read more.
Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination (R2) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article
(This article belongs to the Special Issue Machine Learning for Atmospheric and Remote Sensing Research)
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18 pages, 2986 KB  
Article
Comparing Statistical and Machine-Learning Models for Seasonal Prediction of Atlantic Hurricane Activity
by Xiaoran Chen and Lian Xie
Atmosphere 2026, 17(2), 129; https://doi.org/10.3390/atmos17020129 - 26 Jan 2026
Cited by 1 | Viewed by 781
Abstract
Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950 [...] Read more.
Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950 to 2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches were developed and tested under operationally realistic conditions—Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), XGBoost—using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional major-hurricane targets due to lower event frequencies and stronger predictability limits. Full article
(This article belongs to the Special Issue Machine Learning for Atmospheric and Remote Sensing Research)
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