Statistical and Machine Learning Methods for Climate Sciences: Advances, Applications and Emerging Challenges
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 309
Special Issue Editors
Interests: statistical analysis; statistical modeling; data analysis; applied probability; climate; hydrology; remote sensing data; data manipulation; climate change; extreme events
Interests: population and climate; statistical information; climate statistics
Interests: tropical climatology; soil-plant-atmosphere interactions; climate modeling; extreme events (weather and climate)
Special Issue Information
Dear Colleagues,
Recent advances in statistical and machine learning (ML) methods have revolutionized how climate and hydrometeorological phenomena are investigated. This Special Issue aims to gather studies presenting innovative approaches, practical applications, and methodological developments focused on understanding, predicting, and mitigating the impacts of climate change. Submissions employing classical and modern statistical techniques, as well as ML and deep learning approaches, are welcome for applications such as extreme event detection and prediction, spatio-temporal modeling, dynamic and statistical downscaling, bias adjustment, uncertainty analysis, and integration of multiple data sources (observations, satellites, reanalyses, and climate models). This Special Issue also encourages research exploring teleconnections, ocean–atmosphere interactions, climate services, and scientific communication strategies on climate risk, emphasizing reproducible and open-data approaches. The goal is to foster integration between data science and climatology, highlighting methodological contributions that enhance the predictive capacity, reliability, and applicability of climate analyses across different spatial and temporal scales.
Dr. Daniele Tôrres Rodrigues
Dr. Lára de Melo Barbosa Andrade
Dr. Cláudio Moisés Santos e Silva
Guest Editors
Manuscript Submission Information
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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
- statistical modeling
- machine learning
- climate change
- extreme events
- bias correction
- remote sensing
- data science
- downscaling
- teleconnections
- uncertainty analysis
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