The Applications of Artificial Intelligence (AI) in Climate Sciences
A special issue of Atmosphere (ISSN 2073-4433).
Deadline for manuscript submissions: 31 January 2025 | Viewed by 222
Special Issue Editors
Interests: heatwaves; biometeorology; extreme weather and climate events; climatology, AI tools
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; big data; nonlinear time series analysis; complex systems; dynamical systems; nonlinear methods for dimensionality reduction and pattern extraction; nonparameteric predictions; coherent structures; turbulence; multi spatio-temporal flows; climate dynamics
Interests: machine learning applications in meteorology; unsupervised learning methods; numerical weather prediction; statistical climatology; severe weather outbreaks; tropical cyclone intensification; precipitation predictability; large-scale atmospheric variability; synoptic and dynamic meteorology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The recent emergence of AI tools has been observed across many fields. Beyond everyday applications, AI holds tremendous potential in enhancing scientific research, particularly in climate sciences. AI tools and systems can be applied to data analysis, visualization, the recognition of complex climate patterns, the exploration of new research ideas, and the identification of relevant publications for research papers. These tools include, but are not limited to, Artificial Neural Networks (ANNs), Machine Learning techniques (ML), and Large Language Models (LLMs) such as GPT.
Given the exciting opportunities, new approaches, and innovative methodologies that AI brings to climate sciences, there is a compelling need to consolidate these advancements. This Special Issue of Atmosphere, titled "The Applications of AI in Climate Sciences", aims to do just that. The focus of this Special Issue includes, but is not limited to, the following topics:
- Machine Learning Techniques and Applications: applications in climate modeling, weather forecasting, data analysis, and more.
- Artificial Neural Networks (ANNs) in Climate Science: utilizations for pattern recognition in large climate datasets, prediction of extreme events, etc.
- Large Language Models (LLMs) in Climate Research: automating the extraction of relevant information from scientific papers, generating comprehensive climate reports, predicting future climate research trends based on current data, and educational and academic applications.
- Enhancing Data Quality and Consistency: AI-based error detection and correction.
- Homogenizing Historical Climate Data: application of AI in standardizing historical datasets.
- Downscaling Global Climate Models to Regional Scales: utilizing AI for more localized climate predictions.
- AI in Climate Adaptation and Mitigation Strategies: development of AI tools for optimizing renewable energy deployment and applications in urban planning to mitigate heat island effects.
- Ethical and Societal Implications: addressing ethical considerations in AI applications within climate science, ensuring transparency and accountability in AI-driven climate research.
- Interdisciplinary Approaches: combining AI with traditional climate science methods for enhanced insights and collaborative applications across climatology, meteorology, and environmental sciences.
- Case Studies: showcasing successful interdisciplinary AI projects in climate science.
We eagerly look forward to your submissions.
Dr. Agnieszka Krzyżewska
Dr. Joanna Slawinska
Prof. Dr. Andrew Edward Mercer
Prof. Dr. Jamie Dyer
Guest Editors
Manuscript Submission Information
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Keywords
- artificial Intelligence (AI)
- GPT
- large language models (LLMs)
- artificial neural networks (ANN)
- machine learning (ML)
- ethical AI, AI in climate
- data analysis
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