Advanced AI Methods for Extreme Weather Monitoring and Risk Assessment
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 41
Special Issue Editors
Interests: artificial intelligence; deep learning; disaster prevention and mitigation
Interests: remote sensing of middle and upper atmospheric wind fields; imaging spectropolarimetry
Interests: wastewater treatment; ecological restoration; water management; environmental risk assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to platform recent advances in advanced artificial intelligence methods for extreme weather monitoring, spatiotemporal characteristic analysis, and risk evaluation, which are fundamental to meteorological forecasting, disaster early warning, urban safety governance, and climate change adaptation. Traditional statistical and numerical methods face limitations in capturing nonlinear atmospheric variations and complex extreme weather mechanisms; meanwhile, advanced AI techniques offer powerful tools for high-dimensional meteorological data mining, dynamic pattern recognition, and refined risk quantification, greatly improving the accuracy and efficiency of extreme weather research and practical applications.
The areas of interest for this Special Issue include, but are not limited to, advanced AI modeling for extreme weather; data fusion of AI algorithms with satellite and in situ meteorological observations; probabilistic and statistical AI methods for extreme weather analysis; intelligent identification and prediction of compound extreme weather; AI-driven extreme weather risk assessment and early warning; and engineering and practical applications of meteorological AI models.
Dr. Xin Jin
Dr. Tingyu Yan
Dr. Junyuan Guo
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- extreme weather
- meteorological monitoring
- risk assessment
- meteorological modeling
- deep learning
- compound disasters
- weather prediction
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