Extreme Weather Modeling and Forecasting

Dear Colleagues,

Extreme weather events—including hurricanes and typhoons, flash floods, blizzards, and episodes of extreme heat or cold—pose profound threats to communities and ecosystems worldwide. Recent advances in real-time data acquisition (e.g., satellite- and sensor-based observations), expanding computational capabilities, and rapid developments in machine learning have created new opportunities to enhance classical physics-based approaches for modeling and predicting extreme weather.

This Special Issue of Stats invites high-quality submissions that develop or employ data-driven, physics-informed, or hybrid modeling frameworks for extreme weather analysis and forecasting. Contributions may include, but are not limited to, the following:

• Innovative statistical or machine learning approaches for extreme weather prediction;
• Hybrid data–physics or physics-informed statistical or machine learning models;
• Uncertainty quantification for extreme weather forecasts;
• Advances in data assimilation and use of large-scale observational datasets;
• Case studies demonstrating improved forecasting performance;
• Methodological comparisons between classical and modern approaches.

All submissions will undergo rigorous peer review. We welcome both methodological developments and application-driven research that demonstrate clear advances in understanding or forecasting extreme weather phenomena.

Prof. Dr. Wei Zhu
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Stats is an international peer-reviewed open access semimonthly 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 1600 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.

• extreme weather events
• extreme weather analysis and forecasting
• statistics