Leveraging AI and machine learning for enhanced extreme weather forecasting

Extreme weather events become increasingly intense and frequent, leading to significant economic and social impacts. Forecasting extreme weather remains particularly challenging due to its low baseline frequency, but accurate forecasts could be highly beneficial. In recent years, artificial intelligence and machine learning (AI/ML) methods have demonstrated the potential to rival traditional weather forecasting models in terms of mean performance, often at substantially reduced computational cost. However, evaluating and optimizing AI/ML performance for extreme weather remain critical for their potential contributions to disaster prevention and management.

This Research Topic aims to present and advance the state-of-the-art in AI/ML forecasting of extreme weather. Extreme weather categories to be considered can include tropical cyclones, other severe storms, intense precipitation, heat and cold waves, and those precipitating heightened vulnerability to hazards such as fire, landslides, drought, and pathogen and pest spread. Forecasting timescales can range from minutes to years, contingent on the nature of the extreme weather event and its predictability. By examining both current operational systems and innovative experimental models, this topic aims to refine forecasting tools to ensure they are both effective and sustainable for real-world applications. The scope of this Research Topic encompasses the integration and limitations of AI/ML forecasting techniques for extreme weather. We invite submissions that cover a broad range of themes, including, but not limited to:

• Physics-informed machine learning for forecasting extreme events
• Development of accurate deterministic and probabilistic weather forecasts
• Enhancing the computational efficiency of forecasting systems
• Hybrid models combining AI/ML with classical observation techniques
• Utilization of underexplored data sources for improved prediction
• Addressing weather agency and end-user requirements for AI/ML forecast integration

All article types pertinent to these objectives are encouraged, with a focus on promoting open data practices and algorithm sharing for transparency and communal advancement.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: artificial intelligence, machine learning, extreme weather, probabilistic forecasting, disaster management

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.