Fog and Low Clouds in a Changing Climate: AI-Enhanced Forecasting, Decision Support Systems, and Sustainable Water Harvesting

Fog and low clouds play a critical role in regional climate dynamics, hydrological cycles, aviation safety, agriculture, and ecosystem resilience. Their formation, persistence, and dissipation are governed by complex boundary-layer microphysics (e.g., turbulence), aerosol-cloud interactions, and land-surface feedbacks, making them notoriously difficult to simulate and forecast in numerical weather prediction (NWP) models. Recent advances in artificial intelligence (AI), machine learning (ML), and high-resolution observation networks (e.g., drones, satellite retrievals) are transforming our ability to predict fog onset, duration, and spatial extent. Simultaneously, fog water harvesting has emerged as a promising climate-resilient water resource for arid and semi-arid regions. However, significant challenges remain in characterizing the spatiotemporal variability of fog occurrence, quantifying harvestable water yields, improving collection efficiency, and translating scientific information into actionable tools for stakeholders.

This Research Topic will bridge atmospheric science, data-driven modeling, hydrology, and environmental engineering to advance the science and application of fog and low-cloud systems. We welcome contributions that address, but are not limited to:

• Novel parameterizations and high-resolution modeling of fog/low-cloud microphysics and boundary-layer dynamics
• Integration of AI/ML with NWP and observational data assimilation for improved short-term fog forecasting
• Development and validation of operational decision support systems (DSS) for end-users (aviation, agriculture, water managers, public health)
• Field campaigns, remote sensing, and in-situ observation advances for fog characterization
• Fog water harvesting: climatology, collection efficiency, system design, water quality, and socio-economic viability
• Climate change impacts on fog regimes and low-cloud cover across vulnerable regions

This Research Topic welcomes the following article types: Original Research, Review Articles, Perspectives, Methodologies, and Case Studies.

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
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... 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
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Technology and Code

Keywords: fog climatology, low clouds, numerical weather prediction, artificial intelligence, machine learning, decision support systems, fog water harvesting, arid hydrology, boundary layer meteorology, climate adaptation, open atmospheric data

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.