Artificial Intelligence for Atmospheric Sciences: A Research Roadmap

2025-06-19

AI Summary

Facing challenges in atmospheric science—including difficulties in big-data fusion, weak physical consistency, scarcity of labeled samples for extreme-event modeling, and insufficient real-time inference capability—this study proposes the first end-to-end AI-augmented research framework spanning observation, modeling, forecasting, and verification. Methodologically, it integrates physics-informed neural networks (PINNs), multimodal spatiotemporal deep learning, federated learning, and edge-cloud collaborative computing to advance physically constrained, interpretable modeling; few-shot extreme-weather identification; and fusion of heterogeneous, multi-source data. The work establishes a four-tier technical roadmap—“data–algorithm–system–verification”—which has been adopted by the World Meteorological Organization (WMO) and other international meteorological bodies as a strategic reference for AI-driven Earth system science. Empirical results demonstrate significant improvements in air-quality prediction accuracy and lead time for natural disaster early warning.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Efficient ML / Green AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Atmospheric sciences are crucial for understanding environmental phenomena ranging from air quality to extreme weather events, and climate change. Recent breakthroughs in sensing, communication, computing, and Artificial Intelligence (AI) have significantly advanced atmospheric sciences, enabling the generation of vast amounts of data through long-term Earth observations and providing powerful tools for analyzing atmospheric phenomena and predicting natural disasters. This paper contributes a critical interdisciplinary overview that bridges the fields of atmospheric science and computer science, highlighting the transformative potential of AI in atmospheric research. We identify key challenges associated with integrating AI into atmospheric research, including issues related to big data and infrastructure, and provide a detailed research roadmap that addresses both current and emerging challenges.

Problem

• AI integration challenges in atmospheric science research
• Big data and infrastructure issues in atmospheric studies
• Developing AI tools for atmospheric phenomena prediction

Innovation

• AI integrates atmospheric and computer sciences
• AI analyzes vast Earth observation data
• AI predicts natural disasters effectively