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CDEP Agent is an auditable LLM-agent framework that connects meteorologically detected compound drought-to-extreme-precipitation events to real-world documentary evidence, revealing that most such events go undocumented in current reporting systems.
The article critically examines the use of machine learning in weather and climate modeling, highlighting its practical strengths and inherent limitations while cautioning against overhyped claims of a revolution.
This paper proposes a framework that uses entropy-based diagnostics to harmonize spatial and temporal feature representations, achieving substantial accuracy gains on large-scale spatiotemporal prediction tasks across urban traffic, meteorology, and epidemic datasets.