AO-PHLGMay 22

Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers

arXiv:2605.2406714.0
Predicted impact top 77% in AO-PH · last 90 daysOriginality Highly original
AI Analysis

For operational meteorologists and nowcasting systems, this work addresses the challenge of predicting storm initiation by incorporating atmospheric precursors, offering a significant improvement in early detection.

MeteoLogist integrates thermodynamic, kinematic, and microphysical precursors into radar nowcasting, achieving a 9.7% improvement in high-impact detection (CSI40) and a 37.67% gain during storm development over strong baselines on 3D-NEXRAD data.

Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organized storm evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers -- thermodynamic, kinematic, and microphysical -- that evolve asynchronously (C1) and remain spatially fragmented (C2). To this end, MeteoLogist designs three tightly integrated components. The Physics-Tailored Encoders process radar echoes according to their intrinsic physical scales and semantics, forming thermodynamic, kinematic, and microphysical streams that capture distinct dynamical regimes. The Temporal-Phase Aligner addresses C1 by leveraging causal temporal attention to capture when and how different drivers interact and activate. The Cross-Field Spatial Aggregator addresses C2 through cross-regional fusion, aligning weak and scattered precursors across neighboring cells to expose upstream triggers and enforce spatial coherence. Evaluated on 3D-NEXRAD (2020--2022, US-wide), MeteoLogist boosts high-impact detection (CSI40) by +9.7% over strong baselines, and achieves a remarkable 37.67% gain during the storm-developing stage -- demonstrating true foresight in sensing storms before they appear. The code can be found in the supplementary material.

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