Abstract The atmospheric boundary layer (ABL) controls surface‐atmosphere exchanges, yet accurately capturing its full complexity, particularly the dynamically evolving nature of turbulence across diverse weather conditions, remains a formidable challenge for traditional classification methods. This study introduces a novel framework that integrates coherent Doppler LiDAR observations with machine learning to classify ABL states based on the principal terms of the turbulent kinetic energy (TKE) budget: buoyancy production, shear production, dissipation rate, and turbulent transport. This approach leverages the complete energy cycle—production, transport, and dissipation—offering a physically robust basis for classification. We identify four distinct ABL types (I–IV) that represent a spectrum of turbulence regimes, from continuously shear‐driven to strongly diurnally forced, each with a unique signature in its TKE budget and a clear linkage to specific synoptic‐scale weather patterns. The findings provide a refined understanding of ABL processes and a solid foundation for enhancing turbulence parameterization in numerical models.

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