Machine Learning Model for Inverting Convective Boundary Layer Height with Implicit Physical Constraints and Its Multi-Site Applicability

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH inversion, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show consistent predictions (R2 difference ~0.011, MAE ~0.002 km). Models trained on C1 site ECOR data and tested at E37 and E39 yield R2 values of 0.787 and 0.806, respectively, demonstrating adaptability. Training with all sites’ data enhances C1 ECOR and EBBR performance over C1-only training: ECOR (R2: 0.851 vs. 0.845; MAE: 0.198 km vs. 0.207 km), EBBR (R2: 0.837 vs. 0.834; MAE: 0.203 km vs. 0.205 km). Transferability across ARM Southern Great Plains sites and seasonal performance during summer confirm the model’s robustness, offering a scalable approach for improving boundary layer parameterization in atmospheric models.