Toward efficient and physically consistent collision parameterizations using ML methods

The representation of hydrometeor collision processes remains a significant source of inaccuracy in bulk microphysics schemes. This work reduces these uncertainties through a unified framework that incorporates theoretical refinements of collision kernels together with machine-learning (ML) parameterizations capable of emulating high-resolution kernel behavior with markedly lower computational expense. The theoretical component incorporates realistic liquid–ice collision efficiencies, terminal velocities, and coalescence or sticking efficiencies derived from laboratory studies, together with turbulence-induced enhancements to cloud-drop collision efficiency based on direct particle simulations. The resulting dataset includes the rate of change of the 0th, 2nd, 3rd, and 6th moments for gamma-type size distributions, along with predicted changes in the shape and density of ice particles. Using Latin Hypercube sampling, 100,000 samples were generated for each collision process and used to train XGBoost-based ML parameterizations.

The ML parameterizations were implemented in a two-moment bulk microphysics scheme within the WRF model and evaluated in an idealized squall-line simulation. Execution-time analyses demonstrate substantial performance gains, with runtime reductions of up to 40% relative to the baseline configuration, while maintaining or improving the physical fidelity of the simulated microphysical processes. These results indicate that the proposed ML-based parameterization framework enhances both physical realism and computational efficiency, offering a promising pathway for next-generation microphysics schemes.