Implementation of a Novel Nonlinear Cloud Droplet Spectrum Dispersion Parameterization in Large‐Eddy Model and Its Effects on Cloud and Fog Simulations

Abstract The relative dispersion of the cloud droplet spectrum (ε) is a key parameter that characterizes the spectral shape. Uncertainties in its parameterization can introduce significant biases in the simulation of cloud and fog microphysical and optical properties, making its accurate diagnosis in models critically important. In this study, we conduct sensitivity experiments using the large‐eddy simulation model to evaluate the performance of a newly developed nonlinear ε parameterization based on volume‐mean diameter of cloud droplets (Dv). Four typical surface types associated with cloud and fog processes are tested. Compared to conventional linear parameterizations based on droplet number concentration or Dv, the new parameterization significantly improves the simulation of droplet spectral shape parameters and effective radius, with average improvements of 90.59% and 78.49%, respectively. Moreover, the new parameterization captures the complex relationship between ε and liquid water content observed across different surface types and exhibits distinct impacts on cloud‐to‐rain autoconversion rate from those of default parameterization with a net mean change of +72.21%, thereby affecting the formation and intensity of precipitation. The new parameterization also leads to reduced cloud and fog optical thickness, which weakens cloud radiative cooling. These findings highlight the importance of improved ε parameterizations for the studied regions in China and underscore the potential for enhancing model performance in representing microphysics, radiation, and precipitation processes in low clouds and fog. Assessing the generality of the conclusions across different geographic regions and climatic regimes would be a valuable focus for future work.