Importance of CCN activation for fog forecasting and its representation in the two‐moment microphysical scheme LIMA

The work presented in this article studies the impact of cloud condensation nuclei (CCN) activation for fog forecasting and improves its parameterization in the LIMA (Liquid, Ice, Multiple Aerosols) two‐moment microphysical scheme, building upon the Local And Non‐local Fog EXperiment (LANFEX) field campaign observations, specifically the intensive observation period (IOP) 1 and the DEMISTIFY intercomparison. Large‐eddy simulations were performed with the Meso‐NH model, first using a prognostic supersaturation allowing us to compute the number of activated CCN at each time step, and then with the usual saturation adjustment hypothesis and a diagnostic maximum supersaturation. The prognostic supersaturation method provided very good results, similar to those from earlier simulations of this case using a bin scheme, and was thus used as a reference. In contrast, the diagnostic maximum supersaturation method strongly overestimated droplet numbers and produced a too‐thick fog. Thus, improvements to the maximum supersaturation diagnostic were proposed, by (1) revising the temperature tendency and (2) accounting for pre‐existing cloud droplets in the activation parameterization. These improvements resulted in a simulation in good agreement with observations and the reference simulation, and are promising for use in numerical weather prediction systems with a lower resolution and/or a longer time step.