Ice nucleation from drop-freezing experiments: Impact of droplet volume dispersion and cooling rates.

Because homogeneous ice nucleation is important for atmospheric science, special assays have been developed to monitor ultra-pure nanoscale water droplets for nucleation as the temperature is gradually lowered to deeply supercooled conditions. To analyze the experimental data and predict droplet freezing, we develop model that accounts for the cooling rate and the distribution of droplet sizes. We use the model to analyze two sets of experimental homogeneous nucleation data with carefully controlled cooling rates and droplet sizes. Rate expressions based on classical nucleation theory describes both experiments well and with rate parameters in approximate agreement with theoretical predictions based on the thermodynamics of water. We further demonstrate that a failure to account for dispersion in droplet volumes reduces the apparent barriers for ice nucleation. We provide an open source code to estimate nucleation parameters from drop-freezing assays, and another code to account for dispersion of droplet volumes and predict the outcome of drop-freezing experiments. We also present a sensitivity analysis to find the effect of temperature uncertainty on the measured nucleation spectrum. Our framework may be directly useful in accounting for droplet polydispersity and cooling rates for ice nucleation in clouds. Although our analysis pertains to homogeneous nucleation, we note that similar strategies may be applied to heterogeneous ice nucleation on minerals and organic particles with variable surface areas and nucleation sites.