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Sub-cloud rain evaporation in the trade wind region significantly influences the boundary layer mass and energy budgets. Parameterizing it is, however, difficult due to the sparsity of well-resolved rain observations and the challenges of sampling short-lived marine cumulus clouds. In this study, sub-cloud rain evaporation is analyzed using a steady-state, one-dimensional model that simulates changes in drop sizes, relative humidity, and rain isotopic composition. The model is initialized with relative humidity, raindrop size distributions, and water vapor isotope ratios (e.g., δDv, δ18Ov) sampled by the NOAA P3 aircraft during the Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC), which was part of the larger EUREC4A (ElUcidating the RolE of Clouds–Circulation Coupling in ClimAte) field program. The modeled surface precipitation isotope ratios closely match the observations from EUREC4A ground-based and ship-based platforms, lending credibility to our model. The model suggests that 63 % of the rain mass evaporates in the sub-cloud layer across 22 P3 cases. The vertical distribution of the evaporated rain flux is top heavy for a narrow (σ) raindrop size distribution (RSD) centered over a small geometric mean diameter (Dg) at the cloud base. A top-heavy profile has a higher rain-evaporated fraction (REF) and larger changes in the rain deuterium excess (d=δD-8×δ18O) between the cloud base and the surface than a bottom-heavy profile, which results from a wider RSD with larger Dg. The modeled REF and change in d are also more strongly influenced by cloud base Dg and σ rather than the concentration of raindrops. The model results are accurate as long as the variations in the relative humidity conditions are accounted for. Relative humidity alone, however, is a poor indicator of sub-cloud rain evaporation. Overall, our analysis indicates the intricate dependence of sub-cloud rain evaporation on both thermodynamic and microphysical processes in the trade wind region. 

Two case studies of marine stratocumulus (one nocturnal and drizzling, the other daytime and nonprecipitating) are simulated by the UCLA large-eddy simulation model with bin microphysics for comparison with aircraft in situ observations. A high-bin-resolution variant of the microphysics is implemented for closer comparison with cloud drop size distribution (DSD) observations and a turbulent collision–coalescence kernel to evaluate the role of turbulence on drizzle formation. Simulations agree well with observational constraints, reproducing observed thermodynamic profiles (i.e., liquid water potential temperature and total moisture mixing ratio) as well as liquid water path. Cloud drop number concentration and liquid water content profiles also agree well insofar as the thermodynamic profiles match observations, but there are significant differences in DSD shape among simulations that cause discrepancies in higher-order moments such as sedimentation flux, especially as a function of bin resolution. Counterintuitively, high-bin-resolution simulations produce broader DSDs than standard resolution for both cases. Examination of several metrics of DSD width and percentile drop sizes shows that various discrepancies of model output with respect to the observations can be attributed to specific microphysical processes: condensation spuriously creates DSDs that are too wide as measured by standard deviation, which leads to collisional production of too many large drops. The turbulent kernel has the greatest impact on the low-bin-resolution simulation of the drizzling case, which exhibits greater surface precipitation accumulation and broader DSDs than the control (quiescent kernel) simulations. Turbulence effects on precipitation formation cannot be definitively evaluated using bin microphysics until the artificial condensation broadening issue has been addressed.