An Approach for Simultaneous Estimation of Entrainment and Detrainment Rates in Deep Convective Clouds

This is the authors' abstract. We don't add key points for this paper.

Estimating entrainment and detrainment rates ( ε and δ ) for convective clouds from observational data is critical for quantitatively understanding the physical mechanisms of cloud‐environment interactions. Currently, no direct approaches exist to estimate δ from observational data of deep convective clouds (DCCs), hindering the characterization of detrainment processes in DCCs. To fill this gap, this study proposes a new precipitation efficiency‐based (PE‐based) approach for simultaneously estimating ε and δ in DCCs by introducing precipitation efficiency in the conservation equation of total water, thereby addressing the nonconservation of total water in precipitating DCCs. Evaluation using numerical simulations shows that the PE‐based approach yields ε and δ with magnitudes and vertical variations comparable with those estimated using the bulk‐plume approach, confirming its reliability. Sensitivity tests reveal that the variations of presumed entrained air properties have minimal impact on estimated ε and δ . Increasing the fraction of in‐cloud air in the presumed detrained air leads to larger δ values, while ε remains largely unaffected. Higher precipitation efficiency increases both ε and δ estimates, with a more pronounced effect on δ . Further comparisons between the PE‐based and bulk‐plume approaches identify the optimal precipitation efficiency for accurate estimation. Finally, the application of the PE‐based approach to aircraft observations of DCCs reveals that δ is greater than ε . The proposed approach can be applied to DCC observations to derive ε and δ data sets, ultimately contributing to advancements in parameterizations for DCCs.