Lagrangian investigation of the precipitation efficiency of convective clouds

A new framework is applied to study the precipitation efficiency of convective clouds in large-eddy simulations. Lagrangian water particles are seeded in a large-eddy simulation with each particle representing one water class at a time (e.g., vapor, rain, or graupel). A Monte-Carlo approach is used to facilitate the particles' transitions to other water classes. The new framework permits the tracking of atmospheric water within convective clouds and thereby permits new insights into, e.g., entrainment heights of precipitating water, or the role of warm- vs. mixed-phase formation. On top of that, it allows for a straightforward definition of precipitation efficiency. A comparison to Eulerian estimates of precipitation efficiencies pinpoints the advantages of the Lagrangian retrievals.