Toward a Numerical Benchmark for Warm Rain Processes

Abstract

The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. In the absence of sedimentation and collision-coalescence, the drop size distributions (DSDs) from the LCMs exhibit similar evolution with expected physical behaviors and good inter-scheme agreement, with the volume mean diameter (Dvol ) from the LCMs within 1 to 5% of each other. In contrast, the bin schemes exhibit non-physical broadening with condensational growth. These results further strengthen the case that LCMs are an appropriate numerical benchmark for DSD evolution under condensational growth. When precipitation processes are included, however, the simulated liquid water path, precipitation rates, and response to modified cloud drop/aerosol number concentrations from the LCMs vary substantially, while the bin and bulk schemes are relatively more consistent with each other. The lack of consistency in the LCM results stems from both the collision-coalescence process and the sedimentation process, limiting their application as a numerical benchmark for precipitation processes. Reassuringly, however, precipitation from bulk schemes, which are the basis for cloud microphysics in weather and climate prediction, is within the spread of precipitation from the detailed schemes (LCMs and bin). Overall, this intercomparison identifies the need for focused effort on the comparison of collision-coalescence methods and sedimentation in detailed microphysics schemes, especially LCMs.

Designing a Convection‐Cloud Chamber for Collision‐Coalescence Using Large‐Eddy Simulation With Bin Microphysics

“…Hill et al. (2023) demonstrated that the DSDs in warm rain processes are sensitive to the selection of the collision‐coalescence model. Although testing the sensitivity of simulated cloud properties to the collision‐coalescence model could be a valuable direction for future research, in this study we focus on trends in the LWC and collision‐coalescence rate in response to changes in chamber configuration.…”

“…Finally, it is important to acknowledge that, while the primary goal of this work is to examine factors that can enhance collision-coalescence in a cloud chamber, the collision-coalescence model used in bin microphysics may not be entirely accurate. Hill et al (2023) demonstrated that the DSDs in warm rain processes are sensitive to the selection of the collision-coalescence model. Although testing the sensitivity of simulated cloud properties to the collision-coalescence model could be a valuable direction for future research, in this study we focus on trends in the LWC and collision-coalescence rate in response to changes in chamber configuration.…”

“…Fan et al (2017) conducted an intercomparison of cloud-resolving models, showing that variations in the rain rate can be largely attributed to different parameterizations of the collisioncoalescence process. Moreover, using the same dynamical core, Hill et al (2023) carried out a sensitivity test on various microphysics schemes, revealing that collision-coalescence is a major factor contributing to discrepancies in the simulated liquid water path, DSDs, and precipitation rates. In short, given the numerous uncertainties in precipitation predictions due to the collision-coalescence process, there is a critical need for a laboratory facility capable of observing and quantifying the droplet collision-coalescence process in turbulent flow.…”

Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1

“…Both methods use bins to make sampling of the initial aerosol radius more even, but differ in the way the entire bin range is selected. Recently, Hill et al (2023) found differences in precipitation between different implementations of particle microphysics, both using AON and binned initialization. Differences in details of bin initialization were proposed as one of potential reasons for the observed discrepancies.…”

New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping

“…PySDM supports a PyMPDATA-based (Bartman, Banaśkiewicz, et al, 2022) reimplementation of the 1D kinematically-driven test framework in a recently-published intercomparison of microphysics methods (Hill et al, 2023). The authors are unaware of recent SDM algorithm implementations in open-source packages beyond those mentioned in (Bartman, Bulenok, et al, 2022) and the related list of links in the PySDM README file.…”