Results for “collision-coalescence” — Page 2

NASA NTRS2022

Dilution of Boundary Layer Cloud Condensation Nucleus Concentrations by Free Tropospheric Entrainment During Marine Cold Air Outbreaks

Recent aircraft measurements over the northwest Atlantic enable an investigation of how entrainment from the free troposphere (FT) impacts cloud condensation nucleus (CCN) concentrations in the marine boundary layer (MBL) during cold-air outbreaks (CAOs), motivated by the role of CCN in mediating transitions from closed to open-cell regimes. Observations compiled over eight flights indicate predominantly far lesser CCN concentrations in the FT than in the MBL. For one flight, a fetch-dependent MBL-mean CCN budget is compiled from estimates of sea-surface fluxes, entrainment of FT air, and hydrometeor collision-coalescence, based on in-situ and remote-sensing measurements. Results indicate a dominant role of FT entrainment in reducing MBL CCN concentrations, consistent with satellite-observed trends in droplet number concentration upwind of CAO cloud-regime transitions over the northwest Atlantic. Relatively scant CCN may widely be associated with FT dry intrusions, and should accelerate cloud-regime transitions where underlying MBL air is CCN-rich, thereby reducing regional albedo.

Boundary Layer Cloud Condensation Nucleus Concentr↗

DOE OSTI2024

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

54 ENVIRONMENTAL SCIENCES↗