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https://egusphere.copernicus.org/preprints/2024/egusphere-2024-235/

*Authors*
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and
Lekha Patel

*How to cite*. McMichael, L. A., Schmidt, M. J., Wood, R., Blossey, P. N.,
and Patel, L.: Exploring ship track spreading rates with a physics-informed
Langevin particle parameterization, EGUsphere [preprint],
https://doi.org/10.5194/egusphere-2024-235, 2024.

Received: 29 Jan 2024 – *Discussion started: 23 Feb 2024*

*Abstract*. The rate at which aerosols spread from a point source
injection, such as from a ship or other stationary pollution source, is
critical for accurately representing subgrid plume spreading in a climate
model. Such climate model results will guide future decisions regarding the
feasibility and application of large-scale intentional marine cloud
brightening (MCB). Prior modeling studies have shown that the rate at which
ship plumes spread may be strongly dependent on meteorological conditions,
such as precipitating versus non-precipitating boundary layers and shear.
In this study, we apply a Lagrangian particle model (PM-ABL v1.0), governed
by a Langevin stochastic differential equation, to create a simplified
framework for predicting the rate of spreading from a ship-injected aerosol
plume in sheared, precipitating, and non-precipitating boundary layers. The
velocity and position of each stochastic particle is predicted with the
acceleration of each particle being driven by the turbulent kinetic energy,
dissipation rate, momentum variance, and mean wind. These inputs to the
stochastic particle-velocity equation are derived from high-fidelity
large-eddy simulations (LES) equipped with a prognostic aerosol-cloud
microphysics scheme (UWSAM) to simulate an aerosol injection from a ship
into a cloud-topped marine boundary layer. The resulting spreading rate
from the reduced-order stochastic model is then compared to the spreading
rate in the LES. The stochastic particle-velocity representation is shown
to reasonably reproduce spreading rates in sheared, precipitating, and
non-precipitating cases using domain-averaged turbulent statistics from the
LES.


*Source: EGU Sphere*

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