Preprints
https://doi.org/10.5194/egusphere-2024-825
https://doi.org/10.5194/egusphere-2024-825
19 Apr 2024
 | 19 Apr 2024

Explicit stochastic advection algorithms for the regional scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)

Jeffrey Henry Curtis, Nicole Riemer, and Matthew West

Abstract. This paper presents the development of a stochastic particle method to simulate advection in regional-scale models with a particle-resolving aerosol representation. The new method is based on finite volume discretizations with the flux terms interpreted as probabilities of particle transport between grid cells. We analyze the method in 1D and show that the stochastic particle sampling during transport injects energy at high spatial frequencies, which can be partially compensated for with the choice of a dissipative odd-order finite volume scheme. We then apply the stochastic third- and fifth-order advection algorithms with monotonic limiters in WRF-PartMC, using both idealized and realistic wind fields in 2D and 3D. In all cases we observe the expected convergence rates of the stochastic particle method to the finite volume solution as the number of computational particles is increased. This work enables the use of particle-based aerosol models on the regional scale.

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Jeffrey Henry Curtis, Nicole Riemer, and Matthew West

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Jeffrey Henry Curtis, Nicole Riemer, and Matthew West

Data sets

Data for Explicit stochastic advection algorithms for the regional scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0) Jeffrey Henry Curtis, Nicole Riemer, and Matthew West https://doi.org/10.13012/B2IDB-3847217_V1

Model code and software

open-atmos/wrf-partmc: Version 1.0 Jeffrey Henry Curtis, Nicole Riemer, and Matthew West https://doi.org/10.5281/zenodo.10794890

Jeffrey Henry Curtis, Nicole Riemer, and Matthew West

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Short summary
This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various advection order method’s numerical properties and demonstrate its implementation in a 3D weather prediction model (WRF) for the first time. Particle-based techniques improve the accuracy of aerosol size and composition predictions, which are key for aerosol-cloud and aerosol-radiation interactions.