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DTSTART;TZID=Europe/Stockholm:20230627T193000
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UID:submissions.pasc-conference.org_PASC23_sess116_pos153@linklings.com
SUMMARY:P03 - A Novel Stochastic Parameterization for Lagrangian Modeling 
 of Atmospheric Aerosol Transport
DESCRIPTION:Poster\n\nMichael Schmidt (Sandia National Laboratories)\n\nIn
  recent years, it has become clear that the behavior of atmospheric aeroso
 ls has a non-negligible effect on radiative forcing within Earth's climate
  and the computational models that simulate it [Carslaw, et al., Nature, 2
 013]. Thus, we must obtain descriptive aerosol models that are also predic
 tive, particularly in a time when aerosol-emitting ships may soon traverse
  the polar arctic ocean and there is credible talk about climate intervent
 ion strategies like stratospheric aerosol injection. This begs the questio
 n of how we may accurately describe our changing climate or dynamic weathe
 r patterns in the face of such uncertainty. We propose a novel stochastic 
 model that employs transport parameters that operate on differing scales a
 nd vary according to their respective machine-learned probability distribu
 tion. This parameterization allows our transport variables to be functions
  of space, time, and relevant exogenic properties, and forcing effects may
  be added, subtracted, or altered as we gain more confidence in the machin
 e learning model. To verify and validate this model, particle simulation r
 esults are compared to corresponding LES simulations, data from fog chambe
 r experiments, and satellite imagery of ship tracks in the Pacific Ocean o
 ff the coast of California.
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