BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20241120T082410Z
LOCATION:HG F 30 Audi Max
DTSTART;TZID=Europe/Stockholm:20240603T190600
DTEND;TZID=Europe/Stockholm:20240603T190700
UID:submissions.pasc-conference.org_PASC24_sess156_pos129@linklings.com
SUMMARY:P16 - Enhancing Aerosol Predictions on the Global Scale with Parti
 cle-Resolved Modeling and Machine Learning
DESCRIPTION:Poster\n\nNicole Riemer (University of Illinois Urbana-Champai
 gn), Zhonghua Zheng (University of Manchester), Jeffrey H. Curtis (Univers
 ity of Illinois Urbana-Champaign), Justin L. Wang (WorldQuant), Po-Lun Ma 
 (Pacific Northwest National Laboratory), Xiaohong Liu (Texas A&M Universit
 y), and Matthew West (University of Illinois Urbana-Champaign)\n\nAtmosphe
 ric aerosols play an important role in several key processes related to at
 mospheric chemistry and physics. However, to limit computational expense, 
 current regional and global chemical transport models need to grossly simp
 lify the representation of aerosols, thereby introducing errors and uncert
 ainties in our estimates of aerosol impacts on climate. This work shows ho
 w machine learning (ML) can be used to aid modeling of atmospheric aerosol
 . We illustrate this with two applications that both use detailed particle
 -resolved simulations as a basis to generate training data. The first appl
 ication shows how microscale process of particle coagulation can be learne
 d directly from data. The second application shows how ML can be used to b
 ridge from accurate fine-scale aerosol models to the global scale for the 
 evaluation of climate impacts. We focus on the aerosol mixing state, which
  is an important emergent property that affects the aerosol radiative forc
 ing and aerosol-cloud interactions. In conclusion, the integration of mach
 ine learning methodologies into atmospheric aerosol modeling presents a pr
 omising avenue, offering both enhanced microscale understanding through di
 rect data learning and improved global-scale modeling, thereby paving the 
 way for more accurate estimations of aerosol impacts on climate.\n\nSessio
 n Chair: Erik W. Draeger (Lawrence Livermore National Laboratory)
END:VEVENT
END:VCALENDAR