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MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Kumar, Pankaj 1; Vogel, Heike ORCID iD icon 1; Bruckert, Julia ORCID iD icon 1; Muth, Lisa Janina ORCID iD icon 1; Hoshyaripour, Gholam Ali ORCID iD icon 1
1 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)

Abstract:

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000171227
Veröffentlicht am 03.06.2024
Originalveröffentlichung
DOI: 10.1038/s41612-024-00652-y
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung (IMK)
Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 2397-3722
KITopen-ID: 1000171227
HGF-Programm 12.11.32 (POF IV, LK 01) Advancing atmospheric and Earth system models
Weitere HGF-Programme 12.11.12 (POF IV, LK 01) Atmospheric chemistry processes
Erschienen in npj Climate and Atmospheric Science
Verlag Nature Research
Band 7
Heft 1
Seiten Art.-Nr.: 110
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 23.05.2024
Nachgewiesen in Web of Science
Dimensions
Scopus
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