Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling-Reference-Cited by-同舟云学术

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Published:2021-02 Issue:2 Volume:8 Page:
ISSN:2333-5084
Container-title:Earth and Space Science
language:en
Short-container-title:Earth Space Sci

Author:

Zheng Zhonghua¹^ORCID,Curtis Jeffrey H.²³^ORCID,Yao Yu²^ORCID,Gasparik Jessica T.²^ORCID,Anantharaj Valentine G.⁴^ORCID,Zhao Lei¹⁵^ORCID,West Matthew³^ORCID,Riemer Nicole²^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering University of Illinois at Urbana‐Champaign Urbana IL USA

2. Department of Atmospheric Sciences University of Illinois at Urbana‐Champaign Urbana IL USA

3. Department of Mechanical Science and Engineering University of Illinois at Urbana‐Champaign Urbana IL USA

4. National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA

5. National Center for Supercomputing Applications University of Illinois at Urbana‐Champaign Urbana IL USA

Funder

U.S. Department of Energy

National Science Foundation

Amazon Web Services

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020EA001500

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