Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques-Reference-Cited by-同舟云学术

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques

Published:2021-01-23 Issue:2 Volume:48 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophys Res Lett

Author:

Chiu J. Christine¹^ORCID,Yang C. Kevin¹^ORCID,van Leeuwen Peter Jan¹²^ORCID,Feingold Graham³^ORCID,Wood Robert⁴^ORCID,Blanchard Yann⁵^ORCID,Mei Fan⁶^ORCID,Wang Jian⁷^ORCID

Affiliation:

1. Department of Atmospheric Science Colorado State University Fort Collins CO USA

2. Department of Meteorology University of Reading Reading UK

3. NOAA Earth System Research Laboratory Boulder CO USA

4. Department of Atmospheric Sciences University of Washington Seattle WA USA

5. Department of Earth and Atmospheric Sciences ESCER Centre, University of Quebec at Montreal Montreal QC Canada

6. Pacific Northwest National Laboratory Richland WA USA

7. Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering Washington University in Saint Louis Saint Louis MO USA

Funder

U.S. Department of Energy

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL091236

Reference37 articles.

1. An improved representation of the raindrop size distribution for single-moment microphysics schemes

2. Improving the Representation of Low Clouds and Drizzle in the ECMWF Model Based on ARM Observations from the Azores

3. Effects of turbulence on the geometric collision rate of sedimenting droplets. Part 1. Results from direct numerical simulation