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

Journal Article · · Geophysical Research Letters
DOI:https://doi.org/10.1029/2020GL091236· OSTI ID:1766600
 [1];  [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. Department of Atmospheric Science Colorado State University Fort Collins CO USA
  2. Department of Atmospheric Science Colorado State University Fort Collins CO USA, 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
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Abstract

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1766600
Alternate ID(s):
OSTI ID: 1837838
OSTI ID: 1765318
OSTI ID: 1786685
OSTI ID: 1961663
OSTI ID: 2927214
Journal Information:
Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 2 Vol. 48; ISSN 0094-8276
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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