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Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques
J Christine Chiu1, C Kevin Yang1, Peter Jan van Leeuwen1,2
1Department of Atmospheric Science Colorado State University Fort Collins CO USA.
Geophysical Research Letters
|March 8, 2021
Summary
New parameterizations for warm rain growth processes were developed using machine learning. Drizzle number concentration is a key factor for autoconversion, improving rain growth models.
Area of Science:
- Atmospheric Science
- Cloud Physics
- Machine Learning Applications
Background:
- Accurate representation of warm rain microphysical processes is crucial for climate modeling.
- Existing parameterizations for autoconversion and accretion rates have limitations.
Purpose of the Study:
- To develop and validate improved parameterizations for autoconversion and accretion rates.
- To identify key factors influencing warm rain droplet growth.
Main Methods:
- Utilized machine learning and optimization techniques.
- Constrained parameterizations with in situ cloud probe measurements from the Atmospheric Radiation Measurement Program (ARM) field campaign.
- Analyzed relationships between droplet properties and rain rates.
Main Results:
- New parameterizations show reduced uncertainty (15% for autoconversion, 5% for accretion) compared to existing models.
- Confirmed cloud and drizzle water content as primary drivers of accretion.
- Identified drizzle number concentration as a critical, previously overlooked, factor for autoconversion.
Conclusions:
- The developed parameterizations significantly enhance the representation of warm rain growth.
- Drizzle number concentration must be incorporated into autoconversion parameterizations for improved accuracy.
- Findings have implications for weather and climate models.
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