Hybrid physics-ML & parameterization
Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques
In one sentence. Machine-learning-constrained autoconversion and accretion parameterizations, fitted to Azores cloud-probe data, cut uncertainty to about 15% and 5% and reveal drizzle number's role.
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.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2020gl091236 OpenAlex:W3110725524
- Added
- 2026-10-22
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