An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using "Cloudy," a New n-Moment Bulk Microphysics Scheme
Abstract
The small-scale microphysical processes governing the formation of precipitation particles cannot be resolved explicitly by cloud resolving and climate models. Instead, they are represented by microphysics schemes that are based on a combination of theoretical knowledge, statistical assumptions, and fitting to data ("tuning"). Historically, tuning was done in an ad hoc fashion, leading to parameter choices that are not explainable or repeatable. Recent work has treated it as an inverse problem that can be solved by Bayesian inference. The posterior distribution of the parameters given the data—the solution of Bayesian inference—is found through computationally expensive sampling methods, which require over O(10⁵) evaluations of the forward model; this is prohibitive for many models. We present a proof of concept of Bayesian learning applied to a new bulk microphysics scheme named "Cloudy," using the recently developed Calibrate-Emulate-Sample (CES) algorithm. Cloudy models collision-coalescence and collisional breakup of cloud droplets with an adjustable number of prognostic moments and with easily modifiable assumptions for the cloud droplet mass distribution and the collision kernel. The CES algorithm uses machine learning tools to accelerate Bayesian inference by reducing the number of forward evaluations needed to O(10²). It also exhibits a smoothing effect when forward evaluations are polluted by noise. In a suite of perfect-model experiments, we show that CES enables computationally efficient Bayesian inference of parameters in Cloudy from noisy observations of moments of the droplet mass distribution. In an additional imperfect-model experiment, a collision kernel parameter is successfully learned from output generated by a Lagrangian particle-based microphysics model.
Additional Information
We thank the three anonymous reviewers for their constructive feedback and valuable comments, which significantly improved this paper. This research was supported by Eric and Wendy Schmidt (by recommendation of Schmidt Futures), by the Heising-Simons Foundation, and by the National Science Foundation (award AGS-1835860). E. de Jong was supported by a Department of Energy Computational Sciences Graduate Fellowship.Attached Files
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Additional details
- Eprint ID
- 116294
- Resolver ID
- CaltechAUTHORS:20220815-504980000
- Schmidt Futures Program
- Heising-Simons Foundation
- NSF
- AGS-1835860
- Department of Energy (DOE)
- Created
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2022-08-15Created from EPrint's datestamp field
- Updated
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2022-11-11Created from EPrint's last_modified field
- Caltech groups
- Division of Geological and Planetary Sciences