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An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using "Cloudy," a New n-Moment Bulk Microphysics Scheme

Bieli, Melanie and Dunbar, Oliver R. A. and De Jong, Emily K. and Jaruga, Anna and Schneider, Tapio and Bischoff, Tobias (2022) An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using "Cloudy," a New n-Moment Bulk Microphysics Scheme. Journal of Advances in Modeling Earth Systems, 14 (8). Art. No. e2022MS002994. ISSN 1942-2466. doi:10.1029/2022ms002994.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription for the Calibrate-Emulate-Sample algorithm model code ItemDiscussion Paper
Bieli, Melanie0000-0002-2056-9486
Dunbar, Oliver R. A.0000-0001-7374-0382
De Jong, Emily K.0000-0002-5310-4554
Jaruga, Anna0000-0003-3194-6440
Schneider, Tapio0000-0001-5687-2287
Bischoff, Tobias0000-0003-3930-2762
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.
Group:Division of Geological and Planetary Sciences
Funding AgencyGrant Number
Schmidt Futures ProgramUNSPECIFIED
Heising-Simons FoundationUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Subject Keywords:cloud microphysics; Bayesian inference; model calibration; uncertainty quantification; parameter learning
Issue or Number:8
Record Number:CaltechAUTHORS:20220815-504980000
Persistent URL:
Official Citation:Bieli, M., Dunbar, O. R. A., de Jong, E. K., Jaruga, A., Schneider, T., & Bischoff, T. (2022). An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using “Cloudy,” a new n-moment bulk microphysics scheme. Journal of Advances in Modeling Earth Systems, 14, e2022MS002994.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116294
Deposited By: George Porter
Deposited On:15 Aug 2022 20:25
Last Modified:11 Nov 2022 18:33

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