An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using "Cloudy", a new n-moment bulk microphysics scheme

Bieli, Melanie; Dunbar, Oliver R. A.; Jong, Emily de; Jaruga, Anna; Schneider, Tapio; Bischoff, Tobias

doi:10.1002/essoar.10510248.1

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Some research projects that use this codebase, or modifications of it, are • (Dunbar et al, 2021) • (Bieli et al, 2022) • (Hillier, 2022) • (Howland et al, 2022) • (Dunbar, Howland, et al, 2022) • (Mansfield & Sheshadri, 2022) • (King et al, 2023)…”

Section: Research Projects Using the Packagementioning

confidence: 99%

CalibrateEmulateSample.jl: Accelerated Parametric Uncertainty Quantification

Dunbar,

Bieli,

Garbuno-Iñigo

et al. 2024

JOSS

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A Julia language (Bezanson et al., 2017) package providing practical and modular implementation of "Calibrate, Emulate, Sample" (Cleary et al., 2021), hereafter CES, an accelerated workflow for obtaining model parametric uncertainty is presented. This is also known as Bayesian inversion or uncertainty quantification. To apply CES one requires a computer model (written in any programming language) dependent on free parameters, a prior distribution encoding some prior knowledge about the distribution over the free parameters, and some data with which to constrain this prior distribution. The pipeline has three stages, most easily explained in reverse:1. The goal of the workflow is to draw samples (Sample) from the Bayesian posterior distribution, that is, the prior distribution conditioned on the observed data, 2. To accelerate and regularize sampling we train statistical emulators to represent the user-provided parameter-to-data map (Emulate), 3. The training points for these emulators are generated by the computer model, and selected adaptively around regions of high posterior mass (Calibrate).We describe CES as an accelerated workflow, as it is often able to use dramatically fewer evaluations of the computer model when compared with applying sampling algorithms, such as Markov chain Monte Carlo (MCMC), directly.

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Section: Research Projects Using the Packagementioning

confidence: 99%

CalibrateEmulateSample.jl: Accelerated Parametric Uncertainty Quantification

Dunbar,

Bieli,

Garbuno-Iñigo

et al. 2024

JOSS

View full text Add to dashboard Cite

show abstract

“…For comparison with the BF method, we solve each test case numerically using the flux method for spectral bin microphysics with 32 single‐moment bins (Bott, 1998), a two‐ or three‐moment closure method of moments (Bieli et al., 2022), and a Lagrangian particle‐based code called PySDM (v2.5) (Bartman et al., 2022). The bin method used follows the original setup from Bott (1998), spanning a range of 0.633–817 μm radius with mass doubling between bins, and a time step selected to be sufficiently small as to prevent numerical instability (1–100 s depending on the dynamics).…”

Section: Test Casesmentioning

confidence: 99%

“…Bieli et al. (2022) present a more efficient way to learn these parameters within a similar bulk microphysics framework that still relies on closures. More complex yet, Rodríguez Genó and Alfonso (2022) tackle the challenge of inverting multimodal distribution closures using a machine‐learning based method, which could avoid the necessity for cloud‐rain conversion rate parameterizations.…”

Section: Introductionmentioning

confidence: 99%