Preprints
https://doi.org/10.5194/egusphere-2023-2278
https://doi.org/10.5194/egusphere-2023-2278
23 Jan 2024
 | 23 Jan 2024

Technical Note: General Formulation For the Distribution Problem: Prognostic Assumed PDF Approach Based on The Maximum–Entropy Principle and The Liouville Equation

Jun-Ichi Yano, Vince Larson, and Vaughan T. J. Phillips

Abstract. A general formulation for the distribution problem is presented, which is applicable to frequency distributions of subgrid-scale variables, hydrometeor size distributions, as well as to probability distributions characterizing data uncertainties. The general formulation is presented based upon two well-known basic principles: the maximum-entropy principle and Liouville equation. The maximum-entropy principle defines the most likely general distribution form, if necessary constraints are specified. This paper proposes to specify these constraints as the output variables to be used in a host model. Once a general distribution form is defined, the problem of temporal evolution of the distribution reduces to that of predicting a small number of parameters characterizing it. This paper derives prognostic equations for these parameters from the Liouville equation. The developed formulation, which is applicable to a wide range of atmospheric modelling problems, is specifically applied to condensation growth of cloud droplets as a demonstration.

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Journal article(s) based on this preprint

27 Aug 2025
Technical note: General formulation for the distribution problem – prognostic assumed probability density function (PDF) approach based on the maximum-entropy principle and the Liouville equation
Jun-Ichi Yano, Vincent E. Larson, and Vaughan T. J. Phillips
Atmos. Chem. Phys., 25, 9357–9386, https://doi.org/10.5194/acp-25-9357-2025,https://doi.org/10.5194/acp-25-9357-2025, 2025
Short summary
Jun-Ichi Yano, Vince Larson, and Vaughan T. J. Phillips

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Jun-Ichi Yano on behalf of the Authors (04 Jun 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (17 Jul 2024) by Franziska Glassmeier
RR by Anonymous Referee #2 (18 Jul 2024)
RR by Anonymous Referee #1 (14 Nov 2024)
ED: Reconsider after major revisions (25 Nov 2024) by Franziska Glassmeier
AR by Jun-Ichi Yano on behalf of the Authors (19 Dec 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (23 Jan 2025) by Franziska Glassmeier
RR by Anonymous Referee #1 (05 Apr 2025)
ED: Publish subject to minor revisions (review by editor) (07 May 2025) by Franziska Glassmeier
AR by Jun-Ichi Yano on behalf of the Authors (15 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (06 Jun 2025) by Franziska Glassmeier
AR by Jun-Ichi Yano on behalf of the Authors (12 Jun 2025)

Journal article(s) based on this preprint

27 Aug 2025
Technical note: General formulation for the distribution problem – prognostic assumed probability density function (PDF) approach based on the maximum-entropy principle and the Liouville equation
Jun-Ichi Yano, Vincent E. Larson, and Vaughan T. J. Phillips
Atmos. Chem. Phys., 25, 9357–9386, https://doi.org/10.5194/acp-25-9357-2025,https://doi.org/10.5194/acp-25-9357-2025, 2025
Short summary
Jun-Ichi Yano, Vince Larson, and Vaughan T. J. Phillips
Jun-Ichi Yano, Vince Larson, and Vaughan T. J. Phillips

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Latest update: 27 Aug 2025
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
The distribution problems appear in atmospheric sciences at almost every corner for describing diverse processes. This manuscript presents a general formulation for addressing all these problem.
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