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Feature-Based Parameter Estimation o...

Lunderman, Spencer.

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Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation./
Author:	Lunderman, Spencer.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	87 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
Subject:	Mathematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28022212
ISBN:	9798662474116

Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation.
Lunderman, Spencer.

Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 87 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--The University of Arizona, 2020.

This item must not be sold to any third party vendors.

We introduce numerical methods for Bayesian estimation applications. The first chapter demonstrates the use of feature-based parameter estimation methods in atmospheric science. The nonlinear cloud and rain equation represents emergent behavior of stratocumulus clouds through a simplified predator-prey model with rain acting as a predator of the clouds. We use a large eddy simulation as the ``ground truth'' and extract cycles of cloud growth and decay from the simulation. Our method treats the cycles as features and subsequently performs a Bayesian inversion to estimate the model parameters. In the second chapter, we discuss the uses of global Bayesian optimization in data assimilation. Global Bayesian optimization is a derivative-free optimization technique designed for optimizing computationally expensive functions. We show how it can be coupled to an ensemble Kalman filter to estimate model parameters, model states, and simultaneously tune localization and inflation parameters. To illustrate these ideas, we present numerical experiments with the classical Lorenz models.

ISBN: 9798662474116Subjects--Topical Terms:

515831
Mathematics.
Subjects--Index Terms:

Feature-based parameter estimation methods

Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation.
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100 1 $a Lunderman, Spencer. $3 3559137
245 1 0 $a Feature-Based Parameter Estimation of the Nonlinear Cloud and Rain Equation and Global Bayesian Optimization in Data Assimilation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 87 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
500 $a Advisor: Morzfeld, Matthias.
502 $a Thesis (Ph.D.)--The University of Arizona, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a We introduce numerical methods for Bayesian estimation applications. The first chapter demonstrates the use of feature-based parameter estimation methods in atmospheric science. The nonlinear cloud and rain equation represents emergent behavior of stratocumulus clouds through a simplified predator-prey model with rain acting as a predator of the clouds. We use a large eddy simulation as the ``ground truth'' and extract cycles of cloud growth and decay from the simulation. Our method treats the cycles as features and subsequently performs a Bayesian inversion to estimate the model parameters. In the second chapter, we discuss the uses of global Bayesian optimization in data assimilation. Global Bayesian optimization is a derivative-free optimization technique designed for optimizing computationally expensive functions. We show how it can be coupled to an ensemble Kalman filter to estimate model parameters, model states, and simultaneously tune localization and inflation parameters. To illustrate these ideas, we present numerical experiments with the classical Lorenz models.
590 $a School code: 0009.
650 4 $a Mathematics. $3 515831
650 4 $a Applied mathematics. $3 2122814
650 4 $a Atmospheric sciences. $3 3168354
653 $a Feature-based parameter estimation methods
653 $a Nonlinear cloud
653 $a Rain equation
690 $a 0405
690 $a 0364
690 $a 0725
710 2 $a The University of Arizona. $b Mathematics. $3 1032088
773 0 $t Dissertations Abstracts International $g 82-02B.
790 $a 0009
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28022212