John Harlim

Works (16)

Updated: July 5th, 2023 15:49

2013 journal article

Regression models with memory for the linear response of turbulent dynamical systems

Communications in Mathematical Sciences, 11(2), 481–498.

By: E. Kang *, J. Harlim n & A. Majda*

Source: NC State University Libraries
Added: August 6, 2018

2013 journal article

TEST MODELS FOR FILTERING WITH SUPERPARAMETERIZATION

MULTISCALE MODELING & SIMULATION, 11(1), 282–308.

By: J. Harlim n & A. Majda

author keywords: superparameterization; Kalman filter; no scale separation; data assimilation
TL;DR: This paper considers the Fourier domain Kalman filter for filtering regularly spaced sparse observations of the large-scale mean variables and finds high filtering and statistical prediction skill with superpara-superparameterization. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2013 journal article

The role of additive and multiplicative noise in filtering complex dynamical systems

By: G. Gottwald * & J. Harlim n

author keywords: data assimilation; covariance inflation; stochastic parametrization; additive noise; multiplicative noise; model error
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13. Climate Action (Web of Science)
Source: Web Of Science
Added: August 6, 2018

2012 journal article

Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters

JOURNAL OF COMPUTATIONAL PHYSICS, 235, 143–160.

By: K. Brown n & J. Harlim n

author keywords: Hierarchical Bayesian reduced stochastic filter; Mean Stochastic Model; Data assimilation; Filtering interpolated data
TL;DR: This paper considers a practical filtering approach for assimilating irregularly spaced, sparsely observed turbulent signals through a hierarchical Bayesian reduced stochastic filtering framework and finds that the filtered estimates with ordinary kriging are superior to those with linear interpolation when observation networks are not too sparse. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2012 book

Filtering Complex Turbulent Systems

In FILTERING COMPLEX TURBULENT SYSTEMS (pp. 1–357).

By: A. Majda* & J. Harlim n

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Source: Web Of Science
Added: August 6, 2018

2012 journal article

Filtering nonlinear spatio-temporal chaos with autoregressive linear stochastic models

PHYSICA D-NONLINEAR PHENOMENA, 241(12), 1099–1113.

By: E. Kang * & J. Harlim n

author keywords: AR(p) filter; Autoregressive models; Kalman filter; Lorenz 96 (L-96) model; Non-Markovian linear stochastic model; Ornstein-Uhlenbeck process
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Source: Web Of Science
Added: August 6, 2018

2012 journal article

Optimal filtering of complex turbulent systems with memory depth through consistency constraints

JOURNAL OF COMPUTATIONAL PHYSICS, 237, 320–343.

By: E. Bakunova n & J. Harlim n

author keywords: AR(p)-filter; Optimal multistep filter; Data assimilation; Model error
TL;DR: A linear theory for optimal filtering of complex turbulent signals with model errors through linear autoregressive models is developed and it is shown that the filtered solutions are optimal in the sense that they are comparable to the estimates obtained from the true filter with perfect model. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2012 journal article

Physics constrained nonlinear regression models for time series

NONLINEARITY, 26(1), 201–217.

By: A. Majda* & J. Harlim n

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Added: August 6, 2018

2012 journal article

Test models for filtering and prediction of moisture-coupled tropical waves

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 139(670), 119–136.

By: J. Harlim n & A. Majda*

author keywords: tropical data assimilation; reduced stochastic filters; multicloud models; Madden-Julian Oscillation
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Source: Web Of Science
Added: August 6, 2018

2011 journal article

Filtering Partially Observed Multiscale Systems with Heterogeneous Multiscale Methods-Based Reduced Climate Models

MONTHLY WEATHER REVIEW, 140(3), 860–873.

By: E. Kang  n & J. Harlim n

TL;DR: This reduced filtering strategy introduces model errors in estimating the prior forecast statistics through the (heterogeneous multiscale methods) HMM-based reduced climate model as an alternative to the standard expensive DNS-based fully resolved model. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2011 journal article

INTERPOLATING IRREGULARLY SPACED OBSERVATIONS FOR FILTERING TURBULENT COMPLEX SYSTEMS

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 33(5), 2620–2640.

By: J. Harlim n

author keywords: interpolations; irregularly spaced observation; Kalman filter; data assimilation
TL;DR: The reduced filtering strategy with piecewise linear interpolation produces more accurate filtered solutions than conventional approaches when observations are extremely irregularly spaced and very sparse. (via Semantic Scholar)
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Added: August 6, 2018

2010 journal article

MATHEMATICAL STRATEGIES FOR FILTERING TURBULENT DYNAMICAL SYSTEMS

DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS, 27(2), 441–486.

By: A. Majda, J. Harlim n & B. Gershgorin*

author keywords: stochastic parameter estimation; Kalman filter; filtering turbulent systems; data assimilation; model error
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Source: Web Of Science
Added: August 6, 2018

2010 journal article

Numerical strategies for filtering partially observed stiff stochastic differential equations

JOURNAL OF COMPUTATIONAL PHYSICS, 230(3), 744–762.

By: J. Harlim n

author keywords: Filtering multiscale systems; Data assimilation; Stiff SDE; Heterogeneous Multiscale Methods; Inverse problems
TL;DR: It will be shown that the proposed micro-filter is equivalent to solving an inverse problem for parameterizing differential equations, and that this microscopic reinitialization is an important novel feature for accurate filtered solutions, especially when the microscopic dynamics is not mixing at all. (via Semantic Scholar)
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Added: August 6, 2018

2009 journal article

Filtering Turbulent Sparsely Observed Geophysical Flows

MONTHLY WEATHER REVIEW, 138(4), 1050–1083.

By: J. Harlim n & A. Majda*

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13. Climate Action (Web of Science)
14. Life Below Water (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2009 journal article

Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

JOURNAL OF COMPUTATIONAL PHYSICS, 229(1), 32–57.

By: B. Gershgorin*, J. Harlim n & A. Majda*

author keywords: Stochastic parameter estimation; Kalman filter; Filtering turbulence; Data assimilation; Model error; Predictability
TL;DR: The newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), is extended to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. (via Semantic Scholar)
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13. Climate Action (Web of Science)
Source: Web Of Science
Added: August 6, 2018

2009 journal article

Test models for improving filtering with model errors through stochastic parameter estimation

JOURNAL OF COMPUTATIONAL PHYSICS, 229(1), 1–31.

By: B. Gershgorin*, J. Harlim n & A. Majda*

author keywords: Stochastic parameter estimation; Kalman filter; Filtering turbulence; Data assimilation; Model error
TL;DR: A suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter, which systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. (via Semantic Scholar)
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13. Climate Action (Web of Science)
Source: Web Of Science
Added: August 6, 2018

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