The Application of an Evolutionary Algorithm to the Optimization of a Mesoscale Meteorological Model

O’Steen, Lance; Werth, David

doi:10.1175/2008jamc1967.1

Title: The Application of an Evolutionary Algorithm to the Optimization of a Mesoscale Meteorological Model

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Abstract

It is demonstrated that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is highlighted using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root-mean-square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). It is found that the optimization can be done with relatively modest computer resources; therefore, operational implementation is possible. The overall number of simulations needed to obtain a specific reduction of the cost function is found to depend strongly on the procedure used to perturb the "child" parameters relative to their "parents" within the evolutionary algorithm. Furthermore, the choice of meteorological variables that are included in the rms error and their relative weighting are also found to be important factors in the optimization.

Authors:

O’Steen, Lance ^[1]; Werth, David ^[1]

Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

Publication Date:: Sun Feb 01 00:00:00 EST 2009

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1564669

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Applied Meteorology and Climatology

Additional Journal Information:: Journal Volume: 48; Journal Issue: 2; Journal ID: ISSN 1558-8424

Publisher:: American Meteorological Society

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES; Mesoscale models; Algorithms; Optimization; Parameterization

Citation Formats


                    O’Steen, Lance, and Werth, David. The Application of an Evolutionary Algorithm to the Optimization of a Mesoscale Meteorological Model.  United States: N. p., 2009. 
Web.  doi:10.1175/2008jamc1967.1.

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                    O’Steen, Lance, & Werth, David. The Application of an Evolutionary Algorithm to the Optimization of a Mesoscale Meteorological Model.  United States.  https://doi.org/10.1175/2008jamc1967.1

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                    O’Steen, Lance, and Werth, David. Sun .  
"The Application of an Evolutionary Algorithm to the Optimization of a Mesoscale Meteorological Model".  United States.  https://doi.org/10.1175/2008jamc1967.1.  https://www.osti.gov/servlets/purl/1564669.

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@article{osti_1564669,

  title        = {The Application of an Evolutionary Algorithm to the Optimization of a Mesoscale Meteorological Model},

  author       = {O’Steen, Lance and Werth, David},

  abstractNote = {It is demonstrated that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is highlighted using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root-mean-square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). It is found that the optimization can be done with relatively modest computer resources; therefore, operational implementation is possible. The overall number of simulations needed to obtain a specific reduction of the cost function is found to depend strongly on the procedure used to perturb the "child" parameters relative to their "parents" within the evolutionary algorithm. Furthermore, the choice of meteorological variables that are included in the rms error and their relative weighting are also found to be important factors in the optimization.},

  doi          = {10.1175/2008jamc1967.1},

  journal      = {Journal of Applied Meteorology and Climatology},

  number       = 2,

  volume       = 48,

  place        = {United States},

  year         = {Sun Feb 01 00:00:00 EST 2009},

  month        = {Sun Feb 01 00:00:00 EST 2009}

}

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