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

Journal Article · Sun Feb 01 00:00:00 EST 2009 · Journal of Applied Meteorology and Climatology

DOI:https://doi.org/10.1175/2008jamc1967.1· OSTI ID:1564669

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

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

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1564669
• Journal Information: Journal of Applied Meteorology and Climatology, Journal Name: Journal of Applied Meteorology and Climatology Journal Issue: 2 Vol. 48; ISSN 1558-8424
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Algorithms
Mesoscale models
Optimization
Parameterization