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Title: Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm
Authors: Hong, S
Yu, X 
Park, S.K
Choi, Y.-S
Myoung, B
Keywords: complexity
genetic algorithm
land surface
uncertainty analysis
water budget
Han Basin [Far East]
Issue Date: 2014
Citation: Hong, S, Yu, X, Park, S.K, Choi, Y.-S, Myoung, B (2014). Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm. Geoscientific Model Development 7 (5) : 2517-2529. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2-4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes. © 2014 Author(s).
Source Title: Geoscientific Model Development
ISSN: 1991959X
DOI: 10.5194/gmd-7-2517-2014
Rights: Attribution 4.0 International
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