Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

S. Hong, X. Yu, S. K. Park, Y. S. Choi, B. Myoung

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish
Pages (from-to)2517-2529
Number of pages13
JournalGeoscientific Model Development
Volume7
Issue number5
DOIs
StatePublished - 29 Oct 2014

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