Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

An intercomparison study between optimization algorithms for parameter estimation of microphysics in Unified model : Micro-genetic algorithm and Harmony search algorithm

통합모델의 강수물리과정 모수 최적화를 위한 알고리즘 비교 연구 : 마이크로 유전알고리즘과 하모니 탐색 알고리즘

  • Jang, Jiyeon (Numerical Data Application Division, Numerical Modeling Center, KMA) ;
  • Lee, Yong Hee (Numerical Data Application Division, Numerical Modeling Center, KMA) ;
  • Joo, Sangwon (Numerical Data Application Division, Numerical Modeling Center, KMA)
  • 장지연 (기상청 수치모델링센터 수치자료응용과) ;
  • 이용희 (기상청 수치모델링센터 수치자료응용과) ;
  • 주상원 (기상청 수치모델링센터 수치자료응용과)
  • Received : 2016.11.09
  • Accepted : 2017.02.14
  • Published : 2017.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

기상수치예보모델의 강수물리과정은 강수 발생과 연관된 입자의 낙하속도, 부착 및 자동전환, 입자크기분포 등의 과정을 다룬다. 하지만 수치예보모델의 미세물리과정과 모수에는 상당한 불확실성이 내포되어 있다. 수치예보모델의 불확실성을 줄이기 위하여 일반적으로 모수 추정을 사용한다. 이 연구에서는 모수 추정을 위한 최적화 알고리즘으로 마이크로 유전알고리즘과 하모니탐색 알고리즘을 사용하고 우리나라에서 발생한 강수사례에 대해 통합모델의 강수물리과정에서 사용하는 모수를 최적화하였다. 두 알고리즘의 서로 다른 특성으로 인해 최적화 과정 중의 차이가 보였다. 마이크로 유전알고리즘은 440회 수행 후 약 1.033의 적합도로 수렴하였고 하모니탐색 알고리즘은 60번 수행 후 약 1.031의 적합도로 수렴하였다. 이를 통해 하모니탐색 알고리즘이 마이크로 유전알고리즘보다 더 빨리 최적의 모수를 탐색하는 것을 알 수 있었다. 따라서 계산비용이 방대한 기상수치예보모델의 최적화 문제에서 빠른 시간 내에 최적의 모수를 탐색해야 한다면 하모니 탐색 알고리즘이 더 적합하다는 것을 확인하였다.

Keywords

References

  1. J. Wilkinson, R. Forbes, D. Wilson, The large-scale precipitation parameterization scheme, Unified model documentation paper 026, 2015.
  2. M. Tong, and M. Xye, "Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root kalman filter. Part I: Sensitivity analysis and parameter identifiability", Monthly weather review, vol. 136, pp. 1630-1648, 2008. https://doi.org/10.1175/2007MWR2070.1
  3. H. Moradkhani, S. Sorooshian, H. V. Gupta, P. R. Houser, "Dual state-parameter estimation of hydrological models using ensemble kalman filter", Advances in water resources, vol. 28, pp. 135-147, 2005. https://doi.org/10.1016/j.advwatres.2004.09.002
  4. Y. H. Lee, S. K. Park, and D. E. Chang, "Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast", Annales Geophysicae, vol. 24, pp. 3185-3189, 2006. https://doi.org/10.5194/angeo-24-3185-2006
  5. X. Yu, S. K. Park, Y. H. Lee, and Y. S. Choi, "Quantitative precipitation forecast of a tropical cyclone through optimal parameter estimation in a convective parameterization", SOLA, vol. 9, pp. 36-39, 2013. https://doi.org/10.2151/sola.2013-009
  6. S. Hong, X. Yu, S. K. Park, Y. S. Choi, and B. Myoung, "Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm", Geoscientific Model Development, vol. 7, pp. 2517-2529, 2014. https://doi.org/10.5194/gmd-7-2517-2014
  7. S. Hong, S. K. Park, and X. Yu, "Scheme-Based Optimization of land surface model using a micro-genetic algorithm: Assessment of its performance and usability for regional applications", SOLA, vol. 11, pp. 129-133, 2015. https://doi.org/10.2151/sola.2015-030
  8. X. S. Yang, "Harmony Search as Metaheuristic Algorithm", Studies in Computational Intelligence, Springer Berlinm, vol. 191, pp. 1-14, 2009.
  9. C. Peraza, F. Valdez, and O. castillo, "A Harmony search algorithm comparison with genetic algorithms", Fuzzy logic augmentation of nature-inspired optimizatio nmetaheuristics, vol. 574, pp. 105-123. 2015.
  10. S. J. Abel, and I. A. Boutle, "An improved representation of the raindrop size distribution for single-moment microphysics schemes", Quarterly Journal of the Royal Meteorological Society, vol. 138, pp. 2151-2162, 2012. https://doi.org/10.1002/qj.1949
  11. I. A. Boutle, S. J. Abel, P. G. Hill and C. J. Morcrette, "Spatial variability of liquid cloud and rain : observations and microphysical effects", Quarterly Journal of the Royal Meteorological Society, vol. 140, pp. 583-594, 2014. https://doi.org/10.1002/qj.2140
  12. J. M. Wilkinson, A. N. F. Porson, F. J. Bornemann, M. Weeks, P. R. Field and A. P. Lock, "Improved microphysical parameterization of drizzle and fog for operational forecasting using the Met Office Unified Model", Quarterly Journal of the Royal Meteorological Society, vol. 139, pp. 488-500, 2013. https://doi.org/10.1002/qj.1975
  13. J. Wilkinson, R. Forbes, D. Wilson, The large-scale precipitation parameterization scheme, Unified model documentation paper 026, 2011.
  14. S. Beare, W. Tennant, and C. Sanchez, Stochastic Physics Code in the UM, Unified model documentation paper 081, 2015.
  15. J. Holland, Adaptation in natural and artificial systems, University of Michigan Press, 1975.
  16. D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison-Wesley, 1989.
  17. K. Krishnakumar, "Micro-genetic Algorithm for Stationary and Non-stationary Function Optimization", Intelligent Control and Adaptive Systems, Vol. 1196, pp. 282-296. 1989.
  18. Y. H. Lee, J. E. Nam, S. W. Joo, "Optimization of Z-R relationship in the summer of 2014 using a micro genetic algorithm", JKIIS, vol. 26, no. 1, pp. 1-8, 2016. https://doi.org/10.5391/JKIIS.2016.26.1.001
  19. Z. W. Geem, "Optimal cost design of water distribution networks using harmony search", Dissertation, Korea University, 2000.
  20. X. Wang, X. Z. Gao, K. Zenger An introduction to harmony search optimization method, Springer Briefs in Computation Intelligence, 2015.