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Parameter Estimation Using the Genetic Algorithm and Its Impact on Quantitative Precipitation Forecast : Volume 24, Issue 12 (21/12/2006)

By Lee, Y. H.

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Book Id: WPLBN0004001394
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Parameter Estimation Using the Genetic Algorithm and Its Impact on Quantitative Precipitation Forecast : Volume 24, Issue 12 (21/12/2006)  
Author: Lee, Y. H.
Volume: Vol. 24, Issue 12
Language: English
Subject: Science, Annales, Geophysicae
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2006
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Chang, D., Park, S. K., & Lee, Y. H. (2006). Parameter Estimation Using the Genetic Algorithm and Its Impact on Quantitative Precipitation Forecast : Volume 24, Issue 12 (21/12/2006). Retrieved from http://www.ebooklibrary.org/


Description
Description: Meteorological Research Institute, Korea Meteorological Administration, Seoul, Republic of Korea. In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF) are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA) for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF) scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

Summary
Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

Excerpt
Aksoy, A., Zhang, F., and Nielsen-Gammon, J W.: Ensemble-based simulations state and parameter estimation with MM5, Geophys. Res. Lett., 33, L12801, doi:10.1029/2006GL026186, 2006.; Asselin, R.: Frequency filter for time integrations, Mon. Weather. Rev., 100, 487–490, 1972.; Barth, N. H.: Oceanographic experiment design~II: Genetic algorithms, J. Atmos. Oceanic. Technol., 9, 434–443, 1992.; Bryan, G. H. and Fritsch, J. M.: Unphysical thermodynamic structures in explicitly simulated thunderstorms, presented at: 10th PSU/NCAR Mesoscale Model User's Workshop, NCAR, Boulder, CO, available from http://www.mmm.ucar.edu/mm5, 2000.; Charbonneau, P.: An introduction to genetic algorithms for numerical optimization, NCAR Tech. Note TN-450+IA, 2002.; Deb, K.: An efficient constraint handling method for genetic algorithms, Comput. Methods Appl. Mech. Eng., 186, 311–338, 2000.; Goldberg, D E.: Genetic algorithms in search, optimization and machine learning, Addison-Wesley, MA, 1989.; Grell, G A., Dudhia, J., and Stauffer, D R.: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5), NCAR Tech. Note TN-398+STR, 1994.; Jackson, C., Sen, M. K., and Stoffa, P. L.: An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model predictions, J. Climate, 17, 2828–2841, 2004.; Holland, J.: Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, 1975.; Hong, S.-Y.: Comparison of heavy rainfall mechanisms in Korea and the Central US, J. Meteor. Soc. Japan, 5, 1469–1479, 2004.; Kain, J.: The Kain-Fritsch convective parameterization: An update, J. Appl. Meteoro., 43, 170–181, 2003.; Lee, D.-K., Kim, H.-R., and Hong, S.-Y.: Heavy rainfall over Korea during 1980–1990, Korean J. Atmos. Sci., 1, 32–50, 1998.; Navon, I M.: Practical and theoretical aspects of adjoint parameter estimation and indetifiability in meteorology and oceanography, Dyn. Atmos. Ocean., 27, 55–79, 1997.; Park, S K. and Droegemeier, K K.: Sensitivity analysis of a moist 1D Eulerian cloud model using automatic differentiation, Mon. Weather Rev., 127, 2180–2196, 1999.; Saito, K., Fujita, T., Yamada, U., Ishida, J.-I., Kumagai, Y., Aranami, K., Ohmori, S., Nagasawa, R., and Kumaga, S.: The operational JMA nonhydrostatic mesoscale model, Mon. Weather Rev., 134, 1266–1298, 2006.; Schaefer, J. T.: The critical success index as indicator of warning skill, Weather Forecasting, 5, 570–575, 1990.; Severijns, C. A. and Hazeleger, W.: Optimizing parameters in an atmospheric general circulation model, J. Climate, 18, 3527–3535, 2005.; Zhu, Y. and Navon, I M.: Impact of parameter estimation on the performance of the FSU global spectral model using its full-physics adjoint, Mon. Weather Rev., 127, 1497–1517, 1999.

 

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