최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기대기 = Atmosphere, v.30 no.4, 2020년, pp.335 - 346
장지연 (기상청 수치모델링센터 수치자료응용과) , 이용희 (기상청 수치모델링센터 수치자료응용과) , 최현주 (기상청 수치모델링센터 수치자료응용과)
Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thu...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674-701.
Bechtold, P., N. Semane, P. Lepez, J.-P. Chaboureau, A. Beljaars, and N. Bormann, 2014: Representing equilibrium and nonequilibrium convection in large-scale models. J. Atmos. Sci., 71, 734-753, doi:10.1175/JASD-13-0163.1.
Bae, S. Y., S.-Y. Hong, and W.-K. Tao, 2019: Development of a single-moment cloud microphysics scheme with prognostic hail for the weather research and forecasting (WRF) model. Asia-Pac. J. Atmos. Sci., 55, 233-245, doi:10.1007/s13143-018-0066-3.
Beak, S., 2017: A revised radiation package of G-packed McICA and two-stream approximation: Performance evaluation in a global weather forecasting model. J. Adv. Model. Earth Syst., 9, 1628-1640, doi:10.1002/2017MS000994.
Emanuel, K. A., 1991: A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 2313-2329.
Emanuel, K. A., and M. Zivkovic-Rothman, 1999: Development and evaluation of a convection scheme for use in climate models. J. Atmos. Sci., 56, 1766-1782.
Goldberg, D. E., 1989: Genetic algorithms in search, optimization and machine learning. Addison-Wesley Professional, 432 pp.
Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764-787.
Groenemeijer, P., and G. C. Craig, 2012: Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics. Atmos. Chem. Phys., 12, 4555-4565, doi:10.5194/acp-12-4555-2012.
Han, J.-Y., and S. Y. Hong, 2018: Precipitation forecast experiments using the weather research and forecasting (WRF) model at gray-zone resolutions. Wea. Forecasting, 33, 1605-1616, doi:10.1175/WAF-D-18-0026.1.
Han, J.-Y., S. Y. Hong,, K.-S. Sunny Lim, and J. Han, 2016: Sensitivity of a cumulus parameterization scheme to precipitation production representation and its impact on a heavy rain event over Korea. Mon. Wea. Rev., 144, 2125-2135, doi:10.1175/MWR-D-15-0255.1.
Han, J.-Y., S. Y. Hong,, and Y. C. Kwon, 2020: The performance of a revised simplified Arakawa-Schubert (SAS) convection scheme in the medium-range forecasts of the Korean Integrated Model (KIM). Wea. Forecasting, 35, 1113-1128, doi:10.1175/WAF-D-19-0219.1.
Holland, J. H., 1975: Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, 183 pp.
Hong, S., X. Yu, S. K. Park, Y.-S. Choi, and B. Myong, 2014: Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm. Geosci. Model Dev., 7, 2517-2529, doi:10.5194/gmd-7-2517-2014.
Hong, S. Y., and J. Dudhia, 2012: Next-generation numerical weather prediction: Bridging parameterization, explicit clouds, and large eddies. Bull. Amer. Meteor. Soc., 93, ES6-ES9, doi:10.1175/2011BAMS3224.1.
Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: the Kain-Fritsch scheme. In K. A. Emanuel et al. Eds., The Representation of Cumulus Convection in Numerical Models. Amer. Meteor. Soc., 165-170.
Krishnakumar, K., 1989: Micro-genetic Algorithms for Stationary and Non-stationary Function Optimization. Proc. SPIE Intelligent Control and Adaptive System, 1196, Philadelphia, PA, United States, SPIE, 289-296.
Kwon, Y. C., and S.-Y. Hong, 2017: A mass-flux cumulus parameterization scheme across gray-zone resolutions. Mon. Wea. Rev., 145, 583-598, doi:10.1175/MWR-D-16-0034.1.
Lee, Y. H., S. K. Park, and D.-E. Chang, 2006: Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast. Ann. Geophys., 24, 3185-3189.
Li, S., and Coauthors, 2018: Estimating convection parameters in the GFDL CM2.1 Model using ensemble data assimilation. J. Adv. Model. Earth Syst., 10, 989-1010, doi:10.1002/2017MS001222.
Ma, L.-M., and Z.-M. Tan, 2009: Improving the behavior of the cumulus parameterization for tropical cyclone prediction: Convection trigger. Atmos. Res., 92, 190-211.
Moorthi, S. A., and M. J. Suarez, 1992: Relaxed ArakawaSchubert. A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 978-1002.
Nam, J. E., Y. H. Lee, J. C. Ha, and K. Y. Jung, 2012: Production of rainfall reanalysis data through development of synthetic precipitation production technique on the Korea peninsula. Proc. The Spring Meeting of KMS, Korean Meteorological Society, 136-137.
Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601-608.
Shin, H. H., and S.-Y. Hong, 2013: Analysis of resolved and parameterized vertical transports in convective boundary layers at gray-zone resolutions. J. Atmos., Sci., 70, 3248-3261, doi:10.1175/JAS-D-12-0290.1.
Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgridscale turbulent transport in convective boundary layers at gray-zone resolutions. Mon. Wea. Rev., 143, 250-271, doi:10.1175/MWR-D-14-00116.1.
Shutts, G., and A. C. Pallares, 2014: Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction model. Phil. Trans. R. Soc. A., 372, 20130284, doi:10.1098/rsta.2013.0284.
van Lier-Walqui, M., T. Vukicevic, and D. J. Posselt, 2012: Quantification of cloud microphysical parameterization uncertainty using radar reflectivity. Mon. Wea. Rev., 140, 3442-3466, doi:10.1175/MWR-D-11-00216.1.
Wang, Y., G. J. Zhang, and G. C. Craig, 2016: Stochastic convective parameterization improving the simulation of tropical precipitation variability in the NCAR CAM5. Geophys. Res. Lett., 43, 6612-6619, doi:10.1002/2016GL069818.
Yu, X., S. K. Park, Y. H. Lee, and Y. S. Choi, 2013: Quantitative precipitation forecast of a tropical cyclone through optimal parameter estimation in a convective parameterization. SOLA, 9, 36-39, doi:10.2151/sola.2013-009.
Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate centre general circulation model. Atmos. Ocean, 33, 407-446.
Zou, L., Y. Qian, T. Zhou, and B. Yang, 2014: Parameter tuning and calibration of RegCM3 with MIT-Emanuel cumulus parameterization scheme over CORDEX East Asia domain. J. Climate, 27, 7687-7701, doi:10.1175/JCLI-D-14-00229.1.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.