대기 (Atmosphere)

  • 제33권4호
  • /
  • Pages.331-341
  • /
  • 2023
  • /
  • 1598-3560(pISSN)
  • /
  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
권호 표지
DOI QR코드

DOI QR Code

적응시간 간격 알고리즘을 이용한 KIM의 계산 효율성 개선

The Improvement of Computational Efficiency in KIM by an Adaptive Time-step Algorithm

  • 남현 ((재)차세대수치예보모델개발사업단) ;
  • 최석진 (강릉원주대학교 대기환경과학과)
  • Hyun Nam (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Suk-Jin Choi (Department of Atmospheric Environmental Sciences, Gangneung-Wonju National University)
  • 투고 : 2023.04.04
  • 심사 : 2023.06.13
  • 발행 : 2023.08.31
⟨ 이전 논문 다음 논문 ⟩

초록

A numerical forecasting models usually predict future states by performing time integration considering fixed static time-steps. A time-step that is too long can cause model instability and failure of forecast simulation, and a time-step that is too short can cause unnecessary time integration calculations. Thus, in numerical models, the time-step size can be determined by the CFL (Courant-Friedrichs-Lewy)-condition, and this condition acts as a necessary condition for finding a numerical solution. A static time-step is defined as using the same fixed time-step for time integration. On the other hand, applying a different time-step for each integration while guaranteeing the stability of the solution in time advancement is called an adaptive time-step. The adaptive time-step algorithm is a method of presenting the maximum usable time-step suitable for each integration based on the CFL-condition for the adaptive time-step. In this paper, the adaptive time-step algorithm is applied for the Korean Integrated Model (KIM) to determine suitable parameters used for the adaptive time-step algorithm through the monthly verifications of 10-day simulations (during January and July 2017) at about 12 km resolution. By comparing the numerical results obtained by applying the 25 second static time-step to KIM in Supercomputer 5 (Nurion), it shows similar results in terms of forecast quality, presents the maximum available time-step for each integration, and improves the calculation efficiency by reducing the number of total time integrations by 19%.

키워드

과제정보

이 논문은 기상청 출연사업인 (재)차세대수치예보모델개발사업단의 가변격자체계 기반 통합형수치예보모델 개발(KMA2020-02212) 및 2023년도 강릉원주대학교 학술연구조성비 지원에 의하여 수행되었음을 밝힙니다.

참고문헌

  1. Andraju, P., A. L. Kanth, K. V. Kumari, and S. V. B. Rao, 2019: Performance optimization of operational WRF model configured for Indian Monsoon region. Earth Sys. Environ., 3, 231-239, doi:10.1007/s41748-019-00092-2.
  2. Choi, S.-J., 2018: Structure of eigenvalues in advection-diffusion equation by the spectral element method on cubed-sphere grid. Asia-Pac. J. Atmos. Sci., 54, 293-301, doi:10.1007/s13143-018-0020-4.
  3. Choi, S.-J., F. X. Giraldo, J. Kim, and S. Shin, 2014: Verification of a non-hydrostatic dynamical core using horizontal spectral element method and vertical finite difference method: 2D Aspects. Geosci. Model Dev., 7, 2717-2731, doi:10.5194/gmd-7-2717-2014.
  4. Choi, S.-J., and S.-Y. Hong, 2016: A global non-hydrostatic dynamical core using the spectral element method on a cubed-sphere grid. Asia-Pac. J. Atmos. Sci., 52, 291-307, doi:10.1007/s13143-016-0005-0.
  5. Collier, E., and W. W. Immerzeel, 2015: High-resolution modeling of atmospheric dynamics in the Nepalese Himalaya. J. Geophys. Res. Atmospheres, 120, 9882-9896, doi: 10.1002/2015JD023266.
  6. Gamage, T. D., U. Sonnadara, S. Jayasinghe, and S. Basnayake, 2022: Forecasting the track and the intensity of the cyclone Burevi using WRF. J. National Science Foundation of Sri Lanka., 50, 675-684, doi:10.4038/jnsfsr.v50i3.10761.
  7. Hong, S.-Y., and Coauthors, 2018: The Korean integrated model (KIM) system for global weather forecasting. Asia-Pac. J. Atmos. Sci., 54, 267-292, doi: 10.1007/s13143-018-0028-9.
  8. Hutchinson, T. A., 2007: An adaptive time-step for increased model efficiency. In Extended Abstracts, Eighth WRF Users' Workshop, 4 pp.
  9. Jeworrek, J., G. West, and R. Stull, 2019: Evaluation of cumulus and microphysics parameterizations in WRF across the convective gray zone. Wea. Forecasting., 34, 1097-1115, doi:10.1175/WAF-D-18-0178.1.
  10. Jeworrek, J., and G. West, 2021: WRF precipitation performance and predictability for systematically varied parameterizations over complex terrain. Wea. Forecasting, 36, 893-913, doi:10.1175/WAF-D-20-0195.1.
  11. Morel, B., B. Pohl, Y. Richard , B. Bois, and M. Bessafi, 2014: Regionalizing rainfall at very high resolution over La Reunion Island using a regional climate model. Mon. Wea. Rev., 142, 2665-2686, doi:10.1175/MWR-D-14-00009.1.
  12. Siuta, D., G. West, H. Modzelewski, R. Schigas, and R. Stull, 2016: Viability of cloud computing for real-time numerical weather prediction. Wea. Forecasting., 31, 1985-1996, doi:10.1175/WAF-D-16-0075.1.