Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Aviation Convective Index for Deep Convective Area using the Global Unified Model of the Korean Meteorological Administration, Korea: Part 2. Seasonal Optimization and Case Studies

안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 2. 계절별 최적화 및 사례 분석

  • Yi-June Park (School of Earth and Environmental Sciences, Seoul National University) ;
  • Jung-Hoon Kim (School of Earth and Environmental Sciences, Seoul National University)
  • 박이준 (서울대학교 지구환경과학부) ;
  • 김정훈 (서울대학교 지구환경과학부)
  • Received : 2023.09.12
  • Accepted : 2023.11.03
  • Published : 2023.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACISnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACIYrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACISnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACIYrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACISnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACIYrOpt to ACISnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

Keywords

Acknowledgement

본 논문의 질적 향상을 위해 좋은 의견들을 제시해 주신 두 심사위원 분들께 감사의 말씀을 전합니다. 이 연구는 기상·지진 See-At 기술개발연구사업(KMI2020-01910)의 지원과 기상청 「차세대 항공교통 지원 항공기상 기술개발(NARAE-Weather)」(KMI2022-00310과 KMI2022-00410)의 지원으로 수행되었습니다.

References

  1. Charba, J. P., 1977: Operational system for predicting thunderstorms two to six hours in advance. NOAA [Available online at https://repository.library.noaa.gov/view/noaa/13628/noaa_13628_DS1.pdf].
  2. Chuda, T., and H. Niino, 2005: Climatology of environmental parameters for mesoscale convections in Japan. J. Meteor. Soc. Japan. Ser. II, 83, 391-408, doi:10.2151/jmsj.83.391.
  3. Doswell III, C. A., 1987: The distinction between largescale and mesoscale contribution to severe convection: A case study example. Wea. Forecasting, 2, 3-16, doi:10.1175/1520-0434(1987)002<0003:TDBLSA>2.0.CO;2.
  4. Galway, J. G., 1956: The lifted index as a predictor of latent instability. Bull. Amer. Meteor. Soc., 37, 528-529, doi:10.1175/1520-0477-37.10.528.
  5. George, J. J., 1960: Weather forecasting for aeronautics. Academic press, 684 pp.
  6. Haklander, A. J., and A. V. Delden, 2003: Thunderstorm predictors and their forecast skill for the Netherlands. Atmos. Res., 67, 273-299, doi:10.1016/S0169-8095(03)00056-5.
  7. Hu, S., S.-P. Xie, R. Seager, and M. A. Cane, 2023: Spatial and seasonal variations of sea surface temperature threshold for tropical convection. J. Climate, 36, 4899-4912, doi:10.1175/JCLI-D-22-0545.1.
  8. ICAO, 2012: 'Seventh Meeting of the World Area Forecast System Operations Group (WAFSOPSG)' Lima, Peru, 59 pp [Available online at https://www.icao.int/safety/meteorology/WAFSOPSG/WAFSOPSG%20Meetings%20Metadata/WAFSOPSG.7.Final.Report.pdf].
  9. Melling, L. D., A. G. Laing, M. S. Wandishin, J. E. Hart, and M. A. Petty, 2019: Ensemble Prediction of Oceanic Convective Hazards (EPOCH) Assessment: Part II. NOAA [Available online at https://repository.library.noaa.gov/view/noaa/22921/noaa_22921_DS1.pdf].
  10. Miller, R. C., 1967: Notes on analysis and severe-storm forecasting procedures of the Military Weather Warning Center. Air Weather Service (MAC), United States Air Force, 2 pp.
  11. Miller, R. C., 1975: Notes on analysis and severe-storm forecasting procedures of the Air Force Global Weather Central. AWS, 200 pp.
  12. Poreba, S., M. Taszarek, and Z. Ustrnul, 2022: Diurnal and seasonal variability of ERA5 convective parameters in relation to lightning flash rates in Poland. Wea. Forecasting, 37, 1447-1470, doi:10.1175/WAF-D-21-0099.1.
  13. Reap, R. M., and D. S. Foster, 1979: Automated 12-36 hour probability forecasts of thunderstorms and severe local storms. J. Appl. Meteor. Climatol., 18, 1304-1315, doi:10.1175/1520-0450(1979)018<1304:AHPFOT>2.0.CO;2.
  14. Riemann-Campe, K., K. Fraedrich, and F. Lunkeit, 2009: Global climatology of convective available potential energy (CAPE) and convective inhibition (CIN) in ERA-40 reanalysis. Atmos. Res., 93, 534-545, doi:10.1016/j.atmosres.2008.09.037.
  15. Schmeits, M. J., K. J. Kok, D. H. P. Vogelezang, and R. M. van Westrhenen, 2008: Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm in the Netherlands. Wea. Forecasting, 23, 1253-1267, doi:10.1175/2008WAF2007102.1.
  16. Schultz, D. M., and P. N. Schumacher, 1999: The use and misuse of conditional symmetric instability. Mon. Wea. Rev., 127, 2709-2732, doi:10.1175/1520-0493(1999)127<2709:TUAMOC>2.0.CO;2.
  17. Showalter, A. K., 1953: A stability index for thunderstorm forecasting. Bull. Amer. Meteor. Soc., 34, 250-252, doi:10.1175/1520-0477-34.6.250.
  18. Simon, T., P. Fabsic, G. J. Mayr, N. Umlauf, and A. Zeileis, 2018: Probabilistic forecasting of thunderstorms in the Eastern Alps. Mon. Wea. Rev., 146, 2999-3009, doi:10.1175/MWR-D-17-0366.1.
  19. Slangen, A. B. A., and M. J. Schmeits, 2009: Probabilistic forecasts of winter thunderstorms around Amsterdam Airport Schiphol. Adv. Sci. Res., 3, 39-43, doi:10.5194/asr-3-39-2009.
  20. Sobash, R. A., J. S. Kain, D. R. Bright, A. R. Dean, M. C. Coniglio, and S. J. Weiss, 2011: Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convectionallowing model forecasts. Wea. Forecasting, 26, 714-728, doi:10.1175/WAF-D-10-05046.1.
  21. Sobash, R. A., G. S. Romine, C. S. Schwartz, D. J. Gagne, and M. L. Weisman, 2016: Explicit forecasts of low-level rotation from convection-allowing models for next-day tornado prediction. Wea. Forecasting, 31, 1591-1614, doi:10.1175/WAF-D-16-0073.1.
  22. Wendt, N. A., I. L. Jirak, and C. J. Melick, 2016: Verification of severe weather proxies from the NSSL-WRF for hail forecasting. In 28th Conf. on Severe Local Storms, 15 pp.