한반도 겨울철 강수 유형에 따른 전지구 수치모델(GRIMs) 예측성능 검증
Evaluation of Predictability of Global/Regional Integrated Model System (GRIMs) for the Winter Precipitation Systems over Korea
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This paper evaluates precipitation forecast skill of Global/Regional Integrated Modelsystem (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type(C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequenciesare 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantlyoverestimates precipitation occurrence (0.1 mm d–1) for all types of winter precipitation. Objectivemeasured skill scores of GRIMs are comparably high for L type and O type. Except for precipitationoccurrence, the model shows high predictability for L type precipitation with the mostunbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuringrare events due to its high dependency on the sample size, as in the case of Critical SuccessIndex as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivityon the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplementthe limit of ETS. The evaluation via SEDS shows that the prediction skill score for Ltype is the highest in the range of 5.0, 10.0 mm d–1 and the score for O type is the highestin the range of 1.0, 20.0 mm d–1. C type has the lowest scores in overall range. The differencein precipitation forecast skill by precipitation type can be explained by the spatial distributionand intensity of precipitation in each representative case.
Abstract
This paper evaluates precipitation forecast skill of Global/Regional Integrated Modelsystem (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type(C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequenciesare 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantlyoverestimates precipitation occurrence (0.1 mm d–1) for all types of winter precipitation. Objectivemeasured skill scores of GRIMs are comparably high for L type and O type. Except for precipitationoccurrence, the model shows high predictability for L type precipitation with the mostunbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuringrare events due to its high dependency on the sample size, as in the case of Critical SuccessIndex as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivityon the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplementthe limit of ETS. The evaluation via SEDS shows that the prediction skill score for Ltype is the highest in the range of 5.0, 10.0 mm d–1 and the score for O type is the highestin the range of 1.0, 20.0 mm d–1. C type has the lowest scores in overall range. The differencein precipitation forecast skill by precipitation type can be explained by the spatial distributionand intensity of precipitation in each representative case.
참고문헌 (30)
- Barry, R. G., "Atmosphere, Weather and Climate", pp.101~103 (2003), Routledge
- Casati, B., "Forecast verification : current status and future directions", Meteorol. Appl, 15, pp.3~18 (2008)
- Chang, E. C., "Discussion on differences between public and academic understanding of precipitation prediction process and accuracy", Proceedings of the Korean Meteorological Society, online conference, pp.198~ (2021)
- Cheong, S.-H., "Classification of snowfalls over the Korean Peninsula based on developing mechanism", Atmosphere, 16, pp.33~48 (2006)
- Choi, H.-J., "An updated subgrid orographic parameterization for global atmospheric forecast models", J. Geophys. Res, 120, pp.12445~12457 (2015)[DOI]
- Chun, H. Y., "Momentum flux by thermally induced internal gravity waves and its ap-proximation for large-scale models", J. Atmos. Sci, 55, pp.3299~3310 (1998)
- Ebert, E. E., "The WGNE assessment of shortterm quantitative precipitation forecasts", Bull. Am. Meteorol. Soc, 84, pp.481~492 (2003)
- "Equitable Threat Score: 6.4 Relative skill of IFS models. Forecast user guide" (2011)[URL]
- Ek, M. B., "Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model", J. Geophys. Res. Atmos, 108, pp.8851~ (2003)
- Fritsch, J. M., "Quantitative precipitation forecasting : report of the eighth prospectus development team, US Weather Research Program", Bull. Am. Meteorol. Soc, 79, pp.285~299 (1998)
- Gallus, W. A. Jr, "Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season rainfall forecasts", Mon. Wea. Rev, 134, pp.2632~2641 (2006)
- Jongil Han, "Revision of Convection and Vertical Diffusion Schemes in the NCEP Global Forecast System", Weather and Forecasting, 26(4), pp.520~533 (2011), American Meteorological Society[DOI]
- Hogan, R. J., "Verification of cloud-fraction forecasts", Q. J. R. Meteorol. Soc, 135, pp.1494~1511 (2009)
- Hong, S. Y., "A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation", Mon. Wea. Rev, 132, pp.103~120 (2004)
- Hong, S. Y., "A new vertical diffusion package with an explicit treatment of entrainment processes", Mon. Wea. Rev, 134, pp.2318~2341 (2006)
- Song-You Hong, "A new stable boundary-layer mixing scheme and its impact on the simulated East Asian summer monsoon", Quarterly Journal of the Royal Meteorological Society, 136(651), pp.1481~1496 (2010), Wiley[DOI]
- 홍성유, "The Global", Regional Integrated Model System (GRIMs), 2013, pp.49(2), 219~243[DOI]
- 홍송유, "The Korean Integrated Model (KIM) System for Global Weather Forecasting", Asia-Pacific Journal of Atmospheric Sciences, 54, pp.267~292 (2018), 한국기상학회[DOI]
- Iacono, M. J., "Radiative forcing by long-lived greenhouse gases:calculations with the AER radiative transfer models", J. Geophys. Res, 113, pp.D13103~ (2008)
- Korea Meteorological Administration, "A Hand-Held Forecasting Technique", pp.12~ (2011), KMA
- Lee, H., "The governing factors for heavy snowfalls in Youngdong area", Asia-Pac. J. Atmos. Sci, 30, pp.197~218 (1994)
- 임교선, "Numerical Simulation of Heavy Snowfall over the Ho-Nam Province of Korea in December 2005", Asia-Pacific Journal of Atmospheric Sciences, 43(2), pp.161~173 (2007), 한국기상학회
- Kyo-Sun Sunny Lim, "Simulation of the Summer Monsoon Rainfall over East Asia Using the NCEP GFS Cumulus Parameterization at Different Horizontal Resolutions", Weather and Forecasting, 29(5), pp.1143~1154 (2014), American Meteorological Society[DOI]
- Lim, Y.-J., "Analysis and im- plications of prediction characteristics of winter pre- cipitation in 2021", Proceedings of the Korean Meteorological Society (2022)
- Olson, D. A., "Evaluation of 33 years of quantitative precipitation forecasting at the NMC", Wea. Forecasting, 10, pp.498~511 (1995)
- Park, B. J., "(Column) Interview at a research site - Young-Jun, Kim. Director of Korea Institute of Atmospheric Prediction Systems", The Science &Technology, 525, pp.86~89 (2013)
- Rae-Seol Park, "A Revised Prognostic Cloud Fraction Scheme in a Global Forecasting System", Monthly Weather Review, 144(3), pp.1219~1229 (2016), American Meteorological Society[DOI]
- Sanders, F., "Trends in skill of Boston forecasts made at MIT, 1966–84", Bull. Am. Meteorol. Soc, 67, pp.170~176 (1986)
- Hyeyum Hailey Shin, "Representation of the Subgrid-Scale Turbulent Transport in Convective Boundary Layers at Gray-Zone Resolutions", Monthly Weather Review, 143(1), pp.250~271 (2015), American Meteorological Society[DOI]
- Sud, Y. C., "Influence of land surface roughness on atmospheric circulation and precipitation : a sensitivity study with a general circulation model", J. Appl. Meteorol, 27, pp.1036~1054 (1988)