Improvement in Seasonal Prediction of Precipitation and Drought over the United States Based on Regional Climate Model Using Empirical Quantile Mapping

경험적 분위사상법을 이용한 지역기후모형 기반 미국 강수 및 가뭄의 계절 예측 성능 개선

  • Song, Chan-Yeong (Department of Atmospheric Sciences, BK21 School of Earth and Environmental Systems, Pusan National University) ;
  • Kim, So-Hee (Department of Atmospheric Sciences, BK21 School of Earth and Environmental Systems, Pusan National University) ;
  • Ahn, Joong-Bae (Department of Atmospheric Sciences, Pusan National University)
  • 송찬영 (부산대학교 BK21 지구환경시스템 교육연구단 대기환경과학과) ;
  • 김소희 (부산대학교 BK21 지구환경시스템 교육연구단 대기환경과학과) ;
  • 안중배 (부산대학교 대기환경과학과)
  • Received : 2021.08.17
  • Accepted : 2021.10.20
  • Published : 2021.12.31


The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.



본 논문의 개선을 위해 좋은 의견을 제시해 주신 두 분의 심사위원께 감사드립니다. 이 연구는 농촌진흥청 연구사업(세부과제번호: PJ01475503)의 지원으로 수행되었습니다.


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