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http://dx.doi.org/10.5532/KJAFM.2022.24.3.155

The Advanced Bias Correction Method based on Quantile Mapping for Long-Range Ensemble Climate Prediction for Improved Applicability in the Agriculture Field  

Jo, Sera (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Lee, Joonlee (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Shim, Kyo Moon (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
Hur, Jina (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Kim, Yong Seok (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Choi, Won Jun (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Kang, Mingu (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.24, no.3, 2022 , pp. 155-163 More about this Journal
Abstract
The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.
Keywords
Long-range forecast; Interdisciplinary study; Quantile mapping; Bias correction; Rice phenology;
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Times Cited By KSCI : 8  (Citation Analysis)
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