Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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The Advanced Bias Correction Method based on Quantile Mapping for Long-Range Ensemble Climate Prediction for Improved Applicability in the Agriculture Field

농업적 활용성 제고를 위한 분위사상법 기반의 앙상블 장기기후예측자료 보정방법 개선연구

  • 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)
  • 조세라 (국립농업과학원 기후변화평가과) ;
  • 이준리 (울산과학기술원 도시환경공학부) ;
  • 심교문 (국립농업과학원 기후변화평가과) ;
  • 안중배 (부산대학교 지구환경시스템학부) ;
  • 허지나 (국립농업과학원 기후변화평가과) ;
  • 김용석 (국립농업과학원 기후변화평가과) ;
  • 최원준 (국립농업과학원 기후변화평가과) ;
  • 강민구 (국립농업과학원 기후변화평가과)
  • Received : 2021.11.30
  • Accepted : 2022.08.17
  • Published : 2022.09.30
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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.

본 연구에서는 벼의 생물계절 예측 모형을 예시로 하여 해당 모형의 구동에 필요한 맞춤형 앙상블 상세기후예측자료를 구축하고 해당 자료의 보정방법을 고도화 하였을 때 농업적 활용 분야에서 가지는 부가가치를 확인해 보았다. 이를 위해, 벼의 생물계절 모의를 위해 집중적으로 필요한 기상자료인 1~10월의 일 평균/최저/최고 기온의 앙상블 장기(6개월) 전망자료를 생산하고 해당자료의 질을 높이기 위해 분위사상법 기반의 보정방법의 개선을 수행하였다. 그 결과 최저/최고/평균 기온 모두 대부분의 월에서 20일을 버퍼기간으로 선정하였을 때 4.51~15.37%까지 RMSE가 감소하는 것을 확인하였으며, 8~10월은 변수 및 월 별로 최적 버퍼기간이 다른 것을 확인하였다. 또한, 이러한 기상학적 변수의 개선은 벼의 생육단계별 시작일 예측이 모든 단계에서 7.82~10.60% 감소하였으며, 61개 ASOS 지점 가운데서도 생육단계에 따라 75~100%의 지점에서 RMSE가 감소하는 결과를 확인하였다. 본 연구 결과는 벼의 생물계절뿐만 아니라 감자, 고구마, 옥수수 등 타 작물로의 적용도 가능할 것으로 생각된다. 나아가, 일조시간, 습도, 풍속과 같은 예측변수들의 보정자료가 구축되면 농산물 작황전망, 병해충 예찰 등 다양한 분야의 학제간 연구에 적용하여 더 많은 부가가치 창출이 가능할 것으로 기대된다.

Keywords

Acknowledgement

이 연구는 농촌진흥청 국립농업과학원 농업과학기술 연구개발사업(과제번호: PJ01487901)의 지원으로 수행되었습니다.

References

  1. Ahn, J. B., and J. L. Lee, 2015: Comparative study on the seasonal predictability dependency of boreal winter 2 m temperature and sea surface temperature on CGCM initial conditions. Atmosphere 25, 353-366. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2015.25.2.353
  2. Ahn, J. B., and J. L. Lee, 2016: A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction. Journal of Geophysical Research: Atmospheres 121(16), 9263-9277. https://doi.org/10.1002/2016JD025151
  3. Ahn, J. B., K. M. Shim, M. P. Jung, H. G. Jeong, Y. H. Kim, and E. S. Kim, 2018a: Predictability of temperature over South Korea in PNU CGCM and WRF hindcast. Atmosphere 28, 479-490. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2018.28.4.479
  4. Ahn, J. B., J. Lee, and S. Jo, 2018b: Evaluation of PNU CGCM ensemble forecast system for boreal winter temperature over South Korea. Atmosphere 28, 509-520. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2018.28.4.509
  5. Fauzi, F., H. Kuswanto, and R. M. Atok, 2020: Bias correction and statistical downscaling of earth system models using quantile delta mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ). In Journal of Physics: Conference Series 1538(1), 012050, IOP Publishing. https://doi.org/10.1088/1742-6596/1538/1/012050
  6. Heo, S. I., Y. K. Hyun, Y. Ryu, H. S. Kang, Y. J. Lim, and Y. Kim, 2019: An assessment of applicability of heat waves using extreme forecast index in KMA climate prediction system (GloSea5). Atmosphere 29(3), 257-267. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2019.29.3.257
  7. Hur, J. N., Y.-S. Kim, S. Jo, K. M. Shim, J.- B. Ahn, M.-J. Choi, Y.-H. Kim M. Kang, and W. J. Choi, 2021: Estimation of Waxy Corn Harvest Date over South Korea using PNU CGCM-WRF Chain. Korean Journal of Agricultural and Forest Meteorology 23(4), 405-414. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2021.23.4.405
  8. Im, E. S., S. Ha, L. Qiu, J. Hur, S. Jo, and K. M. Shim, 2021: An evaluation of temperature-based agricultural indices over Korea from the high resolution WRF simulation. Frontiers in Earth Science 9, 357.
  9. Jo, S., and J. B. Ahn, 2017: Statistical forecast of early spring precipitation over South Korea using multiple linear regression. Journal of Climate Research 12, 53-71. doi: 10.14383/cri.2017.12.1.53 (in Korean with English abstract)
  10. Jo, S., J. B. Ahn, D. H. Cha, S. K. Min, M. S. Suh, Y. H. Byun, and J. U. Kim, 2019: The Koppen-Trewartha Climate-Type Changes Over the CORDEX-East Asia Phase 2 Domain Under 2 and 3°C Global Warming. Geophysical Research Letters 46(23), 14030-14041. https://doi.org/10.1029/2019GL085452
  11. Jo, S., K. M. Shim, J. Hur, Y. S. Kim, and J. B. Ahn, 2020: Future changes of agro-climate and heat extremes over S. Korea at 2 and 3 °C global warming levels with CORDEX-EA phase 2 projection. Atmosphere 11(12), 1336. https://doi.org/10.3390/atmos11121336
  12. Kim, Y. H., E. S. Kim, M. J. Choi, K. M. Shim, and J. B. Ahn, 2019: Evaluation of long-term seasonal predictability of heatwave over South Korea using PNU CGCM-WRF chain. Atmosphere 29(5), 671-687. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2019.29.5.671
  13. Lee, J. L., and J. B. Ahn, 2019: A new statistical correction strategy to improve long-term dynamical prediction. International Journal of Climatology 39(4), 2173-2185. https://doi.org/10.1002/joc.5943
  14. Matiu, M., and F. Hanzer, 2021: Bias correction and downscaling of snow cover fraction projections from regional climate models using remote sensing for the European Alps. Hydrology and Earth System Sciences Discussions, 1-25.
  15. Min, Y.-M., V. N. Kryjov, S. M. Oh, and H.-J. Lee, 2017: Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008-2015. Climate Dynamic 49, 4141-4156. https://doi.org/10.1007/s00382-017-3576-2
  16. Passow, C., and R. V. Donner, 2020: Regression-based distribution mapping for bias correction of climate model outputs using linear quantile regression. Stochastic Environmental Research and Risk Assessment 34(1), 87-102. https://doi.org/10.1007/s00477-019-01750-7
  17. RDA (Rural Development Administration), 2018: Implementation of an early warning service for weather risk management in climate-smart agriculture. Rural Development Administration, 58-75.
  18. Shim, K. M., G. Kim, K. Roh, H. Jeong, and D. Lee 2008: Evaluation of agro-climatic indices under climate change. Korean Journal of Agricultural and Forest Meteorology 10, 113-120. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2008.10.4.113
  19. Shim, K. M., Y. S. Kim, M.-P. Jung, I. T. Choi, H. Kim, and K. K. Kang, 2017: Implementation of agrometeorological early warning system for weather risk management in South Korea. Journal of Climate Change Research 8(2), 171-175. (in Korean with English abstract) https://doi.org/10.15531/ksccr.2017.8.2.171
  20. Song, C. Y., S. H. Kim, and J. B. Ahn, 2021: Improvement in seasonal prediction of precipitation and drought over the United States based on regional climate model using empirical quantile mapping. Atmosphere 31(5), 637-656. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2021.31.5.637
  21. Stocker, T. F., D. Qin, G.-K. Plattner, M. M. B. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley (Eds.), 2014: Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge university press.
  22. Yun, J. I., S. O. Kim, J. H. Kim, and D. J. Kim, 2013: User-specific agrometeorological service to local farming community: A case study. Korean Journal of Agricultural and Forest Meteorology 15, 320-331. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2013.15.4.320
  23. Yun, J. I., 2014: Agrometeorological early warning system: A service infrastructure for climate-smart agriculture. Korean Journal of Agricultural and Forest Meteorology 16(4), 403-417. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2014.16.4.403