Data processing system and spatial-temporal reproducibility assessment of GloSea5 model

Soojina Moon; Soohee Han; Kwangsoo Choi; Junghyun Song

doi:10.3741/JKWRA.2016.49.9.761

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2016 Vol.49, Issue 9 Preview Page Next Page

Data processing system and spatial-temporal reproducibility assessment of GloSea5 model GloSea5 모델의 자료처리 시스템 구축 및 시․공간적 재현성평가

30 September 2016. pp. 761-771

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Abstract

The GloSea5 (Global Seasonal forecasting system version 5) is provided and operated by the KMA (Korea Meteorological Administration). GloSea5 provides Forecast (FCST) and Hindcast (HCST) data and its horizontal resolution is about 60km (0.83° x 0.56°) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation (R² = 0.60, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5 (600.1 mm) showed the greatest difference (-26.5%) compared to observations (816.1 mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a –3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran’s I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.

Keywords

Long-Term Probabilistic Forecasts

GloSea5

Statistical Downscaling

Spatial Autocorrelation

기상청에서 운영하고 제공하는 전지구 계절예측시스템 GloSea5 (Global Seasonal forecasting system version 5)자료를 활용하여 용담댐유역에 적용하고자 하였다. GloSea5는 예측자료(Forecast; 이하 FCST)와 과거재현자료(Hindcast; 이하 HCST)로 제공되며 공간 수평해상도는 N216 (0.83°x 0.56°)으로 중위도에서 약 60km이다. 이를 유역단위 물관리에 활용하기 위해서는 시․공간적인 상세화가 필요하므로 통계적 상세화 기법을 수행하여 변수가 갖는 계통적인 지역 오차를 보정함으로써 자료의 신뢰도를 향상시키고자 하였다. HCST자료는 앙상블 형태로 주어지며 용담댐 유역의 앙상블 평균에 대한 6번 격자의 통계적인 상관성(R²= 0.60, RMSE=88.92, NSE=0.57)이 가장 높게 나타났다. 또한 계절분석 시 여름철의 경우 원시 GloSea5 강우량이 600.1mm로 관측값인 816.1mm 대비 –26.5%로 가장 많은 차이를 보였으며 상세화 후 GloSea5 강우량은 –3.1%의 오차율을 보였다. 대부분의 과소 모의된 결과가 여름철 홍수기에 해당되는 강우로 상세화 이후 강우가 회복되는 매우 중요한 결과를 보였다. 계절별 Moran’s I 지수를 이용한 공간적 자기상관분석 결과 역시 통계적으로 유의성 있는 공간적인 분포를 나타냄으로써 자료의 불확실성을 개선하고 시․공간적인 정확도와 타당성을 입증하였다. HCST기간에 대한 GloSea5의 앙상블 강우에 대한 신뢰도를 향상시킴으로써 수문학적인 영향을 평가하기 위한 자료로서의 충분한 가능성을 확보하였으며 이러한 시․공간적인 재현성에 대한 평가결과는 향후 유역단위 물관리를 위한 기초자료로서 매우 중요한 역할을 할 것이다.

키워드

확률장기예보

GloSea5

통계적 상세화

공간자기상관

References

Abatzoglou, J.T., and Brown, T.J. (2011). “A comparison of statistical downscaling methods suited for wildfire applications.” International Journal of Climatology, Royal Meteorological Society, Vol. 32, No. 5, pp. 772-780.

Anselin, L., and Bera, A. (1998). “Spatial dependence in linear regression models with an introduction to spatial econometrics.” Handbook of Applied Economic Statistics.

Anselin, L., Syabri, I., and Smirnow, O. (2002). “Visualizing multivariate spatial correlation with dynamically linked windows.” Urbana, Vol. 51, 61801.

Benestad, R.E., and Mezghani, A. (2015). “On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales.” Tellus A, Vol. 67.

Bowler, N.E., Arribas, A., Beare, S.E., Mylne, K.R., and Shutts, G.J. (2009). “The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system.” Quarterly Journal of the Royal Meteorological Society, Royal Meteorological Society, Vol. 468, No. 640, pp. 767-776.

Feddersen, H., and Andersen, U. (2005). “A method for statistical downscaling of seasonal ensemble predictions.” Tellus A, Vol. 57, No. 3, pp. 398-408.

Hwang, S., and Graham, W.D. (2013). “Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation.” Hydrology and Earth System Sciences, EGU, Vol. 17, No. 11, pp. 4481-4502.

Jung, M.I., Son, S.W., Choi, J., and Kang, H.S. (2015). “Assessment of 6-Month Lead Prediction Skill of the GloSea5 Hindcast Experiment.” Journal of Korean Meteorological Society (Atmospher), KMS, Vol. 25, No. 2, pp. 1-15.

Jung, Y.Y., Moon, I.J., Jang, P.H., Lim, B.H., and Lee, J.H. (2015). “Assessment of accuracy of the ocean-sea meteorological objective of GloSea5.” Proceeings of the Autumn Meeting of KMS, pp. 702-703.

Kim, M.H., Kim, B.H., Kang, H.S., and Byun, Y.H. (2015). “A study on improvement of resolution of GloSea5(TRIP).” Proceeings of the Autumn Meeting of KMS, pp. 619-620.

KMA (Korea Meteorological Administration) (2015). “A Forecast characteristics analysis technical report of GloSea5”, KMA Report.

Lee, S.M., Lim, S.M., Park, J.K., Kang, H.S., and Cho, C.H. (2015). “Verification of forecast skill between global seasonal forecasting system version 5 and unified model to MJO and tropical cyclone.” Proceedings of the Spring Meeting of KMS, pp. 144-145.

MacLachlan, C., Arribas, A., Peterson, K.A., Maidens, A., Pereday, D., Scaife, A.A., Gordon, M., Vellinga, M., Williams, A., Corner, R.E., Camp, J., Xavier, P., and Madec, G. (2015). “Global seasonal forecast system version 5 (GloSea5): a high- resolution seasonal forecast system.” Quarterly Journal of the Royal Meteorological Society, Royal Meteorological Society, Vol. 141, No. 689, pp. 1072-1084.

Moon, S.J. (2015). “Ensemble projection of hydrological extremes using bayesian model averaging scheme.” Ph. D. dissertation, University of Dankook, Gyeonggido, Korea.

Palmer, T.N., Alessandri, A., Andersen, U., and Cantelaube, P. (2004). “Development of a european multimodel ensemble system for seasonal-to-interannual prediction (DEMETER).” Bulletin of the American Meteorological Society, Vol. 85, No. 6, pp. 853.

Ramanarayanan, T.S., Williams, J.R., Dugas, W.A., Hauck, L.M., and McFarland, A.M.S. (1997). “Using APEX to identify alternative practices for animal waste management.” ASAE Paper, No. 972209.

Seo, E.K., Lee, M.I., Jeong, J.H., Kang, H.S., and Won, D.J. (2016). “Improvement of soil moisture initialization for a global seasonal forecast system.” Journal of Korean Meteorological Society(Atmospher), KMS, Vol. 26, No. 1, pp. 35-45.

Son, K.H., Bae, D.H., and Jung, H.S. (2015). “Construction & evaluation of GloSea5-Based hydrological drought outlook system.” Journal of Korean Meteorological Society(Atmospher), KMS, Vol. 25, No. 2, pp. 271-281.

Wood, A.W., Leung, L.R., Sridhar, V., and Lettenmaier, D. (2004). “Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs.” Climatic Change, Springer, Vol. 62, No. 1-3, pp. 189-216.

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 49
No :9
Pages :761-771
Received Date : 2016-06-27
Revised Date : 2016-08-04
Accepted Date : 2016-08-16
DOI :https://doi.org/ 10.3741/JKWRA.2016.49.9.761

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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