多气候模式的全国月降水预测能力评价及偏差校正

林广洪; 朱碧莹; 陈杰; 邱元霖; 刘建华; 陈华

多气候模式的全国月降水预测能力评价及偏差校正

1. 天生桥一级水电开发有限责任公司水力发电厂;
2. 水资源工程与调度全国重点实验室(武汉大学)

基金项目:

国家自然科学基金面上项目(52079093)

湖北省自然科学基金资助项目(2020CFA100)

详细信息

作者简介:
林广洪(1969-),男,工程师,从事水库调度工作。E-mail:651396048@qq.com

通讯作者:
陈杰(1981-),男,教授,博士,研究方向为气候变化与水文水资源。E-mail:jiechen@whu.edu.cn

中图分类号: P457.6
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出版历程
- 收稿日期: 2022-08-05

Evaluation of Monthly Precipitation Prediction Based on Climate Model and Bias Correction in China

1. Hydroelectric Power Plant of Tianshengqiao-I Hydropower Development Limited Liability Company;
2. State Key Laboratory of Water Resources Engineering and Management(Wuhan University)

摘要

摘要: 月降水预测对水资源配置和规划管理等具有重要意义，但其受到多种因素的影响，预测难度和不确定性均较大。为探究基于气候模式的月降水预测在中国区域的表现和偏差校正方法对预测能力的影响，以中国大陆为研究区域，选取1981-2014年为研究时段，评价了九种气候模式（CFSv2, SEAS5, CanSips, GEMNEMO, CCSM4, GFDL, CanCM3, CanCM4, GEOSS2S）在不同预见期下对月降水的预测能力，采用聚类分析方法分析了气候模式的预测能力随预见期的变化规律，并采用线性偏差校正方法（Linear Scaling,LS）和分位数映射校正方法（Quantile Mapping,QM）对降水进行后处理，比较了两种偏差校正方法在验证期（2008-2014年）的校正效果。结果表明：(1)不同气候模式之间对降水的预测精度差异较大，对夏季降水的预测能力因预见期和预测区域而异，其中SEAS5模式在不同经纬度和不同预见期下的综合表现均最优，且其预测能力随预见期的延长变化稳定；(2)偏差校正对所有气候模式的降水预测均有明显的改进效果，两种偏差校正方法的效果相近，但经过LS方法校正后降水的平均绝对相对误差小于50%，总体上略优于QM方法，此外经过偏差校正后SEAS5模式的综合表现依然最优。研究结果揭示了SEAS5模式对中国大陆月降水预测的优势和偏差校正方法对气候模式预测能力的提升作用，可为基于气候模式的降水预测应用提供参考。
- 月尺度降水预测 /
- 气候模式 /
- 偏差校正方法 /
- 预测能力
Abstract: Monthly precipitation prediction is crucial for water resources allocation, planning and management. However, the prediction is influenced by various factors, thus it is extreamly difficult and uncertain. To investigate the performance of monthly precipitation predictions based on climate models in China and the influence of bias correction methods, this paper selects China’s mainland as the research area, adopts 1981 to 2014 as the research period, and evaluates the predictive capability of nine climate models（CFSv2, SEAS5, CanSips, GEMNEMO, CCSM4, GFDL, CanCM3, CanCM4, GEOSS2S） for monthly precipitation prediction in different forecasting periods. Clustering analysis is adopted to analyze the patterns of prediction ability of climate models with forecasting period. The linear scaling（LS） and quantile mapping（QM） bias correction methods are used for the post-processing of the predicted precipitation, and the effects of the two methods are compared in validation（2008 to 2014）. The results show that the accuracy of precipitation prediction varies among climate models, and the capability to summer precipitation prediction depends on the forecasting periods and forecast regions. The SEAS5 climate model performs optimal in different regions and forecasting periods, and its forecasting capability stabilized with the extension of forecasting period.In terms of bias correction, the bias correction methods can significantly improve the prediction ability of precipitation for all of the climate models and the two methods exhibited similar performs, however, the mean absolute relative error of LS method is less than 50%, which indicates that the LS method is generally slightly outperforms QM method. In addition, the SEAS5 climate model still performs best after bias corrections. This paper reveals the superiority of the SEAS5 climate model for monthly precipitation prediction in China’s mainland and the enhancement of the prediction capability of climate models by the bias correction methods. This study provides a reference for the application of precipitation predictions based on climate models.
- monthly precipitation prediction /
- climate model /
- bias correction method /
- forecasting capability

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参考文献(35)

[1]	高雪.浅谈我国短期气候预测的现状及改革措施[J].低碳世界,2019,9(8):313-314. GAO X. The current situation of short-term climate prediction in China and its reform measures[J]. Low Carbon World, 2019,9(8):313-314.
[2]	朱抱真,陈嘉滨,张道民,等.一个修改的σ-坐标初始方程斜压模式[J].气象学报,1983,(2):41-56. ZHU B Z, CHEN J B, ZHANG D M, et al. A modifiedσ-coordinate initial equation oblique pressure model[J]. Acta Meteorologica Sinica, 1983(2):41-56.
[3]	纪立人,陈嘉滨,张道民.一个包含非绝热物理过程的全球谱模式及其初步试验结果中期数值天气预测研究成果汇编[M].北京:气象出版社,1990.
[4]	郭肖容,张玉玲,闫之辉,等.有限区分析预报系统及其业务应用[J].气象学报,1995,53(3):306-318. GUO X R, ZHANG Y L, YAN Z H, et al. Finite area analysis and prediction system and its business application[J]. Acta Meteorologica Sinica, 1995,53(3):306-318.
[5]	周晓平,王东海.短时风暴数值预测模式研究:I.模式的理论框架[J].大气科学,1996,20(1):1-11. ZHOU X P, WANG D H. Study of short-term storm numerical prediction model-I. Theoretical framework of the model[J]. Chinese Journal of Atmospheric Sciences, 1996,20(1):1-11.
[6]	陈德辉,薛纪善,杨学胜,等. GRAPES新一代全球/区域多尺度统一数值预测模式总体设计研究[J].科学通报,2008,(20):2 396-2 407. CHEN D H, XUE J S, YANG X S, et al. Overall design of global/regional assimilation and prediction system[J]. Chinese Science Bulletin, 2008(20):2 396-2 407.
[7]	SAHA S, MOORTHI S, WU X, et al. The NCEP climate forecast system version 2[J]. Journal of Climate, 2014,27(6):2 185-2 208.
[8]	胡胜,罗聪,黄晓梅,等.基于雷达外推和中尺度数值模式的定量降水预测的对比分析[J].气象,2012,38(3):274-280. HU S, LUO C, HUANG X M, et al. Comparisons of one hour precipitation forecast between the radar extrapolation and the mesoscale numerical model[J]. Meteorological Monthly, 2012,38(3):274-280.
[9]	KIM H M, WEBSTER P J, CURRY J A. Seasonal prediction skill of ECMWF system 4 and NCEP CFSv2 retrospective forecast for the northern hemisphere winter[J]. Climate Dynamics, 2012,39(12):2 957-2 973.
[10]	PENG Z, WANG Q J, BENNETT J C, et al. Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China[J]. Journal of Geophysical Research:Atmospheres, 2014,119(12):7 116-7 135.
[11]	KIRTMAN B P, MIN D. Multimodel ensemble ENSO prediction with CCSM and CFS[J]. Monthly Weather Review, 2009,137(9):2 908-2 930.
[12]	PAOLINO D A, KINTER J L, KIRTMAN B P, et al. The impact of land surface and atmospheric initialization on seasonal forecasts with CCSM[J]. Journal of Climate, 2014,25(3):1 007-1 021.
[13]	KIRTMAN B P, MIN D, INFANTI J M, et al. The north American multimodel ensemble:Phase-1 seasonal-to-interannual prediction; Phase-2 toward Developing Intraseasonal Prediction[J].Bulletin of the American Meteorological Society, 2015,95(4):585-601.
[14]	SLATER L J, VILLARINI G, BRADLEY A A. Evaluation of the skill of North-American multi-model ensemble(NMME)global climate models in predicting average and extreme precipitation and temperature over the continental USA[J]. Climate Dynamics, 2017,53(12):7 381-7 396.
[15]	MA F, YE A, DENG X, et al. Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China[J]. International Journal of Climatology,2016,36(1):132-144.
[16]	JUNENG L, TANGANG F T, KANG H W, et al. Statistical downscaling forecasts for winter monsoon precipitation in Malaysia using mult-imodel output variables[J]. Climate, 2010,23(1):17-27.
[17]	ACHARYA N, CHATTOPADHYAY S, MOHANTY U C, et al. On the bias correction of general circulation model output for Indian summer monsoon[J]. Meteorological Applications, 2012,20(3):349-356.
[18]	SINGH A, SAHOO R K, NAIR A, et al. Assessing the performance of bias correction approaches for correcting monthly precipitation over India through coupled models[J]. Meteorological Applications, 2017,24(3):326-337.
[19]	祝诗学,梁忠民,戴昌军,等.丹江口水库流域月尺度降雨与径流预测研究[J].南水北调与水利科技,2016,14(1):96-101. ZHU S X, LIANG Z M, DAI C J, et al. Study on monthly rainfall and runoff prediction in Danjiangkou Basin[J]. South-to-North Water Transfers and Water Science&Technology, 2016,14(1):96-101.
[20]	吴旭树,郭生练,巴欢欢,等.基于海温多极指标的长期降水预测方法[J].水利学报,2018,49(10):1 276-1 283. WU X S, GUO S L, BA H H, et al. Long-range precipitation forecasting based on multi-pole sea surface temperature[J]. Journal of Hydraulic Engineering, 2018,49(10):1 276-1 283.
[21]	赵煜飞,朱江,许艳.近50 a中国降水格点数据集的建立及质量评估[J].气象科学,2014,34(4):414-420. ZHAO Y F, ZHU J, XU Y, Establishment and assessment of the grid precipitation datasets in China for recent 50 years[J]. Journal of the Meteorological Sciences, 2014,34(4):414-420.
[22]	FRALEY C, RAFTERY A E. How many clusters? Which clustering method? Answers via model-based cluster analysis[J]. Computer Journal, 1998,41(8):578-588.
[23]	JAIN A K, MURTY M N, FLYNN P J. Data clustering:A review[J]. ACM Computing Surveys, 1999,31(3):264-323.
[24]	SHERIDAN S C. The redevelopment of a weather-type classification scheme for North America[J]. International Journal of Climatology, 2002,22(1):51-68.
[25]	IYIGUN C, TÜRKEŞM,İNCI B, et al. Clustering current climate regions of Turkey by using a multivariate statistical method[J].Theoretical and Applied Climatology, 2013,114(1-2):95-106.
[26]	ZHAO T T G, LIU P, ZHANG Y, et al. Relating anomaly correlation to lead time:Clustering analysis of CFSv2 forecasts of summer precipitation in China[J]. Journal of Geophysical Research Atmospheres, 2017,122(17):1-13.
[27]	XU R, WUNSCH D C. Survey of clustering algorithms[J]. IEEE Transactions on Neural Networks, 2005,16(3):645-678.
[28]	ZHANG Y Y, FU G B, SUN B Y, et al. Simulation and classification of the impacts of projected climate change onflow regimes in the arid hexi corridor of Northwest China[J]. Journal of Geophysical Research Atmospheres, 2015,120(15):7 429-7 453.
[29]	ROUSSEEUW P J. Silhouettes:a graphical aid to the interpretation and validation of cluster-analysis[J]. Journal of Computational and Applied Mathemetics, 1987,20:53-65.
[30]	HAY L E, WILBY R L, LEAVESLEY G H. A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States[J]. Jawra Journal of the American Water Resources Association, 2000,36(2):387-397.
[31]	DIAZ-NIETO J, WILBY R. A comparison of statistical downscaling and climate change factor methods:impacts on low flows in the River Thames, United Kingdom[J]. Climatic Change, 2005,69(2-3):245-268.
[32]	OUYANG F, LUE H, ZHU Y, et al. Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology[J]. Stochastic Environmental Research and Risk Assessment, 2014,28(4):991-1 010.
[33]	INES A V, HANSEN J W. Bias correction of daily GCM rainfall for crop simulation studies[J]. Agricultural and Forest Meteorology,2006,138(1):44-53.
[34]	HAYHOE K, CAYAN D, FIELD C B, et al. Emissions pathways,climate change, and impacts on California[J]. Proceedings of the National Academy of Sciences, 2004,101(34):12 422-12 427.
[35]	MAURER E P, HIDALGO H G. Utility of daily vs. monthly largescale climate data:an intercomparison of two statistical downscaling methods[J]. Hydrology and Earth System Sciences, 2008,12(2):551-563.