设为首页
加入收藏
Multi-model Downscaling Ensemble Prediction in National Climate Center
-
摘要: 多模式集合和降尺度技术是提升模式预测能力的有效工具。该文对国家气候中心多模式解释应用集成预测 (MODES) 技术与业务应用现状进行了综合介绍。MODES采用欧洲中期天气预报中心、东京气候中心、美国国家环境预报中心和中国气象局国家气候中心4个气候业务季节预测模式输出场，利用EOF迭代、变形的典型相关分析、最优子集回归和高相关回归集成4种统计降尺度方法以及等权平均、经典超级集合等集成方法进行全国月及季节降水和气温预测。目前对MODES进行了夏季回报检验和约1年的实时业务应用。回报检验和业务应用表明，MODES对气温有较好的预测能力 (月预测平均PS评分为76)，对降水有一定预测技巧 (月预测平均PS评分为68)，具有短期气候预测业务应用价值。Abstract: Dynamic model is the dominant tool for the seasonal prediction operation in most climate prediction centers of the world. But now, for any single model, the predictability to seasonal precipitation and temperature is quite limited. Therefore, two kinds of techniques (i.e., multi-model ensemble and downscaling) are developed efficiently to access better prediction ability. Multi-model ensemble can reduce model error and then bring higher prediction skills. Meanwhile, as the model predictability of circulation is better than that of precipitation and temperature, downscaling improves the prediction of temperature and precipitation via regional model or statistic methods.Due to the complex physical mechanism, the seasonal prediction to China climate is much a challenge. China National Climate Center (NCC) develops a new kind of prediction technique combining multi-model ensemble and downscaling. At present, the output variables from four seasonal models from WMO GPCs (including ECMWF, TCC, NCEP and NCC) are used as predictors and four statistic downscaling methods (EOF-ITE, BP-CCA, Optical Subset Regression, Regress Ensemble of High Correlation Factors) are used to set prediction model. Every model output and every downscaling method are used so that 16 model-downscaling components are available. Besides, two methods (equal-weighted average, classic super-ensemble) are employed to access the ensemble result, respectively. As an index showing the prediction ability, the mean PS scores are computed for the reforecast of recent five years for every model-downscaling and ensemble component. The component with highest mean PS score is chosen as the best prediction result.In NCC, the Multi-model Downscaling Ensemble Prediction System (MODES) are set up to realize the above ideas and the operational application of monthly and seasonal temperature with precipitation over China. Reforecast and operational application are carried out. The present reforecast and operational application for seasonal climate indicates that MODES has achieved quite good prediction skills for temperature and also improved precipitation prediction. The real-time application for monthly climate prediction for from September 2012 to July 2013 is assessed with NCC traditional PS methods. For monthly mean temperature, MODES holds the mean and median PS score of 76 and 81, respectively, showing much good prediction ability. Meanwhile, for the monthly precipitation of MODES, the mean and median PS scores are both 68, higher than mean scores of the operational prediction product of NCC. The reforecast and operational application indicate that MODES is a useful tool for the short-term climate operation prediction.
-
Key words:
- multi-model ensemble;
- statistical downscaling;
- MODES
-
图 1 HCRE方法预报流程图
Fig. 1 Diagram of the downscaling method of regress ensemble of high correlation factors
图 2 MODES夏季气温后报平均PS评分
(起报时间为3月，其中降尺度方法后报时间为2003—2012年；集成方法后报时间为2008—2012年)
Fig. 2 The mean PS skill of the reforecast of summer temperature of MODES
(forecast start reference is March, the reforecast period is 2003—2012 for the downscaling methods and 2008—2012 for the ensemble one, respectively)
图 3 MODES最优夏季气温 (a) 和降水 (b) 后报距平符号一致率空间分布
Fig. 3 The spatial distribution of the same sign rate of prediction anomalies of temperature (a) and precipitation (b) in summer
图 4 2011—2012年冬季气温距平与降水距平百分率实况与预测 (a) 气温实况，(b) 气温预测, (c) 降水实况, (d) 降水预测
Fig. 4 The observation and prediction of temperature anomaly and precipitation anomaly percentage during the winter of 2011—2012 (a) temperature observation, (b) temperature prediction, (c) precipitation observation, (d) precipitation prediction
表 1 国家气候中心MODES现有业务气候模式数据
Table 1 The accessible data of operational climate models at NCC
机构 模式 预测时间长度 业务应用起始时间 模式数据原始分辨率 历史回报时间 ECMWF System4 7个月 2011年 1.5°×1.5° 1981—2010年 NCEP CFS V2 9个月 2011年 1°×1° 1982—2010年 TCC MRI-CGCM 3~7个月 2009年 2.5°×2.5° 1979—2008年 NCC CGCM V1 11个月 2005年 2.5°×2.5° 1983—2003年 
下载: 导出CSV
表 2 MODES在2012年9月—2013年7月月预测业务应用PS评分
Table 2 PS skill of the monthly operational prediction of MODES from September 2012 to July 2013
时间 PS评分 气温 降水 2012-09 69 44 2012-10 70 69 2012-11 44 77 2012-12 95 60 2013-01 56 65 2013-02 95 84 2013-03 90 64 2013-04 73 73 2013-05 83 68 2013-06 85 73 2013-07 81 68
下载: 导出CSV
-
[1] Stockdale T N.Understanding and Predicting Seasonal and Inter-annual Climate Variability-the Producer Perspective.White Paper for WCC-3, 2009. [2] 颜宏.关于气候预测与模拟若干问题的思考.应用气象学报, 1997, 8(增刊):6-14. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX7S1.002.htm [3] Atger F.The skill of ensemble prediction systems.Mon Wea Rev, 1999, 127:1941-1953. doi: 10.1175/1520-0493(1999)127<1941:TSOEPS>2.0.CO;2 [4] Krishnamurti T N, Kishtawal C M, La Row T E, et al.Impro-ved skills for weather and seasonal climate forecasts from multimodel superensemble.Science, 1999, 285:1548-1550. doi: 10.1126/science.285.5433.1548 [5] 杜钧.集合预报的现状和前景.应用气象学报, 2002, 13(1):16-28. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020102&flag=1 [6] Stephenson D B, Coelho C A S, Doblas-Reyes F J, et al.Forecast assimilation:A unified framework for the combination of multi-model weather and climate predictions.Tellus A, 2005, 57(3):253-264. doi: 10.3402/tellusa.v57i3.14664 [7] Wang B, Lee June-Yi, Kang In-Sik, et al.Advance and prospe-ctus of seasonal prediction:Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980—2004).Climate Dynamics, 2009, 33(1):93-117. doi: 10.1007/s00382-008-0460-0 [8] 陈超辉, 李崇银, 谭言科, 等.基于交叉验证的多模式超级集合预报方法研究.气象学报, 2010, 68(4):464-476. doi: 10.11898/1001-7313.20100410 [9] Feng Jinming, Lee Dong-Kyou, Fu Congbin, et al.Comparison of four ensemble methods combining regional climate simulations over Asia.Meteorol Atmos Phys, 2011, 111:41-53. doi: 10.1007/s00703-010-0115-7 [10] 丁一汇.季节气候预测的进展和前景.气象科技进展, 2011, 1(3):15-27. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201103005.htm [11] Palmer T, Andersen U, Cantelaube P, et al.Development of a European Multi-model Ensemble System for seasonal to inter-annual prediction (DEMETER).Bull Amer Meteor Soc, 2002:85(6):853-872. [12] Wilby R L.Downscaling general circulation model output:A review of methods and limitations.Journal of the American Water Resources Association, 2000, 36(2):387-397. doi: 10.1111/jawr.2000.36.issue-2 [13] Wilby R L, Wigley T M.Precipitation predictors for downsc-aling:Observed and general circulation model relationships.Int J Climatol, 2000, 20(5):641-661. [14] 范丽军, 符淙斌, 陈德亮.统计降尺度法对华北地区未来区域气温变化情景的预估.大气科学, 2007, 31(5):887-897. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200705011.htm [15] 艾孑兑秀, 孙林海, 宋文玲. NCC-CGCM产品对长江中下游夏季降水预报的释用. 2010, 21(4): 484-490. [16] 覃志年, 陈丽娟, 唐红玉, 等.月尺度动力模式产品解释应用系统及预测技巧.应用气象学报, 2010, 21(5):614-620. doi: 10.11898/1001-7313.20100511 [17] 李江萍, 王式功.统计降尺度法在数值预报产品释用中的应用.气象, 2008, 34(6):41-45. doi: 10.7519/j.issn.1000-0526.2008.06.006 [18] Zhu Congwen, Park Chung-Kyu, Lee Woo-Sung, et al.Statistical downscaling for multi-model ensemble prediction of summer monsoon rainfall in the Asia-pacific region using geopotential height field.Adv Atmos Sci, 2008, 25(5):867-884. doi: 10.1007/s00376-008-0867-x [19] 魏凤英, 黄嘉佑.我国东部夏季降水量统计降尺度的可预测性研究.热带气象学报, 2010, 26(4): 483-488. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX201004013.htm [20] 康红文, 祝从文, 左志燕, 等.多模式集合预报及其降尺度技术在东亚夏季降水预测中的应用.气象学报, 2012, 70(2):192-201. doi: 10.11676/qxxb2012.019 [21] 孙林海, 刘一鸣.区域气候模式对中国夏季平均气温和降水的评估分析.气象, 2008, 34(11):31-39. doi: 10.7519/j.issn.1000-0526.2008.11.005 [22] 孙林海, 艾孑兑秀, 宋文玲, 等.区域气候模式对我国冬春季气温和降水预报评估.应用气象学报, 2009, 20(5):546-554. doi: 10.11898/1001-7313.20090505 [23] 陈丽娟, 李维京.月动力延伸预报产品的评估和解释应用.应用气象学报, 1999, 10(4):486-490. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=199904101&flag=1 [24] 陈丽娟, 李维京, 张培群, 等.降尺度技术在月降水预报中的应用.应用气象学报, 2003, 14(6):648-655. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030682&flag=1 [25] 艾孑兑秀, 董文杰, 张培群.一种基于EOF分析的迭代方法在业务预报中的应用.热带气象学报, 2008, 24(4):320-326. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200804003.htm [26] 柯宗建, 张培群, 董文杰, 等.最优子集回归方法在季节气候预测中的应用.大气科学, 2009, 33(5):994-1002. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200905012.htm [27] 贾小龙, 陈丽娟, 李维京, 等.BP-CCA方法用于中国冬季温度和降水的可预报性研究和降尺度季节预测.气象学报, 2010, 68(3):398-410. doi: 10.11676/qxxb2010.039 [28] 赵振国.中国夏季旱涝及环境场.北京:气象出版社, 1999. http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm [29] 张邦林, 丑纪范, 孙照渤.用前期大气环流预报中国夏季降水的EOF迭代方案.科学通报, 1991, 36(23):1797-1798. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB199123011.htm [30] 沈愈.EOF迭代降尺度方案及其在华东梅汛期降水预测中的应用.高原气象, 2008, 27(1):52-63. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX2008S1007.htm [31] Barnett T P, Preisendorfer R O.Origins and levels of monthly and seasonal forecast skill for United States surface air temperature determined by canonical correlation analysis.Mon Wea Rev, 1987, 115(9):1825-1847. doi: 10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2 [32] 毛恒青, 李小泉.典型相关分析 (CCA) 对我国冬季气温的短期气候预测试验.应用气象学报, 1997, 8(4):385-392. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19970456&flag=1 [33] 黄茂怡, 黄嘉佑.近年来CCA在气候分析与气候预测中的应用.北京大学学报:自然科学版, 2000, 37(1):128-135. http://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ200101022.htm [34] Garside M J.The best sub-set in multiple regression analysis.Applied Statistics, 1965, 14:196-200. doi: 10.2307/2985341 [35] Furnival G M.All possible regressions with less computation.Technometrics, 1971, 13:403-408. doi: 10.1080/00401706.1971.10488794 [36] Furnival G M, Wilson R W.Regression by leaps and bound.Technometrics, 1974, 16:499-511. doi: 10.1080/00401706.1974.10489231 -
图(6) / 表(2)
计量
- 摘要浏览量: 5087
- HTML全文浏览量: 1239
- PDF下载量: 1077
- 被引次数: 0
