气象资料的统计降尺度方法综述

doi:10.11867/j.issn.1001-8166.2011.08.0837

地球科学进展 ›› 2011, Vol. 26 ›› Issue (8): 837 -847. doi: 10.11867/j.issn.1001-8166.2011.08.0837

气象资料的统计降尺度方法综述

刘永和 ¹, 郭维栋 ², 冯锦明 ³, 张可欣 ⁴

1.河南理工大学资源环境学院，河南焦作454000; 2.南京大学气候与全球变化研究院，大气科学学院，江苏南京210093; 3.中国科学院东亚区域气候—环境重点实验室，全球变化东亚区域研究中心，中国科学院大气物理研究所，北京100029;
4.山东省临沂市气象局，山东临沂276004

收稿日期:2011-02-14 修回日期:2011-04-14 出版日期:2011-08-10
通讯作者: 刘永和 E-mail:sucksis@163.com
基金资助:
国家自然科学基金项目“汶川巨震对降水过程激发机制的初步控制”(编号：40975049)；国家自然科学基金重大国际（地区）合作研究项目“亚洲和北美半干旱区大气—植被—水相互作用的比较研究”(编号：40810059003)；淮河流域气象开放研究基金项目“沂沭河流域暴雨洪水预警及灾害评估业务化技术”(编号：HRM200904)资助.

A Summary of Methods for Statistical Downscaling of Meteorological Data

Liu Yonghe ¹, Guo Weidong ², Feng Jinming ³, Zhang Kexin ⁴

1.Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo454000,China;2.ICGCR, School of Atmospheric Sciences, Nanjing University, Nanjing210093,China;3.Key Laboratory of Regional ClimateEnvironment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing100029,China;4.Linyi Meteorological Bureau, Linyi276004,China

Received:2011-02-14 Revised:2011-04-14 Online:2011-08-10 Published:2011-08-10

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统计降尺度是解决由气象模式输出的低分辨率资料到流域尺度资料转换的手段之一，已成为一个重要的研究领域。统计降尺度方法十分丰富，分为传递函数法、天气形势法和天气发生器3类，3类之间并无严格的界限。统计降尺度涉及到时间与空间降尺度、随机型与确定型降尺度、时间自相关与空间相关性以及面向格点与面向站点的降尺度这4个方面的属性与分类问题，各种具体方法在这些方面的表现有所不同。近年来，相似法、隐马尓可夫模型、广义线性模型、Poisson点过程以及乘性瀑布过程获得了较大的发展和应用，并诞生了各种非线性模型以及物理—统计模型等新方法，已有一些影响较大的统计降尺度模型软件。新的方法在不断涌现，其中非线性模型、气候情境随机模拟技术、短期预报资料降尺度技术以及结合物理机理的统计降尺度方法是未来的主要发展趋势。

关键词: 统计降尺度, 天气发生器, 天气分类, 传递函数, 非线性模型

As one of the means for bridging the gap of the data between low resolution data obtained from weather models and that needed in basin scale, statistical downscaling become a important field for study. The approaches for statistical downscaling are abundant and can be roughly divided into three groups: Transfer functions, weather-typing approaches and stochastic weather generators. The transfer functions may be linear methods, such as multivariate linear regression, canonic correlation, and Singular Value Decomposition, or nonlinear methods like artificial neural networks and support vector machine. Weather generators are designed initially for generating the missing weather data, but in downscaling applications it can be used as the output backend of other statistical downscaling techniques. The weather-typing approaches can be regarded as some variant of weather generators combining with some transfer functions or classifications. Thus, no strict boundaries exist among the three groups. The statistical downscaling problems have some attributes involved with temporal downscaling or spatial downscaling, stochastic downscaling or deterministic downscaling, temporal self-correlation and spatial correlation, point-site oriented or grids oriented. The differences of downscaling performance between all techniques are mainly related to these attributes. In recent years, the analog method, weather classifying, hidden Markov modeling, generalized linear models, Poisson clustered point process and the multiplicative cascade process based on multifractal theories are developed and used for statistical downscaling application, and many new nonlinear methods such as generalized additional models and physical-statistical methods are arising. Meanwhile, there is also some widely used model software available. Among all new emerging techniques, the nonlinear methods, stochastic simulation techniques of climate scenario, downscaling models for shortterm weather prediction and the statistical downscaling methods combining physical mechanism may become the main trend of study in the future.

Key words: Statistical downscaling, Weather generator, Weather typing, Transfer function, Nonlinear model

中图分类号:

P432+.1

[1]Fan Lijun, Fu Congbin, Chen Deliang. Review on creating future climate change scenarios by statistical downscaling techniques [J].Advances in Earth Science,2005, 20(3): 320-329. [范丽军, 符淙斌, 陈德亮. 统计降尺度法对未来区域气候变化情景预估的研究进展 [J]. 地球科学进展,2005, 20(3): 320-329.]
[2]Wei Fengying, Huang Jiayou. A study of predictability for summer precipitation on East China using downscaling techniques [J].Journal of Tropical Meteorology,2010, 26(4): 483-488. [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 [J]. 热带气象学报,2010, 26(4): 483-488.]
[3]Huang Junxiong, Xu Zongxue, Liu Zhaofei,et al. Analysis of future climate change in the Taihu Basin using statistical downscaling [J].Resources Science,2008, 30(12): 1 811-1 817. [黄俊雄, 徐宗学, 刘兆飞,等. 统计降尺度法分析太湖流域未来气候变化情景 [J]. 资源科学,2008, 30(12): 1 811-1 817.]
[4]Liu Jifeng, Li Shijie, Ding Yuguo. Forecasting of water level of Qinghai Lake based on statistics downscaling from climatic model [J].Advances in Water Science,2008, 19(2): 184-191. [刘吉峰, 李世杰, 丁裕国. 基于气候模式统计降尺度技术的未来青海湖水位变化预估 [J]. 水科学进展,2008, 19(2): 184-191.]
[5]Zhao Fangfang, Xu Zongxue. Statistical downscaling of future temperature change in source of the Yellow River Basin [J].Plateau Meteorology,2008, 27(1): 153-161. [赵芳芳, 徐宗学. 黄河源区未来地面气温变化的统计降尺度分析 [J]. 高原气象,2008, 27(1): 153-161.]
[6]Tang Jianping, Gao Hongxia, Li Yan,et al. Assessment of wind energy potential in China during the 21st century under the IPCC-A2 scenario [J].Acta Energiae Solaris Sinica,2009, 30(5): 655-666. [汤剑平, 高红霞, 李艳,等. IPCC-A2情景下我国21世纪风能变化的统计降尺度方法研究 [J]. 太阳能学报,2009, 30(5): 655-666.]
[7]Fan Lijun, Fu Congbin, Chen Deliang. Estimation of local temperature change scenarios in North China using statistical downscaling method [J].Chinese Journal of Atmospheric Sciences,2007, 31(5): 887-897. [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 [J]. 大气科学, 2007, 31(5): 887-897.]
[8]Wilby R L, Dawson C W, Barrow E M. SDSM—A decision support tool for the assessment of regional climate change impacts [J].Environmental Modelling & Software,2002, 17(2): 147-159.
[9]Zorita E, Hughes J P, Lettemaier D P,et al. Stochastic characterization of regional circulation patterns for climate model diagnosis and estimation of local precipitation [J].Journal of Climate,1995, 8(5): 1 023-1 042.
[10]Charles S P, Bates B C, Hughes J P. A spatiotemporal model for downscaling precipitation occurrence and amounts [J].Journal of Geophysical Research-Atmospheres,1999, 104(D24): 31 657-31 669.
[11]Kioutsioukis I, Melas D, Zanis P. Statistical downscaling of daily precipitation over Greece [J].International Journal of Climatology,2008, 28(5): 679-691.
[12]Hertig E, Jacobeit J. Assessments of Mediterranean precipitation changes for the 21st century using statistical downscaling techniques [J].International Journal of Climatology,2008, 28(8): 1 025-1 045.
[13]Huth R,Kliegrova S, Metelka L. Non-linearity in statistical downscaling: Does it bring an improvement for daily temperature in Europe? [J].International Journal of Climatology,2008, 28(4): 465-477.
[14]Grecu M, Krajewski W F. A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting [J].Journal of Hydrology,2000, 239(1/4): 69-84.
[15]Tolika K, Maheras P, Vafiadis M,et al. Simulation of seasonal precipitation and raindays over Greece: A statistical downscaling technique based on Artificial Neural Networks (ANNs) [J].International Journal of Climatology,2007, 27(7): 861-881.
[16]Ramírez M C, Ferreira N J, Velho H F C. Linear and nonlinear statistical downscaling for rainfall forecasting over Southeastern Brazil [J].Weather and Forecasting,2006, 21(6): 969-989.
[17]Liu X L, Coulibaly P, Evora N. Comparison of data-driven methods for downscaling ensemble weather forecasts [J].Hydrology and Earth System Sciences,2008, 12(2): 615-624.
[18]Fernandez-Ferrero A, Saenz J, Ibarra-Berastegi G,et al. Evaluation of statistical downscaling in short range precipitation forecasting [J].Atmospheric Research,2009, 94(3): 448-461.
[19]Anandhi A, Srinivas V V, Kumar D N,et al. Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine [J].International Journal of Climatology,2009, 29(4): 583-603.
[20]Chen H, Guo J, Xiong W,et al. Downscaling GCMs using the smooth support vector machine method to predict daily precipitation in the Hanjiang Basin [J].Advances in Atmospheric Sciences,2010, 27(2): 274-284.
[21]Chen S T, Yu P S, Tang Y H. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis [J].Journal of Hydrology,2010, 385(1/4): 13-22.
[22]Tripathi S, Srinivas V V, Nanjundiah R S. Dowinscaling of precipitation for climate change scenarios: A support vector machine approach [J].Journal of Hydrology,2006, 330(3/4): 621-640.
[23]Anandhi A, Srinivas V V, Nanjundiah R S,et al. Downscaling precipitation to river basin in India for IPCCSRES scenarios using support vector machine [J].International Journal of Climatology,2008, 28(3): 401-420.
[24]Stehlik J, Bardossy A. Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation [J].Journal of Hydrology,2002, 256(1/2): 120-141.
[25]Bardossy A, Stehlik J, Caspary H J. Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules [J].Climate Research, 2002, 23(1): 11-22. 
[26]Ghosh S,Mujumdar P P. Future rainfall scenario over Orissa with GCM projections by statistical downscaling [J].Current Science,2006, 90(3): 396-404.
[27]Bardossy A, Bogardi I, Matyasovszky I. Fuzzy rule-based downscaling of precipitation [J].Theoretical and Applied Climatology,2005, 82(1/2): 119-129.
[28]Wetterhall F, Halldin S, Xu C Y. Seasonality properties of four statistical-downscaling methods in central Sweden [J].Theoretical and Applied Climatology,2007, 87(1/4): 123-137.
[29]Bogardi I, Matyasovszky I, Bardossy A,et al. Application of a space-time stochastic-model for daily precipitation using atmospheric circulation patterns [J].Journal of Geophysical Research-Atmospheres,1993, 98(D9): 16 653-16 667.
[30]Wilson L L, Lettenmaier D P, Skyllingstad E. A hierarchical stochastic-model of large-scale atmospheric circulation patterns and multiple station daily precipitation [J].Journal of Geophysical Research-Atmospheres,1992, 97(D3): 2 791-2 809.
[31]Wilks D S. Interannual variability and extreme-value characteristics of several stochastic daily precipitation models [J].Agricultural and Forest Meteorology,1999, 93(3): 153-169. 
[32]Richardson C W. Stochastic simulation of daily precipitation,temprature, and solar-radiation [J].Water Resources Research,1981, 17(1): 182-190.
[33]Roldan J, Woolhiser D A. Stochastic daily precipitation models.1. A comparison of occurrence processes [J].Water Resources Research,1982, 18(5): 1 451-1 459.
[34]Woolhiser D A, Roldan J. Stochastic daily precipitation models.2. A comparison of distributions of amounts [J].Water Resources Research,1982, 18(5): 1 461-1 468. 
[35]Woolhiser D A,Pegram G. Maximum likelihood estimation of Fourier coefficients to describe seasonal-variations of parameters in stochastic daily precipitation models [J].Journal of Applied Meteorology,1979, 18(1): 34-42. 
[36]Woolhiser D A,Roldan J. Seasonal and regional variability of parameters for stochastic daily precipitation models-south-Dakota, USA [J].Water Resources Research,1986, 22(6): 965-978.
[37]Mendes D, Marengo J A. Temporal downscaling: A comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios [J].Theoretical and Applied Climatology,2010, 100(3/4): 413-421.
[38]Olsson J. Evaluation of a scaling cascade model for temporal rainfall disaggregation [J].Hydrology and Earth System Sciences,1998, 2(1): 19-30.
[39]Yang C, Chandler R E, Isham V S,et al. Spatial-temporal rainfall simulation using generalized linear models [J].Water Resources Research,2005, 41(11)，doi.10.1029/2004WR003739.
[40]Busuioc A, Von Storch H, Schnur R. Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions [J].Journal of Climate,1999, 12(1): 258-272.
[41]Wetterhall F, Halldin S, Xu C Y. Statistical precipitation downscaling in central Sweden with the analogue method [J].Journal of Hydrology,2005, 306(1/4): 174-190.
[42]Hughes J P, Lettenmaier D P, Guttorp P. A stochastic approach for assessing the effect of changes in synoptic circulation patterns on gauge precipitation [J].Water Resources Research,1993, 29(10): 3 303-3 315.
[43]Gao X G, Sorooshian S. A stochastic precipitation disaggregation scheme for GCM applications [J].Journal of Climate,1994, 7(2): 238-247.
[44]Wilks D S. Multisite generalization of a daily stochastic precipitation generation model [J].Journal of Hydrology,1998, 210(1/4): 178-191.
[45]Brissette F P, Khalili M, Leconte R. Efficient stochastic generation of multi-site synthetic precipitation data [J].Journal of Hydrology,2007, 345(3/4): 121-133.
[46]Fang J N, Tacher L. An efficient and accurate algorithm for generating spatially-correlated random fields [J].Communications in Numerical Methods in Engineering,2003, 19(10): 801-808.
[47]Kottegoda N T, Natale L, Raiteri E. A parsimonious approach to stochastic multisite modelling and disaggregation of daily rainfall [J].Journal of Hydrology,2003, 274(1/4): 47-61.
[48]Harpham C, Wilby R L. Multi-site downscaling of heavy daily precipitation occurrence and amounts [J].Journal of Hydrology,2005, 312(1/4): 235-255.
[49]Timbal B, Jones D A. Future projections of winter rainfall in southeast Australia using a statistical downscaling technique [J].Climatic Change,2008, 86(1/2): 165-187.
[50]Zorita E, Von Storch H. The analog method as a simple statistical downscaling technique: Comparison with more complicated methods [J].Journal of Climate,1999, 12(8): 2 474-2 489.
[51]Fernandez J, Saenz J. Improved field reconstruction with the analog method: Searching the CCA space [J].Climate Research,2003, 24(3): 199-213.
[52]Moron V, Robertson A W, Ward M N,et al. Weather types and rainfall over Senegal. part II: Downscaling of GCM simulations [J].Journal of Climate,2008, 21(2): 288-307.
[53]Panagoulia D, Bardossy A, Lourmas G. Multivariate stochastic downscaling models for generating precipitation and temperature scenarios of climate change based on atmospheric circulation [J].Global Nest Journal,2008, 10(2): 263-272.
[54]Rabiner L R, Juan B H. An Introduction to Hidden Markov Models [Z].IEEE ASSP Magazine,1986.
[55]Zucchini W, Guttorp P. A hidden markov model for space-time precipitation [J].Water Resources Research,1991, 27(8): 1 917-1 923.
[56]Hughes J P, Guttorp P. Incorporating spatial dependence and atmospheric data in a model of precipitation [J].Journal of Applied Meteorology,1994, 33(12): 1 503-1 515.
[57]Bellone E, Hughes J P, Guttorp P. A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts [J].Climate Research, 2000, 15(1): 1-12. 
[58]Hughes J P,Guttorp P. A non-homogeneous hidden Markov model for precipitation occurrence [J].Journal of the Royal Statistical Society (Series C),1999, 48: 15-30.
[59]Ceo R, Stern R D. Fitting models to daily rainfall data [J].Journal of Applied Meteorology,1982, 21(7): 1 024-1 031.
[60]Chandler R E. On the use of generalized linear models for interpreting climate variability [J].Environmetrics,2005, 16(7): 699-715.
[61]Yan Z W, Bate S, Chandler R E,et al. An analysis of daily maximum wind speed in northwestern Europe using generalized linear models [J].Journal of Climate,2002, 15(15): 2 073-2 088.
[62]Zheng X G, Katz R W. Mixture model of generalized chain-dependent processes and its application to simulation of interannual variability of daily rainfall [J].Journal of Hydrology,2008, 349(1/2): 191-199.
[63]Fealy R, Sweeney J. Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalised linear modelling approach [J].International Journal of Climatology,2007, 27(15): 2 083-2 094.
[64]Buishand T A, Shabalova M V, Brandsma T. On the choice of the temporal aggregation level for statistical downscaling of precipitation [J].Journal of Climate,2004, 17(9): 1 816-1 827.
[65]Northrop P. A clustered spatial-temporal model of rainfall [J].Proceedings of the Royal Society A,1998, 454(1 975): 1 875-1 888.
[66]Cowpertwait P. Further developments of the neyman-scott clustered point process for modeling rainfall [J].Water Resources Research,1991, 27(7): 1 431-1 438.
[67]Cowpertwait P. A generalized spatial-temporal model of rainfall based on a clustered point process [J].Proceedings of the Royal Society of London (Series A),1995, 450(1 938): 163-175.
[68]Cowpertwait P. A Poisson-cluster model of rainfall: High-order moments and extreme values [J].Proceedings of the Royal Society of London (Series A),1998, 454(1 971): 885-898.
[69]Cowpertwait P, Oconnell P E, Metcalfe A V,et al. Stochastic point process modelling of rainfall .1. Single-site fitting and validation [J].Journal of Hydrology,1996, 175(1/4): 17-46.
[70]Cowpertwait P, Oconnell P E, Metcalfe A V,et al. Stochastic point process modelling of rainfall .2. Regionalisation and disaggregation [J].Journal of Hydrology,1996, 175(1/4): 47-65.
[71]Velghe T, Troch P A, Detroch F P,et al. Evaluation of cluster-based rectangular pulses point process models for rainfall [J].Water Resources Research,1994, 30(10): 2 847-2 857.
[72]Veneziano D, Villani P. Identification of rain cells from radar and stochastic modeling of space-time rainfall [J].Meccanica,1996, 31(1): 27-42.
[73]Uijlenhoet R, Stricker J, Torfs P,et al. Towards a stochastic model of rainfall for radar hydrology: Testing the Poisson homogeneity hypothesis [J].Physics and Chemistry of the Earth Part B，1999, 24(6): 747-755.
[74]Marsan D, Schertzer D N, Lovejoy S. Causal space-time multifractal processes: Predictability and forecasting of rain fields [J].Journal of Geophysical Research,1996, 101(D21): 26 333-26 346.[75]Tessier Y, Lovejoy S, Schertzer D. Universal multifractals-theory and observations for rain and clouds [J].Journal of Applied Meteorology,1993, 32(2): 223-250.
[76]Lovejoy S, Duncan M R, Schertzer D. Scalar multifractal radar observer′s problem [J].Journal of Geophysical Research,1996, 101(D21): 26 479-26 491.
[77]Deidda R, Benzi R, Siccardi F. Multifractal modeling of anomalous scaling laws in rainfall [J].Water Resources Research,1999, 35(6): 1 853-1 867.
[78]Kiely G, Ivanova K. Multifractal analysis of hourly precipitation [J].Physics and Chemistry of the Earth Part B: Hydrology Oceans and Atmosphere,1999, 24(7): 781-786.
[79]Ladoy P, Schmitt F, Schertzer D,et al. The multifractal temporal variability of nimes rainfall data [J].Comptes Rendus De L Academie Des Sciences Serie II,1993, 317(6): 775-782.
[80]Olsson J. Validity and applicability of a scale-independent, multifractal relationship for rainfall [J].Atmospheric Research,1996, 42(1/4): 53-65.
[81]Tessier Y, Lovejoy S, Hubert P,et al. Multifractal analysis and modeling of rainfall and river flows and scaling, causal transfer functions [J].Journal of Geophysical Research,1996, 101(D21): 26 427-26 440. 
[82]Garcia-Marin A P,Jimenez-Hornero F J, Ayuso-Munoz J L. Multifractal analysis as a tool for validating a rainfall model [J].Hydrological Processes,2008, 22(14): 2 672-2 688.
[83]Schertzer D, Lovejoy S. Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes [J].Journal of Geophysical Research,1987, 92(D8): 9 693-9 714.
[84]Deidda R. Multifractal analysis and simulation of rainfall fields in space [J].Physics and Chemistry of the Earth Part B: Hydrology Oceans and Atmosphere,1999, 24(1/2): 73-78. 
[85]Deidda R.Rainfall downscaling in a space-time multifractal framework [J].Water Resources Research,2000, 36(7): 1 779-1 794.
[86]Over T M, Gupta V K. Statistical-analysis of mesoscale rainfall-dependence of a random cascade generator on large-scale forcing [J].Journal of Applied Meteorology,1994, 33(12): 1 526-1 542.
[87]Pathirana A, Herath S. Multifractal modelling and simulation of rain fields exhibiting spatial heterogeneity [J].Hydrology and Earth System Sciences,2002, 6(4): 695-708.
[88]Veneziano D, Bras R L, Niemann J D. Nonlinearity and self-similarity of rainfall in time and a stochastic model [J].Journal of Geophysical Research,1996, 101(D21): 26 371-26 392.
[89]Carsteanu A, Venugopal V, Foufoula-Georgiou E. Event-specific multiplicative cascade models and an application to rainfall [J].Journal of Geophysical Research,1999, 104(D24): 31 611-31 622.[90]Menabde M, Harris D, Seed A,et al. Multiscaling properties of rainfall and bounded random cascades [J].Water Resources Research,1997, 33(12): 2 823-2 830.
[91]Over T M, Gupta V K. A space-time theory of mesoscale rainfall using random cascades [J].Journal of Geophysical Research,1996, 101(D21): 26 319-26 331.
[92]Perica S, Foufoulageorgiou E. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions [J].Journal of Geophysical Research,1996, 101(D21): 26 347-26 361.
[93]Menabde M, Seed A, Harris D,et al. Self-similar random fields and rainfall simulation [J].Journal of Geophysical Research,1997, 102(D12): 13 509-13 515.
[94]Harris D, Menabde M, Seed A,et al. Breakdown coefficients and scaling properties of rain fields [J].Nonlinear Processes in Geophysics,1998, 5(2): 93-104.
[95]Bremnes J B. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output [J].Monthly Weather Review,2004, 132(1): 338-347.
[96]Friederichs P, Hense A. Statistical downscaling of extreme precipitation events using censored quantile regression [J].Monthly Weather Review,2007, 135(6): 2 365-2 378. 
[97]Vrac M,Marbaix P, Paillard D,et al. Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe [J].Climate of the Past,2007, 3(4): 669-682.
[98]Hessami M, Gachon P, Ouarda T,et al. Automated regression-based statistical downscaling tool [J].Environmental Modelling & Software,2008, 23(6): 813-834.
[99]Hervada-Sala C, Pawlowsky-Glahn V, Jarauta-Bragulat E. A statistical method to downscale temperature forecasts:A case study in Catalonia [J].Meteorological Applications,2000, 7(1): 75-82. [100]Coulibaly P.Downscaling daily extreme temperatures with genetic programming [J].Geophysical Research Letters,2004, 31(16):L16203. 
[101]Pandey G R, Cayan D R, Dettinger M D,et al. A hybrid orographic plus statistical model for downscaling daily precipitation in northern California [J].Journal of Hydrometeorology,2000, 1(6): 491-506.
[102]Burton A, Kilsby C G, Fowler H J,et al. RainSim: A spatial-temporal stochastic rainfall modelling system [J].Environmental Modelling & Software,2008, 23(12): 1 356-1 369.
[103]Rebora N, Ferraris L, Von Hardenberg J,et al. RainFARM: Rainfall downscaling by a filtered autoregressive model [J].Journal of Hydrometeorology,2006, 7(4): 724-738.
[104]Lim Y K, Shin D W, Cocke S,et al. Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States [J].Journal of Geophysical Research,2007,112,D24102, doi:10.1029/2007 JD 008764.

[1]	范丽军;符淙斌;陈德亮;. 统计降尺度法对未来区域气候变化情景预估的研究进展[J]. 地球科学进展, 2005, 20(3): 320-329.

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