中国汛期降水模式误差主分量相似预报研究
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摘要
本工作主要是针对提高我国季节预报业务模式对我国汛期降水预报水平这一问题而提出。基于相似-动力预报基本原理,在不改进和发展当前模式的基础上,从反问题的角度,充分利用已有历史资料,通过模式的后处理对模式误差进行统计预报。针对动力模式预报误差的估计问题及模式误差的局地性特点,提出将模式误差的直接相似订正问题转化成具有针对性的模式误差主分量的相似预报。客观上将模式误差主分量分成可预报和不可预报两部分,对于可预报部分采用演化相似最优多因子动态配置方案进行相似预报,而对于不可预报部分则用系统平均代替。考虑到外强迫演变对气候的重要影响,提出了演化相似的概念并发展了一种科学合理的演化相似判据,这相当于吸收了预报因子在一个季节时间内的时空演变信息。在对模式误差主分量进行相似预报的过程中,在预报年前期通过所有潜在的预报因子对该主分量预报技巧的排序,确定预报该主分量的主导因子；其后,基于该因子,通过相关分析等方法在预报因子排序中挑选出对该主分量有一定预报技巧且相对独立的预报因子集,并对因子集中的这些因子进行优化配置回报试验,确定最优配置预报因子；最后,用预报该主分量最优配置预报因子预报该主分量预报年时间系数。
基于国家气候中心季节和月动力延伸期预报业务模式、CMAP降水资料、NCEP/NCAR2米温度资料及国家气候中心气候系统诊断预报室74项环流指数和NOAA40个气候指数,首先对该方案中的一些关键技术问题进行了研究。其后,将中国按地理分区分成华南、长江中下游、华北、东北、西北东部、西北西部、西藏和西南8区,对中国各区域季节降水和温度,月降水和温度开展了实际业务预报试验。为检验动力、动力-统计和统计三种预报方法的预报技巧差别,还将该方案推广至季节和月降水、温度的统计预报,并在此基础上研究了降水和温度可预报性的时空分布特征。得到的主要结果和结论如下：
1.本工作的特点之一就是充分利用了预报因子的演化相似信息,在利用因子的演化相似进行统计预报时提出了一种科学的演化相似判据即相似指数,实践证实了该相似判据在判断因子演化相似问题上的合理性。而在预报因子问题上提出了一种确定最优预报因子配置的方法,即最优多因子动态配置方法,该方法先通过确定主导因子及演化相似因子,再对因子进行相关分析和自由配置组合预报的方式克服了预报因子自由度大和预报因子之间的相关关系等问题的困扰,并实践证明了最优多因子动态配置方式对预报技巧的改进。
2.从实际业务预报2005-2011年中国区域汛期降水预报技巧看来,整体而言4种相似预报方案的预报技巧都较系统误差订正预报高,系统误差订正预报2005-2010年的平均ACC为0.02,而4种相似预报方案的ACC分别为0.11、0.19、0.15和0.10,以基于模式误差主分量相似预报因子的CMAP降水主分量相似预报的预报技巧最高,CMAP主分量相似预报次之,其次是模式误差主分量相似预报,在加入2011年预报结果后,系统误差订正预报及4种相似预报方法预报近7年的平均ACC变成0.06、0.14、0.19、0.15和0.13。
3.区域汛期降水可预报性上,近几年模式系统误差订正预报在长江中下游和华北有较高预报技巧；模式误差主分量相似预报除在华北区域预报技巧不及系统误差订正预报,其他区域均有不同程度的改进,以长江中下游、东北、西北西、西藏和西南的改进最大,其中在除西北西以外的区域,相似动力预报体现出了较高的预报技巧；对于基于模式误差主分量相似预报因子的汛期降水主分量相似预报,该方案在除华南和华北以外的区域均表现出较高的预报技巧,特别对于长江中下游、西北东和西藏区域,近6年平均预报ACC都在0.4以上；汛期降水主分量相似预报方案在华南、东北和西藏体现出较高预报技巧,基于其预报因子的模式误差主分量相似预报方案预报技巧位于华南、东北、西北东西藏和西南。
4.从近几年预报各区域汛期降水模式误差第一模态的主导因子看来,模式预报中国东部区域汛期降水误差受北半球副高影响较大,对于华北还有极涡的影响,而西部区域则主要受大西洋副高及nino区海温的影响。近几年CGCM模式预报中国区域夏季温度的误差主要受前冬的一些环流和下垫面异常影响,而西藏高原(25°N-35°N,80°E-100°E)在近几年是主要因素之一,2011年开始,似乎这一因素的影响在减弱而北半球各种极涡的影响在增强。
5.在季节可预报性上,对于中国区域降水的预报,模式在冬春两季的预报技巧明显高于夏秋,而动力统计预报和统计预报则在夏冬更有预报技巧。对于温度,模式预报秋冬两季预报技巧明显高于春夏,可能是模式本身在秋冬两季预报技巧已经很高,因此动力统计预报对这两季节温度预报技巧几乎没有改进,只是在秋季的预报RMSE有一定减小,而对于模式预报技巧相对较低的春夏两季,动力统计预报则使得模式在这两季的预报ACC有一定提高。
6.在近几年前汛期和后汛期降水预报上,对于华南区域,模式误差主分量相似预报方法对DERF模式预报该区域降水几乎没有改进,归纳起来可能的原因有以下几方面：(1)该区域降水的复杂性；(2)模式在这两时段本身没有什么预报技巧；(3)还可能属于方案本身的缺陷。然而就中国区域而言,模式误差主分量相似预报方法对模式预报这两月降水还是略有改进,将近6年前汛和后汛模式预报平均ACC分别由0.07和0.02提高到0.08和0.03,以在华北、东北和西北等区域的提高最为明显。
7.在月降水和温度可预报性上,模式预报2005-2010年的温度和降水的平均技巧分别是0.38和0.13,和前人的结论非常一致。整体看来模式误差主分量相似预报对DERF模式对月降水和温度的改进都比较有限,但对月降水预报的改进要大于温度,对降水预报的改进在有些区域和月份也比较明显,如华南夏季降水及东北春夏降水等。而纯粹的统计预报在月降水和温度预报中的预报能力都不及模式误差主分量相似预报和DERF模式预报。这可能是由于月动力延伸期预报的初值在月预报中起着重要作用,特别是在天气预报的可预报期限内,初值对预报起着决定性的作用,外强迫对于月预报有一定作用,但不是决定性作用的缘故。
8.不管是最优多因子动态配置的模式误差主分量相似预报还是基于其预报因子的降水、温度主分量相似预报在季节及月降水和温度中均表现出了一定技巧,在一些区域改进了模式对该区域降水和温度的预报水平,显示出了动力统计相结合这一思想的优越性。从相似场个数及可预报模态个数对相似动力预报的影响看来,两者对于相似动力预报都有很大影响,合适的相似场个数或可预报模态个数都能较大地提高区域汛期降水和温度的预报技巧。相似动力预报中不同区域,不同气象要素都有各自的最优相似个数,相似预报中相似个数应具有时间、空间和对象的针对性。
This work is proposed mainly to improve the prediction ability of rainy season precipitation in china for operational seasonal prediction model. Based on the basic principle of analogue-dynamical prediction and current model, from the point of inverse problem, historical data is utilized to estimate current unknown model errors using known historical analogical information. Considering the problem of prediction the model error and its local characteristics, a new idea is proposed, which transports the problem of directly estimating the model errors by analogue prediction into the problem of analogue prediction of the principal component of model errors. The principal components of model errors are objectively divided into two parts, predictability and unpredictability. The predictability ones are predicted by analogue prediction of evolving analogues of optimal configuration of multiple predictors, while the rests instead by past climate average. Taking into account the important impact of historical evolution of the external forcing on the climate change, the concept of evolving analogues is brought forth and a new scientific and reasonable criterion similarity coefficient is developed through which the temporal and spatial evolution information of a predictor in a single season could be included. When analogue prediction of the principal component of the model errors in the forecast years, firstly the dominant predictor of this principal component of the year should be determined by sorting the prediction ability of all the potential single predictors through cross-validate prediction this principal component; Subsequently, based on this predictor, predictors of sets of relatively independent and have certain predict skill in predicting this principal component are picked out through the correlation analysis and other methods, based on these factors, then an optimal configuration of multiple predictors is set up through optimal multi-predictor configuration; Finally, predicting the time coefficient of principal component in the forecast year using this optimal configuration of multiple predictors.
Based on the National Climate Center (NCC) of China operational seasonal prediction model and dynamical extended range forecast model results for the period1983-2009and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the74circulation indices of NCC Climate System Diagnostic Division and40climate indices of NOAA of US during1951-2009, the key technical issues in this method are discussed firstly. Subsequently, based on geographical partition compartmentalize china region as8small regions such as South China, the Yangtze River, North China, northeast china, eastern of Northwest, western of Northwest, Tibet and south-west respectively, and actual operational forecasting experiments are implemented on seasonal precipitation, temperature, and monthly precipitation as well as temperature for all these regions. To compare the prediction ability of the dynamical model, dynamical-statistical and statistical method in predicting seasonal and monthly precipitation and temperature, of cause, similar technique is also carried on predicting the principle components of seasonal and monthly precipitation and temperature, and then, the spatial and temporal distribution characteristics of predictability of precipitation and temperature are discussed. The main results and conclusions are as follows:
1. One of the features of this work is the information of evolving analogues of the predictors is utilized and a new scientific criterion similarity coefficient is developed. Practice confirmed that the rationality of the similarity criteria on evolving analogous judgment. While on the problem of selecting predictors, a method to determine the optimal configuration of predictors called dynamic and optimal multi-predictor configuration is proposed. The method overcomes two common problems in nonlinearity prediction as degrees of freedom of prediction factors and the relationship between the prediction factors respectively through determining the dominant predictor and predictors of evolving analogues, correlation analysis and dynamic and optimal multi-predictor configuration scheme. Results in prediction have proved that the scheme dynamic and optimal multi-factor configuration can improve forecast skill.
2. Seem from the forecast skill of actual operational forecast summer rainfall in china in2005-2011, generally the four kinds of analogues prediction are more skillful than model systematic correction of error forecast. The average anomaly correlation coefficients(ACC) for model systematic correction of errors prediction is0.02in2005-2010, while the four kinds of analogues prediction are0.11,0.19,0.15,0.10respectively. The most skillful method is analogue prediction of principle component of CMAP based on predictors of analogue prediction of principle component of model errors; the analogue prediction of principle component of CMAP next, then is analogue prediction of principle component of model errors. After joining the2011forecast, the average ACC for model systematic correction of errors forecasting and four kinds of analogue forecast in the nearly seven-year is0.06,0.14,0.19,0.15and0.13.
3. On the predictability of rainy season precipitation, in recent years, model systematic correction of errors forecast is skillful in prediction of rainy season precipitation in Yangtze River and North China. Analogue prediction of principal component of the model errors has certain improvements on model systematic correction of errors forecast except in North China, especially in Yangtze River, Northeast, western of northwest, Tibet and southwest. In these regions analogue-dynamical prediction is skillful in prediction rainy season precipitation except in western of northwest, while for analogue prediction of principle component of CMAP based on its predictors, which is skillful in prediction rainy season precipitation in most regions except South china and North china, especially in Yangtze River, eastern of northwest and Tibet, the average ACC in the nearly6years is above0.4; analogue prediction of principle component of CMAP is skillful in prediction precipitation in South China, Northeast and Tibet, and for analogue prediction of principal component of model errors based on its predictors, which prediction skill is located in the South china, Northeast, Eastern of Northwest, Tibet and southwest.
4. Opinion from the dominant factor in recent years to forecast the first principle component of model errors in regional rainy season precipitation prediction, the error of model forecast of rainy season rainfall is mainly influenced by the Northern Hemisphere subtropical high in China's eastern region, for North China which is also impacted by the polar vortex, while the western region is mainly affected by the Atlantic subtropical high and sea surface temperature in Nino region. The error of CGCM prediction of summer temperature in china region is mainly affected by the abnormal circulation and surface features in previous winter, and the Tibetan Plateau (25°N-35°N,80°E-100°E), one of the main factors, and since2011, it seems that the impact is weakening while the impact of polar vortex in the northern hemisphere is enhancing.
5. On Seasonal predictability, for the precipitation prediction in china region, the forecast ability, in winter and spring was significantly higher than in summer and autumn, but it is better in summer and winter for the dynamical-statistical forecasting and statistical forecasting. For temperature, model forecast in autumn and winter is significantly higher than in spring and summer, maybe the model itself is already skillful in autumn and winter temperature forecast, so the dynamic-statistical forecasting practically has little improvement in seasonal temperature forecast of the two seasons, only in autumn the forecast RMSE has reduced comparing to model itself. But for the relatively less skill of spring and summer of model forecast, dynamic-statistical forecasting makes certain improvement in ACC for these two seasons.
6. On prediction of the precipitation in the first and second raining season in South China region in recent years, comparing to model prediction of DERF, the analogue prediction of principle components of model error almost has no improvement in the region precipitation prediction. Possible reasons are summed up in the following:(1) the complexity of the regional precipitation;(2) model itself has no forecast skill in these two seasons;(3) maybe the defects of the program itself. However, on China's regional, comparing to model itself forecast, the analogue prediction of principal component of the model error shows a slight improvement in these two months of precipitation prediction, and in the nearly six years, the average ACC for model prediction and dynamical-statistical prediction in this two months is0.07,0.08and0.02,0.03, respectively. The most obvious improvement exists in north china, northeast, northwest and so on.
7. On the predictability of monthly precipitation and temperature,2005-2010, the average ACC of model prediction for temperature and precipitation is0.38and0.13, and very consistent with previous conclusions. As a whole, analogue prediction of principle component of the model error is relatively limited on the improvement of the DERF model on prediction monthly precipitation and temperature, but the improvements for precipitation is greater than that of temperature, and the improvements is obviously in some regions and months such as summer rainfall in South China and spring and summer precipitation in Northeast. Statistical forecasting are not capabilities than the analogue prediction principle components of model error and DERF model in the monthly precipitation and temperature forecasts because of the important effect the initial value of DERF, especially for the weather forecasts in the predictability limit, while external forcing has a certain impact on monthly forecast, but not so crucial as the initial value does.
8. Both analogue prediction of the principal component of model errors based on the dynamic and optimal multi-factor configuration and analogue prediction of the principal component of precipitation and temperature based on its predictors, show certain prediction skill in seasonal and monthly precipitation and temperature prediction, and in some areas the precipitation and temperature prediction levels are improved compared to model systematic correction of errors forecasting, which demonstrated the superiority of combination of dynamic and statistical. From the impact of the numbers of analogues and mode of predictability on analogue-dynamical forecasting, both have great impact on prediction skill. Either the suitable number of analogues or modes of predictability can greatly improve the forecast skill of regional rainy season precipitation and temperature. For analogue-dynamical prediction, in different regions, different meteorological elements have their own optimal numbers of analogues and they should be given due to specific time, space and objects.
基于国家气候中心季节和月动力延伸期预报业务模式、CMAP降水资料、NCEP/NCAR2米温度资料及国家气候中心气候系统诊断预报室74项环流指数和NOAA40个气候指数,首先对该方案中的一些关键技术问题进行了研究。其后,将中国按地理分区分成华南、长江中下游、华北、东北、西北东部、西北西部、西藏和西南8区,对中国各区域季节降水和温度,月降水和温度开展了实际业务预报试验。为检验动力、动力-统计和统计三种预报方法的预报技巧差别,还将该方案推广至季节和月降水、温度的统计预报,并在此基础上研究了降水和温度可预报性的时空分布特征。得到的主要结果和结论如下：
1.本工作的特点之一就是充分利用了预报因子的演化相似信息,在利用因子的演化相似进行统计预报时提出了一种科学的演化相似判据即相似指数,实践证实了该相似判据在判断因子演化相似问题上的合理性。而在预报因子问题上提出了一种确定最优预报因子配置的方法,即最优多因子动态配置方法,该方法先通过确定主导因子及演化相似因子,再对因子进行相关分析和自由配置组合预报的方式克服了预报因子自由度大和预报因子之间的相关关系等问题的困扰,并实践证明了最优多因子动态配置方式对预报技巧的改进。
2.从实际业务预报2005-2011年中国区域汛期降水预报技巧看来,整体而言4种相似预报方案的预报技巧都较系统误差订正预报高,系统误差订正预报2005-2010年的平均ACC为0.02,而4种相似预报方案的ACC分别为0.11、0.19、0.15和0.10,以基于模式误差主分量相似预报因子的CMAP降水主分量相似预报的预报技巧最高,CMAP主分量相似预报次之,其次是模式误差主分量相似预报,在加入2011年预报结果后,系统误差订正预报及4种相似预报方法预报近7年的平均ACC变成0.06、0.14、0.19、0.15和0.13。
3.区域汛期降水可预报性上,近几年模式系统误差订正预报在长江中下游和华北有较高预报技巧；模式误差主分量相似预报除在华北区域预报技巧不及系统误差订正预报,其他区域均有不同程度的改进,以长江中下游、东北、西北西、西藏和西南的改进最大,其中在除西北西以外的区域,相似动力预报体现出了较高的预报技巧；对于基于模式误差主分量相似预报因子的汛期降水主分量相似预报,该方案在除华南和华北以外的区域均表现出较高的预报技巧,特别对于长江中下游、西北东和西藏区域,近6年平均预报ACC都在0.4以上；汛期降水主分量相似预报方案在华南、东北和西藏体现出较高预报技巧,基于其预报因子的模式误差主分量相似预报方案预报技巧位于华南、东北、西北东西藏和西南。
4.从近几年预报各区域汛期降水模式误差第一模态的主导因子看来,模式预报中国东部区域汛期降水误差受北半球副高影响较大,对于华北还有极涡的影响,而西部区域则主要受大西洋副高及nino区海温的影响。近几年CGCM模式预报中国区域夏季温度的误差主要受前冬的一些环流和下垫面异常影响,而西藏高原(25°N-35°N,80°E-100°E)在近几年是主要因素之一,2011年开始,似乎这一因素的影响在减弱而北半球各种极涡的影响在增强。
5.在季节可预报性上,对于中国区域降水的预报,模式在冬春两季的预报技巧明显高于夏秋,而动力统计预报和统计预报则在夏冬更有预报技巧。对于温度,模式预报秋冬两季预报技巧明显高于春夏,可能是模式本身在秋冬两季预报技巧已经很高,因此动力统计预报对这两季节温度预报技巧几乎没有改进,只是在秋季的预报RMSE有一定减小,而对于模式预报技巧相对较低的春夏两季,动力统计预报则使得模式在这两季的预报ACC有一定提高。
6.在近几年前汛期和后汛期降水预报上,对于华南区域,模式误差主分量相似预报方法对DERF模式预报该区域降水几乎没有改进,归纳起来可能的原因有以下几方面：(1)该区域降水的复杂性；(2)模式在这两时段本身没有什么预报技巧；(3)还可能属于方案本身的缺陷。然而就中国区域而言,模式误差主分量相似预报方法对模式预报这两月降水还是略有改进,将近6年前汛和后汛模式预报平均ACC分别由0.07和0.02提高到0.08和0.03,以在华北、东北和西北等区域的提高最为明显。
7.在月降水和温度可预报性上,模式预报2005-2010年的温度和降水的平均技巧分别是0.38和0.13,和前人的结论非常一致。整体看来模式误差主分量相似预报对DERF模式对月降水和温度的改进都比较有限,但对月降水预报的改进要大于温度,对降水预报的改进在有些区域和月份也比较明显,如华南夏季降水及东北春夏降水等。而纯粹的统计预报在月降水和温度预报中的预报能力都不及模式误差主分量相似预报和DERF模式预报。这可能是由于月动力延伸期预报的初值在月预报中起着重要作用,特别是在天气预报的可预报期限内,初值对预报起着决定性的作用,外强迫对于月预报有一定作用,但不是决定性作用的缘故。
8.不管是最优多因子动态配置的模式误差主分量相似预报还是基于其预报因子的降水、温度主分量相似预报在季节及月降水和温度中均表现出了一定技巧,在一些区域改进了模式对该区域降水和温度的预报水平,显示出了动力统计相结合这一思想的优越性。从相似场个数及可预报模态个数对相似动力预报的影响看来,两者对于相似动力预报都有很大影响,合适的相似场个数或可预报模态个数都能较大地提高区域汛期降水和温度的预报技巧。相似动力预报中不同区域,不同气象要素都有各自的最优相似个数,相似预报中相似个数应具有时间、空间和对象的针对性。
This work is proposed mainly to improve the prediction ability of rainy season precipitation in china for operational seasonal prediction model. Based on the basic principle of analogue-dynamical prediction and current model, from the point of inverse problem, historical data is utilized to estimate current unknown model errors using known historical analogical information. Considering the problem of prediction the model error and its local characteristics, a new idea is proposed, which transports the problem of directly estimating the model errors by analogue prediction into the problem of analogue prediction of the principal component of model errors. The principal components of model errors are objectively divided into two parts, predictability and unpredictability. The predictability ones are predicted by analogue prediction of evolving analogues of optimal configuration of multiple predictors, while the rests instead by past climate average. Taking into account the important impact of historical evolution of the external forcing on the climate change, the concept of evolving analogues is brought forth and a new scientific and reasonable criterion similarity coefficient is developed through which the temporal and spatial evolution information of a predictor in a single season could be included. When analogue prediction of the principal component of the model errors in the forecast years, firstly the dominant predictor of this principal component of the year should be determined by sorting the prediction ability of all the potential single predictors through cross-validate prediction this principal component; Subsequently, based on this predictor, predictors of sets of relatively independent and have certain predict skill in predicting this principal component are picked out through the correlation analysis and other methods, based on these factors, then an optimal configuration of multiple predictors is set up through optimal multi-predictor configuration; Finally, predicting the time coefficient of principal component in the forecast year using this optimal configuration of multiple predictors.
Based on the National Climate Center (NCC) of China operational seasonal prediction model and dynamical extended range forecast model results for the period1983-2009and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the74circulation indices of NCC Climate System Diagnostic Division and40climate indices of NOAA of US during1951-2009, the key technical issues in this method are discussed firstly. Subsequently, based on geographical partition compartmentalize china region as8small regions such as South China, the Yangtze River, North China, northeast china, eastern of Northwest, western of Northwest, Tibet and south-west respectively, and actual operational forecasting experiments are implemented on seasonal precipitation, temperature, and monthly precipitation as well as temperature for all these regions. To compare the prediction ability of the dynamical model, dynamical-statistical and statistical method in predicting seasonal and monthly precipitation and temperature, of cause, similar technique is also carried on predicting the principle components of seasonal and monthly precipitation and temperature, and then, the spatial and temporal distribution characteristics of predictability of precipitation and temperature are discussed. The main results and conclusions are as follows:
1. One of the features of this work is the information of evolving analogues of the predictors is utilized and a new scientific criterion similarity coefficient is developed. Practice confirmed that the rationality of the similarity criteria on evolving analogous judgment. While on the problem of selecting predictors, a method to determine the optimal configuration of predictors called dynamic and optimal multi-predictor configuration is proposed. The method overcomes two common problems in nonlinearity prediction as degrees of freedom of prediction factors and the relationship between the prediction factors respectively through determining the dominant predictor and predictors of evolving analogues, correlation analysis and dynamic and optimal multi-predictor configuration scheme. Results in prediction have proved that the scheme dynamic and optimal multi-factor configuration can improve forecast skill.
2. Seem from the forecast skill of actual operational forecast summer rainfall in china in2005-2011, generally the four kinds of analogues prediction are more skillful than model systematic correction of error forecast. The average anomaly correlation coefficients(ACC) for model systematic correction of errors prediction is0.02in2005-2010, while the four kinds of analogues prediction are0.11,0.19,0.15,0.10respectively. The most skillful method is analogue prediction of principle component of CMAP based on predictors of analogue prediction of principle component of model errors; the analogue prediction of principle component of CMAP next, then is analogue prediction of principle component of model errors. After joining the2011forecast, the average ACC for model systematic correction of errors forecasting and four kinds of analogue forecast in the nearly seven-year is0.06,0.14,0.19,0.15and0.13.
3. On the predictability of rainy season precipitation, in recent years, model systematic correction of errors forecast is skillful in prediction of rainy season precipitation in Yangtze River and North China. Analogue prediction of principal component of the model errors has certain improvements on model systematic correction of errors forecast except in North China, especially in Yangtze River, Northeast, western of northwest, Tibet and southwest. In these regions analogue-dynamical prediction is skillful in prediction rainy season precipitation except in western of northwest, while for analogue prediction of principle component of CMAP based on its predictors, which is skillful in prediction rainy season precipitation in most regions except South china and North china, especially in Yangtze River, eastern of northwest and Tibet, the average ACC in the nearly6years is above0.4; analogue prediction of principle component of CMAP is skillful in prediction precipitation in South China, Northeast and Tibet, and for analogue prediction of principal component of model errors based on its predictors, which prediction skill is located in the South china, Northeast, Eastern of Northwest, Tibet and southwest.
4. Opinion from the dominant factor in recent years to forecast the first principle component of model errors in regional rainy season precipitation prediction, the error of model forecast of rainy season rainfall is mainly influenced by the Northern Hemisphere subtropical high in China's eastern region, for North China which is also impacted by the polar vortex, while the western region is mainly affected by the Atlantic subtropical high and sea surface temperature in Nino region. The error of CGCM prediction of summer temperature in china region is mainly affected by the abnormal circulation and surface features in previous winter, and the Tibetan Plateau (25°N-35°N,80°E-100°E), one of the main factors, and since2011, it seems that the impact is weakening while the impact of polar vortex in the northern hemisphere is enhancing.
5. On Seasonal predictability, for the precipitation prediction in china region, the forecast ability, in winter and spring was significantly higher than in summer and autumn, but it is better in summer and winter for the dynamical-statistical forecasting and statistical forecasting. For temperature, model forecast in autumn and winter is significantly higher than in spring and summer, maybe the model itself is already skillful in autumn and winter temperature forecast, so the dynamic-statistical forecasting practically has little improvement in seasonal temperature forecast of the two seasons, only in autumn the forecast RMSE has reduced comparing to model itself. But for the relatively less skill of spring and summer of model forecast, dynamic-statistical forecasting makes certain improvement in ACC for these two seasons.
6. On prediction of the precipitation in the first and second raining season in South China region in recent years, comparing to model prediction of DERF, the analogue prediction of principle components of model error almost has no improvement in the region precipitation prediction. Possible reasons are summed up in the following:(1) the complexity of the regional precipitation;(2) model itself has no forecast skill in these two seasons;(3) maybe the defects of the program itself. However, on China's regional, comparing to model itself forecast, the analogue prediction of principal component of the model error shows a slight improvement in these two months of precipitation prediction, and in the nearly six years, the average ACC for model prediction and dynamical-statistical prediction in this two months is0.07,0.08and0.02,0.03, respectively. The most obvious improvement exists in north china, northeast, northwest and so on.
7. On the predictability of monthly precipitation and temperature,2005-2010, the average ACC of model prediction for temperature and precipitation is0.38and0.13, and very consistent with previous conclusions. As a whole, analogue prediction of principle component of the model error is relatively limited on the improvement of the DERF model on prediction monthly precipitation and temperature, but the improvements for precipitation is greater than that of temperature, and the improvements is obviously in some regions and months such as summer rainfall in South China and spring and summer precipitation in Northeast. Statistical forecasting are not capabilities than the analogue prediction principle components of model error and DERF model in the monthly precipitation and temperature forecasts because of the important effect the initial value of DERF, especially for the weather forecasts in the predictability limit, while external forcing has a certain impact on monthly forecast, but not so crucial as the initial value does.
8. Both analogue prediction of the principal component of model errors based on the dynamic and optimal multi-factor configuration and analogue prediction of the principal component of precipitation and temperature based on its predictors, show certain prediction skill in seasonal and monthly precipitation and temperature prediction, and in some areas the precipitation and temperature prediction levels are improved compared to model systematic correction of errors forecasting, which demonstrated the superiority of combination of dynamic and statistical. From the impact of the numbers of analogues and mode of predictability on analogue-dynamical forecasting, both have great impact on prediction skill. Either the suitable number of analogues or modes of predictability can greatly improve the forecast skill of regional rainy season precipitation and temperature. For analogue-dynamical prediction, in different regions, different meteorological elements have their own optimal numbers of analogues and they should be given due to specific time, space and objects.
引文
Baldwin M P, Stephenson D B, Thompson D W, et al.2003. Stratospheric memory and skill of Extended-Range weather forecasts. Science,301:636-640
Barnett T P, Preisendorfer R.1987. Origins and Levels of Monthly and Seasonal Forecast Skill for United States Surface Air Temperatures Determined by Canonical Correlation Analysis. Mon. Wea. Rev,115(9):1825-1850
Baumhefner D B.1996. Numerical extended-range prediction:forecast skill using a low-resolution climate model. Mon. Wea. Rev,124:1965-1980
Bengtsson L, Schese U, Roeckner E, et al.1993. A two-tiered approach to long-range climate forecasting. Science,261:1027-102
Bennett A F, Leslie L M.1981. Statistical correction of the Australian region primitive equation model. Mon. Wea. Rev,109:453-462
Bjerknes J.1966. A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature. Tellus,18:820-829
Bjerknes J.1969. Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev,97: 163-172
Bjerknes V.1904. Das Problem der Wettervorhersage:betrachtet vom Stadpunkte der Mechanik and der Physik. Meteor. Zeit.,21:1-7
Bryan K, Cox M D.1972. An approximate equation of state for numerical models of ocean circulation.J. Phys. Oceanogr.,2:510-514
Chamey J G, Fjortoft R, von Neumann J.1950. Numerical integration of the barotropic vorticity equation.Tellus,2:237-254
Chen Hong, Lin Zhaohui.2006. A New Correction Method suitable for Dynamical Climate Prediction. Adv. Atmos. Sci,23(3):425-430
Chou Ge-Fen.1974. A problem of using past data in numerical weather forecasting. Scientia Sinica,17(6):814-825
Christine D, Yan G L.2004. Use of a Principal Components Analysis for the Generation of Daily Time Series.J. Appl. Meteor.,43:984-996
Cubasch U, Wiin-Nielsen A C.1986. Predictability studies with the ECMWF spectral model for the extended range: the impact of horizontal resolution and sea surface temperature. Tellus, 38:25-41
Feddersen H, Navarra A, Ward M N.1999. Reduction of model systematic error by statistical correction for dynamical seasonal predictions.J. Climate,12:1974-1989
Feng Guolin, Cao Hongxing, Gao Xinquan, et al.2001. Prediction of Precipitation during Summer Monsoon with Self-memorial Model. Adv. Atmos. Sci.,18(5):701-709
Ferranti L, Palmer T N, Molteni F, et al.1990. Tropical-extratropical interaction associated with the 30-60 day oscillation and its impact on medium and extended range prediction. J. Atmos. Sci,47(18):2177-2199
Fraedrich K, Smith N R.1989. Combining predictive schemes in long-range forecasting. J. Climate,2:291-294
Gent P R, McWilliams J C.1990.lsopycnal mixing in ocean circulation models. J. Phys. Oceanogr, (20)1:150-155
Goddard L, Mason S J, Zebiak S E, et al.2001. Current approaches to seasonal to interannual climate predictions. Int. J. Climatol.,21(9):1111-1152
Gray B H.1981.On the stability of temperature eigenvectors pattern. J Climatology,1:273-281
Haney R L.1971. Surface thermal boundary condition for ocean circulation models. J. Phys. Oceanogr.,1:241-248
Huang J P, Yi Y H, Wang S W, et al.1993. An analogue-dynamical long-range numerical weather prediction system incorporating historical evolution. Quart. J. Roy. Meteor. Soc,119: 547-565
Huang J P, Wang S W.1991. The monthly prediction experiments using a coupled analogy-dynamical model. Acta Meteor. Sinica.,5:8-15
Huang J P, Wang S W.1992. The experiment of seasonal prediction using the analogy-dynamical model.Sci. China Ser. B,35:207-216
Huang Ronghui, Wu Yifang.1989. The Influence of ENSO on the Summer Climate Change in China and Its Mechanism. Adv. Atmos. Sci.,6(1):21-30
Jin X Z, Zhang X H, Zhou T J.1999. Fundamental framework and experiments of the third Generation of IAP/LASG world ocean general circulation model. Adv. Atmos. Sci., 16(2):197-215
Julia N P, Reed B H.1982. Statistical prediction of 500mb height field using eigenvectors. J. Appl. Meteor.,21:127-138
Ke Zongjian, Zhang Peiqun, Dong Wenjie, et al.2009. A New Way to Improve Seasonal Prediction by Diagnosing and Correcting the Intermodel Systematic Errors. Mon. Wea. Rev, 137 (6):1898-1907
Kug J S, Lee J Y, Kang I S.2008. Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method. Mon. Wea. Rev,136 (9):3501-3512
Kuo H L.1974. Further studies of the parameterization of the influence cumulus convection of large-scale flow. y. Atmos. Sci.,31:1232-1240
Latif M, Anderson D, Bamett T, et al.1998. A review of the predictability and prediction of ENSO.y. Geophys. Res.,103:14375-14393
Lorenz E N.1965. A study of the predictability of a 28-variable atmospheric model. Tellus, 17:321-333
Lorenz E N.1973. On the Existence of Extended Range Predictability. J. Appl. Meteor., 12:543-546
Lorenz E N.1982. Atmospheric predictability experiments with a large numerical model.Tellus. 34:505-513
Michael W P, Arun K, James J.2004. Potential predictability in the NCEP CPC Dynamical seasonal forecast system. J. Climate,17(6):3775-3785
Miyakoda K, Sirutis J, Ploshay J.1986. One-month forecast experiments-without anomaly boundary forcings. Mon. Wea. Rev,114:846-869
Mo Ruping, Straus D M.2002. Statistical-Dynamical Seasonal Prediction Based on Principal Component Regression of GCM Ensemble Integrations. Mon. Wea. Rev,130:2167-2187
Morcrette J J.1990. Impact of changes to the radiation transfer parameterization plus cloud optical properties in the ECMWF model. Mon. Wea. Rew,; 118:847-873
Pakanowski R C, Philander G.1981. Parameterization of vertical mixing in numerical models of the tropical ocean. J. Phys. Oceanogr.,11:1442-1451
Palmer T N, Brankovic C, Molteni F, et al.1990. The European centre for medium-range weather forecasts (ECMWF) program on extended-range prediction. Bull. Amer. Meteor. Soc,71(9):1317-1390
Palmer T N, Shutts G J, Swinbank R.1986. Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parameterization. Quart. J. Roy. Meteor. Soc,112:1001-1039
Parkinson C L, Washington W M.1979. A large-scale numerical model of sea ice. J. Geophys. Res.,84:311-337
Robert V, Guy P, Wang R S, et al.1999. Seasonal Prediction of North American Surface Air Temperatures Using Space-Time Principal Components. J. Climate,12:380-394
Rosati A, Miyakoda K, Maskell K, et al.1997. The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev,125:754-772
Rosati A, Miyakoda K.1988. A general circulation model for upper ocean circulation. J. Phys. Oceanogr.,18(11):1601-1626
Shapiro M A, Thorpe A J.2004. THORPEX:A global atmospheric research programme for the beginning of the 21st century. WMO Bulletin,54 (3):see http://www. wmo. int/thorpex/
Shukla J.1981. Dynamical predictability of monthly means. J. Atmos. Sci.,38:2574-2572
Thomas A G.1970. Statistical-Dynamical Prediction. J. Appl. Meteor.,8:333-344
Tippett M K, Goddard L, Barnston A G.2005. Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation. J. Climate,18:1831-1843
Tracton M S, Mo K, Chen W, et al.1989. Dynamical extended range forecasting (DERF) at the National Meteorological Center. Mon. Wea. Rev,117:1604-1635
UNESCO.1981. Tenth report of the joint panel on oceanographic tables and standards. UNESCO Technological Paper in Marine Science,36:1-25
Vannitsem S, Toth Z.2002. Short-Term Dynamics of Model Errors. J. Atmos. Sci.,59 (17):2594-2604
Vitart F.2004. Monthly forecasting at ECMWF. Mon. Wea. Rev,132:2761-2779
Waliser D E, Lau K M, Stem W, et al.2003. Poential predictability of the Madden-Julian Oscillation. Bull. Amer. Meteor. Soc,84:33-50
Wang Huijun, Zhou Guangqing, Zhao Yan.2000. An Effective Method for Correcting the Seasonal-Interannual Prediction of Summer Climate Anomaly. Adv. Atmos. Sci., 17(2):234-240
Xiong Kaiguo, Feng Guolin, Huang Jianping, et al.2011. Analogue-dynamical prediction of monsoon precipitation in Northeast China based on dynamic and optimal configuration of multiple predictors. Acta Meteor. Sinica.,25(3):316-326
Yu Zhiping, Chu P, Schroeder T.1997. Predictive Skills of Seasonal to Annual Rainfall Variations in the U.S. Affiliated Pacific Islands:Canonical Correlation Analysis and Multivariate Principal Component Regression Approaches. J. Climate,12:2586-2599
Zeng N, Neelin J D, Lau K M, et al.1999. Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science,286:1537-1540
Zeng Q C, Yuan C G, Li X, et al.1997. Seasonal and extraseasonal predictions of summer monsoon precipitation by GCMs. Adv. Atmos. Sci,14(2):163-176
Zeng Q C, Zhang B L, Yuan C G, et al.1994. A Note on Some Methods Suitable for Verifying and Correcting the Prediction of Climatic Anomaly. Adv. Atmos. Sci.,11(2):121-127
鲍名,倪允琪,丑纪范.2004.相似-动力模式的月平均环流预报试验.科学通报,49(11)：1112-1115
曹鸿兴.1993.大气运动的自忆性方程.中国科学B辑,23(1)：104-112
巢纪平,季劲钧,何家骅等.1979.长期数值天气预报的滤波方法.中国科学,(1)：75-84
巢纪平,季劲钧,朱志辉等.1977.一种长期数值天气预报方法的物理基础.中国科学,(2)：162-172
陈伯民,纪立人,杨培才等.2003.改善月动力延伸预报水平的一种新途径.科学通报,48(5)：513-520
陈创买,周文,林爱兰.2009.气候场的主分量逐步回归预测模型及应用.热带气象学报,25(2)：196-204
陈菊英.2010.中国旱涝的机理分析和长期预报技术研究,北京：气象出版社,154-212pp
陈丽娟,陈伯民,李维京等.2005.T63模式月动力延伸预报高度场的改进实.应用气象学报,16(增刊),92-96
陈丽娟,李维京.1999.月动力延伸预报产品的评估和解释应用.应用气象学报,10(4)：486-490
陈兴芳,赵振国.2000.中国汛期降水预测及其应用.北京：气象出版社,65-66pp
池艳珍,何金海,吴志伟.2005.华南前汛期不同降水时段的特征分析.南京气象学院学报,28(2)：163-171
丑纪范,任宏利.2006.数值天气预报-另类途径的必要性和可行性.应用气象学报,17：240-244
丑纪范,徐明.2001.短期气候数值预测的进展和前景.科学通报,46(11)：890-895
丑纪范,郑志海,孙树鹏.2010.10-30天延伸期数值天气预报的策略思考-直面混沌.气象科学,30(5)：569-573
丑纪范.1986.为什么要动力-统计相结合?-兼论如何结合.高原气象,5(4)：367-372
丑纪范.2002.大气科学中的非线性与复杂性,北京：气象出版社,131-203pp
丑纪范.2003.短期气候预报的现状问题与出路(一).新疆气象,26(1)：1-4
丁一汇,李清泉,李维京等.2004.中国业务动力季节预报的进展.气象学报,62(5)：598-612
丁一汇,刘一鸣,宋永加等.2002.我国短期气候动力预报模式系统的研究及试验.气候与环境研究,7(2)：236-246
丁一汇,任国玉,石广玉等.2006.气候变化国家评估报告(Ⅰ)：中国气候变化的历史和未来趋势.气候变化研究进展,2(1)：3-8
丁一汇.2011.季节气候预测的进展和前景.气象科技进展,1(3)：14-27
董敏主编.2001.国家气候中心大气环流棋式-基本原理和使用说明.北京：气象出版社,1-152pp
封国林,曹鸿兴,魏风英等.2001.长江三角洲汛期预报模式的研究及其初步应用.气象学报, 59(2)：206-212
封国林,曹鸿兴,魏凤英.1999.区域气候自忆预测模式的计算方案及其结果,应用气象学报,10(4)：470-477
封国林,戴新刚,王爱慧等.2001.混沌系统中可预报性的研究.物理学报,50(4)：606-611
冯耀煌,杨旭.1989.论最优预报因子与最优预报方程.气象学报,47(1)：52-60
高辉,王永光.2007ENSO对中国夏季降水可预测性变化的研究.气象学报.65(1)：131-137
龚建东.1999.统计与动力相结合改进月动力延伸预报的研究.兰州大学,博十学位论文
谷湘潜.1998.一个基于大气自忆原理的谱模式.科学通报,43(1)：1-9
顾思南,杨修群.2006.北半球绕极涡的变异及其与我国气候异常的关系.气象科学,26(2)：135-142
顾震潮.1958.天气数值预报中过去资料的使用问题.气象学报,29(3)：176-184
郭其蕴,沙万英.1998.华南前汛期降水变率的分析.应用气象学报,9(增刊)：9-15
国家减灾委员会办公室、民政部国家减灾中心和《中国减灾》杂志社.2007.2006年度全国“十大自然灾害事件”.中国减灾,1：28-31
何有海,关翠华,林锡贵等.1998.华南后汛期降雨量的振动和分布.热带气象学报,14(4)：359-363
黄吕兴.2005.气候模式的误差及其可预报性研究.中国气象科学研究院与南京信息工程大学联招,博十学位论文,pp19-21
黄刚,周文.2006.华北夏季降水气候场的主分量逐步回归预报模型.气候与环境研究,11(3)：296-301
黄嘉佑,黄茂怡.2000.主分量逐步筛选因子典型相关分析及其预报试验.应用气象学报,11(增刊)：72-78
黄嘉佑,刘舸,赵昕奕.2004.副高、极涡因子对我国夏季降水的影响.大气科学.28(4)：517-526
黄嘉佑.1989.降水分析与长期预报的若干问题.气象科技,2：1-6
黄荣辉.2001.关于我国重大气候灾害的形成机理和预测理论研究进展.中国基础科学,8(1)：4-8
贾小龙,王谦谦.2006.东北地区汛期降水异常的大气环流特征分析.高原气象,25(2)：309-318
金祖辉,陶诗言.1999.ENS循环与中国东部地区夏季和冬季降水关系的研究.大气科学,23(6)：663-672
李芳,林中达,左瑞亭等.2005.基于经验正交函数和奇异值分解对东亚季风区跨季度夏季降水距平的订止方法.气候与环境研究,10(3)：658-668
李建平,J‘瑞强,陈宝花.2006.大气可预报性研究的回顾与展望,21世纪初大气科学前沿与展望.北京：气象出版社,96-103pp
李维京,纪立人.2000.月动力延伸预报研究.北京：气象出版社
李维京,张培群,李清泉等.2005.动力气候模式预报系统业务化及其应用.应用气象学报,16(增刊)：1-11
李维京,张小礼,李小泉等.1993.海温对月平均环流影响的数值试验.应用气象学报,4(增刊)：45-49
廉毅,沈柏竹,高枞亭等.2005.中国气候过渡带干旱化发展趋势与东亚夏季风、极涡活动相关研究.气象学报,63(5)：740-749
林学椿.1978.统计天气预报中相关系数的不稳定性问题.大气科学,2(1)：55-63
陆日宇.2002.华北汛期降水量变化中年代际和年际尺度的分离.大气科学,2(65)：611-624
穆穆,李建平,丑纪范等.2002.气候系统可预报性理论研究.气候与环境研究,7(2)：227-235
倪东鸿,孙照渤,赵玉春2000-ENSO循环在夏季的不同位相对东亚夏季风的影响.南京气象学院学报,23(1)：48-54
钱永甫,郑琼.2001.P-σ全球模式的月尺度动力延伸预报试验.大气科学,25(5)：641-649
秦正坤,林朝晖,陈红等.2011.基于EOF/SVD的短期气候预测误差订止方法及其应用.气象学报,69(2)：289-296
秦止坤.2007.短期气候数值预测的误差订止和超级集合方法研究.南京信息工程大学,博十学位论文
邱崇践,丑纪范.1987.改进数值天气预报的一个新途径.中国科学(B辑),8：903-910
邱崇践,丑纪范.1989.天气预报的相似-动力方法.大气科学,13(1)：22-28
任国玉,初子莹,周雅清等.2005.中国气温变化研究最新进展.气候与环境研究,10(4)：701-716
任宏利,丑纪范.2005.统计-动力相结合的相似误差订止法.气象学报,63(6)：988-993
任宏利,丑纪范.2007.动力相似预报的策略和方法研究.中国科学D辑(地球科学),37(8)：1101-1109
任宏利,丑纪范.2007.数值模式的预报策略和方法研究进展.地球科学进展,22(4)：376-385
任宏利,张培群,李维京等.2006.基于多个参考态更新的动力相似预报方法及应用.物理学报,55(8)：4388-4396
任宏利.2006.动力相似预报的策略和方法.兰州大学,博十学位论文
施能.2009.气象统计预报,北京：气象出版社,128-142pp
王澄海,隆霄,杨毅.2011.大气数值模式及模拟.北京：气象出版社,225pp
王会军,陈丽娟,李维京等.2007.中国区域月平均温度和降水的模式可预报性分析.气象学报,65(5)：725-732
王鹏飞,黄荣辉,李建平.2011.数值积分过程中截断误差和舍入误差的分离方法及其效果检验.大气科学,35(3)：403-410
王启光,封国林,郑志海,等.2010.长江中下游汛期降水优化多因子组合客观定量化预测研究.大气科学,35(2)：287-297
王绍武.1990.月平均环流的长期数值预报.大气科学,14(2)：243-248
王绍武.2001.现代气候学研究进展.北京：气象出版社,306-311pp
魏凤英,曹鸿兴.1990.长江流域旱涝趋势的主分量预测模型.气象,16(8)：20-24
魏凤英.1999.现代气候统计诊断与预测技术.北京：气象出版社,115-122pp
吴尚森,梁建茵.1992.华南前汛期旱涝时空分布特征.热带气象学报,8(1)：87-92
吴志伟,江志红,何金海.2006.近50年华南前汛期降水、江淮梅雨和华北雨季旱涝特征对比分析.大气科学,30(3)：391401
吴志伟,江志红,何金海.2006.近50年华南前汛期降水、江淮梅雨和华北雨季旱涝特征对比分析.大气科学,30(3)：391-401
谢炯光,曾琮,纪忠萍.2003.中国近30年来气象统计预报进展.气象科技,31(2)：67-83
严华生,严晓东.2004.中国降水月季气候变化分析预测研究.北京：气象出版社,pp1
阎惠芳,李社宗,黄跃青等.2003.常用相似性判据的检验和综合相似系数的使用.气象科技,31(4)：211-215
杨杰,王启光,支蓉,等.2011.动态最优多因子组合的华北汛期降水模式误差估计及预报, 物理学报,60(2)：029204
杨金虎,江志红,杨启国等.2007.中国西北汛期极端降水事件分析.中国沙漠,27(2)：320-325
姚秀萍,董敏.2000.东北三江流域夏季旱涝基本特征分析应用气象学报,11(3)：297-303
曾庆存,郭裕福.1999.可问天机-气候动力学和气侯预测理论的研究.湖南：湖南科学技术出版社,134pp
曾庆存,林朝晖,周广庆.2003.跨季节动力气候预测系统IAP DCP-II大气科学,27(1)：101-117
曾庆存,王会军,林朝晖等.气候动力学与气候理论研究.大气科学,2003,27(4)：468-463
张邦林,丑纪范,孙照渤.1991.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,23：1797-1798
张邦林,丑纪范.1991.经验止交函数在气候数值模拟中的应用.中国科学(B辑),4：442-448
张道民,纪立人,李金龙.1996.月数值天气预报的试验研究,大气科学,20(4)：429-438
张恒德,高守亭,刘毅.2008.极涡研究进展.高原气象,2(72)：452-461
张录军,钱永甫.2004.长江流域汛期降水集中程度和洪涝关系研究.地球物理学报,47(4)：622-630
张培群,丑纪范.1997.改进月延伸预报的一种方法.高原气象,16(4)：376-388
张培群,李清泉,王兰宁等.2004.我国动力气候模式预测系统的研制及应用.科技导报,7：17-21
张学洪,赵其庚,杨修群等.2000.海洋环流-海冰模式和海气耦合研究进展.见：项目办公室,项目执行专家组编.短期气候预测业务动力模式的研制.北京：气象出版社,161-169
赵焕宸.1984.东北地区降水分布特性的主成份分析.地理科学,4(8)：225-234
赵振国,王永光,陈桂英等.2000.中国夏季旱涝及环境场.北京：气象出版社,1-110pp
赵振国.1997.影响中国汛期旱涝的主要因素的初步分析,北京：国家气候中心
赵振国等.1999.影响中国汛期降水和长江流域旱涝的主要物理因素的综合分析.1998年特大暴雨(洪涝)学术研讨会论文摘要汇编.北京：中国气象学会
郑庆林,杜行远.1973.使用多时刻观测资料的数值天气预报新模式.中国科学,(2)：289-297
郑庆林,宋青丽,蒋平.1993.一个改进的T42L10谱模式及其在月预报中的应用.应用气象学报,4(增刊)：50-56
郑志海,封国林,丑纪范等.2010.数值预报中自由度的压缩及误差相似性规律.应用气象学报,21(2)：139-148
郑志海,任宏利,黄建平.2009.基于季节气候可预报分量的相似误差订止方法和数值实验.物理学报,58(10)：7359-7367
郑志海.2010.基于可预报分量的6-15天数值天气预报业务技术研究.兰州大学,博士学位论文
钟剑,黄思训,费建芳,等.2011.模式误差动力特征：模式参数误差和物理过程描绘缺失误差.大气科学,3(56)：1169-1176
周家斌,黄嘉佑.1997.近年来中国统计气象学的新进展.气象学报,55(3)：297-305
Barnett T P, Preisendorfer R.1987. Origins and Levels of Monthly and Seasonal Forecast Skill for United States Surface Air Temperatures Determined by Canonical Correlation Analysis. Mon. Wea. Rev,115(9):1825-1850
Baumhefner D B.1996. Numerical extended-range prediction:forecast skill using a low-resolution climate model. Mon. Wea. Rev,124:1965-1980
Bengtsson L, Schese U, Roeckner E, et al.1993. A two-tiered approach to long-range climate forecasting. Science,261:1027-102
Bennett A F, Leslie L M.1981. Statistical correction of the Australian region primitive equation model. Mon. Wea. Rev,109:453-462
Bjerknes J.1966. A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature. Tellus,18:820-829
Bjerknes J.1969. Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev,97: 163-172
Bjerknes V.1904. Das Problem der Wettervorhersage:betrachtet vom Stadpunkte der Mechanik and der Physik. Meteor. Zeit.,21:1-7
Bryan K, Cox M D.1972. An approximate equation of state for numerical models of ocean circulation.J. Phys. Oceanogr.,2:510-514
Chamey J G, Fjortoft R, von Neumann J.1950. Numerical integration of the barotropic vorticity equation.Tellus,2:237-254
Chen Hong, Lin Zhaohui.2006. A New Correction Method suitable for Dynamical Climate Prediction. Adv. Atmos. Sci,23(3):425-430
Chou Ge-Fen.1974. A problem of using past data in numerical weather forecasting. Scientia Sinica,17(6):814-825
Christine D, Yan G L.2004. Use of a Principal Components Analysis for the Generation of Daily Time Series.J. Appl. Meteor.,43:984-996
Cubasch U, Wiin-Nielsen A C.1986. Predictability studies with the ECMWF spectral model for the extended range: the impact of horizontal resolution and sea surface temperature. Tellus, 38:25-41
Feddersen H, Navarra A, Ward M N.1999. Reduction of model systematic error by statistical correction for dynamical seasonal predictions.J. Climate,12:1974-1989
Feng Guolin, Cao Hongxing, Gao Xinquan, et al.2001. Prediction of Precipitation during Summer Monsoon with Self-memorial Model. Adv. Atmos. Sci.,18(5):701-709
Ferranti L, Palmer T N, Molteni F, et al.1990. Tropical-extratropical interaction associated with the 30-60 day oscillation and its impact on medium and extended range prediction. J. Atmos. Sci,47(18):2177-2199
Fraedrich K, Smith N R.1989. Combining predictive schemes in long-range forecasting. J. Climate,2:291-294
Gent P R, McWilliams J C.1990.lsopycnal mixing in ocean circulation models. J. Phys. Oceanogr, (20)1:150-155
Goddard L, Mason S J, Zebiak S E, et al.2001. Current approaches to seasonal to interannual climate predictions. Int. J. Climatol.,21(9):1111-1152
Gray B H.1981.On the stability of temperature eigenvectors pattern. J Climatology,1:273-281
Haney R L.1971. Surface thermal boundary condition for ocean circulation models. J. Phys. Oceanogr.,1:241-248
Huang J P, Yi Y H, Wang S W, et al.1993. An analogue-dynamical long-range numerical weather prediction system incorporating historical evolution. Quart. J. Roy. Meteor. Soc,119: 547-565
Huang J P, Wang S W.1991. The monthly prediction experiments using a coupled analogy-dynamical model. Acta Meteor. Sinica.,5:8-15
Huang J P, Wang S W.1992. The experiment of seasonal prediction using the analogy-dynamical model.Sci. China Ser. B,35:207-216
Huang Ronghui, Wu Yifang.1989. The Influence of ENSO on the Summer Climate Change in China and Its Mechanism. Adv. Atmos. Sci.,6(1):21-30
Jin X Z, Zhang X H, Zhou T J.1999. Fundamental framework and experiments of the third Generation of IAP/LASG world ocean general circulation model. Adv. Atmos. Sci., 16(2):197-215
Julia N P, Reed B H.1982. Statistical prediction of 500mb height field using eigenvectors. J. Appl. Meteor.,21:127-138
Ke Zongjian, Zhang Peiqun, Dong Wenjie, et al.2009. A New Way to Improve Seasonal Prediction by Diagnosing and Correcting the Intermodel Systematic Errors. Mon. Wea. Rev, 137 (6):1898-1907
Kug J S, Lee J Y, Kang I S.2008. Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method. Mon. Wea. Rev,136 (9):3501-3512
Kuo H L.1974. Further studies of the parameterization of the influence cumulus convection of large-scale flow. y. Atmos. Sci.,31:1232-1240
Latif M, Anderson D, Bamett T, et al.1998. A review of the predictability and prediction of ENSO.y. Geophys. Res.,103:14375-14393
Lorenz E N.1965. A study of the predictability of a 28-variable atmospheric model. Tellus, 17:321-333
Lorenz E N.1973. On the Existence of Extended Range Predictability. J. Appl. Meteor., 12:543-546
Lorenz E N.1982. Atmospheric predictability experiments with a large numerical model.Tellus. 34:505-513
Michael W P, Arun K, James J.2004. Potential predictability in the NCEP CPC Dynamical seasonal forecast system. J. Climate,17(6):3775-3785
Miyakoda K, Sirutis J, Ploshay J.1986. One-month forecast experiments-without anomaly boundary forcings. Mon. Wea. Rev,114:846-869
Mo Ruping, Straus D M.2002. Statistical-Dynamical Seasonal Prediction Based on Principal Component Regression of GCM Ensemble Integrations. Mon. Wea. Rev,130:2167-2187
Morcrette J J.1990. Impact of changes to the radiation transfer parameterization plus cloud optical properties in the ECMWF model. Mon. Wea. Rew,; 118:847-873
Pakanowski R C, Philander G.1981. Parameterization of vertical mixing in numerical models of the tropical ocean. J. Phys. Oceanogr.,11:1442-1451
Palmer T N, Brankovic C, Molteni F, et al.1990. The European centre for medium-range weather forecasts (ECMWF) program on extended-range prediction. Bull. Amer. Meteor. Soc,71(9):1317-1390
Palmer T N, Shutts G J, Swinbank R.1986. Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parameterization. Quart. J. Roy. Meteor. Soc,112:1001-1039
Parkinson C L, Washington W M.1979. A large-scale numerical model of sea ice. J. Geophys. Res.,84:311-337
Robert V, Guy P, Wang R S, et al.1999. Seasonal Prediction of North American Surface Air Temperatures Using Space-Time Principal Components. J. Climate,12:380-394
Rosati A, Miyakoda K, Maskell K, et al.1997. The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev,125:754-772
Rosati A, Miyakoda K.1988. A general circulation model for upper ocean circulation. J. Phys. Oceanogr.,18(11):1601-1626
Shapiro M A, Thorpe A J.2004. THORPEX:A global atmospheric research programme for the beginning of the 21st century. WMO Bulletin,54 (3):see http://www. wmo. int/thorpex/
Shukla J.1981. Dynamical predictability of monthly means. J. Atmos. Sci.,38:2574-2572
Thomas A G.1970. Statistical-Dynamical Prediction. J. Appl. Meteor.,8:333-344
Tippett M K, Goddard L, Barnston A G.2005. Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation. J. Climate,18:1831-1843
Tracton M S, Mo K, Chen W, et al.1989. Dynamical extended range forecasting (DERF) at the National Meteorological Center. Mon. Wea. Rev,117:1604-1635
UNESCO.1981. Tenth report of the joint panel on oceanographic tables and standards. UNESCO Technological Paper in Marine Science,36:1-25
Vannitsem S, Toth Z.2002. Short-Term Dynamics of Model Errors. J. Atmos. Sci.,59 (17):2594-2604
Vitart F.2004. Monthly forecasting at ECMWF. Mon. Wea. Rev,132:2761-2779
Waliser D E, Lau K M, Stem W, et al.2003. Poential predictability of the Madden-Julian Oscillation. Bull. Amer. Meteor. Soc,84:33-50
Wang Huijun, Zhou Guangqing, Zhao Yan.2000. An Effective Method for Correcting the Seasonal-Interannual Prediction of Summer Climate Anomaly. Adv. Atmos. Sci., 17(2):234-240
Xiong Kaiguo, Feng Guolin, Huang Jianping, et al.2011. Analogue-dynamical prediction of monsoon precipitation in Northeast China based on dynamic and optimal configuration of multiple predictors. Acta Meteor. Sinica.,25(3):316-326
Yu Zhiping, Chu P, Schroeder T.1997. Predictive Skills of Seasonal to Annual Rainfall Variations in the U.S. Affiliated Pacific Islands:Canonical Correlation Analysis and Multivariate Principal Component Regression Approaches. J. Climate,12:2586-2599
Zeng N, Neelin J D, Lau K M, et al.1999. Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science,286:1537-1540
Zeng Q C, Yuan C G, Li X, et al.1997. Seasonal and extraseasonal predictions of summer monsoon precipitation by GCMs. Adv. Atmos. Sci,14(2):163-176
Zeng Q C, Zhang B L, Yuan C G, et al.1994. A Note on Some Methods Suitable for Verifying and Correcting the Prediction of Climatic Anomaly. Adv. Atmos. Sci.,11(2):121-127
鲍名,倪允琪,丑纪范.2004.相似-动力模式的月平均环流预报试验.科学通报,49(11)：1112-1115
曹鸿兴.1993.大气运动的自忆性方程.中国科学B辑,23(1)：104-112
巢纪平,季劲钧,何家骅等.1979.长期数值天气预报的滤波方法.中国科学,(1)：75-84
巢纪平,季劲钧,朱志辉等.1977.一种长期数值天气预报方法的物理基础.中国科学,(2)：162-172
陈伯民,纪立人,杨培才等.2003.改善月动力延伸预报水平的一种新途径.科学通报,48(5)：513-520
陈创买,周文,林爱兰.2009.气候场的主分量逐步回归预测模型及应用.热带气象学报,25(2)：196-204
陈菊英.2010.中国旱涝的机理分析和长期预报技术研究,北京：气象出版社,154-212pp
陈丽娟,陈伯民,李维京等.2005.T63模式月动力延伸预报高度场的改进实.应用气象学报,16(增刊),92-96
陈丽娟,李维京.1999.月动力延伸预报产品的评估和解释应用.应用气象学报,10(4)：486-490
陈兴芳,赵振国.2000.中国汛期降水预测及其应用.北京：气象出版社,65-66pp
池艳珍,何金海,吴志伟.2005.华南前汛期不同降水时段的特征分析.南京气象学院学报,28(2)：163-171
丑纪范,任宏利.2006.数值天气预报-另类途径的必要性和可行性.应用气象学报,17：240-244
丑纪范,徐明.2001.短期气候数值预测的进展和前景.科学通报,46(11)：890-895
丑纪范,郑志海,孙树鹏.2010.10-30天延伸期数值天气预报的策略思考-直面混沌.气象科学,30(5)：569-573
丑纪范.1986.为什么要动力-统计相结合?-兼论如何结合.高原气象,5(4)：367-372
丑纪范.2002.大气科学中的非线性与复杂性,北京：气象出版社,131-203pp
丑纪范.2003.短期气候预报的现状问题与出路(一).新疆气象,26(1)：1-4
丁一汇,李清泉,李维京等.2004.中国业务动力季节预报的进展.气象学报,62(5)：598-612
丁一汇,刘一鸣,宋永加等.2002.我国短期气候动力预报模式系统的研究及试验.气候与环境研究,7(2)：236-246
丁一汇,任国玉,石广玉等.2006.气候变化国家评估报告(Ⅰ)：中国气候变化的历史和未来趋势.气候变化研究进展,2(1)：3-8
丁一汇.2011.季节气候预测的进展和前景.气象科技进展,1(3)：14-27
董敏主编.2001.国家气候中心大气环流棋式-基本原理和使用说明.北京：气象出版社,1-152pp
封国林,曹鸿兴,魏风英等.2001.长江三角洲汛期预报模式的研究及其初步应用.气象学报, 59(2)：206-212
封国林,曹鸿兴,魏凤英.1999.区域气候自忆预测模式的计算方案及其结果,应用气象学报,10(4)：470-477
封国林,戴新刚,王爱慧等.2001.混沌系统中可预报性的研究.物理学报,50(4)：606-611
冯耀煌,杨旭.1989.论最优预报因子与最优预报方程.气象学报,47(1)：52-60
高辉,王永光.2007ENSO对中国夏季降水可预测性变化的研究.气象学报.65(1)：131-137
龚建东.1999.统计与动力相结合改进月动力延伸预报的研究.兰州大学,博十学位论文
谷湘潜.1998.一个基于大气自忆原理的谱模式.科学通报,43(1)：1-9
顾思南,杨修群.2006.北半球绕极涡的变异及其与我国气候异常的关系.气象科学,26(2)：135-142
顾震潮.1958.天气数值预报中过去资料的使用问题.气象学报,29(3)：176-184
郭其蕴,沙万英.1998.华南前汛期降水变率的分析.应用气象学报,9(增刊)：9-15
国家减灾委员会办公室、民政部国家减灾中心和《中国减灾》杂志社.2007.2006年度全国“十大自然灾害事件”.中国减灾,1：28-31
何有海,关翠华,林锡贵等.1998.华南后汛期降雨量的振动和分布.热带气象学报,14(4)：359-363
黄吕兴.2005.气候模式的误差及其可预报性研究.中国气象科学研究院与南京信息工程大学联招,博十学位论文,pp19-21
黄刚,周文.2006.华北夏季降水气候场的主分量逐步回归预报模型.气候与环境研究,11(3)：296-301
黄嘉佑,黄茂怡.2000.主分量逐步筛选因子典型相关分析及其预报试验.应用气象学报,11(增刊)：72-78
黄嘉佑,刘舸,赵昕奕.2004.副高、极涡因子对我国夏季降水的影响.大气科学.28(4)：517-526
黄嘉佑.1989.降水分析与长期预报的若干问题.气象科技,2：1-6
黄荣辉.2001.关于我国重大气候灾害的形成机理和预测理论研究进展.中国基础科学,8(1)：4-8
贾小龙,王谦谦.2006.东北地区汛期降水异常的大气环流特征分析.高原气象,25(2)：309-318
金祖辉,陶诗言.1999.ENS循环与中国东部地区夏季和冬季降水关系的研究.大气科学,23(6)：663-672
李芳,林中达,左瑞亭等.2005.基于经验正交函数和奇异值分解对东亚季风区跨季度夏季降水距平的订止方法.气候与环境研究,10(3)：658-668
李建平,J‘瑞强,陈宝花.2006.大气可预报性研究的回顾与展望,21世纪初大气科学前沿与展望.北京：气象出版社,96-103pp
李维京,纪立人.2000.月动力延伸预报研究.北京：气象出版社
李维京,张培群,李清泉等.2005.动力气候模式预报系统业务化及其应用.应用气象学报,16(增刊)：1-11
李维京,张小礼,李小泉等.1993.海温对月平均环流影响的数值试验.应用气象学报,4(增刊)：45-49
廉毅,沈柏竹,高枞亭等.2005.中国气候过渡带干旱化发展趋势与东亚夏季风、极涡活动相关研究.气象学报,63(5)：740-749
林学椿.1978.统计天气预报中相关系数的不稳定性问题.大气科学,2(1)：55-63
陆日宇.2002.华北汛期降水量变化中年代际和年际尺度的分离.大气科学,2(65)：611-624
穆穆,李建平,丑纪范等.2002.气候系统可预报性理论研究.气候与环境研究,7(2)：227-235
倪东鸿,孙照渤,赵玉春2000-ENSO循环在夏季的不同位相对东亚夏季风的影响.南京气象学院学报,23(1)：48-54
钱永甫,郑琼.2001.P-σ全球模式的月尺度动力延伸预报试验.大气科学,25(5)：641-649
秦正坤,林朝晖,陈红等.2011.基于EOF/SVD的短期气候预测误差订止方法及其应用.气象学报,69(2)：289-296
秦止坤.2007.短期气候数值预测的误差订止和超级集合方法研究.南京信息工程大学,博十学位论文
邱崇践,丑纪范.1987.改进数值天气预报的一个新途径.中国科学(B辑),8：903-910
邱崇践,丑纪范.1989.天气预报的相似-动力方法.大气科学,13(1)：22-28
任国玉,初子莹,周雅清等.2005.中国气温变化研究最新进展.气候与环境研究,10(4)：701-716
任宏利,丑纪范.2005.统计-动力相结合的相似误差订止法.气象学报,63(6)：988-993
任宏利,丑纪范.2007.动力相似预报的策略和方法研究.中国科学D辑(地球科学),37(8)：1101-1109
任宏利,丑纪范.2007.数值模式的预报策略和方法研究进展.地球科学进展,22(4)：376-385
任宏利,张培群,李维京等.2006.基于多个参考态更新的动力相似预报方法及应用.物理学报,55(8)：4388-4396
任宏利.2006.动力相似预报的策略和方法.兰州大学,博十学位论文
施能.2009.气象统计预报,北京：气象出版社,128-142pp
王澄海,隆霄,杨毅.2011.大气数值模式及模拟.北京：气象出版社,225pp
王会军,陈丽娟,李维京等.2007.中国区域月平均温度和降水的模式可预报性分析.气象学报,65(5)：725-732
王鹏飞,黄荣辉,李建平.2011.数值积分过程中截断误差和舍入误差的分离方法及其效果检验.大气科学,35(3)：403-410
王启光,封国林,郑志海,等.2010.长江中下游汛期降水优化多因子组合客观定量化预测研究.大气科学,35(2)：287-297
王绍武.1990.月平均环流的长期数值预报.大气科学,14(2)：243-248
王绍武.2001.现代气候学研究进展.北京：气象出版社,306-311pp
魏凤英,曹鸿兴.1990.长江流域旱涝趋势的主分量预测模型.气象,16(8)：20-24
魏凤英.1999.现代气候统计诊断与预测技术.北京：气象出版社,115-122pp
吴尚森,梁建茵.1992.华南前汛期旱涝时空分布特征.热带气象学报,8(1)：87-92
吴志伟,江志红,何金海.2006.近50年华南前汛期降水、江淮梅雨和华北雨季旱涝特征对比分析.大气科学,30(3)：391401
吴志伟,江志红,何金海.2006.近50年华南前汛期降水、江淮梅雨和华北雨季旱涝特征对比分析.大气科学,30(3)：391-401
谢炯光,曾琮,纪忠萍.2003.中国近30年来气象统计预报进展.气象科技,31(2)：67-83
严华生,严晓东.2004.中国降水月季气候变化分析预测研究.北京：气象出版社,pp1
阎惠芳,李社宗,黄跃青等.2003.常用相似性判据的检验和综合相似系数的使用.气象科技,31(4)：211-215
杨杰,王启光,支蓉,等.2011.动态最优多因子组合的华北汛期降水模式误差估计及预报, 物理学报,60(2)：029204
杨金虎,江志红,杨启国等.2007.中国西北汛期极端降水事件分析.中国沙漠,27(2)：320-325
姚秀萍,董敏.2000.东北三江流域夏季旱涝基本特征分析应用气象学报,11(3)：297-303
曾庆存,郭裕福.1999.可问天机-气候动力学和气侯预测理论的研究.湖南：湖南科学技术出版社,134pp
曾庆存,林朝晖,周广庆.2003.跨季节动力气候预测系统IAP DCP-II大气科学,27(1)：101-117
曾庆存,王会军,林朝晖等.气候动力学与气候理论研究.大气科学,2003,27(4)：468-463
张邦林,丑纪范,孙照渤.1991.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,23：1797-1798
张邦林,丑纪范.1991.经验止交函数在气候数值模拟中的应用.中国科学(B辑),4：442-448
张道民,纪立人,李金龙.1996.月数值天气预报的试验研究,大气科学,20(4)：429-438
张恒德,高守亭,刘毅.2008.极涡研究进展.高原气象,2(72)：452-461
张录军,钱永甫.2004.长江流域汛期降水集中程度和洪涝关系研究.地球物理学报,47(4)：622-630
张培群,丑纪范.1997.改进月延伸预报的一种方法.高原气象,16(4)：376-388
张培群,李清泉,王兰宁等.2004.我国动力气候模式预测系统的研制及应用.科技导报,7：17-21
张学洪,赵其庚,杨修群等.2000.海洋环流-海冰模式和海气耦合研究进展.见：项目办公室,项目执行专家组编.短期气候预测业务动力模式的研制.北京：气象出版社,161-169
赵焕宸.1984.东北地区降水分布特性的主成份分析.地理科学,4(8)：225-234
赵振国,王永光,陈桂英等.2000.中国夏季旱涝及环境场.北京：气象出版社,1-110pp
赵振国.1997.影响中国汛期旱涝的主要因素的初步分析,北京：国家气候中心
赵振国等.1999.影响中国汛期降水和长江流域旱涝的主要物理因素的综合分析.1998年特大暴雨(洪涝)学术研讨会论文摘要汇编.北京：中国气象学会
郑庆林,杜行远.1973.使用多时刻观测资料的数值天气预报新模式.中国科学,(2)：289-297
郑庆林,宋青丽,蒋平.1993.一个改进的T42L10谱模式及其在月预报中的应用.应用气象学报,4(增刊)：50-56
郑志海,封国林,丑纪范等.2010.数值预报中自由度的压缩及误差相似性规律.应用气象学报,21(2)：139-148
郑志海,任宏利,黄建平.2009.基于季节气候可预报分量的相似误差订止方法和数值实验.物理学报,58(10)：7359-7367
郑志海.2010.基于可预报分量的6-15天数值天气预报业务技术研究.兰州大学,博士学位论文
钟剑,黄思训,费建芳,等.2011.模式误差动力特征：模式参数误差和物理过程描绘缺失误差.大气科学,3(56)：1169-1176
周家斌,黄嘉佑.1997.近年来中国统计气象学的新进展.气象学报,55(3)：297-305