【作者】 熊开国；

【导师】 黄建平； 封国林；

【作者基本信息】 兰州大学 ， 气象学， 2012， 博士

【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。更多还原

【Abstract】 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.更多还原