摘要
由于短期气候预测的难度和不确定性,我国短期气候预测的研究不再是单纯注重物理统计预测方法或者数值模式预测方法的应用,而是同时重视二者的结合。鉴于此,本文基于动力-统计的基本原理,以国家气候中心(NCC)短期气候预测模式CGCM为基础,分别从前期环境场的动态特征、静态特征、异常信号等不同角度构建了多种动力-统计预测方案,开展了多种预测方案的集成预测试验,基于模式预报误差的统计特征发展了模式预报能力的评估方法以及旱涝预测可信度的计算方法。目的是将动力学方法和统计学方法有机结合,取长补短,充分利用历史统计信息修正动力学方法积分过程中的系统性误差,以此来避免直接对数值模式动力框架的大幅修改,以相对较小的代价达到相同的预测效果。最后以此为平台,初步构建动力统计相结合的短期气候客观预测系统,以促进预测准确率的提高,使之更好地为防灾减灾提供决策服务,主要结论如下：
1.动力-统计预测理想试验的结果表明,对于各个区域汛期降水模式的预测误差场而言,历史资料中相似误差场的信息量是相当充足的,每年的降水都可以在历史资料中找到与其较为相似的年份。如果能够通过科学的相似判据准确地选取出最佳的相似年,便可利用动力-统计方法对预报年的降水做出很好的预报。各个区域存在理论上的预报上限,在理想状态下,区域预报的距平相关系数(ACC)最高可达到0.8左右,并且理想相似年个数在4个左右时预报效果达到最佳。
2.从前期环境场的静态特征出发,构建了动态最优多因子组合预报方案。通过对前期因子进行单因子交叉检验筛选,建立适用于我国不同区域的夏季降水特点的关键相似因子集,对关键因子集中的影响因子进行单因子交叉检验回报试验,筛选能反映模式预报误差分布特征的关键预报因子；对关键预报因子进行配置试验,通过独立样本检验ACC得到区域最优多因子配置,考虑到前期关键因子对区域降水影响的年际或年代际变化,结合历史近期的动态多因子配置得到预报时段内稳定的最优关键因子组合。独立样本回报检验显示该方案可以显著提高区域汛期降水的预报技巧。
3.从前期异常强信号的角度出发,研发有针对性的异常因子动力-统计预报方案。以华北区域为例,对其近27年前期因子的异常状况统计分析发现,异常因子个数的偏多或偏少与预报年的降水异常存在很高的一致性,可作为异常年的判断标准。通过相关性检验筛选前期关键异常因子形成影响华北地区汛期降水的关键异常因子集。对前期异常因子进行经验正交函数分解(EOF)压缩自由度后发现其异常信号主要体现在第1模态和第2模态的时间系数上,以这2个模态时间系数作为相似年的选取依据。7年的独立样本检验结果显示,区域ACC从系统订正的0.38提高至0.61,提高了其预报稳定性及其准确率,且能够针对降水异常年给出异常预报,改善区域汛期降水的预报水平。
4.从前期环境场的动态特征出发,尝试从前冬海温关键区的配置及其演变过程进行历史相似选取并进行动力统计预报。对比发现海温不同的演化过程会导致夏季降水分布有明显的差异,进而通过交叉检验筛选赤道中东太平洋、印度洋等7个海温关键区。回报结果显示,赤道中东太平洋海温演化过程相似能够有效且稳定的提高预报水平,将该区海温作为相似选取的主信号,将其他区域中回报效果最好的区域海温作为调制信号。以相似系数作为相似年判断标准来表征各海温区演化过程的相似程度,并通过回报检验确定相似系数的最佳阈值,结果显示当相似阈值取值在0.6时,其预报效果最好,回报检验显示10年全国平均ACC可达0.12。
5.将模式误差动力-统计预报方案应用于副高的客观定量化预测,通过交叉检验ACC筛选对副高区域的500hPa高度场模式预报结果订正较好的因子作为前期关键因子集。对2003-2010年的副高区域的500hPa高度场进行了回报检验,结果显示该方案在数值模式预报结果基础上有了进一步提高,显示出较好的预测水平。在此基础上,从高度场预测结果中提取出与中国降水关系最为密切的两个典型副高特征指数(脊线指数与西伸脊点指数),将其投影在二维平面上得到相应的副高类型,并根据不同类型的副高特征下对应的雨型分类特征得到预报年副高所属类型下中国夏季降水的分布类型,多年检验结果表明预测的投影类型所对应的降水合成分布与实况的降水之间具有较好的一致性。
6.开展了多种动力-统计多方案集成预测试验,结果表明动态优选集成方案预报效果最为稳定、预报技巧最高,因此,将动态优选方案作为最优集成方案应用到实际的业务预测中。具体到每年的预测效果来看,集成预测并不一定比预报效果最好的方案预测技巧高,但是始终会比预报效果最差的预报方案高,使得每年的预测效果能够保持在较为稳定的水平,有效地避免预报效果出现较大的波动,从而提高了预测的可信度。
7.通过统计多年模式降水预报误差发生频次发现模式预报误差的分布满足高斯分布,可通过误差分布的高斯拟合曲线对比分析其预测能力：从高斯曲线的形状来看,若其形态越“瘦长”则表示模式预报性能越好,从高斯曲线的均值(最概然值)来看,越接近于0则预报性能越好。通过对比动力-统计预报误差的高斯曲线和模式预报误差的高斯曲线发现前者的预报性能优于后者,预报效果是否有所改进可以明显地在误差分布曲线上体现出来,误差分布曲线存在两种类型的改进方式：(1)变幅型改进(离差减小)；(2)均值型改进(修正系统漂移)。预报误差频次的年代际分布特征非常一致,且预报误差频次最高的值都在0左右,说明模式预报误差的分布形式较为稳定,没有明显地年代际变化特征。
8.基于预报误差的统计特征提出了预报结果可信度的度量方法,用以定量化地评估不同区域的动力统计的旱涝预测结果在可信度大小。以2012年夏季降水为例,给出了实况、预测以及旱涝等级可信度的分析,对比实况与预测结果可以看出可信度较大的区域实况与预测具有较好的一致性,而在可信度较小的区域预测结果的错误也相对较多,并且相比其他的信度检验方法,该方法对预报异常降水的可信度更为准确,显示出了该方法的有效性。
9.初步构建了动力-统计集成的季节气候预测(FODAS)系统,并开展了2009～2012年的实际预测与检验。在季节预报方面,2009～2012年的夏季降水预测显示出了良好的预测效果,4年的平均预报评分为72.8,平均ACC达到了0.16,明显优于模式预报结果,对于异常降水有一定的预报能力。对2012年的冬季气候趋势做出了较好的预报,2012年冬季温度预测PS评分达到92,降水PS评分达到82分。从月预测的应用效果来看,对月预测也存在一定的预报技巧,对异常降水也具有一定的预报能力,但整体而言月预测不如季节预测,预测效果不够稳定,存在较大波动。FODAS系统在夏季、冬季的温度和降水预测中取得了良好的预测效果,其应用推广的价值初现端倪,并且还存在较大的拓展、完善和应用推广的空间。
Because of the difficulty and uncertainty of short-term climate prediction, the research of short-term climate prediction in China no longer simply focuses on the application of the physical statistical forecasting methods or numerical model prediction method, but also attaches importance to the combination of the two methods. In view of this, based on the basic principles of dynamic-statistics and the short-term climate prediction mode CGCM of National Climate Center, we construct a variety of dynamic-statistics prediction schemes from the angle of the dynamic characteristics, static characteristics and abnormal strong signal of the ambient field of earlier stage. We not only carry out the dynamic-statistics prediction test which is integrated by several schemes, but also develop forecasting performance assessment method as well as droughts and abnormal level reliability calculation method. Thus dynamics methods and statistical methods are combined effectively and learn from each other. Through this way we can avoid substantial modifications on the dynamic framework of numerical model and get the same prediction effect at the relatively small expense, historical statistics are made full use of to correct the systematic error generated from dynamics method in the integration process. Finally, based on the platform, we set up the short-term climate objective prediction system in which dynamic and statistical are combined effectively to promote the improvement of prediction accuracy rate and provide better service to government's disaster prevention and mitigation decision-making. The main conclusions are as follows:
1. The ideal test results of dynamic-statistics prediction show that the information of similar error field in the historical data is quite enough for the prediction error field of precipitation models of the flood season in the various regions. The precipitation characteristics in every year can be find similar years from the historical data. The best similar year is selected by a scientific similar criterion to forecasting precipitation by dynamic-statistics prediction method very well. However, maximum theoretical forecasts exist in each region, for example, under ideal conditions, the maximum ACC of regions is about0.8and the forecasting results is the best when the number of the ideal similar years is four.
2.Based on the idea of using the historical-analogue information to revise the prediction errors of National Climate Centre numerical business model, For North China, based on analysis data of the CMAP from1983to2009,40pieces of climate indices from NOAA,27years of the season prediction model results from1983to2009and74pieces of circulation characteristics materials, using the method of combining data analysis and numerical simulation of diagnostic tests, taking the advantage of the prediction error of the key information of similar model from the historical data, by identifying key factors, optimizing allocation of the different factors of different forecasting years, to establish specific multi-factor dynamic optimal portfolios to revise prediction errors in different periods of the power statistical model in North China, to construct early environmental factors similar to field multiple objective criteria, to develop new technology of revising prediction errors from the power-statistical model based on dynamic optimal combination of multi-factor, to improve the prediction effect in the summer precipitation in North China and the forecasting skills. Results of independent sample return of2005-2009shows that, the score of similarity revised method has improved significantly comparing to the score of Systematic revised method.
3.We studied the correlations between the interactions of prophase key factors and the precipitation of rainy season in China and found the key atmospheric circulation predictor. According to the predictors which are abnormal in prophase, the authors compressed the dimensions of the factors to select the similar years through EOF analysis. Furthermore, a new dynamical analogue prediction scheme is constructed, which is based on the anomalous signals of prophase environment field. Analyses show that there is a good corresponding relationship between precipitation in North China and numbers of atmospheric circulation factors which are abnormal in prophase. The authors developed a comprehensive scheme to revise prediction errors of numerical model combined with the abnormal factors scheme and the optimal dynamic multi-factor scheme.Through the diagnostic analysis, the authors found that the comprehensive scheme has a good adaptability. Results of independent sample return during2003-2009show that the anomaly correlation coefficient (ACC) score has increased from0.38to0.61.The similarity revised method has further improved the prediction capacity of numerical model and has a good application prospect for summer precipitation in North China.
4.Based on dynamic characteristics, we established an objective and quantitative forecasting scheme for summer precipitation in China, which was based on finding the similar evolutions of sea surface temperature (SST) in key areas. Different from the multi-factor scheme which has been founded with an eye to the static state of prophase environmental fields, the new scheme is focused on the dynamic state and is attempting to find the analogous year from the history according to the evolution of prophase SST. Using the observational data of precipitation and SST in recent decades, we have screened out the critical areas, which had a great impact on the summer precipitation of China. Similarity coefficient was used as the criterion to show the similarity degree of evolutions and then we calculated the best threshold value through forecasting verification. Result of independent sample return of2002-2011shows that it has a good consistency between predictions and observations and the accuracy is promoted significantly compared with the system revised prediction. The predictive ability for abnormal precipitation has been improved, making up the deficiency of multi-factor scheme.The new scheme provided us a feasible method to increase the accuracy of objective and quantitative forecasting system.
5.We apply the dynamical analogue prediction scheme to predict the subtropical anticyclone in western Pacific.The key prophase predictors for forecasting subtropical anticyclone in summer were separated from the atmospheric circulation factors through correlation analysis and cross validating the anomaly correlation coefficients (ACC).Then we have made the independent sample return test of2003-2010, the results show that the optimal dynamic multi-factor schemes can help the numerical model improving the accuracy of prediction. Based on this, we extract the two typical indexes (western ridge point and ridge line index) from the prediction of subtropical anticyclone, which can represent the characteristics of subtropical anticyclone.Then we project the two indexes in the two-dimensional plane and associate this work with the first part study of statistical classification of subtropical anticyclone.Furthermore, we get the summer precipitation distribution type of forecasting years correspond to the type of subtropical anticyclone.The result shows that the distributions of precipitation correspond to the projection of the type of subtropical anticyclone are consistent with the observations, which demonstrate the rationality of this type of classification of the subtropical anticyclone and precipitation distribution. Based on this study we could carry our point of forecasting the monsoon precipitation through the objective and quantitative prediction of subtropical anticyclone so as to provide a possible scheme for improving the monsoon precipitation prediction skill.
6.The results of multi-scheme prediction tests indicate that the forecasting effect of the dynamic preferable integrated scheme is the most stable and the forecasting techniques are the highest. Specific to a given year, the integrated forecasting techniques are not necessarily higher than the best single forecasting scheme, but it is always better than the worst forecasting scheme, so that the annual forecasting effect can be maintained at a relatively stable level and the great fluctuation of forecasting effect can be avoided effectively, thereby the credibility of the prediction is enhanced.
7.Through the statistics of the forecast results of summer precipitation in many years and the errors,we find that the distribution of the model prediction errors satisfies the Gaussian distribution. Based on the Gaussian distribution characteristics, prediction ability of mode for summer precipitation in China can be analyzed and compared, for example, viewing from the position of the error distribution, the more the normal distribution is satisfied, the better the forecast performance is:viewing from the shape of the fitting, the "slenderer" distribution pattern is, the better the forecast performance is. The forecast performance of the dynamic-statistics scheme is significantly better than that of system error correction scheme of the model. The dynamic-statistics optimal combination of factors revise is corrected, relative to error distribution pattern of the system revised forecast:(1) improvement of the amplitude (the deviation decreases);(2) improvement of the displacement (the drift of the system correction is revised). The interdecadal distribution characteristics of forecast error frequency are very consistent and there are almost no changes. The maximum prediction errors frequency are all around0, which is indicate that the distributions of the model forecast errors are relatively stable and there is no obvious interdecadal variability.
8. Based on the statistical characteristics of the prediction error, measure of the credibility of model forecast results is came up to quantitatively assess credibility of the numerical model forecast results in different regions. Putting the summer precipitation in2012as an example, we analyse the credibility of actual observations, prediction and drought or flood level. Comparing actual observations and prediction, we find that there is a good consistency where the credibility of them is larger, while there are relatively many bad predictions in the region where the credibility of them is smaller. What's more, compared with other reliability test method, the credibility of the abnormal precipitation forecast by this method is more accurate, showing the effectiveness of the method.
9.The FODAS is constructed initially and used to actually predict and inspect in2009-2012. In terms of seasonal forecast, there is a good predictive effect on summer precipitation forecast in2009-2012. The four-year average forecast score of the FODAS is72.8and the average ACC is0.16. It is obvious that the forecast results of the FODAS are better than that of numerical model and there is a good forecast capability for abnormal precipitation by using the FODAS. Moreover, the FODAS effectively forecasts the winter climate trends of2012as well. The PS score of winter temperature forecast in2012is92and PS score of precipitation is82. At the same time, from the perspective of the application effect of month forecast, there are also a certain degree of forecast skill and a forecast capability for abnormal precipitation. But as a whole, the month forecast effect is not better than the seasonal prediction effect, it is not stable enough and there is greater volatility. The FODAS makes a good performance in the temperature and precipitation forecast of summer and winter. Importantly, the value of the FODAS to be applied and promote is in the beginning of another and there is a big space for the FODAS to be developed, improved and promoted.
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