岷江上游潜在性泥石流堰塞湖危害及判识

汶川地震造成泥石流形成条件的改变,其次生灾害堰塞湖的危害已开始显现。如何对震后潜在性泥石流堰塞湖进行判识,成为迫切需要解决的问题。选取岷江上游映秀至汶川段为研究区,通过分析震后泥石流形成条件的变化、典型泥石流堰塞湖的危害及松散物质储量,选取潜在性泥石流堰塞湖的判识指标,利用模糊物元可拓模型,建立潜在性泥石流堰塞湖的综合判识模式。通过判识,研究区形成泥石流堰塞湖可能性高的一级支沟有17条,主要集中分布在映秀镇至草坡乡段,此段将是今后受堰塞湖危害的高危地段。     

The Mesoscale Predictability of a Heavy Precipitation Event

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.     

混沌系统单变量可预报性研究

对于n维的混沌系统, 不同变量的可预报性是不同的。为了研究混沌系统中单个变量的可预报性, 本文在以前提出的混沌系统整体的非线性局部Lyapunov指数基础上(李建平等, 2006), 引入了单变量的非线性局部Lyapunov指数及其相关统计量, 进一步完善了非线性误差增长理论。通过应用到几个混沌个例, 结果表明单变量的非线性局部Lyapunov指数及其相关统计量可以用来定量地研究多维混沌系统中不同变量的可预报性, 系统不同变量的可预报性之间不是相互独立的, 而是单个变量的可预报期限与系统整体的可预报期限之比都近似保持一个常数, 但各个变量的常数值有所不同。     

A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability

There are three common types of predictability problems in weather and climate, which each involve different constrained nonlinear optimization problems: the lower bound of maximum predictable time, the upper bound of maximum prediction error, and the lower bound of maximum allowable initial error and parameter error. Highly efficient algorithms have been developed to solve the second optimization problem. And this optimization problem can be used in realistic models for weather and climate to study the upper bound of the maximum prediction error. Although a filtering strategy has been adopted to solve the other two problems, direct solutions are very time-consuming even for a very simple model, which therefore limits the applicability of these two predictability problems in realistic models. In this paper, a new strategy is designed to solve these problems, involving the use of the existing highly efficient algorithms for the second predictability problem in particular. Furthermore, a series of comparisons between the older filtering strategy and the new method are performed. It is demonstrated that the new strategy not only outputs the same results as the old one, but is also more computationally efficient. This would suggest that it is possible to study the predictability problems associated with these two nonlinear optimization problems in realistic forecast models of weather or climate.     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of El Nino in Zebiak-Cane Model

With the Zebiak-Cane(ZC)model,the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation(CNOP).The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model.By analyzing the behavior of CNOP- type errors,we find that for the normal states and the relatively weak El Nino events in the ZC model,the predictions tend to yield false alarms due to the uncertainties caused by CNOP.For the relatively strong El Nino events,the ZC model largely underestimates their intensities.Also,our results suggest that the error growth of El Nino in the ZC model depends on the phases of both the annual cycle and ENSO.The condition during northern spring and summer is most favorable for the error growth.The ENSO prediction bestriding these two seasons may be the most diffcult.A linear singular vector(LSV)approach is also used to estimate the error growth of ENSO,but it underestimates the prediction uncertainties of ENSO in the ZC model.This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes.CNOP yields the severest prediction uncertainty.That is to say,the prediction skill of ENSO is closely related to the types of initial error.This finding illustrates a theoretical basis of data assimilation.It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

热带东印度洋海表温度持续性的秋季障碍

利用全球海表海温资料(GISST)和NCEP/NCAR再分析风场、海平面气压场资料,研究了热带东印度洋海表温度持续性的季节差异,发现东印度洋海温持续性存在"秋季障碍"现象。进一步分析了东印度洋"秋季障碍"后冬季海温与中东太平洋海温、海平面气压及850hPa风场的关系,并讨论了热带印度洋—太平洋地区海气系统的季节变化与东印度洋"秋季障碍"的关系,结果表明,秋季热带印度洋—太平洋地区海气系统由以印度洋季风环流为主导转向以太平洋海气系统为主导,太平洋海气系统处于急剧加强期,增强的太平洋海气系统对东印度洋海温持续性"秋季障碍"起着重要的作用。     

台风麦莎渤海转向的可预报性研究

利用MM5模式研究了2005年第9号台风"麦莎"在渤海向东北转向路径的可预报性.试验用不同的积云参数化方案、不同的预报时效分别从确定性和集合预报角度对"麦莎"在渤海向东北方向的转向过程做了模拟.结果表明,"麦莎"在渤海的转向可预报时效为48小时左右.不同的积云参数化方案对台风麦莎路径的48小时预报结果显示台风均转向东北,预示"麦莎 "基本不会直接影响北京.60和72小时的预报结果显示,Kuo和Betts-Miller积云参数化方案的台风模拟路径与实况比较接近,而Grell和KeinFritch积云参数化方案的台风模拟路径却偏向了实际台风位置的西北,台风有可能直接影响北京.研究表明,对于台风麦莎而言,时效超过两天的转向预报可信度较低,Kuo和Betts-Miller积云参数化方案的预报准确性较高.     

中国季降水量的气候噪声和潜在可预报性估计

利用中国130个测站1961—2004年的日降水量资料,使用低频白噪声延伸法和方差分析法估计了中国季降水量的气候噪声方差和潜在可预报性。结果表明：中国季降水量的气候噪声方差由南向北、由沿海向内陆逐渐减小,且有明显的季节变化,夏季最高,其次是春秋季,冬季最小,而且内陆的季节变化比东南沿海的季节变化显著。季降水量的潜在可预报性有较大的季节和区域差异,但总体来说,全国大部分地区的季降水量是潜在可预报的。以绝对误差小于均方差0.68倍作为预测正确标准,全国大部分地区季降水量的预报正确率上限为50%-60%。     

SMIP2试验对亚洲夏季风的模拟能力及其可预报性的分析

利用参加第二次季节预测模式比较计划（Phase 2 of the Seasonal Prediction Model Intercomparison Project,简称SMIP2）的五个大气环流模式的输出结果,比较了这些模式对亚洲夏季降水的模拟能力,并讨论了大气环流模式在季节尺度上对亚洲夏季风的可预报性。结果表明,各模式能够较好地模拟亚洲夏季降水的大尺度分布特征。除了模拟的东亚夏季降水异常经验正交函数分解第一模态（EOF1）的时间系数与观测之间的相关系数较低之外,多数模式可以大致再现东亚、南亚和西太平洋夏季降水异常EOF1及其对应的时间系数。分析表明,热带地区有很好的可预报性,北半球副热带地区的可预报性尽管也较好,但比热带地区要低,南半球热带以外地区的可预报性较差,陆地上的可预报性比海洋上低。在热带和北半球副热带地区,由海温强迫所产生的较大外部方差对此地高可预报性有很大的贡献。与正常年份相比,模式在强El Ni?o年和强La Ni?a年表现出较高的可预报性,在南亚和印度洋地区大多数模式在El Ni?o年比La Ni?a年产生了更好的可预报性,在中西太平洋区域El Ni?o年可预报性的高值中心较La Ni?a年位置偏西。