利用─维时间序列重建动力系统的进一步研究

对月平均气温一维时间序列作相空间拓展，并根据系统的分维数设定重建的动力系统，且在具有二次非线性项的假定下，利用最小二乘法求解相空间状态演化方程中的各项系数，以重建动力系统。结果表明：随相空间维数的增加，重建的动力系统对气候演化过程可作出更精确、细致的描述。     

Preliminary Study of Reconstruction of a Dynamic System Using an One－Dimensional Time Series

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一维气候时间序列所包含的浑沌吸引子的Kalmogorov熵的确定

本文采用上海16年(1970—1985年)逐日平均气压资料,用相空间延拓的方法,计算了它的关联维D和Kalmogorov熵的近似值二阶Renyi嫡K₂。得到D=7.7～7.9,为分数值,K₂约为0.1,是一正数值。证明我国季风区短期天气吸引子是一种浑沌运动。由K₂数值直接估计可预报时间T=(1K₂)约为十天,与早期动力统计学方法所得的可预报时间尺度相一致。 本文计算中,考察了相空间拓展时延滞τ的效应。计算结果表明:关联维D和K₂对τ均是收敛的。对本文所用上海逐日平均气压序列,用延滞τ=5延拓的相空间,各坐标分量是相互独立的,系统动力学特征量是稳定可靠的。      

A Method of Phase Space Modeling of a Climatological Series and its Application

Summary  In this paper, a method of phase space in modeling the climatological series is put forward on the correlation dimension, the theory of phase space and the direct retrieval technique. Then time series of monthly mean temperature are used to build dynamic models of regional climate in phase space in which some dynamic characteristics can be discovered. The results illustrate that the methodology holds promise for a wide variety of applications. Furthermore, the evolution characteristic and the interior interaction mechanism of the regional climate of southern China are also discussed. Received March 29, 1999/Revised November 2, 1999     

利用一维时间序列重建动力系统的初步研究

通过对月平均气温一维时间序列作相空间拓展,和假定相空间状态变量随时间的演化方程含有线性和非线性二次项,再根据拓展后的资料进行拟合,利用最小二乘法求出各项系数,保留其方差贡献大的项,以重建动力系统.实际计算结果表明,重建的低阶动力系统有可能对月平均气温所表征的短期气候演化过程的非线性特性作出描写.     

一维气候时间序列的拓展及其相空间中浑沌吸引子维数的确定

本文利用上海和广州近百年(1873—1980)月平均气温时间序列资料,将一维气候时间序列拓展到多维相空间上去。计算结果表明,月平均气温所表示的我国季风区短期气候演化,在相空间中存在吸引子,具有分维结构,其维数分别是d=3.4和d=2.3,为奇怪吸引子。由此推论,就我国季风区气候短期变化而言,为了能在多维相空间支撑起上述奇怪吸引子,最好选取四个变量或者建立最低为四阶的动力学模式来进行描述。      

500hPa月平均距平场演变的宏观描述

近年来,采用数值方法进行长期预报和气候异常的研究有了很大的进展,但是这些研究多是沿用了中短期预报的概念和方法,由于长期天气系统是一个强迫耗散的非线性系统,而“耗散系统最终将趋向维数比原始相空间低的极限集合——吸引子,许多自由度在演变过程中成为‘无关变量’,最终剩下支撑起吸引子的少数自由度”。如果不考虑系统的极其复杂的暂态行为,我们自然可以把这些‘无关变量’去掉,从而把高维动力系统简化成低维动力系统。这个解释H.Haken称做随动原理。本文的目的旨在应用上述原理探讨长期天气异常演变的宏观描述方法。我们希望这里提出的概念和方法将有助于建立有效的长期数值预报模式。     

用天气变量时间序列估计天气的可预报性

本文从非线性系统的吸引子概念出发,用单个气象时间序列重构维数较高的相空间并嵌入天气吸引子,根据相轨道上初始时刻紧邻的点随时间的演化来估计吸引子的维数和天气的可预报性。用500hPa亚洲环流指数和北京冬季气温的逐日资料计算表明,天气吸引子的维数分别为3.8和5.4;可预报时间尺度约6—14天,考虑相空间e指数膨胀因素后为4—9天。     

一维气候时间序列的李亚普诺夫指数所显示的动态系统的演化特征

本文利用广州1873—1980年共108年的月平均气温时间序列资料,在拓展了的三维相空间中,用Wolf的方法,求出了在不同参数条件下的李亚普诺夫指数λ₁。从中发现,对每一组参数,均得到λ_1,2>0（其中λ₁>λ₂）,λ₃<0。这说明我国季风区短期气候演化存在着浑沌吸引子,因而气候应该分享经历着浑沌力学行为的体系所具有的那种本征的演化特征。      

基于马尔科夫链转移概率极限分布的降水过程持续性研究

各地不同气候条件所具有的天气状态转移概率的极限分布实际上代表了各地天气气候的持续性和转折性特征,同时也表明了它的可预报期限。从马尔科夫链理论出发,初步研究了中国160个代表测站逐日天气状态演变过程的极限分布。结果表明,转移概率的极限分布不但其空间分布有明显差异,而且不同季节的极限分布也有明显差异。例如,有的地区仅有2 d持续期,有的可达4—6 d或更长,充分反映出不同地区因其影响的天气系统差异所造成的逐日天气气候的持续性和转折性特征的差异。其研究意义在于,由此可作为天气气候分型区划的一种理论依据。统计分析结果表明,就全中国平均而言,夏季持续期最短,持续期由北向南、由西向东呈增加趋势,且春季平均降水持续期为5.1 d,夏季平均为5.0 d,秋季平均为6.5d,冬季平均为6.2 d。可见夏半年比冬半年的降水持续期短,这可能是因为春夏季的天气系统比较复杂且中小尺度天气系统较多的缘故。这从另一侧面再次证明,各地逐日降水天气状态演变过程具有一定的天气气候状态自然转折的持续性即自然天气周期的气候状况,从而为短期天气预报提供了气候背景。     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

雾霾天气下DAML空间相干光通信系统的研究

大气湍流引起的折射率起伏会导致光信号的振幅和相位发生随机波动,同时日益严重的城市雾霾现象带来的系统性能降低问题也不可忽视.基于湍流相位波动和激光器相位噪声的缓慢时变特性,本文提出了将DAML相位估计算法应用到空间相干光通信系统中来提高系统性能,并推导了在大气湍流、激光器相位噪声以及各种天气状况衰减效应影响下,针对DQPSK信号的DAML相位估计系统的误码率,最后重点分析了在雾、霾天气条件下该系统的传输距离限制和波长选择特性.仿真结果表明：湍流相位波动造成的信噪比损失低于0.1 dB;在雾和霾天气下,相较于传统的OOK IM／DD FSO通信系统,DAML相位估计系统在不同的大气湍流环境中至少有500 m的传输距离优势;在霾和薄雾天气,传输波长为1 550 nm的DAML系统比波长为850 nm的系统的性能更优,但在能见度低于200 m的浓雾天气,系统性能基本与波长选择无关.     

AOGS第六届学术年会气象学研究报告综述

简要综述了第六届亚洲大洋洲地球科学学会（AOGS）学术年会有关天气和气候的可预报性、协同亚洲季风试验、季风变化及其模拟与预测、气候变化与热带气候灾害、中层大气研究、中尺度气象学与台风研究以及卫星资料应用等方面的报告内容。在全球气候变化的大背景下,季风的年代际变化特征,可能成因及其对天气气候的影响是目前研究的热点问题之一。对亚洲季风系统形成机制的探讨及其与气候系统关系的研究有助于提高数值模式对季风系统的模拟能力,从而能够更好地预测季风系统及其演变。集合预报,特别是多模式超级集合预报方法引起了人们广泛的兴趣。研究报告指出,多个积云对流参数化方案的集成、集合卡曼滤波、条件非线性最优扰动法以及滑动训练期超级集合预报技术对数值天气预报的准确率都有明显改善。＂可预报性障碍＂在数值模式对大气环流的预报中同样存在。＂可预报性障碍＂本质上反映了系统的季节变化对预报结果的影响。研究还表明,热带气旋频率和强度的变化取决于在其形成的海域是动力因子还是热力因子起主导作用,如热力因子起主导作用则气候变暖会使该区域的热带气旋活动频率和强度增加。一些研究指出,中高纬度阻塞形势持续异常及南海的热源异常可能是造成2008年初中国南方低温、雨雪和冰冻灾害的主要原因。Formosat-2和Formosat-3等卫星观测资料的广泛应用将极大地提高数值天气预报、气候监测和空间天气预报水平。     

Challenges posed by and approaches to the study of seasonal-to-decadal climate variability

The tasks of providing multi-decadal climate projections and seasonal plus sub-seasonal climate predictions are of significant societal interest and pose major scientific challenges. An outline is presented of the challenges posed by, and the approaches adopted to, tracing the possible evolution of the climate system on these various time-scales. First an overview is provided of the nature of the climate system’s natural internal variations and the uncertainty arising from the complexity and non-linearity of the system. Thereafter consideration is given sequentially to the range of extant approaches adopted to study and derive multi-decadal climate projections, seasonal predictions, and significant sub-seasonal weather phenomena. For each of these three time-scales novel results are presented that indicate the nature (and limitations) of the models used to forecast the evolution, and illustrate the techniques adopted to reduce or cope with the forecast uncertainty. In particular, the contributions (i) appear to exemplify that in simple climate models uncertainties in radiative forcing outweigh uncertainties associated with ocean models, (ii) examine forecast skills for a state-of-the-art seasonal prediction system, and (iii) suggest that long-lived weather phenomena can help shape intra-seasonal climate variability. Finally, it is argued, that co-consideration of all these scales can enhance our understanding of the challenges associated with uncertainties in climate prediction.     

热带纬向平均臭氧层动力学系统的重建

将ＴＯＭＳ测量的５°Ｎ纬向平均Ｏ３总量逐日序列作相空间拓展,使Ｏ３层的行为特征可用相轨迹刻划。首先计算Ｏ３层系统吸引子的分数维,而后把相点作为动力学系统的离散特解集合,反演出描述系统状态随时间演化的数学模型中的客观控制参数值,建立起反映热带Ｏ３层短期演化特征的非线性动力学系统。     

Polar amplification as a preferred response in an idealized aquaplanet GCM

An aquaplanet atmospheric general circulation model (GCM) coupled to a mixed layer ocean is analyzed in terms of its polar amplified surface temperature response to a 2×CO₂-like steady forcing and in terms of the phase space trajectory of the relaxation of a free perturbation to equilibrium. In earlier studies concerned with linear stability analysis of the same system we have shown that the least stable mode of the linearized surface budget operator has a polar amplified shape. We demonstrate that this shape of the least stable mode is responsible for the polar amplified shape of the response to a uniform forcing and for the manner in which the system relaxes back to equilibrium. Based on GCM and simple energy balance model results it is argued that the decay time-scale of this mode is determined by the sensitivity of the net top-of-atmosphere radiation to surface temperature while its shape (and thus the degree of polar amplification in a climate change experiment) is determined by the sensitivity of poleward heat transports to low- and high-latitude temperatures by the faster time-scale atmospheric dynamics. This implies that the underlying mechanisms for the polar amplification may be obscured when studying feedbacks during the slow evolution of climate change or considering only the new equilibrium state after introduction of a steady forcing.     

Autoregressive logistic regression applied to atmospheric circulation patterns

Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.     

COSMIC及其在气象领域的应用

COSMIC是利用现代遥感技术、通信技术和计算机技术解决当今地球科学重大问题的地球科学卫星探测系统。作为GPS／MET的后续计划，主要是为了进行气象与气候研究、气候监测、太空天气和大地测量等研究。COSMIC计划于2003年之前发射8颗低轨卫星，8颗卫星每天可进行4000次GPS观测。本文介绍COSMIC系统组成及COSMIC数据应用。