Prediction of weather parameters on a very short time scale by an Auto Regressive process for aviation flight planning

Forecasting weather parameters such as temperature and pressure with a reasonable degree of accuracy three hours ahead of the scheduled departure of an aircraft helps economic and efficient planning of aircraft operations. However, these two parameters exhibit a high degree of persistency and have nonstationary mean and variance at sub-periods (i.e. at 0000, 0300, 0600,…, 2100UTC). Hence these series have been standardised (to have mean 0 and variance 1) and thereafter seasonal differenced (lag 8) to achieve almost near stationarity. An attempt has been made to fit the standardised and seasonal differenced series of Chennai (a coastal station) and Trichy (an inland station) airport into an Auto Regressive (AR) process. The model coefficients have been estimated based on adaptive filter algorithm which uses the method of convergence by the steepest descent. The models were tested with an independent data set and diagnostic checks were made on the residual error series. An independent estimation of fractal dimension has also been made in this study to conform the number parameters used in the AR processes. The models contemplated in this study are parsimonious and can be used to forecast surface temperature and pressure.     

A four-parameter,two degree-of-freedom block-spring model: Effect of the driver velocity

We analyze the effect of tectonic plate velocities in the earthquake pattern using a simple mass-spring model of the Burridge and Knopoff type with two blocks and a velocity-weakening friction law. Previous versions of the two-block model assume a steady driver during slip events (limit of zero driver velocity), which, in some cases makes necessary the introduction of artificial parameters to start the numerical integration of the equations of motion at impending slip of any block. Still maintaining the condition of zero driver velocity during slip, we shall introduce a procedure to start the numerical integration without introducing artificial parameters and this will be done by using a linearized version of the equations of motion valid for small velocities and considering nonzero driver velocity. We also introduce a four parameter model in which the driver velocity enters the equations during the whole simulation, and analyze the effect of the new parameter, the driver velocity, in the displacement and time patterns of blocks motion, directly related to earthquake statistics such as coseismic slips and average repeat times.     

基于混沌时间序列的地下水位多步预测模型

利用相空间重构技术，并借助G—P算法、C-C方法和Wolf方法从宁陵地区地下水位一维时间序列中提取Lyapunov指数，结果表明此时间序列具有混沌特征。计算了宁陵地区地下水位时间序列的关联维数、时间延迟和最大Lyapunov指数，将局域加权一阶多步预测模型应用于地下水位预测。预测表明，此模型可有效应用于地下水位时间序列的多步预测。     

基于线性反馈控制的一类混沌系统的同步

研究一类混沌系统的同步问题。基于李雅普诺夫稳定性理论,利用线性反馈法给出了同步混沌系统的3种控制方案,得到了2个混沌系统同步的充分条件。为了更清楚地了解每种方案下系统的同步行为,还给出了以增益为分岔参数时同步误差的变化图。理论分析和数值仿真结果都表明了文中所给方法的有效性和可行性。     

地球化学景观吸引子的发现及其意义

勘查地球化学对异常评价的最大不确定性源于对地球化学景观特性的认识不够。我们应用构造相空间的方法分析了浙江儒岙幅（1:5万）水系沉积物测量的Ag含量数据，揭示了地球化学景观是具有低维吸引子（DZ≈2．9）的混饨系统。这一发现将对勘查地球化学中采样密度的选择、地球化学场模拟、异常评价等根本性问题产生重大影响。     

大规模富社团网络的时空混沌同步

以Plankton时空混沌系统作为网络节点,通过非线性耦合构成富社团(rich-club,RC)网络,研究其时空混沌同步规律.首先给出了RC网络中连接节点之间的非线性耦合函数的一般性选取原则.进而基于Lyapunov稳定性定理,理论分析了实现网络同步的条件.最后,通过仿真模拟检验了网络的时空混沌同步效果.仿真研究表明,RC网络中各富节点之间以及这些富节点各自星形连接的子网络中的所有节点均实现了完全同步.     

On the quest for chaotic attractors in hydrological processes

Abstract

In the last two decades, several researchers have claimed to have discovered low-dimensional determinism in hydrological processes, such as rainfall and runoff, using methods of chaotic analysis. However, such results have been criticized by others. In an attempt to offer additional insights into this discussion, it is shown here that, in some cases, merely the careful application of concepts of dynamical systems, without doing any calculation, provides strong indications that hydrological processes cannot be (low-dimensional) deterministic chaoti. Furthermore, it is shown that specific peculiarities of hydrological processes on fine time scales, such as asymmetric, J-shaped distribution functions, intermittency, and high autocorrelations, are synergistic factors that can lead to misleading conclusions regarding the presence of (low-dimensional) deterministic chaos. In addition, the recovery of a hypothetical attractor from a time series is put as a statistical estimation problem whose study allows, among others, quantification of the required sample size; this appears to be so huge that it prohibits any accurate estimation, even with the largest available hydrological records. All these arguments are demonstrated using appropriately synthesized theoretical examples. Finally, in light of the theoretical analyses and arguments, typical real-world hydrometeorological time series, such as relative humidity, rainfall, and runoff, are explored and none of them is found to indicate the presence of chaos.     

Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSM) models is investigated for modelling reference evapotranspiration (ET₀). The modelling results of the proposed models are validated against the M5 model tree and multi-layer perceptron neural network (MLPNN) methods. Two meteorological stations, Isparta and Antalya, in the Mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET₀. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET₀. Mean absolute error (MAE), root mean square error (RMSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET₀, giving the lowest MAE (0.036 and 0.037 mm) and RMSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.     

地球磁极倒转的分形混沌研究

地球磁场多次发生南北(正负)磁极位置的变换,这已为大家所公认。但造成这种异常现象的原因,则是迄今未能很好解答的一个难题。应用分形混沌理论对地球磁极倒转进行了分析,认为发生地球磁极倒转时间段在时间轴上具有分形分布的性质;地球磁场系统通过不断与外界交换物质和能量,维持一种空间或时间的有序结构。地球磁场极性的随机倒转具有混沌运动的自逆转特性,混沌理论给地磁极性倒转提出了一个简明的动力机制解释。