双偏振雷达差分传播相移的五种滤波方法对比分析

为了对双线偏振雷达的差分传播相移ΦDP进行滤波降噪处理,提高双线偏振雷达识别粒子、估测降水的能力。通过对C波段双线偏振雷达差分传播相移分别采用滑动平均、中值滤波、FIR滤波、卡尔曼滤波和小波分析5种不同方法地介绍,利用中国气象科学研究院灾害天气国家重点实验室移动式C波段双线偏振雷达(POLC)2013年外场试验观测资料,对比分析了经5种方法处理后的差分传播相移ΦDP径向数据,及其分别在降水估测中应用的效果。结果表明:采用FIR滤波方法、卡尔曼滤波方法和小波分析方法对ΦDP径向数据进行处理的效果比传统的滑动平均和中值滤波方法好;其中小波分析处理的ΦDP径向数据波动最小,再经过变距离法拟合的KDP数据的负值能够得到最有效地抑制,最后应用到R(KDP)降水估测中的精度最高。     

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.      

浅议地下室渗漏及预防措施

本文从地下室渗漏的理论分析入手,提出解决渗漏的关键在于工程施工阶段的合理组织和有效预防措施,结合某地下室的实践经验,总结了相应的一些预防措施     

盘锦湿地生态系统野外观测站概况

重点介绍了盘锦湿地生态系统野外观测站建立的背景、研究定位与研究方向,主要观测场及观测仪器设备,已有基础数据积累和科研任务等。在此基础上,提出了未来盘锦站应在建设目标、人才队伍建设及规范管理方面的重点工作,为将研究站建设成为生态气象监测示范与技术支持基地、研制生态气象业务评估方法与模型的平台、生态气象业务人才培养基地以及作为地球系统科学前沿研究的孵化器,最终成为生态学和资源、环境科学等相关学科的发展提供野外试验和研究平台提供保障。     

A 3D discrete FEM iterative algorithm for solving the water pipe cooling problems of massive concrete structures

Water pipe cooling has been widely used for the temperature control and crack prevention of massive concrete structures such as high dams. Because both under‐cooling and over‐cooling may reduce the efficiency of crack prevention, or even lead to great harm to structures, we need an accurate and robust numerical tool for the prediction of cooling effect. Here, a 3D discrete FEM Iterative Algorithm is introduced, which can simulate the concrete temperature gradient near the pipes, as well as the water temperature rising along the pipes. On the basis of the heat balance between water and concrete, the whole temperature field of the problem can be computed exactly within a few iteration steps. Providing the pipe meshing tool for building the FE model, this algorithm can take account of the water pipe distribution, the variation of water flow, water temperature, and other factors, while the traditional equivalent algorithm based on semi‐theoretical solutions can only solve problems with constant water flow and water temperature. The validation and convergence are proved by comparing the simulated results and analytical solutions of two standard second‐stage cooling problems. Then, a practical concrete block with different cooling schemes is analyzed and the influences of cooling factors are investigated. In the end, detailed guidance for pipe system optimization is provided. Copyright © 2015 John Wiley & Sons, Ltd.     

Stage level,volume and time‐frequency information content of Lake Tana using stochastic and wavelet analysis methods

Lake Tana is the largest fresh water body situated in the north‐western highlands of Ethiopia. In addition to its ecological services, it serves for local transport, electric power generation, fishing, recreational purposes, and source of dry season irrigation water supply. Evidence shows that the lake has dried at least once at about 15,000–17,000 before present owing to a combination of high evaporation and low precipitation events. Past attempts to understand and simulate historical fluctuation of Lake Tana based on simplistic water balance approach of inflow, outflow, and storage have failed to capture well‐known events of drawdown and rise of the lake that have happened in the last 44 years. This study tested different stochastic methods of lake level and volume simulation for supporting Lake Tana operational planning decision support. Three stochastic methods (perturbations approach, Monte Carlo methods, and wavelet analysis) were employed for lake level and volume simulation, and the results were compared with the stage level measurements. Forty‐four years of daily, monthly, and mean annual lake level data have shown a Gaussian variation with goodness of fit at 0.01 significant levels of the Kolmogorov–Smirnov test. The stochastic simulations predicted the lake stage level of the 1972, 1984, and 2002/2003 historical droughts 99% of the time. The information content (frequency) of fluctuation of Lake Tana for various periods was resolved using Wigner's Time‐Frequency Decomposition method. The wavelet analysis agreed with the perturbations and Monte Carlo simulations resolving the time (1970s, 1980s, and 2000s) in which low frequency and high spectral power fluctuation has occurred. The Monte Carlo method has shown its superiority for risk analysis over perturbation and deterministic method whereas wavelet analysis reconstructed historical record of lake stage level at daily and monthly time scales. Copyright © 2012 John Wiley & Sons, Ltd.     

A survey of numerical methods for stochastic differential equations

The development of numerical methods for stochastic differential equations has intensified over the past decade. The earliest methods were usually heuristic adaptations of deterministic methods, but were found to have limited accuracy regardless of the order of the original scheme. A stochastic counterpart of the Taylor formula now provides a framework for the systematic investigation of numerical methods for stochastic differential equations. It suggests numerical schemes, which involve multiple stochastic integrals, of higher order of convergence. We shall survey the literature on these and on the earlier schemes in this paper. Our discussion will focus on diffusion processes, but we shall also indicate the extensions needed to handle processes with jump components. In particular, we shall classify the schemes according to strong or weak convergence criteria, depending on whether the approximation of the sample paths or of the probability distribution is of main interest.     

A comparison of state space and multiple regression for monthly forecasts: U.S. Fuel consumption

Monthly consumption forecasts for U.S. oil, natural gas, and coal are made using state space and multiple regression applied to the same data. These forecasts are compared with actual consumption for a test period. The forecasts made using state space are preferred to those made using multiple regression models for both expost and exante cases. The state space forecasts track data cycles better than do the regression forecasts. Average absolute forecast errors are less for the state space models than they are for the multiple regression models.