中尺度业务模式系统侧边界嵌套方法的研究

为提高预报准确率，在对多种嵌套方案试验对比的基础上，选用了一种计算效果较好，方法也不甚复杂的Ｄａｖｉｅｓ物理量松弛方案，对中尺度业务模式系统进行了预报试验。其网格边界值采用Ｔ４２Ｌ９谱模式的预报值。嵌套模式与原模式用同一个例进行了预报对比试验。结果表明，嵌套方案考虑了大尺度环流背景场在边界附近对中尺度系统的强迫作用，提高了有限区域模式的实际预报能力。     

Semivariogram Models Based on Geometric Offsets

Kriging-based geostatistical models require a semivariogram model. Next to the initial decision of stationarity, the choice of an appropriate semivariogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental semivariograms with a nested combination of proven models such as the spherical, exponential, and Gaussian models. These models work well in most cases; however, there are some shapes found in practice that are difficult to fit. We introduce a family of semivariogram models that are based on geometric shapes, analogous to the spherical semivariogram, that are known to be conditional negative definite and provide additional flexibility to fit semivariograms encountered in practice. A methodology to calculate the associated geometric shapes to match semivariograms defined in any number of directions is presented. Greater flexibility is available through the application of these geometric semivariogram models.     

巢式监测井成井技术

从巢式监测井选用的材料、巢式监测井的安装、巢式监测井安装时注意事项、巢式监测井的清洗取样、以及在地下水污染监测方面的实际应用做了简单介绍，为地下水污染监测提供了一种新的参考。     

WRF模式对中国夏季降水的动力降尺度模拟研究

采用NCEP的FNL再分析资料驱动WRF模式,对中国10 a(2000—2009年)夏季降水进行双重动力降尺度(双重嵌套)模拟,将子、母区域模拟结果和观测进行对比,以检验双重动力降尺度对中国夏季降水模拟的"增值"能力。结果表明:单重动力降尺度(单重嵌套)方法能较好模拟出中国10 a夏季平均降水的空间分布,对季风雨带"北跳"特征模拟较好,但模拟降水具有系统性正偏差。在母区域的强迫下,双重动力降尺度模拟的降水分布与单重动力降尺度相比,没有发生根本性变化。但由于子区域的分辨率要高于母区域,双重动力降尺度比单重动力降尺度能提供更多有价值的降水细节。双重动力降尺度的这种"增值"能力存在地域依赖性,在华南地区和江淮地区,双重动力降尺度模拟出的降水分布、量值和逐日演变都要好于单重动力降尺度。但在华北地区,双重动力降尺度没有表现出明显的"增值"。     

Groundwater modelling to quantify the effect of water harvesting structures in Wagarwadi watershed,Parbhani district,Maharashtra, India

Marathwada Agricultural University, Pharbani, has developed about 560 hectares of Wagarwadi watershed in Pharbani district since 1987. Groundwater monitoring on 16 observations wells at weekly intervals commenced in January 1992, and rainfall and pan evaporation has been measured daily at a hydrometeorological station situated in the nearby university campus. Aquifer parameters, namely, transmissivity and specific yield, have been estimated by carrying out a pumping test on a large diameter well. Groundwater recharge resulting from rainfall has been estimated using a water balance model of the soil moisture zone considering soil zone thickness and crops grown. The SCS (Soil Conservation Service) curve number method was used for surface runoff estimation. The groundwater flow model has been constructed using the nested squares, finite difference method. Nested square meshes of sizes 160 m×160 m and 80 m×80 m have been used and the steady-state condition of aquifer system was simulated in the model assuming the June 1992 water level configuration under equilibrium conditions. The model has been calibrated for transient conditions incorporating additional seepage from the water harvesting structures and their contribution to the groundwater regime has been assessed. © 1998 John Wiley & Sons, Ltd.     

A Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies

We introduce the Bayesian hierarchical modeling approach for analyzing observational data from marine ecological studies using a data set intended for inference on the effects of bottom-water hypoxia on macrobenthic communities in the northern Gulf of Mexico off the coast of Louisiana, USA. We illustrate (1) the process of developing a model, (2) the use of the hierarchical model results for statistical inference through innovative graphical presentation, and (3) a comparison to the conventional linear modeling approach (ANOVA). Our results indicate that the Bayesian hierarchical approach is better able to detect a “treatment” effect than classical ANOVA while avoiding several arbitrary assumptions necessary for linear models, and is also more easily interpreted when presented graphically. These results suggest that the hierarchical modeling approach is a better alternative than conventional linear models and should be considered for the analysis of observational field data from marine systems.     

三维斜压台风模式Ⅰ.数值方法

一种多重移动套网格斜压台风模式已被应用于海洋环境数值预报.模式采用σ坐标系的原始方程组作为控制方程.现在用于国家海洋环境预报中心的模式垂直方向为非等距4层,水平方向为Arakawa B型格式,所采用的差分格式满足动量和能量守恒原理.模式控制方程组分离成平流过程和适应过程二组方程,并根据大气运动不同过程的特性,分别采用不同的时间步长和不同的积分方法.预报和后报结果显示该数值方法不仅可以缩短机时,而且可以得到稳定的预报结果.     

三维斜压台风模式I.数值方法

一种多重移动套网格斜压台风模式已被应用于海洋环境数值预报。模式采用坐标系的原始方程组作为控制方程。现在用于国家海洋环境预报中心的模式垂直方向为非等距４层,水平方向为ＡｒａｋａｗａＢ型格式,所采用的差分格式满足动量和能量守恒原理。模式控制方程组分离成平流过程和适应过程二组方程,并根据大气运动不同过程的特性,分别采用不同的时间步长和不同的积分方法。预报和后报结果显示该数值方法不仅可以缩短机时,而且可以得到稳定的预报结果。     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.