随机天气模型参数化方案的研究及其模拟能力评估

文中介绍了随机天气模型 WGEN的基本结构及其模拟原理 ,并针对其中随机过程的统计结构特征和 GCMs输出要素的不同时空尺度特点 ,利用动态数据的参数化分析方法等统计学技术 ,确定了该模型参数的估计方法。同时基于蒙特卡罗数值计算原理 ,给出了 WGEN的随机试验方法 ,并通过模拟基准气候 ,从时间分布和空间场两方面对模型在中国东北地区的模拟效果及其能力进行了评估。结果表明 ,模型对于最高气温、最低气温、降水和辐射等要素均具有较好的模拟效果 ,模拟序列与观测序列的取值分布有较一致的概率特性。由此可以结合 GCMs大尺度网格上输出的月和年要素值 ,通过调控随机过程的参数 ,生成具有不同气候变率的 2× CO2 逐日气候变化情景 ,实现气候预测模式与气候影响模式的嵌套 ,进一步研究气候变率变化的可能影响。     

坑内单管水力反循环取心钻探技术研究

阐明了坑内水力反循环连续取心钻进的原理以及该工艺所具有优点、缺陷及相应的工艺措施。提出了该工艺规程参数的计算原理及计算方法。     

冰雹指数的误差分析及在冰雹云识别中的应用

利用模拟测试方法研究了几个重要因子对冰雹指数误差的影响,并根据遵义新一代多普勒天气雷达资料,使用探测概率、临界成功指数和误报率为衡量指标,对2008年3月到2012年7月12次冰雹天气过程中25个降雹风暴和72个非降雹风暴的冰雹指数进行分析评估。结果表明：H₀、反射率因子垂直廓线和目标离雷达的距离等因子对POSH误差的影响较大,H_m20对其影响较小。POH识别冰雹云的误报率较高,临界成功指数较小;POSH识别效果好于POH,通过增大阈值可以提高临界成功指数和减小误报率,但是当阈值太大时,探测概率会较低,为了获得较大的探测概率和临界成功指数,减小误报率,以POSH=70为阈值时识别效果最好;在POSH识别冰雹云时加上VIL>20 kg·m^-2的条件对探测概率没有影响,但是当阈值小于90时,可以减少误报率和提高临界成功指数。     

Ensemble Forecast for Tropical Cyclone Based on CNOP-P Method: A Case Study of WRF Model and Two Typhoons

In this paper, we set out to study the ensemble forecast for tropical cyclones. The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter (CNOP-P) method and the WRF model to improve the prediction accuracy for track and intensity, and two different typhoons are selected as cases for analysis. We first select perturbed parameters in the YSU and WSM6 schemes, and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters. Finally, perturbations are imposed on default parameter values to generate the ensemble members. The whole proposed procedures are referred to as the Perturbed Parameter Ensemble (PPE). We also conduct two experiments, which are control forecast and ensemble forecast, termed Ctrl and perturbed-physics ensemble (PPhyE) respectively, to demonstrate the performance for contrast. In the article, we compare the effects of three experiments on tropical cyclones in aspects of track and intensity, respectively. For track, the prediction errors of PPE are smaller. The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members. As for intensity, ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time. The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall. The PPE also shows uncertainty in the forecast. Moreover, we also analyze the track and intensity from physical variable fields of PPE. Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.     

边坡工程的爆破效应分析

本文以四川省某中型水电站坝肩边地皮工程为例，讨论了边坡稳定性评价中的爆破效应，论证了目前边坡工程中采用的经验方法。并在此基础上。提出了“动态反演参数优定法”，对边坡工程的有关参数作了合理的调整，所采用优定参数在边坡稳定性评价预测及工程处理中取得了良好的效果。     

等级三星系统的运动学研究

统计分析表明,在目前的观测精度(Imas)下,由于三体效应大多数等级三星系统已不能用经典的双二体运动学模型来描述;即使对于应用需求而言,该模型也不再适用于描述三体视效应较强的等级三星系统.因为在实用星表参考架中不宜采用数据量较大的数值历表来描述子星的运动,所以有必要针对等级三星系统建立尽可能简单实用的运动学模型.借助已有的观测资料和拟合研究结果,给出了6个等级三星系统的质量参数和初始条件,进而根据实用需求讨论了这些系统的运动学描述问题.     

Site response analysis using one-dimensional equivalent-linear method and Bayesian filtering

Site response analysis is crucial to define the seismic hazard and distribution of damage during earthquakes. The equivalent-linear (EQL) is a numerical method widely investigated and used for site response analysis. Because several sources of uncertainty are involved in this type of analysis, parameters defining the numerical models need to be identified from in-situ measurements. In this paper, a Bayesian inference method to estimate the expected values and covariance matrix of the model parameters is presented. The methodology uses data from downhole arrays recorded during earthquakes. Two numerical applications show the good performance and prediction capabilities of the proposed approach.     

羊拉铜矿区岩石力学模拟与采场结构参数确定

采用三维弹塑性有限元程序3D-σ对羊拉铜矿的岩石力学进行模拟,并据此提出采场结构参数,对羊拉铜矿开采具有较好指导作用。     

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R² = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.     

地震活动性参数的确定

本文基于目前国际上比较先进的地震危险性概率法，讨论了其中各地震活动性参数的具体确定方式及在实际工作中应该注意的问题。