北疆暴雪过程的湿位涡诊断

利用NCEP1°×1°再分析资料,对2009年3月19—20日北疆沿天山一带一次暴雪天气过程进行诊断分析,着重探讨了湿位涡诊断在新疆暴雪预报中的应用。分析表明：暴雪的水汽输送有3个源地,低层负散度、向北倾斜的涡度“上负下正”分布、等θe线的陡立密集带、垂直速度负值区与暴雪落区均有较好的对应关系。暴雪主要发生在MPV1〉0、MPV2绝对值迅速增加且等值线密集分布的区域。MPV1下传增大,大气对流不稳定能量释放,低层MPV2绝对值增大,大气湿斜压性增强,下滑倾斜涡度增长是暴雪形成的重要原因。     

条件非线性最优扰动在可预报性问题研究中的应用

本文总结了近年来条件非线性最优扰动方法的应用研究的主要进展.主要包括四个方面:(1)将条件非线性最优扰动(CNOP)方法拓展到既考虑初始扰动又考虑模式参数扰动,形成了拓展的CNOP方法.拓展的CNOP方法不仅能够应用于研究分别由初始误差和模式参数误差导致的可预报性问题,而且能够用于探讨初始误差和模式参数误差同时存在的情形;(2)将拓展的CNOP方法分别应用于ENSO和黑潮可预报性研究,考察了初始误差和模式参数误差对其可预报性的影响,揭示了初始误差在导致ENSO和黑潮大弯曲路径预报不确定性中的重要作用;(3)考察了阻塞事件发生的最优前期征兆(OPR)以及导致其预报不确定性的最优增长初始误差(OGR),揭示了OPR和OGR空间模态及其演变机制的相似性及其局地性特征;(4)研究了台风预报的目标观测问题,用CNOP方法确定了台风预报的目标观测敏感区,通过观测系统模拟试验(OSSEs)和/或观测系统试验(OSEs),表明了CNOP敏感区在改进台风预报中的有效性.具体地,台风OGR的主要误差分布在某一特定区域,空间分布具有明显的局地性特征,在台风OGR的局地性区域增加观测,有效改进了台风的预报技巧,该区域代表了台风预报的初值敏感区.事实上,上述El Ni(n)o事件、黑潮路径变异以及阻塞事件的OGR的空间分布也具有明显的局地性特征,这些事件的OGR刻画的局地性区域可能也代表了初值敏感区.     

工作流技术在地质调查项目管理中的应用

介绍了工作流技术在地质工作项目管理中的应用，阐述了工作流技术的基本概念和工作流模型的建立过程，突出体现了当工作流技术应用于地质工作项目管理时的方便、快捷，代表了当前项目管理系统的一个研究和发展的方向，它通过对工作流的流程定义和运行期控制来解决项目管理中两方面的问题：项目业务流程的多样化的需求和解决项目管理流程的不确定性。     

利用改进灰色模型的钟差预报算法及其精度分析

灰色模型在钟差长期预报中虽具有一定的优势,但是预测的精度还有进一步提高的可能。在本文中,采用经过数据预处理和残差修正对一阶灰色模型进行了改进,再对GPS卫星钟差进行不同时间尺度上的预报。算例利用IGS提供的精密钟差产品,建立了一个基于改进灰色模型的预报算法,改进灰色模型的预报精度较传统的二次项模型有了很大的提高,且预报结果的收敛性也获得了一定程度的改善。     

The Impact of Horizontal Resolution on the CNOP and on Its Identified Sensitive Areas for Tropical Cyclone Predictions

In this study,the impacts of horizontal resolution on the conditional nonlinear optimal perturbation (CNOP) and on its identified sensitive areas were investigated for tropical cyclone predictions.Three resolutions,30 km,60 km,and 120 km,were studied for three tropical cyclones,TC Mindulle (2004),TC Meari (2004),and TC Matsa (2005).Results show that CNOP may present different structures with different resolutions,and the major parts of CNOP become increasingly localized with increased horizontal resolution.CNOP produces spiral and baroclinic structures,which partially account for its rapid amplification.The differences in CNOP structures result in different sensitive areas,but there are common areas for the CNOP-identified sensitive areas at various resolutions,and the size of the common areas is different from case to case.Generally,the forecasts benefit more from the reduction of the initial errors in the sensitive areas identified using higher resolutions than those using lower resolutions.However,the largest improvement of the forecast can be obtained at the resolution that is not the highest for some cases.In addition,the sensitive areas identified at lower resolutions are also helpful for improving the forecast with a finer resolution,but the sensitive areas identified at the same resolution as the forecast would be the most beneficial.     

The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

In this study,a series of sensitivity experiments were performed for two tropical cyclones (TCs),TC Longwang (2005) and TC Sinlaku (2008),to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts.Specifically,three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation (CNOP),first singular vector (FSV),and composite singular vector (CSV) methods.Additionally,random initial errors in randomly selected areas were considered.Based on these four types of initial errors and areas,we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors,and to determine which type of initial errors and areas has the greatest impact on TC forecasts.Overall,results from the experiments indicate the following:(1) The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas.From the perspective of statistical analysis,and by comparison,the impact of random errors introduced into the CNOP target area was greatest.(2) The initial errors with CNOP,CSV,or FSV patterns were likely to grow faster than random errors.(3) The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.     

基于Erdas Image 9.2 LPS区域网平差处理Spot5卫星影像实现方法

介绍基于LPS的正射影像处理流程,以图文的形式描述区域网平差处理Spot-5卫星影像的具体过程,最后得到正射纠正影像,用于1∶50 000DOM生产。     

高原东侧特大暴雨过程中秦岭山脉的作用

利用中尺度数值模式MM5V3.5对2002年6月8～9日陕西特大暴雨过程进行了数值模拟, 在模拟结果和实况较吻合的情况下, 通过敏感性试验, 研究了秦岭山脉在这次暴雨过程中所起的作用。结果表明, 秦岭山脉在这次暴雨过程中对降水的强度有很大的影响, 即秦岭山脉使陕甘宁边界的长城沿线、 秦岭山脉、 汉江河谷一带降水量增加, 使关中盆地、 陕北南部、 大巴山东南坡降水量减少。进一步研究表明, 秦岭山脉主要是通过形成局地环流, 使气流产生辐合, 大量的水汽和位势不稳定能量在迎风坡堆积, 上升运动加强, 从而使位势不稳定能量得以释放, 触发了对流降水。     

台风影响上下海近海风场特性的数值模拟分析

选择近年来影响上海最严重的不同路径台风个例,首先利用TAPM数值模式对出现最大风速过程期间,海岸线的风速变化作了数值模拟计算,然后与海岸测风梯度塔的同步观测数据进行对比,在验证了模式计算结果的准确性和可靠性基础上,对台风影响下上海近海区域最大风速的分布特征、不同高度风速变化规律进行分析评估.同时采用海上测风平台的观测数据,对近海海面上的湍流强度作了计算.结果表明:当台风影响上海地区时,上海近海海上的最大风速有较明显的梯度变化;海面上风速随高度变化远比陆上小,各高度层风速如用指数律公式计算,幂指数可取O.09-0.10;海面上的湍流强度亦较小,基本上在0.10以下范围内波动.     

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.