利用轨迹数据提取城市居民出行时空分布特征

介绍了利用出租车轨迹数据提取城市居民出行时空分布特征的过程,包括利用数理统计的方法对出租车上下客事件基于时间进行特征分析;给出了一种融合核密度估计(KDE)与兴趣点(POI)分类的密度聚类算法,实现了出租车上下客热点区域的挖掘以及居民出行活动规律与城市功能区之间关系的发现.?研究表明:居民的出行活动特征在"工-休"日之...     

玛曲地区夏季强降水的环流分型及水汽轨迹分析

利用青藏高原(下称高原)东北边坡玛曲地区1967—2008年12个常规气象观测站的降水资料和NCEP/NCAR再分析资料,在环流分型的基础上,应用后向轨迹模型分析了不同等级降水的不同环流型的水汽输送轨迹。结果表明:(1)暴雨发生的环流型以高原低槽型为主,大雨的发生以切变型为主。(2)暴雨的不同环流型的水汽输送轨迹差异较大,同一型暴雨不同等压面水汽轨迹较一致;大雨的不同环流型之间水汽轨迹无明显差异,同一型大雨不同等压面的水汽轨迹差异很大。(3)通过追踪不同时间长度的水汽输送的后向轨迹发现,可以用向前追踪360h的水汽输送轨迹代表玛曲地区的水汽输送,水汽轨迹以偏南气流为主。由此可见,虽然不同等级、不同环流型强降水之间的水汽轨迹存在差异,但是玛曲地区强降水的水汽主要来自印度洋-孟加拉湾和南海-孟加拉湾的偏南气流。     

异常气温作用于水稻一维能量模型的数值分析

应用动力学方程建立四川内江市水稻的生长模型,通过FORCAL和MATLAB对方程参数拟合,并进行数值分析和定性分析,为水稻产量预报和气温对水稻生物量的影响评估提供科学依据。在传统的一维能量模型基础上,将水稻对外界能量的摄取能力与水稻生长季中的气温变化规律联系起来,得到水稻的生长曲线x（t）,并通过相平面轨迹得到x—T曲线,定性分析水稻的临界气温和最快生长率出现时间。通过低温对水稻分蘖期和高温对生殖生长期影响的比较,得出如下结论：水稻后期受高温影响比前期受低温影响严重。     

西藏地区强降雪过程的波包分布及传播特征

利用NCEP/NCAR高度场的再分析资料,通过波包传播诊断方法(WPD),对2005年10月19～23日西藏地区中东部及南部边缘地区一次大范围强降雪天气过程进行波包分布与波能传播特征分析。结果表明:500hPa高度场的波包分布和传播特征很好地反映此次过程;强降雪天气发生在波包大值区域内而且反映了过程的爆发和消亡;波包的分布特征与强对流天气的发生状况是一致的;波包的移动路径很好地反映了扰动能量的传播。此次过程是"积累-释放"的三次循环。     

Estimation of the Resolution of Earth＇s Gravity Field for GRACE Follow-On Using the Spectrum Method

利用轨道扰动引力谱和大地水准面累计误差谱分析的方法估计未来GRACE（gravity recovery and climate experimenl）Follow—On卫星反演地球重力场的空间分辨率。基于GRACEFollow—On卫星的轨道特性,计算其在高空所受到的径向扰动引力,并根据谱特性及星载加速度计的测量噪声水平分析该卫星能反演重力场的阶数。利用EGM96重力场模型分别计算200km和250km轨道高度处的扰动引力谱。分析其特性表明：在两个轨道高度处分别能反演281阶和242阶的地球重力场模型。给出大地水准面累计误差谱模型,并计算200km和250km轨道高度处大地水准面累计误差谱。分析其谱特性表明：在两个轨道高度处分别能反演至286阶和228阶的地球重力场模型。     

The Mesoscale Predictability of a Heavy Precipitation Event

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.     

日美成功开发台风预测新模型

由日美研究人员组成的一个联合小组最近成功开发出一种新的气象模型。这种利用超级计算机“地球模拟器”计算气流数据的新模型可以准确预测出在两周后形成的台风的移动轨迹及其对未来天气的影响。     

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.     

一次持续空气污染过程的气象条件分析

2004年12月24-30日,淄博地区出现一次持续一周的强空气污染过程.本文分析了这次过程的主要污染物浓度变化特征和造成强污染的天气形势特点以及主要的气象变量分布特征.结果表明:高空维持稳定纬向环流、逆温层结持续存在、混合层厚度增加、低空风速小,导致污染物不能及时随大气扩散,污染物浓度增大.