三维GIS的城市暴雨积涝灾害模拟

针对传统基于二维GIS进行城市暴雨积涝模拟分析不能立体地展现积涝灾害动态演进过程、地物受灾情况的问题,该文提出了基于三维GIS进行城市暴雨积涝灾害模拟分析。该方法综合应用精细化建模与地物拉伸建模技术,通过区域DEM构建、地表路面建模、建筑物建模、地形匹配构建研究区三维模型;以改进的D8算法进行汇水区划分,应用径流系数法进行汇水区产流计算。研究结果表明,该方法能够有效实现三维场景中暴雨积涝积水深度、积水范围计算以及积水深度、受灾房屋、受灾道路可视化表达,为城市暴雨积涝灾害防灾减灾提供技术支撑。     

A NOVEL CLASSIFICATION METHOD FOR TROPICAL CYCLONE INTENSITY CHANGE ANALYSIS BASED ON HIERARCHICAL PARTICLE SWARM OPTIMIZATION ALGORITHM

Based on the tropical cyclone (TC) observations in the western North Pacific from 2000 to 2008, this paper adopts the particle swarm optimization (PSO) algorithm of evolutionary computation to optimize one comprehensive classification rule, and apply the optimized classification rule to the forecasting of TC intensity change. In the process of the optimization, the strategy of hierarchical pruning has been adopted in the PSO algorithm to narrow the search area, and thus to enhance the local search ability, i.e. hierarchical PSO algorithm. The TC intensity classification rule involves core attributes including 12-HMWS, MPI, and Rainrate which play vital roles in TC intensity change. The testing accuracy using the new mined rule by hierarchical PSO algorithm reaches 89.6%. The current study shows that the novel classification method for TC intensity change analysis based on hierarchic PSO algorithm is not only easy to explain the source of rule core attributes, but also has great potential to improve the forecasting of TC intensity change.     

国内空间数据挖掘研究进展与技术体系探讨

基于空间数据具有的海量、非线性、尺度性等特点,分析了空间数据挖掘技术具有的复杂性、尺度性、综合性、空间性、可视化、多样性特征,并结合调研国内5年来空间数据挖掘研究进展,探讨分析了空间数据挖掘技术体系的组成内容。     

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model

Weather radar echo extrapolation plays a crucial role in weather forecasting. However, traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data. Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors. Moreover, it is difficult to obtain higher accuracy by relying on a single historical radar echo observation. Therefore, in this study, we constructed the Fusion GRU module, which leverages a cascade structure to effectively combine radar echo data and mean wind data. We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions. Based on the Jiangsu Province dataset, we compared some models. The results show that our proposed model, Cascade Fusion Spatiotemporal Network (CFSN), improved the critical success index (CSI) by 10.7% over the baseline at the threshold of 30 dBZ. Ablation experiments further validated the effectiveness of our model. Similarly, the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.     

基于WRF-CALMET的精细化方法在大风预报上的应用研究

针对当前风场预报普遍精细程度偏低,动力降尺度改善风场预报应用偏少的问题,利用CALMET模式的动力诊断模块,结合高分辨率的地形资料对WRF模式输出的风场预报数据进行了动力降尺度处理,进而提高风场的精细化预报。其方法充分借助地形动力学效应,使大尺度的近地层风场经过坡度流与阻碍效应等修正后得到更精细的风场且体现出与局地地形相符的特征。试验部分以广东省为研究区域,并选取个例用观测资料与陆地同化系统资料对模拟结果进行对比检验,结果表明动力降尺度之后的风场较之前更为精细且含有更多与地形相关的信息;与观测资料对比的总体相关性较好,误差也更小,与陆地同化系统资料对比变化趋势基本一致,且误差不大,这表明WRF-CALMET方法不仅能有效地提高风场预报数据的时空分辨率而且其结果更趋近于真实,对风场的精确预报具有重要的参考价值。     

田野考古信息系统的设计方案与实施流程

田野考古信息系统的主要研究内容包括探方剖面图、底面图线图的自动获取与数据处理;通过线图等资料构建发掘现场的原始三维场景,再现人类古环境;将近景摄影测量技术、无线网络传输系统和GPS、GIS的数据相融合,建立空间数据快速采集集成处理系统等集群技术。本文给出了该三组集群技术的设计方案,同时设计出了三维空间数据采集与处理、三维体视化与无线网协同工作、虚拟环境构建与空间决策支持系统建立三个研究阶段的主要实施流程。已完成的第一阶段与第二阶段的部分研究成果表明,该系统的设计方案与实施流程是正确的、可行的。     

欧亚遥相关型位相变化对我国寒潮路径的影响

利用1965—2015年冬半年NCEP/NCAR再分析资料以及中国地面气候资料日值数据集,借助FMM算法对影响我国的寒潮路径进行聚类分析,得到四类主要移动路径;并通过统计分析探讨欧亚遥相关型（EU）活跃位相与我国寒潮发生频次的关系及对路径选择的影响。结果表明：EU处于正位相活跃年时,寒潮活动增多,经向环流增强,冷空气干冷,第二类北路型寒潮频次和强寒潮事件相对增多;EU处于负位相活跃年时,纬向环流增强,冷空气湿润,第三类西北型寒潮频次相对增多,与第四类西路型寒潮的强寒潮事件增加。     

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results. In recent years, post-processing models based on deep learning have become prominent. In this paper, a deep learning model named ED-ConvLSTM based on encoder-decoder structure and ConvLSTM is developed, which appears to be able to effectively correct numerical weather forecasts. Compared with traditional post-processing methods and convolutional neural networks, ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field. In this paper, the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics, convolutional neural network postprocessing methods, and the original prediction by the ECMWF. The results show that the correction effect of ED-ConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes, especially in the long forecast time.     

基于FMM算法的我国寒潮路径分类及气候特征分析

利用1965—2015年冬半年寒潮过程数据和NCEP/NCAR再分析资料,通过FMM算法聚类分析,将影响我国寒潮路径确定为4类,并统计分析了不同类型路径寒潮的活动变化趋势以及环流特征。研究结果表明:各类路径寒潮的频数、强度、季节分布和年际变化存在显著差异,第一类西路转向型寒潮频数最多且逐年减少趋势最大,第三类西北型寒潮强度最大,第四类西路型寒潮在春季频发且变化趋势平缓;在寒潮爆发时刻,500 h Pa温压场配置、风场转变、高层涡度平流和冷暖平流以及地面冷高压分布与寒潮路径的选择密切相关,其中第二类北路型寒潮的低槽、负涡度区和冷平流区偏东分布,第三类则相应偏西。     

碎石堆小行星自旋演化过程的离散元数值模拟

近年的探测研究表明，由引力和范德华粘附力聚合成碎石堆结构可能是小行星的一种重要形成方式。为探究这类小行星的演化机制，文章基于离散元软件MatDEM进行二次开发，实现了单元的万有引力计算。建立直径为1.2 km“碎石堆”小行星数值模型，通过数值模拟分析了不同粘附强度和体积密度下，球形聚集体的旋转重塑和破坏过程。结果表明：球形聚集体受到其角速度初始增量的影响，以启动聚集体的重塑过程，导致其发生变形或者破坏；粘附强度越小，模型越容易被破坏，破坏前小行星会通过向扁圆状椭球体变形来保持自身构型的稳定性；对于粘附强度一定的小行星模型，体积密度的增大会有效增加其结构的稳定性，可以延后小行星破坏行为发生的时间，且颗粒脱落的数量随着密度的增大而减少；采用离散元分析可以较好地模拟小行星自旋演化过程。进一步与真实小行星数据相结合，有利于深入、全面地探索小行星自旋演化破坏机理。