高建筑对周围建筑雷击保护距离的模拟

在已有先导连接参数化方案的基础上,选取近地面层为研究区域,保持方案中其他基本参量不变,通过改变闪电的空间形态,在同一建筑分布的背景下进行多次闪电模拟。研究多个建筑之间的屏蔽作用以及建筑雷击保护距离与建筑相关特征参数之间的关系,结果表明:高建筑对矮建筑具有屏蔽作用,并存在一个临界保护距离,当高、矮建筑高度分别为190 m和165 m,宽度均为20 m时,建筑之间的距离在12 m以内,矮建筑受高建筑完全保护不遭受雷击;建筑之间的距离超过12 m,矮建筑遭雷击次数明显增多。     

适应气候变化的国际行动和农业措施研究进展

针对气候变暖采取稳健的适应措施已成为国际社会共识。该文综合了当前适应气候变化的国际谈判进展及已有的农业适应气候变化措施,指出适应资金严重不足,技术研发、应用与转让难以实施,以及适应气候变化行动实施能力的不足严重制约着适应气候变化行动的有效实施;关于农业适应气候变化的技术措施仍缺乏系统的理论研究与应用示范。在此基础上,提出了未来中国农业适应气候变化需要重点开展的研究任务,即农业气象灾变过程的新特点及其风险管理, 农业适应气候变化的大数据决策管理系统研发及适应气候变化的农业气候区划与减灾保产技术研究,以切实推进农业适应气候变化,为确保国家粮食安全与农业可持续发展提供支撑。     

一次寒潮背景下降水相态变化特征分析

利用常规气象观测资料和NCEP再分析资料,对2013年4月18日到20日河南出现的一次区域性寒潮天气过程的环流背景、影响系统及寒潮期间降水多相态转换的成因进行了分析。结果表明:本次寒潮过程属于横槽转竖型,冷锋迅速南下,导致寒潮暴发;此次寒潮天气的大气温度层结存在逆温层结构特征,温度廓线的变化会导致降水相态发生变化;受低层冷空气持续影响,暖层强度即融化层遭到一定程度的破坏或消失,冷层即冻结层强度增强,0℃层高度下降,以致降水相态发生相应的改变;降水相态的变化与暖层温度及0℃层高度密切相关。根据本次寒潮过程中NCEP再分析数据得到:当降水相态为雨时,暖层温度≥2℃,0℃层高度≤975 h Pa;当降水相态为雪时,暖层温度≤-1℃,0℃层高度≌1000 h Pa;当降水相态为冰粒时,2℃≥暖层温度≥-1℃,1000 h Pa≥0℃层高度≥975 h Pa。     

FDR土壤水分传感器原状土壤标定方法研究

为获得测站准确的土壤水分传感器标定参数,利用自动土壤水分站点的大型原状土壤为样本,在实验室同时进行人工称重观测和仪器自动对比观测,得到一系列的人工自动测量数据,以人工称重数据为准对自动土壤水分传感器进行订正。由于采用原状土壤,样本土壤的质地、密度、土壤颗粒和结合紧密度基本没有受到破坏,订正后的自动土壤体积含水量值能更加真实反映台站实际土壤墒情,能够修订人工对比观测期间由于样本空间不足导致的不合理的田间标定方程。     

基于外弹道计算的人影高炮作业安全评估方法

随着经济的高速发展,各个高炮作业点所在地的周边环境已经发生了很大变化,为了进一步提高人工影响天气作业的安全性,针对高炮作业特点提出了一套安全论证方法。推导了弹道计算的方法,在此基础上结合安全射界法和地理信息系统对作业安全性进行了更精确的评估,并给出实例说明。提出用危险系数的方法进行安全性评估,给出了具体的危险系数定义方法,通过评估系统的一次实际作业评估证明了危险系数进行安全性评估的可行性。以该方法进行安全性评估可有效保障人影作业的安全性,为人影作业提供重要的参考。     

南海海岛自动气象站防护应用技术探索

立足海南及南海的自然条件,结合海岛自动气象站在南海高温、高湿、高盐雾、强辐射、强风等恶劣气候环境下使用情况,通过对历年来海岛自动气象站故障及失效模式的统计和分析,总结分析故障原因。根据设备维护人员多年气象设备保障工作积累总结的经验,从海岛自动气象站结构和防护要求出发,集成应用市场上优质材料、先进工艺和多种成熟、关键设备防护技术,形成一些气象设备防护的有效方法措施,以提高海岛自动气象站对海洋环境的适应性和性能,延长设备使用寿命。     

Precise orbit determination of BeiDou constellation: method comparison

Chinese BeiDou navigation satellite system is in official service as a regional constellation with five geostationary earth orbit (GEO) satellites, five inclined geosynchronous satellite orbit (IGSO) satellites and four medium earth orbit (MEO) satellites. There are mainly two methods for precise orbit determination of the BeiDou constellation found in the current literatures. One is the independent single-system method, where only BeiDou observations are used without help from other GNSS systems. The other is the two-step GPS-assisted method where in the first step, GPS data are used to resolve some common parameters, such as station coordinates, receiver clocks and zenith tropospheric delay parameters, which are then introduced as known quantities in BeiDou processing in the second step. We conduct a thorough performance comparison between the two methods. Observations from the BeiDou experimental tracking stations and the IGS Multi-GNSS Experiment network from January 1 to March 31, 2013, are processed with the Positioning and Navigation Data Analyst (PANDA) software. The results show that for BeiDou IGSO and MEO satellites, the two-step GPS-assisted method outperforms the independent single-system method in both internal orbit overlap precision and external satellite laser ranging validation. For BeiDou GEO satellites, the two methods show close performances. Zenith tropospheric delays estimated from the first method are very close to those estimated from GPS precise point positioning in the second method, with differences of several millimeters. Satellite clock estimates from the two methods show similar performances when assessing the stability of the BeiDou on board clocks.     

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.     

Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.