利用星载和地基激光雷达分析2019年5月东亚沙尘天气过程

2019年5月中旬,中国北方出现大范围沙尘暴天气,此次天气过程持续时间较长,影响范围较大.利用星载激光雷达CALIOP(Cloud-Aerosol Lidar with Orthogonal Polarization)和地基激光雷达AD-NET(Asian Dust and aerosol lidar observat...     

线路上任意一点对应中桩平面坐标在统一模型中的计算

本文给出了交通线路任意点对应中桩平面坐标计算的统一模型,并就直线段、圆曲线段、缓和曲线段三种线型具体展开,给出其理论公式和实际计算方法。     

应用Supernatural GIS进行3维城市数据分区管理的方法研究

探讨了3维城市数据管理的有关问题,基于3维GIS软件Supernatural GIS研究了3维城市模型大场景海量数据的分区管理、动态调度和快速显示问题,提出了应用Supernatural GIS实现城市3维可视化及大范围城市景观动态漫游的解决方案。     

关于GeoMedia Professional坐标系统定义问题的探讨

介绍了GeoMedia Professional软件在油田地面工程数字化工作中的应用,并针对设置工作空间、数据库、特征类的坐标系统时容易混淆的问题,及该问题引起的不同数据库之间的坐标系统不一致,以及一个数据库中个别特征类的坐标系统参数不正确的情况进行了分析,同时总结了操作过程中需要注意的问题及解决方法。     

上海市城市网格化管理的技术实现

城市网格化管理采用数字化网格管理的模式,本文阐述了上海市城市网格化管理的技术实现过程。     

珊溪水库地震台阵建设

为更好地对珊溪水库地震震群进行监测和分析,浙江省地震局自2006年开始建设珊溪水库地震台阵.介绍了该环形小孔径台阵的勘选和建设过程,并结合2次地震震群活动的监测数据,展示了该台阵对地区性地震活动监测的效果.     

基于时空聚类的出租车载客热点区域挖掘研究

针对出租车运营过程缺少路径优化指导造成运营能力分布不均、空载率高的问题,本文以成都市安装有GPS设备的出租车所采集的轨迹数据为研究对象,以提高出租车效益为目标,采用了一种基于网格的出租车载客热点聚类算法,通过对出租车GPS轨迹数据进行处理和聚类分析,充分挖掘出租车载客热点区域,从而为出租车的运营者和管理者提供信息决策服务。     

Local Katabatic Winds of the Russian Federation and Their Observation Using Synthetic Aperture Radar Imagery

Izvestiya, Atmospheric and Oceanic Physics - Data from synthetic aperture radars (SAR) provide new opportunities for observing and studying local katabatic winds (bora and foehn) over the seas in...     

New Circulation Features in the Okhotsk Sea from a Numerical Model

Izvestiya, Atmospheric and Oceanic Physics - Using the eddy-permitting model, circulation in the Okhotsk Sea and in an adjacent area of the Pacific Ocean is retrospectively simulated from 1991 to...     

SMAP卫星海表盐度产品的检验与校正

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.