遥感图像分割中的小波域改进信息割算法

针对传统信息割(IC)算法对存在灰度变化的图像易引起误分割的问题,提出了一种小波域改进信息割(W-MIC)算法。使用一个新的结合像素点灰度关联和空间关联的Parzen窗对IC进行改进,以降低灰度变化对分割的影响;将MIC引入到小波域,利用小波的平滑作用降低参数选取复杂度,改善图像分割效果。遥感图像分割实验结果表明,W-MIC可有效降低参数选择效应,不仅避免了灰度变化引起的误分割,而且还较好地保持了图像边缘。     

地图投影变换浅析

通过研究得出,将地球上各种地理事物与现象表示到平面上,需要采取一系列数学变换,才能使地面上的点与平面上的相应点建立起对应关系,从而才有可能反映出它们的位置、距离、方向、面积、形状及数量大小关系。地图之所以能在平面上反映出球面上事物与现象的空间准确位置和相互关系,就是由于它经过了地图投影的转换。通过研究地图投影变换问题,可以为基于离散网格的数据管理和建模应用提供很好的投影变换基础。     

海量影像并行处理技术效率测试——以地理国情普查影像处理为例

摘要：地理国情普查项目实施过程中有大量的航空、航天遥感影像需要处理,如何高效、快捷、准确地处理这些种类繁多、形式各异的海量数据,成为地理国情普查面临的新的技术挑战。引进集群计算机并行处理海量遥感影像技术,通过测试、分析与对比,总结海量数据集群处理系统的优势。     

利用三维可视化平台快速计算地质灾害区塌方量的方法研究

基于三维可视化平台,利用离散点内插的方法构建地质灾害区表面TIN模型,并利用C#语言实现地质灾害区塌方量的算法.在理论上该方法所描述的地面模型逼真且能保持原始数据的原有精度,而且采用TIN进行体积计算,易于计算机实现.通过塌方区实例数据验证该方法具有速度快、精度高、作业效率好等优点,可以满足地质灾害应急需求.     

基于GPS测站坐标残差序列的小波工具应用与分析

利用小波工具剔除噪声的思想对全球部分GPS测站坐标残差序列进行应用试验。通过选取特定的阈值和小波基函数,成功提取出GPS测站坐标残差序列的一些非线性周期规律,对于进一步深入研究GPS测站坐标非线性变化规律有重要意义。     

基于过程的面向对象时空数据模型数据组织方法

本文在分析现有时空数据模型和面向对象方法的基础上,提出了基于过程的面向对象时空数据模型,该模型从时空语义建模的角度反映空间对象的演变过程,以及在这个过程中产生的事件因果联系,并且通过过程语义和面向对象思想两者的结合解决了时空动态数据的组织、存储和查询问题。     

Reliability of X-band Dual-polarization Phased Array Radars Through Comparison with an S-band Dual-polarization Doppler Radar

Based on the observations of a squall line on 11 May 2020 and stratiform precipitation on 6 June 2020 from two X-band dual-polarization phased array weather radars (DP-PAWRs) and an S-band dual-polarization Doppler weather radar (CINRAD/SA-D), the data reliability of DP-PAWR and its ability to detect the fine structures of mesoscale weather systems were assessed. After location matching, the observations of DP-PAWR and CINRAD / SA-D were compared in terms of reflectivity (ZH), radial velocity (V), differential reflectivity (ZDR), and specific differential phase (KDP). The results showed that: (1) DP-PAWR has better ability to detect mesoscale weather systems than CINRAD/SA-D; the multi-elevation-angles scanning of the RHI mode enables DP-PAWR to obtain a wider detection range in the vertical direction. (2) DP-PAWR’s ZH and V structures are acceptable, while its sensitivity is worse than that of CINRAD/SA-D. The ZH suffers from attenuation and the ZH area distribution is distorted around strong rainfall regions. (3) DP-PAWR’s ZDR is close to a normal distribution but slightly smaller than that of CINRAD/SA-D. The KDP products of DP-PAWR have much higher sensitivity, showing a better indication of precipitation. (4) DP-PAWR is capable of revealing a detailed and complete structure of the evolution of the whole storm and the characteristics of particle phase variations during the process of triggering and enhancement of a small cell in the front of a squall line, as well as the merging of the cell with the squall line, which cannot be observed by CINRAD/SA-D. With its fast volume scan feature and dual-polarization detection capability, DP-PAWR shows great potential in further understanding the development and evolution mechanisms of meso-γ-scale and microscale weather systems.     

地形云催化增雨试验研究进展

地形云作为最具有前景和可行的人工影响云系,受到人工影响天气工作者和研究人员的关注。本文分析了国内外地形云催化增雨野外科学试验的历史进程,总结了野外科学试验中取得的成果,梳理了在地形云催化增雨试验中需关注的几个关键科学问题,包括对地形云自然降水过程的分析、地形云系统中过冷水在云内的分布、山地云系的微物理过程演变特征及其与中尺度动力结构的关联,介绍了宁夏开展地形云野外科学试验的实践,提出了加快地形云催化野外科学试验,提高地形云云水资源开发利用的对策及建议。为解决中国西北地区干旱问题,推动黄河流域生态环境保护及高质量发展提供了一种思路。     

1961-2020年郑州主城区大气自净能力变化特征分析

利用郑州市主城区1961—2020年气象观测资料和2014—2018年空气质量监测数据,分析了郑州主城区大气自净能力指数的长期变化趋势与影响因子以及2014—2018年主城区大气自净能力与PM_(2.5)的关系。结果表明:郑州主城区大气自净能力指数30 a气候均值为4.42 t·(d·km^(2))^(-1),春季大气自净能力最强,为5.20 t·(d·km^(2))^(-1);秋季大气自净能力最弱,为3.88 t·(d·km^(2))^(-1),不利于对大气污染物的清除。1961—2020年郑州主城区大气自净能力呈显著的减弱趋势,其中1969年最强为6.85 t·(d·km^(2))^(-1),2020年最弱为3.06 t·(d·km^(2))^(-1)。影响因子中,1961—1980年混合层厚度与大气自净能力指数呈正相关;日平均风速≥2.5 m·s^(-1)的日数和小风日数与大气自净能力分别呈正、负显著相关;大气自净能力指数与降水日数显著相关,2015年后偏强降水日数的增加对大气自净能力在同时期的增强有一定影响。此外,研究还表明主城区大气自净能力和PM_(2.5)浓度存在显著的负相关,说明大气自净能力强时,对应的PM_(2.5)浓度低,环境空气质量趋好。     

Record Arctic Ozone Loss in Spring 2020 is Likely Caused by North Pacific Warm Sea Surface Temperature Anomalies

Record ozone loss was observed in the Arctic stratosphere in spring 2020. This study aims to determine what caused the extreme Arctic ozone loss. Observations and simulation results are examined in order to show that the extreme Arctic ozone loss was likely caused by record-high sea surface temperatures(SSTs) in the North Pacific. It is found that the record Arctic ozone loss was associated with the extremely cold and persistent stratospheric polar vortex over February–April, and the extremely cold vortex was a result of anomalously weak planetary wave activity. Further analysis reveals that the weak wave activity can be traced to anomalously warm SSTs in the North Pacific. Both observations and simulations show that warm SST anomalies in the North Pacific could have caused the weakening of wavenumber-1 wave activity, colder Arctic vortex, and lower Arctic ozone. These results suggest that for the present-day level of ozone-depleting substances, severe Arctic ozone loss could form again, as long as certain dynamic conditions are satisfied.