青藏高原沙漠化土地空间分布及区划

利用野外调查数据、遥感影像和已有研究成果,构建了一套适用于青藏高原沙漠化土地的分类分级指标体系及遥感解译标志。以此为基础,选取目视解译法监测青藏高原沙漠化土地的空间分布特征。结果表明：2015年青藏高原沙漠化土地面积392 913km²,占高原土地总面积的15.1%,主要包括沙质沙漠化土地、砾质沙漠化土地和风蚀残丘3种类型。沙漠化土地以中度和轻度沙漠化土地为主,重度和极重度沙漠化土地面积仅占沙漠化土地总面积的12.2%。空间上,沙漠化土地集中分布在高原的北部和西部地区,其他地区零散分布。自东南向西北,沙漠化土地面积逐渐增大,沙漠化程度不断加重。以沙漠化土地空间分布数据（面积、类型、程度、空间特征和驱动因素）为基础,结合气候、地貌、第四纪沉积物和人类活动等数据,将青藏高原沙漠化区划分为雅鲁藏布江半干旱高山宽谷沙漠化区、藏北青南高寒高原面沙漠化区、柴达木干旱盆地沙漠化区、黄河上游半干旱河流盆地沙漠化区和“三江”流域湿润半湿润高山沙漠化区。     

Tracking embodied carbon flows in the Belt and Road regions

In the past few decades, economic globalization has driven rapid growth of cross-border trade and a new international division of labor, leading to increasing inter- country embodied carbon flows. Multi-region input-output (MRIO) analysis is used to identify embodied carbon flows between major world regions, including seven regions along the Belt and Road (BR), and the spatial distribution of production- and consumption-based carbon intensities. The results show that current embodied carbon flows are virtually all from BR regions to developed countries, with more than 95% of world net embodied carbon exports coming from BR regions. Consumption in the United States and European Union countries induce about 30% of the carbon emissions in most BR regions, indicating that the former bear a high proportion of consumers’ responsibility for the carbon emitted in the latter. For this reason, measuring environmental responsibilities from consumption rather than a production- based perspective is more equitable, while developing countries should be given a louder voice in the construction through dialogue and cooperation, in part in the context of the Belt and Road Initiative, of an inclusive global climate governance system.     

Village-level multidimensional poverty measurement in China: Where and how

Village is an important implementation unit of national poverty alleviation and development strategies of rural China, and identifying the poverty degree, poverty type and poverty contributing factors of each poverty-stricken village is the precondition and guarantee of taking targeted measures in poverty alleviation strategies of China. To respond it, we construct a village-level multidimensional poverty measuring model, and use indicator contribution degree indices and linear regression method to explore poverty factors, while adopting Least Square Error (LSE) model and spatial econometric analysis model to identify the villages’ poverty types and poverty difference. The case study shows that: (1) Spatially, there is obvious territoriality in the distribution of poverty-stricken villages, and the poverty-stricken villages are concentrated in contiguous poverty-stricken areas. The areas with the highest VPI, in a descending order, are Gansu, Yunnan, Guizhou, Guangxi, Hunan, Qinghai, Sichuan, and Xinjiang. (2) The main factors contributing to the poverty of poverty-stricken villages in rural China include road construction, terrain type, frequency of natural disasters, per capita net income, labor force ratio, and cultural quality of labor force. The main causes of poverty include underdeveloped road construction conditions, frequent natural disasters, low level of income, and labor conditions. (3) Chinese poverty-stricken villages include six main subtypes, and most poverty-stricken villages are affected by multiple poverty-forming factors, reflected by a relatively high proportion of the three-factor dominant type, four-factor coordinative type, and five-factor combinative type. (4) There exist significant poverty differences in terms of geographical location and policy support, and the governments still need to carry out targeted poverty alleviation measures according to local conditions. The research can not only draw a macro overall poverty-reduction outline of impoverished villages in China, but also depict the specific poverty characteristics of each village, helping the government departments of poverty alleviation at all levels to mobilize all kinds of anti-poverty resources.     

An Extended Minimum Spanning Tree method for characterizing local urban patterns

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management.     

北印度洋-南海季风区热带海洋极端波候变化特征

利用1979—2016年ERA-Interim有效波高（SWH）和海表风场数据,分析了南海-北印度洋极端海浪场分布和变化.结果表明：南海-北印度洋极端SWH分布和极端风速分布形态以及年际变化趋势高度一致,说明了涌浪为主的北印度洋和风浪为主的南海一样,极端SWH都由局地的极端风速控制;强极端SWH主要分布在阿拉伯海以及南海北部,阿拉伯海北部增长与该区域气旋强度增强有着密切关系,而南海的极端SWH主要受东北季风控制;东非沿岸极端SWH线性增长趋势则与索马里急流的年代际尺度上有逐渐增强的线性趋势有关.北印度洋及南海海域极端SWH距平场的EOF分析结果表明,南海极端SWH与北印度洋表现出反相变化的特征.北印度洋（南海海域）极端SWH多出现在西南季风（东北季风）期间,因为在西南季风（东北季风）期间,极端风速也相对增强.     

Python在FY 2G定量业务产品自动可视化中的应用

利用Python语言实现了每日对CMACast下发的最新风云二号G星(FY-2G)云导风(AMV)和射出长波辐射(OLR)两个定量业务产品的实时自动解码和可视化出图,并与国家卫星气象中心下发的卫星天气应用平台(SWAP)的显示进行了对比。结果表明:两者显示一致。在显示所有AMV文件风矢量数据的前提下,本文可视化产品较目前版本SWAP的显示更为疏密均匀,没有出现重叠现象;Python出图自带的高洛德(Gouraud)明暗处理渲染方式则在对OLR格点产品可视化显示中提供了一种更为平滑,细腻的方法。利用本研究方法对风云二号卫星定量产品进行可视化具备自动化和视觉美观的特点,值得在基层气象部门业务中推广。     

吉林省最高最低气温非对称变化时空演变特征

利用吉林省45个气象站1970-2014年逐日的最高、最低气温资料,分析吉林省最高、最低气温不对称变化在年、季尺度上的时空演变特征。采用经验模态分解法(EMD)分解年、季最高、最低气温序列,分析变化趋势。结果表明:近45年年平均最高、最低气温均呈增加趋势,且最低气温增幅是最高气温的两倍。四季的最高、最低气温变化形式与年变化一致;不同地区之间最高气温的增幅差异不大,最低气温增幅存在一定区别,东部最低气温增幅小于中、西部。相对稳定的增温区出现在西部和中部,不同季节的增温幅度不同,春、夏要强于秋、冬。经验模态分解(EMD)结果显示吉林省年平均最高、最低气温均为增加趋势,而春、冬季中最高气温趋势分量的倾向率与原始序列不同,为负值。夏、秋季中最高气温及四季中最低气温趋势分量序列与原始数据序列的倾向率一致,均为正值但大小存在差异。     

基于逐时资料的黄山雾凇特征及气象条件分析

利用2004—2016年黄山气象站逐日、逐时地面气象观测资料分析了雾凇的时间分布特征及气象条件。结果表明:(1)黄山年平均雾凇日61.6d,年份之间差异明显;雾凇初日主要在11月,终日主要在3—4月;连续雾凇日数多在3～4d,占40%,各年均出现了连续雾凇日数≥10d的情况。(2)雾凇出现在10月至次年4月,其中12月至次年3月雾凇日数占89.8%;月平均雾凇日数与月平均气温呈显著负相关。雾凇还存在一定的日变化,08:00—09:00最多,18:00最少。(3)逐日资料统计表明,适宜雾凇出现的气象条件是雾日且日平均气温在-8～2℃之间、平均相对湿度≥80%、平均风速2～9m/s。(4)逐时资料统计表明,雾凇的形成主要受气温影响,雾凇形成前需7h以上的累计低温(≤0℃),适宜雾凇形成的气象条件是有雾且气温在-6～1℃之间,湿度≥95%,风速2～11m/s,较好地反映了雾凇形成的临界气象条件。     

浙江省台风报告单自动化制作发布系统设计与实现

目前浙江省气象台的台风报告单制作系统自动化程度低,需要大量手工输入,越来越不能满足现代化天气业务和服务的需求。以浙江省台精细化格点化预报数据为依托,结合数据库系统,将各功能模块整合,设计开发出新一代浙江省台风报告单自动化制作发布系统。新系统可对Word文档进行模块化处理,依据客观化转化规则自动生成文本文字,并自动插入各类必要的图片,从而大大提高了工作效率,减小了出错率。     

省级气象云资源管理平台设计与应用

基于OpenStack架构建立省级云资源管理平台,通过整合省级各类硬件资源,将计算、存储、网络等硬件资源纳入统一资源池管理,在此基础上将省级原有的多套虚拟化系统软硬件资源纳入平台统一管理。平台投入使用后资源有效利用率大幅提升,实现硬件资源动态、弹性、可回收利用,能够更好地为各业务部门提供硬件资源服务。通过接管省级VMware、OS等虚拟化系统,平台可以根据业务需求快速部署交付业务系统,大幅提高对气象业务的服务和支撑能力,提高了系统运维的水平和效率。该平台的建成和应用对省级云计算发展和应用具有示范、服务、探索等多重作用,特别是对带动省级信息化建设进入新阶段,探寻省级新的信息化建设模式具有现实意义。