Wireless Mapping,GIS, and Learning about the Digital Divide: A Classroom Experience

Abstract

The purpose of this article is to describe a capstone course in undergraduate student geographical research in which GIS and other geospatial tools were used to teach undergraduate students basic geographical principles. The course uses the “cooperative learning” pedagogical approach to address one of a number of client-supplied research projects, chosen on the basis of logistical difficulty, time, student ability, and project importance. In the connection of primary data with existing data, students confronted a number of important research issues such as mapping ethics, database design and management, time management, group dynamics, and research limitations.     

基于CMIP5模式的中国气候变化敏感性预估与分析

以CMIP5提供的26个全球气候系统模式的温度和降水数据为基础,采用区域气候变化指数(Regional Climate Change Index,RCCI)分析中国的不同区域对21世纪气候变化响应的敏感性。结果表明,三种排放情景(RCP 2.6、RCP 4.5、RCP 8.5)下,21世纪全期,气候变化最敏感的区域分布在西藏地区,其次为我国西北地区以及东北地区,气候变化敏感性最低的区域分布在我国内蒙古中东部、华北地区以及长江中下游一带,且高排放情景对应更高的气候变化敏感性。对RCCI指数贡献因子分析结果表明,对中国气候变化敏感性贡献的大小依次为Δσ_TΔσ_pΔRRWAF。冬夏两季温度变化的大值区与RCCI指数的大致区分布一致,RCCI大小的分布很大程度上由温度变化的敏感性决定。而夏季降水变化的大值区主要出现在西藏地区、华南地区和东北地区,冬季降水变化的大值区则主要出现在黄河以南长江以北的中原地区以及东北地区。     

国内外雾预报技术研究进展

大雾是引起低能见度的主要天气现象.提高雾的预报技术水平是确保交通安全的重要措施.从统计和数值预报两个方面,回顾了过去几十年来国内外在雾预报技术上的主要研究进展,并总结了各种方法的特点及存在的缺陷.在某些情形下,新统计方法的应用提高了雾的预报准确率,但仍然无法摆脱统计方法本身的缺陷.相比较而言,数值模式在大雾预报方面具有更广泛的应用和更大的潜力.在目前的计算机水平下,使用高分辨率的一维雾模式与中尺度天气模式相结合的方法,在一定程度上可以提高雾的预报准确率,该方法在大雾易发区的机场及高速公路沿线具有重要的应用价值.随着计算机能力的不断提高,包含大雾形成和演变的各种复杂过程、具有先进资料同化过程的高分辨率三维雾模式,以及集合数值预报系统将是未来的发展方向.     

Ensemble simulation of land evapotranspiration in China based on a multi-forcing and multi-model approach

In order to reduce the uncertainty of offline land surface model(LSM) simulations of land evapotranspiration(ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS(Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs(BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982–2007. The results showed that various simulations of each member and their arithmetic mean(Ens Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates(Obs MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens Mean was closer to Obs MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs MTE and Ens Mean exhibited a significant increasing trend during 1982–98; whereas after 1998, when the last big EI Ni ?no event occurred, the Ens Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.     

高层建筑坍塌墙体修复施工安全管理定量化控制模型研究

为了确保震后高层建筑坍塌墙体修复工程的安全性,提出一种高层建筑震后坍塌墙体修复施工安全管理方法。结合主成分分析法与信息熵法对震后高层建筑坍塌墙体修复施工管理的控制特征量进行提取,确定模型评价指标权重;根据加权学习法对坍塌墙体修复的施工进行量化控制;通过灰阶量化评估进行约束参量分析,构建安全管理控制模型;采用管理因素模糊调度算法实现施工安全管理控制,并完成优化决策。通过仿真实验验证坍塌墙体修复施工管理控制的管理效益与累积评价百分率。分析结果表明,采用该方法进行震后高层建筑坍塌墙体修复施工管理控制的管理效益最高可达83.32%,累积管理效果回报率较高,累积评价百分率平均约为75%,能够有效提高施工过程的安全管理效能,确保施工安全。     

数据驱动的蒸散发遥感反演方法及产品研究进展

蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。     

太湖叶绿素a同化系统敏感性分析

太湖叶绿素a同化系统对于不同参数的敏感性将直接影响到该系统能否精确的估算太湖叶绿素a的浓度分布.利用2009年4月21日环境一号卫星(HJ-1B CCD2)影像数据反演太湖叶绿素a浓度场信息.以此作为背景场信息,结合基于集合均方根滤波的太湖叶绿素a同化系统,分析和评价了样本数目、同化时长、背景场误差、观测误差和模型误差对于同化系统性能的影响.结果表明:从计算成本、系统运行时间和同化效果等方面分析,当集合样本数目达到30~40左右时同化系统取得了较好的结果;同化系统对于背景场误差的估计变化不是很敏感,即初始场的估计是否准确对于同化系统的性能影响不是很大;同化系统对于模型误差和观测误差的变化较为敏感,不同的测试点位由于水体动力学性质不一,其敏感性的表现形式有所差异;利用数据同化方法可以有效地估算太湖叶绿素a浓度.     

A STUDY ON THE ENSEMBLE FORECAST REAL-TIME CORRECTION METHOD

Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36h.     

A Comparative Study of Various Supervised Learning Approaches to Selective Omission in a Road Network

Selective omission is necessary for road network generalisation. This study investigates the use of supervised learning approaches for selective omission in a road network. To be specific, at first, the properties to measure the importance of a road in the network are viewed as input attributes, and the decision of such a road is retained or not at a specific scale is viewed as an output class; then, a number of samples with known input and output are used to train a classifier; finally, this classifier can be used to determine whether other roads to be retained or not. In this study, a total of nine supervised learning approaches, i.e., ID3, C4·5, CRT, Random Tree, support vector machine (SVM), naive Bayes (NB), K-nearest neighbour (KNN), multilayer perception (MP) and binary logistic regression (BLR), are applied to three road networks for selective omission. The performances of these approaches are evaluated by both quantitative assessment and visual inception. Results show that: (1) in most cases, these approaches are effective and their classification accuracy is between 70% and 90%; (2) most of these approaches have similar performances, and they do not have any statistically significant difference; (3) but sometimes, ID3 and BLR performs significantly better than NB and SVM; NB and KNN perform significantly worse than MP, SVM and BLR.