Atmospheric Response to Mesoscale Ocean Eddies over the South China Sea

The South China Sea(SCS) is an eddy-active area. Composite analyses based on 438 mesoscale ocean eddies during 2000–2012 revealed the status of the atmospheric boundary layer is influenced remarkably by such eddies. The results showed cold-core cyclonic(warm-core anticyclonic) eddies tend to cool(warm) the overlying atmosphere and cause surface winds to decelerate(accelerate). More than 5% of the total variance of turbulent heat fluxes, surface wind speed and evaporation rate are induced by mesoscale eddies. Furthermore, mesoscale eddies locally affect the columnar water vapor, cloud liquid water, and rain rate. Dynamical analyses indicated that both variations of atmospheric boundary layer stability and sea level pressure are responsible for atmospheric anomalies over mesoscale eddies. To reveal further details about the mechanisms of atmospheric responses to mesoscale eddies, atmospheric manifestations over a pair of cold and warm eddies in the southwestern SCS were simulated. Eddy-induced heat flux anomalies lead to changes in atmospheric stability. Thus, anomalous turbulence kinetic energy and friction velocity arise over the eddy dipole, which reduce(enhance) the vertical momentum transport over the cold(warm) eddy, resulting in the decrease(increase) of sea surface wind. Diagnoses of the model's momentum balance suggested that wind speed anomalies directly over the eddy dipole are dominated by vertical mixing terms within the atmospheric boundary layer, while wind anomalies on the edges of eddies are produced by atmospheric pressure gradient forces and atmospheric horizontal advection terms.     

A STUDY OF NEAR SURFACE WIND PROFILES IN THE HEBEI COASTAL AREA BASED ON OBSERVATIONAL EXPERIMENTS

In order to provide wind profiles for the microscale numerical simulation of wind farm with complex terrain,using the 100 m tower atmospheric turbulence observation experiment data in 2010 in Hebei Province offered by National Climate Center, the variation characteristics of wind profile under the different atmospheric stability conditions are analyzed, and the wind profile expression based on the local similarity theory is established. The results show that:(1) In spring, the occurrence probability of unstable stratification in the Hebei coastal area is as high as 28%, and the probability of stable stratification is more than 43% while, in summer, the probability of occurrence of unstable stratification is as high as 80% with a lower probability for stable stratification; and(2) for stable stratification, the characteristics of atmosphere change is dramatic in terms of the vertical direction, which need to be treated layer by layer.According to the atmospheric turbulence observation experiment data above, under stable stratification, the relationship between the dimensionless velocity gradient and the stability ζ can be expressed as 1 +βmζ, with βm changing with the height: βm takes 4.1-4.3 under 30 m, βm takes 4.6-4.7 between 30-50 m, and βm takes 6.3-6.7 over 50 m.     

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

Texture information offers an extensive solution for image classification by providing better accuracy of image information. However, huge amounts of improper additional texture information may result in a chaotic state, and this leads to uncertainty in the classification process. Considerable portion of earlier works have been carried out through the generally acknowledged procedure of Principal Components Analysis (PCA). However, the PCA method has flaws in the area of influenced and non- influenced attributes. On the whole, whether PCA provides an effective solution to determine the value of knowledge rule in image information still remains a question. This study proposes an innovative method, called Discrete Rough Set method, as a tool for image classification. This study focuses on two crucial issues: (1) The core attributes of the target categories in image classification are systematically analyzed while eliminating surplus attributes rationally; (2) The unique point of each attribute, which influenced the target categories, is successfully found. This is a crucial aspect that is very helpful for the construction of decision rule. Finally, in this study we utilized the expert knowledge classifier and the overall accuracy of Discrete Rough Set (96.67%) exceeds that of the conventional PCA (86.00%) of paddy rice area evaluation from Quickbird image. This result shows that the appropriate classification knowledge can be presented by Discrete Rough Set, and this information can effectively improve the accuracy of image classification. An erratum to this article can be found at     

The knowledge rules of debris flow event: A case study for investigation Chen Yu Lan River, Taiwan

Debris flow, one of the most active hydrological processes, brings about a great loss of life and properties in the Chen Yu Lan River area, Nan-Tou County, Taiwan. The goal of this study is to manage and monitor the debris flows in a vast area. Thus, the strategy is to collect hydraulical and in-situ data easily and quickly through our spatial information system. Then, a novel Data Mining technique will be developed to find a rule-based knowledge system of debris flow. The entire study can be divided into two parts. In the first part, using spatial information the debris flow image is extracted from the outbreak area. The system provides precise positional information pertaining to the debris flow events. Attributes such as effective watershed area, Normalized Difference Vegetation Index (NDVI), and slope are automatically obtained from the system. In the second part of this study, K-mean theory is used to classify the observed hydraulical and in-situ data into different categories. Then, the ‘Effective Rough Set’ method is successfully used to analyze the factors influencing the debris flow phenomenon and delivers rational knowledge rules. The contribution of this study is presented that NDVI, slope and effective watershed area are the major factors influencing debris flow in the study area. The knowledge rules can provide better understanding on the elevation of high potential debris catchments.     

博格达南缘二叠系古土壤类型及其在层序地层研究中的应用

古土壤是在地质时期经成壤作用改造原岩而形成土壤。野外实地详细考察和室内研究表明,博格达南缘二叠系中发育4类古土壤,即有机土壤,发育于土壤剖面的近顶部,属于潮湿古环境的产物;泥质土壤,主要发育于淋滤带和淋滤带底部泥质沉淀带,形成于长期淋滤和比较潮湿的古环境;铁质土壤以富含铁质结核为特征,是长期淋滤和潮湿古环境的标志;钙质土壤以富含钙质结核为特征,是干旱—半干旱古环境的标志。通过古土壤的研究能够精细地划分层序旋回和恢复沉积古环境。     

地质雷达特征图像与典型地质现象的对应关系

地质雷达是浅层地质勘探的有效方法,其对断层破碎带、裂隙带、富水带、岩溶洞穴等不良地质灾害有明显的异常反映。地质雷达测量方式、测线布置及系统参数的选择,可根据测量环境以及探测目标体的大致走向、规模、物性等情况综合分析,做出合理选择。通过用地质雷达对多个工区进行工程勘查,从地质雷达图像的波形、频率、振幅、相位及电磁波能量吸收情况(或自动增益梯度)等细节特征的变化规律出发,建立了典型地质现象与地质雷达特征图像的对应关系,可以作为地质雷达图像解释的重要判据。     

基于激光反射率的点云与图像自动融合

提出一个基于激光反射率的点云图像自动融合算法,数字图像与点云反射率图像特征点匹配,得到数字图像特征点像点坐标和相应物点物方空间坐标对,采用直接线性变换算法(DLT)建立点云数据反投影至二维图像点的投影模型,实现点云图像自动融合,得到目标表面的全面信息,包括物体表面三维坐标和纹理信息。自动地实现点云图像融合,无需人工参与,提高效率和准确率,同时结合实验验证该算法的可行性。     

供给侧改革背景下产业用地供应——以浙江省和贵州省为例

本文分析了浙江省和贵州省土地供应的总体特征,包括土地供应与经济运行态势呈同比下降态势、供地结构调整的显著变化特征是基础设施用地明显加大、建设用地供应逐步由增量扩张型向存量挖潜型转变等.本文总结了浙江省和贵州省深化工业用地市场化配置改革的8条探索性经验.在此基础上,提出了浙江和贵州省土地供给侧改革面临的4个主要问题和9个政策建议.     

改进灰色人工神经网络模型的超高层建筑变形预测

针对灰色人工神经网络模型初始化权值和阈值的随机性导致易产生误差积累和过拟合的缺陷,该文利用遗传算法的全局优化能力训练灰色人工神经网络模型的权值和阈值,构建了基于遗传算法的灰色人工神经网络超高层建筑物变形预测模型。结合长沙北辰新河A1超高层建筑变形监测实例,用该文所提模型与灰色人工神经网络模型分别进行变形数据的处理分析和预测。实验结果表明,该文提出的模型具有更好的预测精度,预测趋势也更加逼近实际测量结果。     

宝鸡市重点景区游客满意度评价研究

重点景区在目的地城市的旅游资源功能发挥中扮演着重要角色.构建宝鸡市重点景区期望差异理论模型,通过李克特五级量表调查游客的游前期望和游后的实际感知,对宝鸡市八大不同级别的重点旅游景区进行分析,得到游客满意度的优势和劣势因子.结果表明:游客对宝鸡旅游总体感到较为满意,旅游资源禀赋好但分布较散,难以满足多元化旅游者的需求;餐饮卫生、住宿便捷度、购物环境和种类、历史文化特色四大类因子的游客期望和实际感知差异值均为负,在景区发展中处于弱势;与法门寺相比,太白山更具有资源和观赏优势,其余景区的观赏价值虽较高,但整体满意度不均衡.针对弱势因子探讨并提出解决对策,为宝鸡市旅游业健康、快速发展提供借鉴.