国外生态旅游研究进展及启示

作为替代性旅游和可持续旅游的实现形式,生态旅游从提出就受到学者们的重视。本文以Web of Science核心合辑收录的国外生态旅游期刊文章为基础,对筛选出的546篇文献进行综述分析,梳理和归纳相应的研究热点和研究方法,并从生态旅游内涵、利益相关者、客源市场、目的地、影响和评估6方面阐明国外生态旅游的研究进展和主要内容。结果表明：国外生态旅游研究以应用为导向,具有研究区域资源特征明显、研究对象不断扩展、研究内容逐渐深入的特点,并呈现跨学科、多视角的研究态势。结合国外的研究经验,本文提出未来中国应就完善生态旅游理论体系、妥善处理利益相关者的关系、总结生态旅游者行为规律、拓展目的地的研究尺度和管理模式、重视生态旅游的影响研究和优化效果评估方法6方面加强研究,以促使研究体系完善和优化。     

Characterizing mixed-use buildings based on multi-source big data

To-date few research has successfully integrated big data from multiple sources to characterize urban mixed-use buildings. In this paper, we introduce a probabilistic model to integrate multi-source and geospatial big data (social network data, taxi trajectories, Points of Interest and remote sensing images) to characterize urban mixed-use buildings. The usefulness of our model is demonstrated with a case study of the Tianhe District in megacity Guangzhou, China. The model predicted building functions at 85% accuracy based on ground truth data from field surveys. We further explored the spatial patterns of the identified building functions. Most mixed-use buildings are located along major streets. Our proposed model can identify mixed-use buildings in a city; information is useful for planning evaluation and urban policymaking.     

Analysis of Spatial Mismatch of Tourism Development in Guangdong Province

The spatial mismatch situation of tourism development in Guangdong Province is analyzed by gravity model and two-dimensional matrix based on the spatial mismatch hypothesis, and the results are visualized using ArcGIS software. This study finds that varying degrees of spatial mismatch exist between the level of tourism development, abundance of tourism resources and accessibility of tourism locations in the 21 cities in Guangdong Province. The gravity centers for tourism economy, tourism resources and tourism location are (113.55° E, 23.00° N), (113.69° E, 23.21° N) and (113.74° E, 22.86° N), respectively. According to the two-dimensional combinatorial matrices, synchronous development is shown in 10 prefecture-level cities for the tourism revenue-resource abundance combination, whereas it is shown in seven prefecture-level cities for the tourism revenue-tourism location combination. Guangzhou and Shenzhen are synchronous-double high zones for both combinations, while Foshan, Qingyuan, Yangjiang, Zhongshan and Jieyang are deviating-negative mismatch zones for both combinations. Furthermore, the vast majority of prefecture-level cities within the province currently present mismatching trends in tourism development. Based on the analysis results, corresponding countermeasures and suggestions are put forward taking into account the actual situation and opportunities for further tourism development in various prefecture-level cities.     

Changes in record-breaking temperature events in China and projections for the future

Theoretical and Applied Climatology - As global warming intensifies, more record-breaking (RB) temperature events are reported in many places around the world where temperatures are higher than...     

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty

This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II. Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60°S and 60°N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.     

BCC S2S模式对亚洲夏季风准双周振荡预报评估

利用1994-2013年ERA-Interim及NCEP/NCAR再分析数据,对国家气候中心（BCC）次季节到季节尺度模式（S2S）1994-2013年的回报试验数据进行亚洲季风区准双周振荡（QBWO）预报能力评估,并诊断模式预报误差来源。结果表明:BCC S2S模式对QBWO的预报能力随着预报提前时间的增长而降低,9 d后预报技巧明显减弱,其周期、传播特征和强度出现误差;在提前9 d预报中,印度洋地区QBWO对流-环流系统结构松散,信号偏弱,对流向东传播,这与印度洋平均态的预报误差有关,夏季对流平均态低层水汽场在西太平洋和阿拉伯海较强,而东印度洋、孟加拉湾一带偏弱;西北太平洋地区QBWO具有向西北传播的特征,但强度偏弱,可能原因是预报低估了QBWO对流西北侧低层涡度的超前信号,经涡度方程诊断发现,地转涡度平流正贡献微弱,相对涡度平流在对流西北侧引发负涡度,从而减弱了对流西北侧由低层正涡度引发的有利条件。     

基于MICAPS4的智能网格预报平台设计与实现

随着我国智能网格预报业务的开展,海量高分辨率客观数据需要便捷的分析显示及产品制作平台高效处理。该文基于MICAPS4（Meteorological Information Comprehensive Analysis and Processing System Version 4.0,人机交互气象信息处理和天气预报制作系统）设计并实现智能网格预报平台,采用MVVM（模型-视图-视图模型,model-view-viewmodel）设计模式,实现业务逻辑与视图的分离,通过对各子功能模块的划分,降低模块之间的耦合度,具有良好的可扩展性。平台实现了高分辨率网格预报数据的显示分析和产品输出,开发了基于等值线、网格、关键点等智能化预报制作工具,集成了降水时间拆分、温度极值订正等客观预报方法,开发了降水、温度、相对湿度等要素一致性处理方法,可有效帮助预报员提高工作效率,同时能够确保产品之间的一致性。平台继承MICAPS4的微内核组件服务、高性能渲染引擎和开放式插件扩展管理等优良特性,实现面向智能网格预报的业务编排、智能编辑和算法集成。目前,该平台已经实现业务应用,为全国智能网格预报业务提供重要支撑。     

Evolution and changes of permafrost on the Qinghai-Tibet Plateau during the Late Quaternary

Due to the uplift of Qinghai-Tibet Plateau(QTP), the cryosphere gradually developed on the higher mountain summits after the Neocene, becoming widespread during the Late Quaternary. During this time, permafrost on the QTP experienced repeated expansion and degradation. Based on the remains and cross-correlation with other proxy records such as those from glacial landforms, ice-core and paleogeography, the evolution and changes of permafrost and environmental changes on the QTP during the past 150,000 years were deduced and are presented in this paper. At least four obvious cycles of the extensive and intensive development, expansion and decay of permafrost occurred during the periods of 150–130, 80–50, 30–14 and after 10.8 ka B.P.. During the Holocene, fluctuating climatic environments affected the permafrost on the QTP, and the peripheral mountains experienced six periods of discernible permafrost changes:(1) Stable development of permafrost in the early Holocene(10.8 to 8.5–7.0 ka B.P.);(2) Intensive permafrost degradation during the Holocene Megathermal Period(HMP, from 8.5–7.0 to 4.0–3.0 ka B.P.);(3) Permafrost expansion during the early Neoglacial period(ca. 4,000–3,000 to 1,000 a B.P.);(4) Relative degradation during the Medieval Warm Period(MWP, from 1,000 to 500 a B.P.);(5) Expansion of permafrost during the Little Ice Age(LIA, from 500 to 100 a B.P.);(6) Observed and predicted degradation of permafrost during the 20 th and 21 st century. Each period differed greatly in paleoclimate, paleoenvironment, and permafrost distribution, thickness, areal extent, and ground temperatures, as well as in the development of periglacial phenomena. Statistically, closer dating of the onset permafrost formation, more identification of permafrost remains with richer proxy information about paleoenvironment, and more dating information enable higher resolution for paleo-permafrost reconstruction. Based on the scenarios of persistent climate warming of 2.2～2.6 °C in the next 50 years, and in combination of the monitored trends of climate and permafrost changes, and model predictions suggest an accelerated regional degradation of plateau permafrost. Therefore, during the first half of the 21 st century, profound changes in the stability of alpine ecosystems and hydro(geo)logical environments in the source regions of the Yangtze and Yellow rivers may occur. The foundation stability of key engineering infrastructures and sustainable economic development in cold regions on the QTP may be affected.     

Classifying urban land use by integrating remote sensing and social media data

Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.     

A linear tessellation model to identify spatial pattern in urban street networks

Street patterns reflect the distribution characteristics of a street network and affect the urban structure and human behavior. The recognition of street patterns has been a topic of interest for decades. In this study, a linear tessellation model is proposed to identify the spatial patterns in street networks. The street segments are broken into consecutive linear units with equal length. We define five focal operations using neighborhood analysis to extract the geometric and topological characteristics of each linear unit for the purpose of grid-pattern recognition. These are then classified by Support Vector Machine, and the result is optimized based on Gestalt principles. The experimental results demonstrate that our method is effective for mining grid patterns in a street network.