基于百度地图的精细化格点预报显示

精细化格点要素预报是目前中国气象局的主推业务和未来天气预报的发展方向,如何有效地将预报产品提供给用户使用,是精细化格点预报的最终环节。本文介绍了基于百度地图API的陕西精细化格点预报显示系统,系统主要实现了:(1)降水预报,以CMORPH(NOAA Climate Prediction Center Morphing Method)卫星与全国3万个自动观测站的逐时降水量融合资料为基础,通过动态偏差订正的方法来提高格点降水的预报能力;温度预报,采用滑动线性回归的方法来改善温度预报效果。(2)任意位置的地理信息获取及对应格点240h预报时效的气象要素实时展示。(3)格点气象要素向站点转换,通过格点值提取全省98个观测站逐3h站点预报值,实时分析过去24h降水、温度预报与观测值的误差,供用户预判未来预报值的可能误差趋势;并提供未来168h逐日要素预报。(4)与以往的数据库后台支撑不同,本系统直接将3.5GB格点预报数据一次性读入内存,进行侦听,解决了数据库检索、调用效率低下的问题。     

不同边界层参数化方案在暴雨数值模拟中的对比分析

本文利用非静力平衡的中尺度模式(MM5),通过对一次华南锋前暖区暴雨的数值模拟,比较了3种边界层参数化方案的作用。结果表明：从24小时累积降水的分布来看,MRF方案的预报与实况对比效果最好,其次是Blaekadar高分辨率PBL模式,最差是no PBI,方案。分析MRF方案的输出结果,发现对应每个时刻的累积雨团上空,925hPa都有较强的辐合辐散偶极子与之相配合,而在300hPa则有较强的辐散。不同边界层方案模拟的辐散场有一定的差别,导致了降水的不同预报效果。     

四维EOF分析检验数值模式模拟气候及其变化能力研究

文章提出了一个四维经验正交函数(4D-EOF)方法,原理是三维经验正交函数(3D-EOF)与扩展经验正交函数(EEOF)简单的组合,此方法不仅提供空间水平分布特征场及其对应的月际变化特征和年际变化特征,而且还提供空间垂直结构特征.利用这个新方法分析东亚季风国际区域模式比较计划(RMIP)MM5V3模拟的1989-1998年10 a积分结果--包括中国大部分区域(4941个格点,格距60 km)月平均100,500,700,1000 hPa 4个位势高度场(模拟场)及其距平场;同时分析对应的NCAR/NCEP再分析资料(观测场),进而对比两者检验模式模拟东亚季风气候及其变化能力.对比分析结果表明:对于月平均高度场的第一特征向量场,模式能比较准确地模拟出平均气候场的分布及其垂直相当正压性的结构特征;对于月平均高度距平场第一、二特征向量场,模式对于距平场的模拟也较成功,垂直方向有明显的相当正压性特征;月平均高度场及其距平场相应的月际变化和年际变化特征也在模拟中得到较好的反映.本研究表明:4D-EOF具有综合检验数值模式模拟气候及其变化的能力,而MM5V3模拟20世纪90年代东亚气候及其变化能力是令人满意的.     

基于Ripley’s K函数的南京市ATM网点空间分布模式研究

运用Ripley’s K函数的相关理论,以南京市ATM网点为研究对象,分别从平面与网络空间两种视角,在中心城区范围与主城区范围两种空间尺度上,通过单变量\begin{array}{c}K\end{array}函数法分析ATM网点的分布模式,通过双变量\begin{array}{c}K\end{array}函数法分析ATM网点与地铁站点的空间关联情况,最后对计算结果进行评价与分析。研究表明,ATM网点在南京主城区与中心城区均呈现出较强的集聚状态;在一定的距离范围内,ATM网点与地铁站点之间也有较强的依赖关系。同时,对于沿着路网分布的地理空间点状对象而言,利用网络\begin{array}{c}K\end{array}函数法进行空间点模式分析比用平面\begin{array}{c}K\end{array}函数法更加符合实际情况。     

Cellular automata as analysis and synthesis engines at the geomorphology–ecology interface

The linkages between ecology and geomorphology can be difficult to identify because of physical complexity and the limitations of the current theoretical representations in these two fields of study. Deep divisions between these disciplines are manifest in the methods used to simulate process, such as rigidly physical-deterministic methods for many aspects of geomorphology compared with purely stochastic simulations in many models of change in landcover. Practical and theoretical research into ecology–geomorphology linkages cannot wait for a single simulation schema which may never come; as a result, studies of these linkages often appear disjointed and inconsistent.The grid-based simulation framework for cellular automata (CA) allows simultaneous use of competing schemas. CA use in general geographic studies has been primarily limited to urban simulations models of change for land cover, both highly stochastic and/or expert rule-based. In the last decade, however, methods for describing physically deterministic systems in the CA framework have become much more accurate. The possibility now exists to merge separate CA simulations of different environmental systems into unified “multiautomata” models. Because CAs allow transition rules that are deterministic, probabilistic, or expert rule-based, they can immediately incorporate the existing knowledge rules in ecology and geomorphology. The explicitly spatial nature of CA provides a map-like framework that should allow a simple and deeply rooted connection with the mapping traditions of the geosciences and ecological sciences.     

金华市水资源承载力分析

从生态学角度出发,提出水资源承载力是在社会发展的某一阶段和生态系统良性循环的条件下,水资源对人类活动的最大支撑能力。指出目前金华市的水荒不是水源型缺水,而是水质型缺水和工程型缺水。根据20多年的历史数据和2020年全面建设小康社会的标准,利用系统动力学(SD)模型,仿真研究了金华市未来政策实施后水资源承载力的动态变化过程。同时指出,对于金华市来说,单方面追求经济的快速发展、以牺牲环境作为代价的高方案和把环境保护作为首要目标、经济慢速发展的低方案都是不可取的,只有经济发展和环境保护同时兼顾的中方案才是首选方案。     

旅游产业生长点的SGGT模式与空间演化——以张家界为例

界定了旅游产业生长点的概念,借鉴“路径依赖”理论重新诠释旅游地产业成长路径,提出具有普遍性SGGT模式,以张家界旅游产业生长点为例,具体分析了SGGT和空间演化过程及其动力机制。结果发现:①历史与现实是武陵源旅游生长点形成的基础,需求与培育是旅游生长点萌发的关键因素,创新与拓展是旅游生长点成长的保证,蜕变促进了旅游生长点成为旅游产业增长极;②张家界国家森林公园、天子山和索溪峪三个旅游生长点,通过“路径创造”,实现了行政管辖权的统一,后被列为世界遗产地,成为张家界旅游产业的核心生长点;③在旅游产业“集聚力”和世界遗产地管控的驱动下,天门山成为张家界新的生长点;④武陵源和天门山两个生长点的形成,是张家界旅游产业集聚和“路径创造”共同作用的结果,也拓展了张家界旅游产业的成长空间。本文有利于为旅游地政府和企业从旅游产业生长点视角理解旅游产业成长路径,更为世界遗产地旅游产业可持续发展提供科学指导。     

Implementing a new data model for simulating processes

The paper describes the development of a new methodological approach for simulating geographic processes through the development of a data model that represents a process. This methodology complements existing approaches to dynamic modelling, which focus on the states of the system at each time step, by storing and representing the processes that are implicit in the model. The data model, called nen, focuses existing modelling approaches on representing and storing process information, which provides advantages for querying and analysing processes. The flux simulation framework was created utilizing the nen data model to represent processes. This simulator includes basic classes for developing a domain specific simulation and a set of query tools for inquiring after the results of a simulation. The methodology is prototyped with a watershed runoff simulation.     

Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach

Many cities in the United States and Canada offer a 311 helpline to their residents for submitting requests for non-emergency municipal services. By dialing 311, urban residents can report a range of public issues that require governmental attention, including potholes, graffito, sanitation complaints, and tree debris. The demand for these municipal services fluctuates greatly with time and location, which poses multiple challenges to effective deployment of limited resources. To address these challenges, this study uses a locally adaptive space-time kernel approach to model 311 requests as an inhomogeneous Poisson process and presents an analytical framework to generate predictions of 311 demand in space and time. The predictions can be used to optimally allocate resources and staff, reduce response time, and allow long-term dynamic planning. We use a bivariate spatial kernel to identify the spatial structure and weigh each kernel by corresponding past observations to capture the temporal dynamics. Short-term serial dependency and weekly temporality are modeled through the temporal weights, which are adaptive to local community areas. We also transform the computation-intensive parameter estimation procedure to a low dimensional optimization problem by fitting to the autocorrelation function of historical requests. The presented method is demonstrated and validated with sanitation service requests in Chicago. The results indicate that it performs better than common industry practice and conventional spatial models with a comparable computational cost.