利益相关者对旅游产业的认知差异分析

基于一般系统论和博弈论理论,对旅游产业利益相关者进行界定,分析了各利益主体对旅游产业认知差异,认为主要表现在政策、经济、社会文化、环境保护等4个方面.由于认知差异的存在,各利益主体行为对旅游产业产生了正面影响和负面影响,针对负面影响提出了相应的对策,旨在促进旅游产业发展达到一种利益均衡、责权对称的状态.     

北京市住宅价格的影响因素及适宜居住面积标准

针对我国城市住宅市场中的问题,如何通过适宜的住宅面积标准来引导住房建设与消费是当前极为重要和紧迫的课题。然而已有的建筑学和比较研究等方法却存在较大的局限性,难以为政策提供有效的支撑。本文运用微观经济学的理论和方法对适宜居住面积标准的理论框架和方法进行探索,以北京市为例进行实证分析。从住房市场的经济分析出发,将住宅面积标准的优化界定为使消费者的边际效应最大化的问题。通过定量分析,提出适合于不同家庭的居住面积的建议值,并就如何合理地引导住宅建设与消费提出政策建议。根据分析结果,建议以50m²和80m²作为北京地区的适宜建筑面积标准。同时,从鼓励阶段性消费的政策视角出发,提出宜针对特定的目标层,通过减税、补贴等手段降低换购费用,从而创造引导居民理性消费的经济动机。     

预测陕西关中地区需水量的改进GM(1,1)模型

需水量评价与预测是水资源规划与管理一项重要的研究内容。本文采用定额法计算需水量。通过对陕西关中地区需水量要素组成的分析,认为需水量要素属平稳时间序列,且一阶累加生成数据系列满足指数规律,符合灰色预测条件。为了提高GM(1,1)模型的精度,采用一种改变背景值的新方法,即:中心逼近方法。通过精度检验,中心逼近式GM(1,1)模型平均误差百分比、误差平方和分别比传统的GM(1,1)模型提高了0.147和1.579。用中心逼近式GM(1,1)模型预测需水量各要素近期和中长期值,经检验,其中非农业人口、农业人口、耕地面积、工业总产值预测模型满足一级模型,等级为良好。牲畜头数预测模型为不合格模型,分析其原因,主要是因为时间序列数据不完全符合指数规律。通过定额法计算,预计75%保证率时2005年和2010年关中地区工农业需水量分别占总需水量的92.21%和89.75%,生活需水量分别占7%和9.04%。     

西方乡村性研究进展

在梳理20世纪60年代以来乡村性研究的社会、学术背景的基础上,聚焦于乡村性的社会建构流派及其对中国乡村转型与乡村地理研究的启示。社会建构流派将乡村性看作是社会、文化建构的过程与产物而非先存的事实,重点关注乡村转型过程中的权力关系与差异性。由描述性流派、乡土流派向社会建构流派的过渡可以看作是从关注乡村性物质层面转向想象层面,由追求乡村性的本体、核心机制向探索乡村性的认识论问题,也就是从追求“乡村性是什么”到研究“不同社会群体如何认识乡村性”“不同社会群体多样化的乡村性话语和体验”。而目前的中国乡村性研究主要关注乡村性指标及其模型建构,相关研究对于各级政府全面地了解现存的乡村问题并合理地制定乡村发展、复兴政策规划具有重要的实践意义。在西方乡村研究中,乡村性指标由于暗含线性发展观、无法解读乡村变迁的机制和过程而饱受批判。在中国乡村急剧变迁的今天,现代化乡村发展话语占据着主导地位,而在这一过程中不公平的权力关系和霸权话语对部分农村、农民的边缘化基本没有提及。在这种社会与学术背景下,乡村性社会建构流派对于解构乡村性主导话语、揭示乡村变迁过程中的权力关系及不同社会群体间利益冲突与协商具有重要意义。     

A Numerical Study on Effects of Land-Surface Heterogeneity from ‘Combined Approach’ on Atmospheric Process Part II: Coupling-Model Simulations

Two land surface schemes, one the standard Biosphere / Atmosphere Transfer Scheme Version le (B0Z) and the other B1Z based on B0Z and heterogeneously-treated by ‘combined approach’ , were coupled to the meso-scale model MM4, respectively. Through the calculations of equations from the companion paper, parameters representing land surface heterogeneity and suitable for the coupling models were found out. Three cases were simulated for heavy rainfalls during 36 hours, and the sensitivity of short-term weath-er modeling to the land surface heterogeneity was tested. Through the analysis of the simulations of the three heavy rainfalls, it was demonstrated that B1Z, compared with B0Z, could more realistically reflect the features of the land surface heterogeneity, therefore could more realistically reproduce the circulation and precipitation amount in the heavy rainfall processes of the three cases. This shows that even short-term weather is sensitive to the land surface heterogeneity, which is more obvious with time passing, and whose influence is more pronounced in the lower layer and gradually extends to the middle and upper layer. Through the analysis of these simulations with B1Z, it is suggested that the bulk effect of smaller-scale fluxes (i.e., the momentum, water vapor and sensible heat fluxes) near the significantly-heterogeneous Land surface is to change the larger-scale (i.e., meso-scale) circulation, and then to influence the development of the low-level jets and precipitation. And also, the complexity of the land-atmosphere interaction was shown in these simulations.     

IMPLICIT NONLINEAR NORMAL MODE INITIALIZATION:A MULTIGRID APPROACH

The implicit nonlinear normal mode initialization (INMI) is applied to a tropical limited area shallow water modelin spherical coordinates.The boundary condition for the INMI scheme is based on the boundary formulation of themodel.The INMI scheme is found to be very efficient in suppressing spurious gravity wave oscillation and providing awell balanced initial data set for the model.The INMI scheme involves solving a number of elliptic type equations withvarying complexity.and hence an efficient numerical technique is required for solving such equations.In order to makeINMI computationally more attractive,we are employing the multigrid method for solving all the elliptic type equationsin the INMI scheme.The numerical procedures for the development of such multigrid solvers are briefly described.     

基于年际增量法的卷积神经网络模型对中国夏季降水的预测研究

将前冬的500 hPa位势高度、向外长波辐射和海表温度的年际增量作为预测因子,建立基于卷积神经网络(Convolutional Neural Network, CNN)的非线性预测模型,对中国160个测站夏季降水展开预测研究,并与基于线性奇异值分解(Singular Value Decomposition, SVD)的预测模型进行效果对比。结果表明：CNN在1981—2020年的交叉检验中所回报的降水平均PS评分和距平相关系数(ACC)分别为74.33和0.12,比SVD高2.15和0.06,说明CNN比SVD在整体上对夏季降水具有更好的预测能力。其中,CNN对SVD预测较好年份的预测效果提升较为明显,对SVD预测较差的年份则改进不大。CNN对中国降水预测存在一定的系统性偏差,订正后CNN对拉尼娜年的降水预测改进较大。结果表明,基于年际增量法的CNN预测模型展示出较好的潜在应用价值。     

Proposed nonlinear 3-D analytical method for piled raft foundations

The load distribution and deformation of piled raft foundations subjected to axial and lateral loads were investigated by a numerical analysis and field case studies. Special attention is given to the improved analytical method (YSPR) proposed by considering raft flexibility and soil nonlinearity. A load transfer approach using p–y, t–z and q–z curves is used for the analysis of piles. An analytical method of the soil–structure interaction is developed by taking into account the soil spring coupling effects based on the Filonenko-Borodich model. The proposed method has been verified by comparing the results with other numerical methods and field case studies on piled raft. Through comparative studies, it is found that the proposed method in the present study is in good agreement with general trend observed by field measurements and, thus, represents a significant improvement in the prediction of piled raft load sharing and settlement behavior.     

Evaluation of the Historical Sampling Error for Global Models

Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is to use an ensemble forecast; however, this is very time-consuming. The historical sampling approach is an alternative way to generate the ensemble,by picking some snapshots from historical forecast series.With this approach, many ensemble-based assimilation schemes can be used in a deterministic forecast environment. Furthermore, considering the time that it saves, the method has the potential for operational application.However, the historical sampling approach carries with it a special kind of sampling error because, in a historical forecast, the way to integrate the ensemble members is different from the way to integrate the initial conditions at the analysis time(i.e., forcing and lateral boundary condition differences, and ‘warm start' or ‘cold start' differences). This study analyzes the results of an experiment with the Global Regional Assimilation Prediction System-Global Forecast System(GRAPES-GFS), to evaluate how the different integration configurations influence the historical sampling error for global models. The results show that the sampling error is dominated by diurnal cycle patterns as a result of the radiance forcing difference.Although the RMSEs of the sampling error are small, in view of the correlation coefficients of the perturbed ensemble, the sampling error for some variables on some levels(e.g., low-level temperature and humidity, stratospheric temperature and geopotential height and humidity), is non-negligible. The results suggest some caution must be applied, and advice taken, when using the historical sampling approach.     

对流尺度集合预报中模式地形扰动对其预报技巧的影响研究

基于ARPS3DVAR+WRF（Advanced Regional Prediction and 3-dimensional variational System）快速同化模式对西南地区近几年发生的4次强降水过程进行模拟试验,对12 h降水预报结果采用升尺度方法,计算邻域平均预报、站点概率预报,最终形成邻域概率预报,并细致分析了这三种预报的特点与效果,讨论了升尺度窗区尺度给不同量级降水带来的影响,最后结合AROC评分与邻域空间检验FSS讨论业务概率预报应用的最佳尺度。结果表明：升尺度邻域平均预报在小雨与大暴雨量级降水上表现不稳定,对中雨的预报提高不明显,但是对大雨与暴雨预报有较好的改善效果;站点概率预报具有一定的误导性,而邻域概率预报可以弥补其缺憾,越高分辨率的模式有更多的降水样本,在降水不确定性上能给出更好的概率分级信息;相对邻域平均的升尺度预报TS检验结果,基于邻域概率的FSS和AROC分析有更好的预报技巧指导意义;36 km升尺度窗区既能消除一定程度的强降水预报不确定性,同时也可以保留适当的对流尺度特征,为最佳升尺度窗区。