AMDAR气温资料和常规高空观测气温资料的对比分析     

罗林艳  罗宇  钟艳雯 《气象与环境科学》2018,41(3):117-121

为了解飞机观测气象资料(AMDAR资料)的可用性,推进AMDAR资料在湖南气象预报业务中的应用,对2013年5月至2016年12月的AMDAR资料时空分布特征进行了分析,并参考美国国家环境预报中心(NCEP)提供的AMDAR资料质量控制方法,对AMDAR气温资料进行质量控制,并在此基础上对湖南范围内的AMDAR气温资料和常规高空观测气温资料(长沙、怀化、郴州)进行了对比分析。结果表明:我国AMDAR资料主要分布在100 h Pa气压层以下,起飞和降落阶段资料约占68. 75%。AMDAR气温资料数据质量稳定,气温疑误率低于0. 13%。在对比样本上,AMDAR观测气温(t_a)与常规高空观测气温(ts)存在非常显著相关性,相关系数为0. 9966,均方根误差为1. 065℃,AMDAR观测气温平均偏低0. 13℃,差值呈准正态分布,且73. 76%的差值在-1~1℃。AMDAR观测气温与常规高空观测气温的差值分布与飞行状态和飞行高度相关,上升状态AMDAR气温偏高,下降状态AMDAR气温偏低。500 h Pa高度以下,样本飞行状态多为下降,t_a较ts平均偏低0. 25℃; 500h Pa高度以上,样本飞行状态多为上升和平飞,t_a较ts平均偏高0. 03℃。  相似文献   

基于ECMWF产品福建省前汛期短时强降水预报方法   总被引：2，自引：1，他引：1       下载免费PDF全文

洪伟  郑玉兰 《应用气象学报》2018,29(5):584-595

利用2014—2016年福建省1605个自动气象站逐时降水资料和ECMWF全球模式细网格预报产品,分析福建省前汛期短时强降水发生背景下模式预报物理量的分布特征,并基于阈值判定的方法建立短时强降水预报模型。结果表明:福建省内陆县市前汛期短时强降水发生频次较高,沿海县市发生频次低,且日变化特征表现出双峰结构。箱型图差异指数（I_bd）在评估相关变量对于区分短时强降水发生与否的敏感程度有较好的作用,比湿、整层可降水量等水汽变量I_bd最为显著,K指数、对流有效位能等变量的I_bd仅次于水汽变量,说明模式预报变量对于预测短时强降水有较好的表征作用。针对短时强降水事件的物理量集合,采用剔除异常值后的最小值作为判定阈值,通过训练集分析结果客观订正对流有效位能和3 h降水量两个高I_bd变量的阈值,建立潜势预报模型。对于福建省西部的关键区,检验集白天时段12 h时间分辨率预报TS评分可达0.5,夜间时段约为0.3。对于福建省进行分区建模预报,检验集预报结果显示白天时段比夜间准确率高、内陆县市比沿海县市准确率高。  相似文献   

基于降水条件的城镇化与植被覆盖协调发展研究     

温小洁  姚顺波  赵敏娟 《地理科学进展》2018,37(10):1352-1361

为探讨城镇化发展与植被覆盖状况的协调发展关系,本文以黄河中上游的144个县域单元为研究对象,基于2000、2005、2010及2015年4期的县域经济发展数据及MODIS-NDVI数据,通过门槛回归模型,以影响研究区域植被覆盖最主要的降水因素作为门槛变量,具体分析不同降水条件下城镇化发展对植被覆盖的影响,并提出以保护植被覆盖为基础的差异化城镇化发展策略。结果表明：①随着城镇化的推进,植被覆盖会进一步遭到破坏,但降水因素在城镇化影响植被覆盖的过程中可发挥显著的正向调节作用,从而促进城镇化与植被覆盖的协调发展;②实现城镇化与植被覆盖的协调发展,不仅取决于自身的降水条件,还依赖于当地的生态工程建设及产业结构优化升级等多种因素。其中,退耕还林(草)政策的实施可加速植被覆盖的修复进程,提高区域环境的生态承载力,有效缓解城镇化发展带给植被覆盖的负面影响;③第二产业在经济发展中的比重增加会给植被覆盖带来严重威胁,阻碍城镇化与植被覆盖的协调发展,尤其是在年均降水量较少且生态脆弱的地区。  相似文献   

Comparison of methods for quantifying consumer spending on retail using publicly available data     

Derek T. Robinson  Andrei M. Balulescu 《International journal of geographical information science》2018,32(6):1061-1086

Data and estimates on the spatial distribution of consumer spending form the foundation of retail location analysis and location-allocation spatial interaction models. The presented research presents and compares four methods to estimate consumer spending that combine different levels of detail in household spending data across multiple levels of household aggregation (i.e. census units). Results demonstrate that these methods, which use publicly available census data, can capture a substantial amount of reported sales. Furthermore, comparison of the best method with the de facto industry standard for business data in Canada shows a strong correlation (R2 > 0.85) across space and is within 10% of aggregate industry-derived spending estimate. Analysis of the spending data across space finds that the majority of spending occurs in census metropolitan areas with high spending clusters that offer unique opportunities for retail location. We interrogate the socio-demographic characteristics of the population in these high spending clusters and find that only a few variables are required to explain a large proportion of the variance in consumer spending. The overarching outcome of the presented research is that small-to-medium businesses, planners, and municipalities do not need expensive proprietary data to estimate and predict consumer spending and market demand.  相似文献   

Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran     

Abdi OMID  Shirvani ZEINAB  F. Buchroithner MANFRED 《地理学报(英文版)》2018,28(12):1933-1952

Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps: (1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986-2000 and 2000-2015, (2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation (BiLISA), and (3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag (SL) model (wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error (SE) model (wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.  相似文献   

长江经济带城乡一体化对能源效率的空间效应分析     

汪小英  李小漫  沈镭  王宜龙 《地球信息科学学报》2020,22(11):2188-2198

城乡一体化作为新的经济增长动能,是建设高效的能源经济体系、平衡经济社会发展与生态环境保护的关键性途径。因此城乡一体化对能源效率的效应分析成为促进经济社会可持续发展的重要课题。本文以长江经济带各省市为研究对象,采用DEA测度了考虑非期望产出的能源效率值,并分析其时空上的分异特征,通过构建经济、社会和基础设施3个维度的综合指标体系评价城乡一体化水平,以此为基础运用Moran's I指数确定了能源效率、城乡一体化水平及其他影响因素的空间相关性,最后运用混合地理加权回归估计城乡一体化及3个控制变量的回归参数值并作空间效应分析。得出结论:① 从空间相关性分析来看,长江经济带的城乡一体化发展水平对能源效率具有正向作用,同时经济发展水平和产业结构也对能源效率有正向影响,而技术进步则在一定条件下与能源效率呈负相关关系;② 从回归系数的估计值来看,城乡一体化对能源效率的影响程度要小于技术进步和产业结构,而经济发展水平对能源效率的影响程度最小;③ 城乡一体化水平对能源效率的正向影响程度随着能源效率的上升呈现出先上升后下降的趋势,并且其影响在空间分布上自东向西呈现出递增的特征;④ 长江经济带的能源效率呈现显著的空间相关性和空间异质性。基于此,对长江经济带推进城乡一体化进程中的能源效率提升具有重要意义。  相似文献   

贵州局地大气加权平均温度模型的建立与精度分析     

李宏达  张显云  王晓红  邹广黔 《大地测量与地球动力学》2020,40(5):496-501

基于贵阳、威宁两个无线电探空站2014~2016年的气象数据,采用一元线性回归方法构建贵州整体、局地及季节大气加权平均温度Tm模型,并分析模型的精度。结果表明,贵州整体Tm模型精度高于Bevis模型、全国模型和亚热带季风气候模型;建立贵州局地、季节模型有助于进一步改善Tm的精度;相较于Bevis模型,局地Tm模型反演的PWV精度更高,与实际降水吻合更好。  相似文献   

宽城台定点形变气压干扰小波分析及干扰排除     

王嘉琦  孙澎涛  任俊峰  袁国旭 《大地测量与地球动力学》2020,40(8):860-864

通过小波分解对宽城台定点形变仪器受气压干扰的典型事件进行分析,得出气压干扰在不同频段对不同仪器的干扰特征,并通过小波阈值去噪对各测项受气压干扰的观测数据进行干扰排除,取得较好的效果。  相似文献   

Indicator Kriging without Order Relation Violations   总被引：2，自引：1，他引：1  

Raimon Tolosana-Delgado  Vera Pawlowsky-Glahn  Juan-Jose Egozcue 《Mathematical Geosciences》2008,40(3):327-347

Indicator kriging (IK) is a spatial interpolation technique aimed at estimating the conditional cumulative distribution function (ccdf) of a variable at an unsampled location. Obtained results form a discrete approximation to this ccdf, and its corresponding discrete probability density function (cpdf) should be a vector, where each component gives the probability of an occurrence of a class. Therefore, this vector must have positive components summing up to one, like in a composition in the simplex. This suggests a simplicial approach to IK, based on the algebraic-geometric structure of this sample space: simplicial IK actually works with log-odds. Interpolated log-odds can afterwards be easily re-expressed as the desired cpdf or ccdf. An alternative but equivalent approach may also be based on log-likelihoods. Both versions of the method avoid by construction all conventional IK standard drawbacks: estimates are always within the (0,1) interval and present no order-relation problems (either with kriging or co-kriging). Even the modeling of indicator structural functions is clarified.  相似文献   

Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping   总被引：2，自引：1，他引：1  

E.J.M. Carranza  M. Hale  C. Faassen 《Ore Geology Reviews》2008,33(3-4):536-558

Data-driven prospectivity mapping can be undermined by dissimilarity in multivariate spatial data signatures of deposit-type locations. Most cases of data-driven prospectivity mapping, however, make use of training sets of randomly selected deposit-type locations with the implicit assumption that they are coherent (i.e., with similar multivariate spatial data signatures). This study shows that the quality of data-driven prospectivity mapping can be improved by using a training set of coherent deposit-type locations. Analysis and selection of coherent deposit-type locations was performed via logistic regression, by using multiple sets of deposit occurrence favourability scores of univariate geoscience spatial data as independent variables and binary deposit occurrence scores as dependent variable. The set of coherent deposit-type locations and three sets of randomly selected deposit-type locations were each used in data-driven prospectivity mapping via application of evidential belief functions. The prospectivity map based on the training set of coherent deposit-type locations resulted in lower uncertainty, better goodness-of-fit to the training set, and better predictive capacity against a cross-validation set of economic deposits of the type sought. This study shows that explicit selection of training set of coherent deposit-type locations should be applied in data-driven prospectivity mapping.  相似文献