基于ARMA模型的基坑沉降监测预测研究

应用ARMA模型对地铁车站基坑监测数据进行预测分析。在参考利用已有的ARMA模型算法进行预报分析的基础上,联合最小二乘原理对模型算法进行改进,结合基坑工程算例,验证了新模型算法的可行性、有效性及稳定性。说明改进之后的新模型算法较老模型算法提高了预测精度且提升了运算效率。     

基于时间序列模型的沉降监测预报分析

时间序列模型预测具有可靠性与高效性的特点。本文结合沉降监测工程,采用Matlab进行建模预报分析,分别基于预测模型(AR、MA、ARMA)进行应用。对比自回归模型、滑动平均模型及自回归滑动平均模型预测结果的精度,表明3种模型可预测期连续分布,模型组合可提高预测精度。     

Functional analysis of landscape connectivity at the landscape,component, and patch levels: A case study of Minqing County,Fuzhou City,China

Maintaining forest landscape connectivity is one of the most effective ways to alleviate natural forest fragmentation and biodiversity loss problems. Recently, graph theory based metrics have been used as powerful tools in the assessment of landscape connectivity. However, the functional features of landscape units at different structure levels, based on which outcomes can be enriched and advice can be given for practical applications, have been overlooked. In this study, a series of graph-based connectivity indices was calculated to 1) evaluate the optimal threshold distance, 2) identify the key landscape units at the component and patch levels, and 3) classify the functional types of components and patches and analyze functional patterns at different structural levels. The relationship between patch size and patch functional performance in maintaining landscape connectivity was discussed. With a natural forest area in Minqing County of China as the study area, recommendations regarding forest conservation and connectivity enhancement were provided based on the research conclusions. This study provides a way to comprehensively analyze habitat fragmentation and functional patterns for local forest conservation.     

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System:Dual-Resolution Implementation and Testing Results

A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.     

SWAN2.0系统的设计与实现

强对流天气短时临近预报系统（Severe Weather Automatic Nowcasting,SWAN）是面向短时临近监测、分析、预报、预警制作等功能为一体的业务平台。SWAN2.0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）二次开发框架,采用C/S架构,服务器部署在省级,负责收集数据,运算SWAN产品;客户端部署在气象台站,实现具体的预报业务,并形成算法二次开发接口。SWAN2.0新增了三维变分风场反演、基于分雨团技术的雷达降水估测、冰雹识别等方法,实现了算法管理、产品生成、分析处理、资料检索显示、实时监控报警、预警产品制作等功能。SWAN2.0业务系统已在全国试用,在强对流天气监测、分析和短时临近预报预警中发挥了重要作用。     

基于BPSO-NBayes的雷暴释用预报技术研究

提出了一种新的雷暴预报法,即二进制粒子群-朴素贝叶斯分类器(Binary Particle Swarm Optimization-Naive Bayesian Classifiers,BPSO-NBayes)方法,以福州、连城、宁波3站为例,对使用T511数值预报产品站点的雷暴释用预报技术进行研究。利用2010—2014年T511数值预报产品和单站观测资料,使用BPSO-NBayes方法,建立了0~72 h雷暴预报模型,并与Fisher判别准则和Bayes判别准则进行比较。预报结果表明,BPSO-NBayes模型临界成功指数都在0.29以上,平均值达到0.33以上,是3种方法中最好的,空报率都在0.59以下,漏报率在0.60以下,而且变化幅度很小。BPSO-NBayes模型明显优于Fisher判别准则和Bayes判别准则,具有良好的稳定性和预报能力。     

基于智慧气象的数据接口的实现

物联网、云计算、移动互联、大数据等新一代智能技术的迅猛发展,为智慧气象产品提供了技术支撑。由于气象信息系统数据类型多种多样,且来自多个部门、跨越多个系统平台,数据结构和格式也不相同,因此数据读取与处理是一项异常复杂的工程。为了解决该问题,开发了基于智慧气象的气象服务数据接口。基于智慧气象的数据接口,具有跨平台性、良好的响应速度、通用的数据交换格式,可使各种应用之间相互通信,并能创建新的应用。基于智慧气象的气象服务数据接口为RESTful风格,返回格式为JSON。数据接口的开发采用JAVA语言,基于Spring MVC+Spring Security Oauth2开发框架实现。数据存储采用Oracle数据库,分为冷、热两种数据。客户端对数据接口的访问,采用HTTP GET的方式发出数据请求,访问服务资源地址,获得JSON格式的数据返回结果。为加强数据接口访问的安全性,采用HTTPS POST方式访问授权接口获取访问令牌。基于智慧气象的数据接口,已为微信、微博、手机APP、网站等多个系统及保险公司、市政等多个单位提供数据支撑,且气象服务数据接口系统安全性高,具有较好的用户体验和兼容性。     

青黄公路交通调查及交通量预测

通过对1998年2月青黄公路的交通调查,分析了青黄公路的交通现状,根据青岛市的国民经济增长速度、交通运输发展及城市出入口资料,利用弹性系数法预测了2000年和2010年青黄公路的城市交通量。     

中期数值天气预报业务的回顾与展望

本文回顾了近年来国内外中期数值天气预报业务的新进展,简略地介绍了当今国外（以欧洲中期天气预报中心为主）和我国中期数值天气预报业务系统。最后对未来中期数值天气预报的发展作一展望,指出其未来发展的主要方向。     

岩湾矿段风化磷矿形成条件及其三种类型

湖北省鹤峰磷矿走马矿区岩湾矿段风化磷矿是湖北境内首次探明的一中型风化磷矿。矿床成因类型有风化残余型、风化淋滤型、洪积型砂磷矿等三类。在时间上 ,风化淋积型矿体位于风化残余型矿体之下 ,是风化残余型的继承发展。在雨水和洪水冲刷下 ,风化残余磷矿在适当的低洼地带堆积成洪积型砂磷矿。对这种风化矿的勘探应因地制宜 ,灵活运用。对岩湾矿段风化磷矿的成因探讨和勘探研究为东山峰寻找和勘探类似风化磷矿积累了经验。