一个在GIS环境下管理开放存取数据的后处理程序

L. Benvenuto, Roberto Marzocchi, I. Ferrando, B. Federici, D. Sguerso
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引用次数: 1

摘要

数据库(DB)是一种广泛的数据来源,对不同科学领域的许多应用程序都很有用。目前的贡献描述了一个自动程序来访问,下载和存储来自不同来源的开放访问数据,在GIS环境中进行处理。特别是,它指的是作者管理气象数据(压力和温度)和GNSS(全球导航卫星系统)天顶总延迟(ZTD)估计的特定需求。借助GRASS GIS软件开发的所谓气象全球导航卫星系统(G4M)程序,这些数据可以生成可降水量(PWV)地图。7.4、及时监测和解释灾害性气象事件。实际上,当前版本的程序包括来自NOAA综合地面数据库(ISD)的气象压力和温度数据,而ZTD数据来自RENAG DB,该数据库收集了1998年至2015年法国-意大利边境地区181个GNSS永久站(ps)的ZTD估计。除了使用GRASS GIS对数据进行细化以生成PWV地图之外,还实现了几个Python脚本来管理从NOAA和RENAG数据库中下载数据、在PostgreSQL/PostGIS geoDB中导入数据。数据管理过程的关键特征是它的可伸缩性和多功能性,适用于不同的数据源和不同的上下文。作为未来的发展,该程序的网络界面将允许用户更容易地进行后处理和实时数据的交互。数据管理过程存储库可从https://github.com/gtergeomatica/G4M-data获得
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A procedure to manage open access data for post-processing in GIS environment
DataBases (DB) are a widespread source of data, useful for many applications in different scientific fields. The present contribution describes an automatic procedure to access, download and store open access data from different sources, to be processed in a GIS environment. In particular, it refers to the specific need of the authors to manage both meteorological data (pressure and temperature) and GNSS (Global Navigation Satellite System) Zenith Total Delay (ZTD) estimates. Such data allow to produce Precipitable Water Vapor (PWV) maps, thanks to the so called GNSS for Meteorology(G4M) procedure, developed through GRASS GIS software ver. 7.4, for monitoring in time and interpreting severe meteorological events. Actually, the present version of the procedure includes the meteorological pressure and temperature data coming from NOAA’s Integrated Surface Database (ISD), whereas the ZTD data derive from the RENAG DB, that collects ZTD estimates for 181 GNSS Permanent Stations (PSs) from 1998 to 2015 in the French-Italian boundary region. Several Python scripts have been implemented to manage the download of data from NOAA and RENAG DBs, their import on a PostgreSQL/PostGIS geoDB, besides the data elaboration with GRASS GIS to produce PWV maps. The key features of the data management procedure are its scalability and versatility for different sources of data and different contexts. As a future development, a web-interface for the procedure will allow an easier interaction for the users both for post-processing and real-time data. The data management procedure repository is available at https://github.com/gtergeomatica/G4M-data
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