EarthCube Data Capabilities: Argovis 2.0: A Next Generation Platform for co-located Oceanic and Atmospheric Data to Accelerate Climate Science Workflows

该项目将开发Argogis 2.0，这是一个网络应用程序，用于绑定共同定位的大气和海洋数据集，以便科学家和非科学家轻松访问它们。该应用程序将重点放在与Argo Floats的观测相结合的位置上，Argo Floats测量全球海洋属性，深达6000米。该应用程序将能够帮助可视化的数据示例包括：飓风轨迹、风和降水；这些数据将与来自Argo剖面的海洋温度和盐度观测相结合。用户将能够查看和下载数据，而不需要安装网络浏览器以外的任何东西，也不需要直接将数据导入他们选择的编程环境。Argois 2.0的目的是既是一个科学研究工具，也是一个教育工具。研究人员设想它将被用于海洋和大气科学的展览和课堂学习活动。该项目将开发Argois 2.0，这是一个将大气和海洋数据与Argo剖面数据搭配在一起的网络应用程序。该应用程序将能够在纬度、经度、深度和时间上配置两个或更多观测数据集。用户将能够使用网络浏览器或通过将数据直接导入到他们选择的编程环境中来查看和下载数据，所选择的编程环境例如是MatLab专有编程语言或开放源码，例如Python或R.Argois 2.0构建在Argois 1.0之上，使用由前端、后端和数据库组成的标准架构。MongoDB数据库用于存储网格和非网格数据，例如基于点和精细网格的数据集。数据库以JSON格式输出，并由Express.js后端框架访问。后端通过向用户发送HTML或JSON数据来发送请求和响应。最后，ANGLE是前端的Web应用程序框架。用户生成的图表和地图是使用平铺地图和制图库LEAFLE和PLOTLY构建的。该项目将服务于广大在研究和教学中使用大气和海洋科学的科学家和教授，以及普通公众。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop Argovis 2.0, a web application for binding co-located atmospheric and oceanographic data sets so that they are easily accessible by scientists and non-scientists. The application will focus on colocation with observations from Argo floats, which measure ocean properties globally as deep as 6000 meters below the surface. Examples of data that the application will be able to help visualize include: hurricane trajectories, winds, and precipitation; these data will be combined with ocean temperature and salinity observations from Argo profiles. Users will be able to view and download data without the need to install anything other than a web browser or to directly import data into their programming environment of choice. Argovis 2.0 is intended to be both a scientific research and educational tool. The investigators envision it being used for exhibits and for classroom learning activities in oceanic and atmospheric sciences.This project will develop Argovis 2.0, a web application for collocating atmospheric and oceanic data with Argo profile data. The application will be able to collocate two or more observational datasets in latitude, longitude, depth, and time. Users will be able to view and download data using a web browser or by directly importing data into their programming environment of choice such as Matlab proprietary programming language or open source such as Python or R. Argovis 2.0 builds on Argovis 1.0, using a standard architecture comprising of a front-end, back-end, and database. The MongoDB database is used to store gridded and non-gridded data such as the point based and finely gridded datasets. The database outputs in JSON format and is accessed by an Express.js back end framework. The backend sends requests and responds by sending either HTML or JSON data to the user. Finally, Angular is the web application framework for the front end. User generated charts and maps are built using tiled mapping and charting libraries Leaflet and Plotly. The project will serve a broad community of scientists and professors who use atmospheric and oceanic science in their research and teaching, as well as the general public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.