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

EarthCube 数据功能：Argovis 2.0：用于同步定位海洋和大气数据以加速气候科学工作流程的下一代平台

• 批准号: 1928305
• 负责人: Donata Giglio
• 金额: $ 48.75万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928305&HistoricalAwards=false
• 关键词: EarthCube; Data; Capabilities; Argovis; 2.0

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.

该项目将开发Argovis 2.0，这是一个网络应用程序，用于绑定位于同一地点的大气和海洋数据集，使科学家和非科学家都能方便地获取这些数据集。该应用程序将专注于与Argo浮标的观测进行共定位，这些浮标测量全球海洋特性，深度可达地表以下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，而不需要安装除Web浏览器之外的任何东西，或者直接将数据导入到他们选择的编程环境中。Argovis 2.0旨在成为科学研究和教育工具。研究人员设想将其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Argovis 2.0，这是一个网络应用程序，用于将大气和海洋数据与Argo剖面数据进行搭配。该应用程序将能够在纬度，经度，深度和时间上配置两个或多个观测数据集。用户将能够使用Web浏览器查看和下载数据，或者直接将数据导入到他们选择的编程环境中，例如Matlab专有编程语言或Python或R等开源。Argovis 2.0建立在Argovis 1.0的基础上，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化和非网格化数据，例如基于点的数据集和精细网格化数据集。数据库以JSON格式输出，并由一个js.js后端框架访问。后端发送请求，并通过向用户发送HTML或JSON数据进行响应。最后，Angular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映射和图表库Leaflet和Plotly构建的。该项目将服务于在研究和教学中使用大气和海洋科学的广大科学家和教授以及公众。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

用于北极海冰运动每日预测的机器学习：模型预测技能的归因评估

• DOI: 10.1175/aies-d-23-0004.1
• 发表时间: 2023
• 期刊: Artificial Intelligence for the Earth Systems
• 作者: Hoffman, Lauren;Mazloff, Matthew R.;Gille, Sarah T.;Giglio, Donata;Bitz, Cecilia M.;Heimbach, Patrick;Matsuyoshi, Kayli
• 通讯作者: Matsuyoshi, Kayli

Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix

使用现有的 Argo 轨迹通过转移矩阵统计预测未来的浮标位置

• DOI: 10.1175/jtech-d-22-0070.1
• 发表时间: 2023
• 期刊: Journal of Atmospheric and Oceanic Technology
• 影响因子: 2.2
• 作者: Chamberlain, Paul;Talley, Lynne D.;Mazloff, Matthew;van Sebille, Erik;Gille, Sarah T.;Tucker, Tyler;Scanderbeg, Megan;Robbins, Pelle
• 通讯作者: Robbins, Pelle

Ocean Surface Salinity Response to Atmospheric River Precipitation in the California Current System

加州洋流系统中海洋表面盐度对大气河流降水的响应

• DOI: 10.1175/jpo-d-21-0272.1
• 发表时间: 2022
• 期刊: Journal of Physical Oceanography
• 影响因子: 3.5
• 作者: Hoffman, Lauren;Mazloff, Matthew R.;Gille, Sarah T.;Giglio, Donata;Varadarajan, Aniruddh
• 通讯作者: Varadarajan, Aniruddh

Argovis: A Web Application for Fast Delivery, Visualization, and Analysis of Argo Data

Argovis：用于快速交付、可视化和分析 Argo 数据的 Web 应用程序

• DOI: 10.1175/jtech-d-19-0041.1
• 发表时间: 2019
• 期刊: Journal of Atmospheric and Oceanic Technology
• 影响因子: 2.2
• 作者: Tucker, Tyler;Giglio, Donata;Scanderbeg, Megan;Shen, Samuel S.
• 通讯作者: Shen, Samuel S.

Argo Reveals the Scales and Provenance of Equatorial Island Upwelling Systems

• DOI: 10.1029/2022gl098744
• 发表时间: 2022-08
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: K. Karnauskas;D. Giglio