Collaborative Research: ELEMENTS: The LROSE Science Gateway LIDAR/RADAR Analysis In The Cloud

合作研究：ELEMENTS：云端 LROSE Science Gateway LIDAR/RADAR 分析

• 批准号: 2103776
• 负责人: Jennifer DeHart
• 金额: $ 29.8万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103776&HistoricalAwards=false
• 关键词: Collaborative; Research; ELEMENTS; LROSE; Science

Remote sensing observations from weather radars and lidars consist of a tremendous amount of raw data. Analysis of this data is often time-consuming, slowing the translation of data to scientific insights. The research team, under prior NSF funding, developed the Lidar Radar Open Software Environment (LROSE) which consists of freely available tools to help analyze and visualize data. This award will take the next step by making LROSE available online, eliminating the need for a complicated installation on local computers and taking advantage of data sets on the cloud. The impact of this project will be on all areas of science that make use of lidar and radar data, such as for severe weather and air quality. The online approach will also make it possible for educators to set up exercises and teaching aids for students in a classroom, along with the relevant data and documentation for learning activities.This project builds upon the Lidar Radar Open Software Environment, which is a software toolbox for analyzing radar and lidar data, by developing a fully functional science gateway for LROSE. The research team will integrate LROSE into a web portal environment, thereby allowing ready accessibility to computational resources via web browser interfaces and allowing users to move data directly within the cloud. The intent of this CSSI Elements award is to facilitate access to analysis and data processing tools, reducing barriers to effective science and expanding the user base. The project has two major development components: 1) the underlying infrastructure, including the gateway, the data handling layer and data visualization capabilities, and 2) the scientific software modules that allow users to perform experiments using the data.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.

气象雷达和激光雷达的遥感观测包含大量原始数据。 分析这些数据通常非常耗时，从而减慢了数据向科学见解的转化。 该研究团队在 NSF 先前的资助下开发了激光雷达开放软件环境 (LROSE)，其中包含可免费使用的工具，可帮助分析和可视化数据。 该奖项将采取下一步措施，使 LROSE 可以在线使用，从而无需在本地计算机上进行复杂的安装并利用云上的数据集。 该项目的影响将涉及利用激光雷达和雷达数据的所有科学领域，例如恶劣天气和空气质量。 在线方法还将使教育工作者能够在课堂上为学生设置练习和教具，以及学习活动的相关数据和文档。该项目建立在激光雷达开放软件环境的基础上，该环境是一个用于分析雷达和激光雷达数据的软件工具箱，通过为 LROSE 开发功能齐全的科学网关。 研究团队将把 LROSE 集成到网络门户环境中，从而允许通过网络浏览器界面随时访问计算资源，并允许用户直接在云中移动数据。 CSSI Elements 奖项的目的是促进分析和数据处理工具的使用，减少有效科学的障碍并扩大用户群。 该项目有两个主要的开发组件：1）底层基础设施，包括网关、数据处理层和数据可视化功能，2）允许用户使用数据进行实验的科学软件模块。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Enabling efficient execution of a variational data assimilation application

实现变分数据同化应用的高效执行

• DOI: 10.1177/10943420221119801
• 发表时间: 2022
• 期刊: The International Journal of High Performance Computing Applications
• 作者: Dennis, John M.;Baker, Allison H.;Dobbins, Brian;Bell, Michael M.;Sun, Jian;Kim, Youngsung;Cha, Ting-Yu
• 通讯作者: Cha, Ting-Yu