RAPID: An Interactive "Human Sensor Web" for Improved Model Predictions of the Dispersion of the Deepwater Horizon Gulf Oil Spill

提案#:10-61621PI(s): Halem, Milton；布朗,谢尔登;托马斯·孔蒂；Yesha， yelen所在机构：马里兰大学巴尔的摩县分校职称：RAPID：合作研究；用于改进深水地平线海湾漏油扩散模型预测的交互式“人类传感器情况网络”项目建议：该项目提出了与Gnome模型预测海湾漏油相关的研究活动（在CHMPR I/UCRC内，位于UMBC， UCSD和GaTech），旨在开发一种工具，该工具可以获取服务器来收集，提取，定位和处理来自现有社交媒体来源的海湾漏油数据，如Flickr， youtube，并将它们整合到云端。这些数据是通过移动设备和卫星传感器收集的。因此，该项目提供了沿海湾边境各州完整海岸线的浮油、焦油球、痛苦和死亡动物的即时空间分布和时间频率。该仪器使用Gnome溢油预测模型作为第一猜测，并将社交媒体数据作为边界条件，执行二维VAR数据同化。该项目将在加州大学圣地亚哥分校的超大液晶瓷砖墙上展示预测的浮油分散产品，并向数千名观众播出。系统预计在15天内交付。在NOAA操作Gnome模型预测墨西哥湾的基础上，也有望有所改进。此外，“人体传感器网”数据可能会导致更准确的操作油分散预测，通过二维VAR数据同化传播给决策者；更广泛的影响：工作原型在未来应该成为对未来事件情况的响应的一部分，无论是自然的还是人为的。通过二维VAR数据同化，“人体传感器网”数据可能导致更准确的操作油分散预测，并传播给决策者；因此，社会方面是非常明显的。这个项目涉及学生，特别是少数民族（在UMBC）。佐治亚理工学院也计划开展类似的拓展活动。此外，加州大学圣地亚哥分校将在一所初中和高中组织实习项目。

Proposal #: 10-61621PI(s): Halem, Milton; Brown, Sheldon; Conte, Thomas M; Yesha, YelenaInstitution: University of Maryland Baltimore CountyTitle: RAPID: Collaborative Research: An Interactive "Human Sensor Situation Network" for Improved Model Predictions of the Dispersion of the Deepwater Horizon Gulf Oil SpillProject Proposed:This project, proposing research activities related to Gnome model predictions of the Gulf oil spill (within the CHMPR I/UCRC, to be located at UMBC, UCSD, and GaTech), aims to develop an instrument that acquires servers to collect, extract, locate, and process Gulf oil spill data from the existing social media sources such as Flickr, You Tube, Twitter and integrates them into a cloud. The data is collected from mobile devices and satellite sensors. As a result, the project provides instantaneous spatial distributions and temporal frequencies of oil slicks, tar balls, distressed and dead animals, along the complete coastline of Gulf Border States. The instrument is used to perform a 2-D VAR data assimilation using the Gnome oil spill forecast model as a first guess and the social media data as boundary conditions. The project will present the forecast oil slick dispersion products on the very large LCD tiled wall at UCSD for broadcast to thousands of viewers. System delivery is expected within 15 days. Improvement upon the NOAA operational Gnome model predictions of the Gulf is also expected. Moreover, the "human sensor web" data is likely to lead to more accurate operational oil dispersion forecasts for dissemination to decision makers through the 2-D VAR data assimilation;Broader Impacts: The work prototypes what in the future should become part of the responses to future event situations, either natural or anthropogenic. The "human sensor web" data is likely to lead to more accurate operational oil dispersion forecasts for dissemination to decision makers through the 2-D VAR data assimilation; hence the social aspects are strongly evident. This project involves students, especially minorities (at UMBC). Similar outreach activities are planned at Georgia Tech. Furthermore, UCSD will organize an internship program at a middle and high school.