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)：哈勒姆，米尔顿；布朗，谢尔顿；孔蒂，托马斯·M；Yesha，Yelena机构：马里兰大学巴尔的摩县分校标题：快速：协作研究：一个互动的“人类传感器情况网络”，用于改进深海地平线墨西哥湾漏油扩散的模型预测项目建议：该项目建议开展与墨西哥湾漏油的Gnome模型预测相关的研究活动(在CHMPR I/UCRC内，将位于UMBC、UCSD和GaTech)，旨在开发一个工具，该工具购买服务器来收集、提取、定位和处理来自现有社交媒体来源，如Flickr、You Tube、Twitter的墨西哥湾漏油数据，并将它们集成到云中。这些数据是从移动设备和卫星传感器收集的。因此，该项目提供了整个海湾边界国家海岸线上浮油、焦油球、遇难动物和死亡动物的瞬时空间分布和时间频率。该仪器用于以Gnome溢油预报模式为初始猜测，以社交媒体数据为边界条件进行二维VAR数据同化。该项目将在加州大学圣迭戈分校的超大LCD瓷砖墙上展示预测的浮油分散产品，并向数千名观众播放。系统预计在15天内交付。对美国国家海洋和大气局业务侏儒模型对墨西哥湾的预测也有望得到改进。此外，“人类传感器网”数据可能导致更准确的业务油类扩散预测，通过二维VAR数据同化传播给决策者；更广泛的影响：这项工作的原型是未来应成为对未来自然或人为事件情况反应的一部分。“人类传感器网”数据可能导致更准确的业务油类扩散预报，通过二维VAR数据同化传播给决策者；因此，社会性方面是非常明显的。该项目涉及学生，特别是少数民族(在密歇根州立大学)。佐治亚理工学院也计划开展类似的外展活动。此外，加州大学圣迭戈分校将在一所初中和高中组织一个实习项目。

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.