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

University Of Maryland Baltimore County, Baltimore MD

Investigators

Abstract

Proposal #: 10-61621 PI(s): Halem, Milton; Brown, Sheldon; Conte, Thomas M; Yesha, Yelena Institution: University of Maryland Baltimore County Title: RAPID: Collaborative Research: An Interactive "Human Sensor Situation Network" for Improved Model Predictions of the Dispersion of the Deepwater Horizon Gulf Oil Spill Project 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.