Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford

Ohio State University 2 Project Team Involves 2 Computer Scientists and 2 Environmental Scientists –G. Agrawal (PI) – Grid Middleware –H. Ferhatosmanoglu – Databases –K. Bedford: Great Lakes Now/Forecasting –R. Li: Coastal Erosion Analysis Collaborations: –NOAA –Ohio Department of Natural Resources (ODNR)

Ohio State University 3 Project Premise and Challenges Limitation of Current Environmental Observation Systems –Tightly coupled systems »No reuse of algorithms »Very hard to experiment with new algorithms –Closely tied to existing resources Our claim –Emerging trends towards web-services and grid-services can help Challenges –Existing Grid Middleware Systems have not considered streaming data or data integration issues –Enabling algorithms (data mining, query planning, data fusion) need to be implemented as grid/web-services

Ohio State University 4 Coastal Forecasting and Change Detection (Lake Erie)

Ohio State University 5 Proposed Infrastructure and Collaboration

Ohio State University 6 Middleware Developed at Ohio State Middleware Developed at Ohio State Automatic Data Virtualization Framework –Enabling processing and integration of data in low- level formats GATES (Grid-based AdapTive Execution on Streams) –Processing of distributed data streams FREERIDE-G (FRamework for Rapid Implementation of Datamining Engines in Grid) –Supporting scalable data analysis on remote data

Ohio State University 7 Application Details: Coastal Erosion Prediction and Analysis Focus: Erosion along Lake Erie Shore –Serious problem –Substantial Economic Losses Prediction requires data from –Variety of Satellites –In-situ sensors –Historical Records Challenges –Analyzing distributed data –Data Integration/Fusion  Long Term Goal : Create Service-oriented implementation oDesign a WSDL to describe available data oDescribe available tools and services oSupport discovery and composition of datasets and services for a given query

Ohio State University 8 Iterative Closest Points (ICP) Algorithm for Bluffline Refinement Bluffline extraction (Liu et al. 2005) LiDAR DSM LiDAR Profile Initial Bluffline from LiDAR (bluff top and toe) Orthophotos Bluffline Extraction

Ohio State University 9 DataAcquisition Time Average Elevation of Shoreline Standard Deviation of Shoreline Water Level from Nearest Gauge Stations IKONOS :17 GMT m0.615 m Port Manatee: m (predicted) St. Petersburg: m Port of Tampa: m QuickBird :58 GMT m0.439 mPort Manatee: m Tampa Bay, FL IKONOS Shoreline QuickBird Shoreline Integration of LiDAR Bathymetry, Water Gauge Data and 3-D Shorelines

Ohio State University 10 Application Details: Great Lakes Now/ForeCasting GLOS: Great Lakes Observing System –Co-designer/project manager: K. Bedford, a co- PI on this project –Collaboration with NOAA Limitations: Hard-wired –Cannot incorporate new streams or algorithms Create a Demand-driven Implementation using GATES Event of Interest –A boat accident, oil leakage Need to run a new model –Time Constraints –Find grid resources on the fly Need to decide: –Spatial and Temporal Granularity –Parameters to Model

Ohio State University 11 Great Lakes Forecasting System Regularly Scheduled Nowcasts /Forecasts of the Great Lakes’ physical conditions Joint venture of OSU Civil Engineering Dept. and NOAA/GLERL Meteorological data and consultation provided by the National Weather Service, Cleveland Office Great Lakes Forecasting System Low water due to negative storm surge on eastern end of Lake Erie - Oct. 25, 2001

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