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Enhancing Coastal Forecasting and Change Detection in Lake Erie through Cyberinfrastructure

This project focuses on improving coastal erosion analysis and forecasting for Lake Erie. By addressing existing limitations in environmental observation systems and leveraging emerging web-services and grid-services, we aim to integrate and analyze various data sources, including satellite and in-situ sensor data. Our proposed infrastructure, featuring tools like GATES and FREERIDE-G, will enable real-time data processing and support scalable data analysis. Collaboration with OSU, NOAA, and GLOS seeks to overcome challenges in data integration and provide a demand-driven implementation for timely coastal erosion predictions.

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Enhancing Coastal Forecasting and Change Detection in Lake Erie through Cyberinfrastructure

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

  2. Coastal Forecasting and Change Detection (Lake Erie) Lake Erie Coastal Erosion Analysis: OSU Geodetic Science and ODNR GLOS: Collaboration between OSU Civil and NOAA

  3. Proposed Infrastructure and Collaboration

  4. 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

  5. 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

  6. 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 Design a WSDL to describe available data Describe available tools and services Support discovery and composition of datasets and services for a given query Application Details: Coastal Erosion Prediction and Analysis

  7. 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 Application Details: Great Lakes Now/ForeCasting

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