Ohio State University

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Cyberinfrastructure for Coastal Cyberinfrastructure for Coastal Forecasting and Change Forecasting and Change

AnalysisAnalysisGagan Agrawal

Hakan FerhatosmanogluXutong Niu

Ron Li Keith Bedford

Ohio State University

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Project TeamProject 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

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Project Premise and ChallengesProject 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

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Coastal Forecasting and Change Coastal Forecasting and Change Detection (Lake Erie)Detection (Lake Erie)

Ohio State University

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Proposed Infrastructure and Proposed Infrastructure and CollaborationCollaboration

Ohio State University

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Middleware Developed at Ohio Middleware Developed at Ohio State 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

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Application Details: Coastal Erosion Application Details: Coastal Erosion Prediction and Analysis 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 implementationo Design a WSDL to describe

available data

o Describe available tools and services

o Support discovery and composition of datasets and services for a given query

Ohio State University

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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

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Data Acquisition TimeAverage

Elevation of Shoreline

Standard Deviation of

ShorelineWater Level from Nearest Gauge Stations

IKONOS2004-07-08 16:17

GMT0.285 m 0.615 m

Port Manatee: -0.1870m (predicted)

St. Petersburg: -0.1546m

Port of Tampa: -0.1734m

QuickBird2003-09-12 15:58

GMT- 0.217 m 0.439 m Port Manatee: -0.017 m

Tampa Bay, FL

Legend

IK_Cal

IK_Cal

cock

<VALUE>

-5.125 - -3.375

-3.375- -0.7

-0.7 - -0.6

-0.6 - -0.5

-0.5 - -0.4

-0.4 - -0.3

-0.3 - -0.2

-0.2 - -0.1

-0.1 - 0

0 - 0.065

orthopo_001000.img

Value

High : 2040

Low : 0

orthopo_159082_pan_0000000.img

Value

High : 2040

Low : 0

utmgrid

Value

High : 31.9283

Low : -29.7271

IKONOSShoreline

QuickBirdShoreline

Integration of LiDAR Bathymetry, Water Gauge Data and 3-D Integration of LiDAR Bathymetry, Water Gauge Data and 3-D ShorelinesShorelines

Ohio State University

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Application Details: Great Lakes Application Details: Great Lakes Now/ForeCasting 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

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Great Lakes Forecasting SystemGreat 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

Ohio State University

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