Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan...
-
Upload
peter-wheeler -
Category
Documents
-
view
216 -
download
1
Transcript of Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan...
![Page 1: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/1.jpg)
Ohio State University
1
Cyberinfrastructure for Coastal Cyberinfrastructure for Coastal Forecasting and Change Forecasting and Change
AnalysisAnalysisGagan Agrawal
Hakan FerhatosmanogluXutong Niu
Ron Li Keith Bedford
![Page 2: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/2.jpg)
Ohio State University
2
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)
![Page 3: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/3.jpg)
Ohio State University
3
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
![Page 4: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/4.jpg)
Ohio State University
4
Coastal Forecasting and Change Coastal Forecasting and Change Detection (Lake Erie)Detection (Lake Erie)
![Page 5: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/5.jpg)
Ohio State University
5
Proposed Infrastructure and Proposed Infrastructure and CollaborationCollaboration
![Page 6: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/6.jpg)
Ohio State University
6
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
![Page 7: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/7.jpg)
Ohio State University
7
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
![Page 8: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/8.jpg)
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
![Page 9: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/9.jpg)
Ohio State University
9
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
![Page 10: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/10.jpg)
Ohio State University
10
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
![Page 11: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/11.jpg)
Ohio State University
11
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
![Page 12: Ohio State University 1 Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford.](https://reader030.fdocuments.net/reader030/viewer/2022032612/56649f045503460f94c18bb7/html5/thumbnails/12.jpg)
Ohio State University
12