a data system for visualizing 4-d atmospheric co 2 models and data tyler a. erickson, ph.d. research...
TRANSCRIPT
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A Data System for Visualizing 4-D Atmospheric CO
2 Models and Data
Tyler A. Erickson, Ph.D.Research Scientist
Adjunct Assistant Professor of Civil & Environmental EngineeringMichigan Technological University, Michigan, USA
22 October 2009
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Collaborators
Prof. Anna M. MichalakUniversity of MichiganAnn Arbor, Michigan, USA– Carbon Cycle Science Researcher
Prof. John C. LinUniversity of WaterlooWaterloo, Ontario, Canada– STILT Atmospheric Transport Model Creator
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Time is Important
Geospatial representation of present conditions is fine,but predicting future conditions is really useful and interesting...
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Temporal Change is Everywhere
Land cover change
Climate Change
Disease Spread
Environmental Change
Economic Change Photo credit: John McColgan of the Bureau of Land Management, Alaska Fire Service
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Spatial-Temporal Data
Location: North Slope of Alaska, USASource: The National Academies
Location: Central CanadaSource: NASA
radio collar(a.k.a. caribou
bling)
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Problem #1: Too Few Data!
Collecting data with in-situ sensors is expensive
Even with dense meshesof sensors, processes are severely undersampled
Soddie Meteorological TowerLocation: Niwot Ridge LTER, Colorado, USA
Source: Tyler Erickson
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Solution #1: Model it!
“Give me a modeland data with which run it,and I shall estimate allthe properties of the worldin both space and time.”
- Archimedes of Syracuse(severely paraphrased)
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Spatial-Temporal Modeling
DATA(x,t) = MODEL(x,t) + ERROR(x,t)
• Models should rigorously represent reality, if possible
• Errors near each other are often similar(i.e. geostatistics)
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Problem #2: Too Much Data!
Data volumes are overwhelming!
How do you go about exploringthe data in space and time?
Soddie Meterological TowerLocation: Niwot Ridge LTER, Colorado, USA
Source: Tyler Erickson
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Solution #2: FOSS4G !!!
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CO2 Monitoring
Source: http://commons.wikimedia.org/wiki/File:Mauna_Loa_Carbon_Dioxide.png
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Carbon Balance
Source: NASA
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Atmospheric Carbon Monitoring
Adapted from work by: K. Mueller, University of Michigan
NOAATall Tower
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By the Numbers...
A Typical Particle Simulation:
500*24 per day, simulated particles
10*24 hours of simulation per particle
6 positions per hour per particle
30 days, total dataset length
TOTAL: ~500 million records per measurement tower
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How does this data get reported?
Source: Lin, J.C. et al., 2003. JGR (Atmospheres) Figure 7
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Another Approach
IDEA: Provide model results in user-friendly, standard data formats– OGC KML 2.2 standard
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What's under the Hood?
ClientApplication
GeospatialServer
DataStorage
SciPy/NumPylibkml
pylibkml
KML
Virtual Globe
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GeoDjango
• A geospatial extension of a web framework designedfor publishing on-line newspapers (Django)created by a law student (Justin Bronn)
• Python-based
• Leverages GEOS, GDAL, Proj.4, and PostGIS
• Templates allow for output in various geospatial formats
“A world-class geographic web framework”
+
Image Sources: Wikipedia and Wikimedia Commons
=
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pylibkml
from pylibkml import Kmlplacemark = Kml().create_placemark({ 'name' : 'FOSS4G 2009', 'description' : 'In Sydney', 'timestamp' : {'when': '8/22/2009'}, 'point' : Kml().create_point({ 'extrude' : True, 'altitudemode' : 'relativetoground', 'coordinates' : Kml().create_coordinates(
151.1998,-33.8761), }) })
pylibkml is a Python wrapper for the libkml C++ library
Allows for easy programmatic creation of valid KML documents
http://code.google.com/p/pylibkml/
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Why Google Earth?
Free (for some uses)
Open Source
Easy to Use Interface
Rich Reference Imagery
Wide User Base
Runs on Linux
Full KML Implementation
Talks to External ServersSource: http://www.flickr.com/photos/gillpoole/
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Google's KML in Research Competition
Earlier this year Google hosted a contest onusing KML to communicate scientific research
Judging criteria:– Usability– Educational value– Visual/interactive appeal– Efficiency– Attribution
KML output from this data system was selected as one of 5 professional winners
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Tall Tower Measurements
Adapted from work by: K. Mueller, University of Michigan
NOAATall Tower
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Sensitivity to Surface Flux
Adapted from work by: K. Mueller, University of Michigan
NOAATall Tower
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Simulated Particle Tracks
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What Else Goes Under The Hood?
SciPy/NumPylibkml
KML
CONSUME(Fuel Consumption &
Emissions Model)
ESRIShapefile
GeoTiff ASCII Grid
netCDF
WKT Raster
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Modeling Wildfire Emissions
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WKT Raster
Adds raster-vector spatial analysis to PostGIS
http://trac.osgeo.org/postgis/wiki/WKTRaster
2 2 2 2 2 2 2 2 2 2 2 2 2 22 2 2 2 2 2 2 2 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 21 1 1 1 1 1 1 1 2 2 2 2 2 22 2 2 2 2 2 2 2 2 2 2 2 2 2
a b
1 1 0 01 1 1 01 1 1 11 1 1 11 1 1 11 1 1 11 1 1 11 1 1 01 1 0 0
2 2 2 2 0 02 2 2 2 2 02 2 2 2 2 22 2 2 2 2 22 2 2 2 2 22 2 2 2 2 22 2 2 2 2 22 2 2 2 2 02 2 2 2 0 0
0 0 10 1 11 1 11 1 11 1 11 1 11 1 10 1 10 0 1
and=
Example:Intersection(geometry,raster) → raster
Source: WKTSpecifications1.0.ppt (Pierre Racine)
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Next Steps
Atmospheric Carbon Application: move from prototypeto a web-accessible tool for researchers
Allow users to upload data from atmospheric transport model runs
Improve KML styling
Create visualizations of additional high-dimensional datasets
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Questions?
Tyler A. Erickson, Ph.D.
Email: [email protected]: http://people.mtri.org/tyler+ericksonTwitter: tylericksonCode: http://bitbucket.org/tylere/geodjango-stilt/