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

Carbon Balance

Source: NASA

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

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)

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: tyler.erickson@mtu.eduWeb: http://people.mtri.org/tyler+ericksonTwitter: tylericksonCode: http://bitbucket.org/tylere/geodjango-stilt/