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Using Google Earth for Exploring Atmospheric Observations of Carbon Dioxide for Understanding the North American Carbon Budget

{align:center} [!kml-icon.png!|http://na-carbon.appspot.com/static/na_carbon_link.kml] ([download KML|http://na-carbon.appspot.com/static/na_carbon_link.kml]) !lef_tower.jpg|thumbnail! !particles_far.jpg|thumbnail! !sensitivity.jpg|thumbnail! _(click to enlarge screenshots)_ {align}
Section
Column
Info

This work was selected as a winner of Google's KML in Research Competition.

Section

Atmospheric

concentrations

of

carbon

dioxide

(CO2)

have

increased

from

approximately

280

ppm

(parts

per

million)

to

380

ppm

since

the

beginning

of

the

industrial

era.

We

know

that

this

increase

contributes

to

global

climate

change.

Carbon

cycle

scientists

are

trying

to

understand

why

plants

and

oceans

are

taking

up

as

much

carbon

as

they

are,

so

that

they

can

better

predict

how

these

carbon

sinks

will

change

in

the

future.

One

important

way

in

which

scientists

gain

this

understanding

is

by

using

atmospheric

CO2

concentration

measurements,

together

with

information

about

wind

and

weather

patterns

from

atmospheric

models,

to

trace

back

where

the

releases

and

uptakes

of

CO2

are

occurring.

This

information

is

used

together

with

existing

computer

models

of

how

plants

take

up

carbon

to

improve

the

understanding

of

what

controls

variability,

in

space

and

time,

of

carbon

sinks.

To

understand

the

carbon

cycle,

scientists

must

work

with

large

volumes

of

data

that

vary

in

space

and

time.

This

work

formats

several

of

these

carbon

cycle

datasets

in

[

KML

|http://code.google.com/apis/kml/]

,

a

descriptive

language

for

geospatial

data,

so

that

that

data

be

explored

using

[

Google

Earth

|http://earth

.

google.com/].

Google

Earth

provides

a

user-friendly

interface

for

viewing

spatial

and

temporal

data

that

be

used

by

a

wide

range

of

user

groups,

including

researchers,

teachers,

and

the

general

public.

This

project

was

led

by

[

Dr.

Tyler

A.

Erickson

|http://people.mtri.org/tyler+erickson/]

of

the

[

Michigan

Tech

Research

Institute

(MTRI)

|http://mtri.org]

,

a

research

institute

of

[

Michigan

Technological

University

|http://mtu

.

edu].

The

visualization

was

made

possible

by

the

ongoing

work

of

many

organizations

and

researchers

working

in

the

area

of

carbon

cycle

science.

In

particular,

the

CO2

concentration

measurements

were

obtained

from

[

NOAA's

Tall

Tower

Network

|http://www.esrl.noaa.gov/gmd/ccgg/towers/]

of

observing

sites

and

the

model

results

were

produced

through

an

ongoing

NASA-funded

project

led

by

[

Dr.

Anna

M.

Michalak

|http://www-personal.umich.edu/~amichala/]

at

the

University

of

Michigan,

with

several

members

of

her

[

PUORG

research

group

|http://www-personal.umich.edu/~amichala/Group/]

contributing

data

directly

for

this

application.

Additional

details

on

models

and

datasets

are

described

within

the

KML

file

itself.

The

KML

file

is

an

output

from

an

internet-based

information

system

designed

for

storing,

analyzing,

and

communicating

geospatial

and

temporal

data.

MTRI

researchers

build

similar

systems,

based

on

open-source

geospatial

software

components,

to

manage

data

for

a

wide

variety

of

government

clients.

The

following

open

source

geospatial

packages

were

used

to

generate

the

final

KML

file:

{align:center} || [GeoDjango|http://code.djangoproject.com/wiki/GeoDjango] |

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Wiki Markup

GeoDjango

Python-based

geographic

web

framework || || [GEOS|http://trac.osgeo.org/geos/] |

framework

GEOS

2-dimensional

geometry

modeling

and

manipulation

library || || [

library

proj.4

|http://trac.osgeo.org/proj/] | cartographic projections library || || [GDAL/OGR|http://www.gdal.org] | raster/vector translation and processing libraries || || [libkml|http://code.google.com/p/libkml] | KML object generation library || || [numpy / scipy|http://numpy.scipy.org/] | Python scientific computing libraries || || [PostgreSQL|http://www.postgresql.org] / [PostGIS|http://postgis.refractions.net] | open source relational database with geometric object support || {align} To learn more about this project, contact [Dr. Tyler A. Erickson|http://people.mtri.org/tyler+erickson/] at [mailto:tyler.erickson@mtu.edu].
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Wiki Markup

cartographic projections library

GDAL/OGR

raster/vector translation and processing libraries

libkml

KML object generation library

numpy / scipy

Python scientific computing libraries

PostgreSQL / PostGIS

open source relational database with geometric object support

To learn more about this project, contact Dr. Tyler A. Erickson at tyler.erickson@mtu.edu.

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titleDownload Dataset
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Click on the following icon to access the dataset, which can be viewed in Google Earth.

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(click to enlarge screenshots)