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\begin_body

\begin_layout Title
A Data System for Visualizing 4-D Atmospheric CO2 Models and Data
\end_layout

\begin_layout Author
Tyler A.
 Erickson
\end_layout

\begin_layout Address
Michigan Tech Research Institute
\end_layout

\begin_layout Address
Michigan Technological University
\end_layout

\begin_layout Address
3600 Green Court, Suite 100
\end_layout

\begin_layout Address
Ann Arbor, MI 48105
\end_layout

\begin_layout Address
tylerickson@gmail.com
\end_layout

\begin_layout Author
Anna M.
 Michalak
\end_layout

\begin_layout Address
Department of Civil and Environmental Engineering, and
\end_layout

\begin_layout Address
Department of Atmospheric, Oceanic and Space Sciences
\end_layout

\begin_layout Address
The University of Michigan
\end_layout

\begin_layout Address
183 EWRE Building
\end_layout

\begin_layout Address
Ann Arbor, MI 48109-2125
\end_layout

\begin_layout Address
anna.michalak@umich.edu
\end_layout

\begin_layout Author
John C.
 Lin
\end_layout

\begin_layout Address
Department of Earth and Environmental Sciences
\end_layout

\begin_layout Address
University of Waterloo
\end_layout

\begin_layout Address
200 University Avenue West
\end_layout

\begin_layout Address
Waterloo, ON, Canada N2L 3G1
\end_layout

\begin_layout Address
jcl@uwaterloo.ca
\end_layout

\begin_layout Abstract
This paper describes a geospatial data system that produces KML representations
 of complex spatio-temporal datasets related to modeling the atmospheric
 carbon cycle.
 KML is an open standard language for transferring annotated geospatial
 data that can be used by many modern geospatial software packages, particularly
 virtual globe applications.
 The server component of the data system is built using a variety of open
 source software packages, which provide flexibility for creating custom
 geospatial representations of the datasets.
 The paper shows examples of how KML representations of atmospheric CO2
 datasets and model outputs can be visualized with virtual globe client
 applications, allowing a diverse group of users to explore the complex
 scientific datasets that are central to the discussion of climate change
 and global warming.
\end_layout

\begin_layout Section
Introduction
\end_layout

\begin_layout Standard
The general population’s awareness of, and interest in, climate change has
 increased dramatically in recent years.
 The consensus among climate scientists is that climate change is occurring,
 and that there is “very high confidence (9 out of 10 chance of being correct)
 that the global average net effect of human activities since 1750 has been
 one of warming” 
\begin_inset CommandInset citation
LatexCommand cite
key "Keeling76"

\end_inset

.
 Although the need for further work on specific scientific aspects remains,
 the discussion has largely progressed from “is climate change occurring?”
 to “how will climate change progress in the future?” and “how can human
 society mitigate or adapt to climate change?”
\end_layout

\begin_layout Standard
Several factors affect the energy balance of the climate system, including
 greenhouse gases, aerosols, and land surface properties.
 Of all the components, the increase in the atmospheric concentration of
 carbon dioxide (CO2) has been responsible for the largest increase in radiative
 forcing, or tendency to warm the Earth’s surface.
 In 2007, the International Panel on Climate Change (IPCC) published a summary
 report stating that “carbon dioxide is the most important anthropogenic
 greenhouse gas that contributes to climate change” 
\begin_inset CommandInset citation
LatexCommand cite
key "Keeling76"

\end_inset

.
\end_layout

\begin_layout Standard
CO2 is continuously exchanged between the atmosphere and the Earth’s surface,
 including land and oceans.
 The rate of exchange, or flux, is spatially and temporally variable, with
 this variability itself changing across scales.
 Overall, approximately half of current anthropogenic emissions of CO2 are
 taken up by land and oceans, which act as natural carbon “sinks.” However,
 there is a lack of understanding of where these sinks occur, how they vary
 in time, and how they are affected by climate variability and other processes.
 This, in turn, limits the skill of existing models in predicting future
 changes in net carbon balance 
\begin_inset CommandInset citation
LatexCommand cite
key "Friedlingstein06"

\end_inset

, and, therefore, the future atmospheric concentrations of CO2.
 Because CO2 flux can only be measured directly at relatively small spatial
 scales and at a limited number of sites (e.g.
 
\begin_inset CommandInset citation
LatexCommand cite
key "Baldocchi00"

\end_inset

), the estimation of CO2 fluxes on regional to continental scales relies
 heavily on models and indirect measurements.
\end_layout

\begin_layout Standard
One approach that carbon cycle scientists use to characterize the spatial
 and temporal variability of CO2 fluxes is to relate concentrations of atmospher
ic CO2 measured in the atmosphere to fluxes occurring in upwind regions,
 through a process called inverse modeling (e.g.
 
\begin_inset CommandInset citation
LatexCommand cite
key "Enting02"

\end_inset

).
 This approach couples atmospheric CO2 observations with numerical models
 describing winds and weather patterns, and any additional information relevant
 to estimating carbon exchange, in order to trace fluctuations in atmospheric
 concentration measurements of CO2 backwards in space and time to the sources
 and sinks.
 This process allows scientists to characterize variability in upwind carbon
 exchange between the Earth’s surface and the atmosphere.
\end_layout

\begin_layout Standard
Modeling and understanding the sensitivity of available CO2 observations
 to upwind fluxes is a key component of the inverse modeling framework.
 One approach, described in this paper, involves the use of a Lagrangian
 model, which simulates large numbers of particle trajectories backwards
 in time, starting at the location and time of the measurement, to identify
 the regions (in space and time) that influence the measured concentration.
 This modeling approach produces a diverse set of spatial and temporally
 varying datasets, with concentration measurement locations that are fixed
 in 3-D space and variable in time, particle trajectories that are variable
 in 3-D space and time, and sensitivity maps of measurements to surface
 fluxes that are variable in 2-D space and time.
\end_layout

\begin_layout Standard
The focus of this paper is the development of an approach for sharing these
 complex spatial and temporal datasets with a diverse set of users, ranging
 from the general public to carbon cycle scientists and decision makers.
 These spatial and temporal datasets can be difficult to explore and visualize,
 due to the high dimensionality of the data.
 An ideal tool for exploring these datasets would:
\end_layout

\begin_layout Itemize
possess capability for displaying 3-D spatially and temporally referenced
 data; 
\end_layout

\begin_layout Itemize
have an easy-to-use interactive user interface, that allows the user to
 navigate the data in both space and time, and to query attributes of the
 data; 
\end_layout

\begin_layout Itemize
have support for overlaying other georeferenced datasets; and 
\end_layout

\begin_layout Itemize
be freely available and be compatible with commonly used software.
 
\end_layout

\begin_layout Standard
While many past approaches for visualizing complex geospatial data have
 some of these characteristics, all of the characteristics are important
 for communicating the data to a diverse set of users.
 
\end_layout

\begin_layout Standard
Fortunately, recent advances in geospatial software have reduced the barriers
 for visualizing 3-D and 4-D datasets.
 Virtual globes, which are interactive client applications, can be used
 to present custom 4-D datasets over a richly detailed reference model of
 the Earth 
\begin_inset CommandInset citation
LatexCommand cite
key "Butler06"

\end_inset

.
 Accessing this type of visualization no longer requires advanced training
 in geospatial information systems or computer science, but rather is accessible
 by typical computer users.
 Google Earth, a popular virtual globe with a simple user interface, has
 been downloaded over 350 million times 
\begin_inset CommandInset citation
LatexCommand cite
key "Ohazama08"

\end_inset

.
 
\end_layout

\begin_layout Standard
This paper presents a client-server geospatial information system that allows
 users to visualize several complex datasets used for understanding terrestrial
 carbon fluxes.
 The geospatial data server is built using free and open source software
 (FOSS) components that store, process, and format spatial and temporal
 datasets so that they can be easily visualized, using modern virtual globe
 software packages.
 The system described in this paper incorporates the desired characteristics
 listed above, and is built on a flexible platform that can be easily enhanced
 in the future.
\end_layout

\begin_layout Section
Background
\end_layout

\begin_layout Standard
This section presents an overview of the scientific background for the models
 used to generate datasets included in the visualization and of open source
 software development.
\end_layout

\begin_layout Subsection
Atmospheric CO2 Data and Modeling
\end_layout

\begin_layout Standard
Regular atmospheric measurements of CO2 began in 1958 with observations
 taken at Mauna Loa, Hawaii 
\begin_inset CommandInset citation
LatexCommand cite
key "Keeling76"

\end_inset

.
 Since that time, global atmospheric CO2 monitoring networks have expanded
 significantly.
 The National Oceanic and Atmospheric Administration (NOAA) Earth System
 Research Laboratory (ESRL) Global Monitoring Division (GMD) maintains the
 NOAA-ESRL Cooperative Air Sampling Network, which currently includes over
 150 sites globally 
\begin_inset CommandInset citation
LatexCommand cite
key "NOAA09a"

\end_inset

.
 More recently, NOAA-ESRL-GMD has developed a Tall Tower Network of sites
 with continuous observations of CO2 and related gases 
\begin_inset CommandInset citation
LatexCommand cite
key "NOAA09b"

\end_inset

.
 This network focuses on the continental United States, and currently includes
 eight tall tower sites.
 Continuous measurements are particularly useful for inverse modeling studies,
 because they allow individual sampling locations to “see” large regions,
 as a function of changing wind directions and weather patterns.
 
\end_layout

\begin_layout Standard
Data from two of the Tall Tower Network sites are used for illustration
 purposes in the presented application.
 The first site is the LEF tower in Park Falls, Wisconsin, a 396m tower
 that has been operating since 1994.
 The second site is the 107m AMT tower in Argyle, Maine, which has been
 operating since 2003.
 In the presented analysis, the concentration measurements from these towers
 were averaged to 3-hour intervals over the time period of June 1 to July
 8, 2004.
 Meteorological information derived from the WRF model and atmospheric trajector
ies from the STILT model (described later in this section) were used to
 calculate the influence of atmospheric CO2 arriving from outside the examined
 domain on the available observations, and this impact was pre-subtracted
 from observations.
 As a result, the CO2 concentration variations examined here are influenced
 only by carbon sources and sinks within the examined North American domain
 
\begin_inset CommandInset citation
LatexCommand cite
key "Gourdji09"

\end_inset

.
 
\end_layout

\begin_layout Standard
As discussed in Section 1, one method used by carbon cycle scientists to
 quantify the sensitivity of atmospheric measurements to surface fluxes
 involves simulating collections of particles backwards in time from the
 measurement location, while modeling the turbulent dispersion as a stochastic
 process 
\begin_inset CommandInset citation
LatexCommand cite
key "Obukhov59"

\end_inset

.
 The Stochastic Time-Inverted Lagrangian Transport Model (STILT) is one
 such model that estimates subgrid particle movement by interpolating gridded
 meteorological fields to the location of the particle and parameterizing
 the turbulent motions as functions of these meteorological variables 
\begin_inset CommandInset citation
LatexCommand cite
key "Lin03"

\end_inset

.
 The STILT model is an adaptation of the HYSPLIT trajectory model 
\begin_inset CommandInset citation
LatexCommand cite
key "Draxler98"

\end_inset

 but incorporates four key improvements:
\end_layout

\begin_layout Itemize
a modified turbulence scheme that ensures adherence to the “well-mixed criterion
”, a manifestation of the 2nd Law of Thermodynamics 
\begin_inset CommandInset citation
LatexCommand cite
key "Thompson87"

\end_inset

;
\end_layout

\begin_layout Itemize
close coupling to atmospheric models to minimize deviations from mass conservati
on
\begin_inset CommandInset citation
LatexCommand cite
key "Nehrkorn10"

\end_inset

 ; 
\end_layout

\begin_layout Itemize
capability to account for errors in the meteorological fields using a Monte
 Carlo method 
\begin_inset CommandInset citation
LatexCommand cite
key "Lin05"

\end_inset

; 
\end_layout

\begin_layout Itemize
a revised method for estimating the height of the planetary boundary layer
 (PBL) that generalizes to unstable, neutral, and stable conditions 
\begin_inset CommandInset citation
LatexCommand cite
key "Vogelezang96"

\end_inset

.
 The PBL is the lower portion of the atmosphere in which trace gas concentration
s are most sensitive to surface fluxes 
\begin_inset CommandInset citation
LatexCommand cite
key "Stull88"

\end_inset

.
 
\end_layout

\begin_layout Standard
The STILT model runs can utilize gridded meteorological datasets from a
 variety of sources, including high-resolution limited area models like
 the Regional Atmospheric Modeling System (RAMS) 
\begin_inset CommandInset citation
LatexCommand cite
key "Cotton03"

\end_inset

 and the Weather Research and Forecasting (WRF) model 
\begin_inset CommandInset citation
LatexCommand cite
key "Skamarock05"

\end_inset

.
 For this study, WRF v2.2 was used to generate meteorological fields used
 by STILT 
\begin_inset CommandInset citation
LatexCommand cite
key "Nehrkorn10"

\end_inset

.
 A 3-level nested grid domain was used, with base grid of 40-km spanning
 10°N to 70°N, and 170°W to 50°W with higher resolution grids of 10-km resolutio
n over the Eastern half of the United States, and 2-km resolution grids
 surrounding three tall towers, including the two examined in the current
 study.
 10 day back-trajectories of 500 particles per hour from each receptor (tall
 tower location) were simulated.
 From these trajectories, a temporal grid was produced, representing the
 sensitivity of atmospheric concentrations to upwind surface fluxes on a
 3-hour time interval and a 1°×1° grid.
 The sensitivities were derived using times and locations where the particles
 were below the planetary boundary layer, indicating that the air parcel
 is sensitive to fluxes occurring at that location.
 These sensitivity grids, a.k.a.
 
\emph on
footprints
\emph default
, provide the linkage between locations measuring atmospheric concentrations
 with upwind fluxes.
 
\end_layout

\begin_layout Standard
Biospheric fluxes play a critical role in the carbon cycle and atmospheric
 CO2 concentrations, imposing marked diurnal and seasonal cycles on CO2
 variations.
 The terrestrial biosphere absorbs CO2 through photosynthesis during the
 daytime of the growing season and releases CO2 back to the atmosphere through
 respiration during the nighttime and the winter season 
\begin_inset CommandInset citation
LatexCommand cite
key "Schlesinger97"

\end_inset

.
 To simulate these biospheric processes, a wide variety of biospheric models
 have been developed over the past several decades 
\begin_inset CommandInset citation
LatexCommand cite
key "Sellers97"

\end_inset

.
 
\end_layout

\begin_layout Standard
In this project, CO2 surface fluxes generated using the CASA terrestrial
 carbon cycle model 
\begin_inset CommandInset citation
LatexCommand cite
key "Potter93"

\end_inset

 were used, which simulates ecosystem processes and is driven by satellite
 observations and meteorology.
 The surface fluxes, which were presented in Olson and Randerson 
\begin_inset CommandInset citation
LatexCommand cite
key "Olsen04"

\end_inset

, were mapped every 3 hours and at 1°×1° resolution, matching the sensitivity
 datasets.
 
\end_layout

\begin_layout Standard
The biospheric processes controlling carbon dioxide fluxes are complex functions
 of a large number of variables, including solar radiation, temperature,
 vegetation type, nutrient availability, disturbance history, and soil moisture,
 among other factors.
 The interactions between these variables result in a heterogeneous distribution
 of fluxes that vary both in space and time.
\end_layout

\begin_layout Subsection
Geovisualization
\end_layout

\begin_layout Standard
Geovisualization is an emerging field that includes approaches from a variety
 of disciplines, including cartography, scientific visualization, exploratory
 data analysis, and geographic information science, which provide tools
 for the visual exploration, analysis, synthesis and presentation of data
 that contain geographic information 
\begin_inset CommandInset citation
LatexCommand cite
key "Dykes05b,MacEachren97"

\end_inset

.
 Geovisualization systems may include support for temporal information,
 which is often lacking in traditional geographic information systems 
\begin_inset CommandInset citation
LatexCommand cite
key "Mountain05"

\end_inset

 but may be of critical importance for understanding temporally-variable
 environmental datasets.
 
\end_layout

\begin_layout Standard
Many geovisualization systems allow for significant levels of interaction,
 which allows users to explore data, synthesize, confirm and communicate
 ideas through guided discovery 
\begin_inset CommandInset citation
LatexCommand cite
key "Dykes05a"

\end_inset

.
 In recent years the general public has become familiar with interactive
 visualization in several forms such as online maps used for driving directions
 and virtual globes used by television news programs to give spatial context
 to remote events.
 
\end_layout

\begin_layout Standard
The scientific community has begun to utilize freely available virtual globe
 applications as geovisualization tools to communicate scientific results
 
\begin_inset CommandInset citation
LatexCommand cite
key "Butler06"

\end_inset

 such as meteorological data 
\begin_inset CommandInset citation
LatexCommand cite
key "Smith05"

\end_inset

, disease transmission observations 
\begin_inset CommandInset citation
LatexCommand cite
key "Janies07"

\end_inset

, and vertical profile data obtained from satellite sensors 
\begin_inset CommandInset citation
LatexCommand cite
key "Chen09"

\end_inset

.
 Many virtual globes can display custom user content that is spatially (and
 may also be temporally) referenced, provided the data is in a standard
 format.
 
\end_layout

\begin_layout Standard
The Keyhole Markup Language (KML) is an open standard XML-based language
 for exchanging georeferenced feature data, styling, and annotation.
 KML was originally created by Keyhole, Inc.
 and further expanded by Google after it acquired Keyhole, Inc.
 in 2004.
 The KML 2.2 specification was submitted to the Open Geospatial Consortium
 (OGC) standards organization, and became an official OGC standard (the
 OpenGIS KML Encoding Standard) on April 14, 2008 
\begin_inset CommandInset citation
LatexCommand cite
key "OGC08KML"

\end_inset

.
 The adoption of the standard by the OGC should encourage the development
 of visualization clients and server software applications that use KML
 to exchange spatial and temporal data.
 
\end_layout

\begin_layout Standard
One import feature of the KML language is the ability to access additional
 KML-formatted data at a specified URL using the Network Link functionality.
 This allows KML viewers to hierarchically link to large external datasets
 stored on or generated by remote servers.
 This paper describes a data system, built with open source software components,
 that produces KML formatted data with spatial and temporal attributes.
 Open source is a software development method that allows wide accessibility
 to the software’s code base for use, improvement, and redistribution in
 modified or unmodified forms.
 
\end_layout

\begin_layout Standard
Open source software has been adopted in many areas of academic research,
 such as the R language for statistical analysis 
\begin_inset CommandInset citation
LatexCommand cite
key "Ihaka96"

\end_inset

.
 Rey 
\begin_inset CommandInset citation
LatexCommand cite
key "Rey09"

\end_inset

 describes past interactions between academic geospatial researcher and
 the open source communities, as well potential opportunities for future
 cross-collaboration.
 In many ways, the open source software development process is similar to
 the scientific process of knowledge development 
\begin_inset CommandInset citation
LatexCommand cite
key "vonKrogh07"

\end_inset

 in that it promotes peer review by external developers 
\begin_inset CommandInset citation
LatexCommand cite
key "OSI07"

\end_inset

 and the open source licenses allow for continuous enhancements and improvements
 to a body of knowledge.
\end_layout

\begin_layout Section
Data System
\end_layout

\begin_layout Standard
A flexible three-tier data system architecture was designed to enable visualizat
ion of the carbon cycle datasets (Figure 1).
 The database and application tiers store, manipulate, and format the carbon
 cycle datasets as requested by client applications via a HTTP connection,
 enabling access by any client computer that has access to the application
 server.
 The database and application tier are created using open source geospatial
 components, and communicate with the client applications using KML, an
 open standard language for transferring geospatial data.
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Graphics
	filename data_system.png

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Overview of the open source data system components (shaded grey) and their
 relationship to the Google Earth virtual globe client application.
 The core of the application server, GeoDjango, uses the functionality of
 several open source libraries for formatting and manipulating data.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Data Storage
\end_layout

\begin_layout Standard
The foundation tier of the data system is PostGIS 
\begin_inset CommandInset citation
LatexCommand cite
key "Refractions09"

\end_inset

, a spatial extension to PostgreSQL 
\begin_inset CommandInset citation
LatexCommand cite
key "Postgresql09"

\end_inset

, a client-server relational database.
 The PostGIS extension adds geographic data types and spatial operators
 to PostgreSQL, which enables the database to store spatial, temporal and
 attribute information as records.
 PostGIS was selected because it is a widely used, free and open source,
 and follows the 
\emph on
Simple Feature Access Specification for SQL
\emph default
 
\begin_inset CommandInset citation
LatexCommand cite
key "OGC06SFA"

\end_inset

, which is an open international standard for storing and accessing geographic
 features.
 
\end_layout

\begin_layout Subsection
Application Server
\end_layout

\begin_layout Standard
The GeoDjango web framework is the core of the data system.
 GeoDjango is an integration of the Django 
\begin_inset CommandInset citation
LatexCommand cite
key "Django09"

\end_inset

 web framework with several open source geospatial libraries (GEOS, proj.4,
 GDAL) that includes support for spatial databases and provides spatial
 processing functionality.
 GeoDjango exposes the geometric data types and operators of provided by
 PostGIS at the database level.
 GeoDjango is written in Python 
\begin_inset CommandInset citation
LatexCommand cite
key "Python09"

\end_inset

, a general-purpose object-oriented programming language that can be used
 for many kinds of software development and is known for its code readability
 and its ability to integrate with other languages and tools.
 GeoDjango is a web server shares content using the Hypertext Transfer Protocol
 (HTTP).
 While the content is typically a web page, for this data system we have
 customized GeoDjango to serve KML documents.
 
\end_layout

\begin_layout Standard
The functionality of GeoDjango is enhanced by integrating with several open
 source libraries, as shown in Figure 1.
 The libkml C++ library 
\begin_inset CommandInset citation
LatexCommand cite
key "mashbridge09"

\end_inset

 provides a structured way of authoring valid KML documents.
 The pylibkml library 
\begin_inset CommandInset citation
LatexCommand cite
key "Erickson09"

\end_inset

 is a Python wrapper for libkml, which simplifies the use of the libkml
 objects from within Python code.
 Key elements of the KML language that are used by this data system are:
 (1) features that have temporal attributes to denote a specific time or
 interval of time, (2) network link elements that allow for accessing remote
 datasets, and (3) model elements that can incorporate 3-D models in the
 virtual globes to symbolize attributes.
 
\end_layout

\begin_layout Standard
Measurement and model data used by the data system originate from a variety
 of sources and occur in a variety of formats.
 Several open source packages, used by the data system, are summarized in
 Table 1.
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Summary of the open source packages used to import datafiles.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Tabular
<lyxtabular version="3" rows="4" columns="3">
<features>
<column alignment="center" valignment="top" width="0">
<column alignment="center" valignment="top" width="0">
<column alignment="center" valignment="top" width="0">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Datafile Format
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Packages Used
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Dataset Examples
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
text file
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
SciPy 
\begin_inset CommandInset citation
LatexCommand cite
key "Anon09"

\end_inset


\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
land-water mask
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
MATLAB 
\begin_inset CommandInset citation
LatexCommand cite
key "Mathworks09"

\end_inset

 data file
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
SciPy
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
sensitivity maps, biospheric flux maps 
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
R formatted data file
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
RPy 
\begin_inset CommandInset citation
LatexCommand cite
key "Moreira09"

\end_inset

 & R 
\begin_inset CommandInset citation
LatexCommand cite
key "Ihaka96"

\end_inset


\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
particle locations 
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Client Application
\end_layout

\begin_layout Standard
The client application’s role is to present spatially and temporally referenced
 data to the user.
 Although any application that implements the OGC KML standard could be
 used to view the data, the visualizations shown in this paper were produced
 using the Google Earth virtual globe (version 5.0) 
\begin_inset CommandInset citation
LatexCommand cite
key "GoogleEarth09"

\end_inset

.
 
\end_layout

\begin_layout Section
Application: Visualizing Atmospheric CO2 Data
\end_layout

\begin_layout Standard
This section describes how the data system was configured to manage the
 atmospheric CO2 datasets.
 
\end_layout

\begin_layout Subsection
Model Representation
\end_layout

\begin_layout Standard
The atmospheric transport and biospheric models produce datafiles with numerous
 attributes.
 Data models were created in the GeoDjango framework to describe data objects,
 their attributes, and relationships between objects.
 Table 2 gives a summary of the conceptual objects that are used in this
 visualization and highlights the spatial and temporal attributes of each
 object, while Figure 2 shows how the conceptual data objects are interrelated.
 
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout

\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Summary of the GeoDjango data model objects used to manage the measured
 and modeled datasets.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Tabular
<lyxtabular version="3" rows="8" columns="3">
<features>
<column alignment="center" valignment="top" width="1in">
<column alignment="center" valignment="top" width="2in">
<column alignment="center" valignment="top" width="1.5in">
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Conceptual Object
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Description
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Spatial & Temporal Attributes
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Sensor
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A tall tower measurement location, which includes the static 3D location
 of the sensor.
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
3D point
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Particle
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A single simulated particle that represents a parcel of air at a specific
 time that will later arrive at the sensor location
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
time instant
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Location
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A simulated 3D point location of a particle at a specified time that corresponds
 to a specified concentration measurement.
 Also includes the height of the planetary boundary layer.
 
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
3D point; time; corresponding to a specific time interval (measurement)
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Sensor Measurement 
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Average CO2 concentration measured over a time interval 
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
time interval (measurement) 
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Sensitivity
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
The sensitivity of a measurement to a surface flux
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
time interval (measurement) & time interval (surface flux)
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Surface Region
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A discretized portion of the Earth's surface
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
2D polygon
\end_layout

\end_inset
</cell>
</row>
<row>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
Surface Region Flux
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
A modeled surface flux value 
\end_layout

\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text

\begin_layout Plain Layout
time interval (surface flux)
\end_layout

\end_inset
</cell>
</row>
</lyxtabular>

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Graphics
	filename stilt_modelviz.eps
	width 5in

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Overview of the GeoDjango data models and their relationships that are used
 to model the CO2 measurement and model output datasets.
 
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
In conjunction with the imported data, GeoDjango uses the data model objects
 to create, populate, and save instances of the data records to the PostGIS
 database.
 Similarly, in order to access the data for the visualizations, GeoDjango
 uses the data model objects to query data stored in the PostGIS database.
\end_layout

\begin_layout Subsection
Atmospheric Measurements of CO2
\end_layout

\begin_layout Standard
The data for the atmospheric CO2 measurements, described in the background
 section, was obtained as MATLAB data files (one per measurement sensor).
 A Python script is used to parse the data files and import records into
 the PostGIS data tables, using the SensorMeasurement object of the GeoDjango
 data models (Figure 2).
 KML-formatted representations of the CO2 concentrations are obtained by
 submitting a URL request to the GeoDjango server application.
 For example, a request for a KML representation of a measurement data series
 for the LEF Tall Tower sensor between June 1, 2004 and August 8, 2004 would
 be:
\end_layout

\begin_layout Quotation
http://localhost/measurement/station=LEF/start=2004-06-01T00:00:00Z/ end=2004-07
-08T00:00:00Z/series.kml 
\end_layout

\begin_layout Standard
An example rendering of the CO2 concentration relative to background measured
 at the LEF Tall Tower is shown in Figure 3(a), and an example KML representatio
n is shown in Table 3.
 The KML model representation uses the 
\emph on
<Location>
\emph default
 element to set the 3-D position and the 
\emph on
<Link>
\emph default
 element to reference an external COLLADA model of a unit-sized, colored
 sphere (green_sphere.dae).
 The volume of the sphere is set to be proportional to the absolute value
 of the difference between the measured and background concentrations using
 the 
\emph on
<Scale>
\emph default
 element.
 CO2 concentrations that are greater (less) than the background concentration
 are symbolized in green (blue).
 Although KML is itself an OGC standard, this encoding of the sensor data
 does not conform to any of the current OGC Sensor Web Enablement specifications
 
\begin_inset CommandInset citation
LatexCommand cite
key "OGC09SWE"

\end_inset

.
 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Graphics
	filename LEF_tower_ab_900x1800.TIF
	lyxscale 50
	height 7in

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Screenshots of Google Earth renderings of the KML generated by the data
 system.
 (a) CO2 concentrations measured at the LEF Tall Tower.
 The volume of the sphere is proportional to the difference between the
 measured and the background concentrations.
 The sphere color (green/blue) is used to denote whether the measured concentrat
ions are below/above the background concentration.
 (b) Simulated particle locations (grey) representing air parcels that are
 sampled by the LEF Tall Tower.
 The paths between simulated locations are colored green/red to indicate
 whether the particle is below/above the atmospheric boundary layer.
 
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout

\end_layout

\end_inset


\end_layout

\begin_layout Standard
When a user navigates time using the time slider control in Google Earth,
 the color and size of the sphere change according to the selected time,
 conveying the temporal variability of the concentrations.
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout

\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Example excerpt of KML for displaying a CO2 measurement observation.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset listings
lstparams "basicstyle={\tiny},language=XML,numbers=left,tabsize=4"
inline false
status open

\begin_layout Plain Layout

<Placemark>
\end_layout

\begin_layout Plain Layout

  <name>2004-06-01
\begin_inset space ~
\end_inset

01:00</name>
\end_layout

\begin_layout Plain Layout

  ...
\end_layout

\begin_layout Plain Layout

  <TimeSpan>
\end_layout

\begin_layout Plain Layout

    <begin>2004-05-31T23:30:00Z</begin>
\end_layout

\begin_layout Plain Layout

    <end>2004-06-01T02:30:00Z</end>
\end_layout

\begin_layout Plain Layout

  </TimeSpan> 
\end_layout

\begin_layout Plain Layout

  <Model
\begin_inset space ~
\end_inset

id="model_1366">
\end_layout

\begin_layout Plain Layout

    <altitudeMode>relativeToGround</altitudeMode>
\end_layout

\begin_layout Plain Layout

    <Location>
\end_layout

\begin_layout Plain Layout

      <longitude>-90.273157</longitude> 
\end_layout

\begin_layout Plain Layout

      <latitude>45.945048</latitude>
\end_layout

\begin_layout Plain Layout

      <altitude>396</altitude>
\end_layout

\begin_layout Plain Layout

    </Location> 
\end_layout

\begin_layout Plain Layout

    <Scale> 
\end_layout

\begin_layout Plain Layout

      <x>28.1536</x> 
\end_layout

\begin_layout Plain Layout

      <y>28.1536</y> 
\end_layout

\begin_layout Plain Layout

      <z>28.1536</z>
\end_layout

\begin_layout Plain Layout

    </Scale>
\end_layout

\begin_layout Plain Layout

    <Link>
\end_layout

\begin_layout Plain Layout

      <href>green_sphere.dae</href>
\end_layout

\begin_layout Plain Layout

    </Link> 
\end_layout

\begin_layout Plain Layout

  </Model>
\end_layout

\begin_layout Plain Layout

</Placemark>
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Air Parcel Simulation
\end_layout

\begin_layout Standard
The source of the particle location data are R-formatted datafiles produced
 by the STILT atmospheric transport model.
 A Python script is used to parse the data files and import records into
 the PostGIS data tables, using the GeoDjango data models.
 
\end_layout

\begin_layout Standard
KML-formatted representations of the particle data are obtained by submitting
 a URL request to the GeoDjango server application.
 For example, a request for single particle trajectory captured by the LEF
 Tall Tower measurements on 2004-06-10 at 19:00 (UTC) is:
\end_layout

\begin_layout Quotation
http://localhost/particle/station=LEF/capture=2004-06-10T019:00:00Z/
\end_layout

\begin_layout Quotation
index=1/track.kml
\end_layout

\begin_layout Standard
A user may also request a series of particle trajectories.
 For example, a request for a series of 100 particle trajectories is:
\end_layout

\begin_layout Quotation
http://localhost/particle_tracks/station=LEF/capture=2004-06-10T19:00:00
\end_layout

\begin_layout Quotation
Z/index_start=1/index_end=100/track.kml
\end_layout

\begin_layout Standard
Sample KML representations of the particle locations and paths between consecuti
ve locations are shown in Table 4.
 An example Google Earth rendering of the particle tracks KML corresponding
 to measurements collected at the LEF Tall Tower is shown in Figure 3(b)
 and a screenshot of several paths is shown in Figure 4(a).
 The particle paths are symbolized, using the 
\emph on
<StyleUrl>
\emph default
 and 
\emph on
<Style>
\emph default
 elements, according to whether the particle location is above (red) or
 below (green) the atmospheric boundary layer.
 Particle locations that are below the atmospheric boundary layer contribute
 to the sensitivity of the sensor measurements to surface flux.
 Similarly, the path between consecutive particle locations is constructed
 with posts extending from each measurement location to the ground surface.
 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Graphics
	filename sensitivity_abc_1800x1800.TIF
	lyxscale 50
	width 5in

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Screenshots of Google Earth renderings of the KML generated by the data
 system.
 The screenshots all represent the conditions at the same time: 6/20/2004
 at 1:00 (UTC).
 (a) Particle locations (grey) and trajectories (red and green) for air
 parcels that are sampled by the LEF Tall Tower, along with the estimated
 sensitivity (purple) of the LEF tower measurements to surface fluxes.
 The height of the column is proportional to the sensitivity.
 (b) The overlaid sensitivity maps for the LEF and AMT tall towers.
 The sensitivity of CO2 measurements taken at the LEF/AMT tall tower to
 surface flux are symbolized in purple/cyan.
 (c) The sensitivity of CO2 measurements to the modeled biospheric flux
 of CO2 for measurements taken at the LEF and AMT tall towers.
 The height of the columns is proportional to the sensitivity multiplied
 by the model biospheric flux.
 Fluxes from the ground surface to atmosphere (respiration) are symbolized
 blue, while fluxes from the atmosphere to the ground surface (photosynthesis)
 are symbolized green.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout

\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Portion of the KML document that defines a particle location for a specified
 time and particle paths between consecutive locations.
 Although the location is valid for an instant in time for a moving particle,
 the KML <TimeSpan> tag for the particle location specifies a short interval
 to enhance the visualization.
 Paths between consecutive locations are extended to the ground surface.
\end_layout

\end_inset


\begin_inset listings
lstparams "basicstyle={\tiny},language=XML,numbers=left,tabsize=4"
inline false
status open

\begin_layout Plain Layout

<Document>
\end_layout

\begin_layout Plain Layout

  <Style
\begin_inset space ~
\end_inset

id="style_particle">
\end_layout

\begin_layout Plain Layout

    <IconStyle> 
\end_layout

\begin_layout Plain Layout

      <Icon>
\end_layout

\begin_layout Plain Layout

        <href>http://maps.google.com/mapfiles/kml/shapes/shaded_dot.png</href>
\end_layout

\begin_layout Plain Layout

      </Icon>
\end_layout

\begin_layout Plain Layout

    </IconStyle>
\end_layout

\begin_layout Plain Layout

  </Style>
\end_layout

\begin_layout Plain Layout

  <Style id="style_particle_path_above_bnd">
\end_layout

\begin_layout Plain Layout

    <LineStyle>
\end_layout

\begin_layout Plain Layout

      <color>ff0000ff</color>
\end_layout

\begin_layout Plain Layout

      <width>4</width> 
\end_layout

\begin_layout Plain Layout

    </LineStyle>
\end_layout

\begin_layout Plain Layout

    <PolyStyle> 
\end_layout

\begin_layout Plain Layout

      <color>400000ff</color>
\end_layout

\begin_layout Plain Layout

    </PolyStyle>
\end_layout

\begin_layout Plain Layout

  </Style>
\end_layout

\begin_layout Plain Layout

  <Style id="style_particle_path_below_bnd">
\end_layout

\begin_layout Plain Layout

    <LineStyle> 
\end_layout

\begin_layout Plain Layout

      <color>ff00ff00</color>
\end_layout

\begin_layout Plain Layout

      <width>4</width>
\end_layout

\begin_layout Plain Layout

    </LineStyle>
\end_layout

\begin_layout Plain Layout

    <PolyStyle>
\end_layout

\begin_layout Plain Layout

      <color>4000ff00</color>
\end_layout

\begin_layout Plain Layout

    </PolyStyle>
\end_layout

\begin_layout Plain Layout

  </Style>
\end_layout

\begin_layout Plain Layout

  <Folder> 
\end_layout

\begin_layout Plain Layout

    <name>locations</name>
\end_layout

\begin_layout Plain Layout

    <visibility>0</visibility>
\end_layout

\begin_layout Plain Layout

    <open>1</open>
\end_layout

\begin_layout Plain Layout

    <Placemark>
\end_layout

\begin_layout Plain Layout

      <visibility>0</visibility>
\end_layout

\begin_layout Plain Layout

      <TimeSpan>
\end_layout

\begin_layout Plain Layout

        <begin>2004-06-03T04:30:00Z</begin>
\end_layout

\begin_layout Plain Layout

        <end>2004-06-03T04:50:00Z</end> 
\end_layout

\begin_layout Plain Layout

      </TimeSpan>
\end_layout

\begin_layout Plain Layout

      <styleUrl>#style_particle</styleUrl>
\end_layout

\begin_layout Plain Layout

      <Point>
\end_layout

\begin_layout Plain Layout

        <extrude>1</extrude>
\end_layout

\begin_layout Plain Layout

        <altitudeMode>relativeToGround</altitudeMode>
\end_layout

\begin_layout Plain Layout

        <coordinates>-83.0984,77.8721,4240.77</coordinates>
\end_layout

\begin_layout Plain Layout

      </Point>
\end_layout

\begin_layout Plain Layout

    </Placemark>
\end_layout

\begin_layout Plain Layout

  </Folder>
\end_layout

\begin_layout Plain Layout

    <Folder>
\end_layout

\begin_layout Plain Layout

    <name>path</name>
\end_layout

\begin_layout Plain Layout

    <Placemark> 
\end_layout

\begin_layout Plain Layout

      <visibility>0</visibility>
\end_layout

\begin_layout Plain Layout

      <TimeSpan>
\end_layout

\begin_layout Plain Layout

        <begin>2004-06-03T04:40:00Z</begin>
\end_layout

\begin_layout Plain Layout

        <end>2004-06-10T19:00:00Z</end>
\end_layout

\begin_layout Plain Layout

      </TimeSpan> 
\end_layout

\begin_layout Plain Layout

      <styleUrl>#style_particle_path_above_bnd</styleUrl>
\end_layout

\begin_layout Plain Layout

      <LineString>
\end_layout

\begin_layout Plain Layout

        <extrude>1</extrude>
\end_layout

\begin_layout Plain Layout

        <tessellate>1</tessellate>
\end_layout

\begin_layout Plain Layout

        <altitudeMode>relativeToGround</altitudeMode>
\end_layout

\begin_layout Plain Layout

        <coordinates> -83.0984,77.8721,4240.77 -83.1489,77.8293,4255.398 </coordinates
>
\end_layout

\begin_layout Plain Layout

      </LineString>
\end_layout

\begin_layout Plain Layout

    </Placemark>
\end_layout

\begin_layout Plain Layout

    <Placemark>
\end_layout

\begin_layout Plain Layout

      ...
\end_layout

\begin_layout Plain Layout

  </Folder>
\end_layout

\begin_layout Plain Layout

</Document>
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Sensitivity Maps
\end_layout

\begin_layout Standard
Spatially discretized sensitivity maps are produced by aggregating particle
 locations that are below the atmospheric boundary layer for a specified
 time interval (flux time), and that correspond to a specified measurement
 time interval (measurement time).
 
\end_layout

\begin_layout Standard
A sample KML representation of a sensitivity map element is shown in Table
 5.
 The TimeSpan element refers to the time interval of the surface flux, and
 the symbolized map elements correspond to aggregate sensitivity of the
 all measurements taken 0-10 days after the surface flux time interval.
 
\end_layout

\begin_layout Standard
Examples of Google Earth screenshots rendering the sensitivity map KML files
 are shown in Figure 4(a) and 4(b).
 The extruded height of the sensitivity map elements is proportional to
 the sensitivity of the CO2 measurements to the surface flux, and the elements
 are uniquely colored to correspond to the measurement sensor (magenta –
 LEF Tall Tower; cyan – AMT Tall Tower).
 As can be seen by the overlapping regions shown on the center section of
 Figure 4(b), more than one measurement sensor may be sensitive to surface
 fluxes occurring for a particular region.
 
\end_layout

\begin_layout Standard
\begin_inset Float table
wide false
sideways false
status open

\begin_layout Plain Layout

\end_layout

\begin_layout Plain Layout
\begin_inset Caption

\begin_layout Plain Layout
Portion of the KML document that symbolizes the sensitivity of CO2 measurements
 made at the LEF Tall Tower to a surface flux for a 1°×1° ground region
 and 3-hour time interval.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset listings
lstparams "basicstyle={\tiny},language=XML,numbers=left,tabsize=2"
inline false
status open

\begin_layout Plain Layout

<Document> 
\end_layout

\begin_layout Plain Layout

  <Style id="style_positive"> 
\end_layout

\begin_layout Plain Layout

    <PolyStyle>
\end_layout

\begin_layout Plain Layout

      <color>bfff00ff</color>
\end_layout

\begin_layout Plain Layout

      <outline>0</outline> 
\end_layout

\begin_layout Plain Layout

    </PolyStyle>
\end_layout

\begin_layout Plain Layout

  </Style> 
\end_layout

\begin_layout Plain Layout

  <Folder> 
\end_layout

\begin_layout Plain Layout

    <name>LEF sensitivity</name>
\end_layout

\begin_layout Plain Layout

    <Placemark>
\end_layout

\begin_layout Plain Layout

      <TimeSpan>
\end_layout

\begin_layout Plain Layout

        <begin>2004-06</begin>
\end_layout

\begin_layout Plain Layout

        <end>2004-06-01T03:00:00Z</end>
\end_layout

\begin_layout Plain Layout

      </TimeSpan>
\end_layout

\begin_layout Plain Layout

      <styleUrl>#style_positive</styleUrl>
\end_layout

\begin_layout Plain Layout

      <MultiGeometry>
\end_layout

\begin_layout Plain Layout

        <Polygon>
\end_layout

\begin_layout Plain Layout

          <extrude>1</extrude>
\end_layout

\begin_layout Plain Layout

          <tessellate>1</tessellate> 
\end_layout

\begin_layout Plain Layout

          <altitudeMode>relativeToGround</altitudeMode>
\end_layout

\begin_layout Plain Layout

          <outerBoundaryIs>
\end_layout

\begin_layout Plain Layout

            <LinearRing>
\end_layout

\begin_layout Plain Layout

        <coordinates> -96,44,15036.9 -95,44,15036.9 -95,45,15036.9 -96,45,15036.9
 -96,44,15036.9 </coordinates>
\end_layout

\begin_layout Plain Layout

            </LinearRing>
\end_layout

\begin_layout Plain Layout

          </outerBoundaryIs>
\end_layout

\begin_layout Plain Layout

        </Polygon>
\end_layout

\begin_layout Plain Layout

      </MultiGeometry>
\end_layout

\begin_layout Plain Layout

    </Placemark> 
\end_layout

\begin_layout Plain Layout

    <Placemark>
\end_layout

\begin_layout Plain Layout

      ...
\end_layout

\begin_layout Plain Layout

</Document> 
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
While the sensitivity maps describe regions that may have affected the concentra
tion measurements, a more informative variable to visualize is the sensitivity
 value multiplied by a modeled CO2 flux.
 The bottom section of Figure 4(c) shows a Google Earth rendering of this
 variable using biospheric fluxes of CO2 estimated by the CASA biospheric
 model.
 The extruded height of the map elements is proportional to the absolute
 value of the sensitivity multiplied by the biospheric flux.
 The elements are colored according to the direction of the flux, with green
 indicating transfer of CO2 from the atmosphere to the land surface (photosynthe
sis), and blue indicating release from the land surface to the atmosphere
 (respiration).
 
\end_layout

\begin_layout Section
Discussion 
\end_layout

\begin_layout Subsection
CO2 Visualizations
\end_layout

\begin_layout Standard
The use of a user-friendly virtual globe application, such as Google Earth,
 makes the datasets accessible to a wide group of users.
 One use of the data system has been to create data layers that can introduce
 carbon cycle science to non-specialists such as educators.
 A KML document that included many of the datasets discussed in the paper
 was selected as a winner of Google For Educators 2009 KML in Research competiti
on because “it represented a novel and compelling representation of science
 using Google Earth and the KML language.”
\begin_inset Foot
status open

\begin_layout Plain Layout
Ryan Falor (Google), personal communication, 2 March 2009
\end_layout

\end_inset

 The ease of use of virtual globe applications enables other non-specialist
 groups, such as the general public and decision makers whom are neither
 familiar with carbon cycle science or geospatial information tools, to
 access and explore the datasets.
\end_layout

\begin_layout Standard
Although carbon cycle scientists have long had other tools such as mapping
 applications for understanding the datasets, this group of advanced users
 can also benefit from having access to visualization tools for viewing
 their data.
 Virtual globe applications can be used for exploratory data analysis, helping
 scientists to identify issues with their data that are difficult to detect
 using traditional tools.
 For example, the virtual globe interface allows users to easily change
 perspective to view both the ‘forest’ (carbon sensitivity variations across
 North America) and the ‘trees’ (particle tracks that the sensitivity values
 are based on).
 This has been used to identify potential issues with the simulation of
 individual particle tracks which are not apparent when viewing the sensitivity
 footprints at the continental scale.
 Also, due to the three-dimensional nature of trajectories and the spatially
 and temporally varying footprints, the visualization software described
 in this paper provides a valuable tool to probe changes due to dynamic
 changes in the wind patterns.
 The tool enables the user to investigate the dispersion pattern of trajectories
 and quickly reveals the land areas whose emissions are sampled by the trajector
ies, whenever they dip within the PBL.
 
\end_layout

\begin_layout Standard
A key advantage of using a virtual globe to visualize spatio-temporal data
 is the ability to interactively navigate the temporal aspect of the datasets.
 The ability to select a specific time and to play forward and backward
 in time allows users to explore the temporal variability in the ‘footprints’
 of each concentration measurement location.
 This conveys to users the effect that constantly changing meteorological
 fields have on the potential information can be extracted from the concentratio
n measurements, a concept that is difficult to fully convey with a non-temporal
 representation that can only contain data for a single pre-specified time
 interval.
 While past work has used geovisualization tools to create movies to present
 changes over time 
\begin_inset CommandInset citation
LatexCommand cite
key "Gardiner06"

\end_inset

, the non-interactive nature of movies does not facilitate exploration of
 the data.
 The sensitivity dataset actually has two temporal dimensions, which cannot
 be directly represented in KML because KML objects can only contain a single
 primitive element for time.
 
\end_layout

\begin_layout Standard
The sensitivity dataset relates a concentration measured over a time interval
 (time 1) to a surface flux that occurred over a previous time interval
 (time 2).
 The approach taken in this paper is to generate a KML representation of
 sensitivity integrated over the entire time that concentrations were measured
 (time 1), so that the temporal primitive element in the KML refers to the
 time of the surface flux (time 2).
 This allows the user to use the Google Earth time slider to see variations
 in surface flux sensitivity.
 A complementary and equally useful approach would be to create a second
 KML representation that integrates the sensitivity data over the surface
 flux time (time 2), so that users could use the time slider to see variations
 in regions that a particular concentration measurement is sensitive to,
 regardless of when the surface flux occurred.
 
\end_layout

\begin_layout Subsection
Using Open Source Software for Research
\end_layout

\begin_layout Standard
The data system described in this paper was designed to manage a variety
 of spatially and temporally referenced datasets, which is a typical need
 for scientists that monitor the Earth’s environment.
 The data system was constructed using a variety of open source software
 components because of the numerous advantages that open source software
 has over proprietary components in terms of flexibility, maintainability,
 simplicity, and the developer community.
 
\end_layout

\begin_layout Standard
Because the source code is available, modifications can be made to extend
 the functionality provided by the component.
 If these modifications are contributed back to the open source project,
 they may be incorporated into the core product.
 This can be advantageous to the author of the modification, because future
 enhancements to the core product will be compatible with the modifications.
 This is particularly important for code developed as part of academic research
 projects, which generally have a finite project length and do not support
 long-term maintenance of software.
 
\end_layout

\begin_layout Standard
In the lead author’s experience, the process of designing prototype geospatial
 applications for research with open source software is quite different
 than with closed source proprietary systems.
 When an issue is encountered with closed source software, a user is restricted
 in their options for resolving the issue because they are prevented from
 inspecting and modifying the source code.
 When developing with open source software, there is always a way forward.
 If the user has sufficient technical skills they can debug and fix the
 issue themselves, or if not they can hire someone to fix the issue.
 Online forums and chat groups for both types of systems provide support
 to programmers, but for open source projects there is less separation between
 the developers and the users of the software, resulting in quicker and
 more relevant answers to questions.
 
\end_layout

\begin_layout Standard
Google Earth, a freely available proprietary closed source application,
 was primarily chosen as the visualization client because of its support
 of the full KML specification and its availability on multiple operating
 systems (Linux, Mac, Windows).
 Other beneficial features are the easy-to-use interface, the direct access
 to detailed vector data layers and high-resolution imagery that provide
 spatial reference, and the availability of rendering effects such as atmosphere
 and sun options that enhance the user’s perception of spatial and temporal
 change, However the KML documents created by this data system can be rendered
 by any virtual globe application that supports the full OGC-KML specification
 
\begin_inset CommandInset citation
LatexCommand cite
key "OGC09KML"

\end_inset

.
\end_layout

\begin_layout Section
Conclusions
\end_layout

\begin_layout Standard
This paper has described a prototype data system for producing visualizations
 of datasets related to atmospheric CO2 modeling.
 The intent has been to present an example of using open source geospatial
 software to manage complex spatial and temporal data, and to produce datasets
 in an open standard format that can be viewed in virtual globe applications
 as well as used by other geospatial software.
 While this paper focused on datasets related to modeling the atmospheric
 CO2 cycle, the data management and visualization techniques are appropriate
 for other regional to global-scale spatial-temporal datasets.
 
\end_layout

\begin_layout Standard
Providing a means of visualizing spatial-temporal datasets is important
 step toward increasing the non-specialists knowledge of the complex processes
 that cause climate change.
 Geovisualizations, such as those presented in this paper, can be used to
 familiarize the general public, decision makers, and future researchers
 with an understanding the current state of knowledge and challenges in
 modeling Earth’s systems and predicting future responses.
 
\end_layout

\begin_layout Standard
The data system presented in this paper is a work in progress, with numerous
 enhancements envisioned.
 Additional KML representations of the sensitivity datasets could be added
 to allow users to visualize according to the time of concentration measurement
 (in addition to the currently implemented time of surface flux).
 The current approach of storing discretized spatial variables (i.e.
 sensitivity maps or biospheric flux maps) as polygons could be improved
 by implementing raster data storage.
 Additional complex datasets used for inverse modeling, such as best estimate
 maps and covariance matrices, could be included in the visualization.
 
\end_layout

\begin_layout Section*
Acknowledgements
\end_layout

\begin_layout Standard
The authors gratefully acknowledges Adam Hirsch for discussions related
 to particle tracking as well as Sharon Gourdji, Kimberly Mueller, Vineet
 Yadav, and Deborah Huntzinger who provided the modeling datasets used in
 this visualization.
 The modeling datasets were created with support from the National Aeronautics
 and Space Administration under grant NNX06AE84G 
\emph on
Constraining North American Fluxes of Carbon Dioxide and Inferring Their
 Spatiotemporal Covariances through Assimilation of Remote Sensing and Atmospher
ic data in a Geostatistical Framework
\emph default
 issued through the ROSES A.6 North American Carbon Program to the University
 of Michigan.
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Vogelezang D, Holtslag A (1996) Evaluation and model impacts of alternative
 boundary-layer height formulations.
 Boundary-Layer Meteorology.
 81(3):245-269
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