Improving Air Quality Analysis and Planning through the Integration of Satellite Data
with Ground-based Observations, Modeling Results, and Emissions Estimates:
Enhancements to the VIEWS/TSS System with Data from CALIPSO, Aura, Terra, and Aqua
Air Quality Deliverables – Roles, Responsibilities, and Process
UNC/UMBC
3)
4)
Incorporation of three to fourdimensional (2-3 spatial, 1
temporal) pollutants fields (e.g.,
aerosol extinction profiles, NO2,
O3) into the DSS to improve
boundary inputs and evaluation
of outputs from gridded
chemistry- transport models
(CTMs) such as CMAQ.
Development of advanced
analysis tools for examining the
satellite and model data to better
understand the relevant
atmospheric processes and their
representation in the CTMs.
CIRA/UMBC
Both
1)
Routine
capture,
analysis, and
processing
algorithms
with high
temporal and
spatial
resolution to
provide land
use/land
cover data as
inputs to
emissions
and air
quality
modeling
analyses.
2)
Acquisition of satellite data to
obtain increased temporal and
spatial resolution of activity data
and emission rates from natural
and anthropogenic area,, point,
and cluster sources in both
remote and urban areas .
5)
Visualization and quantitative
analysis of satellite data in
combination with existing
monitoring and emissions data,
and modeling results within a
unified data analysis and
decision support platform.
Tasks
1) Routine capture, analysis, and processing algorithms with high temporal and spatial resolution to provide land
use/land cover data as inputs to emissions and air quality modeling analyses.
 What satellite data products are available, from which sensors, and how long have they been/will they be around?
 What is the best use of these data in air quality assessments? What processing will be needed of the level 2 data to
improve land cover information in, e.g., wind-blown dust modeling? Biogenic emissions modeling? Sea salt
emissions estimates? Fire emissions estimates? Dry deposition velocity estimates?
 How often are these data sampled in time relative to monitoring sites, counties, grid cells – what about frequency
and types of interferences and instrument uncertainties (e.g., cloud cover interference in Aerosol Optical Depth
[AOD] data)?
 What is the spatial coverage and resolution of these data?
 What land use data types are available?
 Routine capture, processing and storage considerations – does the data format lend itself to be used easily in
inputs to emissions and air quality models? How much processing will be needed?
2) Acquisition and incorporation of satellite data for improving the temporal and spatial resolution of activity data and
emission rates from natural and anthropogenic emission sources, from both remote and urban areas, and from point
sources and source clusters, and for helping constrain current emissions estimates in inventories.
 What activity data and emission rates do we want to correlate with satellite data? Which sensors/platforms are
appropriate? How long have they been /will they be around?
 What is the sensitivity of these satellite data products in the boundary layer?
 How often are these data sampled in time? What is the frequency and type of interferences?
 What is the spatial coverage and resolution of the data relative to counties and model grid cells?
 Can we derive an apples-to-apples relationship (e.g., between modeled and satellite-derived PM2.5 emission
estimates)?

See Rosetta Stone: http://vista.cira.colostate.edu/tss/help/parameterkey.aspx

See slides 14-30: http://www.wrapair.org/forums/toc/meetings/080729m/WRAPCalNexPres.pdf

See WRAP FETS http://www.wrapfets.org/index.cfm - already combines ground-based fire activity tracking
classified into fire type with satellite fire detects from: http://maps.geog.umd.edu/firms/shapes.htm
 What are the data storage considerations for routine capture and processed output? How should the data be
formatted for input to emissions analysis, and how should they be fed back to air quality models?
3) Acquisition and incorporation of three to four-dimensional (2-3 spatial, 1 temporal) pollutants fields (e.g., column
NO2 and O3; CALIPSO extinction profiles) to improve boundary inputs and evaluation data for gridded chemistrytransport models (CTMs) such as CMAQ
 What 3-D (time-varying column) or 4-D measurements do we want to integrate into the DSS? From which
sensors and platforms? How long have they/will they be around?
2
 How often are these data sampled in space and time relative to sources or clusters of sources – what is the
frequency and type of interferences?
 Are the satellite data products sensitive enough in the boundary layer to enable a one-to-one comparison to model
outputs? (For example, satellite data are not sensitive to PBL ozone, so we know the answer is no in that case, but
recent research suggests the answer is yes for OMI NO2).
 What is the spatial coverage and resolution in case of level 2 columnar data? Gridded data?
 Can we derive a one-to-one relationship between modeled and observed data?

See Rosetta Stone: http://vista.cira.colostate.edu/tss/help/parameterkey.aspx

See slides 14-30: http://www.wrapair.org/forums/toc/meetings/080729m/WRAPCalNexPres.pdf
 Routine capture, processing and storage considerations – does the data format lend itself for use as inputs to
emissions modeling, air quality model inputs (e.g., boundary conditions), and air quality model performance
evaluation? How much processing is required for such use?
 For what analyses would we use L3 satellite data (re-gridded to CMAQ grid)? What are instances when we
would need to use L2 gridded satellite data products (e.g., if the user is examining diurnal processes)?
 What types of metadata are appropriate to provide with these satellite data products for the average user? Should
there be multiple levels of detail for a spectrum of users and applications? How should these data be provided and
housed in the DSS?
 Metadata could be grouped into 3 tiers depending on the expertise of the user community that they are targeted to
serve. Tier 1 metadata would be targeted toward the novice user who may not be familiar with satellite data
products, although generally experienced in using air quality data. Tier 2 metadata would target a somewhat more
experienced user with some expertise in interpreting the data. Tier 3 metadata would target the advanced user with
expertise in subsetting and manipulating the observational data and model outputs for customized analyses. More
detailed information on the data products would be provided under help/training.
 Develop and integrate metadata into the database:

Create simplified interface to assist novice users. Potentially could group together a series of windows or
panels to connect analysis/metadata/help
o


Include window for metadata
Examples of Tier 1 metadata
o
Definitions or brief descriptions of metrics and their units (e.g., AOD, column O3)
o
Map of QC cells
o
Map of cloud mask for satellite
o
Instrument or level processing information
o
Satellite data level
o
CMAQ version and release notes
o
Emission information
Examples of Tier 2 metadata
o
Satellite averaging kernels, calculations from prior data versus additional information in retrieval
o
Details on speciation of model data
o
Map of emissions data
3

Examples of Tier 3 metadata
o
Descriptions of physicochemical processes relevant to PyPA output
o
Details on trajectory met data if doing back trajectory analyses
o
Episode information pertinent to transport from aloft layers, high-level jets, etc.
4) Development of advanced analysis tools to enhance our understanding of relevant atmospheric processes and their
representation in the CTMs. This task also supports Task 3.)
 What other ozone precursor data are available? What other column measurements relevant to speciated PM and
O3 analysis are available?

Need to provide an indication of measurement uncertainties and caveats on data use
 What processes are well-understood in the atmosphere, but need improved representation in the air quality
models, and which satellite data products could help achieve that? What are the sensors/platforms? How long
have they been /will they be around? Which satellite data products would be useful in achieving these
improvements? For example data are needed to improve:

Specification of lateral boundary conditions, especially over oceans and remote locations with sparse
coverage from surface-based monitors

Vertical profiles of ozone and aerosols to better understand boundary layer mixing, tropospheric budgets, and
shortcomings in the chemical mechanisms (satellites do not “see” O3 well in the PBL although there are
ongoing efforts to combine instruments for getting more information within the PBL)

Estimates of the surf zone area fraction within a model grid cell for better estimation of high sea salt emission
fluxes along coastlines (important for both ozone and speciated PM predictions in coastal areas)

Estimates of areas burned in various types of fires
 What new analysis tools are available to analyze these data alongside model data to improve our understanding of
the required model representations vs. actual model behavior?
 What are the best ways to make these analysis tools available in the DSS?
 Develop and integrate Tier 2 and Tier 3 analysis tools:

Leverage satellite tools for plots (Giovanni, RSIG, etc)

Leverage model tools for plots (Pave, AMET, etc)

For some of these analyses we might create "canned" or simplified interfaces to help the user. Others might
require tailoring to a more sophisticated user.
Examples of Tier 2 analysis tools: These are more complicated than Tier 1 tools described under Task 5, and
require some expertise in interpreting the results. They typically operate on a two-data type domain (combined
from among satellite, monitor and model data) or include more complicated analytical techniques within a single
data type.

Examples of potential analyses:
o
Model/monitor comparisons (at location of monitors). A lot of this can come from present VIEWS
capability or AMET.
o
Satellite/monitor data comparison; scatter plots of AOD vs. PM2.5 from monitors
o
Model/satellite data comparisons with modeled aerosol extinction integrated over model layers.
o
Kriging monitor data augmented by satellite or model data (for spatial pattern)
4
Examples of Tier 3 analysis tools: These require significant expertise in interpreting the results. They typically
operate on a multiple-data type domain (satellite vs. monitor vs. model data) and require a thorough
understanding of the satellite data and the model data.

Examples of potential analyses
o
pyPA
o
Maps that integrate model, satellite and monitor data.
o
Specification of lateral boundary conditions for regional AQ model, especially over oceans and remote
locations with sparse coverage from surface-based monitors
o
Lidar maps and comparisons with model data
5) Development of tools for visualization and quantitative analysis of satellite data with the existing monitoring,
emissions, and modeling results within a unified data analysis and decision support platform.
 What level of training is necessary to use these tools? How will it be provided to the user? Will there be a multitiered approach to providing it?
User training and help would follow the 3-tiered approach used for metadata and analysis tools.

Create a simplified interface to assist the novice user. Potentially could group together a series of windows or
panels to connect analysis/metadata/help
o



Include a window for Tier 1 training and help
Examples of Tier 1 help/training
o
Description of the type of quantity measured or modeled
o
Related definitions (e.g. definition of AOD)
o
Caveats or warnings (e.g. difficulty of directly comparing PM2.5 results with AOD)
o
Correlations pre-determined by NASA between AOD and PM2.5 concentrations (e.g., on Giovanni)
Examples of Tier 2 help/training
o
Discussion of averaging kernel role in retrieval
o
Mid-level summary information about L2 and L3 algorithms, e.g. inputs and assumptions
o
Explanation of point vs. area-averaged data comparisons, and the difficulty of making direct comparisons
between monitor data and model or satellite data
Examples of Tier 3 help/training
o
Detailed documentation (from NASA) on retrieval algorithms
o
Discussion of process analysis (Integrated Reaction Rates vs. Integrated Process Rates)
 What satellite data products do we want to make available in existing and new monitoring/emissions/modeling
analysis and visualization tools? From which sensors/platforms? How do we put them together in the DSS?

Example data products and platforms:
o
2-D AOD (OMI/Aura, GOES/GASP, MODIS/Aqua, MODIS/Terra)
o
aerosol extinction profiles (CALIPSO LIDAR)
o
column NO2
o
column O3
o
column HCHO (OMI/Aura)
5
A detailed list including NAS/NOAA contact information is also being compiled on
http://vista.cira.colostate.edu/AirDataWiki/ROSES2007_Dataset_Inventory.ashx
 Choose 5 key variables (e.g., speciated PM, ozone, AOD, temperature, RH) to cross-compare among each other
based on feedback from the Steering Committee.
 Is there a one-to-one comparison between modeled and observed data that can result from permutations of these
variables?

Need enhanced Rosetta Stone: http://vista.cira.colostate.edu/tss/help/parameterkey.aspx
 Which systems exist out there for integrated data analysis and decision support to complement the vision and
mission for our DSS, and how do we leverage them?

WRAP FETS http://www.wrapfets.org/index.cfm

AQS

WRAP EDMS http://www.wrapedms.org/default_login.asp

VIEWS/TSS

EPA data federation through CMAS

GEOSS Architecture Implementation Pilot Phase II (see http://www.ogcnetwork.net/AIP2develop )
 Routine capture, processing and storage considerations – how do we allow users to view, assess, and retrieve the
data easily? Do these include data that they have found or only those that we will provide?
 What are the paradigms we should consider to establish data portals to these other systems? [see
http://vista.cira.colostate.edu/AirDataWiki/GetFile.aspx?File=DSS_Extension_Strategy.doc ]
 Integrate and leverage Tier 1 analysis tools, and help
Examples of Tier 1analysis tools: These are potentially most user-friendly and need the least amount of expertise
in interpreting the results. They typically operate on a single data type (satellite or monitor or model data)

Leverage satellite data tools for plots (Giovanni, RSIG, etc)

Leverage model analysis tools for plots (Pave, AMET, etc., other tools already available in VIEWS)

Create simplified interface to assist novice user in creating analyses. Potentially could group together a series
of windows or panels to connect analysis/metadata/help
o

Include window for analytic tool results
Example Tier 1 analyses:
o
Map of AOD from MODIS L3 data for a particular day
o
Map of OMI NO2 L2 gridded data for a particular hour
o
MAP of daily mean PM2.5 data from CMAQ model output for a particular day
o
Map of average NO2 concentration from CMAQ for a particular hour
o
Time series of model results for O3 at a model location (grid cell)
o
Time series of satellite data at a location (grid cell or swath "cell")
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