Transcript Slide 1

Assessing U.S Air Quality with
Remote Sensing Data via
Goddard Interactive Online Visualization ANd aNalysis
Infrastructure
NASA Goddard Space Flight Center
http://giovanni.gsfc.nasa.gov/
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Content
• What is Giovanni?
• Giovanni for Air Quality
• Trace gases in Giovanni
• Interoperability of Giovanni
• Case studies
• Giovanni and Google Earth
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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About Giovanni
• Giovanni is a Web-based application
developed by the NASA Goddard Earth
Sciences Data and Information Services
Center (GES DISC).
• Giovanni provides a simple and easy
way to explore, visualize, analyze, and
access vast amounts of Earth science
remote sensing and model data.
http://giovanni.gsfc.nasa.gov/
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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10-6 ppmv
Data
Inputs
Aerosol from GOCART model
AIRS
MODIS
MISR
Parasol
Particulate Matter (PM 2.5) from AIRNow
Aerosol from MODIS and GOCART model
Carbon Monoxide from AIRS
MODIS vs SeaWiFS Chlorophyll
Ozone Hole from OMI
Giovanni
Instances
CloudSat
CALIOP
TOMS
OMI
MLS
HIRDLS
HALOE
TRMM
AMSR-E
SeaWiFS
Models
and more…
Water Vapor from AIRS
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RTP, NC
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Basic (one-parameter):
• Area plot – averaged or accumulated over any data period for any rectangular area
(various map projections)
• Time plot – time series averaged over any rectangular area
• Hovmöller plots –longitude-time or latitude-time cross sections
• ASCII output – for all plot types (can be used with GIS apps)
• Image animation – for area plot
• Vertical profiles
• Vertical cross-sections, zonal means
Beyond basics:
• Area plot - geographical intercomparison between two parameters
• Time plot - an X-Y time series plot of several parameters
• Scatter plot of parameters in selected area and time period
• Scatter plot of area averaged parameters - regional (i.e., spatially averaged) relationship
between two parameters
• Temporal correlation map - relationship between two parameters at each grid point in the
selected spatial area
• Temporal correlation of area averaged parameters - a single value of the correlation
coefficient of a pair of selected parameters
• Difference plots
• Anomaly plots
• Acquiring parameter and spatial subsets in a batch mode through Giovanni
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
http://giovanni.gsfc.nasa.gov/
Capabilities
RTP, NC
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 Only a Web browser is needed.
 No need to learn data formats
and programming.
 No need to download large amounts
of data.
 Customized data and analyses can be
obtained with only a few mouse clicks.
Caution: Giovanni is an exploration tool!
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
http://giovanni.gsfc.nasa.gov/
The Power of Simplicity
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AOT for June 2006
Terra MODIS
Parasol POLDER
Aqua MODIS
Envisat MERIS
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RTP, NC
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AOT Differences for June 2006
Terra MODIS – Aqua MODIS
Aqua MODIS - POLDER
Terra MODIS – MERIS
MERIS – POLDER
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RTP, NC
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Scatter plots
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RTP, NC
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Time series
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RTP, NC
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Maps of NO2
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RTP, NC
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Maps of CO
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RTP, NC
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Profile Data Comparisons
HIRDLS
MLS
HIRDLS and MLS
ozone (top) and
temperature
(bottom) profiles
acquired
March 12, 2007,
over France during
the passage of a
weather front. Note
the tropopause fold
(arrow) in the
ozone profiles.
MLS vertical
resolution
is ~3 km, HIRDLS
vertical resolution
is ~1 km.
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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California fires by MODIS
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RTP, NC
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PM2.5 from AirNow in Giovanni
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RTP, NC
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Visualizing California’s Wildfires from Space
23-27 October 2007
Data from NASA’s Aura OMI (Tropospheric NO2 and UV Aerosol Index), Aqua AIRS (Total Column CO) and
Terra MODIS (Aerosol Small Fraction, Cloud Optical Thickness and Aerosol Mass Concentration Over Land)
Tropospheric
NO2
OMI
OMI
Aerosol Small
Mode Fraction
MODIS
UV Aerosol
Index
Cloud Optical
Thickness
MODIS
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
Total Column
CO
AIRS
Aerosol Mass
over Land
MODIS
RTP, NC
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Data Fusion (prototype) in
Terra
Terra
+
Aqua
Aqua
Dust
event,
MayAQ
23,
2007
Gregory Leptoukh
& Frank
Lindsay,
Data
Summit
RTP, NC
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Interoperability
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RTP, NC
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RTP, NC
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MODIS
Aerosol Data
TOMS
already in
Giovanni 3.06
WMS
OMI
Protocols
Binary
SATs in
Giovanni
Sea
WIFS
HDF
4/5
NASA GES DISC
KML
Format
IDL
MAPSS
ESDC
ASCII
NetCDF
Open
DAP
WCS
Data
Fed
CALIPSO
MATLAB
etc.
ICARE
Interoperability
BAMGOMAS
AMAPS
SERVIR
etc.
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RTP, NC
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OGC and other m2m protocols
•
WMS:
– Via Map Server
– Via Giovanni
•
WCS:
– Via WebGIS
– Via Giovanni
•
OPeNDAP Hyrax 4 serving data on 12 machines. Sample URLs:
http://acdisc.sci.gsfc.nasa.gov/opendap/
http://atrain.sci.gsfc.nasa.gov/opendap/
•
WMS sample URL to get WMS data for 'LAYER=AIRX3STD_TOTO3_A‘:
http://g0hep12u.ecs.nasa.gov/mapservbin/wms_ogc?TARGET_SRS=EPSG:4326&Service=WMS&VERSION=1.1.
1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=768&HEIGHT=512&BB
OX=-180,-90,180,90&LAYERS=AIRX3STM_TOTO3_A,coastline
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GDS (Grads-DODS):
http://agdisc.gsfc.nasa.gov/dods/
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RTP, NC
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WMS and WCS in
Serving MODIS data via WMS: Maps (decorated and undecorated), time-series,
Hovmoller, time-averaged maps, difference maps
Example of Maps:
http://giovanniplus-ts1.sci.gsfc.nasa.gov/daac-bin/G3/giovanniwms.cgi?SERVICE=WMS&WMTVER=1.0.0&REQUEST=GetMap&SRS=EPSG:4326&EXCEPT
IONS=INIMAGE&FORMAT=GIF&BBOX=-130,24,-60,52&TIME=2006-0201T00:00:00Z&WIDTH=800&HEIGHT=400&LAYERS=MOD08_D3.005::Optical_Depth_Land_A
nd_Ocean_Mean
Serving data via WCS:
Here is a get capabilities url:
http://giovanniplus-ts1.sci.gsfc.nasa.gov/daac-bin/G3/giovanniwcs.cgi?SERVICE=WCS&WMTVER=1.0.0&REQUEST=GetCapabilities
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RTP, NC
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WCS support outside of
OMI NO2 (Level 3):
getCapabilities and describeCoverage requests
http://acdisc.sci.gsfc.nasa.gov/daac-bin/wcsNO2?service=wcs&version=1.0.0&request=describeCoverage
http://acdisc.sci.gsfc.nasa.gov/daac-bin/wcsNO2?service=wcs&version=1.0.0&request=getCapabilities
• getCoverage example request:
http://acdisc.sci.gsfc.nasa.gov/daacbin/wcsNO2?service=WCS&version=1.0.0&request=getCoverage&CRS=WGS84&resx=0.5&resy=0.5&cov
erage=NO2Total&bbox=-179.75,-89.75,179.75,89.75&TIME=2006-08-01/2006-08-04&format=netCDF
AIRS X2RET (Level 2 collection 5):
getCapabilities and describeCoverage requests
http://g0dup05u.ecs.nasa.gov/cgi-bin/ceopAIRX2RET?service=wcs&version=1.0.0&request=getCapabilities
http://g0dup05u.ecs.nasa.gov/cgibin/ceopAIRX2RET?service=wcs&version=1.0.0&request=describeCoverage
• getCoverage request:
http://g0dup05u.ecs.nasa.gov/cgibin/ceopAIRX2RET?service=WCS&version=1.0.0&request=getCoverage&coverage=H2OMMRStd&crs=W
GS84&bbox=-179.75,-89.75,179.75,89.75&resX=0.5&resY=0.5&time=2004-07-28&format=netCDF
• The server now supports 28 variables, including both 2D and 3D fields:
TSurfAir, TAirStd, GP_Height, GP_Surface, PSurfStd, TSurfStd, totH2OStd, H2OMMRStd, H2OMMRSat,
H2OMMRSat_liquid, O3VMRStd, totO3Std, PCldTopStd, TCldTopStd, olr, clrolr, CO_total_column,
CO_VMR_eff, CO_eff_press, CH4_total_column, CH4_VMR_eff, CH4_eff_press, GP_Height_MWOnly,
sfcTbMWStd, EmisMWStd, totH2OMWOnlyStd, totCldH2OStd, and numCloud.
• OMI UVB and O3 (Level 3) coming shortly
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Air Quality Tools and Datasets
on aerosols
Available Science Data Sets (examples)
• PM2.5 station data - EPA AirNow (via WCS)  DataFed
(aggregated and gridded)  (via WCS)  Giovanni
• MODIS TERRA and AQUA total and Fine mode Aerosol Optical
Depth
• CALIOP Aerosol Feature Mask curtain plots
• OMI NO2 Tropospheric column and Aerosol Index
Useful Tools for Air Quality Applications
• AOD/ PM2.5 scatter plots, correlation maps, time series and
difference plots
• AOD and PM2.5 loops for examining long range transport of aerosols
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Prototyping PM25 data in
PM2.5 (EPA DataFed  Giovanni)
The standard MODIS AOT
Deep Blue MODIS Aerosol Optical Depth
GOCART AOT
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Giovanni Air Quality Data (July 7th, 2006)
EPA AirNow PM2.5 (ug/m3)
OMI Aerosol Index
• MODIS and OMI imagery show smoke
aerosols over the northeast, southeast and
Great Lakes.
Level-3 MODIS AQUA AOD
CALIOP Aerosol Flag (yellow)
• CALIOP Aerosol Flag (yellow) confirms
that aerosols are above the boundary layer
• EPA AirNow PM2.5 doesn’t show anything
around Great Lakes, i.e. aerosols are
primarily above the boundary layer
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Giovanni Air Quality Services:
AOD/PM2.5 Correlation Maps and Time Series
May 2007 AOD/PM2.5
correlation map over the U.S
Moderate to good correlation in
the eastern U.S
No significant differences were
found when using the Fine
Mode MODIS AOD.
May 2007- AOD and PM2.5
Time series over the southeast
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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CALIPSO:
Elevated Smoke Layers over the
US
Midwest
Giovanni MODIS Terra AOD map
Giovanni PM2.5 Map
Smoke
Smoke in Great Lakes
region moving east
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Giovanni Air Quality tools:
Understanding AOD/PM2.5 correlations
Level-3 MODIS AOD
May 22nd, 2007: Smoke over
North Carolina. High AOD
and low PM2.5 (r=0.54).
There is also haze in the
southeast
EPA PM2.5 (ug/m3)
• Improved correlation over this
region when excluding smoke
areas (r=0.80)
• Giovanni data sets and tools
help provide a more complete
understanding of the origin,
evolution, and vertical
distribution of aerosol pollution
over the continental U.S.
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Giovanni Air Quality tools:
Understanding AOD/PM2.5 correlations
EPA PM2.5 (ug/m3)
• CALIOP Aerosol Flag (yellow) for
examining the vertical aerosol
distribution.
Level-3 MODIS AOD
• Aerosols in Georgia and Alabama
from the surface to 4 km,
AOD/PM2.5 correlation is
moderately good
• Aerosols in the northeast are
above boundary layer,
AOD/PM2.5 correlation is poor
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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July 31st, 2007
In Canada and the north-central US,
MODIS and OMI show thick aerosols
plumes. CALIOP overpass has a plume
above the boundary layer
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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July 31, 2007:
Haze over the south eastern US
• In the southeast (Tennessee,
Mississippi, Alabama and Arkansas)
MODIS and PM2.5 show good spatial
agreement and have moderately good
correlation (see scatter plot)
• Low OMI Aerosol Index and CALIPSO
Aerosol flag (see previous slide) also
indicate aerosols are primarily confined
to the boundary layer in these states
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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July 31, 2007:
Haze over the south eastern US
OMI contours over MODIS AOD. White
lines indicated CALIPSO overpass
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RTP, NC
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RTP, NC
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Importing Giovanni Data into Google Earth
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RTP, NC
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A-Train in Google Earth
via Giovanni: Calipso Lidar
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RTP, NC
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Analysis of U.S Air Quality Via
• Examined AOD and PM2.5 maps, correlation maps and time series
plots. Fine Mode AOD also available
• High AOD/PM2.5 correlation indicates the MODIS algorithm is
capturing aerosols at the surface in addition to elevated aerosols (if
any)
• High AOD and low PM2.5 may indicate the presence of aerosol
plumes above the boundary layer
• CALIPSO overpass (if available) together with AOD/PM2.5
correlations and scatter plots to qualitatively assess the vertical
distribution of aerosols
• OMI measurements are less sensitive to aerosols in the boundary
layer, so if OMI doesn’t show high aerosol while MODIS does, it may
indicate aerosol being in the boundary layer
• MODIS algorithm issues (e.g. retrieval problems over bright
surfaces) may affect correlations
Gregory Leptoukh & Frank Lindsay, AQ Data Summit
RTP, NC
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Test Case - NO2 Air Pollution Data from
Aura OMI
Iamges Courtesy of Mark O. Wenig, Eric J. Bucsela, Edward A. Celarier, James F. Gleason, NASA
J. Pepijn Veefkind, K. Folkert Boersma, Ellen Brinksma, KNMI