Increased Availability of Satellite Remote Sensing Data for

Increased Availability of Satellite
Remote Sensing Data for Model
Evaluation through 3D-AQS
AMS Paper 11ATCHEM 3.3
January 12, 2009 Phoenix, AZ
Ray Hoff, Ana Prados, Hai Zhang, Meloë Kacenelenbogen, and Ruben Delgado
University of Maryland Baltimore County, [email protected]
Shobha Kondragunta, Jim Szykman, Fred Dimmick, Brad Johns, Chieko Kittaka
Jay Al-Saadi, Jill Engel-Cox, Amy Huff, Stephanie Weber,
Tony Wimmers, and Steve Ackerman
Overview of NASA 3D-AQS Project
(Hoff et al, JAWMA, 2009; http://alg.umbc.edu/3DAQS)
 Integrate satellite sensor and lidar data into
EPA’s air quality data systems: AIRQuest, RSIG
NOAA NESDIS AQS system: IDEA
 Provide greater accessibility and usability of
satellite and lidar data to users of these systems
through IDEA and US Air Quality Smog Blog
 Enable monitoring in horizontal and vertical
dimensions for forecasting and retrospective
analysis
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Some key air quality satellite sensors
GOES
MODIS
AIRS
OMI
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Integrated System Solutions for 3-D AQS Impacting Air Quality & Public Health
Sun-Earth Observations and Models for
Predictions/Assessments/ Forecasts
Observations
Terra/Aqua
MODIS
AIRS
GALION Models
NOAA
CREST
Hysplit
MPLNet
GOES
LaRC
GASP
modified
Aura
IMPACT
OMI
trajectory
Calipso
model
CALIOP
AERONET
INPUTS
NESDIS
IDEA NRT
3D-AQS
USAQ NRT
Weblog
OUTPUTS
NASA/NOAA/EPA/ UMBC/CIMSS/BMI
Partnership Area
Decision-Support Tools
AIRNow/AQS-EPA/NOAA
•Increase synoptic data for
PM2.5 forecasters
AQS/AIRQuest (EPA)
•Multi-dimensional aerosol
related data and analyses:
•Assess general state of air
quality and trends
•Air quality rule
development
Remote Sensing
Information Gateway
(RSIG)
•Comparison with forecast
models
•Interpolation between sites
OUTCOMES
Value & Benefits to
Citizens & Society
Increase accuracy
in AQ forecast:
reduce poor air
quality health
impacts.
Increase knowledge
in causes or poor air
quality – leading to
improvements in
AQ and confidence
in government.
Improved
prevention initiative
targeting.
IMPACTS
EPA/NOAA/CDC
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US Air Quality Smog Blog
http://alg.umbc.edu/usaq
December 7, 2008
This example uses:
MODIS RGB (UW)
MODIS Fire Product (NOAA)
MADIS winds (NOAA)
AIRNow PM2.5 (EPA)
Webcam (public sources) 5
Smog Stories
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Example:
Northern CA
Fires of July September
2008
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Current Datasets Sent to AIRQuest
http://epa.gov/oar/umbc/ (request password from EPA)
 MODIS and GOES AOD matched to ground-based
PM2.5 monitors
• MODIS - fully incorporated
• GOES/GASP - fully incorporated
Data created and pushed from the NOAA IDEA site
http://www.star.nesdis.noaa.gov/smcd/spb/aq/
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Current Datasets Sent to AirQuest
MISR AOD, matched  OMI NO2 matched
to PM monitors
to NOy monitors
developed by NASA JPL
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Current Datasets Sent to RSIG
http://badger.epa.gov/rsig/datainventory.html
 Ground-based LIDAR data
by station (UMBC,
CCNY, HU, UPRM,
CoralNet)
0.5 Altitude (km)
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 MODIS AOD matched to 12 km CMAQ grid
 GASP AOD matched to 12 km CMAQ grid
15:00 Time (UTC)
00:00
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User Desired Datasets
3D-AQS End User Rankings of Remote Sensing Datasets
Ranking
Pollutant
Sensor
1
Particulate Matter (PM2.5)
ground-based LIDAR
2
Nitrogen Dioxide (NO2)
OMI
3
Tropospheric Ozone (O3)
OMI-derived
4
Particulate Matter (PM2.5)
CALIOP (late 2009)
5
Sulfur Dioxide (SO2)
OMI
6
Particulate Matter (PM2.5)
MISR
7
Carbon Monoxide (CO)
AIRS
8
Carbon Monoxide (CO)
MOPITT
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National MODIS AOD and PM2.5
Case Study (Zhang et al, 2009)
• 2-year (2005 and 2006) comparison of v.4.0.1 and v.5.2.6
MODIS AOD data to surface PM2.5 concentration
measurements across the US by EPA Region
• v.5.2.6 AOD more highly correlated with surface PM2.5 than
v.4.0.1 AOD, and thus v.5.2.6 AOD data are more accurate
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National MODIS AOD and PM2.5
Case Study (cont.)
•
v.4.0.1 AOD coverage is approximately 40% greater nation-wide than
v.5.2.6 AOD, due to differences in the cloud screening algorithms.
•
v.5.2.6 AOD are more accurate, but v.4.0.1 AOD have greater coverage.
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3D VISUALIZATION
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3D-AQS Remote Sensing Data:
Conclusions
•
•
•
•
NOAA IDEA now encompasses Terra and Aqua MODIS plus the
GASP product in NRT
Satellite aerosol optical depth data matched to monitors and on CMAQ
grid now available in AirQuest and RSIG
The USAQ weblog (“the Smog Blog”) is being cloned to other regions
of the globe (http://alg.umbc.edu/mac for Central America)
Applications include:
–
–
–
–
Location specific evaluation of satellite data versus EPA monitored data
Supplemental monitoring at increased spatial and temporal scales
Air quality model evaluation
Back trajectory studies to evaluate pollutant transport for CAIR
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