air quality products from noaa operational satellites in support of nws air quality forecasting...
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Air Quality Products from NOAA Operational Satellites in Support of NWS
Air Quality Forecasting Efforts
Shobha KondraguntaNOAA/NESDIS Center for Satellite Applications and Research
Project 1: Using satellite-derived biomass burning PM2.5 emissions to improve NWS air quality forecastingProject 2: Trace gas products from IJPS GOME-2 for air quality applications
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Biomass Burning PM2.5 Emissions Project
• Major Accomplishments (FY05 funding)Algorithm to derive PM2.5 emissions during biomass burning
events developed and evaluated New fuel load database using MODIS land products. Zhang
and Kondragunta, GRL, 2006 New fuel moisture category maps using AVHRR NDVI
Test PM2.5 emissions datasets have been created to be used by NOAA/OAR-EPA to test the impact on PM2.5 predictions. After testing, NOAA/OAR to make a recommendation whether the product is useful or not for NWS operational applications
Developed 2005 PM2.5 emissions data for EPA National Emissions Inventory database
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Modeling Biomass Burning Emissions
GOES fire size
Fuel type AVHRR moisture condition
MODIS vegetation properties
CMAQ model
Emissions
Fuel loading Fraction of fuel consumption
Emission FactorBurned area
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Tree Biomass Components
A. Foliage biomass (tons/ha)
B. Branch biomass (tons/ha)
C. Aboveground biomass (tons/ha)
Zhang, X., and S. Kondragunta (2006), Estimating forest biomass in the USA using generalized allometric models and MODIS land products, Geophysical Research Letter, 33, L09402, doi:10.1029/2006GL025879.
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Moisture Category --derived from AVHRR VCI
Early January in 2002 Early July in 2002
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GOES Fire Detection for 2002
• Spatial resolution: 4km• Temporal resolution: 30min• Instantaneous fire sizes in subpixels detected from 3.9 µm and 10.7 µm infrared bands
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Half-hourly PM2.5 Emissions
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Annual PM2.5 Emissions
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Variation in GOES Fire and PM2.5 Emission with Land Cover Type
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PM2.5 Emissions in Each State
0
5000
10000
15000
20000
25000
30000
35000
40000
Washington
IdahoMontanaMainem
innesotaN. DakotaOregonN.HMichiganV.TW
isconsNew YorkS. DakotaMichinganW
yoming
MA CaliforniaConnNevadaPENNIowaUtahNebraskaNew JerseyOhioIllinoisIndianaColoradoW
VirginiaMD DELVirGiniaMissouriKansasKentuckyArizonaN CarolinaNewTennesseeOklahom
aTexasArkansasS CarolinaGeorGiaAlabam
aMississippiLouisianaFloridaR.I.
PM2.5
(ton)
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Fire
size
(km2
)
PM2.5--2002
PM2.5--2003
PM2.5--2004
Fire-size-2002
Fire-size-2003
Fire-size-2004
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Comparison of Daily Emissions--April-December 2002
PM2.5 in 2002
0
5000
10000
15000
20000
25000
99 109
119
129
139
149
159
169
179
189
199
209
219
229
239
249
259
269
279
289
299
309
319
329
339
349
359
Day of year
Pm2.
5 (to
ns)
National-Wildfire-Emissions-Inventory NFDRS fuelNESDIS fuelFCCS fuel
Trace Gas and Aerosol Products from IJPS GOME-2
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EPA Criteria Pollutants
• Ozone• SO2• CO• NO2• H2CO**• Aerosols (PM2.5 and PM10)
– Dust– Smoke
– Sulfate
– Organic Carbon
Criteria pollutants are those chemical species for which EPA has set standards and routinely monitors them over the US to determine if counties and states are in compliance or not.
** not a criteria pollutant but important ozone precursor
474 Counties with a population of 159M in non-attainment
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User Needs
• EPA– Track Clean Air Interstate
Rule (CAIR). Are NOx and SO2 controls working? Is visibility improving in our national parks?
• Long-term monitoring from satellites critical to track trends
• NWS– Improve air quality forecast
accuracy• Near real term monitoring
from satellites critical for satellite data assimilation
0
5
10
15
20
1980 1985 1990 1995 2000 2005 2010 2015 2020
Mil
lio
n T
on
s
National NOx and SO2 Power Plant Emissions:Historic and Projected with CAIR
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Societal Benefits
• Annually tens of thousands of deaths• > $100B in impacts
– Hospital visits (asthma, bronchitis, upper respiratory diseases, heart failure)
• > $20B spent on air pollution controls
Accurate forecasts will warn sensitive population (children and elderly) to stay
indoors on days with poor air quality
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Can NESDIS Meet User Requirements?
• MetOP Global Ozone Monitoring Experiment (GOME)-2 can provide tropospheric amounts of most of the EPA criteria pollutants– O3, NO2, H2CO, SO2, CHOCHO, and aerosols for air
quality applications– BrO, OClO for stratospheric ozone monitoring
applications
• Aura Ozone Monitoring Instrument (OMI) with similar capabilities is already providing data and helping NESDIS scientists prepare for MetOP launch
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Shortfalls that Must be Met
• To meet user requirements, NESDIS has to invest substantial effort towards algorithm and product development– Acquire capabilities,– Use Aura OMI data as risk reduction,– Collaborate with the group at Harvard Smithsonian
Astrophysical Organization (SAO) which developed OMI trace gas algorithms,
– Collaborate with NASA scientists who developed OMI total ozone and aerosol product algorithms (this falls under NASA Research to Operations activity),
– Coordinate with the users on product development and user application
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EPA Timeline for Use of GOME-2 data for Air Quality Applications involves on-going collaboration with NOAA &
NASA
Applications Research
Validation & Verification
Applications Demonstration
2004 - 2008 2008- 20012 2012 and beyond
SCIENCE
NationwideData
APPLICATIONS
Implementation asAir Quality
Management Tool (mature products)
On-going involvement from EPA, State, Local, and Tribal Air Quality Management Organizations
On-going studies on use of GOME, SCIA and OMI data.
Demonstrate Linkages of Regional Scale SatelliteMeasurements to In-situmeasurements and emissioninventories.
Evaluate GOME-2 operational products.Intercomparison and continuity studies with heritage sensors. Evaluation of first 5 year of GOME-2 data for trends.
On-going assessment of air quality trends withGOME-2 data against traditional benchmark data sets and incorporation into as an indicator for accountability.
GOME, SCIA, OMI (NASA) sensors provideprototype data sets forGOME-2. (Ozone, NO2, SO2, Aerosol, HCHO)
NOAA/NESDIS starts production of air quality GOME-2 data products.
GOES-R and potential future NASA Geostationarytropospheric chemistry missions.
Operations
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Operational GOES/MODIS AODs cannot retrieve aerosols when they are co-located with clouds. Instrument like OMI and GOME-2 with spectral coverage in UV/VIS can distinguish smoke aerosols from clouds
OMI data courtesy of NASA
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A. Richter et. al., Nature, Letters 2005
Trends in NOx emissions from GOME data
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GOME Tropospheric NO2 GEOS-CHEM Tropospheric NO2
1015 molecules cm-2
r=0.75 bias 5%
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Near Real Time Air Quality Products from IJPS GOME-2 at NOAA/NESDIS
Product User Application
NO2 EPANWS
• AssessmentsConstrain NOx emissions in air quality forecast modelVerification of precursor forecast fields
H2CO EPANWS
• AssessmentsConstrain isoprene emissions in air quality forecast modelVerification of precursor forecast fields
Ozone NWS • Ozone forecast improvements
Aerosol optical Depth (absorption vs scattering)
EPANWSNESDIS
• PM2.5 MonitoringPM2.5 and ozone forecast improvementsHazard Mapping System
Volcanic SO2 NESDIS • Hazard Mapping System
• Algorithm development to begin in 2006
• OMI DOAS algorithms will be employed, tested, and implemented
• Products will be made available in NRT in 2008
• Products will be available at 40 X 40 km2 spatial resolution. Measurements from Polarization Monitoring Device will be at 10 km X 40 km
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