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

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Washington

IdahoMontanaMainem

innesotaN. DakotaOregonN.HMichiganV.TW

isconsNew YorkS. DakotaMichinganW

yoming

MA CaliforniaConnNevadaPENNIowaUtahNebraskaNew JerseyOhioIllinoisIndianaColoradoW

VirginiaMD DELVirGiniaMissouriKansasKentuckyArizonaN CarolinaNewTennesseeOklahom

aTexasArkansasS CarolinaGeorGiaAlabam

aMississippiLouisianaFloridaR.I.

PM2.5

(ton)

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200

400

600

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1000

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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

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99 109

119

129

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149

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269

279

289

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309

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329

339

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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

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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