1. Air Quality Products from NOAA Operational Satellites in Support of NWS Air Quality Forecasting Efforts Shobha Kondragunta NOAA/NESDIS Center for Satellite Applications and Research Project 1: Using satellite-derived biomass burning PM2.5 emissions to improve NWS air quality forecasting Project 2: Trace gas products from IJPS GOME-2 for air quality applications

2. 2 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

3. 3 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 Factor Burned area

4. 4 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.

5. 5 Moisture Category --derived from AVHRR VCI Early January in 2002 Early July in 2002

6. 6 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

7. 7 Half-hourly PM2.5 Emissions

8. 8 Annual PM2.5 Emissions

9. 9 Variation in GOES Fire and PM2.5 Emission with Land Cover Type

10. 10 PM2.5 Emissions in Each State

11. 11 Comparison of Daily Emissions --April-December 2002

12. Trace Gas and Aerosol Products from IJPS GOME-2

13. 13 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

14. 14 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 National NO x and SO 2 Power Plant Emissions: Historic and Projected with CAIR

15. 15 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

16. 16 Can NESDIS Meet User Requirements? MetOP Global Ozone Monitoring Experiment (GOME)-2 can provide tropospheric amounts of most of the EPA criteria pollutants –O 3, NO 2, H 2 CO, SO 2, 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

17. 17 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

18. 18 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 - 20082008- 200122012 and beyond SCIENCE Nationwide Data APPLICATIONS Implementation as Air 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 Satellite Measurements to In-situ measurements and emission inventories. 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 with GOME-2 data against traditional benchmark data sets and incorporation into as an indicator for accountability. GOME, SCIA, OMI (NASA) sensors provide prototype data sets for GOME-2. ( Ozone, NO2, SO2, Aerosol, HCHO) NOAA/NESDIS starts production of air quality GOME-2 data products. GOES-R and potential future NASA Geostationary tropospheric chemistry missions. Operations

19. 19 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

20. 20 A. Richter et. al., Nature, Letters 2005 Trends in NOx emissions from GOME data

21. 21 GOME Tropospheric NO 2 GEOS-CHEM Tropospheric NO 2 10 15 molecules cm -2 r=0.75 bias 5%

22. 22 Near Real Time Air Quality Products from IJPS GOME-2 at NOAA/NESDIS ProductUserApplication NO2EPA NWS Assessments  Constrain NOx emissions in air quality forecast model  Verification of precursor forecast fields H2COEPA NWS Assessments  Constrain isoprene emissions in air quality forecast model  Verification of precursor forecast fields OzoneNWS Ozone forecast improvements Aerosol optical Depth (absorption vs scattering) EPA NWS NESDIS PM2.5 Monitoring  PM2.5 and ozone forecast improvements  Hazard Mapping System Volcanic SO2NESDIS 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 km 2 spatial resolution. Measurements from Polarization Monitoring Device will be at 10 km X 40 km