Satellite observations of AOD and fires for Air Quality applications

1. Satellite observations of AOD and fires for Air Quality applications Edward Hyer Naval Research Laboratory AQAST 3 13-15 June, Madison, Wisconsin Hyer AQAST 3

2. In This Talk • Update: Production of assimilation-ready MODIS AOD product at NASA LANCE • Plans for MODIS and VIIRS assimilation-ready products • Other activities Hyer AQAST 3

3. Why Does Assimilation-Grade AOD Matter? • Aerosol analysis and forecasting requires AOD for assimilation • Assimilation has specific requirements • Minimize outliers • Correct persistent bias • Quantify residual uncertainty • Level 2 AOD products are not good enough • Correlated bias • Limited error characterization Hyer AQAST 3

4. Surface Boundary Condition Issues in over-land MODIS AOD • Strong systematic bias with surface albedo • Positive or negative biases for different land areas Hyer AQAST 3

5. Surface Boundary Condition Issues in over-land MODIS AOD • Albedo correction calculated empirically • Corrects negative bias in S. America, positive bias over arid surfaces Correction reduces occurrence of negative AODs AFTER CORRECTION Hyer AQAST 3

6. Microphysical bias S. AMERICA N. AFRICA CORRELATION Vs. AERONET SLOPE vs AERONET Regional slope correction improves global correlation of MODIS AOD vs AERONET from r2=0.62-0.65 to r2=0.71-0.73 • MODIS c5 uses a coarse climatology of aerosol optical properties • Two problems: • Bias over regions • Uncaptured variability • Above: Bias has general regional trends • Below: High correlation + variable bias = uncaptured variability in aerosol properties • Result: NRL L3 product has a regional correction applied Hyer AQAST 3

7. Obs. Uncertainty = instrument error + representativeness error Representativeness error = SD of AOD within grid cell Instrument error: Over ocean: f(AOD, fine mode fraction) Over land: f(AOD, region) Error = MAX(“noise floor”,linear relation) Below left: Estimated uncertainty for over-land product without corrections QA=Very Good, Cloud =0 Below right: Error ratio of corrected vs. uncorrected AOD Australia: filtering changes distribution of AOD, increases noise floor error Prognostic Error Estimation Hyer AQAST 3

8. NRL MODIS L3 AOD for Data Assimilation • Filtered • QA • Surface • Clouds • Textural • Corrected • Surface • Microphysics • Gridded • 0.5 degree • 6 hours • Error estimation • Empirical based on AERONET Our goal is to make this product available in near real time for any user with an air quality or visibility forecasting application Hyer AQAST 3

9. Production at LANCE: Progress • Updated code for unified production of over-land and over-ocean AOD • Improved resolution from 1 degree to 0.5 degree • Changed surface wind correction (over-ocean) from Navy NOGAPS to GMAO GEOS-5 • Created HDF-EOS output file format • Added metadata for traceability Hyer AQAST 3

10. Production at LANCE: Product 1 • Product will be called MCDAODHD • Product will be produced with 2-3 hour latency • Terra-only product will be produced first (Terra has lower latency) • LANCE will make production code available to the public • Documentation and sample code for using this product available (C, FORTRAN, IDL, Python) Hyer AQAST 3

11. Plans for DA-ready AOD products • Working on product from NPP VIIRS • MODIS Collection 6? • May permit improved spatial resolution • Improvements to error estimation • Prognostic error model has skill, but does not account for some known factors (atmospheric path, surface properties) • Investigate possible residual snow contamination • Snow filtering is already very strict, but suspicioushigh AODs continue to appear Hyer AQAST 3

12. Other activity: NPP VIIRS, fire products • Fire products: see Hyer & Westphal poster this afternoon • NPP VIIRS: ask me about NPP VIIRS! • To right: NRL NexSat (http://www.nrlmry.navy.mil/NEXSAT.html) GOES animation of New Mexico Gila Fire Hyer AQAST 3