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Satellite observations of AOD and fires for Air Quality applications

Edward HyerNaval Research Laboratory

AQAST 3 13-15 June, Madison, Wisconsin

15 June 2012

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In This Talk

• Update: Production of assimilation-ready MODIS AOD product at NASA LANCE

• Plans for MODIS and VIIRS assimilation-ready products

• Other activities

15 June 2012

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

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Surface Boundary Condition Issues in over-land MODIS AOD

• Strong systematic bias with surface albedo• Positive or negative biases for different land areas

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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

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Microphysical bias• 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

N. AFRICA

S. AMERICA

Regional slope correction improves global correlation of MODIS AOD vs AERONET from r2=0.62-0.65 to r2=0.71-0.73

SLOPE vs AERONET

CORRELATIO

N

Vs. AERON

ET

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Prognostic Error Estimation• 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

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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

15 June 2012

Our goal is to make this product available in near real time for any user with an air quality or visibility forecasting application

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Production at LANCE: Progress

1. Updated code for unified production of over-land and over-ocean AOD

2. Improved resolution from 1 degree to 0.5 degree

3. Changed surface wind correction (over-ocean) from Navy NOGAPS to GMAO GEOS-5

4. Created HDF-EOS output file format5. Added metadata for traceability

15 June 2012

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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)

15 June 2012

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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 suspicious high

AODs continue to appear

15 June 2012

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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

15 June 2012