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The Second TEMPO Science Team Meeting

The Second TEMPO Science Team Meeting. Physical Basis of the Near-UV Aerosol Algorithm. Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry and Dynamics Laboratory. National Institute of Aerospace Hampton, VA May 21-22, 2014.

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The Second TEMPO Science Team Meeting

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  1. The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry and Dynamics Laboratory National Institute of Aerospace Hampton, VA May 21-22, 2014

  2. Land aerosol Remote Sensing from Space using near-UV observations Lsfc Laer L0 F0 Aerosol Attenuation In the near UV, ~ 70-80% of the measured signal is Rayleigh scattering, 5% surface reflection. At 645 nm, 20% is Rayleigh scattering whereas 50 % is surface reflection. Accounting for Rayleigh scattering in the near UV is easier than accounting for surface reflection effects in the visible.

  3. Operational AOD Retrieval Approaches over Land • MODIS Dark Target Technique • -AOD in the Infrared (~ 2.1μm) is negligibly small. • -MODIS uses an empirical relationship between surface reflectance in the SWIR and visible to account for surface effects. • Two approaches are available: SDT and MAIAC (research algorithm) • MISR Multi-angle Technique (9 cameras, 4 wavelengths) • -Multiple viewing geometry allows accurate surface characterization (BRDF effects) • OMI Near-UV Technique • - Takes advantage of the fact that all terrestrial surfaces are dark at near UV and deep blue wavelengths

  4. Surface Reflectance 380 nm 440 nm 630 nm GOME observations

  5. Uncertainty in retrieved AOD due to uncertainty in surface albedo Actual Surface Albedo (Rs) : 0.05 ∆Rs = 0.01 Actual AOD: 0.5 388 nm 470 nm 645 nm 388 nm 470 nm 645 nm 0.06 0.05 0.04 The uncertainty of retrieved AOD over land is smaller in the near UV because the land reflection component to TOA is significantly smaller than in the visible and near IR. At TEMPO’s spectral coverage near UV observations are most insensitive to land surface effects.

  6. Sensitivity to Aerosol Absorption Lsfc Lss L0 F0 Net Aerosol Reflectance Aerosol Attenuation Sensitivity to Aerosol Absorption Rayleigh scattering attenuation by particle absorption generates an aerosol absorption signal. Aerosol absorption signal is largest in the near UV.

  7. Sensitivity of 388 nm TOA to Aerosol Optical Depth and Single Scattering Albedo (BL) 0.98 0.89 0.85 0.81 0.78 Change Relative to Rayleigh Scattering Case Sensitivity to BL absorbing aerosols decreases rapidly at AOD’s less than ~ 0.5

  8. Sub-pixel Cloud Contamination (Spatial and temporal resolution dependent) MODIS True-color RGB Jul 1, 2012 Average availability over the Continental US as a function of spatial resolution (based on Remer et al., AMT, 2012) • -Sub-pixel cloud contamination reduces the availability of retrieval opportunities (AOD yield) and • affects accuracy (AOD over-estimates) of retrieval product. • Thin cirrus clouds are also a source of error in AOD retrieval. • Sub-pixel cloud contaminated scenes are detected using high spatial resolution observations.

  9. Near UV Retrieval Procedure Two channel – two parameter algorithm Assumes Aerosol Height, retrieves AOD, SSA Level2 calibrated radiances at 354 and 388 nm Other input: -Surface Albedo (354, 388 nm) -Aerosol Type -Aerosol Vertical Distribution Assumed aerosol parameters: -Particle size distribution -Real comp. refractive index -Relative spectral dep. of imag. refractive index. Calculate Aerosol Index (AI) and Reflectivity N Cloud Free? No retrieval One-channel retrieval Assume Non-absorbing aerosol Retrieved parameter Extinction optical depth τext (354, 388, 500 nm) Y Two-channel retrieval Retrieved parameters: Extinction optical depth τext Single Scattering Albedo, SSA (354, 388, 500 nm) N Y AI > 0.5? A similar retrieval approach can be used to simultaneously retrieve SSA and aerosol layer height if AOD is known.

  10. Conclusions • TEMPO Near-UV observations will be used to retrieve aerosol optical depth • and single scattering albedo. • Planned TEMPO aerosol retrieval approach is based on current OMI algorithm. • At TEMPO’s 4.5X2 km spatial resolution sub-pixel cloud contamination is the largest source of uncertainty. • The combination of TEMPO and GOES-R ABI observations present a great opportunity for more accurate retrieval of aerosol properties.

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