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Satellite Remote Sensing of Aerosol Yang Liu yliu@rsmas.miami.edu. Outline. Introduction why aerosol is important Aerosol optical properties: Mie scattering Satellite Remote Sensing Background (visible sensor, orbit) A lgorithm example : MODIS ocean

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Satellite Remote Sensing of Aerosol Yang Liu yliu@rsmas.miami


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    1. Satellite Remote Sensing of Aerosol Yang Liu yliu@rsmas.miami.edu

    2. Outline • Introduction • why aerosol is important • Aerosol optical properties: • Mie scattering • Satellite Remote Sensing • Background (visible sensor, orbit) • Algorithm example : MODIS ocean • Other Satellite sensors and Application

    3. Natural generated (90%) or anthropogenic (10%) • PM (decrease of life expectancy) eg. PM2.5 • Cooling or Warming effect eg. Sulfates or Black carbon • Cloud nuclei eg. Cloud formation; Precipitation • Climate Effect • Magnitude and scientific understanding of aerosol effect on • climate is still low. ( IPCC 2007 )

    4. Aerosol direct effects Health effects

    5. Direct radiative effects (DRE) Modulation of atmospheric scattering and absorption properties due to changes in the concentration and optical properties of the existing aerosol. Soot: +0.2 W/m2 Sulfates: -0.4 W/m2

    6. Aerosol indirect effects

    7. Outline • Introduction • why aerosol is important • Aerosol optical properties: • Mie scattering • Satellite Remote Sensing • Background (visible sensor, orbit) • Algorithm example : MODIS ocean • Other Satellite sensors and Application

    8. Important Parameters • The single scatter albedo(SSA), , is defined as is the mass extinction coefficient • Aerosol Optical depth(AOD) is defined as (0,h) = h∞ z) dz is the volume extinction coefficient • Angstrom exponent

    9. Effective radius Where n(r) is the number distribution of aerosol particles as function of radius. Aerosol phase function where is the normalized phase function Asymmetry parameter

    10. Scattering regimes χ = 2πr /λ = q

    11. Rayleigh Scattering Scattering by molecules: Where R is distance from the scatterer, λ is wavelength, α is the polarizability (related to the refractive index of a material, but for individual molecules). θ is the scattering angle. http://en.wikipedia.org/wiki/Rayleigh_scattering

    12. Aerosol optical properties g? “1”

    13. Mie scattering See http://www.philiplaven.com/p2.html

    14. Outline • Introduction • why aerosol is important • Aerosol optical properties: • Mie scattering • Satellite Remote Sensing • Background (visible sensor, orbit) • Algorithm example : MODIS ocean • Other Satellite sensors and Application

    15. Aerosol Scattering

    16. Radiative Transfer Equation dL / dz = - abs L - scat L + abs B(T(z,t)) +scat/(4) 020 L (,) P(s) sin dd

    17. Non-emissive atmospheres Non-emissive atmospheres (an approximation used in the visible): dL/d = - L(,) + (/4)02-11L(,) p(s) d d It is often acceptable to assume that clouds and aerosols are non-absorbing, so  = 1. This is called the conservative scattering assumption, because radiant energy is conserved.

    18. Atmospheric transmission

    19. Visible sensors • Atmosphere is relatively transparent (reason why our eyes work) • No atmospheric or (generally) surface emission (exceptions, fires, lightning, lava flows, city lights) • Some absorption by O3 and H2O • Scattering is important Rayleigh can be dealt with quite well Aerosols are a problem Clouds  discard data • Signal is reflected sunlight Works only during daylight Incident solar radiation must be known

    20. Sun synchronous orbit The plane of the orbit has to rotate once per year as the earth rotates about the sun.

    21. Whisk broom scanner Linear scan eg, AVHRR, MODIS

    22. Outline • Introduction • why aerosol is important • Aerosol optical properties: • Mie scattering • Satellite Remote Sensing • Background (visible sensor, orbit) • Algorithm example : MODIS ocean • Other Satellite sensors and Application

    23. MODIS Facts Instrument Specifications Orbit: 705 km, sun-synchronous 10:30AM (Terra), 1:30PM (Aqua) Over same point every 16 days Swath: 2330 km (55° cross track) Spectral Range: 0.4 - 14.4mm (36 bands) Spatial Resolution: 250m (2 bands) 500m (5 bands) 1000m (29 bands) Calibration: On-board Aerosol Retrieval Bands Band Bandwidth Resolution 1 620-670 nm 250 m 2 841-876 nm 250 m 3 459-479 nm 500 m 4 545-565 nm 500 m 5 1230-1250 nm 500 m 6 1628-1652 nm 500 m 7 2105-2155 nm 500 m D. Herring Data Gridded: Daily, 8-Day, 30-Day Atmosphere: Cloud and Aerosol Ocean: Color, Chlorophyll, Temp Land: Vegetation, Change, Fires Global Remote Sensing of Aerosol using MODIS: Algorithm and Validation Kaufman et al.

    24. Ocean algorithm and LUT Combination of two modes to best mimic the observed spectral reflectance Fine mode weighting: Fine Coarse  MODIS ATBD_MOD04 document: Algorithm for remote sensing of Tropospheric aerosol over dark targets from MODIS

    25. N(nr + ini), rg, σ, re MODIS ATBD_MOD04 document: Algorithm for remote sensing of Tropospheric aerosol over dark targets from MODIS

    26. βe g MODIS ATBD_MOD04 document: Algorithm for remote sensing of Tropospheric aerosol over dark targets from MODIS

    27. MODIS Geometry and Ocean LUT rmodis = raer + rrayleigh + T(rsfc + rwater) F 0 r r = ( q ,q,f , l) i i 0 r ray r aer r sfc Fine t= t + t q f c 0 q Coarse r r w w f Global Remote Sensing of Aerosol using MODIS: Algorithm and Validation Kaufman et al.

    28. Land:  ~ 0.05+15% Ocean:  ~ 0.05+5% In dusty situations the Collection 004 (red) data reported a too high proportion of fine mode aerosols with average Fine Weighting (η or η) of ~0.5. In smoke or pollution aerosols it retrieved roughly the correct fmw of ~0.8. MODIS ATBD_MOD04 document: Algorithm for remote sensing of Tropospheric aerosol over dark targets from MODIS

    29. Example “Granule” July 21, 2000 Puerto Rico RGB AOD at 550 nm Glint Mask Global Remote Sensing of Aerosol using MODIS: Algorithm and Validation Kaufman et al.

    30. Sunglint http://www.etl.noaa.gov/eo/pdf/glitter.html

    31. Outline • Introduction • why aerosol is important • Aerosol optical properties: • Mie scattering • Satellite Remote Sensing • Background (visible sensor, orbit) • Algorithm example : MODIS ocean • Other Satellite sensors and Application

    32. H. Yu et al. In-Situ and Remote Sensing Measurements of Aerosol Properties, Burdens, and Radiative Forcing. Climate Change Science Program

    33. MISR Facts Instrument Specifications Orbit: 705 km, sun-synchronous 10:30AM (Terra), Over same point every 16 days • Swath: • 400 km (9 pushbroomcameras) • 9 view angles at Earth surface: • 70.5º forward to 70.5º aftward • Multiple spectral bands at each angle: • 446, 558, 672, 866 nm Calibration: On-board • Resolutions: 275 m - 1.1 km sampling

    34. OMI facts Instrument Specifications Orbit: 705 km, sun-synchronous 1:30PM (Aura) “A train” Calibration: On-board Resolutions: 13 km× 24 km at nadir 13 km × 12km zoom mode Bands: UV channel 270 - 380 nm VIS channel 350 - 500 nm Swath: 2600 km (Daily global coverage)

    35. Active sensors: Calipso

    36. Aerosol AOD Evaluation • Self Consistency • Visual • No Angle dependency • Valid cloud mask • Land/Ocean continuity • AODcomparison with sunphotometer AERONET:  ~ 0.02

    37. Sunphotometer http://pages.usherbrooke.ca/cimel/index.php/CIMEL_318_-_general_specifications

    38. AAI(AI) AOD AOD

    39. Summary Satellites can not only give us continuous and global information about aerosol distribution, but also the spectral optical depth, particle size over both ocean and land, more direct measurements of polarization and phase function and even the vertical profiles. CERES measures broadband solar and thermal infrared fluxes that are used to derive the aerosol direct radiative effect and forcing. Currently, satellite measurements alone are not adequate to characterize complex aerosol properties over complex surfaces and hence can not be used to derive the aerosol direct effect over land with high accuracy. Observational estimates of aerosol indirect radiative effects are still in their infancy. aerosol-cloud interactions continue to be an enormous challenge from both the observational and modeling perspectives Could and sun glint are still problems, but other uncertainties exist and affect retrieval quantitively, including the Global mass mixing ratio distributions, humidity effects on aerosol, and uncertainty in Black carbon absorption efficiency, etc.