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GOES-R ABI Aerosol Algorithms

GOES-R ABI Aerosol Algorithms. GOES-R Algorithm Working Group Aerosol, Atmospheric Chemistry and Air Quality (AAA) Application Team. Presentations. Suspended Matter/Aerosol Optical Depth Algorithm – Istvan Laszlo, STAR Aerosol Detection Algorithm – Shobha Kondragunta , STAR

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GOES-R ABI Aerosol Algorithms

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  1. GOES-R ABI Aerosol Algorithms GOES-R Algorithm Working Group Aerosol, Atmospheric Chemistry and Air Quality (AAA) Application Team

  2. Presentations Suspended Matter/Aerosol Optical Depth Algorithm – Istvan Laszlo, STAR Aerosol Detection Algorithm – ShobhaKondragunta, STAR Proving Ground and User Interaction – ShobhaKondragunta, STAR

  3. Suspended Matter (SM) Aerosol Optical Depth (AOD) Presented byIstvan Laszlo With contributions from Mi Zhou, PubuCiren, and Hongqing Liu 3 3

  4. SM/AOD Retrieval: Physical Basis • The aerosol portion of the atmospheric radiation (aerosol reflectance) observed by satellites is determined by the amount and type (size, shape and chemical composition) of aerosol. • Over dark surfaces, aerosol reflectance increases with increasing amount of aerosol (as measured by AOD) → Used for estimating AOD • The spectral dependence of aerosol reflectance is a function of aerosol type. → Used for estimating aerosol type (model) model 1: urban; model 2: smoke; top: 0.64 μm, bottom: AOD=0.4; solar zenith angle = 40o; view zenith angle = 40o; relative azimuth = 180o 4 4

  5. SM/AOD Algorithm: Features of the GOES-R/ABI SM/AODalgorithm: Based on the MODIS/VIIRSheritages Separate algorithms for land and water Uses multiple channels to estimate AOD and aerosol type Advantages A lot of ground work has already been done with MODIS Has been tested in an operational environment Potential synergy with MODIS/VIIRS aerosol product Estimates aerosol type Disadvantages Sensitive to radiometric error (multi-channel retrieval) No retrievals over bright surface (sun-glint, bare soil, desert) Dependence on aerosol model assumptions Over land, uses Lambertian surface model and spectral regression with large variance for surface albedo, which can lead to large AOD error for not dark enough surface 5 5

  6. SM/AOD Retrieval: Illustration of Methodology aerosol model 1 aerosol model 2 TOA reflectance in red band Residual 1 Observation Residual 2 Retrieved AOD550 TOA reflectance in blue band Illustration of aerosol retrieval concept • Aerosol retrieval is accomplished by comparing observed spectral reflectances with calculated ones. • AOD and aerosol model corresponding to calculated reflectances best matching observed ones are selected as solutions. • AOD and model is from the “minimum” residual between observed and calculated spectral reflectances. • Residual 2 < Residual 1, so retrieved AOD ≈ 1.0 and aerosol model is model 2. 6 6

  7. SM/AOD Algorithm InputSensor Input 7 7 land only both land and ocean ocean only

  8. SM/AOD Mathematical DescriptionCalculation of TOA Reflectance The satellite-observed reflectance (ρtoa) is approximated as the sum of atmospheric (ρatm) and surface components (ρsurf) TOA reflectance surface contribution atmospheric contribution • Calculated reflectances account for transmission and absorption of radiation in the atmosphere and reflection at the surface. • Atmospheric reflectances and transmittances are pre-calculated using the 6S RTM (Vermote et al., 1997) and stored in LUT for speed. • Surface reflectance of ocean is calculated; that over land is retrieved. → Separate algorithms for aerosol retrieval over ocean and land.

  9. SM/AOD Mathematical DescriptionAtmospheric Contribution Calculation of atmospheric reflectance term ρR+A : reflectance due to molecules (R) and aerosol (A) together – calculated with 6S RTM and stored in LUT ρR : reflectance due to molecules – calculated in the code following 6S; P0 and P are standard and actual pressures, respectively T : gas transmittance (parameterized) gas transmittance atmosphere LUT top of atmosphere bottom of atmosphere 9 9 9

  10. SM/AOD Mathematical DescriptionSurface Contribution Calculation of surface reflectance term gas transmittance atmosphere LUT land and ocean reflectances Total (direct+diffuse) downward and upward transmittance TR+A and spherical albedo SR+A of molecular and aerosol atmosphere are calculated with 6S RTM and stored in LUT

  11. SM/AOD Mathematical DescriptionOcean Surface Reflectance Water reflection includes three components: • Water-leaving radiance (Lambertian) • Whitecap (Lambertian) • Sunglint(bi-directional) Whitecap effective reflectance Wind speed (m/s) ρwccorresponds to constant chlorophyll concentration (0.4 mg m-3) 11 11

  12. SM/AOD Mathematical DescriptionOcean Surface Reflectance Term 1 Term 2 Sunglint calculated • Formulation follows 6S RTM • Cox and Munk (1954) ocean model • Constant salinity (34.3 ppt) • Fixed westerly wind direction Term 3 Term 5 Term 4 Sunglint LUT All , , and from atmosphere LUT 12 12

  13. SM/AOD Mathematical DescriptionLand Surface Reflectance a Surface reflectances in the visible and NIR ABI channels • Lambertian reflection is assumed. • Surface reflectances at 0.47 (ρ0.47) and 0.64 μm (ρ0.64) are estimated from those at 2.25 μm (ρ2.25). • Use NDVI to separate vegetation- and soil-based surface types (VIIRS approach) • For vegetation-based surface • For soil-based surface Mid-IR NDVI 13 13

  14. Φ θs θs Z θg θv SM/AOD Mathematical DescriptionSelection of Dark Pixel • Land – select pixels with low SWIR reflectance: • 0.01≤ρ2.25 μm ≤ 0.25 • Ocean – avoid areas effected by glint: • glint angle θg > 40o • θg is the angle between the viewing direction θvand the direction of specular reflection θs: • θg= cos-1( cosθscosθv + sinθssinθvcosΦ )

  15. SM/AOD Mathematical DescriptionAerosol Models WATER: Four fine mode and five coarse mode aerosol models (MODIS C5) Single scattering albedo and asymmetry parameter as a function of wavelength for the fine (left) and coarse mode (right) models over ocean. LAND: Four aerosol models: dust, smoke, urban, generic (MODIS C5, Levy et al., 2007) Single scattering albedo and asymmetry parameter as a function of wavelength for the four land aerosol models 15 15

  16. SM/AOD Mathematical DescriptionSM/AOD Retrieval over Land • Retrieve ρ2.25 , AOD and aerosol model simultaneously by matching the observed TOA reflectance of the reference channel 0.47µm and calculate the corresponding residuals at 0.64µm for each of the four aerosol models Lookup Table Satellite & Ancillary Data Y calculate TOA reflectance at 0.47µm match 0.47um observation ? calculate residual at 0.64µm Each aerosol model retrieved AOD N Increase AOD at 550nm where residual is calculated as: • Select the aerosol model and AOD with the minimum residual as the “best” solution 16 16

  17. SM/AOD Mathematical DescriptionSM/AOD Retrieval over Ocean • TOA reflectance is assumed to be a linear combination fine and coarse mode aerosols • Retrieve AOD and fine mode weight for each combination of candidate fine and coarse aerosol models. Lookup Table Satellite & Ancillary Data For each fine & Coarse model combination calculate residuals in channels 2, 5 & 6 calculate TOA reflectance in ABI channel Y match 0.87 μm obs.? Minimum residual? retrieved AOD & Weight & residual residual retrieved AOD N Increase AOD at 550nm Change fine mode weight where residual is calculated as: • Select the AOD and combination of fine and coarse modes with minimum residual as the “best” solution. 17 17

  18. SM/AOD Mathematical DescriptionSize Parameter and SM • The Ångström exponent (α) is used as proxy for particle size: • Large/small values of Ångström exponent indicate small/large particles, respectively. • The Ångström exponent is calculated from AODs and two pairs of wavelengths (MODIS heritage): • SM: The retrieved AOD is scaled into column integrated suspended matter in units of µg/cm2 using a mass extinction coefficient (cm2/µg) computed for the aerosol models identified by the ABI algorithm. 18 18

  19. SM/AOD Algorithm VerificationComparison with MODIS/Terra MODIS/Terra aerosol reflectances are used; 03/15/2012 ABI AOD MODIS-ABI AOD

  20. SM/AOD Algorithm VerificationComparison with AERONET • Retrievals are from MODIS Terra and Aqua from 2000-2009 • All available AERONET stations • AOD at 550 nm • Same overall performance of MODIS and ABI over land • Slightly smaller overall ABI bias over water Land Water 20 20

  21. Aerosol Detection (Smoke & Dust) Presented byShobhaKondragunta With contributions from PubuCiren

  22. Input for both Dust and smoke Input for smoke Input for dust Aerosol Detection Sensor Inputs

  23. Physical Basis of the Algorithm • Aerosols, surface, and clouds have different spectral and spatial characteristics • Aerosol and surface signals can be separated through analysis of spectral differences in BTs and reflectances • Cloud mask information is passed on by the cloud algorithm but internal tests for additional cloud screening and snow/ice have been implemented • Thresholds based on simulations and observations from existing satellite instruments.

  24. Physical Basis of the Algorithm Clear Sky Thin Dust Thick Dust

  25. smoke clear Clear Regime Thick Smoke Regime Heavy smoke Smoke Regime Physical Basis of the Algorithm • Spectral (wavelength dependent) thresholds can separate thick smoke, light smoke, and clear sky conditions

  26. Global Smoke/Dust Flags (May 26, 2008) Mongolia desert dust ABI smoke/dust detection algorithm is tested by using MODIS as proxy data smoke dust Biomass burning Saharan desert dust Aerosol Detection – Example

  27. Routine Validation Tools • Product validation: using CALIPSO Vertical Feature Mask (VFM) as truth data (retrospective analysis not near real time. Data downloaded from NASA/LaRC) • Tools (IDL) • Generates match-up dataset between ADP and VFM along CALIPSO track, spatially (5 by 5 km) and temporally (coincident) • Visualizing vertical distribution of VFM and horizontal distribution of both ADP and VFM • Generating statistics matrix

  28. ”Deep-Dive” Validation Tools Percentage of Pixels (%)

  29. Proving Ground and User InteractionShobhaKondraguntaWith contributions from P. Ciren, C. Xu, H. Zhang

  30. Air Quality Proving Ground (AQPG) http://alg.umbc.edu/aqpg/ • NOAA has created the AQPG – a subset of the GOES-R Proving Ground – focusing on the aerosol products that will be available from the ABI. • Goal: build a user community that is ready to use GOES-R air quality products as soon as they become available. • This distinction is important because the air quality community has very different needs than the majority of NOAA users (NWS meteorologists). • AQPG is using simulated GOES-R ABI data for training and interaction with the user community.

  31. Proxy ABI Aerosol Optical Depth AOD indicates areas of high particulate concentrations in atmosphere AOD is unitless; high AOD values (yellow, orange, red) indicate high particulate concentrations Clouds block AOD retrievals

  32. Proxy ABI Aerosol Type • New product - not available with current GOES imager • Qualitative and untested • Useful for distinguishing between smoke and dust but can be noisy, especially at low AOD values

  33. Proxy ABI Synthetic Natural Color (RGB) • No green band on ABI • Algorithm development underway to improve RGB product

  34. Haboob (intense dust storm) over Pheonix, Arizona in the evening of July 5, 2011. Photo by Nick Ozac/ The Arizona Republic MODIS RGB Image (bottom left) and Aerosol Optical Depth (bottom right) the next morning during Terra overpass show widespread dust. Neither Aqua nor Terra captured the event as it happened on July 5th because it happened at the night fall

  35. Compared to a single snapshot of Terra overpass (bottom) the morning after haboob, 30-min refresh rate movie of GOES shows changing dust plume features. However, note the noise in GOES data. For GOES-R, 5-min refresh rates with good quality “MODIS-like retrievals” will be the norm to track episodic events such as dust storms and smoke plumes.. Widespread dust over Phoenix on July 6th : the remnant of the haboob

  36. http://www.star.nesdis.noaa.gov/smcd/spb/aq/ NOAA’s IDEA Site (dynamic flat webpages)

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