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

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

3 33 Suspended Matter (SM) Aerosol Optical Depth (AOD) Presented by Istvan Laszlo With contributions from Mi Zhou, Pubu Ciren, and Hongqing Liu

4 44  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) SM/AOD Retrieval: Physical Basis model 1: urban; model 2: smoke; top: 0.64 μm, bottom: AOD=0.4; solar zenith angle = 40 o; view zenith angle = 40 o; relative azimuth = 180 o

5 55 SM/AOD Algorithm: Features of the GOES-R/ABI SM/AOD algorithm:  Based on the MODIS/VIIRS heritages  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

6 66  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. SM/AOD Retrieval: Illustration of Methodology TOA reflectance in blue band TOA reflectance in red band aerosol model 1 aerosol model 2 Residual 1 Residual 2 Retrieved AOD550 Observation Illustration of aerosol retrieval concept  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.

7 77 SM/AOD Algorithm Input Sensor Input ABI Band WavelengthRange(μm)CentralWavelength(μm)CentralWavenumber(cm-1)Sub-satelliteIGFOV(km) Sample Use AOD Land AOD Land and Ocean AOD Ocean AOD Land and Ocean Estimate land surface reflectance land only both land and oceanocean only

8 SM/AOD Mathematical Description Calculation of TOA Reflectance TOA reflectance atmospheric contribution surface contribution The satellite-observed reflectance (ρ toa ) is approximated as the sum of atmospheric (ρ atm ) and surface components (ρ surf ) 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 99 SM/AOD Mathematical Description Atmospheric Contribution 9 gas transmittanceatmosphere LUT Calculation of atmospheric reflectance term ρ R+A : reflectance due to molecules (R) and aerosol (A) together – calculated with 6S RTM and stored in LUT ρ R : reflectance due to molecules – calculated in the code following 6S; P 0 and P are standard and actual pressures, respectively T : gas transmittance (parameterized) O 3, O 2, CO 2, N 2 O, CH 4 molecules, aerosol, H 2 O top of atmosphere bottom of atmosphere

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

11 SM/AOD Mathematical Description Ocean Surface Reflectance 11 Water reflection includes three components: Water-leaving radiance (Lambertian) Whitecap (Lambertian) Sunglint (bi-directional) Whitecap effective reflectance Wind speed (m/s) ρ wc corresponds to constant chlorophyll concentration (0.4 mg m -3 ) ABI Channel (wavelength in µm) 1 (0.47) (0.64) (0.865) (1.61) (2.25)

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

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

14 SM/AOD Mathematical Description Selection of Dark Pixel  Land – select pixels with low SWIR reflectance: »0.01 ≤ ρ 2.25 μm ≤ 0.25  Ocean – avoid areas effected by glint: »glint angle θ g > 40 o – θ g is the angle between the viewing direction θ v and the direction of specular reflection θ s : θ g = cos -1 ( cosθ s cosθ v + sinθ s sinθ v cosΦ ) θsθs θsθs θvθv θgθg Φ Z

15 SM/AOD Mathematical Description Aerosol Models 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 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.

16 calculate TOA reflectance at 0.47 µ m match 0.47um observation ? calculate residual at 0.64 µ m Each aerosol model Lookup Table Satellite & Ancillary Data Increase AOD at 550nm retrieved AOD ●Select the aerosol model and AOD with the minimum residual as the “best” solution Retrieve ρ 2.25, AOD and aerosol model simultaneously by matching the observed TOA reflectance of the reference channel 0.47 µ m and calculate the corresponding residuals at 0.64 µm for each of the four aerosol models where residual is calculated as: SM/AOD Mathematical Description SM/AOD Retrieval over Land Y N

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

18 SM/AOD Mathematical Description Size 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/cm 2 using a mass extinction coefficient (cm 2 /µg) computed for the aerosol models identified by the ABI algorithm.

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

20 SM/AOD Algorithm Verification Comparison with AERONET Retrievals are from MODIS Terra and Aqua from 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

21 Aerosol Detection (Smoke & Dust) Presented by Shobha Kondragunta With contributions from Pubu Ciren

22 Aerosol Detection Sensor Inputs Future GOES Imager (ABI) Band Nominal Wavelength Range (μm) Nominal Central Wavelength (μm) Nominal Central Wavenumber (cm-1) Nominal sub-satellite IGFOV (km) Sample Use Dust/Smoke Dust/Smoke Dust/Smoke Dust Dust/Smoke Smoke Dust/Smoke Dust/Smoke Dust/Smoke Input for both Dust and smoke Input for smokeInput for dust

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. 23

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

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

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

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 27

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

Proving Ground and User Interaction Shobha Kondragunta With contributions from P. Ciren, C. Xu, H. Zhang 29

30 Air Quality Proving Ground (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.

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 31

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

Haboob (intense dust storm) over Pheonix, Arizona in the evening of July 5, 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

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 6 th : the remnant of the haboob

NOAA’s IDEA Site (dynamic flat webpages)