NESDIS Center for Satellite Applications and Research Air Quality Products from GOES-R Advanced Baseline Imager (ABI) S. Kondragunta, NOAA/NESDIS/STAR Chair, GOES-R Aerosols/Atmospheric Chemistry/Air Quality Application Team Contributions from: P. Ciren, C. Xu, X. Zhang (IMSG) I. Laszlo (NESDIS/STAR) H. Zhang (UMBC) First Air Quality Proving Ground Workshop UMBC, September 14, 2010

Outline Air Quality Products from current operational GOES satellites Examples of user applications Limitations of current GOES GOES-R Products and capabilities 2

3 Aerosol Retrievals from a Geostationary Platform Single snap shot of MODIS Composite image from multiple snap shots of GOES-12 Example Image of GOES AOD Full Disk Quantitative measure of atmospheric aerosol loading that has been shown to be a proxy for surface particulate matter pollution, PM2.5 (aerosol mass in µg/m 3 ).  Single channel retrieval using measured visible channel reflectances from GOES Imager.  IR channels used in identifying clouds.  Physical retrieval that separates contribution of surface from aerosols.  Retrievals over clear sky and dark vegetative pixels only.  Product specifications  Name: GOES Aerosol and Smoke Product (GASP)  Satellites: GOES-East and GOES- West  Accuracy: 0.04 (GOES-E) ; 0.06 (GOES-W)  Spatial resolution: 4 km at nadir  Temporal resolution: 30 min.  Latency: within one hour of data capture  Data format: binary file and JPEG imagery  Data availability: 2003 – present

PM2.5 Monitoring using GOES AOD Data Figures courtesy of Philip Morefield, EPA

5 Aerosol Retrievals from a Geostationary Platform (cont.) GOES vs AERONET AOD comparisons for 10 eastern US stations show retrievals have: –0.13 rmse –Absolute bias less than 30% when AOD is > 0.1 Results are similar to MODIS vs AERONET On average, 39% of days in a month of have good retrievals compared to 11% for MODIS and 5% for MISR. Prados et al., JGR, 2007 Paciorek et al., Envin. Sci. and Tech., 2008 Percent Good Retrievals GOES AOD Validation

6 Seasonal Burned Area over Alaska from GOES-11 in 2009 Fuel Load (kgC/ha) Biomass Burning Products Fire hot spots  Detection and instantaneous fire size from GOES Burned area  Based on algorithm that combines GOES and MODIS/AVHRR fire hot spots/sizes in a fourier model to compute burned area Fuel load  Based on MODIS land products Emissions  Product of burned area (ha), fuel load KgC/ha), emissions factors (g/KgC)  Product specifications  Name: GBBEP  Satellites: GOES-East and GOES-West  Accuracy:30%  Spatial resolution: 4 km at nadir  Temporal resolution: houtly  Latency: one day  Data format: binary file and JPEG imagery  Data availability: 2002 – present 5-yr mean over CONUS

7 Animation of Smoke Plume Detection Original AOD Image Smoke AOD Image Semi-quantitative retrieval of column average smoke concentration (µg/m 3 ) using AOD and fire hot spots from GOES  Uses source apportionment and pattern recognition techniques to isolate smoke aerosols from other type of aerosols  Smoke mass concentration (m c ) is obtained using AOD (τ), mass extinction efficiency (k), and aerosol height (h) Product specifications  Name: ASDTA  Satellites: GOES-East and GOES-West (includes Alaska and Hawaii)  Accuracy: 40%  Spatial resolution: 0.15 o  Temporal resolution: hourly  Latency: one day  Data format: binary file, GRIB file, JPEG imagery  Data availability: present GOES Smoke Concentration Product (1)

NWS testing of smoke verification for Alaska using NESDIS GOES-W smoke product GOES Smoke Concentration Product (2)

9 GOES-12: GOES-13: Geostationary Satellite Platform Challenges Navigation errors are a big problem for current GOES satellites as shown in adjacent figure for a fire event –Spacecraft jitter despite 3-axes stabilization Recently launched GOES-13 (new spacecraft) has less jitter VisibleShortwave IR

10 UTC 14:45 07/20/2008 GOES-West GOES-East Viewing Geometry: GOES-West (135 o W) and GOES-E (75 o W) observing the same location on the Earth can provide totally different AODs. In this example, GOES-W views in a forward scattering mode (scattering angles less than 90 o ) whereas GOES-E views in a backward scattering mode (scattering angles greater than 90 o ). Since sensitivity (change in top of the atmosphere reflectance to change in AOD) is greater for forward scattering mode, retrievals are more accurate for those conditions. GOES-W AOD sector does not have coverage Optimum scattering angle retrieval range Geostationary Satellite Platform Challenges (cont.)

11 UTC:1415UTC:2315 Bad retrievals due to viewing geometry effects etc. can be removed in post- processing of data. NESDIS developed a data quality flag based on scattering angle and spatial variability tests. Only data for viewing geometry corresponding to scattering angles greater than 70 o and less than 150 o are retained. Before screening After screening Geostationary Satellite Platform Challenges (cont.)

What about GOES-R? Similar and some new products but with enhanced accuracy, spatial resolution, and temporal resolution. 2-km resolution allows picking up localized events; 5-min resolution allows peeking through clouds, now-casting; Night-time retrievals (except for dust) not possible due to reliance on visible channels 12 GOES-R ABI in five minutesCurrent GOES in five minutes 24 April, 2004, Central America, 5 minute resolution loop Note black dots that are fire detections

13 Aerosol Retrievals from Next Generation Geostationary Satellite GOES-R capabilities –16 channel Advanced Baseline Imager. –AOD at 2 X 2 km resolution at nadir –5-min CONUS, 30-sec mesoscale, 15-min full disk modes or full disk in five minutes. –AOD retrieval is a multi- channel MODIS/VIIRS type retrieval. Current GASP retrieval is a single channel retrieval. 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 Laszlo et al., Advances in Space Research,

14 ±0.05 ±0.02 ±0.04 ±0.1 ±0.12 Aerosol Retrievals from Next Generation Geostationary Satellite (cont.) GOES-R ABI products –AOD –Aerosol Particle Size –Smoke/dust mask –Fire hot spots and emissions AOD Accuracy Dust Mask Smoke Mask AOD ABI Aerosol Detection CALIPSO Matchups Accuracy (%) Smoke mask 2280 Dust mask2681

Proving Ground Experiments with the National Weather Service (1) 15 NESDIS/STAR is developing a global biomass burning emissions product from a network of geostationary satellite sensors using GOES- R emissions algorithm. STAR scientists are working closely with NWS/NCEP scientists in validating and testing the product. Adjacent figure shows an example of seasonal mean Fire Radiative Power (FRP) for Fall FRP is used to compute trace gas and aerosol emissions that are used in the model to improve forecasts. Funded by Joint Center for Satellite Data Assimilation (JCSDA) but the concept is similar to AQPG

Proving Ground Experiments with the National Weather Service (2) 16 Version 3 Version 5 STAR scientists worked closely with the NWS scientists in testing the use of satellite-derived dust mask (MODIS used as a proxy for GOES-R Advanced Baseline Imager) in verifying dust forecasts. In working with the NWS, we learned that the algorithm is flagging some of the thin dust plumes (localized) as clouds. Algorithm (V3) was changed (V5) to rectify the identification of dust as cloud.

Proving Ground Experiments with the National Weather Service (3) V3 Funded by GOES-R program office and National Weather Service Office of Science and Technology but the concept is similar to AQPG V3 V5

GOES AOD ABI AOD ABI Suspended Matter (µg/m 3 ) ABI AOD and Aerosol Model Method 2Method 1 GOES-R ABI Suspended Matter UMCP aircraft spirals

Preliminary Validaiton of GOES-R ABI Suspended Matter Profile No. GOES AOD Aircraft AOD ABI AOD ABI Mass (ug/m 3 ) GOES Mass (ug/m3) Aircraft Mass (ug/m 3 ) ± ± ± ± ± ± ± ±0.01 Method 1: 8.32±0.01 Method 2: 12.06± ± ± ± ± ±0.01 Method 1: 20.0±0.83 Method 2: 19.6± ± AODSuspended Matter ABI AOD Derived using MODIS radiances as proxy

Conclusions 20 Air quality products from GOES-R ABI have been in the development at NOAA/NESDIS for the past three years.  Algorithms built based on experience with GOES and MODIS. MODIS and model simulated data are being used as proxy data. Algorithm developers working closely with NWS. Algorithm developers would like to work closely, through the proving ground, with EPA, state/local air quality forecasters.  User/focus group feedback very critical for improving product quality, usage, distribution, and the development of new applications including specific data formats.