1. Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA), Chuanyu Xu (IMSG), and Pubu Ciren (IMSG) AERONET data provided by NASA (PI: Brent Holben) Aircraft data provided by UMD (PI: Jeff Stehr) AIRNOW data provided by EPA (PI: John White)

2. GOES-R Advanced Baseline Imager (ABI) Aerosol Products Aerosol Optical Depth (AOD) Suspended Matter (µg/cm 2 ) Aerosol detection (qualitative flag for smoke and dust) ABI Products generated using proxy data – MODIS (Aqua and Terra) radiances – Theoretical radiances calculated from CMAQ 3D aerosol fields 2 km 10 km All validation work reported in this presentation is based on the 10 km ABI products

3. Suspended matter is calculated from ABI AOD using: Where m c is mass concentration, τ is ABI AOD, k is mass extinction efficiency (cm 2 /µg) and h is height. – k is a function of aerosol type and and h is assumed to be 3 km (from 6S model used to create LUTs) – Errors in ABI aerosol type (k) and h being a non-derivable parameter can lead to errors in m c GOES-R ABI Suspended Matter

4. Validating the ABI Aerosol Products AOD validation (routinely done) – AERONET measurements Suspended matter validation (spot check verification only) – EPA AIRNOW observations Knowledge of aerosol type and height not available. – Aircraft observations Capture vertical profile of aerosols Information on scattering vs. absorption can be used as a proxy for aerosol type Limited to data between surface and 3 km. Aerosols aloft are not captured In this study, we attempt to use aircraft, AIRNOW, and AERONET data to assess if aircraft data can be useful to evaluate GOES-R SM product.

5. Study Domain Aircraft data – July 2011 (13 flight days with multiple spirals each day). GOES data – 4 km (nadir) and 30 min temporal resolution. GOES-R data – GOES-R ABI algorithm run on MODIS (Aqua and Terra) 10 km (nadir) resolution radiances as proxy. AIRNOW data – Hourly surface PM2.5 measurements. AERONET data – 15 minute AOD measurements. – DRAGON network (10s of stations within 100 km x 100 km over eastern US. AOD Matchups GOES/GOES-R vs. AERONET: ± 15 min; nearest GOES/GOES-R pixel GOES/GOES-R vs. Aircraft: ± 30 min; nearest GOES/GOES-R pixel GOES/GOES-R vs. AIRNOW: ± 30 min; nearest GOES/GOES-R pixel

6. AIRCRAFT vs. GOES AODs Bias0.089 RMS0.137 Integrated aerosol extinction profiles

7. Aircraft vs. AERONET AODs Bias0.187 RMS0.203

8. GOES-R ABI vs. Aircraft AODs Agreement between aircraft and AERONET AODs is similar to aircraft and GOES AOD results (previous slide) indicating that during this time period (July 2011), the aerosol was not necessarily confined to PBL. Note aircraft measures between surface and 3 km while both GOES and AERONET measurements are for total column. Bias0.095 RMS0.229

9. GOES-R ABI vs. AERONET AODs GOES-R ABI AODs retrieved using MODIS (Aqua and Terra) as proxy agree with AERONET observations better compared to aircraft AODs. This could be due to the fact that aerosols were not necessarily confined to PBL. The scatter plots also show some strange features (highlighted) that need further investigation. ABI AOD, however, meets GOES-R specification set at 0.04 for AOD ranging between 0.2 and 0.8. Bias0.037 RMS0.109 Could be issues with surface reflectance retrievals at low optical depths.

10. GOES-R ABI vs. AIRNOW PM2.5 Bias13.53 RMS14.5

11. GOES-R ABI vs. Aircraft Mass Concentrations Aircraft particle number density measured at the lowest altitude converted to mass concentration using density of aerosol typical for eastern US and measured particle size Bias8.94 RMS24.30 Bias-4.97 RMS11.49

12. Left panel: AOD converted to mass concentration using aerosol type that the ABI algorithm identified Right panel: AOD converted to mass concentration using urban aerosol type The two data points (highlighted) changed marginally but no significant change to the overall results. Ideal way to observe the impact is to run the ABI algorithm using urban aerosol model LUT and then convert the AOD to mass concentration using urban aerosol type. That work will be done in the future. Aerosol Type

13. Aerosol Height

14. Aircraft data: Lowest Layer Aircraft data: Lower than 500m Aircraft data: Lower than 1000m Aircraft data: Column Average Density 1.7 g/cm 3 Aircraft data: Lower than 1000m

15. Aircraft data: Column Average Aircraft data: Lowest Layer Aircraft data: Lowest 500 m Aircraft data: Lowest 1000 m Density 1.0 g/cm 3

16. Profile for one spiral case on July 18, 2011 Profile for one spiral case on July 20, 2011 Profile for all spiral cases (averaged) Variability in aerosol concentration as a function of height.

17. Conclusions AOD comparisons – GOES-R ABI AOD correlates well with aircraft AOD (integrated extinction profile) but biased high (~0.1). Potential reasons:  Aircraft data extend only up to 3 km  ABI algorithm picked the wrong aerosol model (type)  Uncertainty in surface reflectance retrieval PM2.5 comparisons – GOES-R ABI estimates of surface PM2.5 correlate well with AIRNOW (ground) and aircraft observations but biased high. The bias between ABI and AIRNOW is ~13.5 µg/m 3 and bias between ABI and aircraft is 9 µg/m 3 when the aircraft data are averaged for the whole column. – These comparisons are sensitive to assumed density of aerosol as well as the aerosol height. Limitations of aircraft data – Uncertainty in converting particle number density to mass concentration (UMD working on calibrating this conversion) – Aircraft ceiling of ~3 km – Limited geographic coverage Analysis indicates that although suspended matter product from ABI has reasonable values, it is biased high. Knowing aerosol height is more important than aerosol type. ABI AOD meets specification when compared to AERONET but not when compared to aircraft AOD but the sample size is very small for aircraft data.