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)
Aircraft spiral on July
20, 2011 at 14 UTC
GOES-R Advanced Baseline
Imager (ABI) Aerosol Products
• Aerosol Optical Depth
(AOD)
• Suspended Matter
(µg/cm2)
• 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
All validation work reported in
this presentation is based on the
10 km ABI products
2 km
10 km
GOES-R ABI Suspended Matter
• Suspended matter is calculated from ABI AOD using:
mc 

k h
Where mc is mass concentration, τ is ABI AOD, k is mass
extinction efficiency (cm2/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 mc
Mass Extinction Efficiency (cm2/µg)
for Different Aerosols
Inverse of k
–
–
80
70
60
50
40
30
20
10
0
Generic
Urban
Smoke
Dust
0
1
2
3
4
Aerosol Optical Thickness
5
6
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.
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
AIRCRAFT vs. GOES AODs
Integrated aerosol extinction profiles
Bias
0.089
RMS
0.137
Aircraft vs. AERONET AODs
Bias
0.187
RMS
0.203
GOES-R ABI vs. Aircraft AODs
Bias
0.095
RMS
0.229
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.
GOES-R ABI vs. AERONET AODs
Could be issues with surface
reflectance retrievals at low
optical depths.
Bias
0.037
RMS
0.109
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.
GOES-R ABI vs. AIRNOW PM2.5
Bias
13.53
RMS
14.5
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
Bias
8.94
RMS
24.30
Bias
-4.97
RMS
11.49
Aerosol Type
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 Height
Density
1.7 g/cm3
Aircraft data:
Column Average
Aircraft data:
Lower than 500m
Aircraft data:
Lowest Layer
Aircraft
Aircraft data:
data:
Lower
than
Lower than 1000m
1000m
Density
1.0 g/cm3
Aircraft data:
Column Average
Aircraft data:
Lowest Layer
Aircraft data:
Lowest 500 m
Aircraft data:
Lowest 1000 m
Profile for all spiral cases (averaged)
Variability in aerosol concentration as a
function of height.
Profile for one spiral case on July 18, 2011
Profile for one spiral case on July 20, 2011
• AOD comparisons
Conclusions
– 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/m3 and bias between ABI and aircraft is 9 µg/m3 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.