ammonia from space: how good are current measurements and...
TRANSCRIPT
Ammonia from space: how good are current measurements and what could future
instruments tell us
Karen Cady-Pereira1, Mark Shephard2, Daven Henze3, Juliet Zhu3, Jonathan Wrotny1, Jesse Bash5, Armin Wisthaler6,
Kang Sun7, Amy Jo Scarino4, Ming Luo8
1. Atmospheric and Environmental Research (AER) 2. Environment Canada 3. University of Colorado
4. NASA Langley 5. Environmental Protection Agency (EPA)
6. University of Innsbruck 7. Princeton University
8. NASA Jet Propulsion Laboratory (JPL)
NH3 Sources
Bi-directional Flux
AGRICULTURE • Animal waste
(temperature dependent) • Fertilizer application
Industry • Fertilizer • Coal Mining • Power generation
Biomass burning
Automobiles (catalytic converters) • Large urban centers
• 50% of NH3 in LA area
NH3 in the atmosphere
Long-range import
Long-range export
PM2.5 Particles
NH3 + HNO3NH4NO3 2 NH3 + H2SO4(NH4)2SO4
• Increase incidence of cardiovascular and respiratory diseases
• Increase number of CCN #• Affect cloud radiative properties #• Climate change
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SO2, NOX decreasing but NH3 forecast to increase
Monitoring NH3 is difficult
NH3 is highly reactive highly variable in space and time
• Data from an Open path Quantum Cascade Laser (QCL) on a moving platform in the San Joaquin Valley during DISCOVER-AQ 2013.
NH3 from aircraft DISCOVER-AQ campaign in January 2013 in the San Joaquin Valley
Aircraft campaigns provide high quality data, but are not feasible for long term monitoring over large areas
Janu
ary
30
PBL from airborne HSRL
backscatter
NH3 from PTR and Picarro
Janu
ary
21
TES and CrIS instruments
Two TES observation modes: • Global Surveys: 26 hours
long, return to starting point every 16 days
• Special Observations: higher sampling density over shorter tracks
TES CrIS Satellite AURA NPP Launch July 2004 October 2011 Resolution 0.06 cm-1 0.625 cm-1
Footprint 5x8 km rectangle 14 km diameter circle Repeat cycle Once every 16 days Daily Equatorial crossing 1:30 am and 1:30 pm 1:30 am and 1:30 pm Noise in NH3 window 0.09 – 0.12 K 0.03 – 0.06 K
NH3 from TES and CrIS Simulated spectra and NH3 signal
18 ppbv at surface
• Detectability is ~ 1 ppbv under ideal conditions
• But thermal contrast also plays a role
TES
CrIS
NH3 from TES and CrIS
• Both instruments most sensitive to NH3 between 950 and 600 mbar • TES is more sensitive to amounts lower in the atmosphere • 1 piece of information or less: DOFS<1.0 • Collapse all information to a single point: RVMR
• Easier to compare with in situ measurements, models and other instruments
TES CrIS
Global NH3 results TES NH3 from GS: 2004-2011
• Large increase from NH winter to summer
• Hotspot over
India • Biomass burning
signal over South America and Africa
Eastern China: 2007-2009
Surface NH3
Seasonal means from TES
TES NH3 TES transect path
Bei
jing Beijing
Beijing Shangdianzi
Meng et al., ACP, 2011
Shangdianzi
TES and aircraft NH3 from DAQ January 30: aircraft tracks under TES transect
• P3B aircraft @ 300 m • TES maximum sensitivity between 1 and 2 km
Total Column RVMR
TES and aircraft NH3 are well correlated
Surface and satellite NH3 in DISCOVER-AQ 2013
• Satellite and QCL NH3 measured in January 2013 are spatially well correlated
• QCL almost directly under TES transect in the San Joaquin Valley on January 28, 2013
Measuring NH3 daily variations • North Carolina in situ data • CMAQ model with different temporal emission profiles
• Static profile • Dynamic profile
NH3 from a geostationary platform
CMAQ vs Simulated Retrievals • TES-like instument • Geostationary platform • Provides data every three hours • CMAQ profiles over California for
a warm July day • Difference in CMAQ NH3 matches
difference in retrieved NH3
Final comments
• Limitations • TES and CrIS are sensitive to only higher amounts (> 1.0 ppbv) of NH3 • Require some thermal contrast to detect NH3 • Clouds reduce amount of useful data
• TES and CrIS data • Show seasonal and spatial variability consistent with in situ
measurements • Have greater temporal and spatial coverage than aircraft campaigns or
surface networks
• TES-like instrument on a geostationary platform could provide information on daily variability
• TES NH3 are operational data available at:
• http://avdc.gsfc.nasa.gov/index.php?site=635564035&id=10&go=list&path=/NH3
• netcdf files with all NH3 retrievals for each month since September 2004
Acknowledgements • Markus Mueller and Tomas Mikoviny from the PTR instrument team • PICARRO instrument team • TES team at JPL • Jesse Bash and Rober Pinder from the EPA • Research was supported by
• the Jet Propulsion Laboratory, California Institute of Technology under contract to the National Aeronautics and Space Administration (NASA).
• NOAA Climate Program Office (CPO) Grant number NA130AR4310060 • CU support from NASA grant NNX10AG63G and EPA-STAR RD83455901
Better emissions with TES NH3
April
July
October April
July
October
GC
(p
pb
) I
nit
ial
GC
(p
pb
) O
pti
miz
ed
AMoN obs (ppb) 0 1 2 3 4 5 6
01
23
45
6
Observation NH3, (ppb)
GE
OS!
Ch
em
, N
H3
, (p
pb
)
October
y =
0.494
+
0.564
x
0 1 2 3 4 5 6
01
23
45
6
Observation NH3, (ppb)
GE
OS!
Ch
em
, O
ptim
ize
d,
NH
3,
(pp
b)
October
y =
0.455
+
0.999
x
0 1 2 3 4 5 6
01
23
45
6
Observation NH3, (ppb)
GE
OS!
Chem
, N
H3, (p
pb)
October
y =
0.494
+
0.564
x
0 1 2 3 4 5 6
01
23
45
6
Observation NH3, (ppb)
GE
OS!
Chem
, O
ptim
ized, N
H3, (p
pb)
October
y =
0.455
+
0.999
x
R2=0.545 RMSE=0.952
NMB=-0.138
R2=0.693 RMSE=0.862
NMB=0.166
0 2 4 6 8 10 12
02
46
81
01
2Observation NH3, (ppb)
GE
OS!
Ch
em
, N
H3
, (p
pb
)
July
y =
1.5
+
0.51
x
0 2 4 6 8 10 12
02
46
81
01
2
Observation NH3, (ppb)
GE
OS!
Ch
em
, O
ptim
ize
d,
NH
3,
(pp
b)
July
y =
2.22
+
1
x
0 2 4 6 8 10 12
02
46
81
01
2
Observation NH3, (ppb)G
EO
S!
Ch
em
, N
H3
, (p
pb
)
July
y =
1.5
+
0.51
x
0 2 4 6 8 10 12
02
46
81
01
2
Observation NH3, (ppb)
GE
OS!
Ch
em
, O
ptim
ize
d,
NH
3,
(pp
b)
July
y =
2.22
+
1
x
R2=0.281 RMSE=1.990
NMB=-0.045
R2=0.365 RMSE=3.534
NMB=0.659
0 2 4 6 8 10
02
46
81
0
Observation NH3, (ppb)
GE
OS!
Ch
em
, N
H3
, (p
pb
)
April
y =
0.215
+
0.225
x
0 2 4 6 8 10
02
46
81
0
Observation NH3, (ppb)
GE
OS!
Ch
em
, O
ptim
ize
d,
NH
3,
(pp
b)
April
y =
!0.189
+
1.02
x
0 2 4 6 8 10
02
46
810
Observation NH3, (ppb)
GE
OS!
Chem
, N
H3, (p
pb)
April
y =
0.215
+
0.225
x
0 2 4 6 8 10
02
46
810
Observation NH3, (ppb)
GE
OS!
Chem
, O
ptim
ized, N
H3, (p
pb)
April
y =
!0.189
+
1.02
x
R2=0.497 RMSE=1.834
NMB=-0.069
R2=0.406 RMSE=2.107
NMB=-0.678
Largest changes western US and Mexico
• Used GEOS-Chem adjoint with TES NH3 profiles, averaging kernels and error covariances to optimize model
• Optimized GC shows better agreement with AMoN network measurements
Zhu et al., 2013, JGR
South Asia: July-August 2007
Indus River Valley High NH3 north of New Delhi and in the northern Indus valley
TES NH3 and CO Monthly NH3 RVMR
July 2010
Aug 2010
Sept 2010
Monthly CO at 681 hPa
NH3 from TES V005 operational product
Biomass burning in South America is evident in both CO and NH3 maps
High NH3 values over northern India
CalNex 2010: California spring CalNex: Campaign focused on air quality and climate change
Aircraft and surface measurements and TES transects
Central Valley, southern California basin, Pacific Ocean
CALNEX: TES vs Aircraft TES May 14 transect @13:15 local
Partial P3 May 12 track (flying at ~ 300 AGL @ 17:30 local)
NH3 measurements from TES and aircraft are well correlated
Day vs Night: TES DAY • Lower NH3 values • Sensitivity peaks between 900 and 750 mbar
NIGHT • Greater range of NH3 values
• More high values • Sensitivity peaks between surface and 900 mbar
Collapse of boundary layer Pooling of NH3
CMAQ simulated NH3 profile
And now …
NH3
Shephard et al.[2011]
TES NH3 Validation
North Carolina Intense livestock
farming (hogs, chickens, turkeys)
• EPA CAMNet NH3 monitoring network
• TES high spatial density observations (transects)
• Feb – Dec 2009
• Allows detection of spatial variability and seasonal trends CHALLENGE
• TES: instantaneous profile over 5x8 km
• CAMNet: two week average at a surface point
• Cloudy summers!
Seasonal and spatial variability
NH3 vs time NH3 vs source concentration
TES and surface measurements are qualitatively well correlated
Pinder et al., GRL, 2011