1. GEO-CAPE Atmosphere SWG activities
Daniel J. Jacob
Co-Lead, GEO-CAPE Atmosphere Science Working Group

2. The SWG defines mission requirements, evaluates implementation options
Science Traceability
Matrix (STM)
Science Value
Matrix (SVM)
Weiss et al., IEEAC 2004
GEO-CAPE also has an Applications Traceability Matrix (ATM)
and corresponding Applications Value Matrix (AVM)
STM leads: Doreen Neil. Daniel Jacob
SVM leads: Doreen Neil, David Edwards
ATM/AVM leads: Jessica Neu, Rob Pinder

3. Science Questions for GEO-CAPE - Atmosphere
What are the temporal and spatial variations of emissions of gases and aerosols important for air quality and climate?
How do physical, chemical, and dynamical processes determine tropospheric composition and air quality over scales ranging from urban to continental, diurnal to seasonal?
3. How does air pollution drive climate forcing and how does climate change affect air quality on a continental scale?
4. How do we improve air quality forecasts and assessments for societal benefit?
5. How does intercontinental transport affect air quality?
6. How do episodic events such as wild fires, dust outbreaks, and volcanic eruptions affect atmospheric composition and air quality?
Fishman et al.
[2012]

4. Measurement requirements for GEO-CAPE: spectral regions and precision
Species
Precision
Rationale O3
Stratosphere: 5%
2 km-tropopause: 15 ppb
0-2 km: 10 ppb
Surface AQ, transport, climate forcing CO
2 km – tropopause: 20 ppb
0-2 km: 20 ppb
CO emission, transport
Aerosol
0.05 (AOD)
Surface AQ, aerosol sources and transport, climate forcing
NO2
1x1015 cm-2
NOx emissions, chem.
HCHO
1x cm-2
VOC emissions, chem.
SO2
SOx emissions, chem.
CH4
Troposphere: 20 ppb
CH4 emissions
NH3
0-2 km: 2 ppb
NH3 emissions
CHOCHO
4x cm-2
VOC emissions, chem., aerosol formation
Absorbing aerosol
0.02 (AAOD)
Climate forcing
Aerosol index
0.1
Aerosol events
Aerosol centroid height
1 km
Aerosol plume height, large-scale transport, AOD to PM conversion

5. Measurement requirements for GEO-CAPE:
viewing geometry, resolution, frequency
Orbit centered over ~100o W, observing domain north of 10o N
Hourly data over land/coastlines
with pixel resolution of 1x1 km2 (aerosols), 4x4 km2 (gases), for SZA<70o (some species), <50o (others)
Daily data over open oceans (O3, CO, aerosol) with pixel resolution of 16x16 km2
Observing
domain

6. What do TEMPO selection and geostationary constellation mean for achieving GEO-CAPE goals?
TEMPO (UV/Vis) will monitor tropospheric ozone (2 levels), aerosols, NO2, SO2, formaldehyde, glyoxal with 1-hour temporal resolution, 4x2 km2 spatial resolution
TEMPO will be part of a geostationary constellation with other sensors observing Europe and East Asia
Kelly Chance, PI
Next frontier in satellite observations
of atmospheric composition!
TEMPO Sentinel GEMS

7. Mapping of TEMPO on the GEO-CAPE Science Value Matrix
GCIRI addition
Doreen Neil,
NASA LaRC
TEMPO delivers 70% of GEO-CAPE science;
A concurrent GEO-CAPE IR Instrument (GCIRI) can deliver the other 30%
Preliminary mapping on the Applications Value Matrix has similar results

8. Current role of the GEO-CAPE Atmosphere SWG
Evaluate TEMPO capabilities, develop science +application products for TEMPO
Assess value of different GCIRI additions to TEMPO
Develop value of constellation for mission science
Four reconstituted WGs for FY13:
Working Grouo
Chairs
Motivating question
Aerosols
Mian Chin,
Jun Wang
What can TEMPO really do for aerosols, and is there a measurement need beyond TEMPO+GOES-R?
Emissions
Daven Henze, Greg Frost
How well can TEMPO and GCIRI constrain emissions?
Urban/Regional OSSE
Brad Pierce, Kevin Bowman
How well can TEMPO and GCIRI inform urban-scale and regional AQ?
Global OSSE
David Edwards, Arlindo daSilva
How well can TEMPO and GCIRI inform continental-scale atmospheric composition, and how can we best exploit the constellation?

9. Observing System Simulation Experiment (OSSE)
How can knowledge of a state x be improved by the proposed measurement of y?
We need three models:
CTM 1 y = F1(x) defines the “true” atmosphere (Nature Run)
CTM 2 y = F2(x) +  is the forward model (Control Run)
Instrument model describes measurement of y including smoothing, noise
Model 1
Instrument model
“True” x
“True” y
“Measured” y’
Prior estimate
xA ± SA
Model 2
Data assimilation
Compare x’ ± S’ to x and to xA ± SA :
Is the measurement worth it?
improved estimate
x’ ± S’
Good error characterization for instrument model
Independence of Model 1 and Model 2
Realistic atmospheric variability in Model 1
State of science for Model 2
A reliable OSSE requires:

10. Global OSSE WG Foci: Improve realism of GEO-CAPE OSSEs
Demonstrate value of constellation
GEOS-5 10-km resolution “true” atmosphere
with aerosols, chemistry (Arlindo daSilva, NASA)
ECMWF workshop, Oct 2012
Spread in MOPITT AKs for surface & 500 hPa CO retrievals
(CONUS)
Improving instrument models:
variability of averaging kernel matrix
for MOPITT CO (Worden et al., 2013)

11. Urban/Regional OSSE WG
Foci:
Continuation of regional OSSE development initiated in FY12
Initiation of urban OSSE for GEOCAPE multi-spectral ozone retrievals.
Atlanta ozone (CMAQ)
“True” regional and urban atmospheres generated
Aerosol extinction, surface reflectance, variable AK matrices included for more realistic instrument modeling
Atlanta surface reflectance,
different times of day
Atlanta aerosol extinction
(CMAQ)

12. Emissions WG
Focus:
Assess GEO-CAPE constraints on emissions of NOx, VOCs, NH3, aerosols
Can we detect doubling of CH4 emissions from natural gas in western US?
Step 1: Perturb model natural gas emissions by 2x in west.
Step 3: Assimilate pseudo-obs into GEOS-Chem adjoint inversion
Perturbed/prior emissions
GEOS-Chem CTM
“Observed” enhancement
GEOS-Chem Adjoint
1.0
1.0
2.0
2.0
-1.0
[ppb]
1.0
1.0
Step 2: Sample model as GEOCAPE OSSE
Observation Platform
Error reduction in perturbed region
Error reduction in North America
GEOCAPE
88 %
96 %
TES-like LEO
8 %
93 %
0.75
0.0
200
1000
Pressure [hpa]
Rows of GEOCAPE
averaging kernel matrix
GEOS-Chem CTM
0.1 hPa
954 hPa .
Only GEOCAPE locates emissions correctly

13. Aerosols WG ln(Io/I) Foci:
Improve understanding of TEMPO capabilities
Assess added value from concurrent GOES-R observations
Aerosol retrieval availability
(April 10, 2012)
Fraction clear sky
1x1 km (GEO-CAPE)
4x2 km (TEMPO)
σ <
σ < 0.005
σ < 0.01 a b c
1x1 km2
MODIS cloud criteria
TEMPO
4x2 km2
relaxed cloud criteria
Satellite simulator for geo aerosol OSSEs
ln(Io/I)
Successful mapping w/TEMPO
will require relaxed cloud criteria