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![Page 1: Scattering by Earth surface Instruments: Backscattered intensity I B absorption Methane column Application of inverse methods to constrain methane.](https://reader035.fdocuments.net/reader035/viewer/2022062806/56649ed15503460f94bdfc4b/html5/thumbnails/1.jpg)
Scattering by Earth surface
Instruments:
Backscatteredintensity IB
abso
rpti
on
l1 l2
Methane column
2 1ln[ ( ) / ( )]
AMF
B BI I
Application of inverse methodsto constrain methane emissions from satellite data
Methane observable by solar backscatter at 1.6 and 2.3 µmnear-unit sensitivityat all altitudes
Remove air mass factor (AMF) dependence using CO2 retrieval for nearby wavelengths:
44 2
2
CHCH CO
CO
X X dry column mixing ratio
2002 2005 2009 20016 ?
SCIAMACHY
60 km, 6-day GOSAT5 km, 3-day, sparse
TROPOMI Geostationary 7 km, 1-day 2 km, 1-hour
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Global distribution of methane observed from space
Sources: wetlands, livestock, landfills, natural gas… Sink: atmospheric oxidation (10-year lifetime)
Global source is 550 60 Tg a-1, constrained by knowledge of global sink
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Long-term trends of methane are not understood
Source attribution is difficult due to diversity, complexity of sources
Livestock90
Landfills70
Gas60
Coal40
Rice40
Other natural40
Wetlands180
Fires50
Global sources,Tg a-1
Individual sources uncertain by at least factor of 2; emission factors are highly variable, poorly constrained
the last 1000 years
the last 30 years
E. Dlugokencky, NOAA
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Satellite data as constraints on methane emissions
“Bottom-up” emissions (EDGAR):best understanding of processes
2009-2011537 Tg a-1
Satellite data for methane columns
Optimal estimate inversionusing GEOS-Chem model adjoint
Ratio of optimal estimateto bottom-up emissions
Turner et al. [2015]
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Building a continental-scale methane monitoring system
Can we use satellites together with suborbital observations of methane to monitor methane emissions on the continental scale?
CalNex
INTEX-A
SEAC4RS
1/2ox2/3o grid of GEOS-Chem
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Bottom-up methane emissions for N. America (2009-2011)
total: 63 Tg a-1 wetlands: 20
oil/gas: 11livestock: 14
waste: 10 coal: 4
CONUS anthropogenic emissions: 25 Tg a-1 (EDGAR) 27 Tg a-1 (EPA) 8 oil/gas 9 livestock 6 waste 3 coal Aircraft/surface data indicate that these bottom-up estimates are too low
Turner et al. [2015]
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High-resolution inversion of methane emissions
GEOS-Chem CTM and its adjoint1/2ox2/3o over N. America
nested in 4ox5o global domain
Observations
Bayesianinversion
Optimized emissions (“state vector”)at up to 1/2ox2/3o resolution
Validation Verification
EDGAR 4.2 + LPJprior bottom-up emissions
Three applications: 1. Summer 2004 using SCIAMACHY 2. CalNex May-June 2010 aircraft campaign over California 3. 2009-2011 using GOSAT
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First step: validate the satellite methane data
SCIAMACHY validation using vertical profiles from INTEX-A aircraft campaignSCIAMACHY column methane mixing ratio XCH4 INTEX-A methane below 850 hPa
C. Frankenberg(JPL)
D. Blake(UC Irvine)
C. Frankenberg(JPL)
H2O retrieval bias: remove it!Differencebetween satelliteand aircraft
after bias correction
Wecht et al. [2014a]
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Second step: check model background
Model mean methane for Jul-Aug 2004 (background) and NOAA data (circles)
Wecht et al. [2014a]
4ox5o 1/2o2/3o
Include time-dependent boundary conditionsin state vector
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Third step: choose state vector
1-1 ) () ( )( ) OAx AS K S F x -(x - x 0yx TJ
If state vector is too large, cost function is dominated by prior: smoothing error
Correct this by aggregating state vector elements, but this incurs aggregation error
There is an optimal state vector dimension for fitting observations:1ˆ ˆ( ) ( )T Oy F(x) S y F(x)
# state vector elements
aggregation smoothing
As dim(x) increases, the importance of the prior terms increases
Prior Observations
native grid aggregated grid
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Selection of state vector for inversion of SCIAMACHY dataOptimal clustering
of 1/2ox2/3o gridsquares
Correction factor to bottom-up emissions
Number of clusters in inversion1 10 100 1000 10,000
34
28
Optim
ized US
emissions (T
g a-1)
Native resolution (7,906 gridsquares) 1000 clusters
Wecht et al. [2014a]
1ˆ ˆ( ) ( )T Oy F(x) S y F(x)
aggregation smoothing
Inverse model fit to observations
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Verification of inversion results with INTEX-A aircraft data
Prioremissions
Optimizedemissions
GEOS-Chem simulation of INTEX-A aircraft observations below 850 hPa:
with prior emissions with optimized emissions
Wecht et al. [2014a]
Tg CH4 a-1
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Attribution of geographical source contributions to source type is complicated by spatial overlap
For a given cluster, assume that prior emission attribution by source type (i) is correct:
,A i A ii
E f Eand apply inversion scaling factor for that cluster to all source types weighted by fi
with 1ii
f
Livestock and natural gas emissions are often collocated
Eagle Ford Shale, Texas
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North American methane emission estimatesoptimized by SCIAMACHY (Jul-Aug 2004)
1700 1800ppb
SCIAMACHY column methane mixing ratio Correction factors to a priori emissions
Livestock Oil & Gas Landfills Coal Mining Other0
5
10
15US anthropogenic emissions (Tg a-1)
EDGAR v4.2 26.6
EPA 28.3
This work 32.7
Wecht et al. [2014a]
1000 clusters
Livestock emissions are underestimated by EDGAR/EPA, oil/gas emissions are not
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Constraining methane emissions in CaliforniaStatewide greenhouse gas emissions must decrease to 1990 levels by 2020
Large difference between bottom-up emission inventories:EDGAR v4.2 (2010) vs. California Air Resources Board (CARB)
Wecht et al. [2014b]
CARB: 1.51
CARB: 0.86CARB: 0.18
CARB: 0.39
Tg a-1
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Inversion of methane emissions using aircraft campaign data
CalNex aircraft observations GEOS-Chem w/EDGAR v4.2Correction factors to EDGAR(analytical inversion, n= 157)
May-Jun2010
Wecht et al. [2014b]
California emissions (Tg a-1)
G. Santoni (Harvard)
May-Jun2010
EDGAR v4.2 1.92
CARB 1.51
This work 2.86 ± 0.21
State totals
Livestock Gas/oil Landfills Other0
0.20.40.60.8
11.2
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Diagnosing the information content from the inversion
ˆ ( )Ax = x + (I - A) x x + Gεsolution = truth + smoothing + noise
averaging kernel matrix prior
x is the state vector of emissions (n = 157)
Diagonal elements of ˆ / A x x
• Diagonal elements of A range from 0 (no local constraint from observations) to 1 (no constraint from prior)
• Degrees Of Freedom for Signal (DOFS) = tr(A) = total # pieces of information constrained by inversion
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Comparing information content from aircraft and satellites
TROPOMI will provide information comparable to a continuous aircraft campaign; a geostationary satellite instrument will provide even more Wecht et al. [2014b]
Diagonal elements of A
OSSE of satellite observations during CalNex period (May-June 2010)
CalNex GOSAT:precise but sparse
TROPOMI (2016):daily coverage
Geostationary:hourly coverage
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Temporal averaging can overcome GOSAT data sparsity
2.5 years of GOSAT data
Turner et al. [2015]
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GOSAT validation using CTM as intercomparison platform
Model provides continuous 3-D fields to compare different observational data sets
Satellite (GOSAT)
GEOS-Chem with prior emissions
aircraft+surface data
Are the comparisons consistent?
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GEOS-Chem (with prior emissions) compared to in situ data
Latitude, degrees
GEOS-ChemHIPPO
Jan09 Oct-Nov09 Jun-Jul11 Aug-Sep11
Met
hane
, ppb
v
GE
OS
-Ch
em
NOAA US observations
• GEOS-Chem is unbiased for background methane• US enhancement is ~30% too low, to be corrected
in inversion
Turner et al. [2015]
HIPPO aircraft data over Pacific
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GEOS-Chem (prior) comparison to GOSAT data
High-latitude bias could be due to satellite retrieval or GEOS-Chem stratosphere:in any case, we need to remove it before doing inversion
Turner et al. [2015]
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State vector choice to balance smoothing & aggregation error
Native-resolution 1/2ox2/3o emission state vector x (n = 7096)
Aggregation matrix
x =x
Reduced-resolutionstate vector x (here n = 8)
Posterior error covariance matrix: ˆ T T
ω ω ω A ω ω ωTT
ω O ωAG (K - K Γ )S (K - K Γ ) G (IS = + +- A) G SS I- ) G( A Aggregation Smoothing Observation
Choose n = 369 for negligible aggregation error; allows analytical inversion with full error characterization
1 10 100 1,000 10,000Number of state vector elements
M
ean
erro
r s.
d., p
pb
Posterior errordepends on choice
of state vectordimension
observation aggregationsmoothingtotal
Turner and Jacob [2015]
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Using radial basis functions (RBFs) with Gaussian mixing model
as state vector
• State vector of 369 Gaussian 14-D pdfs optimally selected from similarity criteria in native-resolution state vector
• Each 1/2ox2/3o grid square is unique linear combination of these pdfs• This enables native resolution (~50x50 km2) for major sources and much
coarser resolution where not needed
Dominant Gaussians for emissionsin Southern California
Turner and Jacob [2015]
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Global inversion of GOSAT datafeeds boundary conditions for North American inversion
GOSAT observations, 2009-2011
Adjoint-based inversionat 4ox5o resolution
Dynamicboundaryconditions
Analytical inversionwith 369 Gaussians
Turner et al. [2015]
correction factors to EDGAR v4.2 + LPJ prior
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Averaging kernel sensitivities and inversion results
Turner et al. [2015]
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Evaluation of posterior emissionswith independent data sets in contiguous US
Comparison of California resultsto previous inversions of CalNex data
(Los Angeles)
Turner et al. [2015]
GEOS-Chem simulationwith posterior vs. prior emissions
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Methane emissions in US:comparison to previous studies, attribution to source
types
• EPA national inventory underestimates anthropogenic emissions by 30%• Livestock is a contributor: oil/gas production probably also
Ranges from prior errorassumptions
Turner et al. [2015]
2004satellite
2007surface,aircraft
2009-2011satellite
• What is needed to improve source attribution in future?• Better observing system (more GOSAT years, TROPOMI,…)• Better bottom-up inventory (gridded EPA inventory, wetlands)
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Source attribution is only as good as bottom-up prior pattern
Little confidence and detail in EDGAR gridded inventory; construct our ownin collaboration with US EPA data including detailed info on processes
Large point sources(oil/gas/coal, waste)reporting emissions to EPA
GIS data for location of wells, pipelines, coal mines,…
Livestock and rice data at (sub)-county level
Process-level emission factors including seasonal variation
National bottom-up US inventory of methane emissions at 0.1ox0.1o grid resolution
J.D. Maasakkers (in prep.)
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New EPA-based gridded emission inventory: natural gas production
J.D. Maasakkers (in prep.)
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Natural gas processing
J.D. Maasakkers (in prep.)
New EPA-based gridded emission inventory: natural gas production + processing
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Natural gas transmission
J.D. Maasakkers (in prep.)
New EPA-based gridded emission inventory: natural gas production + processing + transmission
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Total natural gas: production + processing + transmission + distribution
J.D. Maasakkers (in prep.)
New EPA-based gridded emission inventory: natural gas production + processing + transmission + distribution
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EDGAR v4.2FT 2010 total natural gas emissions
J.D. Maasakkers (in prep.)
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Difference with EDGAR
J.D. Maasakkers (in prep.)