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Transcript of Scattering by Earth surface Instruments: Backscattered intensity I B absorption Methane column ...
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
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
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
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]
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
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]
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
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]
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
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
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
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
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
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
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
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
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
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
Temporal averaging can overcome GOSAT data sparsity
2.5 years of GOSAT data
Turner et al. [2015]
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?
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
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]
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]
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]
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
Averaging kernel sensitivities and inversion results
Turner et al. [2015]
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
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)
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.)
New EPA-based gridded emission inventory: natural gas production
J.D. Maasakkers (in prep.)
Natural gas processing
J.D. Maasakkers (in prep.)
New EPA-based gridded emission inventory: natural gas production + processing
Natural gas transmission
J.D. Maasakkers (in prep.)
New EPA-based gridded emission inventory: natural gas production + processing + transmission
Total natural gas: production + processing + transmission + distribution
J.D. Maasakkers (in prep.)
New EPA-based gridded emission inventory: natural gas production + processing + transmission + distribution
EDGAR v4.2FT 2010 total natural gas emissions
J.D. Maasakkers (in prep.)
Difference with EDGAR
J.D. Maasakkers (in prep.)