Validation of TES Methane with HIPPO Observations

Kevin J. Wecht, DJ Jacob, SC Wofsy, EA Kort, A Eldering, JR Worden, SS Kulawik, GB Osterman, VH Payne

9 November 2010Atmospheric and

Environmental Research, Inc

Application to Adjoint Inverse Modeling of Methane Sources

Atmospheric CH4: RF and trends

0.6 0.3 [W m-2]

Emission-based Radiative Forcing

Radiative forcing from CH4 emissions ≈ 50% radiative forcing from CO2 emissionsCause of slowdown in CH4 growth rate remains unclear, but is likely driven by emissions.

CH

4 [pp

b]

?

Bottom-up CH4 Emission Inventories

Annual CH4 Emissions (IPCC) [Tg a-1]

Bottom-up inventories do not capture slowdown in CH4 growth rate or confidently apportion emissions between source types.1978 2005

EDGAR CH4

Anthropogenic Emissions [Tg a-1]350

300

?

CH4

[ppb]

Intelligent climate change legislation requires more tightly constrained sources.

200

100

200

100

50

150

Inverse Methods Help to Constrain Emissions

[ppb]

GEOS-Chem and NOAA GMD surface methane

Forward

Model

Inverse Model

CH 4

Forward Model:GEOS-Chem 3-D chemical transport model (CTM)

Satellite observations represent potentially huge source of information on CH4 emissions

GEOS-ChemNOAA GMD

Adjoint Inverse Modeling of Methane Sources

Adjoint inverse analysis

OPTIMIZATION OF SOURCES

GEOS-Chem CTM

ValidationHIPPO

Methane(Wofsy, Kort)

Apriori Sources

GEOS-Chem Adjoint

EDGAR v4, Kaplan, GFED 2, Yevich and Logan [2003]

HIPPO Methane provides:• Large number of profiles• Wide latitudinal coverage• Remote from sources

(reduces colocation error)

TES Methane (Worden, Kulawik, Payne)

Other Methane

Remote Sensing of CH4

Wavelength, μm20 1015 89 7

CH4

Radi

ance

, 10-3

W m

2 sr-1

Jacob 1999

Terrestrial Radiation Spectrum

Calibrated Radiance

Inte

nsity

Wavelength

CH4

Retrieved Atmospheric CH4

Pres

sure

Mixing ratio [ppb]

Inverse Model

1 TES “global survey”16 orbits, 26 hours, 2300 observations

15-16 global surveys per month

8 km

5 km

One obs

I validate the TES CH4 product and evaluate its utility for use in inverse modeling.

TES V004 CH4

• Methane retrieval 7.658 – 7.740 μm• Averaging kernels peak 200-400 hPa• Degrees of Freedom for Signal 0.5-2.0• ~50% of TES observations pass CH4 quality filter• Unit of comparison for this work is

Representative Tropospheric Volume Mixing Ratio (RTVMR)

Rows of Typical Averaging Kernel

RTVMR (Payne et. al. 2009)• Map retrieval to 4-level pressure grid defined by AK.• Concentration at 2nd pressure level is the RTVMR.

0 0.05 0.10 1600 1700 1800 1900 [ppb]

Pres

sure

[hPa

]

10

1000

100

aprioriTES

Corresponding CH4 Retrieval RTVMR Grid

Reduce all profiles to 1 piece of information representative of tropospheric mixing ratio.

HIAPER Pole-to-Pole Observation Program (HIPPO)

HIPPO I - interpolated methanePacific transect (flights 3-7 of 10)Dominant variability with latitudeLittle variability in vertical

• Five missions:- Jan, Nov 2009- April 2010, June & Aug 2011

• > 700 vertical profiles by finish- 80% surface – 330 hPa- 20% surface – < 200 hPa

• Methane Instrument Properties- Frequency: 1 Hz- Accuracy/Precision: 1.0/0.6 ppb

Pres

sure

[hPa

]

1000

800

600

400

200

Latitude0 20 6040-60 -20-40 80

1600 1775 1950[ppb]

HIPPO I HIPPO II

Using HIPPO to Define Coincidence Criteria

Mean Bias

HIPP

O I

Residual Standard Dev.

[ppb

]

# Observations

Coincidence requirements of +/- 750km and +/- 24h are sufficient.

Standard error of the mean

24 hour12 hour

24 hour12 hour

~ 250 km

HIPPO profiles

TES observation 5 x 8 km

Validation characterizes mean bias and residual error.Residual error contains contributions from: 1) error in the retrieval 2) colocation error r

-6 h+20 h

250 500 750 250 500 750 250375

750500625Distance [km]Distance [km]

N

HIPPO v. TES by Latitude

RTVM

R [

ppb]

RTVM

R [

ppb]HIPPO 1

Jan 9-30,2009

HIPPO 2Oct 22 – Nov 20,

2009

Positive bias and significant noise, but latitudinal gradient roughly captured.

TES

HIPPO w/ TES op.HIPPO 250-450 hPa

# TES = 204# HIPPO = 76

# TES = 183# HIPPO = 72

HIPPO v. TES Difference by Latitude

HIPPO 1Jan 9-30,

2009

HIPPO 2Oct 22 – Nov 20,

2009

RTVM

R [

ppb]

RTVM

R [

ppb] TES – HIPPO over land

TES – HIPPO over ocean

Mean Bias

HIPPO I: bias = 73.7 ppb, residual std dev = 43.1 ppb ≈ 3 x self-reported errorHIPPO II: bias = 59.9 ppb, residual std dev = 48.4 ppb marginally sig. land-ocean diff

Distribution of TES Residual Errors

HIPPO I1/9 - 1/30,

2009

HIPPO II10/22 – 11/20,

2009

Normally distributed errors are important for derivation of inverse cost function.

ScatterplotsError distributions

TES

RTVM

R [p

pb] 2000

1900

1800

1700

2000

1900

1800

1700TES

RTVM

R [p

pb]

HIPPO RTVMR [ppb]1700 1800 1900

N = 76 r = 0.65

N = 72 r = 0.59

bias = 73.3σ = 43.1

bias = 59.9σ = 48.4

The Ability of TES to Capture Latitudinal Gradients

TES captures HIPPO I & II latitudinal gradients on a scale of ~20°

HIPPO I 15° Latitude Bins

HIPPO I and TES GradientsTESHIPPO

Latitude0 20 6040-60 -20-40

Gra

dien

t [pp

b / °

lat]

0

2

-2

4

RTV

MR

[ppb

] 1900

1800

1700

Bin Size [° latitude]0 20 6040 80

[ppb

/ °la

t]

0

1

2

3

4

5

mean( |HIPPO| )mean( |TES – HIPPO difference| )

Adjoint Inverse Modeling of Methane Sources

Adjoint inverse analysis

OPTIMIZATION OF SOURCES

GEOS-Chem CTM

ValidationHIPPO

Methane(Wofsy, Kort)

Apriori Sources

GEOS-Chem Adjoint

EDGAR v4, Kaplan, GFED 2, Yevich and Logan [2003]

HIPPO Methane provides:• Large number of profiles• Wide latitudinal coverage• Remote from sources

(reduces colocation error)

TES Methane (Worden, Kulawik, Payne)

Other Methane

Adjoint Inverse Modeling

Analytical Inversion Adjoint InversionJ(x)

xx*xa

Jacob 2007

Define cost function

Adjoint approach allows us to constrain emissions at the resolution of the model.

Computationally impractical for my goals

Twin Tests (OSSE) for Inversion of Methane Sources1-D example

x = CH4 emissions, F(x) = CH4 [ppb] F(xtrue) true stateF(xpert) apriori, perturbedF(xpost) post. best guess

F(x)

distance

F(x)

distance

F(xtrue) true statePseudo-observations

Why perform twin tests?• Test the inverse calculation• Understand the effects of TES error and apriori• Find optimum length of assimilation period

Twin test methodology:• Generate pseudo-observations from “true” state• Perform inversion with purposefully incorrect apriori• Goal: recover true atmospheric state by adjusting emissions

Through twin tests, we can evaluate the utility of TES CH4 for retrieving CH4 emissions.

Twin Tests (OSSE) for Inversion of Methane Sourceswith TES CH4

Why perform twin tests?• Test the inverse calculation• Understand the effects of TES error and apriori• Find optimum length of assimilation period

The twin test methodology• “Observe” GEOS-Chem with TES• Add error to pseudo-observations of magnitude

determined by HIPPO validation• Begin adjoint inversion with incorrect apriori• Goal: to recover original emission field.

GEOS-Chem RTVMR July 2008

w/ error

A priori emissions

= -50% = +50%

# of Observations July 2008 total = 22038 ppb

2.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Twin Tests Results

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%0

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%02.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Tests Results

Iteration #0 5 10 15 20 25

Cos

t Fun

ctio

n

-50% +50%0

Convergence!

2.0 4.0 6.00

July 2008True CH4 Emissions [102 Mg]

Assimilate July 2008 observations 22,038 total

Observational Error = 40ppb

Twin Test Results

Assimilation Length # observationsTop 1 month 22038Bottom 2 months 40060

TES alone is not sufficient for use in a global adjoint inversion

0 +50%-50%

Optimize July 2008 emissions, assimilating data for variable lengths of time.

Additional work:• Test longer assimilation periods• Understand how the solution changes as a function of assimilation length

New TES CH4

Update this slide for new retrieval • Extend spectral domain of retrieval• Joint retrieval for CH4, N2O, H2O, HDO• “N2O correction” to mitigate systematic errors

• New retrieval has more DOFS and sensitivity lower in troposphere(key for inverse modeling)

New Retrieval has sensitivity lower in troposphere, some vertical information.

New TES CH4

During HIPPO I & II (Jan/Nov 2009)

DOFS have strong seasonal and latitudinal dependence

RTVMR-like levels

New TES CH4

First look comparison to HIPPO I

• Reproduce HIPPO TES difference by latitude and error characterization slides here• Maybe reproduce whether TES can capture latitudinal gradients.• Make a slide on whether TES captures vertical gradients as measured

by HIPPO (let dCH4/dz ~ diff b/wn upper and lower pieces of info)

Error <= that of old TES CH4 retrieval. Real benefits: higher DOFS, lower sensitivity

TES – HIPPO difference by latitude

Upper Troposphere

Mid-lower Troposphere

TES – HIPPO over landTES – HIPPO over ocean

ppbppb

ppbppb

• Further validation of New TES CH4 for use in inverse modeling of CH4 sources.• Evaluate utility of new TES product in constraining CH4 emissions.• Assimilate new TES product to optimize emissions.

• Joint SCIAMACHY/TES assimilation.– SCIAMACHY provides complimentary sensitivity and coverage to TES

• Evaluate inversion results with NOAA GMD surface observations

• Incorporate additional satellite CH4 products (AIRS, GOSAT) and focus on North America.

Future Work: Optimal Estimation of CH4 Sources

Evaluate utility of new TES CH4 for inverse modeling.Assimilate new TES and additional satellite CH4 to optimize emissions.

Old TES CH4 - Most recent public release• TES captures latitudinal gradient in HIPPO data at ~20° resolution• TES is biased high and residual instrument error is 3 x self-reported range• Colocation error in validation is negligible• Enabling Inverse Modeling:

– Quantification of bias, characterization of error– Robust latitudinal gradient with greater coverage than surface stations– TES alone is not sufficient for use in global adjoint inversion

New TES CH4

• Sensitivity lower in troposphere (important for inverse modeling)• Error <= that of old TES CH4

Future Work• Assimilate new TES CH4

• Evaluate inversion results with NOAA GMD surface observations• Incorporate additional satellite CH4 products and focus on N.A.

Thank You!