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Validation of TES Methane with HIPPO Observations

Application to Adjoint Inverse Modeling of Methane Sources. Validation of TES Methane with HIPPO Observations. Atmospheric and Environmental Research, Inc. 9 November 2010. Kevin J. Wecht, DJ Jacob, SC Wofsy , EA Kort , A Eldering , JR Worden, SS Kulawik , GB Osterman , VH Payne.

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Validation of TES Methane with HIPPO Observations

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  1. Application to Adjoint Inverse Modeling of Methane Sources Validation of TES Methane with HIPPO Observations Atmospheric and Environmental Research, Inc 9 November 2010 Kevin J. Wecht, DJ Jacob, SC Wofsy, EA Kort, A Eldering, JR Worden, SS Kulawik, GB Osterman, VH Payne

  2. Atmospheric CH4: RF and trends ? CH4 [ppb] 0.6 0.3 Emission-based Radiative Forcing [W m-2] Radiative forcing from CH4 emissions ≈ 50% radiative forcing from CO2 emissions Cause of slowdown in CH4 growth rate remains unclear, but is likely driven by emissions.

  3. Bottom-up CH4Emission Inventories Annual CH4 Emissions (IPCC) [Tg a-1] 200 200 ? 150 100 100 50 CH4 [ppb] EDGAR CH4 Anthropogenic Emissions [Tg a-1] 350 300 Bottom-up inventories do not capture slowdown in CH4 growth rate or confidently apportion emissions between source types. 1978 2005 Intelligent climate change legislation requires more tightly constrained sources.

  4. Inverse Methods Help to Constrain Emissions GEOS-Chem and NOAA GMD surface methane GEOS-Chem NOAA GMD Inverse Model CH4 Forward Model [ppb] Forward Model: GEOS-Chem 3-D chemical transport model (CTM) Satellite observations represent potentially huge source of information on CH4 emissions

  5. TES Methane (Worden, Kulawik, Payne) HIPPO Methane (Wofsy, Kort) Validation Adjoint Inverse Modeling of Methane Sources GEOS-Chem CTM Other Methane Apriori Sources GEOS-ChemAdjoint • HIPPO Methane provides: • Large number of profiles • Wide latitudinal coverage • Remote from sources • (reduces colocation error) Adjoint inverse analysis EDGAR v4, Kaplan, GFED 2, Yevich and Logan [2003] OPTIMIZATION OF SOURCES

  6. Remote Sensing of CH4 1 TES “global survey” 16 orbits, 26 hours, 2300 observations 15-16 global surveys per month Calibrated Radiance Terrestrial Radiation Spectrum CH4 Intensity One obs Wavelength CH4 8 km Radiance, 10-3 W m2 sr-1 5 km Retrieved Atmospheric CH4 20 15 10 7 9 8 Jacob 1999 Wavelength, μm Pressure Inverse Model Mixing ratio [ppb] I validate the TES CH4 product and evaluate its utility for use in inverse modeling.

  7. TES V004CH4 • 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) • RTVMR (Payne et. al. 2009) • Map retrieval to 4-level • pressure grid defined by AK. • Concentration at 2nd pressure • level is the RTVMR. Rows of Typical Averaging Kernel Corresponding CH4 Retrieval RTVMR Grid 10 apriori TES Pressure [hPa] 100 0 0.05 0.10 1600 1700 1800 1900 [ppb] 1000 Reduce all profiles to 1 piece of information representative of tropospheric mixing ratio.

  8. HIAPER Pole-to-Pole Observation Program (HIPPO) HIPPO II HIPPO I HIPPO I - interpolated methane Pacific transect (flights 3-7 of 10) Dominant variability with latitude Little variability in vertical 200 400 • 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 Pressure [hPa] 600 800 80 20 40 60 -60 -40 0 -20 1000 Latitude 1600 1775 1950 [ppb]

  9. Using HIPPO to Define Coincidence Criteria Standard error of the mean Validation characterizes mean bias and residual error. Residual error contains contributions from: 1) error in the retrieval 2) colocation error -6 h N +20 h r Mean Bias Residual Standard Dev. # Observations ~ 250 km 24 hour 24 hour HIPPO profiles 12 hour 12 hour HIPPO I [ppb] TES observation 5 x 8 km 250 250 500 500 750 750 250 500 750 Distance [km] Distance [km] 375 625 Coincidence requirements of +/- 750km and +/- 24h are sufficient.

  10. HIPPO v. TES by Latitude HIPPO w/ TES op. HIPPO 1 Jan 9-30, 2009 HIPPO 250-450 hPa TES RTVMR [ppb] # HIPPO = 76 # TES = 204 HIPPO 2 Oct 22 – Nov 20, 2009 RTVMR [ppb] # HIPPO = 72 # TES = 183 Positive bias and significant noise, but latitudinal gradient roughly captured.

  11. HIPPO v. TES Difference by Latitude TES – HIPPO over ocean HIPPO 1 Jan 9-30, 2009 TES – HIPPO over land Mean Bias RTVMR [ppb] HIPPO 2 Oct 22 – Nov 20, 2009 RTVMR [ppb] HIPPO I: bias = 73.7 ppb, residual std dev = 43.1 ppb ≈ 3 x self-reported error HIPPO II: bias = 59.9 ppb, residual std dev = 48.4 ppb marginally sig. land-ocean diff

  12. Distribution of TES Residual Errors Error distributions Scatterplots 2000 bias = 73.3 σ = 43.1 HIPPO I 1/9 - 1/30, 2009 1900 TES RTVMR [ppb] 1800 N = 76 r = 0.65 1700 2000 bias = 59.9 σ = 48.4 HIPPO II 10/22 – 11/20, 2009 1900 TES RTVMR [ppb] 1800 N = 72 r = 0.59 1700 1800 1700 1900 HIPPO RTVMR [ppb] Normally distributed errors are important for derivation of inverse cost function.

  13. The Ability of TES to Capture Latitudinal Gradients HIPPO I 15° Latitude Bins HIPPO HIPPO I and TES Gradients 1900 TES 5 1800 RTVMR [ppb] mean( |TES – HIPPO difference| ) 4 mean( |HIPPO| ) 1700 3 [ppb / °lat] 4 2 2 1 Gradient [ppb / °lat] 0 0 0 20 40 60 80 Bin Size [° latitude] -2 20 40 60 -60 -40 0 -20 Latitude TES captures HIPPO I & II latitudinal gradients on a scale of ~20°

  14. TES Methane (Worden, Kulawik, Payne) HIPPO Methane (Wofsy, Kort) Validation Adjoint Inverse Modeling of Methane Sources GEOS-Chem CTM Other Methane Apriori Sources GEOS-ChemAdjoint • HIPPO Methane provides: • Large number of profiles • Wide latitudinal coverage • Remote from sources • (reduces colocation error) Adjoint inverse analysis EDGAR v4, Kaplan, GFED 2, Yevich and Logan [2003] OPTIMIZATION OF SOURCES

  15. Adjoint Inverse Modeling Analytical Inversion Adjoint Inversion J(x) Define cost function x xa x* Jacob 2007 Computationally impractical for my goals Adjoint approach allows us to constrain emissions at the resolution of the model.

  16. Twin Tests (OSSE) for Inversion of Methane Sources 1-D example • 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. F(xtrue) true state F(xpert) apriori, perturbed F(xpost) post. best guess x = CH4 emissions, F(x) = CH4 [ppb] F(x) F(xtrue) true state Pseudo-observations F(x) distance distance

  17. Twin Tests (OSSE) for Inversion of Methane Sources with TES CH4 GEOS-Chem RTVMR July 2008 • 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. w/ error # of Observations July 2008 total = 22038 ppb A priori emissions = -50% = +50%

  18. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  19. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  20. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  21. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  22. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  23. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  24. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  25. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  26. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  27. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  28. Twin Tests Results Assimilate July 2008 observations 22,038 total Observational Error = 40ppb Cost Function Convergence! 25 20 15 10 5 0 July 2008 True CH4 Emissions [102 Mg] Iteration # 4.0 0 2.0 6.0 -50% 0 +50%

  29. Twin Test Results 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 +50% 0 -50% TES alone is not sufficient for use in a global adjoint inversion

  30. 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.

  31. New TES CH4 • During HIPPO I & II (Jan/Nov 2009) RTVMR-like levels DOFS have strong seasonal and latitudinal dependence

  32. 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) • New TES CH4 • First look comparison to HIPPO I TES – HIPPO difference by latitude TES – HIPPO over ocean TES – HIPPO over land Upper Troposphere ppb ppb Mid-lower Troposphere ppb ppb Error <= that of old TES CH4 retrieval. Real benefits: higher DOFS, lower sensitivity

  33. Future Work: Optimal Estimation of CH4 Sources • 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. Evaluate utility of new TES CH4 for inverse modeling. Assimilate new TES and additional satellite CH4 to optimize emissions.

  34. Thank You! 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.

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