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Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide

Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide. Dylan B. A. Jones, Paul I. Palmer, Daniel J. Jacob Division of Engineering and Applied Sciences, and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA.

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Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide

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  1. Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide Dylan B. A. Jones, Paul I. Palmer, Daniel J. Jacob Division of Engineering and Applied Sciences, and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA Kevin W. Bowman, John Worden California Institute of Technology,Jet Propulsion Laboratory, Pasadena, CA Ross N. Hoffman Atmospheric and Environmental Research, Inc. Lexington, MA November 19, 2002

  2. Tropospheric Emission Spectrometer (TES) • an infrared, high resolution Fourier spectrometer • covers the spectra range650 - 3050 cm-1 (3.3 - 15.4 mm) • has 2 viewing modes: a nadir view and a limb view • will be launched in early 2004 • makes 14.56 orbits per day • repeats its orbit every 16 days (or 233 orbits)

  3. OBJECTIVE: Determine whether nadir observations of CO from TES will have enough information to reduce uncertaintiesin our estimates of regional sources of CO Assume that we know the true sources of CO APPROACH: Use GEOS-CHEM 3-D model to simulate “true” CO concentration field Apply averaging kernels for TES to simulate nadir retrievals of CO Obtain a posteriori sources and errors; How successful are we at finding the true source and reducing the error? Make a priori estimate of CO sources by applying errors to the “true” source Use an optimal estimation inverse method

  4. Source Distribution Fossil Fuels Biofuels Biomass Burning • Other Sources: oxidation of methane and other VOCs • Main sink: reaction with atmospheric OH

  5. Inversion Analysis State Vector EUFF NAFF ASFF ASBB AFBB CHEM SABB RWFF RWBB • FF = Fossil Fuel + Biofuel • All sources include contributions from oxidation of VOCs • OH is specified • Use a “tagged CO” method to estimate contribution from each source • Use inverse method to solve for annually-averaged emissions “True” Emissions (Tg/yr) NAFF: 121.3 SABB: 96.5 EUFF: 131.1 RWBB: 98.0 ASFF: 258.3 RWFF: 149.8 ASBB: 96.0 CHEM: 1125.0 AFBB: 193.9 Total = 2270 Tg/yr

  6. Modeled COLevel 13 (6 km) 15 Mar. 2001, 0 GMT North Americanfossil fuel CO tracer

  7. Simulating the Data • Sample the synthetic atmosphere along the orbit of TES • Assume that the TES nadir footprint of 8 x 5 km is representative of the entire GEOS-CHEM 2º x 2.5º grid box • We consider data only between the equator and 60ºN CO at 510.9 hPa for March 15, 2001

  8. P > 250 mb Simulating the Data The retrieved profile is: yret = ya + A(F(xt) – ya) ya = a priori profile xt = true emissions yt = F(xt) F(x) = GEOS-CHEM A = averaging kernels yret ya

  9. Methodology • Minimize a maximum a posteriori cost function • Gauss-Newton iterative method xa=a priori estimate of the CO sources (state vector) xi = estimate of the state vector for the ith iteration yret= retrieved profile ymod= forward model simulation of xi K=Jacobian (from tagged CO method) Ŝ= a posteriori error covariance Sx= error covariance of sources Sy= error covariance of observations = instrument error (Si) + model error (Sm) + representativeness error (Sr)

  10. The Forward Model We apply the same ya and A used for the retrieved profileto GEOS-CHEM (forward model) } The retrieved profile andthe forward model mustbe consistent Gein = instrument noise G = gain matrix associated with A ei = NESR of instrument Si = G<(ein)(ein)T>GT n = Gaussian white noise with unity variance sn = model noise based on variance of (Sm + Sr) (Sm + Sr) = s2I

  11. Constructing the Covariance Matrix for the Model Error • The NMC method (Parrish and Derber, 1992): • assume that the difference between forecasts on different timescales, valid at the same time, is representative of the forecast error structure Assimilation Model t0+24hr forecast 2 t0 forecast 1 t0+48hr assume that the dominant error structures for the 24-hr period are representative of the errors in the assimilation

  12. Forecast Error • root mean square error based on the difference of 89 pairs of 48-hr and 24-hr forecasts of CO during TRACE-P, Feb-April 2001 Model Level 16 (8 km) CO Mixing Ratio (ppb)

  13. Scaling the Forecast Errors • Compare model with observations of CO from TRACE-P and estimate the relative error • Assume mean bias in relative error is due to emission errors • Remove mean bias and assume the residual relative error (RRE) is due to transport

  14. Scaling the Forecast Errors • Compare model with observations of CO from TRACE-P and estimate the relative error • Assume mean bias in relative error is due to emission errors • Remove mean bias and assume the residual relative error (RRE) is due to transport Mean forecast error RRE RRE  2-3 x mean forecast error

  15. Model Error (15 March 2001, 0 GMT) Model Level 16 (8 km) Relative error Model Level 8 (1.5 km) Relative error

  16. TRACE-P GEOS-CHEM 2x2.5o cell Representativeness Error (Sr) • Use TRACE-P aircraft observations to estimate the subgrid-scale variability of CO • Estimate 1s about 2x2.5omean value of TRACE-P observations sr = 5%

  17. Assumptions • Assume that sources are uncorrelated with uncertainty(Sx)jj = 50% (for the chemistry source we assume 25%) • Assume that the transport errors are spatially uncorrelated • Cloud free conditions • A posteriori results are based on statistics from an ensemble of 100 realizations of the instrument noise and model noise

  18. a priori a posteriori true Inversion Results (5 days of TES data,Mar. 10-15th, 2001) Model successfully retrieves the true emissions for all sources. Caveat: perfect Gaussian error statistics with zero bias

  19. Resolution Matrix (KTSy-1K + Sx-1)-1KTSy-1K Sensitivity of a posteriori solution to true state

  20. How well do we constrain the sources with only upper tropospheric (P < 500 hPa) observations? Good constraint (because of large volume of data), but errors are larger  lower tropospheric retrievals provide useful additional information

  21. Conclusions • TES retrievals of CO have the potential to constrain regional sources of CO, but proper error characterization will be crucial – particularly model error; real data will not have unbiased Gaussian error statistics which will reduce the information we can extract • Upper tropospheric retrievals alone may provide considerable information to constrain the sources • Since most of the CO information is in the lower troposphere, TES retrievals below 500 hPa will be useful in constraining the sources, despite the instrument’s reduced sensitivity in the lower troposphere

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