Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide
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Transcript of Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide
Assessment of the Potential of Observations from TESto Constrain Emissions of Carbon Monoxide
Dylan B. A. Jones, Paul I. Palmer, Daniel J. JacobDivision of Engineering and Applied Sciences,
and Department of Earth and Planetary Sciences,Harvard University, Cambridge, MA
Kevin W. Bowman, John WordenCalifornia Institute of Technology,
Jet Propulsion Laboratory, Pasadena, CA
Ross N. HoffmanAtmospheric and Environmental Research, Inc.
Lexington, MA
November 19, 2002
• an infrared, high resolution Fourier spectrometer
• covers the spectra range650 - 3050 cm-1 (3.3 - 15.4 m)
• has 2 viewing modes: a nadir view and a limb view
Tropospheric Emission Spectrometer (TES)
• will be launched in early 2004 • makes 14.56 orbits per day• repeats its orbit every 16 days (or
233 orbits)
Products Nadir Limb
Temperature X X
Surface temp. X
Land surf. emissivity X
O3 X X
CO X X
H2O X X
CH4 X X
NO X
HNO3 X
OBJECTIVE: Determine whether nadir observations of CO from TES will have enough information to reduce uncertainties in our estimates of regional sources of CO
Assume that we know the true sources of CO
Use GEOS-CHEM 3-D model to simulate “true” CO concentration field
Apply averaging kernels for TES to simulate nadir retrievals of CO
Make a prioriestimate of CO sources
by applying errorsto the “true” source
Use an optimal estimation
inverse method
Obtain a posteriori sources and errors;
How successful are we at finding the true source and reducing the error?
APPROACH:
Source DistributionFossil Fuels Biofuels
Biomass Burning
• Other Sources: oxidation of methane and other VOCs• Main sink: reaction with atmospheric OH
Inversion Analysis State Vector
CHEM
NAFF
RWFF RWBB
AFBB
EUFF
SABB
ASFFASBB
NAFF: 121.3 SABB: 96.5EUFF: 131.1 RWBB: 98.0ASFF: 258.3 RWFF: 149.8ASBB: 96.0 CHEM: 1125.0AFBB: 193.9
“True” Emissions (Tg/yr)
Total = 2270 Tg/yr
• 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
Modeled COLevel 13 (6 km)15 Mar. 2001, 0 GMT
North Americanfossil fuel CO tracer
• 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
Simulating the Data
CO at 510.9 hPa for March 15, 2001
The retrieved profile is: yret = ya + A(F(xt) – ya)
A = averaging kernels
ya = a priori profileyt = F(xt)
ya
yret
xt = true emissions
F(x) = GEOS-CHEM
P > 250 mb
Simulating the Data
Methodology
• Minimize a maximum a posteriori cost function
xa = a priori estimate of the CO sources (state vector)
xi = estimate of the state vector for the ith iteration
yret = retrieved profileymod = forward model simulation of xi
K = Jacobian (from tagged CO method)Ŝ = a posteriori error covarianceSx = error covariance of sources Sy = error covariance of observations
= instrument error (Si) + model error (Sm) + representativeness error (Sr)
ret mod T 1 ret mod T 1y a x a(x) (y y ) S (y y ) (x x ) S (x x )J
T -1 -1 -1 T -1 ret mod -1i+1 i i y i x i y x i ax = x +(K S K +S ) [K S (y -y )-S (x -x )]
T -1 -1 -1i y i xS = (K S K +S )
• Gauss-Newton iterative method
We apply the same ya and A used for the retrieved profileto GEOS-CHEM (forward model)
ret ta a iy y A(F(x ) y ) G nε
moda ay y A(F(x) n y )
n = model noise based on variance of (Sm + Sr)
Gin = instrument noise
(Sm + Sr) = 2I
G = gain matrix associated with Ai = NESR of instrument Si = G(in)(in)TGT
n = Gaussian white noise with unity variance
} The retrieved profile andthe forward model mustbe consistent
The Forward Model
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
t0+24hr
forecast 1
forecast 2
t0+48hr
assume that the dominant error structures for the 24-hr period are representative of the errors in the assimilation
• 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
Forecast Error
Model Level 16 (8 km)
CO Mixing Ratio (ppb)
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
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
Model Error (15 March 2001, 0 GMT)
Model Level 16 (8 km)
Model Level 8 (1.5 km)
Relative error
Relative error
r = 5%
GEOS-CHEM
2x2.5o cell
TRACE-P
Representativeness Error (Sr)
• Estimate 1 about 2x2.5o mean value of TRACE-P observations
• Use TRACE-P aircraft observations to estimate the subgrid-scale variability of CO
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
Inversion Results (5 days of TES data, Mar. 10-15th, 2001)
a priori a posteriori true
Model successfully retrieves the true emissions for all sources.Caveat: perfect Gaussian error statistics with zero bias
Resolution Matrix
(KTSy-1K + Sx
-1)-1KTSy-1K
Sensitivityof a posteriorisolution totrue state
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
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