further enhance speed of processing and performance. A more 
detailed description of the ASPECT Retrieval Algorithm, 
illustrated in Figure 7, is the first step is to run the NCEP 
atmospheric profile, sun/sensor geometry, through KLAYERS 
algorithm and then kCARTA algorithm two times, this is done 
to raise the simulated methane column amount, and rerun 
kCARTA (once using optical depth KLAYER profile (A) and 
once with perturbed KLAYER profile (B)). 
From profiles A and B we have formed our Jacobian matrix. 
This matrix is the change in the radiance as a function of a 
change in the amount of methane; the Jacobian derived is then 
used in a comparison to the measured radiometrically calibrated 
data to the reflected scaled unit matched spectral radiance. This 
is a spectral comparison of the difference in measured data and 
the reflectance scaled unit matched radiance spectra divided by 
the perturbation amount (or the change of methane in the state 
   
Rene KG 
  
   
jacobian 
optical depths 
, Reflectance scaled f 
i matrix {model 
nit matched — 4 
fadiance spectra 
   
  
     
Figure 7. Lockheed Martin Methane Retrieval Processing 
Diagram 
of the system). The result is a calculated spectra that is 
generated applying the methane perturbation amount, and the 
difference from the measured spectra and is determined. If this 
value is near the order of the noise then the spectra agrees with 
the measurement, if not then one must resume the loop 
illustrated in Figure 7 and iterate the Jacobian calculation once 
more while applying small changes in methane amount, CH,, 
until the spectral difference converges (i.e., is less than noise). 
This retrieval algorithm is shown in Figure 7. The calculation 
from KLAYERS through kCARTA and are done, and we 
manually adjust the reflectivity factor and iterations of Jacobian 
calculations to calculate total column CH,. As is illustrated in 
the retrieval algorithm figure, there are components of the 
retrieval algorithm that are currently done manually (green 
boxes in Figure 7). We propose in future efforts to automate 
these to enable an algorithm for ASPECT retrieval that does not 
include a ‘man in the loop.’ We have done retrievals on both 
the data over the sunglint with and without the methane cell. 
Data and calculations are shown in Figure 8. The final results of 
running the ASPECT Retrieval Algorithm (Figure 7) upon the 
sunglint collections retrieved the amount 0.903 atm-cm of CH, 
in the gas cell. The gas cell was known to contain 0.908 atm- 
cm of CH,to 196 accuracy. 
  
  
  
  
  
Figure 8. This graph shows a methane retrieval spectra 
collected over the sunglint. The dark blue line is the 
observation, green is with no perturbation, red is the 
methane amount perturbed, aqua is the reflectivity 
factor and purple is the final calculation with 
reflectivity applied. 
3. CONCLUSIONS 
The analysis demonstrated retrieval of a gas cell simulated 
enhancement in the total atmospheric CH4 column of 0.908 atm 
— cm with 1% accuracy. This is an equivalent sensitivity to an 
enhancement of 200 ppm in the first meter of the atmosphere. 
In a parallel effort Kumer et al., 2011, have calibrated and used 
the data from consecutive frames with the absorption cell NOT 
in the beam and showed retrieval of total atmospheric column to 
1% precision. There is potential to considerably improve the 
precision. For example, for the use of all spectra (in this study 
only two out of the 512 taken along a given slit of a TIMS 
collection) the precision is theoretically expected to improve to 
the order of an 18 ppm enhancement in the first meter of the 
atmosphere sensitivity. This research constitutes an important 
step in demonstrating sounding CH4 enhancements over water 
by the use of high-resolution spectral measurement of sun glint. 
REFERENCES 
Desouza-Machado, S., Strow, L., Hannon, S., Dec. 1997. 
kCompressed Atmospheric Radiative Transfer Algorithm 
(kCARTA). Proceedings SPIE, 3220, Satellite Remote Sensing 
of Clouds and Atmosphere II, Joanna D. Haigh; Ed., pp. 156- 
162. 
Kumer, J.B., Roche, A.E., Rairden, R.L., Mergenthaler, J.L., 
Doolittle, J.H., Blatherwick, R.D., Hawat, T., Chatfield, R.B., 
July 2008. Tropospheric Infrared Mapping Spectrometers 
(TIMS) to Provide Measurements with much Improved 
Vertical, Temporal, and Spatial Resolution in the Lower 
Troposphere. Geoscience and Remote Sensing Symposium, 
2008. IEEE International. 111-522 — III-525. 
Kumer, J.B., Rairden, R., Roche, A., Chatfield, R., 2011. 
NASA ESTO  IIP  Tropospheric Infrared Mapping 
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Airship: Preliminary Results. Hyperspectral Imaging and 
Sounding of the Environment, Optical Society of America 
Technical Digest (CD) (Optical Society of America), paper 
HTuD2. 
   
Larsen, N 
from Spa 
016202.1- 
We thank 
Departmer 
his deve 
Radiative 
using this