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Principal Component Analysis of MGS-TES Data and Comparison with Modeling Guo, Xin October 7 th 2004 Advisor: Yung, Yuk L.

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Presentation on theme: "Principal Component Analysis of MGS-TES Data and Comparison with Modeling Guo, Xin October 7 th 2004 Advisor: Yung, Yuk L."— Presentation transcript:

1 Principal Component Analysis of MGS-TES Data and Comparison with Modeling Guo, Xin October 7 th 2004 Advisor: Yung, Yuk L.

2 Outline  Mars, Mars Global Surveyor (MGS), Thermal Emission Spectrometer (TES)  Principal Component Analysis (PCA)  Results of PCA on TES data  Results of PCA on synthetic data  Results of PCA on GCM output  Conclusions and future work

3 Mars Facts  A Martian year is 668 sols (Martian days), 687 Earth days  A Martian day (sol) is 24 hours 37 minutes 22 sec  Atmospheric gaseous components: CO 2 (95%), CO (700ppm), H 2 O (100ppm), N 2, O 2,O 3, NO, H 2, Noble Gases  Major Aerosol Components: Dust, Water Ice  Atmosphere shows annual variation and diurnal variation Pater, I.d. and L. Jack J, Planetary Sciences. 2001, Cambridge: Cambridge University Press.

4 MGS & TES MGS (Mars Global Surveyor)  Orbit covers almost the whole surface of Mars  One orbiting period of MGS at normal mapping phase is 118 minutes  At normal mapping phase, a global mapping takes 7 sols = 3.78 º Ls = hours TES (Thermal Emission Spectrometer)  Spatial resolution 3 km  Spectral range 200 cm -1 to 1700 cm -1  Spectral resolution 10 wavenumbers (cm -1 ) or 5 wavenumbers (cm -1 )  SNR around 400 at 1000 cm -1  Sample rate around 800 per second

5 Principal Component Analysis (PCA) [Terminology: Meteorologists call it Empirical Orthogonal Function (EOF) Analysis, Factor Analysis … I am trying to be a statistician here]  Linearly transforms an original set of variables to a substantially smaller set of uncorrelated variables that represents most of the information in the original set of variables  Capture the variation of data  1 st principal component (PC1) captures the largest variation  2 nd principal component (PC2) captures the largest variation orthogonal to that captured by the 1 st principal component

6 Previous work of Huang et al.  PC1 is associated with surface or near surface brightness temperature  PC2 is associated with atmospheric variability  Signal from surface emission (surface or near surface temperature) is dominant

7 Manipulation of Data When (nadir view), and (thus ). Ignore the strong CO 2 absorption band between 510 cm -1 and 810 cm -1 Apply PCA to the residual spectra.

8 PCA on TES data: MY25 L s 30 º -45 º

9 PCA on TES data: MY25 L s 90º-105º

10 PCA on TES data: MY25 L s 330º-345º

11 Discussion of Results  Variability of atmospheric dust and water ice  Incompleteness of the removal of surface emission Smith, M.D., J.L. Bandfield, and P.R. Christensen, Separation of atmospheric and surface spectral features in Mars Global Surveyor Thermal Emission Spectrometer (TES) spectra. Journal of Geophysical Research, (E4): p Smith, M.D., Interannual variability in TES atmospheric observations of Mars during Icarus, : p a

12 PCA on Synthetic Data  Feed the Radiation Model with temperature profile, pressure profile, atmospheric dust mixing ratio profile, atmospheric water ice mixing ratio profile (12 levels)  Generate IR radiation spectra with different abundance of dust and water ice  Get rid of the surface emission and CO 2 absorption band  Apply PCA on data

13 PCA on GFDL Mars GCM Based Data  Geophysical Fluid Dynamic Laboratory (GFDL) Mars General Circulation Model (GCM)  Spatial resolution: 6 degrees longitude, 5 degrees latitude, 20 vertical levels  Output fields: eight 3D fields, eleven 2D fields  Output interval: 2 sols, 2 Martian hours

14 Comparison between GCM and TES [Smith 2004]

15 Conclusion and Future Work  Atmospheric aerosol variability is well captured using this method. It is independent of the retrieval.  Better removal of surface emission would lead to better results.  A better radiation model (such as MODTRAN) would improve the understanding of the roles of various species.  PCA is a good way to test the GCM and help to improve it. Eventually, we would like to predict the weather on Mars.

16 The End Thank you for listening Acknowledgements Xianglei Huang, Yuk Yung, Michael Smith, Run-Lie Shia, Xun Jiang, Dave Camp for useful guidance and discussions Oded Aharonson for the access of Martian surface emissivity data Mark Richardson, Shabari Basu, Michael Mischna, Jiafang Xiao for the access of GCM outputs

17 References  Pater, I.d. and L. Jack J, Planetary Sciences. 2001, Cambridge: Cambridge University Press.  Albee, A.L., et al., Overview of the Mars Global Surveyor mission. Journal of Geophysical Research, (E10): p  Christensen, P.R., et al., Mars Global Surveyor Thermal Emission Spectrometer experiment: Investigation description and surface science results. Journal of Geophysical Research, (E10): p  Weisberg, S., Applied Linear Regression. Second Edition ed. Wiley Series in Probability and Mathematical Statistics, ed. V. Barnett, et al. 1985, New York: John Wiley & Sons.  Jolliffe, I.T., Principal Component Analysis. Springer Series in Statistics, ed. D. Brillinger, et al. 1986, New York: Springer-Verlag.  Huang, X., J. Liu, and Y.L. Yung, Analysis of Thermal Emission Spectrometer data using spectral EOF and tri-spectral methods. ICARUS, : p  Smith, M.D., J.L. Bandfield, and P.R. Christensen, Separation of atmospheric and surface spectral features in Mars Global Surveyor Thermal Emission Spectrometer (TES) spectra. Journal of Geophysical Research, (E4): p  Richardson, M.I. and R.J. Wilson, Inverstigation of the nature and stablility of the Martian seasonal water cycle with a general circulation model. Journal of Geophysical Research, (E5).  Smith, M.D., Interannual variability in TES atmospheric observations of Mars during Icarus, : p

18 Solar Longitude (Ls)  A Martian year is defined 360 degree of Solar Longitude (L s ) or Heliocentric Longitude  Ls = 0, northern hemisphere vernal equinox  1 Ls ~ hours

19 Manipulation of Data where is the surface emissivity at frequency, is the surface temperature, is the normal column-integrated (aerosol) opacity, is the cosine of the emission angle. is the Planck function, is the temperature profile Denote When (nadir view), and (thus ). Ignore the strong CO 2 absorption band between 510 cm -1 and 810 cm -1

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26 (Michael Carrol, space artist) “Blue Mars”

27 Simplified Geologic Map

28 (Boynton et al, Science, 2002) Epithermal Neutrons


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