Analyzing TES Data Using the Tri-spectral and Spectral EOF Techniques

Details Analyzing TES Data Using the Tri-spectral and Spectral EOF Techniques Analyzing TES Data Using the Tri-spectral and Spectral EOF Techniques

Sep 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002dps....34.0503y&link_type=abstract

American Astronomical Society, DPS Meeting #34, #05.03; Bulletin of the American Astronomical Society, Vol. 34, p.842

Astronomy and Astrophysics

Astronomy

Scientific paper

Two new insights into analyzing the Thermal Emission Spectrometer (TES) data from the Mars Global Surveyor (MGS) are presented. First, we use the tri-spectral technique to detect cirrus, dust and clear sky spectra in TES measurements. The use of three bands can effectively decrease the ambiguity between dust and clear sky spectra. We design two algorithms to carry out the classification: (1) use 12.5 and 9 micron bands to distinguish between cirrus spectra and dust/clear sky spectra, and use 9 and 7.3 micron bands to distinguish between dust and clear sky/cirrus spectra; (2) use 12.5 and 9 micron bands to distinguish between cirrus and dust/clear sky spectra, and use 9 and 7.7 micron bands to distinguish between dust and clear sky/cirrus spectra. The two algorithms give very similar results when we apply them to a sample of TES data. Using our improved Martian atmospheric radiative transfer model, we verify our algorithm and study the sensitivity of our algorithm to the cirrus and dust optical depths. Second, we apply spectral Empirical Orthogonal Function (EOF) analysis to the TES data. Because of the temporal and spatial sampling patterns of TES data, averaging over certain time period and area is necessary before we can get meaningful results. For the regions that we study, the first principal component is due to the surface temperature change. The variations of cirrus and dust are captured in the following two leading principal components. The spectral EOF analysis of the TES data can be helpful for validating Martian general circulation models (GCM).

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