Vertically Resolved Water Ice Aerosol Opacity from Mars Global Surveyor Thermal Emission Spectromete

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Vertically Resolved Water Ice Aerosol Opacity
from Mars Global Surveyor Thermal Emission
Spectrometer (TES) Limb Sounding

• Tim McConnochie, Mike Smith
• NASA Goddard Space Flight Center

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Individual TES limb scans for a typical sol
Radiance spectra from a single TES limb scan
14hrs LST
2hrs LST
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Retrieval Method

• Pseudo-spherical forward radiative transfer
  model
• Start with TES-derived temperature profile and
  pre-determined particle size distributions (reff
  2?m for ice and1.5?m for dust).
• Vary dust and ice MIXING RATIOS at six levels to
  match absolute TES radiance between 200 and 1200
  cm-1.
• Use Levenberg-Marquardt algorithm to find best
  fit.
• Points are weighted by the quadratic sum of the
  instrumental noise and an estimate of the model
  uncertainty, which is treated as a constant
  fraction of the signal level.

3.51? N 212.53? W 269.89? Ls 1.80 hr LST
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Accounting for Correlated noise

• TES instrumental background noise is highly
  correlated in wavenumber.
• 90 99 of this noise is contained in the first
  3 principal components of the background noise.
• We can (and do) eliminate this portion of the
  noise by excluding those 3 principal components
  from the least squares fitting.
• To exclude these components we
• Transform the model and the data to the basis
  defined by the background noise principal
  components.
• Set the model equal to the data in the first 3
  dimensions of this new basis.
• Transform back to the original basis.

When signal levels are high, other sources of
uncertainty (chiefly model uncertainties) become
comparable to that contributed by some of the
instrumental components. In these cases its
optimal to use fewer than 3 of the components.
Without correlated noise compensation
With correlated noise compensation
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Individual dust-ice-temperature profiles
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More individual dust-ice-temperature profiles (a
series from a particular orbit)
51? N
29? N
18? N
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More individual dust-ice-temperature profiles (a
series continued)
18? N
7? N
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Example data product I Night-time column ? vs.
Lat. and Ls First year of MGS-TES mapping (MY 24
25)
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Example data product II Extinction and
Temperature MY 24, Ls 197 199
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Example data product IIb Extinction scaled by
gas density MY 24, Ls 197 199
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Example data product III Map of ?? in a 10km
deep layer 35 45 km altitude, MY 24, Ls 195
205
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Example result 1 Water ice optical depth cross
sections Extinction, scaled by gas density

• Reassuring seasonal pattern in the daytime
  condensation level
• Night-time clouds are partially ANTI-CORRELATED
  with daytime clouds perhaps this behavior is
  tracing the diurnal tides.
• The failure of daytime clouds to persist into the
  night places a constraint on the lifetime of
  cloud particles, which in turn constrains the
  cloud-advection component of water transport.

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Example result 2 Diurnal Evolution of the
equatorial cloud belt total column opacities
Ls 105 - 115
Ls 115 - 125


peak night-time opacities gt 2.5
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Example result 3 The polar hoods

• Confined to low altitude
• Southern hood is weak, variable, and present
  mainly near the equinoxes
• Dust is confined just south of the polar hood
  boundary. Is this dynamical confinement by the
  polar vortex, or is it ice scavenging of dust?

Dust ? at surface scale 0.0 0.4
Map views, ice ? at surface scale 0.0 0.4
Ice ? at surface scale 0.0 0.4
Ls 250-255
Ice ? at 20 km altitude scale 0.0 0.4
Ls 25-30
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Example result 4 Mesospheric water ice clouds
Latitude vs Ls, MY 24 25, Water Ice ?? 45 55
km
Night
Day
Latitude vs Ls, MY 24 25, Water Ice ?? 55 65
km
Night
Day