Vertically Resolved Water Ice Aerosol Opacity from Mars ...
TES limb-scan radiance spectra
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 Ice Extinction (km-1) MY 24, Ls 199–201
Dayside
Nightside
Individual TES limb scans for a typical sol ~14hrs LST
~2hrs LST
Radiance spectra from a single TES limb scan
Retrieval Method Pseudo-spherical forward radiative transfer model: 1. Start with TES-derived temperature profile and pre-determined particle size distributions (reff = 2μm for ice and1.5μm for dust). 2. Vary dust and ice MIXING RATIOS at six levels to match absolute TES radiance between 200 and 1200 cm-1. 3. 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
Accounting for Correlated noise 1. 2. 3. 4.
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: a) Transform the model and the data to the basis defined by the background noise principal components. b) Set the model equal to the data in the first 3* dimensions of this new basis. c) 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 it’s optimal to use fewer than 3 of the components.
Without correlated noise compensation
With correlated noise compensation
Individual dust-ice-temperature profiles
More individual dust-ice-temperature profiles (a series from a particular orbit) 51° N
29° N
18° N
More individual dust-ice-temperature profiles (a series… continued)
18° N
7° N
Example data product I: Night-time column vs. Lat. and Ls First year of MGS-TES mapping (MY 24 – 25)
Example data product II: Extinction and Temperature MY 24, Ls 197 – 199
Example data product IIb: Extinction scaled by gas density MY 24, Ls 197 – 199
Example data product III: Map of in a 10km deep layer 35 – 45 km altitude, MY 24, Ls 195 – 205
Example result 1: Water ice optical depth cross sections Extinction, scaled by gas density 1.
2.
3.
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.
Day
per mb 0.00 0.09 0.18 0.27 0.36
60 48 Altitude 36 (km) 24 12
Northern
Spring Ls 22°
60 48 Altitude 36 (km) 24 12
Northern
Summer Ls 108°
60 48 Altitude 36 (km) 24 12 60 48 Altitude 36 (km) 24 12 -90 -60
Night
Northern
Fall Ls 198° Northern
Winter Ls 290° -30 0 30 60 Latitude (°North)
90
-90 -60
-30 0 30 60 Latitude (°North)
90
Example result 2: Diurnal Evolution of the equatorial cloud belt total column opacities Ls 105 - 115
Ls 115 - 125
*
* peak night-time opacities > 2.5
*
Example result 3: The polar hoods 1. 2.
Dust at surface [scale 0.0 – 0.4]
Ice at surface [scale 0.0 – 0.4]
Ice at 20 km altitude [scale 0.0 – 0.4]
3.
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?
Map views, ice at surface [scale 0.0 – 0.4] Ls 250-255
Ls 25-30
Example result 4: Mesospheric water ice clouds
Latitude vs Ls, MY 24 – 25, Water Ice 45 – 55 km Day
Night
Latitude vs Ls, MY 24 – 25, Water Ice 55 – 65 km Day
Night
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 Ice Extinction (km-1) MY 24, Ls 199–201
Dayside
Nightside
Individual TES limb scans for a typical sol ~14hrs LST
~2hrs LST
Radiance spectra from a single TES limb scan
Retrieval Method Pseudo-spherical forward radiative transfer model: 1. Start with TES-derived temperature profile and pre-determined particle size distributions (reff = 2μm for ice and1.5μm for dust). 2. Vary dust and ice MIXING RATIOS at six levels to match absolute TES radiance between 200 and 1200 cm-1. 3. 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
Accounting for Correlated noise 1. 2. 3. 4.
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: a) Transform the model and the data to the basis defined by the background noise principal components. b) Set the model equal to the data in the first 3* dimensions of this new basis. c) 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 it’s optimal to use fewer than 3 of the components.
Without correlated noise compensation
With correlated noise compensation
Individual dust-ice-temperature profiles
More individual dust-ice-temperature profiles (a series from a particular orbit) 51° N
29° N
18° N
More individual dust-ice-temperature profiles (a series… continued)
18° N
7° N
Example data product I: Night-time column vs. Lat. and Ls First year of MGS-TES mapping (MY 24 – 25)
Example data product II: Extinction and Temperature MY 24, Ls 197 – 199
Example data product IIb: Extinction scaled by gas density MY 24, Ls 197 – 199
Example data product III: Map of in a 10km deep layer 35 – 45 km altitude, MY 24, Ls 195 – 205
Example result 1: Water ice optical depth cross sections Extinction, scaled by gas density 1.
2.
3.
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.
Day
per mb 0.00 0.09 0.18 0.27 0.36
60 48 Altitude 36 (km) 24 12
Northern
Spring Ls 22°
60 48 Altitude 36 (km) 24 12
Northern
Summer Ls 108°
60 48 Altitude 36 (km) 24 12 60 48 Altitude 36 (km) 24 12 -90 -60
Night
Northern
Fall Ls 198° Northern
Winter Ls 290° -30 0 30 60 Latitude (°North)
90
-90 -60
-30 0 30 60 Latitude (°North)
90
Example result 2: Diurnal Evolution of the equatorial cloud belt total column opacities Ls 105 - 115
Ls 115 - 125
*
* peak night-time opacities > 2.5
*
Example result 3: The polar hoods 1. 2.
Dust at surface [scale 0.0 – 0.4]
Ice at surface [scale 0.0 – 0.4]
Ice at 20 km altitude [scale 0.0 – 0.4]
3.
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?
Map views, ice at surface [scale 0.0 – 0.4] Ls 250-255
Ls 25-30
Example result 4: Mesospheric water ice clouds
Latitude vs Ls, MY 24 – 25, Water Ice 45 – 55 km Day
Night
Latitude vs Ls, MY 24 – 25, Water Ice 55 – 65 km Day
Night
