Studies of Jovian Atmospheric Structure and

Coloring Agents using Hyperspectral Imaging

Paul Strycker

Doctoral Dissertation Proposal

April 29, 2009

ABSTRACT

This project will contribute to the understanding of Jupiter’s atmosphere by ex-

amining the vertical cloud structure and correlating it with the following: (1) atmo-

spheric features on a wide range of spatial scales, (2) the wavelength dependence of

the single-scattering albedo in the visible and near-infrared continuum, and (3) the

distribution of coloring agents (chromophores). This will be accomplished through

an analysis of observations of Jupiter taken with the Hubble Space Telescope and

the New Mexico State University Acousto-optic Imaging Camera (NAIC) at Apache

Point Observatory (APO). The observations will be modeled with a radiative transfer

code to retrieve atmospheric parameters, and characteristics of the chromophores will

be derived from multivariate and spectral analyses.

1 Introduction and Background

The vast majority of Jupiter’s atmosphere and its physical properties are not directly

observable. Jovian hazes and clouds often obscure even the highest altitude compo-

nents beneath them. Trace chemicals mask the spectral signatures of constituents

that are orders of magnitude more abundant. Ground-based observatories cannot

resolve the fine detail present in atmospheric features as revealed by spacecraft, and

spacecraft cannot feasibly carry the wealth of scientific instruments necessary for a

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thorough analysis of what they can resolve. Yet, observations of the limited regions

accessible to modern instruments contain many clues as to the nature of the Jovian

atmosphere.

1.1 The Current Tropospheric Model

The current model of Jupiter’s tropospheric vertical cloud and haze structure is de-

scribed in West et al. (2004) (Figure 1). With decreasing pressure (temperature), the

following volatiles are expected to condense: a water ice or water-ammonia solution

cloud near 6 bars (273 K), an ammonium hydrosulfide (NH4SH) cloud at 1.5 bars

(210 K), and an ammonia (NH3) cloud at 750 mbar (150 K). Above the ammonia

cloud is a ubiquitous haze of sub-µm particles, probably consisting mostly of ammo-

nia ice, extending up to a maximum of 200 mbar. The haze is highest over the Great

Red Spot (GRS) and the Equatorial Zone (EZ). Note that the temperature never

drops below methane’s condensation temperature. Therefore, methane is well-mixed

throughout the troposphere and stratosphere up to ∼1 mbar, where photolysis breaks

it down.

The West et al. model is a synthesis of predictions from thermochemical equi-

librium models and retrievals of cloud height and composition from observations.

Thermochemical equilibrium models require Jovian temperature-pressure profiles, el-

emental abundances, chemical reaction paths, and temperature-pressure dependent

reaction rates to predict the final equilibrium state of the resulting chemical species.

The model inputs are often poorly constrained, especially the elemental abundances

and reaction rates. Reaction rates at the relevant temperatures and pressures are dif-

ficult to study in the laboratory. Elemental abundances must be measured in situ in

a well-mixed atmospheric region. The Galileo probe made the only in situ measure-

ments of Jupiter to date on December 7, 1995. Unfortunately, it entered a 5-µm hot

spot: an anomalously cloud-free region of the atmosphere with strong downwelling

(Orton et al. 1998). The probe returned data from 0.51 bars down to 21.1 bars but

did not necessarily reach the depth where volatile abundances are representative of

their global mean values (Taylor et al. 2004).

Direct observations of structure are extremely limited due to the impossibility of

remotely observing what lies beneath optically thick clouds. Water and NH4SH clouds

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can be observed only if the above cloud(s) are absent or optically thin. For example,

deep water clouds (cloud top at ∼3 bars) have been observed only in a few isolated

locations in Galileo Solid State Imager (SSI) data. These clouds are typically found

on the perimeters of towering storm clouds reaching ∼450 mbar (Banfield et al. 1998,

Gierasch et al. 2000). Water clouds were identified spectroscopically in 1% of Voyager

Infrared Interferometer Spectrometer (IRIS) data (Simon-Miller et al. 2000). These

water clouds were all located in regions that typically have strong vertical transport:

(1) latitudes near the hot spot/convective plume pairs (∼7◦N), (2) close in proximity

to the GRS, and (3) latitudes containing white ovals and other convective features.

NH4SH clouds have never been detected spectroscopically, but aerosol opacity has

been inferred in the appropriate pressure range. The Galileo probe saw “a tenuous

cloud based at about 0.5 bar, a small well-defined cloud, sharply based at 1.34 bars,

several thin clouds of small vertical extent, especially one at 1.6 bar, and a very

tenuous structure of particles in the region of about 2.4 to 3.6 bars” (West et al.

2004). Irwin et al. (2001) and Irwin and Dyudina (2002) inferred a cloud between 1-2

bar from Galileo Near Infrared Mapping Spectrometer (NIMS) data, consistent with

either NH4SH or the top of the water cloud.

Jovian ammonia ice is quite difficult to detect, despite its presumed ubiquity. It

was first detected spectroscopically by Encrenaz et al. (1996) with a disk-averaged

spectrum from the Infrared Space Observatory (ISO). The absorption feature used

for this detection was located at 3 µm, which is inaccessible to ground-based ob-

servers due to Earth’s atmospheric CO2. Discrete spectrally identifiable ammonia

ice clouds (SIACs) were then detected with Galileo NIMS by Baines et al. (2002),

using absorption features at 2.00 and 2.74 µm. These SIACs averaged 2.8◦ in latitude

and longitude and were found to cover <1% of planet. Their spatial coverage was

correlated with regions of strong vertical transport instead of the zonal distribution

of the visible cloud cover, which is presumably composed of ammonia ice. The tur-

bulent wake region to the northwest of the GRS often contains SIACs, and they also

appear in phase with hot spots (Baines et al. 2002). Most SIAC cloud particles are

determined to have an apparent lifetime of ∼2 days, which could apply to the lifetime

of the particles’ spectroscopic properties or to the lifetime of the particles themselves

(Baines et al. 2002). The current understanding of the temporal and localized na-

ture of SIACs is that the ice particles are coated with hydrocarbons, which mask the

spectral signature of the underlying ice (Kalogerakis et al. 2008).

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1.2 Chromophores

The chemical identity, horizontal and vertical distribution, and number of the coloring

agents (chromophores) in Jupiter’s atmosphere are still unknown. All of the ices

predicted to exist in the jovian atmosphere are white at visible wavelengths (West

et al. 2004), so one or more non-equilibrium species is necessary to account for the

observed color variations between the belts, zones, and weather systems (e.g. the

GRS and Oval BA). Table I contains a list of chromophore candidates from the West

et al. (1986) review. This list is still current. “It is likely that at least one of the

candidates . . . is responsible for the coloration, but the problem is that few of them

can be ruled out on the basis of observation. There are no narrow distinguishing

spectral features which could identify one candidate” (West et al. 2004).

Simon-Miller et al. (2001a) studied the number and crude spectral characteris-

tics of the chromophores through a principal component analysis (PCA) using two

sets of Hubble Space Telescope (HST) images. The first set contained only 3 contin-

uum wavelengths (410, 555, and 953 nm) and the second contained one additional

wavelength (673 nm). PCA decomposes a data set into components (a.k.a. empiri-

cal orthogonal functions, or EOFs) that describe successively smaller amounts of the

total variance, where the number of components is equal to the number of filters.

They determined that only 3 spectral components are required to explain the

deviations from the mean albedo spectrum. The first component, containing 91%

of the variance, was spectrally gray, and therefore does not correspond to a chro-

mophore. The second component contained 8% of the variance and described a red

chromophore. The third component, representing a second chromophore, contained

1% of the variance and was present in the GRS and some other anticyclonic ovals.

However, due to the nature of PCA, its spectral shape was constrained to be orthog-

onal to the higher components; thus, its spectral shape is not necessarily indicative of

any color actually present in the clouds. The data set with 4 filters yielded a fourth

component (variance � 1%) containing only noise.

Simon-Miller et al. (2001b) studied the vertical aerosol structure and particle

absorption properties in the continuum with a radiative transfer analysis of Galileo

SSI data. The only continuum wavelength for which they had data was 410 nm. They

determined that the coloration was entirely due to chromophores in the tropospheric

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haze above the main ammonia cloud deck. This agrees to some extent with Smith

and Tomasko (1984), who found the coloration to be in both the tropospheric haze

and the main ammonia cloud deck.

2 Scientific Goals

We propose to study vertical aerosol structure and chromophore absorption simultane-

ously (with the same data set) in the visible to near-IR with multi- and hyperspectral

imaging. Given our access to a unique and powerful instrument, the New Mexico

State University Acousto-optic Imaging Camera (NAIC), we are in a position to con-

tribute to an unsampled region in the observational phase space of spatial resolution,

spatial coverage, spectral resolution, and spectral coverage. As a precursor to this

hyperspectral analysis, a multispectral data set from the HST will be analyzed with

the same set of tools.

There are three main science goals for this project. (1) Model the vertical aerosol

structure of the Jovian troposphere with a unique combination of spatial resolution

and spectral coverage. This will help constrain the current model of the vertical

aerosol structure and provide insight into the vertical transport of condensates and the

transfer of heat in the jovian atmosphere. (2) Derive values for the single-scattering

albedo ($0) as a function of wavelength, aerosol layer (i.e. pressure), and horizon-

tal location. This will aid in determining the horizontal and vertical distributions

of the chromophores. This yields information concerning their origin: whether pro-

duced photochemically from being suspended at high altitudes or produced chemically

within the interior and dredged up from below. (3) Determine the number and spec-

tral characteristics of the chromophores. This will narrow the search for the chemical

identities of the chromophores.

These goals are in line with the science goals of the Outer Planets Assessment

Group (OPAG). In their July 2006 report entitled Scientific Goals and Pathways

for Exploration of the Outer Solar System, one of their three main goals under the

theme of Making Solar Systems is to “[d]etermine composition, structure, and other

properties of the interiors of planetary bodies to provide vital clues about planetary

formation and evolutionary processes.” This project can directly contribute to fur-

thering this goal by characterizing the vertical aerosol structure, which is directly

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affected by the interior dynamics and composition. The OPAG report also priori-

tize the following scientific questions, technological advancements, and measurements

that are pertinent to this project:

• How do processes that shape the contemporary character of planetary bodies

operate and interact?

• Create low-power, low-mass, radiation tolerant components.

• Create advanced passive and active remote sensing instruments.

• Map atmospheric properties as functions of depth, latitude, and longitude.

The NAIC instrument certainly falls under the category of the 2nd and 3rd items.

Successful observation and published analyses of NAIC data will greatly help to fur-

ther the progress of AOTF technology.

3 Project Overview

3.1 HST Data

We have HST Wide Field Planetary Camera 2 (WFPC2) data from 2008 tracking

the passage of Oval BA and the GRS. The data are from three epochs: 15 May, 28

June, and 8 July. Nine filters were used to sample the continuum (255, 343, 375,

390, 410, 437, 469, 502, and 673 nm), and the 889 nm methane filter was used to

obtain cloud height information. This data set is ideal for high spatial resolution color

studies using PCA and nonnegative matrix factorization (NMF, which is described

in section 5.4). Most of these wavelengths are blueward of NAIC’s functioning range,

and will complement the NAIC color analyses. We also propose to use this data in

a radiative transfer code to retrieve regional cloud structure for comparison with the

color analyses.

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3.2 NAIC Data

We propose to acquire spatially resolved spectrophotometry of Jupiter from 470-900

nm with a resolution of 2 nm. This data will be modeled with a radiative transfer

code to retrieve atmospheric parameters and spectrally decomposed to determine the

properties of the chromophores. Each spectral image cube (two spatial dimensions

and one spectral dimension) resulting from this project will have ∼1◦×1◦ resolution

covering ±50◦ latitude for all longitudes. At this spatial resolution, the GRS, Oval

BA, white ovals, 5-µm hot spots, brown barges (if any), and some convective plumes

are resolvable.

The calibrated data set itself will be an asset to the planetary atmospheres com-

munity. Previously published spectra in this wavelength range fall into two general

categories: (1) low spatial resolution with high spectral resolution and (2) high spa-

tial resolution with low spectral resolution/sampling. The spectra in category (1) are

typical of ground-based telescopes. They produce averages of unique features (e.g.

the GRS, white ovals, and brown barges), maps with very low resolution (>10◦×10◦),

averages of latitudinal bands, or full-disk averages. Category (2) includes data from

large ground-based telescopes with adaptive optics (AO), HST, Galileo SSI and NIMS,

and Cassini Imaging Science Subsystem (ISS) and Composite Infrared Spectrometer

(CIRS). The spacecraft that visited Jupiter had very limited spatial coverage for their

observations that contain large numbers of spectral samples.

Also, Jupiter has never before been imaged with narrowband filters at many of

these wavelengths. Due to the wavelength dependencies of scattering and absorption,

sampling at many wavelengths can be used to constrain vertical location through

radiative transfer modeling. The spatial frequencies present in the contrast across

the disk also yield information on the vertical location of the observed structures.

High spatial frequencies are associated with the level of the variable cloud deck, while

low spatial frequencies are typical of the high-altitude hazes and lower ubiquitous

clouds.

The proposed data set has limitations in the atmospheric parameters that can be

retrieved. Temperature profiles (horizontal or vertical) will not be possible to derive,

because the spectral resolution is not sufficient to resolve the absorption line profiles.

The spatial resolution will be too low to track clouds, so the wind velocity fields

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cannot be determined.

4 Data

4.1 Instrument and Data Characteristics

The data for this project will be acquired with NAIC. This instrument contains an

Acousto-Optic Tunable Filter (AOTF) that operates between 0.45 and 1 µm with a

typical bandpass of ∼40 A FWHM. The AOTF contains a birefringent TeO2 crystal,

which allows an incident beam of broadband light to be split into separate beams:

one broadband beam with no refraction and two orthogonally polarized narrowband

beams with equal and opposite angles of refraction (Figure 2).

The incident beam will be split only when a standing acoustic wave is present

within the crystal. Internal acoustic vibrations are induced by sending a radio fre-

quency (RF) signal from an RF generator into the crystal via a transducer. The

frequency of the RF signal determines the frequency of the standing acoustic wave,

which determines the wavelength of the refracted narrowband beams. Only one of

the narrowband beams is sent to the CCD. The crystal is cut in such a way as to

minimize the width of the filter transmission function of this beam.

When the RF signal is off, only scattered light reaches the CCD. This scattered

light is a significant portion of the flux at the CCD and must be imaged in an RF-off

frame in order to remove it from RF-on frames. The RF-off frames must have an

exposure time equal to the RF-on image so that subtracting the RF-off from the RF-

on not only removes the scattered light but the bias and dark current as well. These

are taken frequently due to changes in the scatted light. The scattered light will

vary with any spatial change in the light incident upon the metal AOTF housing and

the crystal, which can be caused by temporal variations in the Earth’s atmospheric

transmission, the quality of the seeing, the target position within the field of view

(FOV), and target rotation, causing brightness features on the planet to move.

The broadband beam is always present whether or not the crystal is receiving

RF power. This light is picked off by a mirror and sent to a video camera, which

currently serves as the guide camera. For this project we propose to update the

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video camera with one that has a characterized range of linearity so that it may be

used as a photometric calibrator. The removal of the narrowband beam from the

broadband beam (when the RF power is on) results in a negligible loss of flux, so the

count rate in the video images can be used to monitor small changes in atmospheric

transmission. In practice, a small area on the planet will need to be selected for

monitoring, because the full disk of Jupiter will exceed the FOV and the fraction of

the disk in the FOV will shift with wavelength selection, telescope pointing drift due

to inaccurate tracking procedures, and manual pointing offsets.

The NAIC filter transmission is a function of both wavelength and the incidence

angle of the broadband light. At a given wavelength and incidence angle, the filter

function is well approximated by a sinc2 function (Figure 3), however there is an

asymmetry in the side lobes. Most of the light (∼90%) is contained within the

first two pairs of lobes from the central peak. The width of each peak and the

separation between peaks increase with increasing wavelength. The focal length of

the instrumental setup at the telescope will determine the divergence of the ray bundle

as it passes through the crystal, thus contributing a given width to the filter function.

It is important for the optical axis of NAIC to be aligned correctly when it is mounted

on the telescope; otherwise, the narrowband wavelength may be altered from the RF

tuning curve and width will be added to the transmission function.

The instant wavelength selection capability of the AOTF makes it possible to cre-

ate hyperspectral image cubes. Creating spectral image cubes involves collecting the

observations themselves plus a significant amount of subsequent processing. Science

images are taken with the RF power on, selecting a specific narrowband filter. This

process is usually done as an automated wavelength scan in 2 nm increments from

470 nm 900 nm. When preparing for observations, the ability to create custom scan

scripts, especially to image in the methane absorption bands multiple times per full

wavelength scan, will be added.

Many calibration images are needed in addition to spectrophotometric standards.

Flat fielding is done by observing the illuminated (closed) dome of the telescope. In

principle, this must be done at every wavelength tuning used for science images, but

the integration times necessary make this unfeasible. Flat fields are taken in 50 nm

intervals as a trade-off between the signal to noise ratio and the wavelength coverage.

Any wavelength offset due to misalignment at the telescope or anomalies in the RF

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tuning curve should be determined by scans of calibration lamps in the hours before

and after observing.

After the science and calibration images are collected, the image reduction, planet

registration, and map projection must be done before analysis. Figure 4 contains

a flow chart detailing the reduction process and data products. The resulting data

products will have better spectral resolution and coverage than feasible with standard

narrowband filters and better spatial resolution than possible with a spectrograph.

4.2 Previous Observations

We obtained a few thousand narrowband images of Jupiter with NAIC during 2007,

covering 470-900 nm in 2 nm steps. Jupiter and Saturn were observed on 27 February

and 1-2 March at the Advanced Electo-Optic System (AEOS) 3.67 meter telescope

located at the Maui Space Surveillance System. These observations were scheduled

for support of the New Horizons closest approach to Jupiter on 28 February 2007.

AEOS is equipped with an advanced AO system, which provided us with tip-tilt

correction. Use of the full AO was not feasible due to the large angular size of Jupiter

and Saturn. Loss of light from the AO 50/50 beam-splitter and the low throughput

of NAIC necessitated 20-40 second exposures, but the tip-tilt correction still yielded

images with seeing down to 0.7 arcsec. Roughly two nights of the four full nights

awarded at AEOS were lost due to high humidity and clouds.

Jupiter was also observed on 26-27 June and 4 July at the APO 3.5 meter tele-

scope. Exposure time was reduced to ∼2 seconds by an increase in pixel binning,

and the seeing ranged from ∼0.7-2.0 arcsec. Roughly 70% of Jovian longitudes were

imaged from 480-900 nm in 2 nm steps. Four half-nights of the 6 half-nights awarded

were lost due to the early onset of seasonal rains.

Neither of these observation campaigns had photometric weather, which is neces-

sary for radiative transfer modeling. These data (see Figures 5 and 6) were analyzed

with methods that merely use the spatial contrast in each filter (Strycker et al. 2007,

Strycker et al. 2008). These analyses were negatively affected by rapidly changing

seeing during the acquisition of some image cubes, but they provided the necessary

proof-of-concept for this project as well as successful field tests for NAIC. If possible,

these data will be included in the color analyses of this project.

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4.3 Proposed Observations

We propose to observe Jupiter in September of 2009 with NAIC at the APO 3.5 meter

telescope. Jupiter reaches opposition in August, which falls within the typical extent

of monsoon season, so our observations are planned to maximize the angular size of

Jupiter while minimizing the probability of poor weather. In order to obtain full

longitudinal coverage at two epochs and to minimize the time spent recharacterizing

NAIC, we will request to be scheduled in full nights instead of the standard half-

night blocks. Two separate campaigns will be requested to increase the chances of

obtaining photometric observations. Both are planned for ∼4 nights of bright time

in September.

In the event that both campaigns are successful, the extra epoch of data will

allow for time-variable studies of the vertical structure and chromophores. If neither

campaign has photometric data, a method of spectral normalization may be applied

to the data for a radiative transfer analysis, though this is less desirable. Analyses

of spectral deviations from the mean will only be affected by changes in seeing, and

will still be possible.

If no data is able to be collected in 2009, creation of the NAIC-specific analysis

tools can still proceed using the 2007 data for testing purposes. A limited study of

NAIC data would follow, pending observations in 2010. The HST study can proceed

as planned. If absolutely necessary, the HST study can be expanded with additional

(archived) data sets to become the main focus of the dissertation.

5 Analysis

5.1 Radiative Transfer Model

This project will adapt the Simon-Miller et al. (2001b) adding-doubling radiative

transfer code to model vertical structure, which is an extension of the Banfield et al.

(1998) model. Banfield et al. designed the model for use with 727, 756, and 890 nm

data from Galileo SSI. Aerosol scattering, gas scattering, and methane absorption

are incorporated through Mie scattering, Rayleigh scattering, and modified transmis-

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sivities from Karkoschka (1994), respectively. Methane absorption is approximated

by fitting Beer’s Law profiles to the calculated absorption profile for each filter (see

Figure 7). For each aerosol layer the free parameters were the base pressure and

opacity at 756 nm. Simon-Miller et al. added 410 nm data to the model and included

particle size and single-scattering albedo at 410 nm as free parameters. Although

Simon-Miller et al. solve for particle size, they found it was poorly constrained by the

data, and did not report the fit values.

The radiative transfer code accepts the following inputs: 4 I/F calibrated filters

(410, 727, 756, and 890 nm) at 3 separate viewing geometries. Each viewing geometry

is defined by cylindrical maps containing the latitude, longitude, angle of incidence

(µ0), angle of emission (µ), and phase angle for each pixel. The latitude and longitude

are not taken into account in the model but are necessary for reference. The time to

acquire images in all 4 filters was short compared to the viewing geometry’s time rate

of change. Therefore, only one viewing geometry is defined for each set of 4 filters.

Thus, the input requires a total of 12 cylindrically projected images and 3 sets of

viewing geometry data.

To adapt the code for use with HST data, several changes must be made. Ten

HST filters (255, 343, 375, 390, 410, 437, 469, 502, 673, and 889 nm) will be used

(Figure 7). Separate viewing geometry data is required for each image because the

viewing geometry changed significantly between the exposures in each filter. A single

scattering albedo ($0,λ,i) at each wavelength (λ) needs to be fit for each cloud/haze

layer (i) in the model. The current plan is to approximate the wavelength dependence

of $0,λ,i as a linear function. It may be necessary to allow them to vary independently.

A functional change must also be made from the user’s end. The Galileo data

contains images of a given Jovian location when it is near the terminator, the limb,

and in between the two extremes. With this span of viewing geometries (µ0 and µ),

the cloud structure at individual locations can be modeled with the radiative transfer

code. The user simply chooses a single location to model at a time. However, in the

proposed HST study, the parameter space of the viewing geometry is not well-covered

for any single location. This necessitates that the user selects multiple positions across

the disk to fill in the viewing angle parameter space in order to fit a cloud structure

model. The assumption that a uniform cloud structure exists is only valid (if ever)

within a latitudinal band. The cloud structure retrieval should be valid as long as any

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actual structural variations in the clouds produce albedo variations that are within

the uncertainty of the data.

The NAIC data will present new challenges for radiative transfer modeling with

the large number of filters that can be included (Figure 8). This will aid cloud height

discrimination, enable fits to $0 over a broad range of continuum wavelengths, and

provide the ability to retrieve the local ammonia humidity. A unique advantage of the

NAIC data is that multiple viewing geometries and multiple spectra will be available

for each mapped location.

The radiative transfer analysis in this project will differ from the Temma et al.

2005 study of Saturn with AOTF data. In their study, Saturn’s vertical aerosol struc-

ture was modeled by taking latitudinal cuts across spectral image cubes to produce

limb-darkening curves. Thus, the spatial resolution of their retrievals was limited to

latitudinal averages. Our analysis will create limb-darkening curves for each latitude

and longitude, allowing us to model them individually.

5.2 Automated Model Fitting

We do not expect to fully constrain the model with our inclusion of additional wave-

lengths and viewing geometries. Radiative transfer analysis is limited by degeneracy.

At depths below the stratosphere, the multiple scattering of photons creates a source

function that is non-linear with aerosol density. This results in “multiple solutions

to the radiative transfer equation that fit the data to within the uncertainties. It is

important to understand that a purely objective technique such as direct retrieval is

not possible for tropospheric clouds, and the models rely also to some extent on a

priori assumptions about cloud structure” (West et al. 2004).

Most published analyses approach this problem by finding the simplest cloud

model that fits the data within the error. They acknowledge the non-uniqueness of

their solution by stating general rules for the degeneracy. For example, a tropospheric

cloud of a given model opacity is often equivalent to moving it down in altitude and

increasing its opacity.

This project will approach the problem of a priori assumptions by using an initial

set of archetypal models. Two- and three-layer models will certainly be included.

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Best-fit parameters will be derived for each model at each location, and the simplest

model to converge will be chosen to represent the cloud structure there. Due to our

large data set, an automated fitting routine will be required to accomplished this task

in a timely manner.

To test the automated model fitting routine, it will be applied to the Galileo data

that was modeled manually by Banfield et al. (1998) and Simon-Miller et al. (2001b).

Three variations of testing can be done with this data. The first test is designed to

quantify the dependence of the derived model on the initial model. Each carefully

selected region modeled by Banfield et al. and Simon-Miller et al. will be processed

by the automation technique starting with cloud models that differ slightly from the

manually derived model. The initial model will be made progressively divergent from

the manually derived model until the automated routine is not able to converge on

the manually derived parameters. The automated routine will then be tried over the

whole field of view surrounding the selected regions to see where it is able to converge

on a solution. This will provide a measure of the robustness and will validate the

automation technique.

The performance of the automation routine can also be tested on a variety of

spatial scales. Beginning with the regions with manually derived parameters, an

automated fit to each pixel within the region can be found. Banfield et al. and

Simon-Miller et al. used the local pixel variations within each region to find the

vertical location of the variable cloud opacity. They accomplished this by finding a

regional fit to the average of all pixels and then finding a second fit (by increasing the

opacity of a single aerosol layer) to the match the slope of the 756 nm versus 727 nm

scatter plot (Figure 9). If the automated routine finds fits to the individual pixels

that have the same range/distribution in opacity in the same aerosol layer as found

manually, then this will also validate the automation technique.

A third test that can be applied to the automation technique is to vary the size

of the region to be modeled. This test should be applied to whole fields of view from

the Galileo data and should encompass a range of regional sizes that extends above

and below the spatial resolution of NAIC. The regional parameters retrieved for sizes

comparable to NAIC’s resolution will be compared those obtained with finer scales.

Internal consistency tests can be performed on just the NAIC data. A criterion

will be defined to select locations with similar center-to-limb variation (CTLV), and

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the model results for those locations will be compared for consistency. Locations with

similar model results will be compared to check for consistency in the CTLV. If more

than one epoch is available, then the results for each model at each location will be

compared between epochs.

5.3 Computational Time-Saving Methods

Full map retrievals may be impossible due to the required computational time. If

this is the case, a variation of the Irwin and Dyudina (2002) approach can be used.

They find a representative set of spectra, tabulate radiative transfer retrievals, and

interpolate these to find the structure of individual locations. This introduces little

error while greatly reducing the required number of radiative transfer calculations.

The major difference with this work in the application of their approach is that their

retrieval model required only one input spectrum. This will need to be modified for

data with multiple viewing geometries.

The full process as used by Irwin and Dyudina (2002) is as follows. They used

PCA to derive a small number of EOFs that explain most of the spectral variance

present in the data. The original spectral data is then approximated in terms of the

average spectrum and the EOFs. The range of coefficients for each EOF determines

the parameter space that must be included in the radiative transfer analysis. For

example, if 90% of the values of c1 (coefficients in the transformed data for EOF 1)

fall between -0.8 and 1.3, representative spectra will be generated with c1 = [-0.8,

-0.4, 0.0, 0.4, 0.8, 1.3]. The appropriate coefficient values are thus determined by

inspection of a histogram of the transformed data for each EOF. The final number

of representative spectra created for analysis is N1× N2 × · · · × NI , where I is the

number of EOFs included and Ni is the number of individual coefficient values selected

for EOF i.

5.4 Principal Component Analysis and Nonnegative Matrix

Factorization

The Simon-Miller et al. (2001a) PCA study was limited by the number of continuum

filters for which global data was available and “less than optimal filters, including a

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broad-band filter that spanned weak methane absorption bands” (Simon-Miller et al.

2001a). The study proposed here will contain 9 continuum wavelengths in the HST

data and 50-100 in the NAIC data, all of which are narrowband. This will allow for

confirmation of the number of chromophores in this spectral region and a much better

determination of their spectral characteristics.

A similiar multivariate technique to PCA is nonnegative matrix factorization

(NMF). Unlike PCA, NMF has the advantage of constraining all derived components

to be nonnegative, which is physically appropriate for reflectance spectra. Also, NMF

does not require the components to be orthogonal, so the spectral shape is more rep-

resentative of the true colors present.

Ultimately, this project will provide better constraints on the spectral properties

of chromophores, which can be compared to existing data and may help guide future

laboratory investigations.

6 Timeline

Summer 2009:

• Modify radiative transfer code for HST.

• Prepare NAIC for observing.

Fall 2009:

• Analyze HST data.

• Observe with NAIC at APO.

• Annual DPS meeting in Fajardo, Puerto Rico (4-9 October).

Spring 2010:

• Meet with collaborators at Cornell.

• Publish HST analysis.

• Modify radiative transfer code for NAIC data.

Summer 2010:

• Analyze NAIC data.

Fall 2010:

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• Publish NAIC analysis.

• Apply for jobs.

• Annual DPS meeting in Madison, Wisconsin (18-22 October).

Spring 2011:

• Meet with collaborators at Cornell.

• Write dissertation.

Spring/Summer 2011:

• Defend dissertation.

7 References

Baines, K. H., Carlson, R. W., Kamp, L. W. 2002. Fresh Ammonia Ice Clouds inJupiter. I. Spectroscopic Identification, Spatial Distribution, and Dynamical Impli-cations. Icarus 159, 74-94.

Banfield, D. et al. 1998. Jupiter’s Cloud Structure from Galileo Imaging Data. Icarus135, 230-250.

Dyudina, U. A. et al. 2001. Interpretation of NIMS and SSI Images on the JovianCloud Structure. Icarus 150, 219-233.

Gierasch, P. J. et al. 2000. Observation of moist convection in Jupiter’s atmosphere.Nature 403, 628-630.

Ingersoll, A.P. et al. 2004. Dynamics of Jupiter’s Atmosphere. In Jupiter: ThePlanet, Satellites and Magnetosphere (F. Bagenal, T. Dowling, and W. McKinnon,Eds.), pp. 105-128. Cambridge University Press, Cambridge.

Irwin, P. G. J. et al. 2001. The Origin of Belt/Zone Contrasts in the Atmosphere ofJupiter and Their Correlation with 5-µm Opacity. Icarus 149, 397-415.

Irwin, P. G. J. and Dyudina, U. A. 2002. The Retrieval of Cloud Structure Maps in theEquatorial Region of Jupiter Using a Principal Component Analysis of Galileo/NIMSData. Icarus 156, 52-63.

Kalogerakis, K. S. et al. 2008. The coating hypothesis for ammonia ice particles inJupiter: Laboratory experiments and optical modeling. Icarus 196, 202-215.

Karkoschka, E. 1994. Spectrophotometry of the Jovian Planets and Titan at 300- to1000-nm Wavelength: The Methane Spectrum. Icarus 111, 174-192.

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Karkoschka, E. 1998. Methane, Ammonia, and Temperature Measurements of theJovian Planets and Titan from CCD-Spectrophotometry. Icarus 133, 134-146.

OPAG 2006. Scientific Goals and Pathways for Exploration of the Outer Solar System.http://www.lpi.usra.edu/opag/pathways 07 06.pdf

Orton, G. S. et al. 1998. Characteristics of the Galileo probe entry site from Earth-based remote sensing observations. Journal of Geophysical Research 103, E10, 22791-22814.

Simon-Miller, A. A. et al. 2000. A Detection of Water Ice on Jupiter with VoyagerIRIS. Icarus 145, 454-461.

Simon-Miller, A. A., Banfield, D., and Gierasch, P. J. 2001a. An HST Study of JovianChromophores. Icarus 149, 94-106.

Simon-Miller, A. A., Banfield, D., and Gierasch, P. J. 2001b. Color and the VerticalStructure in Jupiter’s Belts, Zones, and Weather Systems. Icarus 154, 459-474.

Simon-Miller, A. A. et al. 2006. Jupiter’s White Oval turns red. Icarus 185, 558-562.

Smith, P. H. and Tomasko, M. G. 1984, Photometry and Polarimetry of Jupiter atLarge Phase Angles. II. Polarimetry of the South Tropical Zone, South EquatorialBelt, and the Polar Regions from the Pioneer 10 and 11 Missions. Icarus 58, 35-73.

Strycker, P. D. et al. 2007. Hyperspectral Imaging of Jupiter and Saturn. Workshopon Planetary Atmospheres.

Strycker, P. D. et al. 2008. Jovian Ammonia Cloud Identification and Color Analysesfrom Hyperspectral Imaging. Division for Planetary Sciences Meeting.

Taylor, F. W. et al. 2004. The Composition of the Atmosphere of Jupiter. In Jupiter:The Planet, Satellites and Magnetosphere (F. Bagenal, T. Dowling, and W. McKin-non, Eds.), pp. 59-78. Cambridge University Press, Cambridge.

Temma, T. et al. 2005. Vertical structure modeling of Saturn’s equatorial regionusing high spectral resolution imaging. Icarus 175, 464-489.

West, R. A. et al. 1986. Clouds, Aerosols, and Photochemistry in the Jovian Atmo-sphere. Icarus 65, 161-217.

West, R. A. et al. 2004. Jovian Clouds and Haze. In Jupiter: The Planet, Satellitesand Magnetosphere (F. Bagenal, T. Dowling, and W. McKinnon, Eds.), pp. 79-104.Cambridge University Press, Cambridge.

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8 Figures and Tables

Figure 1: A model of the vertical aerosol structure of Jupiter’s troposphere (West et al. 2004,Figure 5.15).

Figure 2: Rays of light passing through an acousto-optic tunable filter (AOTF) crystal when aradio frequency (RF) signal is applied to produce standing acoustic waves (shaded area). The lightentering the AOTF is broadband. The exiting beams are broadband extraordinary (E), broadbandordinary (O), narrowband extraordinary (e′), and narrowband ordinary (o′), with polarization asindicated.

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Figure 3: Filter transmission function for NAIC at 543 nm.

Figure 4: NAIC data reduction flow chart. Diamonds represent independent input, rectanglesrepresent data manipulation, ovals represent data products, and hexagons represent analyses.

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Figure 5: NAIC images from Apache Point Observatory. From left to right and top to bottom,the filters are 480, 530, 600, 702, 842, and 890 nm. Note the high clouds and hazes visible as brightfeatures in the 890 nm methane absorption band (bottom right image), especially the EquatorialZone and Oval BA (lower right).

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Figure 6: Jupiter’s full-disk albedo spectrum from NAIC images from Apache Point Observatory(black) and Karkoschka’s (1998) full-disk albedo (gray). In the top plot, Karkoschka’s data isdisplayed as published. In the bottom plot, Karkoschka’s data has been convolved with NAIC’sfilter function.

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Figure 7: Two-way gas transmissivity from space to the indicated pressure level for HST filters255, 343, 375, 390, 410, 437, 469, 502, 673, and 889 nm. The gas absorption (left) is calculatedfrom Karkoschka’s (1994) methane absorption coefficients. The dotted lines are fits to Beer’s Lawprofiles.

Figure 8: Two-way gas transmissivity from space to the indicated pressure level for NAIC filtersfrom 480-900 nm at 2 nm intervals. The gas absorption (left) is calculated from Karkoschka’s (1994)methane absorption coefficients.

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Figure 9: The Simon-Miller et al. (2001b) radiative transfer model (their Figure 4). Model A isa fit to the regional mean of the data. Model B is a fit to the slope of the individual data pointswithin the region, which is obtained by varying the opacity in only one aerosol layer. In this case,the ammonia cloud near 900 mbar has about twice the opacity in Model B as it does in Model A.

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Table I

West et al. 1986, Table V.

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