Studies of Jovian Atmospheric Structure and Coloring Agents using Hyperspectral Imaging

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1 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 examining the vertical cloud structure and correlating it with the following: (1) atmospheric 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 components 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 1

2 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 described 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 (NH 4 SH) cloud at 1.5 bars (210 K), and an ammonia (NH 3 ) cloud at 750 mbar (150 K). Above the ammonia cloud is a ubiquitous haze of sub-µm particles, probably consisting mostly of ammonia 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 equilibrium models and retrievals of cloud height and composition from observations. Thermochemical equilibrium models require Jovian temperature-pressure profiles, elemental 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 difficult 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 measurements of Jupiter to date on December 7, 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 NH 4 SH clouds 2

3 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. NH 4 SH 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 NH 4 SH 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 observers due to Earth s atmospheric CO 2. 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 turbulent 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 nature of SIACs is that the ice particles are coated with hydrocarbons, which mask the spectral signature of the underlying ice (Kalogerakis et al. 2008). 3

4 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 characteristics of the chromophores through a principal component analysis (PCA) using two sets of Hubble Space Telescope (HST) images. The first set contained only 3 continuum 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. empirical 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 chromophore. 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 orthogonal 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 4

5 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 simultaneously (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 contribute 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 (ϖ 0 ) as a function of wavelength, aerosol layer (i.e. pressure), and horizontal location. This will aid in determining the horizontal and vertical distributions of the chromophores. This yields information concerning their origin: whether produced photochemically from being suspended at high altitudes or produced chemically within the interior and dredged up from below. (3) Determine the number and spectral 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 furthering this goal by characterizing the vertical aerosol structure, which is directly 5

6 affected by the interior dynamics and composition. The OPAG report also prioritize 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 further 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. 6

7 3.2 NAIC Data We propose to acquire spatially resolved spectrophotometry of Jupiter from 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 community. Previously published spectra in this wavelength range fall into two general categories: (1) low spatial resolution with high spectral resolution and (2) high spatial 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 7

8 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 Å FWHM. The AOTF contains a birefringent TeO 2 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 frequency (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 RFon 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 8

9 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 sinc 2 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 create 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 9

10 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 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 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 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 June and 4 July at the APO 3.5 meter telescope. Exposure time was reduced to 2 seconds by an increase in pixel binning, and the seeing ranged from arcsec. Roughly 70% of Jovian longitudes were imaged from 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 necessary 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. 10

11 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 halfnight 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 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- 11

12 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 (µ 0 ), 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 (ϖ 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 ϖ 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 (µ 0 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 12

13 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 ϖ 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 study of Saturn with AOTF data. In their study, Saturn s vertical aerosol structure 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 wavelengths 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. 13

14 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 14

15 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 c 1 (coefficients in the transformed data for EOF 1) fall between -0.8 and 1.3, representative spectra will be generated with c 1 = [-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 N 1 N 2 N I, where I is the number of EOFs included and N i 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 15

16 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 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 representative 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: 16

17 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 Fresh Ammonia Ice Clouds in Jupiter. I. Spectroscopic Identification, Spatial Distribution, and Dynamical Implications. Icarus 159, Banfield, D. et al Jupiter s Cloud Structure from Galileo Imaging Data. Icarus 135, Dyudina, U. A. et al Interpretation of NIMS and SSI Images on the Jovian Cloud Structure. Icarus 150, Gierasch, P. J. et al Observation of moist convection in Jupiter s atmosphere. Nature 403, Ingersoll, A.P. et al Dynamics of Jupiter s Atmosphere. In Jupiter: The Planet, Satellites and Magnetosphere (F. Bagenal, T. Dowling, and W. McKinnon, Eds.), pp Cambridge University Press, Cambridge. Irwin, P. G. J. et al The Origin of Belt/Zone Contrasts in the Atmosphere of Jupiter and Their Correlation with 5-µm Opacity. Icarus 149, Irwin, P. G. J. and Dyudina, U. A The Retrieval of Cloud Structure Maps in the Equatorial Region of Jupiter Using a Principal Component Analysis of Galileo/NIMS Data. Icarus 156, Kalogerakis, K. S. et al The coating hypothesis for ammonia ice particles in Jupiter: Laboratory experiments and optical modeling. Icarus 196, Karkoschka, E Spectrophotometry of the Jovian Planets and Titan at 300- to 1000-nm Wavelength: The Methane Spectrum. Icarus 111,

18 Karkoschka, E Methane, Ammonia, and Temperature Measurements of the Jovian Planets and Titan from CCD-Spectrophotometry. Icarus 133, OPAG Scientific Goals and Pathways for Exploration of the Outer Solar System pdf Orton, G. S. et al Characteristics of the Galileo probe entry site from Earthbased remote sensing observations. Journal of Geophysical Research 103, E10, Simon-Miller, A. A. et al A Detection of Water Ice on Jupiter with Voyager IRIS. Icarus 145, Simon-Miller, A. A., Banfield, D., and Gierasch, P. J. 2001a. An HST Study of Jovian Chromophores. Icarus 149, Simon-Miller, A. A., Banfield, D., and Gierasch, P. J. 2001b. Color and the Vertical Structure in Jupiter s Belts, Zones, and Weather Systems. Icarus 154, Simon-Miller, A. A. et al Jupiter s White Oval turns red. Icarus 185, Smith, P. H. and Tomasko, M. G. 1984, Photometry and Polarimetry of Jupiter at Large Phase Angles. II. Polarimetry of the South Tropical Zone, South Equatorial Belt, and the Polar Regions from the Pioneer 10 and 11 Missions. Icarus 58, Strycker, P. D. et al Hyperspectral Imaging of Jupiter and Saturn. Workshop on Planetary Atmospheres. Strycker, P. D. et al Jovian Ammonia Cloud Identification and Color Analyses from Hyperspectral Imaging. Division for Planetary Sciences Meeting. Taylor, F. W. et al The Composition of the Atmosphere of Jupiter. In Jupiter: The Planet, Satellites and Magnetosphere (F. Bagenal, T. Dowling, and W. McKinnon, Eds.), pp Cambridge University Press, Cambridge. Temma, T. et al Vertical structure modeling of Saturn s equatorial region using high spectral resolution imaging. Icarus 175, West, R. A. et al Clouds, Aerosols, and Photochemistry in the Jovian Atmosphere. Icarus 65, West, R. A. et al Jovian Clouds and Haze. In Jupiter: The Planet, Satellites and Magnetosphere (F. Bagenal, T. Dowling, and W. McKinnon, Eds.), pp Cambridge University Press, Cambridge. 18

19 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 a radio frequency (RF) signal is applied to produce standing acoustic waves (shaded area). The light entering the AOTF is broadband. The exiting beams are broadband extraordinary (E), broadband ordinary (O), narrowband extraordinary (e ), and narrowband ordinary (o ), with polarization as indicated. 19

20 Figure 3: Filter transmission function for NAIC at 543 nm. Figure 4: NAIC data reduction flow chart. Diamonds represent independent input, rectangles represent data manipulation, ovals represent data products, and hexagons represent analyses. 20

21 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 bright features in the 890 nm methane absorption band (bottom right image), especially the Equatorial Zone and Oval BA (lower right). 21

22 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 is displayed as published. In the bottom plot, Karkoschka s data has been convolved with NAIC s filter function. 22

23 Figure 7: Two-way gas transmissivity from space to the indicated pressure level for HST filters 255, 343, 375, 390, 410, 437, 469, 502, 673, and 889 nm. The gas absorption (left) is calculated from Karkoschka s (1994) methane absorption coefficients. The dotted lines are fits to Beer s Law profiles. Figure 8: Two-way gas transmissivity from space to the indicated pressure level for NAIC filters from nm at 2 nm intervals. The gas absorption (left) is calculated from Karkoschka s (1994) methane absorption coefficients. 23

24 Figure 9: The Simon-Miller et al. (2001b) radiative transfer model (their Figure 4). Model A is a fit to the regional mean of the data. Model B is a fit to the slope of the individual data points within 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. 24

25 Table I West et al. 1986, Table V. 25