The Retrieval of Cloud Structure Maps in the Equatorial Region of Jupiter Using a Principal Component Analysis of Galileo/NIMS Data

The cloud structure of the jovian atmosphere at pressures less than 2 bars has previously been estimated using near-infrared observations such as those by both the Solid State Imager (SSI) and Near-Infrared Mapping Spectrometer (NIMS) instruments on board the Galileo spacecraft. Unfortunately, complete near-infrared spectra, such as those measured by NIMS, take a long time to be analyzed with multiple-scattering radiative transfer models and thus it has until now been rather difficult to use these data to produce wide-area cloud maps.In this paper we show how principal component analysis may be employed to isolate a small number of empirical orthogonal functions (EOFs) from spectra of Jupiter made by Galileo/NIMS. These EOFs may be used to represent the variance of real NIMS spectra to a high degree of accuracy and with good noise and “drop-out” discrimination. Because of this, a small set of representative spectra may then be calculated using these EOFs and input into a retrieval model that generates a table of fitted cloud profiles for each case. This approach avoids the long times required for analyzing a large number of spectra with full multiple-scattering radiative transfer models and allows us to represent the variability of the 3000 spectra contained in the observations of the North Equatorial Belt (NEB) used in this study with only 75 representative spectra. The cloud structures fitted to these representative spectra were interpolated for the spectra found at individual locations in the measured NIMS data set to produce maps of cloud opacity and mean particle size. We find that the dominant opacity variation, anticorrelated with 5-μm brightness, exists in the 1-2 bar pressure range. The distribution of the cloud at 0.72 bars is mapped and found to be more zonally diffuse than the lower clouds. We find at least one 2000-km-sized deep convective cloud in the NEB vertically extending to all the pressure levels that can be sensed by NIMS.