The noise emissions of jets from full-scale engines installed on military aircraft pose a significant hearing loss risk to military personnel. Noise reduction technologies and the development of operational procedures that minimize noise exposure to personnel are enhanced by the accurate characterization of noise sources within a jet. Hence, more than six decades of research have gone into jet noise measurement and prediction. In the past decade, the noise-source visualization tool near-field acoustical holography (NAH) has been applied to jets. NAH fits a weighted set of expansion wave functions, typically planar, cylindrical, or spherical, to measured sound pressures in the field. NAH measurements were made of a jet from an installed engine on a military aircraft. In the present study, the algorithm of statistically optimized NAH (SONAH) is modified to account for the presence of acoustic reflections from the concrete surface over which the jet was measured. The three dimensional field in the jet vicinity is reconstructed, and information about sources is inferred from reconstructions at the boundary of the turbulent jet flow. Then, a partial field decomposition (PFD) is performed, which represents the total field as the superposition of multiple, independent partial fields. This is the most direct attempt to equate partial fields with independent sources in a jet to date.