P34A-06
High-resolution gravity field modeling using GRAIL mission data
Wednesday, 16 December 2015: 17:09
2011 (Moscone West)
Frank G Lemoine1, Sander J Goossens2, Terence J Sabaka3, Joseph B Nicholas4, Erwan Mazarico5, David D Rowlands3, Gregory A Neumann5, Bryant Loomis6, Douglas S Chinn6, David E Smith7 and Maria T Zuber7, (1)NASA Goddard SFC, Greenbelt, MD, United States, (2)University of Maryland Baltimore County, Baltimore, MD, United States, (3)NASA Goddard Space Flight Center, Planetary Geodynamics Laboratory, Greenbelt, MD, United States, (4)Emergent Space Technologies, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (6)Stinger Ghaffarian Technologies (SGT), Greenbelt, MD, United States, (7)Massachusetts Institute of Technology, Cambridge, MA, United States
Abstract:
The Gravity Recovery and Interior Laboratory (GRAIL) spacecraft were designed to map the structure of the Moon through high-precision global gravity mapping. The mission consisted of two spacecraft with Ka-band inter-satellite tracking complemented by tracking from Earth. The mission had two phases: a primary mapping mission from March 1 until May 29, 2012 at an average altitude of 50 km, and an extended mission from August 30 until December 14, 2012, with an average altitude of 23 km before November 18, and 20 and 11 km after. High-resolution gravity field models using both these data sets have been estimated, with the current resolution being degree and order 1080 in spherical harmonics. Here, we focus on aspects of the analysis of the GRAIL data: we investigate eclipse modeling, the influence of empirical accelerations on the results, and we discuss the inversion of large-scale systems. In addition to global models we also estimated local gravity adjustments in areas of particular interest such as Mare Orientale, the south pole area, and the farside. We investigate the use of Ka-band Range Rate (KBRR) data versus numerical derivatives of KBRR data, and show that the latter have the capability to locally improve correlations with topography.
