Impact of a novel hybrid accelerometer on satellite gravimetry performance

The state-of-the-art electrostatic accelerometers (EA) used for the retrieval of non-gravitational forces acting on a satellite constitute a core component of every dedicated gravity field mission. However, due to their difficult-to-control thermal drift in the low observation frequencies, they are also one of the most limiting factors of the achievable performance of gravity recovery. Recently, a hybrid accelerometer consisting of a regular EA and a novel cold atom interferometer (CAI) that features a time-invariant observation stability and constantly recalibrates the EA has been developed in order to remedy this major drawback. In this paper we aim to assess the value of the hybrid accelerometer for gravity field retrieval in the context of GRACE-type and Bender-type missions by means of numerical closed-loop simulations where possible noise specifications of the novel instrument are considered and different components of the Earth's gravity field signal are added subsequently. It is shown that the quality of the gravity field solutions is mainly dependent on the CAI's measurement accuracy. While a low CAI performance (10(-8) to 10(-9) m/s(2)/Hz(1/2)) does not lead to any gains compared to a stand-alone EA, a sufficiently high one (10(-11) m/s(2)Hz(1/2)) may improve the retrieval performance by over one order of magnitude. We also show that improvements which are limited to low-frequency observations may even propagate into high spherical harmonic degrees. Further, the accelerometer performance seems to play a less prominent role if the overall observation geometry is improved as it is the case for a Bender-type mission. The impact of the accelerometer measurements diminishes further when temporal variations of the gravity field are introduced, pointing out the need for proper de-aliasing techniques. An additional study reveals that the hybrid accelerometer is - contrary to a stand-alone EA - widely unaffected by scale factor instabilities. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.