Determination of a high spatial resolution geopotential model using atomic clock comparisons

TL;DR

This study evaluates how incorporating high-precision atomic clock measurements can significantly improve high-resolution geopotential models, especially in complex terrains, by reducing bias and enhancing accuracy.

Contribution

It introduces a synthetic data-based method to assess the impact of atomic clock data on high-resolution geopotential mapping, demonstrating notable accuracy improvements.

Findings

Adding a few clock data points reduces bias substantially.

Standard deviation of the geopotential map improves by a factor of 3.

Clock data enhances model accuracy even with limited measurements.

Abstract

Recent technological advances in optical atomic clocks are opening new perspectives for the direct determination of geopotential differences between any two points at a centimeter-level accuracy in geoid height. However, so far detailed quantitative estimates of the possible improvement in geoid determination when adding such clock measurements to existing data are lacking. We present a first step in that direction with the aim and hope of triggering further work and efforts in this emerging field of chronometric geodesy and geophysics. We specifically focus on evaluating the contribution of this new kind of direct measurements in determining the geopotential at high spatial resolution (~ 10 km). We studied two test areas, both located in France and corresponding to a middle (Massif Central) and high (Alps) mountainous terrain. These regions are interesting because the gravitational field strength varies greatly from place to place at high spatial resolution due to the complex topography. Our method consists in first generating a synthetic high resolution geopotential map, then drawing synthetic measurement data (gravimetry and clock data) from it, and finally reconstructing the geopotential map from that data using least squares collocation. The quality of the reconstructed map is then assessed by comparing it to the original one used to generate the data. We show that adding only a few clock data points (less than 1 % of the gravimetry data) reduces the bias significantly and improves the standard deviation by a factor 3. The effect of the data coverage and data quality on the results is investigated, and the trade-off between the measurement noise level and the number of data points is discussed.