Using Data Assimilation to Understand the Systematic Errors in CHAMP Accelerometer-Derived Neutral Mass Density Data

Abstract

Accelerometer-derived neutral mass density (NMD) is an important quantity that describes the variability of the upper atmosphere. NMD is widely used to calibrate and validate some models used for satellite orbit determination and prediction. Quantifying the true NMD is nearly impossible due to, among others, the lack of simultaneous in-situ measurements to cross-validate and the incomplete characterization of the uncertainties of these NMD products. Using multiple data assimilation (DA) experiments and robust statistical techniques, this study investigates the error distribution of three different accelerometer-derived NMD products from the CHAMP satellite mission during a time period of low solar and geomagnetic activities. The strategies applied here may be useful and applicable to other space missions spanning over longer time periods. The results indicate considerable differences among the three CHAMP data sets and also show a pronounced latitudinal dependency for the estimated error distributions. On average, the error estimates for NMD vary in the range 6.5-15.6% of the signal. The results further demonstrate that DA considerably enhances the capability of the physical model as well as an excellent tool to assess data uncertainties.