Global Terrestrial Water Storage Product:  Error Assessment and Improvements

The GRACE (2002-2017) and GRACE-FO (since 2018) satellite missions measure spatial and temporal variations in the Earth’s gravity field. Over the continents, water mass redistributions are the dominant source of these gravity variations. Unlike all other remote sensing techniques, GRACE/GRACE-FO can observe the whole water column, called terrestrial water storage (TWS), including groundwater. Thus, with its global coverage and over 20 years of monthly data, the TWS data set is invaluable for geosciences, particularly hydrology. 

The last years have brought considerable improvements in the GRACE/GRACE-FO gravity field processing that leads to Level-2 gravity field products and their so-called formal error characterisation (Jäggi et al., 2020 and ISSI International Team COST-G). Meanwhile, the subsequent derivation of unconstrained and filtered TWS global grids (Level-3 products) as provided via the GravIS portal (Gravity Information Service, gravis.gfz-potsdam.de) has undergone only minor updates. Parallel to these unconstrained and filtered TWS grids, JPL (Jet Propulsion Laboratory) provide a constrained TWS product known as Mascons (Wiese et al., 2016). At the same time, many applications in the geosciences and climate sciences need uncertainty estimations for drawing reliable conclusions from the TWS data set.

This working group aims at improving of the TWS processing by bringing together scientist working with different Level-3 processing strategies. Its uncertainty estimation will be improved by recent developments in the statistical modelling necessary for Level-2 processing. Further, the uncertainty estimation will expand to take temporal correlation into account.

Improvements of TWS processing and its uncertainty estimations will benefit the applications of the data sets such as gravity-based ground water products, estimation of solid Earth deformations, or regional studies.

The objectives of this working group are improving the TWS processing and its uncertainties together with a throughout validation of the new data sets in the aforementioned applications, including a comparison to Mascon solutions.