Advancing Global Precipitation Estimation Using Next-Generation Gravity Missions

The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) provide valuable observations of terrestrial water storage (TWS) dynamics from regional to global scales. However, their limited spatio-temporal resolution impedes the reliable separation of individual hydrological fluxes, especially precipitation. To overcome these challenges, a joint collaboration between NASA and ESA initiated the Mass change and Geosciences International Constellation (MAGIC), aiming to deploy next-generation gravity missions with enhanced spatio-temporal resolution to better monitor hydrological extremes such as droughts and floods. The primary objective of this work is to examine the impact of improving the spatio-temporal resolution of NGGM and MAGIC on precipitation estimation by developing multiple synthetic experiments globally. Precipitation used as forcing in an Earth System Model (ESM) is compared against reference precipitation, with ERA5-Land precipitation serving as the benchmark, to evaluate the reliability of the SM2RAIN approach (Brocca et al., 2014) when driven by equivalent water height (EWH) data (in the past it was implemented by using surface soil moisture data). The global correlation analysis shows median and mean correlation coefficients of 0.74 and 0.69, respectively, indicating satisfactory performance of the EWH-based SM2RAIN framework across most terrestrial regions. Stronger correlations are observed over Northern Hemisphere mid-latitudes, including Europe, northern Asia, and North America, reflecting robust performance in temperate climates, while reduced performance is evident in several tropical regions such as central Africa, parts of the Amazon Basin, and Southeast Asia. Subsequently, synthetic experiments were developed using filter and unfiltered configurations of GRACE-C, NGGM, and MAGIC missions. The performance of NGGM and MAGIC filtered configurations indicates their capability to capture precipitation dynamics effectively as compared to unfiltered ones. The results of the study clearly highlight the benefit of NGGM and MAGIC missions in improving our capability to estimate various hydrological components, particularly for precipitation estimation relying on satellite data as inputs.