Open-Earth-Monitor Global Workshop 2025

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide observations of terrestrial water storage (TWS) dynamics on regional to global scales. However, they lack high spatio-temporal resolution, making them challenging to interpret different hydrological fluxes. A join collaboration between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), initiated a decade ago, is known as the Mass- change And Geosciences International Constellation (MAGIC). The aim of this collaboration to launch new high resolution missions in order to improve capacity for monitoring extreme events such droughts and floods. The primary objective of this work is to examine the impact of improving the spatial-temporal resolution of NGGM and MAGIC on precipitation estimation by developing multiple synthetic experiments on a European scale. The study employed SM2RAIN algorithm by inverting the soil water balance equation to estimate the rainfall accumulated between two consecutive TWS measurements (Brocca et al., 2014). Initially, the ERA5L based TWSA at daily time scale was incorporated into SM2RAIN to check reliability of the model against ERA5L precipitation with spatial resolution of 100 km over Europe with range of latitudes 30 to 60°N and longitudes 10°W to 50°E. The results shows SM2RAIN exhibited satisfactory performance at a daily temporal resolution, with mean values of R, RMSE, BIAS (0.85, 13.76, -0.29) against ERA5L precipitation. Based on statistical analysis, SM2RAIN-simulated precipitation shows good agreement across the most of Europe except in some areas of the northern Italy, northeastern states (Estonia, Latvia) and costal regions of Norway. Subsequently, synthetic experiments were developed by degrading the temporal resolution of TWS data from daily into 5-day interval and by introducing error ranging from 1.9 mm to 42 mm. The results shows that degrading temporal resolution and larger error make the model quite difficult to capture and represent meaningful rainfall patterns, as the error completely overshadows the underlying dynamics captured in the SM2RAIN-simulated rainfall. The results of the study clearly highlight the benefit of NGGM and MAGIC in improving our capability to estimate various hydrological components relying on satellite data as inputs.

References

Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., & Levizzani, V. (2014). Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research: Atmospheres, 119(9), 5128–5141.