Assimilation of satellite observed brightness temperature and terrestrial water storage into the Catchment land surface model for improved soil moisture estimation

Root zone soil moisture (0-1m) is an important variable for hydrological and weather forecast models. Its prediction accuracy depends on a good initialization of soil moisture because it regulates the energy and mass balance exchange between land surface and atmosphere. Observations from recent (or near future) soil moisture missions (e.g. SMOS/SMAP) have been (or will be) used in innovative studies to provide high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture estimates from brightness temperature observations. However, these missions are only sensitive to near-surface soil moisture (0–5 cm). In contrast with other routinely available global remote sensing measurements, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column. GRACE is characterized by low spatial (i.e. 400 km) and temporal (i.e. monthly) resolutions, therefore it requires disaggregation to higher spatial and temporal scales. In this work we have investigated the potential for using TWS and brightness temperature observations to improve root zone soil moisture. We have assimilated GRACE and SMOS observations into the Catchment land surface model, using the NASA Goddard Earth Observing System, version 5 (GEOS-5) land surface data assimilation system. The ensemble-based assimilation scheme is used to disaggregate the GRACE observations in space and time (from observation to model resolution scales), and also to vertically decompose the observations into individual land surface moisture components (i.e.: groundwater, surface and root zone soil moisture). Model estimates with and without observations assimilation are compared against independent measurements of groundwater and soil moisture over the Continental U.S. Results suggest that the joint assimilation of GRACE and SMPOS data has the potential to improve soil moisture estimates. However, the performance of the assimilation strongly depends on the chosen measurement and model error structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.