The framework for satellite gravity data assimilation into land surface models

The Gravity Recovery and Climate Experiment (GRACE) mission has provided an unprecedented global, homogeneous observational dataset of the time variation in terrestrial water storage (TWS) since 2002. This product has seen widespread use in the study of processes in hydrology, oceanography, the cryosphere, and is particularly critical to inform, improve, and validate computational models of the Earth system. Assimilation of the GRACE TWS fields into current land surface models can correct model deficiencies due to errors in the model structure, atmospheric forcing datasets, parameters, etc. However, the assimilation process is complicated by spatial and temporal resolution discrepancies between the model and observational datasets, characterization of the error in each, and requires tuning to the unique characteristics of satellite gravity data. This study establishes a framework for hydrological data assimilation of terrestrial water storage data from GRACE, closes the loop between GRACE product development and its scientific use, and analyzes the assimilated results for use with current GRACE products and future satellite gravity missions. The framework fuses the strengths of the observational and land surface model datasets into an assimilated product representative of the signal strength and large scale structures of the GRACE dataset effectively downscaled to the high resolution land surface dynamics. The data assimilation framework was developed through a comprehensive analysis of the deficiencies and potential improvements of the satellite data products, the assimilation procedures and error characterization, and the assimilation effectiveness over time. This analysis motivated the development of a higher frequency GRACE dataset more representative of the hydrometeorological signal content with reduced temporal aliasing of the TWS signal. Three innovations were implemented in the product development: regularization, sliding windows, and mascon basis functions, to develop a high-fidelity daily gravity field product (RSWM). The signal and error profile of the RSWM product was comprehensively analyzed via an end-to-end simulation analysis of the GRACE mission. The simulation analysis developed an error covariance representative of the magnitude, correlation, and spatial pattern of error in the RSWM dataset available for use in the data assimilation system. The assimilation algorithms and tools were advanced to optimally incorporate the GRACE TWS data and error covariance information. Daily assimilation was performed globally at the one degree gridcell level, significantly reducing spatial and temporal smoothing of the assimilation update from previous basin-scale assimilation of the monthly mean GRACE datasets. Framework elements additionally defined the mechanisms of the assimilation process: (i) the Gaspari-Cohn localization radius to spatially smooth the coarser resolution GRACE data, (ii) the necessary assimilation update rate to balance assimilation performance and computational efficiency, and (iii) open-loop error growth after assimilation has conditioned the system to advise data latency requirements. The GRACE data assimilation framework is versatile and adaptable to other land surface models, different formulations of data from the current GRACE mission, and future satellite gravity datasets.