Abstract
The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.
Published Version
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