Multi-sensor Assimilation of Synthetic AMSR-E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals to Improve Terrestrial Snow Mass
Abstract
This study explores multi-sensor, multi-variate data assimilation (DA) using synthetic AMSR-E passive microwave brightness temperature spectral differences (dTb) and synthetic GRACE terrestrial water storage (TWS) retrievals in order to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS over snow-covered terrain. AMSR-E dTb DA using support vector machine regression as the observation operator improves SWE estimates, but adds little value to subsurface storage estimates. A physically-informed GRACE TWS DA approach discretizes TWS into SWE and subsurface components more accurately. Simultaneous assimilation of AMSR-E dTb and GRACE TWS (a.k.a., dual DA) significantly improves SWE estimates with 14.1% reduction in RMSE (relative to the Open Loop without assimilation) and provides the greatest improvements in TWS (i.e., smallest RMSE) in conjunction with the most reasonable subsurface water storage ensemble spread (spread-error ratio = 1.08) when compared to the single-sensor DA experiments. However, dual DA can lead to contradictory changes in SWE. That is, the assimilation of dTb generates positive SWE increments whereas assimilation of GRACE TWS retrievals removes SWE, which can ultimately degrade posterior SWE estimates. This synthetic experiment provides useful insight into future DA experiments combing real-world AMRS-E/AMSR-2 dTb and GRACE/GRACE-FO TWS retrievals in order to better characterize terrestrial freshwater storage across regional and continental scales.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH079...07W
- Keywords:
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- 1240 Satellite geodesy: results;
- GEODESY AND GRAVITY;
- 1829 Groundwater hydrology;
- HYDROLOGY;
- 1836 Hydrological cycles and budgets;
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY