Improved Terrestrial Snow Mass via Multi-sensor Assimilation of Synthetic GRACE Terrestrial Water Storage Retrievals and Synthetic AMSR-E Brightness Temperature Spectral Differences
Authors: Wang Jing , Forman Barton 
Published: October 2020

&lt;p&gt;This study explores multi-sensor, multi-variate data assimilation (DA) using synthetic GRACE terrestrial water storage (TWS) retrievals and synthetic AMSR-E passive microwave brightness temperature spectral differences (dTb) in order to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS over snow-covered terrain. In order to better assess the performance of joint assimilation, a series of synthetic twin experiments, including the Open Loop (model-only run), single-sensor DA (GRACE TWS DA or AMSR-E dTb DA), and simultaneous assimilation of GRACE TWS and AMSR-E dTb (a.k.a., dual DA), are conducted. The baseline assimilation of GRACE TWS retrievals is further modified using a physically-informed approach during the application of the analysis increments. A well-trained support vector machine (SVM) is used as the observation operator during the assimilation of AMSR-E dTb observations.&lt;/p&gt;
&lt;p&gt;Results suggests that the single-sensor GRACE TWS DA experiment using the physically-informed update approach leads to statistically significant improvements in SWE, subsurface water storage, and TWS estimation. The application of increments based on the presence (or absence) of snowmelt further discretizes TWS into SWE and subsurface water storage more accurately, and hence, effectively enhances TWS vertical resolution. Similarly, the single-sensor AMSR-E dTb DA approach yields improvements in SWE, subsurface water storage, runoff, and TWS estimation. However, the efficacy of SVM-based PMW dTb DA is limited by the fundamentally ill-posed nature of SWE estimation using PMW radiometry coupled with limited controllability of the SVM-based observation operator during deep, wet snow conditions. Furthermore, the PMW dTb assimilation approach (i.e., multiple observations assimilated daily) can lead to SWE ensemble collapse, which can ultimately degrade the SWE estimates.&lt;/p&gt;
&lt;p&gt;Dual assimilation, in general, maintains the benefits introduced by the single sensor assimilation of GRACE TWS retrievals and AMSR-E dTb observations. Dual DA yields the best TWS estimates (in terms of smallest RMSE) and the most reasonable ensemble spread of subsurface water storage compared to the OL and single sensor DA experiments. The assimilation of dTb observations significantly reduces the SWE ensemble spread while the assimilation of TWS retrievals reduces the ensemble spread of subsurface water storage. The assimilation of TWS helps mitigate the SWE ensemble collapse often caused by daily assimilation of dTb's, and hence, improves the SWE ensemble reliability. The assimilation of dTb observations, in general, removes snow mass whereas the assimilation of TWS retrievals, in general, adds snow mass to the system, which can, at times, lead to SWE degradation given this juxtaposed, contradictory behavior. These synthetic experiments provide valuable insights into the assimilation of &amp;#8220;real-world&amp;#8221; GRACE / GRACE-FO TWS retrievals and AMRS-E / AMSR-2 dTb observations in order to better characterize terrestrial freshwater storage across regional scales.&lt;/p&gt;