This study explores multi-sensor data assimilation (DA) using synthetic Advanced Microwave Scanning Radiometer (AMSR-E) passive microwave brightness temperature spectral differences () and synthetic Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) retrievals to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS across snow-covered terrain. Results show that multi-sensor DA improves SWE estimates by reducing the RMSE by 14.1% relative to a model-only simulation. Multi-sensor assimilation also yields the smallest TWS RMSE (reduced by 13.0% relative to a model-only simulation). However, multi-sensor DA does not always yield complementary updates, and can sometimes lead to conflicting changes to SWE, where the assimilation of synthetic generates positive SWE increments while the assimilation of synthetic TWS removes SWE, which can ultimately degrade the posterior SWE estimates. This synthetic experiment provides useful insight for future DA experiments using real-world AMSR-E/AMSR-2 observations and GRACE/GRACE-FO TWS retrievals to better characterize terrestrial freshwater storage across regional scales.

中文翻译：

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通过合成 AMSR-E 亮度温度谱差异和合成 GRACE 陆地储水检索的多传感器同化估计陆地雪量

本研究使用合成高级微波扫描辐射计 (AMSR-E) 被动微波亮温谱差 ( ) 和合成重力恢复和气候实验 (GRACE) 陆地储水 (TWS) 反演探索多传感器数据同化 (DA)以改进估计雪水当量 (SWE)、地下蓄水量和跨雪覆盖地形的 TWS。结果表明，相对于仅模型模拟，多传感器 DA 通过将 RMSE 降低 14.1% 来改进 SWE 估计。多传感器同化还产生最小的 TWS RMSE（相对于仅模型模拟减少了 13.0%）。然而，多传感器 DA 并不总是产生互补更新，有时会导致 SWE 发生冲突的变化，其中合成的同化产生正 SWE 增量，而合成 TWS 的同化消除了 SWE，这最终会降低后验 SWE 估计。该合成实验使用真实世界的 AMSR-E/AMSR-2观测和 GRACE/GRACE-FO TWS 反演为未来的 DA 实验提供了有用的见解，以更好地表征跨区域尺度的陆地淡水储存。