Abstract Global monitoring of soil moisture and snow is now available through various satellite observations from optical, microwave, and gravitational sensors. However, very few modeling frameworks exist that conjointly use the above sensors to produce mutually and physically consistent earth system records. To this goal, a prototype of multi-sensor land data assimilation system is developed by linking the Community Land Model version 4 (CLM4) and a series of forward models with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global soil moisture and snow by assimilating brightness temperature, snow cover fraction, and daily total water storage observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Moderate Resolution Imaging Spectroradiometer (MODIS), and Gravity Recovery and Climate Experiment (GRACE), respectively. A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to introduce ensemble spread in CLM4 land states and some methods are used to reduce the computational load. Data assimilation with different combinations of sensors is implemented for 2003–2009 to investigate individual contributions from different satellite observations. Evaluation results and cross-comparison of open-loop and data assimilation cases suggest that 1) assimilation of MODIS snow cover fraction slightly improves snow estimation in mid and high latitudes; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system.

中文翻译：

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多传感器土地数据同化：走向稳健的全球土壤水分和雪估计

摘要 现在可以通过光学、微波和重力传感器的各种卫星观测对土壤水分和雪进行全球监测。然而，很少有建模框架可以结合使用上述传感器来生成相互和物理一致的地球系统记录。为此，通过将社区土地模型第 4 版（CLM4）和一系列前向模型与数据同化研究试验台（DART）相连接，开发了多传感器土地数据同化系统的原型。DART 中的确定性集合调整卡尔曼滤波器 (EAKF) 用于通过同化亮温、积雪比例和来自地球观测系统高级微波扫描辐射计 (AMSR-E) 的每日总储水量观测来估计全球土壤水分和雪), 中分辨率成像光谱仪 (MODIS) 和重力恢复和气候实验 (GRACE)，分别。采用社区大气模型第 4 版 (CAM4) 的 40 成员再分析来引入 CLM4 陆地状态中的集合传播，并使用一些方法来减少计算负载。2003-2009 年实施了使用不同传感器组合的数据同化，以研究来自不同卫星观测的个人贡献。开环和资料同化案例的评价结果​​和交叉比较表明：1）MODIS积雪部分的同化略微改善了中高纬度地区的积雪估算；2）AMSR-E亮温的较低和较高频率在改善全球土壤水分和雪估计方面发挥互补作用；3) GRACE 的同化往往会降低土壤水分估计，但在改善大多数高纬度地区的雪深估计方面具有潜力。通常，MODIS、GRACE 和 AMSR-E 空间位置观测的结合有望通过多传感器土地数据同化系统提供可靠的全球土壤水分和雪估计。