Abstract A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.

中文翻译：

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使用局部集合变换卡尔曼滤波器将 SMAP 和 ASCAT 土壤水分反演同化到 JULES 地表模型中

摘要 开发了一种土地数据同化系统，以使用局部集合变换卡尔曼滤波器 (LETKF) 将卫星土壤水分反演合并到联合英国土地环境模拟器 (JULES) 地表模型 (LSM) 中。该系统在基于累积分布函数拟合的偏差校正后同化来自土壤水分主动被动 (SMAP) 辐射计和高级散射计 (ASCAT) 的微波土壤水分反演。土壤水分同化估计值是通过美国大陆连续五个暖季（2015 年 5 月至 9 月 - 2019 年）的地面土壤水分测量来评估的。结果表明，SMAP 和 ASCAT 反演均提高了土壤水分估计的准确性。尤其，与没有卫星数据同化的LSM模拟相比，SMAP单传感器同化实验表现出最佳性能，表层土壤水分的时间异常相关性增加ΔR~0.05，根区土壤水分的时间异常相关性增加ΔR~0.03。SMAP 同化比 ASCAT 同化更熟练，主要是因为同化的 SMAP 检索与 ASCAT 检索相比具有更高的技能。技能的提高也很大程度上取决于地区；与美国东部相比，美国西部的技能提升更高是由两个实验中的卡尔曼增益来解释的。此外，单传感器同化实验中的区域技能差异归因于同化观察的数量。最后，