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Assimilation of SMOS Retrievals in the Land Information System

机译：在土地信息系统中同化SMOS检索

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The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in roughly the upper 5 cm with a 30-50-km resolution and a mission accuracy requirement of 0.04 cm3/cm-3. These observations can be used to improve land surface model (LSM) soil moisture states through data assimilation (DA). In this paper, SMOS soil moisture retrievals are assimilated into the Noah LSM via an Ensemble Kalman Filter within the National Aeronautics and Space Administration Land Information System. Bias correction is implemented using cumulative distribution function (cdf) matching, with points aggregated by either land cover or soil type to reduce the sampling error in generating the cdfs. An experiment was run for the warm season of 2011 to test SMOS DA and to compare assimilation methods. Verification of soil moisture analyses in the 0-10-cm upper layer and the 0-1-m root zone was conducted using in situ measurements from several observing networks in central and southeastern United States. This experiment showed that SMOS DA significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10-cm layer. Time series at specific stations demonstrates the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared with using a simple uniform correction curve.
机译：土壤水分和海洋盐度（SMOS）卫星以大约30-50公里的分辨率和0.04 cm3 / cm-3的任务精度要求提供了大约5 cm上部的土壤水分的取回。这些观察结果可用于通过数据同化（DA）改善土地表面模型（LSM）的土壤水分状态。在本文中，通过国家航空航天局土地信息系统中的集成卡尔曼滤波器，将SMOS土壤水分反演吸收到了Noah LSM中。偏差校正是使用累积分布函数（cdf）匹配实现的，其点通过土地覆盖或土壤类型进行汇总，以减少生成cdfs时的采样误差。在2011年的温暖季节进行了一项实验，以测试SMOS DA并比较同化方法。使用美国中部和东南部几个观测网络的原位测量，对0-10-cm上层和0-1-m根区的土壤水分分析进行了验证。该实验表明，SMOS DA在0-10-cm层中显着增加了诺亚土壤湿度与站点测量值的异常相关性，从0.45增加到0.57。特定站点的时间序列证明SMOS DA能够以与站点测量一致的方式增加土壤水分的动态范围。在偏差校正方法中，基于土壤类型的校正在减少偏差方面效果最佳，但相关性也有所降低。与使用简单的均匀校正曲线相比，基于植被的校正没有产生任何显着差异。

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