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Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

机译:将SMOS亮度温度或土壤水分反演吸收到陆地表面模型中

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摘要

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degrees incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.
机译:来自土壤水分海洋盐度(SMOS)任务的三种不同数据产品被分别吸收到Goddard地球观测系统模型第5版(GEOS-5)中,以改善对表层和根区土壤水分的估计。第一个产品包括从1级数据中提取的在大气底部的多角度双极化亮度温度(Tb)观测值。第二种产品是派生的SMOS Tb产品,它以土壤水分主动被动(SMAP)任务以40度入射角模拟数据。第三种产品是可操作的SMOS 2级表层土壤水分(SM)检索产品。同化系统使用空间分布的集合卡尔曼滤波器(EnKF),通过季节性变化缓解气候变化,以实现Tb同化,而时不变累积密度函数匹配用于SM检索同化。在2010年7月1日至2015年5月1日期间以及在美国187个地点,无偏均方根差和异常相关性方面,与仅模型模拟相比,所有同化试验均改善了土壤湿度估计。尤其是在卫星数据对表层土壤湿度最敏感的地区,表层土壤湿度得到了很大的提高(例如,异常相关性增加了0.1)。在各种同化实验中,领域平均表面和根区域技能指标相似,但是在本地发现的技能差异很大。观测值减去预测值的残差和分析增量显示出观测值在Tb和SM检索同化系统中如何增加价值的巨大差异。在两个系统中,这些诊断的独特模式反映了在分配的EnKF错误参数中未很好捕获的观​​察和模型错误模式。因此,需要对EnKF错误参数进行局部优化,以进一步改善Tb或SM检索同化。

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