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David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

departure of the current state of the variable of interest relative to the long-term average. Despite the stated emphasis, most near-real-time LDAS environments only include limited assimilation of terrestrial hydrological observations. Several recent efforts have focused on mitigating this limitation in the NLDAS environment through assimilation studies of soil moisture, snow, and terrestrial water storage, both serially and concurrently (e.g., Kumar et al. 2014 , 2016 , 2019a ). Studies in other

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

.agwat.2008.09.022 . 10.1016/j.agwat.2008.09.022 Kolassa , J. , P. Gentine , C. Prigent , F. Aires , and S. Alemohammad , 2017 : Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation . Remote Sens. Environ. , 195 , 202 – 217 , . 10.1016/j.rse.2017.04.020 Kolassa , J. , and Coauthors , 2018 : Estimating surface soil moisture from SMAP observations using a Neural Network technique . Remote

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Yizhou Zhuang, Amir Erfanian, and Rong Fu

calculated by least squares fitting of the predictand and predictor time series for the 1992–2017 period. This period was selected for the regression analysis to avoid the impact of the artificial trend in the time series of ERAI moisture budget terms introduced by the abrupt changes in the Special Sensor Microwave Imager (SSM/I) observations and the retrieval of total column water vapor in 1992 ( Trenberth et al. 2011 ). Traditional multiple linear regression models determine regression coefficients by

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