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Husayn El Sharif, Jingfeng Wang, and Aris P. Georgakakos

products. This soil moisture data product is then used to “filter” an ensemble of DSSAT-CSM runs using synthetic weather input data. In this study, stochastic forcing is introduced by adding measurement noise to daily weather inputs. The “control” scenario refers to DSSAT-CSM results using the entire ensemble of synthetic input data in DSSAT-CSM runs. The “SMAP” scenario refers to DSSAT-CSM runs in which modeled top soil moisture is consistent with the SMAP-like data. Agreement is assessed via the

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John D. Hottenstein, Guillermo E. Ponce-Campos, Julio Moguel-Yanes, and M. Susan Moran

.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) site, and one DOE AmeriFlux site ( Baldocchi et al. 2004 ). The data were obtained from the International Soil Moisture Network (ISMN), and within this network, all data were subjected to automated quality control to ensure reliability of the dataset ( Dorigo et al. 2013 ). Locations of sites ranged from a small grassland field approximately 150 m × 200 m to a site surrounded by everal square kilometers of grassland or scrubland ( Table 1

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Amy McNally, Gregory J. Husak, Molly Brown, Mark Carroll, Chris Funk, Soni Yatheendradas, Kristi Arsenault, Christa Peters-Lidard, and James P. Verdin

1. Introduction Soil moisture is a critical variable for weather and climate forecasting and early warning for natural disasters like drought, floods, landslides, and fire. Soil moisture also plays an important role in the early warning of human health concerns like hunger and malaria. The Soil Moisture Active Passive (SMAP) mission ( https://smap.jpl.nasa.gov/ ) aims to provide high-quality soil moisture data and enhance predictive models for many applications. However, new tools need to be

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C. Albergel, W. Dorigo, R. H. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, and W. Wagner

://koeppen-geiger.vu-wien.ac.at/present.htm ). 3. Results a. Comparison with in situ measurements This section presents the results of the comparison versus in situ observations of SM-MW, MERRA-Land, and ERA-Land, beginning with surface and then addressing root-zone soil moisture. For all the stations used in this study, a first visual quality check was performed. When suspicious data were observed, they were discarded. Also, spurious soil moisture observations from the ISMN website were detected using a global quality control (QC) procedure

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Sujay V. Kumar, Kenneth W. Harrison, Christa D. Peters-Lidard, Joseph A. Santanello Jr., and Dalia Kirschbaum

et al. 2009 ; Konings 2009 ; Piles et al. 2009 ). A typical OSSE includes the following components: 1) a “nature” or a “truth” run, which is a free-running simulation of the physical model with high-quality inputs and without data assimilation; 2) simulated observations, which are generated from the truth run after incorporating realistic errors and limitations of the observing system; 3) an open-loop (OL) simulation that employs a set of lower-quality inputs with a different physical model and

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

1. Introduction It has become increasingly clear that an accurate initialization of the land surface is important for skillful weather and seasonal climate predictions (e.g., Koster et al. 2004 ; Drusch 2007 ; Drusch and Viterbo 2007 ; Gao et al. 2008 ; Mahfouf 2010 ; Douville 2010 ). Soil moisture, snow characteristics, surface temperature, and vegetation properties of the land surface influence both the water and energy budgets, exerting important controls on land

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