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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

model forecast with precipitation observations. In the second part of this study, results from a continental-scale synthetic data assimilation experiment are presented where passive microwave L-band brightness temperatures TBs are assimilated. The added skill in the analysis of the soil moisture state from the inclusion of these TB observations is quantified by means of domain-averaged performance metrics. Innovation diagnostics are presented to evaluate the assimilation scheme performance. The

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Fan Chen, Wade T. Crow, and Dongryeol Ryu

hydrologic prediction systems operating in ground data–poor regions. Therefore, the precipitation dataset used to force the SAC model, and subject to correction via SMART, is the daily accumulation from Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis, version 7 (TMPA), 3B40RT ( Ostrenga et al. 2013 ). This is a real-time, microwave-only product generated without any ground-based rain gauge observations. The NOAA Climate Prediction Center’s (CPC) 0.25°-grid unified gauge

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

-band (~6 GHz) microwave radiometers. However, none of these sensors were specifically designed to measure soil moisture until the launch of the Soil Moisture Ocean Salinity (SMOS; since late 2009) from the European Space Agency (ESA), which provides global observations for soil moisture and salinity from an L-band radiometer. Compared to the X and C bands, the L-band-based measurements have reduced attenuation of the signal under moderate vegetation conditions and increased penetration depth for

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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

different microwave sensors into a single dataset covering 1979–2010. This led to the preparation of a consistent global soil moisture data record based on active and passive microwave sensors (hereafter referred to as SM-MW; Dorigo et al. 2012 ). The typical validation approach for model- and satellite-based data products is to compare them to in situ observations. Hence, in situ measurements of soil moisture are a highly valuable source of information for assessing the quality of model and satellite

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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

finally, the method of analysis used to compare the different WRSI estimates. a. Meteorological forcing data description Across our Sudano-Sahel domain, we used the bias-corrected African Rainfall Estimation, version 2.0 (RFE2), rainfall product from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). RFE2 is derived from rain gauge data merged with satellite (infrared and microwave) observations ( Xie and Arkin 1997 ) and is available from 2000 to the present

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Catherine Champagne, Andrew Davidson, Patrick Cherneski, Jessika L’Heureux, and Trevor Hadwen

moisture extremes has been done through in situ monitoring networks ( Keyantash and Dracup 2004 ; Nandintsetseg and Shinoda 2013 ; Sridhar et al. 2008 ; Vicente-Serrano 2006 ) and land surface models ( Sheffield and Wood 2007 ). Progress in the development of satellite technology and retrieval algorithms for quantifying soil moisture from active and passive microwave satellite platforms has provided a new suite of tools to the risk assessment community that are now able to provide near

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M. Susan Moran, Bradley Doorn, Vanessa Escobar, and Molly E. Brown

SMAP-simulated data into military maneuver planning and ground vehicle mobility predictions ( Frankenstein et al. 2015 ). Early adopters working with USGS as part of the Famine Early Warning Systems Network (FEWS NET) found that a water requirement satisfaction index could be reformulated to take advantage of a SMAP-like product to improve crop yield estimates ( McNally et al. 2015 ). Simulated L-band microwave measurements have been used to assess the economic utility of these observations for

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

have been derived from signals of active and passive microwave sensors on satellites ( Bartalis et al. 2007 ; Njoku et al. 2003 ; Owe et al. 2008 ) since the early 1980s. Without such observations, soil moisture estimates often depend on reanalysis data subject to large uncertainties ( Dorigo et al. 2012 ; Ferguson and Wood 2011 ). Satellite missions include Skylab ( Entekhabi et al. 2010 ), ERS-1 , ERS-2 , AMSR-E, SMMR, SSM/I, TMI, Advanced Scatterometer (ASCAT), and SMOS ( Dorigo et al. 2010

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Randal D. Koster, Gregory K. Walker, Sarith P. P. Mahanama, and Rolf H. Reichle

observations. Wood and Lettenmaier (2006) outline strong arguments for the expectation that the land-modeling strategy will, in time, eclipse the regression approach as the preferred means of providing streamflow forecasts. Recent work has provided important insights into the science of predicting streamflow with the land-modeling strategy. For example, a number of studies have used such systems to examine the relative contributions of state initialization and forecasted meteorological forcing to

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Susan Frankenstein, Maria Stevens, and Constance Scott

) to calculate soil moisture and hence soil strength as a function of forecasted weather. The ability of the models to accurately predict soil moisture at a location strongly depends on the quality of the precipitation forecast and the scale of the underlying terrain information. Currently, the scale of the forecast data is 1–15 km. These problems can be partially mollified with assimilation of observations (whether ground based or remote; Margulis et al. 2002 ) and by downscaling techniques

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