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

1. Introduction Because of the importance of accurate streamflow forecasts for water resources planning (e.g., Yao and Georgakakos 2001 ; Hamlet et al. 2002 ), the development of approaches for producing useful streamflow forecasts and the evaluation of these approaches over time has a rich history, going back to at least the 1930s ( Pagano et al. 2004 ). Operational streamflow forecasts generally rely on statistical techniques (e.g., Garen 1992 ). Using various quantities describing the

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

so-called “nature run.” The domain is the North American landmass, and the ISBA model was integrated at 1-km resolution for the period from 31 March to 30 September 2009. The atmospheric forcing data used to drive the ISBA land surface model were derived from 6–18 h forecasts from the operational Regional Deterministic Prediction System (RDPS) model ( Mailhot et al. 2006 ). A combination of different data sources was used to describe the geophysical characteristics of the land surface state that

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Fan Chen, Wade T. Crow, and Dongryeol Ryu

al. 2012 ) and are often the only source of precipitation data available for ungauged basins. However, real-time products with relatively low latency (less than daily) typically contain significant bias and errors that can propagate into other model variables during operational streamflow forecasting. Left uncorrected, such errors can significantly impact subsequent hydrologic model predictions ( Harris et al. 2007 ; Li et al. 2009 ; Pan et al. 2010 ; Gebregiorgis et al. 2012 ). Recent work

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

. , Wigneron J. , and Jeu R. , 2008 : A global simulation of microwave emission: Error structures based on output from ECMWF’s operational integrated forecast system . IEEE Trans. Geosci. Remote Sens. , 46 , 846 – 856 , doi: 10.1109/TGRS.2007.914798 . Jackson, T. J. , 1993 : Measuring surface soil moisture using passive microwave remote sensing . Hydrol. Processes , 7 , 139 – 152 , doi: 10.1002/hyp.3360070205 . James, B. , and Freeze R. , 1993 : The worth of data in predicting aquitard

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

precipitation forcing and assimilation of L-band brightness temperatures in the Canadian Land Data Assimilation System (Carrera et al. 2014, manuscript submitted to J. Hydrometeor. ). The Institute for Climate and Society (IRI) at Columbia University tested a data assimilation framework for forecasting yields of grain crops in Africa and reported the potential to detect irrigation applications otherwise not possible through model simulations ( Ines et al. 2013 ; Das et al. 2014, manuscript submitted to J

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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

floods. For many applications, global- or continental-scale soil moisture maps are needed. Among the first soil moisture analysis systems used for operational NWP was the system implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) in 1994 to prevent the land surface model (LSM) drifting to dry conditions in summer. Since then, major upgrades have been implemented in the land surface modeling and analysis systems of the high-resolution component of the Integrated Forecasting

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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 ( ) 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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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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