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Jiarui Dong, Mike Ek, Dorothy Hall, Christa Peters-Lidard, Brian Cosgrove, Jeff Miller, George Riggs, and Youlong Xia

agriculture. Knowledge of various snowpack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project ( Mitchell et al. 2004 ). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions

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Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, Rolf Reichle, Yuqiong Liu, Kristi R. Arsenault, Youlong Xia, Michael Ek, George Riggs, Ben Livneh, and Michael Cosh

is spun up by running from 1979 to 2012 twice and then reinitializing the model in 1979. Noah LSM is used operationally at the National Centers for Environmental Prediction (NCEP) as the land component of regional and global weather forecasting models and at the Air Force Weather Agency (AFWA) in the offline land analysis system. More recent upgrades to the model have focused on improving the snow physics within Noah ( Barlage et al. 2010 ; Livneh et al. 2010 ; Wang et al. 2010 ) by providing

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Youlong Xia, Michael B. Ek, David Mocko, Christa D. Peters-Lidard, Justin Sheffield, Jiarui Dong, and Eric F. Wood

1. Introduction The North American Land Data Assimilation System (NLDAS) runs four land surface models (LSMs) over the NLDAS domain covering southern Canada, the contiguous United States (CONUS), and northern Mexico in support of improved weather prediction and land data assimilation. The NLDAS was initiated in 1999 via the collaboration among the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), and several universities as a tool

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Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

expect CGCMs would provide more dynamical and objective drought forecasts for land regions such as Africa and the transition of advances in climate research to climate services. In fact, CGCM-based seasonal forecasting of drought has become operational in recent years at many national weather centers. Several previous studies have used these operational systems to hindcast specific drought events ( Luo and Wood 2007 ; Yuan et al. 2011 ; Dutra et al. 2012 ), while other work has systematically

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