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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

; Zaitchik et al. 2010 ; Xia et al. 2012c ). These studies note that the model-based estimates suffer from uncertainties in the forcing inputs, model parameters, and model structural errors. Data assimilation (DA) techniques have been employed as an effective strategy to combine the strengths of both modeling and observations to generate superior estimates by appropriately weighting their respective sources of errors ( Reichle 2008 ). There have been several studies that have examined the assimilation

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Johnna M. Infanti and Ben P. Kirtman

( Ropelewski and Halpert 1986 , 1987 ; Mo and Schemm 2008a , b ; Seager et al. 2009 ). In contrast to Mo and Schemm (2008a) , Seager et al. (2009) concluded that rainfall is more closely related to internal atmospheric variability, particularly in summer. Noise or internal atmospheric variability, for the purposes of this paper, is unpredictable, that is, we cannot relate it to specific forcing or feedback (ocean–atmosphere or atmosphere–land). This unpredictable variability is due to internal

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Martha C. Anderson, Christopher Hain, Jason Otkin, Xiwu Zhan, Kingtse Mo, Mark Svoboda, Brian Wardlow, and Agustin Pimstein

measurements are strongly tied to observations, but may have limits in spatial sampling and portability to other domains that lack dense in situ monitoring networks. Prognostic land surface models (LSMs) can provide quantitative estimates of a full suite of hydrologic variables, adding value to the precipitation data used as a primary input. However, model output may have significant biases because of inaccurate modeling assumptions, observational errors in the forcing data, and a reliance on surface

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Jason A. Otkin, Martha C. Anderson, Christopher Hain, Iliana E. Mladenova, Jeffrey B. Basara, and Mark Svoboda

rapid development of large negative ESI and the introduction of drought conditions in the USDM by the end of July. The switch from positive to negative ESI during July was due not only to the increasing rainfall deficit and its effect on surface moisture but also to the increased evaporative demand associated with the warmer conditions and strong radiative forcing due to predominantly clear skies. Unlike the previous case studies, in this event the dewpoint depression anomalies did not increase

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Zengchao Hao and Amir AghaKouchak

-Era Retrospective Analysis for Research and Applications . J. Climate , 24 , 3624 – 3648 , doi:10.1175/JCLI-D-11-00015.1 . Seager, R. , Kushnir Y. , Herweijer C. , Naik N. , and Velez J. , 2005 : Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000 . J. Climate , 18 , 4065 – 4088 , doi:10.1175/JCLI3522.1 . Sheffield, J. , and Wood E. F. , 2007 : Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from

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Paul A. Dirmeyer, Jiangfeng Wei, Michael G. Bosilovich, and David M. Mocko

atmosphere, but may not use data from every time step of the atmospheric model producing the analysis. When using reanalyses as the source of the meteorological forcing data, the issue of lack of closure of the water budget still arises but is less severe, particularly when observed precipitation data are used to constrain the model simulations. This method has been used to independently validate other methods of estimating precipitation recycling and its variability ( Brubaker et al. 2001 ; Sudradjat

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