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Kingtse C. Mo and Dennis P. Lettenmaier

for Environmental Prediction (NCEP) and the University of Washington (UW) both routinely produce hydroclimate fields, including soil moisture and runoff, from NLDAS-derivative systems in near–real time with 1–3 days latency that support the U.S. Drought Monitor (USDM; Svoboda et al. 2002 ). Current drought monitoring systems (e.g., the UW and NCEP systems) are able to detect droughts but are challenged by the classification of drought into, for instance, the D0–D4 categories used by the USDM, in

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Eric F. Wood, Siegfried D. Schubert, Andrew W. Wood, Christa D. Peters-Lidard, Kingtse C. Mo, Annarita Mariotti, and Roger S. Pulwarty

predictability and related aspects such as the dependence on time scales, regions, seasons, and variables, and improvements in forecast models and procedures; (iii) improving current drought monitoring capabilities, including the exploitation of new data, methodologies, and metrics that would improve society’s capability to manage drought; and (iv) improving drought information systems through incorporating the latest advances in monitoring and prediction, objective metrics relevant to various societal

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M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager

at least qualitatively consistent with a longer time scale climate control associated with natural oceanic variability. In the Southwest, it is also consistent with the expected climate response to rising greenhouse gases (e.g., Seager and Vecchi 2010 ), though that influence on precipitation is likely smaller at the current time than the influence of natural long-term variability. However, despite the role of these ocean and radiative boundary conditions in tilting the odds toward a dry state

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

county) if long-term fine-resolution (<4 km) hydrometeorological products are available, as the USDM website provides drought area percentage for each state and each county. This is possible as the EMC land-hydrology group is extending its current NLDAS system to a high-spatial-resolution (4 km) NLDAS system. This may bring some discontinuity of products between state–county boundaries, but this can be overcome using spatial smoothing. We also realize that only NLDAS products cannot totally capture

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Richard Seager, Lisa Goddard, Jennifer Nakamura, Naomi Henderson, and Dong Eun Lee

( Seager et al. 2007 ; Seager and Vecchi 2010 ) is quite likely currently masked by the presence of large-amplitude natural variability on interannual to multidecadal time scales ( Hoerling et al. 2013 ). Fig . 16. Time history of observed JJA temperature (bars, K) and precipitation (line, mm day −1 ) anomalies for the TexMex region and the 1950–2011 period. 9. How well was the 2010/11 drought forecast by operational seasonal-to-interannual prediction systems? Understanding the dynamical causes of

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

, to infer the surface energy budget. Use of time-differential observations reduces model sensitivity to errors in absolute temperature retrievals resulting from sensor calibration and atmospheric correction. A simple model of atmospheric boundary layer (ABL) growth ( McNaughton and Spriggs 1986 ) is used to provide closure to the time-integrated energy balance equations over the morning period, alleviating the need for specifying near-surface boundary conditions in air temperature, which often

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

natural time scale of evolution. The current strategy in operational drought monitoring is to assemble a suite of independent indicators, sampling different types of relevant impacts at different temporal scales, and then to blend these indicators into a concise, integrated report using both subjective and objective approaches. This is the strategy used to construct the U.S. Drought Monitor (USDM; Svoboda et al. 2002 ), the primary record of drought classification for the United States since 1999

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Johnna M. Infanti and Ben P. Kirtman

damage, it is important to understand the current prediction capability within the region. It is now fairly well understood that a multimodel approach to prediction is an imperfect but still pragmatic method to estimating forecast uncertainty ( Krishnamurti et al. 1999 , 2000 ; Doblas-Reyes et al. 2000 ; Palmer et al. 2004 ; Hagedorn et al. 2005 ; Weigel et al. 2008 ; Kirtman and Min 2009 ). In this paper we utilize phase-1 data from the North American Multi-Model Ensemble (NMME) system, a

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Shahrbanou Madadgar and Hamid Moradkhani

natural environment and human lives are strongly affected. Almost all the continents throughout the globe have been affected by various drought phenomena during recent decades ( Mishra and Singh 2010 ). According to the U.S. Drought Monitor, more than 70% of the United States currently experiences dry spells of severities ranging from abnormally dry to exceptional droughts ( Showstack 2012 ). Several recent efforts have attempted to enhance forecast accuracy, mitigation policies, and damage estimate

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