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Kingtse C. Mo and Dennis P Lettenmaier

link our forecasts to an operational medium range weather forecast model, linked with an offline macroscale hydrologic model to produce the combination of physical ( P and T air ) and hydrologic (primarily SM and ET) variables needed to produce forecasts of both heat wave and P -deficit flash droughts. We then compare the (ensemble) forecasts with equivalent quantities reproduced by observations (analysis) by driving the same hydrologic model by observed P and T air. While our skill

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

seasonal to interannual time scale, their onsets and terminations are likely controlled by internal atmosphere variability and fall between the initial value and ocean boundary condition sources of predictability. However, SNT was a purely observational study. Here we report on how well DO&Ts are forecast in the operational seasonal predictions systems of the National Multimodel Ensemble (NMME; Kirtman et al. 2014 ). These systems forecast SST from imposed initial conditions. Several of the models

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Chul-Su Shin, Paul A. Dirmeyer, Bohua Huang, Subhadeep Halder, and Arun Kumar

). Realistic soil moisture initialization has been standard practice in coupled climate forecasts systems for about a decade (e.g., Vitart et al. 2008 ). Nonetheless, land surface initial states in many current weather and climate forecast systems are far from perfect ( Vitart et al. 2017 ), mainly due to the lack of operational near-real-time monitoring for the land surface unlike atmosphere and ocean surface. Satellite data assimilation shows great promise to address this shortcoming ( Carrera et al

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Keyhan Gavahi, Peyman Abbaszadeh, Hamid Moradkhani, Xiwu Zhan, and Christopher Hain

, 2003 : Assimilation of remotely sensed latent heat flux in a distributed hydrological model . Adv. Water Resour. , 26 , 151 – 159 , https://doi.org/10.1016/S0309-1708(02)00089-1 . 10.1016/S0309-1708(02)00089-1 Seo , D. J. , V. Koren , and N. Cajina , 2003 : Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting . J. Hydrometeor. , 4 , 627 – 641 , https://doi.org/10.1175/1525-7541(2003)004<0627:RVAOHA>2.0.CO;2 . 10

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Chul-Su Shin, Bohua Huang, Paul A. Dirmeyer, Subhadeep Halder, and Arun Kumar

4 , respectively, followed by summary and discussions in section 5 . 2. Identical-twin CFSv2 reforecast experiments (1979–2010) CFSv2 is the current U.S. operational seasonal prediction system at the National Centers of Environmental Prediction (NCEP) and is a fully coupled climate forecast system composed of interacting atmospheric, oceanic, sea ice, and land components ( Saha et al. 2014 ). The atmospheric model of the CFSv2 is a lower resolution version of the Global Forecast System (GFS

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David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

1. Introduction Estimates of terrestrial water budget components from land surface models (LSMs) are routinely used in drought monitoring and forecasting environments (e.g., Mo et al. 2011 ; Houborg et al. 2012 ; Hao et al. 2014 ; Sheffield et al. 2014 ). In particular, Land Data Assimilation Systems (LDASs), which employ LSMs forced with observed meteorology, are used for operational drought monitoring at continental and global scales. The North American Land Data Assimilation System

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Wen-Ying Wu, Zong-Liang Yang, and Michael Barlage

have demonstrated the role of groundwater in modulating land surface fluxes and vegetation during droughts ( Barlage et al. 2015 ; Maxwell and Kollet 2008 ; Fan et al. 2017 ) and in the partitioning of evapotranspiration ( Maxwell and Condon 2016 ). Vegetation phenology can affect the land surface energy budget, and accounting for vegetation growth and groundwater in coupled land surface and climate models could improve seasonal precipitation forecasts over the central United States in summer

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Lu Su, Qian Cao, Mu Xiao, David M. Mocko, Michael Barlage, Dongyue Li, Christa D. Peters-Lidard, and Dennis P. Lettenmaier

Forecasting (WRF) regional atmospheric model in the NWM. Noah-MP extends the capabilities of the Noah LSM ( Chen et al. 1996 ; Chen and Dudhia 2001 ) and incorporates multiple options for key land–atmosphere interaction processes, such as surface water infiltration, runoff, groundwater transfer, and options for representing snow albedo and vegetation growth ( Niu et al. 2007 ; Niu et al. 2011 ). Here we used only Noah-MP, with its forcings provided by observations as described above, rather than in the

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