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Anthony M. DeAngelis, Hailan Wang, Randal D. Koster, Siegfried D. Schubert, Yehui Chang, and Jelena Marshak

provided useful information if issued weekly in the summer of 2012. We do this by analyzing reforecasts (or hindcasts) from the Subseasonal Experiment (SubX), a multimodel global forecast ensemble that was recently developed to advance the research and operational capabilities of subseasonal prediction ( Pegion et al. 2019 ). Unlike weather and seasonal forecasting where skill is largely derived from a single source (atmospheric initial conditions and SSTs, respectively), the potential for skillful

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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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