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Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

1. Introduction Seasonal prediction of regional hydroclimate is typically based on deterministic physical models or statistical techniques, yet both approaches exhibit limited predictive ability ( Wang et al. 2009 ; National Academies of Sciences, Engineering, and Medicine 2016 ). Precipitation predictions based on deterministic physical models (regional climate models) exhibit high uncertainty due to imperfect physical conceptualizations, sensitivity to initial and boundary conditions, and

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Stephen E. Lang and Wei-Kuo Tao

radiative characteristics to satellite microwave radiometric observations via a Bayesian technique. This approach later evolved into the “trained radiometer” or TRAIN algorithm ( Grecu and Olson 2006 ; Grecu et al. 2009 ) wherein the passive microwave algorithm is “trained” using space-borne radar profiles; those reflectivity profiles are in turn linked to heating profiles from CRM simulations in a manner similar to the SLH algorithm. The hydrometeor heating (HH) algorithm ( Yang and Smith 1999a , b

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