“…The nonlinear error dynamics are encoded into the forecast error covariance matrix to enable coupling of a potentially sparsely observed driver system with a numerical model as the response system. In NWP, this forecast error covariance information is either estimated from a long time-averaged history of the system's forecast errors (i.e., a climatology) typically denoted as B, produced adaptively to estimate the instantaneous "errors of the day" (Kalnay, 2003) typically denoted as P b , or some combination of the two (Hamill and Snyder, 2000;Wang et al, 2007aWang et al, , 2007bWang et al, , 2008aWang et al, , 2008bWang et al, , 2010Wang et al, , 2013Kleist 2012;Penny, 2014;Penny et al, 2015;Hamrud et al, 2014;and Bonavita et al, 2015). Such methods that combine static and dynamic error representations are typically referred to as hybrid methods and have recently been reviewed by Asch et al (2017) and Bannister (2017).…”