2015
DOI: 10.1175/mwr-d-15-0037.1
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A Linearized Prognostic Cloud Scheme in NASA’s Goddard Earth Observing System Data Assimilation Tools

Abstract: A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA’s Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by det… Show more

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Cited by 5 publications
(3 citation statements)
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“…The linearized version of GEOS has a complete tangent linear and adjoint of the FV3 dynamical core, as well as linearization of the Relaxed Arakawa-Schubert convection scheme (Holdaway et al, 2014, hereafter H14), single moment cloud scheme (Holdaway et al, 2015) and a simplified boundary layer scheme.…”
Section: The Nasa Geos Model Andmentioning
confidence: 99%
“…The linearized version of GEOS has a complete tangent linear and adjoint of the FV3 dynamical core, as well as linearization of the Relaxed Arakawa-Schubert convection scheme (Holdaway et al, 2014, hereafter H14), single moment cloud scheme (Holdaway et al, 2015) and a simplified boundary layer scheme.…”
Section: The Nasa Geos Model Andmentioning
confidence: 99%
“…Yet another challenge is the development of useful TLMs and adjoints for the model's parametrisations of the effects of poorly resolved processes (Janisková et al ., 1999; Janiskov'a et al ., 2002; Tompkins and Janisková, 2004; Janiskov'a and Morcrette, 2005; Lopez, 2011; Janisková et al ., 2012; Janisková and Lopez, 2013; Holdaway et al ., 2014; Holdaway and Errico, 2014; Holdaway et al ., 2015). Janisková and Lopez (2013) outline four major steps within this development; the first of which involves developing a simplified version of the non‐linear model's parametrisations that are more suitable for linearisation than the original parametrisation scheme.…”
Section: Introductionmentioning
confidence: 99%
“…Beyond representing the inherent nonlinear behaviour of the atmosphere, models often rely on the use of highly nonlinear numerics. This can be to achieve the modelling of discrete‐like behaviour, as in the moist physics (Holdaway et al , 2015), or to control something undesirable, such as the removal of negative tracer densities. Note that there are ongoing attempts to overcome some of the assumptions fundamental in current data assimilation, though computational efficiency remains a significant challenge (van Leeuwen, 2009).…”
Section: Introductionmentioning
confidence: 99%