An inherently mass‐conserving semi‐implicit semi‐Lagrangian discretization of the deep‐atmosphere global non‐hydrostatic equations

By N. Wood, Andrew Staniforth, A. White, T. Allen, M. Diamantakis, M. Gross, T. Melvin, Chris Smith, S. Vosper, M. Zerroukat and 1 more. 2014

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The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations (2017, background): “…in PMSL (resulting in a drop in the overall metric) which arose through a combination of the introduction of the ENDGame (Even Newer Dynamics for Global Atmospheric Modelling of the Environment; Wood et al., 2014) dynamical core and revised gravity wave drag scheme (Vosper et al., 2009).”
Can deep learning beat numerical weather prediction? (2021, background): “Simultaneously, remarkable progress has been achieved in designing discretization approaches which enable the grid-scale dynamics to follow the conservation laws of energy, enthrophy and mass [33,34] while also minimizing the need for numerical filters to suppress artificial numerical modes [35].”
The Non-hydrostatic Icosahedral Atmospheric Model: description and development (2014, methodology): “Several authors (Semazzi et al. 1995; Cullen et al. 1997; Qian et al. 1998; Côté et al. 1998; Yeh et al. 2002; Davies et al. 2005; Wedi and Smolarkiewicz 2009; Wood et al. 2013) have used the lat-lon grid to solve a set of non-hydrostatic equations using the SLSI approach in order to acquire a large”