Results 91 to 100 of about 190 (113)
Toward low‐cloud‐permitting cloud superparameterization with explicit boundary layer turbulence [PDF]
Journal of Advances in Modeling Earth Systems, 2017 Systematic biases in the representation of boundary layer (BL) clouds are a leading source of uncertainty in climate projections. A variation on superparameterization (SP) called “ultraparameterization” (UP) is developed, in which the grid spacing of the
Hossein Parishani +2 more
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Test Models for Filtering with Superparameterization
Multiscale Modeling and Simulation, 2013Superparameterization is a fast numerical algorithm to mitigate implicit scale separation of dynamical systems with large-scale, slowly varying “mean” and smaller-scale, rapidly fluctuating “eddy” term. The main idea of superparameterization is to embed parallel highly resolved simulations of small-scale eddies on each grid cell of coarsely resolved ...
John Harlim, Andrew J Majda
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Stochastic superparameterization in quasigeostrophic turbulence [PDF]
Journal of Computational Physics, 2014 In this article we expand and develop the authors' recent proposed methodology for efficient stochastic superparameterization (SP) algorithms for geophysical turbulence. Geophysical turbulence is characterized by significant intermittent cascades of energy from the unresolved to the resolved scales resulting in complex patterns of waves, jets, and ...
Ian Grooms, Andrew J Majda
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New perspectives on superparameterization for geophysical turbulence [PDF]
Journal of Computational Physics, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andrew J Majda, Ian Grooms
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Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers
Multiscale Modeling and Simulation, 2017Summary: Data assimilation or filtering combines a numerical forecast model and observations to provide accurate statistical estimation of the state of interest. In this paper, we are concerned with accurate data assimilation of a sparsely observed passive tracer advected in turbulent flows using a reduced-order forecast model.
Yoonsang Lee, Andrew J Majda, Di Qi
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Climate sensitivity and cloud response of a GCM with a superparameterization [PDF]
Geophysical Research Letters, 2006 The climate sensitivity of an atmospheric GCM that uses a cloud‐resolving model as a convective superparameterization is analyzed by comparing simulations with specified climatological sea surface temperature (SST) and with the SST increased by 2 K. The model has weaker climate sensitivity than most GCMs, but comparable climate sensitivity to recent ...
Matthew C Wyant +2 more
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Stochastic superparameterization in a quasigeostrophic model of the Antarctic Circumpolar Current
Ocean Modelling, 2015Stochastic superparameterization, a stochastic parameterization framework based on a multiscale formalism, is developed for mesoscale eddy parameterization in coarse-resolution ocean modeling. The framework of stochastic superparameterization is reviewed and several configurations are implemented and tested in a quasigeostrophic channel model – an ...
Ian Grooms +2 more
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MJO Intensification with Warming in the Superparameterized CESM
Journal of Climate, 2015Abstract The Madden–Julian oscillation (MJO) is the dominant mode of tropical intraseasonal variability, characterized by an eastward-propagating envelope of convective anomalies with a 30–70-day time scale. Here, the authors report changes in MJO activity across coupled simulations with a superparameterized version of the NCAR Community
Nathan P. Arnold +4 more
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Multiscale Models with Moisture and Systematic Strategies for Superparameterization [PDF]
Journals of the Atmospheric Sciences, 2007 AbstractThe accurate parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate through numerical models. Superparameterization is a promising recent alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model.
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An Improved Framework for Superparameterization
Journal of the Atmospheric Sciences, 2004This paper discusses a large-scale modeling system with explicit representation of small-scale and mesoscale processes provided by a cloud-resolving model embedded in each column of a large-scale model, the superparameterization. In the original formulation, referred to as the cloud-resolving convection parameterization (CRCP), thermodynamic variables ...
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