Results 231 to 240 of about 1,864 (260)

Non-precipitating cumulus convection and its parameterization

Quarterly Journal of the Royal Meteorological Society, 1973
AbstractThis paper discusses the thermodynamic transports of heat, liquid water and (briefly) water vapour by non‐precipitating cumulus convection. It is shown that because of the irreversible mixing between cloud and environment, there is a downward transport of enthalphy in the cumulus layer.
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A framework for parameterization of heterogeneous ocean convection

Ocean Modelling, 2014
Abstract We propose a new framework for parameterization of ocean convection processes. The new framework is termed “patchy convection” since our aim is to represent the heterogeneity of mixing processes that take place within the horizontal scope of a grid cell.
Mehmet Ilıcak, Alistair J. Adcroft, Sonya Legg   +2 more
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Validation of parameterized convective fluxes with DIAMOD

Meteorology and Atmospheric Physics, 1995
A diagnostic model (DIAMOD) for the atmosphere over Europe is use at the University of Vienna. Central parameters in each diagnostic column (horizontal resolution 100 km, time resolution 12 hours) are the vertical moisture plus heat flux (the total convective heat fluxh) and the vertical rain flux (r); both are functions of pressure.
M. Hantel, H. -J. Jakobs, Y. Wang
openaire   +1 more source

Statistical Parameterization of Heterogeneous Oceanic Convection

Journal of Physical Oceanography, 2007
Abstract A statistical convective adjustment scheme is proposed that attempts to account for the effects of mesoscale and submesoscale variability of temperature and salinity typically observed in the oceanic convective regions. Temperature and salinity in each model grid box are defined in terms of their mean, variance, and mutual ...
PASQUERO, CLAUDIA, Tziperman, E.
openaire   +4 more sources

Using Deep Learning for Convection Parameterization

Data-driven approaches using machine learning to parameterizing model physical processes in Earth System Models have been actively explored in recent years. Deep-learning-based convection parameterization is one such example. While significant progress has been made in emulating convection using neural networks (NN), serious roadblocks remain ...
Guang Zhang, Yilun Han, Yong Wang
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The Kain–Fritsch Convective Parameterization: An Update

Journal of Applied Meteorology, 2004
Numerous modifications to the Kain‐Fritsch convective parameterization have been implemented over the last decade. These modifications are described, and the motivating factors for the changes are discussed. Most changes were inspired by feedback from users of the scheme (primarily numerical modelers) and interpreters of the model output (mainly ...
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The Physics and Parameterization of Moist Atmospheric Convection

1997
Preface R.K. Smith. A: Moist Convection. 1. Overview of Atmospheric Convection K.A. Emanuel. 2. Thermodynamics of Moist and Cloudy Air R.K. Smith. 3. The Mesoscale Organization of Deep Convection J.-L. Redelsperger. 4. Trade Cumulus: Observations and Modelling A.K. Betts. 5. Convection in Stratocumulus-Topped Atmospheric Boundary Layers C.S. Bretherton.
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Parameterization of Atmospheric Convection

2014
Robert S Plant, Jun-Ichi Yano
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Organized Convection Parameterization for GCMs

2022
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Stochastic aspects of convective parameterization

2015
R.S. Plant, L. Bengtsson, M.A. Whitall
openaire   +1 more source