Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5393–5411, 2021
Geosci. Model Dev., 14, 5393–5411, 2021

Model description paper 02 Sep 2021

Model description paper | 02 Sep 2021

The Grell–Freitas (GF) convection parameterization: recent developments, extensions, and applications

Saulo R. Freitas et al.

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Cited articles

Alcântara, C. R., Silva Dias, M. A. F., Souza, E. P., and Cohen, J. C. P.: Verification of the Role of the Low Level Jets in Amazon Squall Lines, Atmos. Res., 100, 36–44,, 2011. 
Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I, J. Atmos. Sci., 31, 674–701,<0674:IOACCE>2.0.CO;2, 1974. 
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742,, 2011. 
Baba, Y.: Spectral cumulus parameterization based on cloud-resolving model, Clim. Dynam., 52, 309–334,, 2019. 
Bacmeister, J. T., Suarez, M. J., and Robertson, F. R.: Rain reevaporation, boundary layer-convection interactions, and Pacific rainfall patterns in a AGCM, J. Atmos. Sci., 63, 3383–3403, 2006. 
Short summary
Convection parameterization (CP) is a component of atmospheric models aiming to represent the statistical effects of subgrid-scale convective clouds. Because the atmosphere contains circulations with a broad spectrum of scales, the truncation needed to run models in computers requires the introduction of parameterizations to account for processes that are not explicitly resolved. We detail recent developments in the Grell–Freitas CP, which has been applied in several regional and global models.