Articles | Volume 9, issue 11
https://doi.org/10.5194/gmd-9-4273-2016
https://doi.org/10.5194/gmd-9-4273-2016
Model description paper
 | 
25 Nov 2016
Model description paper |  | 25 Nov 2016

Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS)

Brian M. Griffin and Vincent E. Larson

Related authors

A new subgrid-scale representation of hydrometeor fields using a multivariate PDF
Brian M. Griffin and Vincent E. Larson
Geosci. Model Dev., 9, 2031–2053, https://doi.org/10.5194/gmd-9-2031-2016,https://doi.org/10.5194/gmd-9-2031-2016, 2016
Short summary

Related subject area

Atmospheric sciences
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description and preliminary experimental results
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev., 18, 4855–4876, https://doi.org/10.5194/gmd-18-4855-2025,https://doi.org/10.5194/gmd-18-4855-2025, 2025
Short summary
Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025,https://doi.org/10.5194/gmd-18-4667-2025, 2025
Short summary
Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025,https://doi.org/10.5194/gmd-18-4625-2025, 2025
Short summary
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025,https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary

Cited articles

André, J. C., de Moor, G., Lacarrère, P., and du Vachat, R.: Modeling the 24-hour evolution of the mean and turbulent structures of the planetary boundary layer, J. Atmos. Sci., 35, 1861–1883, 1978.
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., and Bormann, N.: Representing Equilibrium and Nonequilibrium Convection in Large-Scale Models, J. Atmos. Sci., 71, 734–753, 2014.
Böing, S. J., Jonker, H. J., Siebesma, A. P., and Grabowski, W. W.: Influence of the subcloud layer on the development of a deep convective ensemble, J. Atmos. Sci., 69, 2682–2698, 2012.
Boutle, I., Abel, S., Hill, P., and Morcrette, C.: Spatial variability of liquid cloud and rain: Observations and microphysical effects, Q. J. Roy. Meteor. Soc., 140, 583–594, 2014.
Download
Short summary
Microphysical process rates, such as the formation, growth, and evaporation of precipitation, affect the variances, covariances, and fluxes of moisture and heat content. These effects appear as covariance terms within the Reynolds-averaged predictive equations for the scalar (co)variances and fluxes. Using a multivariate probability density function (PDF) and a simple warm-rain microphysics scheme, these microphysical covariance terms can be obtained by analytic integration over the PDF.
Share