Articles | Volume 9, issue 6
https://doi.org/10.5194/gmd-9-2031-2016
https://doi.org/10.5194/gmd-9-2031-2016
Model description paper
 | 
03 Jun 2016
Model description paper |  | 03 Jun 2016

A new subgrid-scale representation of hydrometeor fields using a multivariate PDF

Brian M. Griffin and Vincent E. Larson

Related authors

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
Geosci. Model Dev., 9, 4273–4295, https://doi.org/10.5194/gmd-9-4273-2016,https://doi.org/10.5194/gmd-9-4273-2016, 2016
Short summary

Related subject area

Atmospheric sciences
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025,https://doi.org/10.5194/gmd-18-1017-2025, 2025
Short summary
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025,https://doi.org/10.5194/gmd-18-621-2025, 2025
Short summary
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025,https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025,https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025,https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary

Cited articles

Anderson, T. W.: On the Distribution of the Two-Sample Cramer-von Mises Criterion, Ann. Math. Statist., 33, 1148–1159, 1962.
Bogenschutz, P. A. and Krueger, S. K.: A simplified PDF parameterization of subgrid-scale clouds and turbulence for cloud-resolving models, J. Adv. Model. Earth Syst., 5, https://doi.org/10.1002/jame.20018, 2013.
Bogenschutz, P. A., Krueger, S. K., and Khairoutdinov, M.: Assumed Probability Density Functions for Shallow and Deep Convection, J. Adv. Model. Earth Syst., 2, 10, https://doi.org/10.3894/JAMES.2010.2.10, 2010.
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
A multivariate probability density function (PDF) can be used to represent the subgrid (below grid-box size) variability of atmospheric fields. The PDF was previously extended to include hydrometeor fields, such as rain water mixing ratio. Now, the PDF of hydrometeor fields is altered to account for precipitating and precipitation-less regions of the subgrid domain. Accounting for these regions allowed the hydrometeor PDF to produce an improved match to results from large-eddy simulations.
Share