Articles | Volume 9, issue 1
Geosci. Model Dev., 9, 413–429, 2016
Geosci. Model Dev., 9, 413–429, 2016

Development and technical paper 29 Jan 2016

Development and technical paper | 29 Jan 2016

A flexible importance sampling method for integrating subgrid processes

E. K. Raut and V. E. Larson

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Cited articles

Barker, H. W., Pincus, R., and Morcrette, J.-J.: The Monte Carlo independent column approximation: application within large-scale models, in: Proceedings of the GCSS workshop, Kananaskis, Alberta, Canada, American Meteorological Society, 2002.
Barker, H. W., Cole, J. N. S., Morcrette, J.-J., Pincus, R., Räisänen, P., von Salzen, K., and Vaillancourt, P. A.: The Monte Carlo independent column approximation: an assessment using several global atmospheric models, Q. J. Roy. Meteor. Soc., 134, 1463–1478, 2008.
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.
Cheng, A. and Xu, K.-M.: A PDF-based microphysics parameterization for simulation of drizzling boundary layer clouds, J. Atmos. Sci., 66, 2317–2334, 2009.
Chowdhary, K., Salloum, M., Debusschere, B., and Larson, V. E.: Quadrature methods for the calculation of subgrid microphysical moments, Mon. Weather Rev., 143, 2955–2972, 2015.
Short summary
Numerical models of weather and climate can estimate grid-box-averaged rates of physical processes such as microphysics using Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain into categories, and allows the modeler to prescribe the sampling density in each category.