Articles | Volume 9, issue 1
Geosci. Model Dev., 9, 413–429, 2016
https://doi.org/10.5194/gmd-9-413-2016
Geosci. Model Dev., 9, 413–429, 2016
https://doi.org/10.5194/gmd-9-413-2016

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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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Eric Raut on behalf of the Authors (20 Dec 2015)  Author's response
ED: Publish as is (15 Jan 2016) by Klaus Gierens
AR by Eric Raut on behalf of the Authors (17 Jan 2016)  Author's response    Manuscript
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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.