Articles | Volume 9, issue 1
https://doi.org/10.5194/gmd-9-413-2016
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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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.