A flexible importance sampling method for integrating subgrid processes

Raut, E. K.; Larson, V. E.

doi:https://doi.org/10.5194/gmd-9-413-2016

Articles | Volume 9, issue 1

https://doi.org/10.5194/gmd-9-413-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-413-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 1

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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Feb 2016	71	35	2	108
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Jan 2018	9	4	1	14
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Apr 2018	6	7	2	15
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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.

A flexible importance sampling method for integrating subgrid processes

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