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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Final revised paper (published on 29 Jan 2016)
Preprint (discussion started on 22 Oct 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C2790: 'Review of Raut and Larson (2015)', Anonymous Referee #1, 04 Nov 2015
- AC C3278: 'Response to Anonymous Referee #1', Eric Raut, 07 Dec 2015
RC C2919: 'Review of Raut and Larson', Anonymous Referee #2, 17 Nov 2015
- AC C3286: 'Response to Anonymous Referee #2', Eric Raut, 07 Dec 2015

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

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.