A flexible importance sampling method for integrating subgrid processes
Numerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgri...
Main Authors: | , |
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Format: | Article |
Language: | English |
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Copernicus Publications
2016-01-01
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Series: | Geoscientific Model Development |
Online Access: | http://www.geosci-model-dev.net/9/413/2016/gmd-9-413-2016.pdf |
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author | E. K. Raut V. E. Larson |
author_facet | E. K. Raut V. E. Larson |
author_sort | E. K. Raut |
collection | DOAJ |
description | Numerical models of weather and climate need to compute
grid-box-averaged rates of physical processes such as microphysics.
These averages are computed by integrating subgrid variability over
a grid box. For this reason, an important aspect of atmospheric
modeling is spatial integration over subgrid scales.<br><br>
The needed integrals can be estimated by 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 of integration into eight categories, such
as the portion that contains both precipitation and cloud, or
the portion that contains precipitation but no cloud. It then
allows the modeler to prescribe the density of sample points
within each of the eight categories.<br><br>
The new method is incorporated into the Subgrid Importance
Latin Hypercube Sampler (SILHS). The resulting method is
tested on drizzling cumulus and stratocumulus cases. In the
cumulus case, the sampling error can be considerably reduced
by drawing more sample points from the region of rain
evaporation. |
first_indexed | 2024-12-20T06:38:03Z |
format | Article |
id | doaj.art-88cdafe0acf64a76afc6129da393e2e9 |
institution | Directory Open Access Journal |
issn | 1991-959X 1991-9603 |
language | English |
last_indexed | 2024-12-20T06:38:03Z |
publishDate | 2016-01-01 |
publisher | Copernicus Publications |
record_format | Article |
series | Geoscientific Model Development |
spelling | doaj.art-88cdafe0acf64a76afc6129da393e2e92022-12-21T19:49:57ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032016-01-019141342910.5194/gmd-9-413-2016A flexible importance sampling method for integrating subgrid processesE. K. Raut0V. E. Larson1University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USAUniversity of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USANumerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgrid scales.<br><br> The needed integrals can be estimated by 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 of integration into eight categories, such as the portion that contains both precipitation and cloud, or the portion that contains precipitation but no cloud. It then allows the modeler to prescribe the density of sample points within each of the eight categories.<br><br> The new method is incorporated into the Subgrid Importance Latin Hypercube Sampler (SILHS). The resulting method is tested on drizzling cumulus and stratocumulus cases. In the cumulus case, the sampling error can be considerably reduced by drawing more sample points from the region of rain evaporation.http://www.geosci-model-dev.net/9/413/2016/gmd-9-413-2016.pdf |
spellingShingle | E. K. Raut V. E. Larson A flexible importance sampling method for integrating subgrid processes Geoscientific Model Development |
title | A flexible importance sampling method for integrating subgrid processes |
title_full | A flexible importance sampling method for integrating subgrid processes |
title_fullStr | A flexible importance sampling method for integrating subgrid processes |
title_full_unstemmed | A flexible importance sampling method for integrating subgrid processes |
title_short | A flexible importance sampling method for integrating subgrid processes |
title_sort | a flexible importance sampling method for integrating subgrid processes |
url | http://www.geosci-model-dev.net/9/413/2016/gmd-9-413-2016.pdf |
work_keys_str_mv | AT ekraut aflexibleimportancesamplingmethodforintegratingsubgridprocesses AT velarson aflexibleimportancesamplingmethodforintegratingsubgridprocesses |