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...

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Main Authors: E. K. Raut, V. E. Larson
Format: Article
Language:English
Published: Copernicus Publications 2016-01-01
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.
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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
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