 |
| Titel |
The Subgrid Importance Latin Hypercube Sampler (SILHS): a multivariate subcolumn generator |
| VerfasserIn |
V. E. Larson, D. P. Schanen |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1991-959X
|
| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 6, no. 5 ; Nr. 6, no. 5 (2013-10-29), S.1813-1829 |
| Datensatznummer |
250085007
|
| Publikation (Nr.) |
 copernicus.org/gmd-6-1813-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
Coarse-resolution climate and weather forecast models cannot accurately
parameterize small-scale, nonlinear processes without accounting for
subgrid-scale variability. To do so, some models integrate over the subgrid
variability analytically. Although analytic integration methods are
attractive, they can be used only with physical parameterizations that have a
sufficiently simple functional form. Instead, this paper introduces a method
to integrate subgrid variability using a type of Monte Carlo integration. The
method generates subcolumns with suitable vertical correlations and feeds
them into a microphysics parameterization. The subcolumn methodology requires
little change to the parameterization source code and can be used with a wide
variety of physical parameterizations.
Our subcolumn generator is multivariate, which is important for physical
processes that involve two or more hydrometeor species, such as accretion of
cloud droplets by rain drops. In order to reduce sampling noise in the
integrations, our subcolumn generator employs two variance-reduction methods,
namely importance and stratified (Latin hypercube) sampling. For this reason,
we name the subcolumn generator the Subgrid Importance Latin Hypercube
Sampler (SILHS).
This paper tests SILHS in interactive, single-column simulations of a marine
stratocumulus case and a shallow cumulus case. The paper then compares
simulations that use SILHS with those that use analytic integration. Although
the SILHS solutions exhibit considerable noise from time step to time step,
the noise is greatly damped in most of the time-averaged profiles. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|