On-demand Post-processing In
Modeling Systems
Author(s)
G. Latini, G. Passerini & S. Tascini
Abstract
We present the first results of a project aimed at developing and implementing an
on-demand post-processing service based on a nonstop modeling system with the
purpose of enhancing and optimizing the usability of simulation results.
The on-demand post-processing implementation is part of a general project
we called AUTOMET. The project foresees the development of an autonomous
system able to run nonstop simulations of pollution dispersion over a certain area
by covering all the involved aspects from data collection to internet publication.
The on-demand approach has given encouraging results. It allows the user to
access the widest range of data processed by a modeling chain from
meteorological to diffusive parameters. Whoever accesses the service has the
chance to visualize any datum or data set without any knowledge about its
implementation or programming language.
The interface adopted is user friendly and gives the opportunity, even to nonmeteorologists,
to query and interpret graphics. Users who may be interested in
this information-rendering approach are environmental policy managers,
technicians for quick observations, and general public.
At present the AUTOMET project is limited to meteorological modeling. The
model employed for this purpose is RAMS (Regional Atmospheric Modeling
System) but a MM5 implementation for modeling comparisons is planned.
The RAMS model has been implemented on a parallel Linux platform
following an open-source orientation of the whole project farther then Linux
configuration capabilities. A GrADS is employed due to its diffusion and its
batch programming features. Other technologies considered by this project are
PHP and Javascript.
Keywords
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