[CAnet - news] SOA for Mesoscale Weather forecasting

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[Excellent paper on the use of SOA for use in research and education for
dynamically interacting with mesoscale weather. Thanks to Mathew Arnott for
this pointer -- BSA]

http://lead.ou.edu/pdfs/LEAD_CiSE.pdf

Abstract

Within a decade after John von Neumann and colleagues conducted the first
experimental weather forecast on the ENIAC computer in the late 1940s,
numerical models of the atmosphere became the foundation of modern day
weather forecasting and one of the driving application areas in computer
science. This article describes research that is enabling a major shift away
from a 50-year paradigm - in which weather sensing and prediction
technologies, and computing infrastructures, operate in fixed modes
independent of weather conditions - to a new one involving dynamically
adaptive response to rapidly changing conditions. The authors demonstrate
how this capability offers hope for improving the detection and prediction
of intense local weather.

1. Introduction
Each year across the United States, flash floods, tornadoes, hail, strong
winds, lightning, and localized winter storms - so-called mesoscale weather
events -- cause hundreds of deaths, routinely disrupt transportation and
commerce and result in average annual economic losses greater than $13B).
Although mitigating the impacts of such events would yield enormous economic
and societal benefits, research leading to that goal is hindered by rigid
information technology (IT) frameworks that cannot accommodate the real
time, on demand,and dynamically-adaptive needs of mesoscale weather
research; its disparate, high volume data sets and streams; and the
tremendous computational demands of its numerical models and data
assimilation systems.

In partial response to this pressing need for a comprehensive national
cyberinfrastructure in mesoscale meteorology, particularly one that can
interoperate with those being developed in other relevant disciplines, the
National Science Foundation (NSF) in 2003 funded a Large Information
Technology Research (ITR) grant known as Linked Environments for Atmospheric
Discovery (LEAD). A multi-disciplinary effort involving 9 institutions and
more than 100 scientists, students and technical staff in meteorology,
computer science, social science and education, LEAD is addressing the
fundamental IT research challenges, and associated development, needed to
create an integrated, scalable framework for identifying, accessing,
preparing, assimilating, predicting, managing, analyzing, mining, and
visualizing a broad array of meteorological data and model output
independent of format and physical location.

The foundation of LEAD is dynamic workflow orchestration and data management
in a web services framework. 
[...]

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