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@InProceedings{IEEEVIS15-seu,
author = {Sujan Anreddy and Song Zhang and Andrew Mercer and Jamie Dyer
and J. Edward {Swan~II}},
title = {Visual Scalability of Spatial Ensemble Uncertainty},
booktitle = {VIS Posters, Proceedings of IEEE VIS 2015},
location = {Chicago, Illinois, USA},
date = {October 25--30},
month = {Oct},
year = 2015,
pages = {187--188},
note = {DOI: 10.1109/VAST.2015.7347671}
abstract = {
Weather Research and Forecasting (WRF) models simulate weather
conditions by generating 2D numerical weather prediction ensemble
members either through perturbing initial conditions or by changing
different parameterization schemes, e.g., cumulus and microphysics
schemes. These simulations are often used by weather analysts to
analyze the nature of uncertainty attributed by these simulations to
forecast weather conditions with good accuracy. The number of
simulations used for forecasting is growing with the advent of
increase in computing power. Hence, there is a need for providing
better visual insights of uncertainty with growing number of ensemble
members. We propose a geo-visual analytical framework that uses visual
analytics approach to resolve visual scalability of these ensemble
members. Our approach naturally fits with the workflow of an analyst
analyzing ensemble spatial uncertainty. Meteorologists evaluated our
framework qualitatively and found it to be effective in acquiring
insights of spatial uncertainty associated with multiple ensemble runs
that are simulated using multiple parameterization schemes.
},
}