Visual Scalability of Spatial Ensemble Uncertainty
Sujan Anreddy, Song Zhang, Andrew Mercer, Jamie Dyer, and J. Edward Swan II. Visual Scalability of Spatial Ensemble Uncertainty. In VIS Posters, Proceedings of IEEE VIS 2015, pp. 187–188, Oct 2015. DOI: 10.1109/VAST.2015.7347671
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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.
BibTeX
@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.
},
}