Information
- Publication Type: Journal Paper with Conference Talk
- Workgroup(s)/Project(s):
- Date: October 2017
- Date (from): 2017
- Date (to): 2017
- Event: Pacific Graphics 2017
- Journal: Computer Graphics Forum 36(7) 135-144 (2017)
- Lecturer: Meister Eduard Gröller
Abstract
Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog
regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors
from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization
tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel
approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive
visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their
associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the
presented solution following a participatory methodology together with domain experts. Several meteorologists with different
backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the
analysis of uncertainty and systematic model biases for improved decision-making and process-quality measurements.
Additional Files and Images
Weblinks
BibTeX
@article{Diehl-2017-Albero,
title = "Albero: A Visual Analytics Approach for Probabilistic
Weather Forecasting",
author = "Alexandra Diehl and L. Pelorosso and Claudio Delrieux and
Kresimir Matkovic and Meister Eduard Gr\"{o}ller and Stefan
Bruckner",
year = "2017",
abstract = "Probabilistic weather forecasts are amongst the most popular
ways to quantify numerical forecast uncertainties. The
analog regression method can quantify uncertainties and
express them as probabilities. The method comprises the
analysis of errors from a large database of past forecasts
generated with a specific numerical model and observational
data. Current visualization tools based on this method are
essentially automated and provide limited analysis
capabilities. In this paper, we propose a novel approach
that breaks down the automatic process using the experience
and knowledge of the users and creates a new interactive
visual workflow. Our approach allows forecasters to study
probabilistic forecasts, their inner analogs and
observations, their associated spatial errors, and
additional statistical information by means of coordinated
and linked views. We designed the presented solution
following a participatory methodology together with domain
experts. Several meteorologists with different backgrounds
validated the approach. Two case studies illustrate the
capabilities of our solution. It successfully facilitates
the analysis of uncertainty and systematic model biases for
improved decision-making and process-quality measurements.",
month = oct,
journal = "Computer Graphics Forum 36(7) 135-144 (2017)",
URL = "https://www.cg.tuwien.ac.at/research/publications/2017/Diehl-2017-Albero/",
}