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Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Alexandra Diehl, Leandro Pelorosso, Kresimir Matkovic, Juan Ruiz, M. Eduard Gröller, Stefan Bruckner

JOURNAL ARTICLE: Computer Graphics Forum, vol. 36, no. 7, pp. 135–144, 2017. DOI: 10.1111/cgf.13279

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.

Published

Computer Graphics Forum

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BibTeX

@ARTICLE{Diehl-2017-AVA,
  author = {Alexandra Diehl and Leandro Pelorosso and Kresimir Matkovic and Juan Ruiz and M. Eduard Gr{\"o}ller and Stefan Bruckner},
  title = {Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting},
  journal = {Computer Graphics Forum},
  year = {2017},
  volume = {36},
  number = {7},
  month = oct,
  pages = {135--144},
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
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.},
  keywords = {visual analytics, weather forecasting, uncertainty},
  doi = {10.1111/cgf.13279},
}






 Last Modified: Stefan Bruckner, 2017-10-13