Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Alexandra Diehl, L. Pelorosso, Claudio Delrieux, Kresimir Matkovic, Meister Eduard Gröller, Stefan Bruckner
Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting
Computer Graphics Forum 36(7) 135-144 (2017), October 2017.

Information

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.

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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/",
}