Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

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 wit...

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Vydáno v:Computer graphics forum Ročník 36; číslo 7; s. 135 - 144
Hlavní autoři: Diehl, A., Pelorosso, L., Delrieux, C., Matković, K., Ruiz, J., Gröller, M.E., Bruckner, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.10.2017
Témata:
Analytics
Automation
Categories and Subject Descriptors (according to ACM CCS)
Decision analysis
Decision making
I.3.3 [Computer Graphics]: Picture/Image Generation—Viewing algorithms
I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques
I.3.8 [Computer Graphics]: Applications—Probabilistic Weather Forecasting
Mathematical models
Regression analysis
Statistical analysis
Uncertainty analysis
Weather forecasting
Workflow
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí: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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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13279