Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting. (13th October 2017)

• Record Type: Journal Article
• Authors: Diehl, A.; Pelorosso, L.; Delrieux, C.; Matković, K.; Ruiz, J.; Gröller, M.E.; Bruckner, S.
• Is Part Of: Computer graphics forum. Volume 36:Number 7(2017)
• Page Start: 135
• Page End: 144
• Publication Date: 2017-10-13
• Subjects: Categories and Subject Descriptors (according to ACM CCS) -- 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; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13279
• Languages: English
• ISSNs: 0167-7055

Abstract

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.