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Visual Analysis of Spatio‐Temporal Data: Applications in Weather Forecasting
Author(s) -
Diehl A.,
Pelorosso L.,
Delrieux C.,
Saulo C.,
Ruiz J.,
Gröller M. E.,
Bruckner S.
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12650
Subject(s) - timeline , visualization , computer science , weather forecasting , data visualization , interactive visualization , numerical weather prediction , global forecast system , field (mathematics) , data mining , representation (politics) , visual analytics , interface (matter) , model output statistics , domain (mathematical analysis) , meteorology , geography , mathematical analysis , mathematics , archaeology , bubble , maximum bubble pressure method , politics , political science , parallel computing , pure mathematics , law
Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the atmospheric flow. Interactive visualization of geo‐spatial data has been widely used in order to facilitate the analysis of NWP models. This paper presents a visualization system for the analysis of spatio‐temporal patterns in short‐term weather forecasts. For this purpose, we provide an interactive visualization interface that guides users from simple visual overviews to more advanced visualization techniques. Our solution presents multiple views that include a timeline with geo‐referenced maps, an integrated webmap view, a forecast operation tool, a curve‐pattern selector, spatial filters, and a linked meteogram. Two key contributions of this work are the timeline with geo‐referenced maps and the curve‐pattern selector. The latter provides novel functionality that allows users to specify and search for meaningful patterns in the data. The visual interface of our solution allows users to detect both possible weather trends and errors in the weather forecast model. We illustrate the usage of our solution with a series of case studies that were designed and validated in collaboration with domain experts.