ProWis: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913245062758400 |
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| author | de Souza, Carolina Veiga Ferreira Bonnet, Suzanna Maria de Oliveira, Daniel Cataldi, Marcio Miranda, Fabio Lage, Marcos |
| author_facet | de Souza, Carolina Veiga Ferreira Bonnet, Suzanna Maria de Oliveira, Daniel Cataldi, Marcio Miranda, Fabio Lage, Marcos |
| contents | Weather forecasting is essential for decision-making and is usually performed using numerical modeling. Numerical weather models, in turn, are complex tools that require specialized training and laborious setup and are challenging even for weather experts. Moreover, weather simulations are data-intensive computations and may take hours to days to complete. When the simulation is finished, the experts face challenges analyzing its outputs, a large mass of spatiotemporal and multivariate data. From the simulation setup to the analysis of results, working with weather simulations involves several manual and error-prone steps. The complexity of the problem increases exponentially when the experts must deal with ensembles of simulations, a frequent task in their daily duties. To tackle these challenges, we propose ProWis: an interactive and provenance-oriented system to help weather experts build, manage, and analyze simulation ensembles at runtime. Our system follows a human-in-the-loop approach to enable the exploration of multiple atmospheric variables and weather scenarios. ProWis was built in close collaboration with weather experts, and we demonstrate its effectiveness by presenting two case studies of rainfall events in Brazil. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05019 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ProWis: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime de Souza, Carolina Veiga Ferreira Bonnet, Suzanna Maria de Oliveira, Daniel Cataldi, Marcio Miranda, Fabio Lage, Marcos Human-Computer Interaction Computers and Society Weather forecasting is essential for decision-making and is usually performed using numerical modeling. Numerical weather models, in turn, are complex tools that require specialized training and laborious setup and are challenging even for weather experts. Moreover, weather simulations are data-intensive computations and may take hours to days to complete. When the simulation is finished, the experts face challenges analyzing its outputs, a large mass of spatiotemporal and multivariate data. From the simulation setup to the analysis of results, working with weather simulations involves several manual and error-prone steps. The complexity of the problem increases exponentially when the experts must deal with ensembles of simulations, a frequent task in their daily duties. To tackle these challenges, we propose ProWis: an interactive and provenance-oriented system to help weather experts build, manage, and analyze simulation ensembles at runtime. Our system follows a human-in-the-loop approach to enable the exploration of multiple atmospheric variables and weather scenarios. ProWis was built in close collaboration with weather experts, and we demonstrate its effectiveness by presenting two case studies of rainfall events in Brazil. |
| title | ProWis: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2308.05019 |