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Weather forecasting is essential for decision-making and is usually performedusing numerical modeling. Numerical weather models, in turn, are complex toolsthat require specialized training and laborious setup and are challenging evenfor weather experts. Moreover, weather simulations are data-intensivecomputations and may take hours to days to complete. When the simulation isfinished, the experts face challenges analyzing its outputs, a large mass ofspatiotemporal and multivariate data. From the simulation setup to the analysisof results, working with weather simulations involves several manual anderror-prone steps. The complexity of the problem increases exponentially whenthe experts must deal with ensembles of simulations, a frequent task in theirdaily duties. To tackle these challenges, we propose ProWis: an interactive andprovenance-oriented system to help weather experts build, manage, and analyzesimulation ensembles at runtime. Our system follows a human-in-the-loopapproach to enable the exploration of multiple atmospheric variables andweather scenarios. ProWis was built in close collaboration with weatherexperts, and we demonstrate its effectiveness by presenting two case studies ofrainfall events in Brazil.

ProWis: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime