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.

This paper presents the results obtained from the utilization of a public dominion software that, through data mining and neural networks with Bayesian training is capable of laying the foundation for the selection of the most appropriate natural inflow forecast used in the PREVIVAZ stochastic modeling system. This technique utilizes precipitation information, forecasted and observed, a well as verified natural inflow data recorded over the weeks that precede the actual forecast target made at the water courses at the Foz do Areia and Jordao hydroelectric plants located in the Iguacu River Basin. The results obtained indicate that the usage of these tools can provide a simple and efficient solution to reduce natural inflow forecast errors on a weekly forecast basis for the Iguacu River Basin.