  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.
