Visually Comparing Weather Features in Forecasts


Meteorologists process and analyze weather forecasts using visualization so as to look at the behaviors of and relationships among weather features. During this style study conducted with meteorologists in decision support roles, we identified and tried to handle two significant common challenges in weather visualization: the employment of inconsistent and usually ineffective visual encoding practices across a big selection of visualizations, and an absence of support for directly visualizing how completely different weather features relate across an ensemble of potential forecast outcomes. In this work, we gift a characterization of the issues and data associated with meteorological forecasting, we have a tendency to propose a group of informed default encoding selections that integrate existing meteorological conventions with effective visualization observe, and we have a tendency to extend a collection of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the mixing of those contributions into a functional prototype tool, and conjointly mirror on the various practical challenges that arise when working with weather data.

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