Read the full story from North Carolina State University.
Predicting snowfall from winter storms is tricky, in no small part because heavy snow and regions of mixed precipitation look very similar in weather radar imagery. Mixed precipitation falls as a blend of rain, freezing rain, sleet and snow and can be mistaken for heavy snow on radar imagery, while translating to less snow accumulation on the ground.
Information about the consistency of precipitation particles’ shapes and sizes, derived from weather radar, can help meteorologists distinguish between uniform and mixed precipitation. But visualizing that has traditionally been difficult, especially as precipitation features within a winter storm move in complicated ways, shifting through time and traveling with prevailing winds across a landscape.
To address this problem, researchers at North Carolina State University developed a new way to seamlessly integrate standard weather radar imagery and information about precipitation type, so that weather forecasters and atmospheric scientists can quickly and easily distinguish heavy snow from mixed precipitation and improve understanding of the dynamics of winter storms.
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