It can be challenging to predict how much snow will fall during winter storms, mainly because of the similar representation of areas of heavy snow and mixed precipitation on weather radar images.

To solve this problem, researchers at North Carolina State University have developed a new method to seamlessly integrate standard weather radar imagery and precipitation type data. This method allows weather forecasters and atmospheric scientists to quickly and easily distinguish between heavy snowfall and mixed precipitation and enhance their understanding of the dynamics of winter storms.

The new method, known as “image attenuation,” makes snow-only or rain-only areas more visible by reducing the visual prominence of mixed precipitation in moving radar images. It mainly relies on integrating two sources of information into one visual representation: radar reflectivity and correlation coefficient.

Reflectivity indicates the intensity of precipitation detected by the radar. Correlation coefficient values ​​indicate the consistency of the shapes and sizes of precipitation particles within a storm.

Laura Tomkins, a doctoral student at NC State’s Center for Geospatial Analytics and lead author of the study, said: “For scientists studying snowfall from winter storms, image suppression helps ensure they’re analyzing the right features.”

“However, values ​​for reflectivity and correlation coefficients are usually mapped as separate products. The way it works is that forecasters switch back and forth between reflectivity and correlation coefficient to see where the mixed precipitation is.

“Although switching maps repeatedly is tricky enough, keeping up with the changing shapes of moving objects is particularly challenging.”

Scientists devised a way to identify regions of a winter storm with values ​​typical of mixed precipitation to lessen the burden of mentally comparing reflectivity and correlation coefficient values ​​as storms change over time and geography. They then changed the appearance of these regions in conventional moving reflectivity maps, turning them to grayscale, while leaving the rest of the map in a color scale accessible to colorblind people that shows the intensity of precipitation.

Tomkins says, “We didn’t want to get rid of the melting [from the map] altogether, just reduce the visual familiarity. It gives us the confidence to say, we know this isn’t heavy snow because it’s contaminated with melting.”

“Weather forecasters are interested in how much snow will fall and when, but mixed precipitation is also dangerous.”

“Hence the importance of simply dampening mixed precipitation with a gray scale instead of removing it from the map. The technique, developed specifically for analyzing snowfall during winter storms, will help atmospheric scientists better understand where snow tires occur and why, and [that research will] trickling into improving snowfall forecasts. We designed this for our snow analysis, but it also has potential applications for weather forecasters.”

“For the average person looking at weather maps to plan their day, muting images would help them better understand where and when to expect transitions from rain and sleet to snow. It may start raining now, but will it turn to snow ?” Specifically, her method uses information about the precipitation particles measured by radar, rather than observations of temperature, which are typically used to infer precipitation type on weather maps.

“As a visualization technique, image attenuation can be used in other research applications, such as reducing the visual prominence of data associated with high uncertainty. The technique is freely available for others to use.”

Magazine reference:

  1. Laura M. Tomkins et al. Mixed Precipitation Image Attenuation to Improve Identification of Heavy Snow Areas in Radar Data. Atmospheric measurement techniques. DOI: 10.5194/amt-15-5515-2022