If you’ve been a viewer of Tennessee Valley Weather for any amount of time, you’d have heard us discuss several times before just how many various tools we have at our disposal to investigate radar data to find possibly dangerous areas of rotation – be it from analyzing the structure of storms on radar, or their doppler velocities, these tools have proven invaluable since their nationwide rollout some 35 years ago. In more recent years, however, new tools have entered the radar users toolkit and have opened up a new dimension of radar analysis – literally – and as a Meteorologist who specializes in Radar Analysis, I’d like to take you into that world, and show you how easy to understand it truly is.
The basics are simple enough, and we’re mostly all familiar with them – a radar shoots a beam out, it bounces off of objects (rain, ideally!), and tells us how intense it is. But with the advent of what we call “dual-pol” in recent years, our radars can now shoot out two different beams at two different angles, which enables the radar to process the three-dimensional shape of the objects it hits, from birds to bees to rain and hail, and interestingly enough, tornadic debris… perhaps you see why I call it our “best friend” during severe weather, eh? One main way we do this is by taking the “Correlation Coefficient”… perhaps the most useful of these tools, and also the simplest to understand. Bear with me.. I’ll explain!
Now look, as intimidating as the term “Correlation Coefficient” sounds (typically abbreviated to just CC), it’s actually a fairly simple principle to grasp. Let’s walk through this – you think of it as a 0 to 100 scale, with closer to 100 (in red) indicating very similarly shaped objects being reflected by the radar, and closer to 0 (in greens and blues) indicating very peculiar, unusual objects being reflected by the radar. Early uses of this proved it useful in findng hail, but an interesting phenomena began occurring during tornado events; debris – be it glass, wood, or trees – actually was visible in the data. How exactly can you tell this, though? How do you know it isn’t just say, hail or something else? Well, let’s look at an example. Context reigns supreme in this field!
Above is our example, with the normal reflectivity radar on top and our so-called “Correlation Coefficient” on the bottom. On April 28th, 2014, a significant tornado struck near Graysville, Alabama, just on the outskirts of Birmingham. Luckily, nobody was killed, but the tornado inflicted notable damage on nearby homes and structures. On radar, the presentation was clear – if you’re familiar with more standard radar outputs, you see a large hook wrapped into the supercell, and velocities indicated high winds. At the time, CC was relatively new, however, and showed us what exactly was going on – an area of low CC (blue) surrounded by high CC (red). This indicated that, within the very similar, consistent mass of rain the supercell was producing, there were some very atypical, non-weather objects being lifted INTO the storm… and what does that? Tornadoes. Sure enough, the data proved to us that a tornado was ongoing, and this helped confirm the tornado which was rain-wrapped, moving fast, and occurring at night. Without this tool, the proper high-impact warnings may not have been issued, and indeed to this day may still elude us when tornadoes like this come around. Here in the southern US, they are often extremely difficult to see, so it goes without saying – CC can be a meteorologists best friend when confirmation is scant on the ground.
