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Fun fact: there are a lot of useful things to see in correlation coefficient radar data. If you're just using it as a tornadic "debris tracker" you're missing out.
OK by request here's more information. Clearly đťžşhv = |<s'hh * svv>| / (<|shh|^2><|svv|^2>)^1/2 ... where shh and svv are derived from the 2x2 backscattering matrix for an ensemble of scatterers S from the horizontal (h) and vertical (v) channels. Glad I could help!
Oh people wanted something useful. Correlation Coefficient is a measure of the consistency of the returned power and phase comparing between the vertical and horizontal channels. It ranges from 0 to 1 like all CC measures (but might rarely exceed 1 in 88D due to miscalibration).
One great use of CC data is discriminating meteorological echoes from non-met'l. Here's an example from @PSU_RadarMeteo . This is because most areas of precip are ~uniform (high correlation) whereas mosts non-met targets are more chaotic (low correlation) https://twitter.com/PSU_RadarMeteo/status/783100609981394944
This is particularly useful when precipitation echoes are interspersed with similar-looking areas of reflectivity that are non-meteorological: in this case, chaff. https://twitter.com/wxkev/status/804365606246109186
Or bats! These bats are flying out of a well-known bat cave in south Texas at sunset. You'll also notice the general ground clutter also has low CC. https://twitter.com/ChrisSuchanWOAI/status/1037137919897739265
Sea spray is something else chaotic leading to low CC. And in this case, from @kudrios, you can see the spray only getting kicked up in the deeper waters further away from the FL Keys: https://twitter.com/kudrios/status/1103211692341846016
Another use: mixture of precip types, such as rain/hail or rain/snow. In those cases, the different sizes, shapes, and tumbling properties of the hydrometeors lead to a more chaotic power/phase return signal than a field of pure rain or snow hydrometeors, driving down the CC.
A great example is the melting layer as snow falls into warmer temps & melts to rain. In this winter storm example, you can see the snow aloft (far from radar) and rain (near) w/ melting layer between contorting with time due to warm/cold advection. https://twitter.com/PSU_RadarMeteo/status/939314430059450368
It can really help visualize the movement of the rain/snow line as dynamics come into play in a major winter storm, like this from @ScottNogueira https://twitter.com/ScottNogueira/status/1242273474489528320
Pure fields of giant hail tends to be lower CC. Why? Because there's usually a mixture of hail sizes and shapes with a diversity of tumbling characteristics while falling. In this example, also the non-meteorological three-body scattering spike has low CC. https://twitter.com/NWSSanAntonio/status/991803866877452288
So those are some cool non-tornadic-debris things you can do with CC. I hope you will vote for it for best dual-pol product. I'm Correlation Coefficient and I approved this message.
