Tuesday, May 1, 2012

Using Doppler, dual-pol radar to interrogate storms

Last night there were some pretty impressive-looking storms that moved across northern Oklahoma, dropping a few tornadoes along the way.  I pulled a few screenshots of radar images from these storms to look at a lot of the helpful and hazardous things that our radar system gives us.

First, one of the primary advantages of having a Doppler radar--that is, a radar that uses the Doppler effect to measure wind velocity--is that it lets us more accurately see areas of rotation within thunderstorms.  Before we had Doppler radars (and we just had regular radars), all we had was reflectivity to look at.  Sometimes we got lucky and tornadic storms would produce a well-defined "hook echo" that let us know where a tornado was likely to be.  Here's an image (borrowed from Cliff Mass's blog, where he borrowed it from someone else) of an old-style, 1960s era radar image:

This works out great if you have a storm that has a well-defined hook echo structure.  But we don't always see clear hook echoes in tornadic storms.  Take a reflectivity image from last night, for example.

There are tornado warnings out for these storms and storm spotters reported a tornado on the ground with at least one of them at this time.  But where is the tornado?  There are a few candidate locations that look a little like hook echoes, but nothing too clear.

This is where being able to see Doppler wind velocities is really helpful.  Here's the base velocity image from this time:
I've circled the areas where there are nice velocity couplets that clearly show locations of strong circulation where there could potentially be tornadoes.  If you want to know a bit more about how to look for rotation in these radar velocity images, I wrote a blog post some time back about it that can be found here.

So getting Doppler radars was a huge boon to our ability to forecast and warn for tornadoes.  In fact, a lot of research suggests that adding Doppler capabilities to our radars is the single most important advance we made to increase our warning lead times and probability of detection of tornadoes.

But now we've added even more capabilities to our radars--this whole dual-polarization business.  What can dual-pol show us about these thunderstorms?

If you go through the training material for dual-pol, there are a lot of nice, relatively clean examples that are given that show you how to differentiate between things like rain, hail and even tornadic debris using dual-pol products.  However, in my experience, real-world interpretation of these products is often not nearly so clear-cut.  There are some things that dual-pol nails every time and other things that it doesn't.  Let's start looking at the dual-pol images from the time I showed above.

We'll start with differential reflectivity.  In dual-pol parlance, this is simply the difference between the strength of the horizontally-oriented radar return and the vertically-oriented radar return (eventually I'll write a more detailed blog post about all of this).  This means that the bigger and fatter objects (like, big raindrops which tend to flatten out as they fall) will have higher ZDR (differential reflectivity) values.  Nearly spherical objects or objects that are tumbling as they fall (like hail) will tend to have much lower, near zero ZDR values.  If you have a lot of different objects in an area (like, for instance, if there is tornadic debris being thrown around) we'd expect to see very noisy ZDR values.  Here's my annotations on the ZDR radar image for the time above:

There's a lot going on in that image.  Let's start with my top comment.  If we compare this to the reflectivity image above, you'll notice that in an area where there's a lot of high reflectivity to the north of the potential tornado, we see low, zero, or nonexistent ZDR.  This can happen if the radar beam is traveling through so much material (so much rain or hail) that too much of the beam gets absorbed to make reasonable inferences about what we're actually getting back from the radar beam.  There's just too little left to work with.  This is a phenomenon called attenuation--the absorption of the radar beam by some material that decreases the radar beam's power and resulting sensitivity.  I'll get back to that again later.  For now, it's somewhat disappointing that this northern part of the storm seems to be missing a lot of signal.

Just south of that, closer to the core of the storm, we have a patch of higher ZDR--on the order of 6-7 dB.  We know that higher ZDR values ten to correspond to big, flat objects like big raindrops. So we could probably infer that there was a very heavy downpour going on in the middle of the storm.

But what about the area right near the potential tornado?  The ZDR values are somewhat weaker there, though still slightly positive.  We know that near-zero ZDR values are typical of hail, so maybe there is hail there?  They also look a little noisy, and the base velocity image suggests there could be a tornado there.  Perhaps this is a debris signature?  It's really difficult to tell.

Let's try looking at another dual-pol image for help--the correlation coefficient.  Correlation coefficient basically tells us whether or not everything the radar beam is bouncing off of in that area of space is the same thing or not.  If we have high correlation coefficient, we're probably looking at all rain or all hail.  Lower correlation coefficients mean more of a mix of different precipitation types.  Really low correlation coefficients mean that what the radar beam is bouncing off of is probably not normal precipitation and is instead something like debris, bugs, dust...

Here's the correlation coefficient image from the same time.

Let's start with that area to the north of the core of the storm again, where we saw the low to nonexistent ZDR before.  We see here that that area is full of very noisy, lower correlation coefficients. This could either mean that we're seeing a lot of different sized and shaped objects floating around in that area, but another way to get low, noisy correlation coefficient is for there to be a very weak radar signal.  This is consistent with my early statement that the radar beam is probably somewhat being attenuated in that area.

Looking back down toward the area with the potential tornado in it, we see that the correlation coefficient is still quite high in that area.  This isn't very consistent with debris, as tornadic debris is all different shapes and sizes and should have a lower correlation coefficient.  So, high reflectivity with lower ZDR and high correlation coefficient makes me lean more toward hail being seen in this area.  But, it's very difficult to tell.  Even though we get so much more information from these dual-pol products, it still can be very difficult to interpret.

Let's look at a slightly later time when the storms had merged more together.  Our old friend reflectivity can show us a lot about the storm structure:

One of the striking features of this reflectivity image is the clear outflow boundary seen on reflectivity.  This is a narrow band of slightly higher returns that's making an arc shape out in front of the main storms.  An outflow boundary is the leading edge of the cool, downdraft air that accompanied the rain falling out of the thunderstorms.  As this cool air falls to the ground with the rain, it spreads out and races along the ground out away from the storm.  Those higher returns along the leading edge of the boundary are not actually caused by rain--they're caused by dust, dirt, insects and birds that are caught along the leading edge of the advancing air.  Want proof of this?  We only have to turn to the correlation coefficient image from the same time:
Remember that hydrometeors like rain and hail tend to be rather highly correlated--they're the same everywhere.  Even in mixed precipitation the correlation only drops a small amount.  But other things that are not so evenly shaped--things like dust, birds, insects--those show up as very low correlations.  You can see int he correlation coefficient image above that the outflow boundary is completely missing--the correlations of those returns are so low that they're actually off the color chart.  In fact, a lot of the clutter off to the west of the storms also basically disappears in correlation coefficient.  This highlights one of the best uses of correlation coefficient--to differentiate between what's actually precipitation and what's not.

Here's a look at the velocity image from this time.  Note the strong velocity couplets where there are potential tornadoes.  I've drawn in arrow to help show where the radar says the air is moving--remember green is toward the radar and red is away from the radar.  You can also see how the air behind the outflow boundary is moving to the south, out away from the storms.  There's also strong convergence right along the outflow boundary.

One final thing to look at here.  Sometimes when a radar gets hit with a particularly heavy downpour, the dome around the radar gets coated with a thin layer of water.  As the radar beam tries to pass through the radar dome, some of it gets absorbed by that layer of water, creating more attenuation.  Since the radar beam suddenly loses power as soon as it crosses through the layer of rain, it causes the beam's sensitivity to drop--we have less power going out, so there's even less power that can be reflected back.  This can make radar echoes suddenly appear weaker as a heavy downpour moves over the radar.  This happened last night.  Here's a reflectivity image from right before a downpour hit the radar:

Now watch what happens to the radar returns in the circled area in the next radar scan when there is a downpour right over the radar:

The strength of the return suddenly drops off!  This happens actually quite frequently, and we often call this the "wet radome effect".  Something to watch for if you suddenly see the radar echoes all weaken dramatically.

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