[josephhardin]

This is neat, though there are far better methods for doing this type of thing. Now that the Nexrads are dual-pol, you can look at the correlation between the horizontal and the vertical polarizations to filter out the vast majority of the noise.

The noise you are filtering out is primarily insect returns. If you watch it, it will get worse at some times of the day rather than others. If inversions happen, you'll also see some beam bending that can cause you to pick up powerlines/roads, etc. You can verify it is mostly bugs by looking at the differential reflectivity. The differential reflectivity(difference of horizontal and vertical returned powers) will be somewhat random for ground clutter, and higher values for bugs.

With this approach, without knowing exactly how you do it, I'd be worried that it would have the tendency to filter out initial formations of stratiform clouds, and just leave convective formations. I can point you to some theory on how a lot of the filtering is done for research in atmos if you'd like, feel free to message me(I'm working on my Ph.D. in weather radar).

[thegrossman]

Dual pol wasn't deployed when we first started Dark Sky. Now that it's becoming available, it's definitely going to make our job a lot easier!

Not knowing much about it, though, it'll take some time for us to become comfortable with the data.

[josephhardin]

Well it's very impressive that you got that performance without using any dual-pol parameters. Abstract submission for the AMS radar conference is next week, and I'm sure they'd be interested in this. http://www.ametsoc.org/MEET/fainst/201336radar.html

[thegrossman]

Also, correct me if I'm wrong (and I very well could be), but the most egregious noise -- the giant Bagel Blobs -- aren't insect reflections, but rather night-time temperature inversions. Can dual-pol correlation help identify those?

[josephhardin]

It's likely a combination of both. A quick way to discern would be to look at velocity, zdr, and rhohv. For ground clutter and returns caused by the inversion, you will expect to see very low velocities(close to 0) relative to the background precipitation. Additionally Zdr will end up looking like a roughly random field. If it is insects, then the velocity will roughly match the background precipitation, but you will have a high zdr(as insects look like very very oblate bags of water). In both cases you should get a drop in rho_hv, the correlation coefficient between the channels which will help to differentiate it from actual precipitation. Also it looks like you're primarily concerned with pulling out the rainfall in several cases. For this, I'd look at the specific differential phase(K_DP) as it is a much better estimator of rainfall than reflectivity. In general, it is linearly proportional(exponential, with an exponent close to 1) to the rainfall rate. I'm not sure how good the nexrad estimators are for kdp though.

[josephhardin]

Also I didn't see it mentioned, but are you pulling in level 2 or level 3 data?