0119Z

The supercell cluster coming into Wayne County, Indiana, is exhibiting solid rotation on the lowest slice. NTDA from KIND/KILN are both showing values in the 80% range. Both radars are also indicating two separate mesocyclone detections, and a qualitative analysis of the storm does suggest two separate updrafts and storm cores. As one would expect, the southern core (the newer development) is driving the NTDA detection which matches with the location of the southern NMDA detection. This makes sense conceptually, as the tornado potential will be much greater with the unimpeded inflow associated with the southern core.

0157Z

A broken cluster of supercells is now entering the state of Ohio. Radar data is largely quite mottled and the majority of the NMDA detections are weak, but there are a lot of them — including in places where conceptually you would not really expect a circulation to exist. There are data artifact issues, related somewhat to the distance from the KILN radar, and also perhaps related to the complex interaction between different parts of different supercells and storm clusters. This would likely be a case where extremely weak / transient detections might be desired to be toggled off under a certain threshold. NMDA detections on the strongest circulations have not necessarily been stronger detections, but NTDA detection probabilities have been tracking fairly well with where tornadoes appear most likely to occur.

0237Z

Both the NTDA and NMDA are making high-end detections in Miami County, Ohio, near West Milton. The NTDA has had values >95%, and at times >99%, with this tornado. Perhaps more interesting is that this is the first case I have yet seen in which the NMDA has shown such a consistent, high-end, well-tracked detection across a span of 10-15 minutes — with extreme (>0.03) values of AzShear. Overall, the NMDA algorithm doesn’t seem to trip into the higher values very frequently, so it really calls your attention to it when it does.

0302Z

A messy storm evolution has occurred in Auglaize County, with some kind of hybrid QLCS / supercell structure based on Z/V data. Although qualitatively, the velocity data is not particularly impressive, the NTDA has done a very nice job at consistently detecting a tornadic signal from around 0241Z to 0251Z. A tornado did occur at this location, including a TDS that was apparent along the same track of the NTDA detection just a few minutes later. This is a very good detection by NTDA, correctly identifying a tornado in an area where eyeballing the velocity data would not yield as much certainty.

–Insolation

A strong circulation has developed in Dallas County, with Vr values of as much as 70-75 knots. Appropriately, the NTDA algorithm has identified a 99% chance that this is a tornado.

0216Z UPDATE

This feature appears to be a tornado based on base velocity analysis, but NTDA is assigning a probability of only about ~20%

Just after writing this post, an LSR of damage at this location was provided.

0224Z UPDATE

A small area of bad velocity data has been occasionally (but repeatedly) tripping off low-end NTDA detections near the KFWS radar site.

The detections have generally been <20% so in theory this could be easily ignored with a probability threshold.

–Insolation

At approx 0200Z, the NTDA depicted a 99% probability of a tornado and azimuthal shear rated as “extreme”. This was coincident with a CC drop on the 0.5 deg tilt and a BWER, which increased confidence that a tornado was occurring.

– Angelica Schuyler

Prior to 21:09Z, the NTDA algorithm was keying in on sidelobeing issues ahead of the storm, the the NMDA held strong indicating no rotation. At 21:09Z, a hook echo began to form with a solid rotational signature in a good dat region, and both algorithms picked up on the signature quickly. Using the objective analysis, seeing the NTDA put out a Probability >50% with the NMDA put out an Avg AZShear in the bottom 3 scans of 22.9 *10 ^-3 /s, helped add confidence to the decision to warn quickly.

A couple times though (like at 21:15Z as seen below), the algorithms were still keying in on bad low-level data due to sidelobing from time to time. Velocity images compared to Correlation Coefficient show where more reliable data is located.

-#ProtectAndDissipate

A Severe Thunderstorm Warning was issued on a storm in Marshall County, IA with a much more robust storm further south in Marion/Mahaska Counties. After checking up on the southern storm, I did a quick check-in on the Z/V fields at 0.5 degrees for the northern storm. At the time, the storm didn’t look immediately capable of tornadic production, but earlier LSRs with low thresholds prompted the Severe/Tor possible. At 0.5 degrees, Velocity showed a bit of increased shear but nothing too substantial with a Delta-V of approx 30kts, and NROT of approx 15kts. The TDA picked up on this, but what intrigued me was the MDA did as well which had been very stingy at picking up mesocyclones which caught my eye. The readout suggested a meso signature was being detected within several bottom layers, which prompted me to look at all tilts, and voila! Above the 0.5 degree scan a decent-looking mesocyclone was much more prevalent. This mesocyclone indicated the storm was much more organized than originally expected and lead to the issuance of a tornado warning while anticipating further strengthening of the circulation. Eventually, the Tornado Warning was verified by an LSR report at 2122Z.

-#ProtectAndDissipate

The readouts for the NMDA & NTDA are one of the biggest improvements for these products. It adds the objective analysis I would want out of these products with users ability to (1) pull them up at will thus reducing screen real estate and (2) has them linked to each individual storm/icon so I can easily relate the objective analysis to the storm under investigation. The 1 advantage of a table format highlighting the most intense circulations are solved with color attributes of the icons, which is better in my mind because it relates the severity of the rotational signatures to climatology versus a ranking of storms on any given day.

The drawback of the readouts is that size and the massive amount of information thrown out at once, there is so much in these tables that it’s hard to point out any one thing to follow. In my opinion, each readout should be cut in half to optimize the deluge of information. In addition, the value behind multiple fields should be combined where possible and some organization can help cut down on redundancies between titles thus reducing the amount of space in the product descriptions. For example:

…etc. While I wouldn’t advocate for all these fields, this could be a more organized way to organized the information in the NMDA, that would allow user to find information faster. If awips can handle Unicode, special arrows could also be used as symbols.

–#ProtectAnd Dissipate