I’ve been trying really hard to come up with some useful observations regarding the three mesoscale detection algorithms, but struggling to come up with anything insightful. A loop all 3 algorithms (MDA top left, DMD top right, NMDA bottom left) is below. The three algorithms are overlayed on SRM and the bottom right panel is base V.

Two persistent mesoscyclones in Hall county are largely well-detected by all 3 algorithms with only minor differences is tracking. A relevant limitation for this storm is range from the radar (>65 nm) and interference from range folding. The New Mesocyclone Detection Algorithm (NMDA) is limited in its latency, making its real-time applicability limited. I rarely use the mesoscale detection algorithms operationally because I find the table difficult to read. It is easier for me to interrogate the radar data than to use the MDA or DMD. Perhaps reformatting the data display would help make the NMDA more usable.

On the contrary, the AzShear product performed remarkably well on this particular cell. It is more visually obvious and helps focus forecaster attention in a very simple way. The loop is below:

For the purpose of identifying low-level rotation, AzShear does a much better job than any of the mesocyclone detection algorithms with respect to low-level rotational velocity. The mesocyclone detection algorithms do not add much value to my warning decision process. -Atlanta Braves