Parameters in the Significant Tornado Parameter index

[Tim Vasquez]

Hey guys, I've been starting to drop in at Stormtrack now that I've gotten a bunch of projects out of the way and severe weather season is finally in the upswing. I don't know how many of you use PivotalWeather but I've been seeing something confusing on there, and I figured if anyone would know the answer it would be you guys



If you check out that image at the bottom right, where STP is broken down, you can see that there are CAPE, LCL, ESRH, and EBWD. Those right there are the core elements of STP, so no problem there. However it then goes on to list STPC and STP_fixed. Does anyone know what this is referring to? I Googled around and can't find any references to this except some SHARP Python code that unfortunately isn't commented.

Thanks all.

 

[Greg Blumberg]

Hi Tim,

As you're probably familiar with STP, you may know that there are two formulations of STP. The first uses fixed-layer parameters such as 0-6 km BWD and 0-1 SRH. The second uses parameters calculated using the effective-inflow layer such as the effective bulk wind difference (EBWD) and the effective storm-relative helicity (ESRH). The first is called STP_fixed, the second is called STPC or STP with CIN (an extra term). The CAPE and LCL terms refers to MLCAPE and MLLCL. In SHARPpy, the formulation for STPC comes from Thompson et al. 2012, WAF, and the formulation for STP_fixed comes from Thompson et al. 2003, WAF. The probabilities shown in that small box are showing the probability of a EF2+ tornado given the current MLLCL, ESRH, etc. values being shown in the sounding, and are conditional on there being a right-moving supercell in the environment. That inset is based on the work done in Thompson et al. 2012, WAF.

If I remember correctly, the Thompson 2012 paper shows that the STPC formulation does a better job at discriminating between significant tornado environments than STP_fixed (STPF). The catch is that you need to ensure that the the thermodynamics in the profile you're calculating it from is accurate, so using the index in conjunction with NWP may mislead you if the quality of the sounding is poor.

In case you want more information about SHARPpy, I recommend this link (I'm not sure if BAMS is behind a paywall for others; I can access it from my home computer) to a paper we just published in BAMS on the SHARPpy program. This paper provides a pretty comprehensive overview of SHARPpy . We describe a lot of the indices, insets, and features of the program in the paper. Unfortunately it hasn't gotten the nice formatting treatment typical of BAMS articles yet as it is an Early Online Release, but I've been told it should be out in the August edition of BAMS:

http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00309.1

 

[Tim Vasquez]

That's some solid information... thank you! I also appreciate the references.

SHARPpy sounds really interesting... now I have some reasons for learning Python (currently 95% of my programming is in Delphi).