As noted in the recent HSLC conference call, Matt and Keith plan to submit an article for submission in AMS’s Weather and Forecasting within the next two weeks. This article will focus on a nationwide climatology of HSLC significant severe weather in addition to the parameter-based work detailed through conference calls and blogs over the last several months.
While writing the article, the methods utilized to develop the SHERB were revisited. In our initial development, we had utilized HSLC events and nulls datasets from our CSTAR region. Both datasets used archived SPC Mesoanalysis (aka Surface Objective Analysis, or SFCOA) data; however, the events database took SFCOA data from the nearest grid point at the preceding hour, while the null database points were spatially interpolated. To alleviate these differences, the development methods were repeated using a new null dataset provided by the SPC that utilized the nearest grid point at the preceding hour, consistent with the events dataset.
After re-calculating skill scores for individual environmental parameters, it was found that the two lapse rates used in the SHERB formulation (the 0-3 km, or low-level – LLLR, and 700-500 mb – LR75) were the two conditionally most skillful parameters, and using a product of these lapse rates considerably improved the skill at discriminating between HSLC significant severe reports and nulls over conventional composite parameters. However, the third conditionally most skillful parameter was somewhat less obvious, as multiple wind and shear parameters combined with the lapse rates exhibited skill. This was further compounded by the fairly small sample size when attempting to determine the third conditionally most skillful parameter, which led to differing results between our TSS tests and subsequent Monte Carlo simulations. As a result, we tested multiple formulations of the “SHERB” (defined in the article as the product between the LLLR, LR75, and a wind/shear parameter; see Table 1) across our development dataset and verification dataset.
Within the regional development dataset, multiple versions of the SHERB were more skillful than conventional composite parameters at discriminating between HSLC significant severe reports and nulls (Table 2). In particular, the SHERB6 (using the 6 km wind magnitude) and SHERBS6 (using the 0-6 km shear magnitude) were especially skillful. When looking at just HSLC significant tornadoes against nulls, the SHERBE and the original SHERB (i.e., SHERBS3) stood out as best performers (Table 3). These trends continued when investigating nationwide skill, as shown in Table 4. Also note that the skill of the SHERB6 and SHERBS6 diminished in the nationwide verification dataset, suggesting their skill is conditional.
Here is a summary of some findings from skill and climatology comparisons over multiple subsets:
- In the winter (DJF), deep-layer shear and winds appear to be most skillful in conjunction with LR75 and LLLR, suggesting that in highly dynamic, strongly forced environments, system propagation speed and momentum transfer are important discriminators between events (particularly significant winds) and nulls. Lapse rates are crucial in facilitating this momentum transfer.
- Regionally, the typical “regime” of HSLC significant severe weather varies: across our CSTAR region, surface-based, low LCL cases are most common; across the Plains and Midwest, elevated cases are most common, and in the far western U.S., high-based, dry boundary layer cases are common. All of these are possible given our definition of HSLC environments.
- As a result, the most skillful parameter varies from region to region. The SHERBE is particularly skillful in these “CSTAR-style” regimes and elevated regimes, but using SRH rather than shear or winds seems to have greater utility in high-based cases.
- However, nationwide (and in our region), the SHERBS3 (i.e., original SHERB) and SHERBE are the most appropriate for widespread use due to their relatively consistent skill and optimal thresholds when compared to conventional parameters and other SHERB formulations.
Ultimately, there seems to be no one parameter to encompass all potential HSLC hazards, which is what should be expected. After all, there is no magic bullet parameter (which is good for job security!). However, the SHERBS3 and SHERBE can continue to be used with confidence as guidance tools in HSLC environments when convection is anticipated.
There are some slight adjustments to the parameters’ normalized values after the new tests. The LR75 term is now normalized by 5.6 K/km, while the shear terms are normalized by 26 m/s (SHERBS3/SHERB) and 27 m/s (SHERBE). If there are interests in testing the other SHERB formulations, let us know, and we can post the normalization values for the other wind/shear parameters.
Table 1. Wind and shear magnitude parameters exhibiting skill as the third conditionally most skillful parameter in the development dataset using TSS tests or Monte Carlo simulations.
|Parameter||Label When Combined With Lapse Rates|
|1 km wind magnitude||
|3 km wind magnitude||
|6 km wind magnitude||
|Cloud-bearing layer mean wind magnitude||
|0-1 km shear magnitude||
|0-3 km shear magnitude||
|0-6 km shear magnitude||
|Effective shear magnitude||
|0-1 km storm relative helicity||
|0-3 km storm relative helicity||
Table 2. Maximum true skill statistic (TSS), optimal threshold, and integrated area under the ROC curve (AUC) for given composite parameters at discriminating between HSLC significant severe reports and nulls within the development dataset. Composite parameters include the Craven-Brooks Significant Severe Parameter, the Energy Helicity Index (EHI), the Supercell Composite Parameter (SCP), the Significant Tornado Parameter (STP), and the Vorticity Generation Parameter (VGP).
|Parameter||Maximum TSS||Optimal Threshold||AUC|
Table 3. As in Table 2, but for HSLC significant severe tornado reports and nulls.
|Parameter||Maximum TSS||Optimal Threshold||AUC|
Table 4. Maximum true skill statistic (TSS) using any threshold for (second column) all HSLC significant severe reports against nulls, (third column) HSLC significant tornadoes against nulls, (fourth column) HSLC significant winds against nulls, and (fifth column) HSLC significant hail reports against nulls within the nationwide verification dataset.