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For Day 4, we were DDC. There was an outdoor event ahead of the convection, and prob lightning did a great job 1) detecting some convection ahead of it and 2) showing that it would be east of our IDSS event.
The animated GIF above shows both versions of the lightning cast, with the outdoor event marked by the “Home”. In this case, both versions accurately predicted lighting (white dashes), and also showed it staying east of the event.
– Updraft
For Day 3, we were Midland TX. There were a few storms that formed off the higher terrain. We did not have radar, so we made use of the GREMLIN synthetic radar imagery, and Octane to make severe warning decisions.
First of all, the Gremlin showed increasing reflectivity, and at one point had 60+ dBZ. This factored into the warning decision.
The image above shows Gremlin reflectivity (top left) with LightningCast (bottom right). ENL was in the top right, but it wasn’t loading. The reflectivity of the GREMLIN factored into the “yes” warning decision.
Secondly, the OCTANE divergence showed a persistent area of cloud top divergence on the upshear side of the updraft. This also pushed our team to issue a severe thunderstorm warning.
The animated GIF above shows a persistent cloud top divergence signal with the storm of interest across southern Texas. There is another storm in the southeast part of the 4-panel imagery.
Unfortunately there were not any reports, but I did go back and look at the ProbSevere, and the MESH maxed out at around 1.13”. When evaluation these storms, they seemed small, so having ProbSevere in addition to the satellite imagery may have led me to hold off on the warning.
The image to the right shows ProbSevere. This was looked at after the fact, but does line up with the marginal severe storm, and warning that was issued for 1” hail.
– Updraft
LightningCast
Towards the beginning of operations, there wasn’t much to look at in the MAF CWA, however just to our south, LightningCast was able to pick up on the cell shown in Figure 1 below pretty well before the first lightning strike. It was interesting to see that v2 increased the probabilities to 50% before v1, however v1 increased to 70-90% before v2 a couple frames before GLM depicted the first lightning flash. So in this case, both versions did well in detecting this cell’s lightning potential, with version 1 taking the lead in the higher probabilities right before the lightning occurred.
Figure 1: LightningCast v1 (left) and v2(right)
Another example of version 1 taking the lead is in a different cell just south of Redford, TX shown below in Figure 2. Both versions caught on to the cell at the same time with the 10% probabilities, however as the cell continued growing, version 1 seemed to hold on to the higher probabilities more so than version 2 before GLM showed the first lightning flash.
Figure 2: LightningCast v1 (left) and v2 (right)
Octane & GREMLIN
I really liked assessing the cloud top cooling in the Octane 4-panel in the image below. You can really parse out that cell just south of Redford, which ended up also upticking in LightningCast probabilities (not shown). This was a great way to keep up the situational awareness and determine which cells needed more focus, especially being without radar to assist.
Figure 3: Octane 4 Panel
GREMLIN also picked up on this cell, which I thought did a pretty good job. It’s hard to assess whether or not it matched up with radar since we weren’t using radar today, however I think with the lack of lightning, and a newer cell, the GREMLIN imagery looked fairly good.
Figure 4: GREMLIN
This cell later went on to grow fairly tall, with GREMLIN actually depicting a >60 dbZ echo and Octane showing pretty consistent divergence (not shown), so we ended up issuing a warning. I thought GREMLIN did really well, and led to higher confidence in issuing a warning without having actual radar data.
Comparing Octane Color Curves
With little convection in our CWA, I was able to take some time to compare the Octane colorcurves (Stoplight vs. Original). Before today, I tended to gravitate more towards the original colorcurve with Magenta hues as the divergence and the stoplight colors as the cloud top cooling. However the two images below show both color curves at 20:22Z – In this example, the magenta color curve in Figure 6 would lead me to believe the divergence was fairly good in this cell. But the stoplight color curve shows the divergence actually isn’t as good. Comparing this to lightning, GREMLIN, and IR satellite imagery, I like how the stoplight color curve “talked me down” to be more realistic of what was actually going on. So for day 3, the stoplight color curve took the lead.
Figure 5: Octane Stoplight Color Curve (for Divergence)
Figure 6: Octane Magenta Color Curve (for Divergence)
-Fropa
Day 3 of the testbed offered a unique opportunity to issue real-time warnings without access to radar data. Having never been in such a situation before, it was an eye-opening experience.
Mesoanalysis showed very steep lapse rates, a long and straight hodograph, high LCLs and a fairly deep mixed layer with relatively low surface dew points. This allowed us to key in on large hail and severe winds being the primary hazards. Additionally, equilibrium levels were only around 9km so hail much larger than 1” was determined to be unlikely.
We issued our first warning upon noticing a rapid uptick in updraft intensity on OCTANE, which showed cooling cloud tops, fast motions, and strong divergence aloft. This storm was located west of Albuquerque in a sparsely populated area so it is difficult to say if the warning verified or not. Gremlin showed a similar uptick in simulated reflectivity which added weight to our decision to warn.
We issued additional warnings as the storm traveled into the Albuquerque area. A second and third cell began to strengthen as well, and two more warnings were issued with the southwest storm looking the most intense on OCTANE. Our mesoanalysis determined that the low-level cumulus field east of the northeastern cell appeared flat and was therefore stable…so weakening was anticipated as it moved off the Raton Mesa.
As expected the northeastern cell began to weaken and dissipate, which was evident on OCTANE and Gremlin. The two cells further southwest continued to look strong, and warnings were maintained into the Albuquerque metro. A hail report of 1” was received at this point on the southern margin of the city.
Lastly, lightningCast showed the northeastern cell begin to weaken before its appearance on satellite degraded significantly. This allowed us to cancel the warning early, in conjunction with noticing the downward trends in OCTANE (weakening cloud top divergence).
During the course of the event we had to keep tabs on a fictitious DSS event, and used LightningCast and its associated dashboard to determine when lightning was approaching their critical threshold (10 miles). LighgtningCast did a good job with lead time as it had 70 percent or higher before any lightning was detected nearby.
– WxAnt
For Day 2, we were the Wichita Office. Our IDSS event had a high probability of lightning. Our group had a discussion about the idea of “lighting ending”, as that is a common question from outdoor events. In this case, ProbLighting did a great job of forecasting the ending time of lightning by using the time of arrival tool on the back gradient of the convection. Of course this would not work for back building storms or new development, but it performed well in this case. We incorporated that time info into our messaging.
The image above shows the ProbLighting (V2, left, V1 right) with ENL pulses on the left. This time of arrival tracker is shown in white on the left. Unfortunately AWIPS locked up toward the end, but this time of arrival gave a fairly accurate forecast that was used in the graphic.
The image above shows the graphic that we created at 346pm that showed the storms ending at 6pm using the time of arrival tracker on the back side of the Prob Lightning gradient. This turned out to be fairly accurate. In a real scenario, we could have probably briefed this information out to the decision maker.
– Updraft
Synopsis: A broken line of thunderstorms formed along a warm front from southern Oklahoma to east-central Kansas Tuesday afternoon. Storms entered a very moist environment with moderate to high MLCAPE and marginal shear. Due to a southwest to northeast storm motion, the largest threat was flash flooding, if storms became strong, damaging winds and large hail were possible.
Most of the activity in the ICT CWA was sub-severe (with the exception of flash flooding,) however there were times when a few storms pulsed-up enough to potentially produce damaging winds and/or large hail. One storm in particular had a lightning jump (GLM and ENTLN) at 2019Z over Greenwood county. This storm was entering an area that was untapped, however isolation was low.
Image 1 Caption: Snapshot of MRMS Reflectivity at -10C, GLM Flash Density, LightningCast ABI+MRMS, 5 Minute CG Flash NLDN and ENTLN 1 Minute Update Lightning
Using the OCTANE Speed and Direction Sandwich product it was evident that the storm of concern in Greenwood county showed changes in local shear coinciding with the lightning jump. The combination of this product with radar and lightning trends increased my confidence to issue a severe thunderstorm warning for damaging winds for 60 minutes.
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I tested the OCTANE, GREMLIN, and LightningCast products during an actual severe weather event on 6/3/2025. My role during this testbed was that of the mesoanalyst.
Initial environmental analysis shows weak to moderate shear, which was determined via ARARS soundings and SPC Mesoanalysis, along with OCTANE imagery showing divergent / accelerating speeds within the storm anvils. VAD hodographs were used as convection developed to see rapid changes within the shear profile during the course of the event (as convection altered the broader environment). Shear increased as the event progressed. OCTANE and LightningCast were both useful showing the uptick in storm intensity as shear increased.
LightningCast was very useful picking out developing updrafts and embedded updrafts within broader areas of convection. We used this product to gauge which updrafts had the greatest potential to become severe in the near term. A strong uptick in lightning would indicate a rapidly strengthening updraft which would warrant further interrogation.
Similar to LightningCast, OCTANE was useful in determining which updrafts were trending towards severe. While in the mesoanalyst role, I would check to see which updrafts looked most intense (warmer colors paired with a very bubbly/convective appearance) and showed strong divergence. Radar analysis would then help us determine which individual cells to warn on, especially if the area of convection is multicellular and warning the entire thing isn’t ideal.
I didn’t use GREMLIN as much, since this area had good radar coverage. However, I did use it to keep tabs on its performance. The product seems to do well with picking out the strongest discrete/semi-discrete cells and potentially struggles with smaller/shallower storms and mergers.
Using these products, and working as a team with good communication, we were able to successfully warn a tornado in the Norman area along with various severe wind and hail.
– WxAnt
LightningCast
The LightningCast contours didn’t provide much insight due to high probability (>90%) of lightning pretty much the entire event. However we were able to utilize the dashboard for a DSS event. In Figure 1 below, the first thing I noticed was that the first lightning flash was recognized at approximately 2:57PM CDT where both v1 and v2 showed 90-100% probabilities. Looking back within the past hour at around 2:05 PM (not shown in the image), probabilities of lightning occurring within the next hour were approximately in the 50-60% range. It makes sense that the probabilities would increase with shorter lead times, however if this were being utilized for a DSS event and a partner was briefed at 2:05pm, they might decide to take a risk and hold off on sheltering since the probability is only 55% (therefore giving them a 50/50 chance in their eyes). Whereas around 2:20 PM when the probabilities started increasing to 80+%, there was only about a 30 minute lead time at that point. So the DSS events that require additional lead time due to further sheltering options or larger crowds may not be able to fully shelter by the time the first lightning flash occurs.
All that to say, I really like the utilization of this dashboard, however it would need to be used with additional tools (satellite, radar, etc.) in order to provide the most accurate information.
Figure 1: LightningCast Dashboard
Another item that was pointed out was that in Figure 2 below, you can see that the probabilities in v1 (red line) start to decrease around 4:10pm whereas v2 (green) remains above 95%. This could be due to the fact that maybe there were warming cloud tops, however with the ongoing lightning flashes in the vicinity, v2 would be the more reliable tool in my opinion
Figure 2: LightningCast Dashboard
Octane
The first cell that caught our attention was the cell in southwest Butler County. Figure 3 below shows the cloud top cooling and cloud top divergence (top right and bottom two panels), and you can see that cell shoot up with decent divergence aloft. We didn’t end up warning on it since radar looked pretty subsevere, however it was a good situational awareness tool to keep an eye on where the stronger storms were located.
Figure 3: Octane four panel
Later in the period, we did end up issuing two different warnings. The gif below (Figure 4) honestly doesn’t do it justice since I grabbed it a little too late, but there was a pretty pronounced divergence signature that started in Harper County near the city of Anthony that later pushed east into Sumner county. With the divergence remaining consistent and radar showing a pretty good wind signature, we ended up issuing a warning.
Figure 4: Octane four panel
I messed around with the colortables a little bit in Octane, switching to a stoplight color scale for the divergence and the magenta hue for the cooling. I’m still not fully sure which colorscale I prefer, so I’ll need to continue playing with both. However, comparing the three smoothing techniques for the divergence, I found myself looking at the highest smoothing (bottom right panel) more frequently since the lowest smoothing (top right panel) often looked too noisy. I think for situational awareness and assessing which storms to dive deeper into, the highest smoothing should work well.
-Fropa
LightningCast
For this first day, I started out looking at Lightning Cast to gain familiarity with version 2 and see how it compares to version 1. The first thing I noticed was in southwest Pueblo County, where there seemed to be fairly frequent lightning. Version 1 in the top left panel (Figure 1 below) actually decreased in probability from 70% to 50%, whereas Version 2 in the top right panel remained at 70%. With both GLM and ENTLN depicting ongoing lightning, I think both versions should be showing higher probabilities. I’m wondering if it’s because both versions are so focused on the convection moving into southeast Pueblo County that they’re less focused on the stratiform lightning/less mature convection?
Figure 1: Four panel comparing LightningCast v1 (left panels) and LightningCast v2 (right panels)
Additionally, I tested out using the LightningCast dashboard for Fowler, CO beginning at 3PM MDT. One interesting thing to note was that it seemed to match better with the version 2 LightningCast in AWIPS versus with version 1, however both versions weren’t too far off. In the Figure 2 below, the left panel (version 1) shows between 30-50% probability of lightning, whereas the right panel (version 2) shows Fowler (purple dot in the image) right on the border of the 70% probability. Comparing that to the dashboard (Figure 3) for the same time, the yellow line (version 1) depicts a 54% probability, with the green line (version 2) showing an 84% probability for 21:18Z. With MRMS reflectivity at the -10C level showing a cell up to 42 dBz just southeast of Fowler, I would tend to lean towards utilizing version 2.
Figure 2: LightningCast v1 (left panel) and LightningCast v2 (right panel)
Figure 3: LightningCast Dashboard
One final note on the LightningCast Dashboard – I thought it was interesting to see that version 1 in Figure 4 below, the yellow line (version 1) shows two separate upticks in lightning probability versus the green line (version 2) showing a steady decline in probability.
Figure 4: LightningCast Dashboard
GREMLIN
I was also able to look at GREMLIN, which was my first time assessing this product. Figure 5 below shows a four-panel, with GREMLIN (top left), MRMS Reflectivity (top right), Satellite IR sandwich (bottom left), and GLM Flash Extent Density (bottom right). Just looking at MRMS and IR, the first cell that draws my attention is the cell in southeast Pueblo County as it has higher reflectivities and cooler cloud tops. The cell in southern Otero county looks like the cloud tops are slightly warming with time. However once we start looking at GREMLIN, those two cells look to go back and forth in reflectivity, leading to less confidence in overall intensity. If I were located in an area with poor radar coverage, or if a radar was down and I had to rely on GREMLIN, it may not be straightforward as to which cell could eventually warrant a warning.
Figure 5: Four Panel comparing GREMLIN (top left), MRMS Reflectivity (top right), Satellite IR Sandwich (bottom left), and GLM (bottom right).
That being said, Figure 6 below shows a screenshot of the same four-panel at 21:41Z, which shows GREMLIN having a pretty good grasp on the convection in Stanton and Morton counties (just outside of the PUB CWA). So in this instance, confidence in the GREMLIN product would at least be higher than the previous example shown.
Figure 6: Four Panel comparing GREMLIN (top left), MRMS Reflectivity (top right), Satellite IR Sandwich (bottom left), and GLM (bottom right).
Final Thoughts for Day 1
Overall I enjoyed testing out both of these products. I definitely want to get more hands-on experience with GREMLIN as well as the LightningCast dashboard in order to see these in different scenarios/environments.
– Fropa
LightningCast V2 did a great job predicting lighting development with developing convection along a frontal boundary in northwest Iowa. It outperformed version 1, as shown by the loop and images below.
Animated GIF showing LightningCast V1 (top) and V2 (bottom) with the day cloud phase darkened to show detail. The ENI total lighting (yellow CTG flashes, white cloud flashes) is also displayed.
At 1946Z, V2 has a higher probability of lightning (50%) than V1 (30%).
This trend continued throughout, and at 2016Z the first lighting strike was detected. That’s 30 minutes of lead time, which would be helpful for outdoor event IDSS.
LightningCast at 2016Z with initial cloud to ground strike shown in the yellow dash.
– Updraft
