Day 3 – The Train Is On The Tracks OCTANE

I definitely felt more comfortable using OCTANE today and now that I understand it better, I was able to look at it compared to 10.3 imagery and I found that useful in differentiating which storms were still strengthening.

Jason showed us how to play around with the color tables for the OCTANE product and basically invert them so that the purples/pinks (cooler colors) represent cooler cloud tops, and the yellows/oranges/reds are the divergence. Conceptually I think this makes more sense to me putting cooler colors = cooling cloud tops.

GREMLIN

I feel like I saw very mixed results with GREMLIN with this event. There were times when the meso performed the best I’ve seen it all week (below)

…and then just two minutes later, not so much.

I’m not sure what attributed to the sudden drop off on what on radar appeared to be the strongest storm.

Later, GREMLIN seemed to be doing very well with the areas of more stratiform precip, which I don’t believe I’d gotten to see up until this point. Was curious if it typically does better in that type of environment.

LightningCast

I wish I had grabbed more of the LightningCast plots since it was probably the product I was looking at the most since I was doing the DSS messages, but the plot below was the only one I did grab.

I was curious about the sudden dip in the V2 product because I don’t think I’d seen it be lower than both V1 before.

– Lightning McQueen

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Storm Top Divergence During Low Topped Storms

Storms in the area this afternoon were mainly low-topped supercells with neutral values of speed (green) with a weaker sheared environment. The main thing that was noticeable was the threshold for CTD was not very strong with the storms this afternoon in the area (Figure 1). Thus, adjustments were made to the colorbar maximum thresholds with CTD for each smoothing technique (see Figure 2). The highest smooth CTD is normally 4, so it was maintained. Meanwhile, the medium smooth CTD was lowered to 3 as the colorbar maximum value and the regular CTD (non-smoothed) colorbar maximum was lowered to 2. The intensity of CTD becomes more notable in the lower thresholds, which may be needed/more helpful in these low-topped storm modes where the updrafts and cloud tops are not going to be near as cold (higher in altitude) as other convective modes. More research may be needed to look into whether the storm that originally created subtle CTD values (before the adjustment to the colorbar) ended up going on to become severe and/or produce hazardous weather. Therefore, local calibration may be needed by offices when it comes to different convective modes.

Figure 1: OCTANE Speed/Direction and CTC/CTD.

Figure 2: OCTANE Speed/Direction and CTC/CTD with adjusted colorbar maximum values for CTD across all smoothing levels.

Lightning cast V1 showed slightly lower probabilities than V2 for the convective initiation mentioned above.

Figure 3: Lightning cast V1 (left) and V2 (right) with GLM Flash Extent Density and Day Cloud Phase Distinction RGB.

– Aurora Borealis

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Utilizing OCTANE to Determine Failed Convection

Typically forecasters will utilize cloud top cooling and glaciation for an initial look into convective initiation. OCTANE cloud-top cooling clearly depicts the rapid intensification of an updraft with the quick change of colors (green to red). However, the OCTANE divergence component of this product helps to signify mature convection and a strong persistent updraft. Notice how there is no signal for cloud-top divergence (CTD) in the animated loop below (Figure 1). No signature for CTD and warming cloud-top temperatures became an apparent signature for failed convection.

Figure 1: OCTANE cloud-top cooling and divergence. Notice a rapidly developing updraft initiating south of the cluster of storms before it quickly warms as the storm fails to maintain strength.

Lightning cast clearly signifies a low probability for convection developing south of the main cluster. Additionally, day cloud phase distinction reveals an orphan anvil present in the storm that showed a quick signal for cloud-top cooling in the OCTANE product. Thus, failed convection led to no signal for a storm at the base reflectivity scan on radar.

Figure 2: Lightning cast V1 (left) and V2 (right), along with GLM Flash Extent Density and Day Cloud Phase Distinction RGB.

Figure 3: Local radar KILX base reflectivity at 0.5 degree tilt.

– Aurora Borealis

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DSS messaging with the Lightningcast Dashboard

While doing the DSS for our event in Memphis (the Memphis Firework Preview Show 2) noticed a rather quick uptick in the Lightning V2 and eventually V1 probabilities about 45 mins or so prior to lightning strikes occurring near and around the site. This would give valuable lead time to any partner that was concerned about an outdoor event. It was made slightly easier to have confidence in this decision to mention an increased lightning threat especially looking at the line in satellite and on radar data.

– Sting Jet

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LightningCast and GREMLIN Identifying Threats Fast w/ OCTANE Being Great with Initiation

LightningCast seems to be doing its job quite well as it honed in on a small area in southeast KS with ample lead time (2042Z with more strikes appearing at 2056Z west of Jasper). This would be extremely useful for event deployments and getting ample lead time for our partners as seen below.

Regarding GREMLIN, it does a spectacular job identifying CI along boundaries (in this case, the dryline), somewhat ahead of time compared to MRMS reflectivity.

In this case, it appears to have picked up on more robust CI well ahead of time in TOP’s area. However, it does not appear to be super consistent later on with really capturing how robust some of these cells ended up later on, likely due to overall resolution of the product as well as lightning activity as these cells matured.

Lastly, regarding OCTANE, it did a great job on picking up a cell with ample cooling initially (reds and yellows) followed by strong divergence aloft (purples and pinks). This storm would go on to further intensify down the road.

This cluster of storms would then go on to produce multiple 60mph+ severe gusts.=

– Ryan Cooper

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Day 1 GREMLIN and LightningCast

Noticed that the meso GREMLIN was a little jumpy when it came to the apparent ‘strength’ of the storm. Tried to get a gif of it, but this isn’t the best…

Noticed that the CONUS did a better job of matching what the radar was showing (at this time frame). Same issues as above with the ‘jumpy-ness’.  GREMLIN seemed to pick up more on the new convection (cell furthest to the south in image below).

When using ProbSevere with GREMLIN, can more easily see the slight shift in what it shows vs radar

LightningCast V2 appeared to consistently do a better job

– Lightning McQueen

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LightningCast V1 vs V2

 When initially getting into the Lightningcast product I noticed just some subtle differences between Lightningcast V1 (left window) vs V2 (right window) around the 1851Z timeframe to 1921Z. Near the Choctow and Pushmataha Counties had jumped up to 30% with even a small area of 50%, while the version 2 had a lower probability of lightning closer to 10-30% during that same timeframe. Will note on both, given the anvil cirrus from a strong storm off to the west may have been obscuring the area which looks like it lead to abnormally higher lightning probabilities than what otherwise would have occurred without the cirrus overhead. That being said, felt the Lightningcast V2 captured the threat better overall and kept the probabilities lower which would have lead me to not mentioning the area for thunderstorm probabilities just yet,

-Sting Jet

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LightningCast Comparison

While the training touted cases where Version 2 (V2) of LightningCast had a clear lead time advantage over Version 1 (V1), many of the new cells I tracked today showed the opposite, with V1 showing higher probabilities sooner than V2.

It seemed like many of the cells were producing lightning very quickly after achieving even modest returns at the -10C level. In this case, the significant instability in the region today led to very rapid vertical cloud growth, which likely outpaced significant radar returns aloft. And while it is purely an educated guess on my part, that might have something to do with the weight given to the -10C reflectivity – if it needs a certain reflectivity threshold to really boost the probability, then the V2 product would be artificially slowing the increase in probabilities. Then in addition to waiting for the -10C reflectivities, you have to add in processing and dissemination lag time.

The V1 product, being based entirely on satellite data, was able to key in on just the rapid vertical growth and boost probabilities based on that alone, and not have to wait for the -10C returns to show up in MRMS.

Fig 1: Loop comparing LightningCast V1 (left) to LightningCast V2 (right)

Fig 2: Comparison of LightningCast V1 (left) and V2 (right) at 2041Z on 19 May 2025, showing V1 being first to have a 30% contour over the cell of interest in NW Arkansas.

Fig 3: Comparison of LightningCast V1 (left) and V2 (right) at 2046Z on 19 May 2025, showing both having a 50% at the same time, though V1 is larger in area.

Fig 4: Comparison of LightningCast V1 (left) and V2 (right) at 2051Z on 19 May 2025, showing V1 being first to have a 70% contour, while V2 still only has a small 50% area.

Fig 5: Comparison of LightningCast V1 (left) and V2 (right) at 2056Z on 19 May 2025, with the first GLM Flash Extent Density return (blue square) noted at 2059Z. Note that V1 had a 70% contour nearly coincident with the GLM Flash square, while V2’s highest return was still only 50%, and well displaced from where the lightning actually happened.

While I don’t doubt that the -10C reflectivity can help in many scenarios, on days like today it didn’t seem to help much, if at all, and in many cases the satellite-only V1 seemed to do a bit better. As I said, it’s my hypothesis that this is due to the rapid vertical development outstripping the production of -10C returns. Additionally, I would be curious if testing out MRMS Vertically Integrated Ice (VII) instead of -10C reflectivity would produce better results (if it hasn’t been tried already)

– Marko Ramius

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Odd LightningCast Dashboard Reading

An odd or false reading from LightningCast occurred on the Dashboard readout on May 8, 2025.  The IDSS event was the Southern Skies Music Festival in Knoxville, TN. The screenshot below shows the Max Lightning Potential (10-mile radius) increasing dramatically after 14:40 UTC while the other LightningCast options don’t suggest any potential for lightning (in next 60 minutes) until after 15:45 UTC and even then, the others didn’t have any probability over 30%. On the plainview map comparing LightningCast 1 vs LightningCast 2 (Figures 1, 2, 3), the contours finally overlap the 10-mile event ring by 20:31 UTC and match up with the Dashboard probability at the same time (See Figure 2).  Discussions within the HWT noted the issue could likely be a domain or pixel issue for the Max P.

Screenshot of the LightningCast Dashboard for Southern Skies Music Festival on May 8, 2025 from 19:40 UTC to 21:25 UTC. Interesting to note the blue arrows pointing to high probability of lightning depicted by the Max while the others remained less than 10% until 15:45 UTC.

Figure 1: At 19:46 UTC, the lightning potential approaches the 10-mile radius when the Max probability suggests nearly a 50% probability of lightning in the next 60 minutes.

Figure 2: At 20:31 UTC, the 10 and 25 probability of lightning enter the 10-mile radius.

Figure 3: At 21:26 UTC is when the probability of lightning really tapers off, even the Max P version.

Loop of the LightningCast version 1 and 2 surrounding the Southern Skies Music Festival.

– Podium

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Severe thunderstorm warnings ONLY using satellite data

May 8, 2025 was an interesting day for the experiment because “WFO GSP” was not allowed to utilize radar to issue severe thunderstorm warnings. I relied heavily on OCTANE Speed and CTD/cooling products and GREMLIN for making these decisions. At first, I struggled a bit with mentally shifting everything to the southeast due to parallax. After I began to utilize the ENTLN data as a proxy for storm core, it was better to estimate storm location and movement (although still not perfect). By the end of the day, I ended up issuing seven severe thunderstorm warnings. I found myself relying more on the CTD and cloud top cooling panel than the speed sandwich. Since it was my second day using the OCTANE products, I was quicker at picking up on signals in the CTD panel that suggest the presence of stronger thunderstorms. Having the CTD in one number compared to having to mentally calculate it while using the speed sandwich was helpful when working in simulated operations when a couple seconds does make a difference.  I also found GREMLIN useful as a situational awareness tool to help distinguish which cells should potentially be interrogated more.

Here is a loop of OCTANE Speed Sandwich and the CTD and CTC products from two severe thunderstorm warnings I issued. The severe thunderstorm warnings were issued for the same storm, with the second one being issued as the first one was expiring. Looking at the OCTANE data combined with ENTLN (Image 1), it was clear that the storm was taking a turn to the right.  I don’t know the specific values that CTD was showing, but I do believe this storm had values of 4+.

Image 1: Two severe thunderstorm warnings in OCTANE SS and CTD/CTC with Lightning Cast v1 and v2 overlaid. ~21:20Z to 21:56Z

GREMLIN (Image 2) also subtly shows this change in direction. What is interesting is that GREMLIN using ECONUS actually maxed out with a value of 60.4 dBZ.

Image 2: GREMLIN loop for the same two severe thunderstorm warnings in Image 1.

Image 3: Severe thunderstorm warning with OCTANE SS and CTD/CTC. ~21:56Z-22:22Z.

Image 4: Severe thunderstorm warning for the same time but with GREMLIN.

Another severe thunderstorm warning I issued, was showing CTD values of 4+ that prompted a warning. In GREMLIN, the strength of this cell was not as obvious in the ECONUS version, but slightly more prominent in EMESO-2. GREMLIN shows these cells basically merging, but I don’t know if that was reality. Looking at these loops compared to the movement of the lightning data, I don’t think my strom track was very good. In cases where radar is unavailable, I could see other novice warning forecasters also struggling with identifying storm track and motion when using strictly satellite data and also trying to mentally correct for parallax. This may not be an issue for more experienced warning operators though.

– Golden Retriever Lover

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