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Took a look at GREMLIN to see how it was handling the convection, as storms have developed a bit earlier than expected. It’s doing a great job with the overall picture of the line of discrete storms (formation and placement), but perhaps not as much with the intensity of those individual storms.
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| Reflectivity from KUDX on the left, GREMLIN in the center, and MRMS on the right |
Forecaster Cumulus
It was interesting to see the cell in Glasscock County, TX moving into Sterling CO via KMAF. OCTANE shows the divide really well and the stronger right mover. However, GREMLIN hadn’t picked up on the split yet.
Forecaster Cumulus
The storm in Reagan County, depending on the data you look at, looked like they may merge! When looking at OCTANE/Visible satellite, the cell moving out of Reagan actually seemed like it was moving fairly rapidly eastward towards the stronger cell in Tom Green County. But, when viewing radar data, it wasn’t moving quite that quickly. Interestingly, GREMLIN has been hinting that these may merge (or is this a smoothing effect?).
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| OCTANE |
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| KMAF (left) and GREMLIN (right) |
Forecaster Cumulus
This lengthy post will cover a number of interesting observations with developing and splitting cells near Sterling County TX. This post will mainly focus on GREMLIN, but also a few other products.
Initially, we were focused on new updraft development Glasscock County TX. GOES-E GREMLIN (left panel) depicted Z values of about 57dBZ. KMAF comp reflectivity (right panel) was also picking up some 50+ dBZ. The 0.5 slice on KMAF (middle panel) was yet to really show anything of interest. This is evidence that GREMLIN is picking up on situations where echoes aloft have not yet started reaching near the ground. This has a degree of predictive value.
The loop from here shows that this storm then proceeds to split as it moves into Sterling County TX. The storm split is apparent on the KMAF lowest slice and comp reflectivity, as early as 2020Z. On GOES-E GREMLIN, the split does not start to show up until 2040Z, and is not readily apparent and clear until 2100Z.
So, let’s look a little closer into why GOES-E GREMLIN may have struggled with picking up the split. The loop below compares GOES-W satellite imagery (left panel) with GOES-E satellite imagery (right panel). While GOES-E data masks the updraft of the left-mover under the anvil from the right-mover, GOES-W has a better view of the left-mover updraft.
Comparing GOES-W GREMLIN and GOES-E GREMLIN, it’s clear that GOES-W had the better view of the left-mover updraft, and picked up on the split much more accurately (though it was low on dBZ values). On the contrary, GOES-W GREMLIN did a much less consistent job in handling the right-mover.
–Insolation
The GREMLIN radar product is shown above in the top left while MRMS composite reflectivity is on the top right. The two storms in question have severe warnings on them in this loop. The thunderstorm in the southwest is more isolated from other convective cloud debris, while also displaying a more intense thermal couplet and cloud top divergence signal than the thunderstorm to the northeast. It seems likely that this is the reason why the northeast storm has a lower ceiling for GREMLIN reflectivity than the southwest storm, even though the MRMS composite includes a 60 dBZ core in the northeast storm.
-Joaq
LightningCast has been running high near the PGA Tournament today near Fort Worth as a result of several thunderstorms around the region, but not over the region. As a result, decided to swap over towards trying to use GREMLIN to find the time of arrival of the boundary noted earlier.
So an attempt was made to highlight this in the DSS image to provide an estimate of when these hazards would arrive. Perhaps another proofread would’ve caught the initial typo from copying it over. However, a value of about 40 percent was noted. Partners still had information that storms would become increasingly likely over a narrower time frame in addition to the probabilistic information from LightningCast.
Kadic
It was interesting when comparing the Warning side of the house today versus the DSS side. When we (Cumulus and Kadic) were discussing this and picked two of the tools we used the most, there were similarities and differences:
For Warning Ops: OCTANE and LightningCast
OCTANE proved very useful in interrogating convection. LightningCast was also a helpful diagnostic tool in highlighting the potential for and track of intense convection when used with ProbSevere.
OCTANE:
Here’s a look at 2 particular instances from OCTANE:
A combination of cloud top cooling in OCTANE and subsequent divergence aloft was a helpful clue in assessing the potential of a storm that was distant from the radar. It was caught a little later in analysis, but OCTANE proved helpful in diagnosing the storm and deciding to pull the trigger.
This next instance was a warning that was issued solely using OCTANE and seeing how well it lined up with radar. The warning targeted the center of where the maximum storm top divergence was taking place, and then stretched down towards the south to account for parallax. The warning decision was made for the impressive cloud top cooling and pronounced divergence that appeared in the scans leading up to the warning. The panel on the top left shows the OCTANE speed, and it transitioned to a blue color leading up to the event.
LightningCast and Radar:
Below is the example of what MRMS looked like the moment DMXSVR005 was issued solely based on OCTANE. Much of the SVR encompassed the highest LightningCast values with a probability of 10 flashes of 70% in yellow and the various ProbSevere contours. Again, this highlights how useful these tools can be in performing storm interrogation. However, when thunderstorms are numerous, this may be a lot to run through. They are definitely useful tools in the tool belt, though.
For DSS: LightingCast (especially the Dashboard) and GREMLIN/GLM.
– LightingCast: I REALLY like the form and Dashboard. It helps focus on the DSS site specifically and organizes the data really well to where I would feel comfortable explaining/showing an EM the graph of lightning probabilities. Honestly, I could bring this back to my home WFO right now and use this for DSS events this summer. A couple things that could be added to make it even more awesome: adding more options for ranges (right now there is only 10 miles, perhaps adding 15 and/or 20 miles). Folks could then choose which to display in the graph. The other thing (fairly minor), perhaps reversing the size of the bubbles for the GLM data (smaller range, smaller bubble). But, this is personal preference – maybe if this could be customized by the user like the colors?
– GREMLIN/GLM: GREMLIN followed the storms a lot better today (seems to do better with more intense storms versus run of the mill/sub-severe ones). I used a two panel display with GREMLIN on the left and MRMS on the right with GLM and LightningCast and compared the two. I used time of arrival for the storms to 10 miles outside the DSS event and also at the site itself. GREMLIN was able to keep up with MRMS really well! I am becoming more and more convinced that this could be a really great product to help if a radar goes down or there is a radar hole (in data).
Overall, it seems as though OCTANE was used more for warning ops versus DSS, but LightningCast was used by both the warning operator and DSS forecaster.
Forecasters Cumulus and Kadic
GREMLIN continues to perform well with regards to the overall picture of precipitation and convection. In fact, in the example below, GREMLIN seems to be catching on to the northern extent of the line breaking apart and the southern portion becoming more intense. However, it seems to be a bit slower than the MRMS data.
LightningCast could be more useful in this case if the thresholds were modified (25 flashes versus 10) to better identify and focus on the more intense convection.
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| LightningCast overlayed on GREMLIN emulated radar (left) and MRMS Composite Reflectivity (right) |
Looking at the LightningCast dashboard for our DSS event, probabilities of lightning are increasing. I found that this is actually an easier way of being able to communicate lightning probabilities for a site (or a range around the site) versus using the map (seen above). This would allow me to let an EM or site official know that probability of lightning is near 80% for 10 miles form the site (within the next hour) and 50% at the site itself.
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| LightningCast Dashboard for Belin Quartet Summer Concert Series (Des Moines, IA) |
We provided valuable support. A message was sent nearly 45 minutes ahead of time following the issuance of a Severe Thunderstorm Warning to the west.
If our partners are simply looking to delay, LightningCast may also prove helpful in giving the all clear if you don’t see anything upstream on radar. The values react well as convection clears the site. This is also great in helping you know for sure when the last lightning flash took place, and it can help us give better information.
Forecasters Cumulus and Kadic
Day 2 has featured more convection, and has been a helpful day testing these products and how they help in warning operations. Although I might not feel confident making warning decisions solely based on any of these tools, I think that each tool provides a valuable piece of information.
Pre-Storm
To keep things short here with all the observations, PHS was very helpful today in showing how the QLCS situation would evolved with several areas of embedded rotation. Having CAPE with SRH together showed how these came together, and in conjunction with velocity highlighted rotation updrafts within PHS. This proved to be a helpful pre-storm evaluation. A few storms began rotating, and then everything began rotating as the PHS model indicated.
The developing squall had a linear appearance at first. As time progressed with more embedded areas of rotation, this became a lot less neatly organized.
One thing to note early was that the PHS forecast had a lot of convective debris lingering in Iowa that was not present in reality. This does not appear to have impacted the instability parameters very much.
We’d mentioned looking at the dewpoints for the tendency for aggressive convection. But it only seemed slightly high compared to reality.
We did have a blob near Sioux City on Gremlin that didn’t really correspond with any signal on radar, and it didn’t seem to have satellite signal to go with it. Not sure where it came from, but we were able to see it was erroneous.
Here’s a look at GREMLIN with waves and wobbles following the GLM lightning.
Kadic
Thoughts at the end of Day 1…
The LightningCast product I think would be VERY useful for DSS. Overall, when seeing it perform in real-time, the increasing LC probabilities seem to eventually correlate well with GLM flash density. I look forward to using the DSS form this week and seeing how that works for specific sites.
The GREMLIN product seems to be a great way to see the overall picture of precipitation (say, for a region). I think it struggles with precipitation intensity a bit (>45 dbZ) both for storm cells and for heavy stratiform precipitation. At the “storm” level, I have seen instances of the model not following the evolution well (either too intense or not enough).
For OCTANE, it was easier to pick out an example of CI and divergence with the IR versus the Visible products. I could use the direction product on its own in operations, but I really like having the speed, direction, and cloud top divergence all together in a 3 panel to identify convection.
PHS did a great job today identifying convective initiation when overlayed on visible satellite imagery. I look forward to seeing how this performs in other areas of the country this week.
Still learning how best to utilize the GLM DQP; but, when looking over Cuba, I was able to better understand how it locates areas where the data might not be the best. I hope to learn more about this product through the week and see more examples of its application.
Forecaster Cumulus
