Category: GREMLIN

Columbia SC: Trucks with Food

Posted in DSS, GREMLIN, OCTANE Speed Sandwich, PHSnMWnABI, SPG2024 on by Kevin Thiel.

Protecting the Foodies

A Severe Thunderstorm watch that covered most of Columbia’s CWA was where we concentrated our forecasting efforts today. We found ourselves with no radar and were forced to make decisions on warnings with satellite only. As a result we made some modifications and combined the power of GREMLIN and OCTANE. Here’s what that love child looks like:

 

 

 

One of the issues we had in making decisions based on GREMLIN data was the lack of information it provided. GREMLIN provides a radar emulation and given that it’s a satellite based product it would be nice to see more information in the sample tool of what’s being shown. Values like temperature at highest reflectivity and echotops could be inferred by GREMLIN to help forecasters make better decisions if radar wasn’t available. The other issue we had with GREMLIN was the latency. Products were running anywhere from 15-20 minutes behind the rest of the satellite products that we were using.

A few minutes later we issued our first SVR warning for the eastern edge of CAE CWA for winds over 60MPH and nickle sized hail (sub severe).

 

As our storm moved out of the CWA we allowed the SVR to expire and took a look at the PHS Forecast model and compared it to the HRRR to prepare for the next round of thunderstorms. But both models seemed to agree that more TSRA was unlikely:

 

Storms in GSP came together and eventually created a good line of thunderstorms from GREMLIN’s point of view. GREMLIN was picking up some areas of higher DBZ and a lightning jump through the line was consistent with what we’d expect to see on radar for a SVR. A warning was issued on a line of storms:

 

The line started to fall apart as soon as it hit the CWA border. We allowed the warning to expire without feeling the need to re-issue downstream. After that, storms no loner had access to some of the peak daytime heating that allowed them to become sub-severe during the afternoon.
-Charmander

The Machine Learning Foretold the Future

Posted in GREMLIN, OCTANE Speed Sandwich, SPG2024 on by Kevin Thiel.

While monitoring convective initiation in a sea of Altocumulus castellanus clouds as they moved into a more convectively unstable environment, I noticed that the GREMLIN Emulated reflectivity in Briscoe County was higher than the MRMS (GIF 1).  The GREMLIN emulation was producing a DbZ of 50+ while the MRMS was showing a 30 DbZ. To me, this difference was operationally significant as I would pay closer attention to the developing 50 DbZ feature than I would the 30 Dbz feature. So far in our experiment, this was the opposite of what I had experienced and I initially thought the emulated radar was wrong. I jumped over to the OCTANE products to get a sense of how rapidly the cell was building (GIF 2). My assessment of the cell seemed to confirmed my initial opinion that it was not growing as rapidly as I would have expected given that there was not a strong gradient in the speed or direction products.

Gif One: GREMLIN Emulated Radar on the top and MRMS Composite Reflectivity on the bottom.

 

Gif Two: OCTANE Products (Speed Upper-left, Direction Upper-Right, Divergence Bottom-Left) and Day cloud Phase RGB (bottom-right)
My opinion changed 10 minutes later.  Seen in image one, the MRMS with the next update showed a similar storm intensity to the GREMLIN emulation. This is impressive when one considers the latency of GREMLIN is greater (about 10 min) than the latency of MRMS (about 2 min). The GREMLIN product actually delivered a more operationally useful product sooner, despite having a greater lag. The GREMLIN product continued to show this ability for two more storms developing further back in the line!
Image One: GREMLIN Emulated Radar on the top and MRMS Composite Reflectivity on the bottom.

-Kilometers

MesoAnalysis Support and Lack of Radar

Posted in GREMLIN, OCTANE Speed Sandwich, SPG2024 on by Kevin Thiel.
More emphasis has been put on mesoanalysis in severe weather operations over the last several years and utilizing the OCTANE products has demonstrated that it has utility in confirming objective analysis. Scattered thunderstorms were developing across parts of the Carolinas today, but the mesoanalysis suggested that severe thunderstorms would be favored farther south and east of our area of concern through the late afternoon. A quick look at the SPC mesoanalysis of effective shear showed values of 40-50 kt to the south of our area of concern. As thunderstorms started to develop, we were able to use the OCTANE products to confirm that larger areas of shear were present with our southern storms.
Thunderstorms were ongoing across parts of northern SC and southern NC through mid afternoon. The OCTANE speed products clearly show slower wind speeds aloft to the north, while our southern storms show much higher wind speeds aloft and even some divergent signatures to the southwest of our event. A few of these southern storms would eventually become severe. This product will be beneficial to short term operations in confirming local objective analysis.

Our case also required us to look at information without utilizing radar. The GREMLIN product filled the role of radar data, but some notable issues were present. The GREMLIN latency approached 20 minutes at time, which really makes the product unusable for real time warning operations. However, discussion with the developers indicated that some of these latency issues could be overcome. The utility in the product comes from its visual representation. Forecasters are used to looking at radar data, and this product offers something similar. Latency issues must be overcome for it to be usable in operations. The product overlaid with real time lightning data would offer utility in areas with poor radar coverage or during times of significant radar outages. After working a few days with the other products, the GREMLIN utility really became evident in the absence of regular radar data. An example is shown below.

Getting in Shape

Posted in GREMLIN, SPG2024 on by Kevin Thiel.

Two aspects for what constitutes operational relevance jump to mind when discussing radar imagery. The first is the shape and the second is the intensity. In the image below a line of storms is moving through central Tennessee. The GREMLIN emulated radar is doing a fine job at showing the location of the convection. Where it is still lacking some usefulness to warning operations is not having high enough DbZ returns. Even so, between the two aspects, I believe GREMLIN is resolving the more operationally useful aspect because we can use the prob-severe tool to infer strength and warn on the meso-scale analysis.

 

Image one: GREMLIN Emulated Radar on the left and the MRMS composite reflectivity on the right.

Image two: GREMLIN Emulated Radar on the left and the MRMS composite reflectivity on the right later in the event.

 

-Kilometers

Gremlins are dismantling the nebula!

Posted in GREMLIN, LightningCast, SPG2024 on by Kevin Thiel.

Hi everyone!

First blog post for the Satellite Convective Applications Experiment – Week 1, let’s go!

The loop below shows an example of this from the Corpus Christi, Texas. Notice the convection moving out of the frame to the northeast is bounded by prob-lightning contours (Gif 1). My desire would be to have these better matched to the storms. Right now, the contours are too nebulous.

GIF one: MRMS reflectivity at -10 C overlaid with lightning cast 60-min probability.
Why do I care about it’s nebulousness? When I am providing decision support to an event, I want to know which cell is driving the highest probability, which is building and be able to anticipate the lightning threat based on the cells movement.
As my partner in the testbed pointed out, the anvil(s) (see image one below) were merging and this was likely causing the nebulousness.
   Image one: GOES East Day Cloud Phase RBG channel.
Our discussion began to expand to others in the testbed and an idea emerged to try and reduce the nebulousness. The idea was to use the GREMLIN Radar Emulation product to further train the lightning cast dataset so that the probabilities become anchored by the emulated MRMS product.
Below is a GIF of the GREMLIN and MRMS product. With the GREMLIN product using some of the same satellite features as the lightning cast; the two products have some base level of compatibility. And so my challenge to the developers of these products is, an these two be combined such that lightning cast is mapped to the convective feature causing the probability.
GIF Two: GREMLIN Emulated Radar on the left, and MRMS composite reflectivity on the right.

-Kilometers