EWP Blog

One of my favorite things today about the ProbSvr is the ability to make it a “quick look.” This image is after marginally severe weather a little earlier, so environmental wise in general, it isn’t favorable. But what if it was favorable and I was just glancing, waiting for things to pop? Like the example below, I can quick glance at this mess and see that all the ProbSvr is under 10% so I shouldn’t be worried for severe weather without having to look through various levels of radar data.

Lauren13

A special 18Z radiosonde launch was done at Wilmington, Ohio. The special launch allows for direct comparison to a dervived NUCAPS sounding.

Here is the observed radiosonde data:

Here is a NUCAPS sounding from nearby:

Note that the NUCAPS sounding is not representative, especially near the surface. The surface temperature is 77F and the dew point is 55F on the derived sounding. A nearby METAR close by the NUCAPS sounding was 85F/61F.

However, if the boundary layer temperature and dew point profile is modified using nearby METAR observations (85/61), the SBCAPE is more representative to the observed sounding (1761 vs. 1688 J/kg):

Therefore, it is critical to look at the near-surface temperature and dew point profile when using NUCAPS derived soundings.

Polarimetric Researcher

Day 1, two words: Data. Overload. So many new products to look at. However, once you dig into each individually, there is a lot of cool stuff there. Just getting an initial look and feel for all the GOES-R and ENI products available to us today.  Decided to focus mainly on the ENI stuff today. The ENI total lightning counts are really eye-opening. The addition of the in-cloud component reveals a lot more activity that we weren’t previously aware of.

The ENI Cell flash rates and cell polygons are really interesting as well. The two combined would give a better indication of which cells to focus on. Especially in the West, for fire weather/dry lightning situations, this will be very helpful. Due to the pop-up, nearly stationary nature of the storms over terrain, the cell tracking and motion projection features may not be very useful, but can be turned off or simply not used. Need to remember if you are using the cell flash rates and comparing to the ENI total lightning counts, to use the 1-min TL counts since the cell flash rates update every 1 min – compare apples to apples.

Loving the ENI time series plots. It’s helpful to see the trends with a particular cell. Is it strengthening/weakening electrically, and a visual distribution of the different flash types and how they vary over time. These would be especially helpful in the Southwest in the warning process, as a component in whether to warn or not based on the overall upward or downward trends in flash rates. The storms there tend to be short-lived and more isolated in nature, which this product seems geared for. Interesting to note and a caveat with the time series is trying to pick out possible lightning jumps with it. The “jump” on the time series could be tied to an increase in cell polygon size, not flash rate, caused by mergers of two or more individual cell polygons.

First impressions of the ENI Thunderstorm Alerts…jury is still out on the usefulness of these alerts. I tended to use the time series if looking at a specific storm or the Cell Flash Rates if looking more generally at which cell is more active.

Still trying to figure out which products among all the ENI and GOES-R can be overlaid or used together (taking into account what goes into each product); that is, if you can overlay Lightning Jump with cell flash rates and have them be compatible.

~ Regina Phalange

One of the pitfalls of automated warnings (regardless of what they are based on) is they tend to “hold on” to warnings too long, since they cannot be expired early. Here is an example where a cell had been weakening for some time (>20 min), yet the 45 min Dangerous Thunderstorm Alert (DTA) from ENTLN was still valid. I imagine a forecaster in this situation would expire the warning long before the DTA expired.

Ertel

Here is a good example showing the similarities in trends of ProbSevere, ENTLN total lightning data, and radar data. At 1908 UTC, lightning flash rates neared a peak and ProbSevere was ~50%. At  1919 UTC, the lightning flash rates had dropped significantly as did ProbSevere. By 1925 UTC ProbSevere quickly increased to ~75% at the same time that lightning flash rates were rapidly increasing. Finally, by 1945 UTC, ProbSevere decreased to 9% and lightning flash rates plummeted. Soon after this the storm dissipated. These types of products could help forecasters “hold onto” or “let go” of a warning sooner.

Ertel

Depending on when you look at the last frame of the gridded lightning data you might not get the whole picture. The two screen captures below show this. One of the screen captures was taken at 1924 UTC, but if you look at the legend, the gridded lightning data says 1925 UTC. However, there are only 4 minutes of lightning data going into the 5 minute lightning product.  The next image was taken at 1925 UTC and includes the whole 5 minutes of lightning data. As you might expect, there is more lightning when it includes the whole time frame. If a forecaster was not aware of this, it might appear as though the storm is weakening when in reality it is just an artifact of how the product is generated. I would suggest waiting until the full time period is over before displaying the product…which would make analyzing trends much easier!

Ertel

The NOAA/CIMSS ProbSevere Model is an excellent tool for situational awareness–especially in situations with widespread convection. The tool essentially allows for a quick all-tilts view without looking at all the radar tilts because it integrates data from the entire radar volume (and assimilates other non-radar data as well). For example, overlaying the tool on the lowest radar elevation (e.g., 0.5 degrees), you can monitor trends and areas of interest by the rate of change of the probability of severe. Rapidly increasing probability of severe gives the warning forecaster insight that the storm is rapidly intensifying aloft. At this point, a forecaster can do further interrogation on the storm of interest.

Here’s an example where there is widespread convection in southeast Texas:

Note the probability of severe rapidly increases (10% to 86% in 8 minutes on one storm) across the western storms. This immediately signals to the warning forecaster that these storms need further interrogation and are the storms of interest.

Polarimetric Researcher

The default color scale for gridded lightning data is not very useful…as it goes all the way to 30,000 flashes. This is pretty unreasonable even for the most electrically active storm. Shown here is the 5 min total lightning data for the ENTLN gridded data at 8 km, 5 km, 3 km, and 1 km using the default color scale (with 30000 as the upper limit) and another image showing a more reasonable color scale (with 300 as the upper limit). Clearly, analysis is far easier using the adjusted color scale as the finer scale details are much easier to see. I also find that interpolating the gridded data makes visualization far easier.

Ertel