Tornadic Panhandle storm emblematic of the differences between ProbTor v3 and v2

A powerful storm that saw it's genesis in northeast New Mexico and it's demise in south-central Oklahoma, spanned nearly 11 hours. It produced tornado reports near the Oklahoma-Texas border in the Panhandle region, as well as numerous gigantic hail (5" diameter) and wind reports (up to 75 mph) (Figure 1).

Figure 1: ProbSevere v3 contours (outer contour is colored by ProbTor v3), MRMS MergedReflectivity, and NWS severe weather warnings for a supercell in the OK/TX Panhandles.

This storm clearly demonstrates some important differences between ProbTor version 3 (PTv3) and version 2 (PTv2). Early on in the storm's lifecycle, PTv3 was much greater than PTv2. At 21:24 UTC (see Figure 2), PTv3 = 34%, vs. PTv2 = 1%. The first tornado report was at 21:51 UTC, when PTv2 finally increased to 14% (PTv2 later increased to about 70%). Looking into this further, we found several things contributing to the elevated PTv3 and much diminished PTv2:

• In PTv2, which uses RAP, the SRH 0-1 km AGL was only 50 m2/s2, vs. 100 m2/s2 in the HRRR, which PTv3 utilizes.
• Similarly, PTv2 strongly relies on the mean wind in the 1-3 km AGL layer, which was only 16 kt. This also was dampening the PTv3 probability.
• The 0-2 km azimuthal shear (i.e., low-level storm rotation) was modest. This was not enough to overcome the poor low-level kinematics in PTv2 (until much later), while at the same time, it was not too harsh on PTv3.
• The top contributing predictors for PTv3 were the 0-2 km azimuthal shear, the effective bulk shear (~ 65 kt), the very high MESH (3.43") and surprisingly, a strong low-level laspe rate (8.8 C/km). 

Figure 2: ProbSevere v3 contours (outer contour is colored by ProbTor v3), MRMS MergedReflectivity, and NWS severe weather warnings for the tornadic supercell. Note that PTv3 is 34% here, while PTv2 was only 1%. 

Later on, as the azimuthal shear went off the charts (see Figure 3), PTv2 was much higher than PTv3, but also much more erratic. PTv3 consistently remained in the 30-40% range for awhile, despite very high azshear. This indicates that the PTv3 model has learned that azshear can be quite noisy (due to things such as radar sidelobe contamination), and it learned not to overemphasize the radar-based rotation predictors.

The PTv3 model correctly ramped up probabilities to 30-35% when rotation increased in a decent environment (from about 20:20 to 21:00 UTC), but hedged when rotation (as observed by MRMS azshear) was very high, as evidenced by probabilities below 40%. As of now, there were no other tornado reports after 22 UTC, so this seems like a sensible hedge (the storm was tornado-warned until 02:15 UTC).

In practice, we hope that forecasters find value in earlier ramp-ups in the probability of tornado, while understanding the uncertainty of using scalar-based predictors leads to lower probabilities overall, compared to PTv2 (and fewer erratic swings). We hope that using image-based methods will improve the guidance even further. 

Interactive version of the plots in Figure 3. 

Figure 3: Time series of PSv2 and PSv3 probabilities and certain predictors for this storm.

The value of data fusion

We had an interesting storm develop in a radar gap in far eastern New Mexico yesterday. This is a great case study to demonstrate the value of data fusion in ProbSevere.

Figure 1 shows where the storm developed (the red circle), which was in a region of very poor "radar quality", as the eastern New Mexico KFDR radar was down. Thus, the closest radar was KAMA in Amarillo, TX.

Figure 1: Radar Quality Index for eastern New Mexico yesterday. The red circle is the approximate location of where the storm first developed. 

Figure 2: ProbSevere IntenseStormNet contours with GOES-16 ABI vis-IR sandwich product for a rapidly developing storm in eastern New Mexico.

One input into ProbSevere v3 is the probability of "intense" convection, as computed from IntenseStormNet. This is a deep-learning model that uses images of ABI 0.64-µm reflectance, 10.3-µm brightness temperature, and GLM flash-extent density to compute a probability of how "intense" the storm looks from a satellite perspective [paper]. 

The rapidly increasing IntenseStormNet probability, along with a favorable environment, and increasing total lightning flash rates helped jump the probability of severe despite poor radar reflectivity.

As the storm moved south and east into better radar coverage, radar reflectivity increased and the probabilities of severe further increased to above 70%. 

Figure 3: ProbSevere contours, MRMS MergedReflectivity, and NWS severe weather warnings.

Forecasters at the HWT have noted numerous times how ProbSevere v3 has increased before v2, particularly noticeable in the regime we've experienced this week, where the storms have had a dearth of lightning at the developing stages. At the time in Figure 4, this storm had PSv3 of 36% vs PSv2 of 12%. 

Figure 4: ProbSevere and MRMS MergedReflectivity for a developing storm in a radar gap in eastern New Mexico.

Later on, this storm produced numerous large hail, severe wind, and several tornado reports. Interestingly, the ProbTor v3 was much higher than ProbTor v2 prior to the first tornado report. In Figure 5, we can see PTv3 is 47% while PTv2 is only 9%. Looking into this deeper, we found that the environmental information such as the 0-1 km storm-relative helicity (~ 30 m^2/s^2) and the 1-3 km mean wind (~15 kt) were very low. The HRRR values in PTv3 were much better (~100 m^2/s^2 for SRH and 27 kt for the low-level mean wind). I believe this is an indication that PTv2 was too dependent on environmental information, compared to PTv2. This also demonstrates that the HRRR had a better handle on the environment than the RAP. You can see the low 0-1 km storm-relative helicity in the SPC mesoanalysis (Figure 6).

Figure 5: ProbSevere contours (the outer contour is colored by the probability of tornado), MRMS MergedReflectivity, and NWS severe weather warnings.

Figure 6: 0-1 km SRH (contours) and storm motion (vectors) prior to tornadogenesis. The red circle shows where the approximate location of the storm prior to producing tornadoes. 

Figure 7 demonstrates how ProbTor v3 was much higher than ProbTor v2 early on. The vertical black lines in the top-left two panels represent the times of the first and last tornado reports. The interactive version of these time series have been saved off and are available here.  

Figure 7: Time series of ProbSevere probabilities and radar, satellite, lightning, and HRRR attributes for the tornadic storm in Figure 6. 

East Coast Tornadoes

The past few days have seen several strong tornadoes along the U.S. east coast. A shortwave trough with ample upper-level diffluence provided a forcing mechanism for severe storms from Florida to Virginia.

Near Juno Beach, FL, a tornado damaged power lines, homes, buildings, and cars. Maximum wind speeds were estimated at 130 mph (rated EF2). Oddly enough, this tornado was only about 20 miles north of a weaker tornado from the day before.

Figure 1: ProbSevere v3 contours, MRMS MergedReflectivity, and NWS severe weather warnings for a storm near Juno Beach, FL. Outer contours on ProbSevere objects are colored by the probability of tornado.

ProbTor v3 (PTv3) is better calibrated than its v2 counterpart. There was a distinct ramp up in the tornado probability for this storm prior to tornadogenesis, compared to PTv2 (Figure 2). Part of this ramp up was due to higher 0-1 km storm-relative helicity depicted in the HRRR (~160 J/kg), which was much higher than the RAP. Storm rotation was also slowly increasing. Interestingly, this occurred at the same time that lightning and reflectivity-based parameters were decreasing. Despite low overall probability for tornado (20-30%), the ramp up, coupled with the fact that PTv3 remains on the low end overall (max of ~60%) could perhaps have tipped off users to look more closely at this developing storm.

Figure 2: Time series of PTv3 and PTv2 for a tornadic storm on the Florida coast, along with severe reports and NWS severe weather warnings.

The next day, Virginia Beach, VA was hit with an EF3 tornado, with peak winds estimated between 140 and 150 mph. Remarkably, no injuries were reported despite damage to 100 homes. In this case, PTv3 exceeded PTv2, and even hit 60%, which is a very high value for v3. The dip in probability shortly before the tornado was likely due to a pronounced reduction in mid-level azimuthal shear, which quickly rebounded (the 1-3 km mean wind also dropped from 37 kt to 30 kt during that time). 

Figure 3: ProbSevere v3 contours, MRMS MergedReflectivity, and NWS severe weather warnings for a tornadic storm near Virigina Beach, VA. Outer contours on ProbSevere objects are colored by the probability of tornado.

Figure 4: Time series for ProbSevere v3 probabilities, along with reports and NWS severe weather warnings.

Midwest mayhem

Warm muggy weather under northwest flow churned out a number of supercell thunderstorms last Saturday, June 11th, producing huge hail (up to 3.75" in diameter), damaging winds, and tornadoes. 

ProbSevere LightningCast gave a heads up on the quickly developing convection in Nebraska and Iowa (see Figure 1), with lead-times ranging from 10 to 40 minutes prior to the first flashes in several storms (measured from the 25% probability threshold). LightningCast is adept at predicting lightning in the next hour using GOES-R ABI images as predictors.

Figure 1: LightningCast contours, GOES-16 ABI day land cloud convection RGB, and GOES-16 GLM flash-extent density over the Missouri River valley.

ProbSevere v3, being evaluated at the HWT this month, quickly picked up on the developing convection, using the juicy environmental parameters, increases in total lightning flash rates, and strong satellite growth rates to predict increasing probabilities of severe weather. Numerous forecasters at the HWT have noted that increases in ProbSevere helps them identify potential threats more quickly.

Figure 2: ProbSevere v3 contours, MRMS MergedReflectivity, and NWS severe weather warnings.

Several supercells that spawned in Nebraska were soon tornado-warned and at least one produced multiple tornadoes, destroying trees, power lines, outbuildings, as well as industrial buildings in Maryville, KS. ProbTor v3, which is generally more conservative than version 2, had elevated probabilities of tornado before version 2 did. At 21:26 UTC, 6 minutes before the first tornado warning was issued, ProbTor v3 was 25%, whereas ProbTor v2 was only 1% (for the storm in Figure 3). In this case, the strong STP (2.6) and effective storm-relative helicity (SRH, 200 m2/s2), as well as the high MRMS MESH, were contributing to the enhanced probability of tornado for version 3, whereas the 0-1 km SRH in version 2 was relatively low (60 m2/s2). Forecasters should take note when ProbTor v3 is markedly higher than ProbTor v2.

Figure 3: ProbSevere contours (outer contours is colored by ProbTor v3 probability), MRMS MergedReflectivity, and NWS severe weather warnings for supercells in southeast Nebraska.

Salado Tornado

A supercell quickly developed on the southern flank of an arc of storms in central Texas on Tuesday, April 12. High CAPE (≥ 3000 J/kg), effective shear (≥ 50 kt) and effective SRH (≥ 170 J/kg) all contributed to an elevated probability of tornado from ProbTor v3 (PTv3) by 22:00Z. About 30 minutes later, the supercell produced an EF-3 tornado west of Salado and south of Killeen, TX. 

Figure 1: ProbSevere contours, MRMS MergedReflectivity, and NWS severe weather warnings in central Texas. The storm that produced the EF-3 tornado traveled south of Killeen, Texas. 

In the critical early stages of storm development, PTv3 probabilities exceeded PTv2 probabilities, which is noteworthy given PTv3's better-calibrated guidance. From Figure 2, we see that prior to the initial NWS tornado warning, PTv3 was consistently 10-20% greater than PTv2. Because PTv3 is better calibrated than PTv2 (i.e., probability value better match tornado occurrence), PTv3 will rarely exceed 60%, whereas PTv2 regularly hits 80-90% (but PTv2 over-predicts in that range).

Figure 2: Time series of PTv3 and PTv2 for the tornadic storm west of Salado, TX.

At 22:14Z, PTv3 = 30%, while PTv2 = 7%. In a post-mortem analysis, we found that the MESH, mid-level azimuthal shear, and effective bulk shear were the top-3 contributing predictors. The 4th leading predictor was the probability of intense convection produced by the ProbSevere IntenseStormNet. A rapid increase in this value from 29% to 99% occurred from 21:52Z to 22:14Z (see "ICP" in the meteograms). IntenseStormNet is a GOES-ABI and GOES-GLM-based convolutional neural network, which picked up an a developing cold-U signature and increasing lighting to produce a very high probability of "intense" convection (see the animation below). In this way, it provides a holistic method of leveraging important values, textures, and spatial features found in geostationary imagery. In ProbSevere v3 models, IntenseStormNet computes one value per storm per time step, which is used as a predictor. 

Figure 3: Intense convection probability contours overlaid GOES-16 "sandwich" imagery from a 1-min mesoscale scan. Note the rapidly developing supercell on the south flank of the developed convection.

ProbSevere v3 infuses spatially important satellite information into its predictions. This example shows that forecasters should pay especially close attention to developing storms when PTv3 is exceeding PTv2.

Allendale, SC tornado

Allendale, South Carolina sustained much damage after a major tornado tore through parts of the town on April 5th, 2022. The NOAA Storm Prediction Center issued a 10%-hatched risk of tornadoes through parts of Mississippi, Georgia, and South Carolina the morning of the storms. Strong low-level flow and destabilization led to supercells forming ahead of a squall line, such as the tornadic supercell that hit Allendale.

Figure 1: SPC tornado outlook and 06Z verification. 

A ProbSevere v3 (PSv3) model, ProbTor, tracked this storm from Georgia into South Carolina. The probability of tornado rapidly increased about 30 minutes before and again 15-20 minutes before a tornado was reported around Allendale. Increasing azimuthal shear, radar reflectivity, and lightning were noted prior to tornadogenesis. See here for meteograms of different predictors for this storm.

Figure 2: ProbSevere v3 (storm contours), MRMS MergedReflectivity, and NWS severe weather warnings. The outer PSv3 contour is colored by the probability of tornado.

One new feature at the HWT last year was the ProbSevere time series or meteogram function in AWIPS, which can be activated by double-clicking a ProbSevere time object. The window displays the latest 2 hours of probability history for the featured storm for all four ProbSevere models (hail, wind, tornado, any severe). The meteogram updates automatically as new data are processed. This feature helps forecasters more quickly interrogate storm trends and will again be available to forecasters at the 2022 HWT. 

Figure 3: The ProbSevere time series window for the tornadic supercell in Allendale, SC.

ProbTor v3 uses a different machine-learning model than ProbTor v2 (gradient-boosted decision trees vs. naive Bayesian classifier). While the maximum CSI for PTv3 is about the same as PTv2, the PTv3 probabilities are much better calibrated. What this means is that the output probability values much better match the observed frequencies of tornadoes, for any given probability value. Users should see much lower false alarm rates at higher probability bins. Given the inherent noise in doppler radar velocity data (and downstream MRMS azimuthal shear), and inherent uncertainties in detecting tornadoes, this also means that PTv3 values over 60% are exceedingly rare.

Compare the attributes diagrams for PTv3 and PTv2 below. A perfectly calibrated or "reliable" model will have predictions follow the 1:1 line. Notice how PTv2 over-predicts, while PTv3 is very close to the 1:1 line, except for some under-prediction around 50-60%. The most-skillful (i.e., highest CSI) probability range for PTv3 is 20-40%. The University of Wisconsin / CIMSS is actively working on improving ProbTor, experimenting with additional data and methods that make better use of the spatial patterns found in satellite and radar data. 

Figure 4: Attributes diagrams for PTv3 and PTv2 on a validation dataset from 2021.

The IntenseStormNet detects particularly intense storms from a satellite-only perspective, using deep learning and images of ABI and GLM data. Using GOES-16 one-minute mesoscale scans, IntenseStormNet reached over 90% on this storm about 10 minutes before the first tornado report. The higher IntenseStormNet probabilities corresponded well to a GLM lightning jump and overshooting tops in ABI imagery. While a tornado warning was already in effect, seeing this feature could add confidence to the warning forecaster. The output of IntenseStormNet is also used in the ProbSevere v3 models.

Nocturnal tornado in Arkansas

 A strong, upper-level trough ejected from the Southern Plains toward the Mississippi Valley on Saturday night, carrying with it severe storms along and ahead of a cold front in Arkansas, fueled by low-level moisture and a very strong mid-level jet streak (75 - 90 kt). A portion of the squall line quickly increased in the probability of tornado, indicated by the ProbTor model (Figure 1).

Fig. 1: ProbTor contour north of Little Rock, AR, 66% probability of tornado; MRMS MergedReflectivity (shaded).

Soon after the timestamp in Figure 1, this storm dropped an EF-1 tornado in the small town of Romance, AR, which destroyed or damaged numerous homes and resulted in at least 4 injuries. The NWS in Little Rock, AR noted that there was a brief but concentrated area of rotation as well as a debris signature. 

Figure 3 demonstrates the large increase in 0-2 km azimuthal shear at around 06:40 UTC, coupled with a very conducive environment for tornadoes (eff. shear ≥ 50 kt, 1-3 km AGL mean wind ≥ 60 kts, 0-1 km storm-relative helicity ~ 400 J/kg) led to the rapid increase in ProbTor. There was very little lightning activity evident with this storm.

With MRMS v12 now in operations, NWS forecasters can receive ProbSevere output in their offices. There was also an update to the azimuthal shear products in MRMS v12, improving their accuracy and reducing false alarms. This should help reduce the false alarms of algorithms dependent on MRMS azimuthal shear, such as ProbTor.

Fig. 2: Time series of the ProbSevere models as well as local severe storm reports and NWS severe weather warnings.

Fig. 3: Time series of the ProbTor model, select constituent predictors, as well as local severe storm reports and NWS severe weather warnings.

Nocturnal storms wreak havoc in Tennessee

  A subtle 500-mb shortwave trough brought enough instability to pair with 60-70 kts of 0-6 km bulk shear to create some potent storms last night and early this morning. The Storm Prediction Center had issued a slight risk of storms, including tornadoes, for the mid-Mississippi Valley at 2000 UTC yesterday.

Fig. 1: SPC 2000Z categorical outlook with preliminary verification.

The severe storm activity began with one isolated storm that formed in southeast Missouri and traveled through Cairo, IL and southern Kentucky, dropping large hail. NOAA/CIMSS ProbSevere highlighted this storm early on, aided by a "strong" satellite growth rate from GOES-16 and a quickly-increasing total lightning flash rate, from ENTLN. The storm exhibited robust radar signatures by the time it was first warned.

Fig. 2: ProbSevere (contour) with MRMS MergedReflectivity and NWS severe thunderstorm warning (yellow polygon).

Below, we see the rapid increase in ProbHail and ProbWind around 22:00 UTC, while ProbTor increased much later in the storm's life (and produced a tornado report). You can see the ProbSevere predictor time series for this storm here.

Fig. 3: Time series of ProbSevere models' probabilities for an isolated storm in MO/IL/KY.

Numerous large hail reports (and several tornado reports, later on), were a result of convective storms in southeast Missouri.

Fig. 4: ProbSevere contours (outer contours are colored by the ProbTor value, present if ≥ 15%), MRMS MergedReflectivity, and NWS severe weather warnings.

Another storm formed in western Tennessee and was quickly warned by the NWS. This storm spawned several damaging and deadly tornadoes in Nashville and Cookeville, as well as hail up to the size of baseballs. ProbTor probabilities ramped up in response to increasing MRMS azimuthal (i.e., rotational) shear and total lightning density in an environment characterized by 40 kts of effective bulk shear, 50 kts of 1-3km AGL mean wind, and 400 J/kg of 0-1km AGL storm-relative helicity. More time series plots of ProbSevere predictors are saved here and here.

Fig. 5: ProbSevere contours (outer contours are colored by the ProbTor value; present if ≥ 15%), MRMS MergedReflectivity, and NWS severe weather warnings. 

Fig. 6: Time series of ProbSevere models' probabilities for a long-lived, deadly storm in Tennessee.

Fig. 7: Time series of ProbTor probabilities and constituent predictors for the long-lived, deadly storm in Tennessee.

Tornadic thunderstorms menace Mississippi

Figure 1: SPC 1630Z outlook with 06Z verification.

A deep shortwave trough and strong 850mb jet brought ample low-level moisture and instability to a well-sheared environment in the Southeast U.S. yesterday. The NOAA SPC issued "Enhanced" outlook noting the potential for strong tornadoes.

ProbTor captured the tornadic threats in Mississippi during the afternoon and then during a second round of storms in the overnight hours. The animations in Figures 2 and 3 show outer contours colored by the ProbTor value (inner contours are colored by probability of any severe), which were configured to only appear when ProbTor ≥ 15%. NWS forecasters can configure this threshold using these instructions.

In the first bout of storms, tornadoes were observed for storms in Simpson and Smith counties (see storm time series of predictors), as well as Yazoo and Holmes counties (see storm time series), and Leake county.

Figure 2: ProbSevere/ProbTor contours with MRMS MergedReflectivity and NWS severe weather warnings for 19Z -- 23Z.

During the second round of storms, a potent thunderstorm dropped tornadoes in Jasper, Clarke, and Lauderdale counties (storm time series).

Figure 3: ProbSevere/ProbTor contours with MRMS MergedReflectivity and NWS severe weather warnings for 02Z -- 05:30Z.

Strong tornadoes in the Deep South

A seasonally strong shortwave trough tapped into abundant Gulf of Mexico moisture forcing severe and tornadic storms across Louisiana, Mississippi, and Alabama. The Storm Prediction Center issued a Moderate Risk outlook with tornadoes and strong wind gusts being the primary threats (see Figure 1).

Fig. 1: SPC outlook with 06Z 12/17 verification (dots).

ProbSevere/ProbTor models show the evolution of storms throughout the afternoon (Figure 2). The outer contours represent the ProbTor probability and are only present if ProbTor is ≥ 15%, here.

This environment was characterized by 1000 - 2500 J/kg of MLCAPE, 45-60 kts of effective bulk shear, and 1-3km AGL mean wind of 40-55 kts. The ProbSevere models generally track and discern the most dangerous threats well (see the NWS warning polygons). However, there are several storms with erroneously high ProbTor values (outer polygons with high probabilities) that quickly appear and disappear during the animation. Most of these false alarms are due to spurious MRMS azimuthal shear values which are produced by noisy Doppler velocity data. Work is ongoing to mitigate these errors in ProbTor.

Fig. 2: ProbSevere and ProbTor contours, MRMS MergedReflectivity, and NWS severe weather warnings.

A storm that formed in east Texas/west Louisiana spawned numerous tornadoes and prompted a tornado emergency for Alexandria, LA at 18:41 UTC. This storm was in a primed thermodynamic and kinematic environment, with 250-300 J/kg of 0-1km storm-relative helicity. The low-level and mid-level MRMS azimuthal shear values increased the ProbTor probabilities from 30% to 91% in about 20 minutes. You can see the saved time series of attributes for this storm here. Figure 3 shows how the ProbSevere products evolved for this storm in comparison to NWS severe weather warnings and local storm reports.

Fig. 3: Time series of ProbSevere probabilities for tornadic storm in Louisiana. The bottom axis plots durations for NWS severe weather warnings and times of preliminary LSRs.

At least one this this storm's tornadoes was expected to be rated as significant (EF3+).

Tornadoes in the desert

Early in the morning on the day after Thanksgiving, a long-wave upper-air trough with an embedded short wave disturbance and associated diffluent flow forced thunderstorms in the Phoenix, AZ region. There was enough low-level moisture return to provide adequate CAPE within a well-sheared kinematic environment, providing storm organization and maintenance.

ProbTor (from NOAA/CIMSS ProbSevere) captured the evolution of these storms, two of which spawned three tornadoes in the Phoenix metro area. The twisters uprooted trees, and caused damage to powerlines and roofs.

Fig. 1: ProbSevere contours (ProbTor is the outer contour), MRMS MergedRef, and NWS severe weather warnings. 

The tornado that traveled north of downtown Phoenix was rated EF1. From the time series below, ProbWind and ProbTor were about 30% when the NWS issued a severe thunderstorm warning. Then, ProbTor spiked to about 65% as the 0-2km MRMS AzShear increased markedly. The paltry lightning activity and weak 3-6km MRMS AzShear in this storm show that the rotation was shallow in the troposphere and this was not a supercellular storm. The 0-2km AzShear, along with very strong effective bulk shear and 1-3km mean wind helped the ProbTor values increase rapidly.

Fig. 2: ProbSevere time series for ProbTor, ProbWind, and ProbHail for the northern tornadic storm. NWS warnings and preliminary local storm reports are plotted on the lower axes.

Fig. 3: ProbSevere time series for ProbTor, ProbWind, and ProbHail for the northern tornadic storm. NWS warnings and preliminary local storm reports are plotted on the lower axes.

Further south along the line of storms, ProbTor values behaved in a more cyclic manner, first hitting 40% before dropping to 15%, then increasing rapidly to 30% (at the time of the first tornado LSR) and 60% (at the time of the second tornado LSR). ProbTor values then decreased to 20% and rebounded to 55%. This cyclic nature followed the 0-2km AzShear somewhat closely.

Fig. 4: ProbSevere time series for ProbTor, ProbWind, and ProbHail for the southern storm. NWS warnings and preliminary local storm reports are plotted on the lower axes. 

Fig. 5: ProbSevere time series for ProbTor, ProbWind, and ProbHail for the southern storm. NWS warnings and preliminary local storm reports are plotted on the lower axes.

Fall severe weather outbreak over the Southern Plains

A strong, negatively tilted, diffluent short wave trough forced severe thunderstorms in the Southern Plains ahead of and along a potent cold front yesterday. Figure 1 shows a high-level evolution of the storms and ProbSevere v2 (PSv2) from discrete to more linear storm modes as the event proceeds.

Fig. 1: GOES-16 IR, MRMS MergedRef, ProbSevere storm contours, and NWS warnings.

One supercell that traveled through downtown Dallas, TX dropped a strong, EF3 tornado which produced much damage, which an NWS survey marked 01:58 UTC as the initial touchdown time. The storm went on to produce an EF1 tornado, starting at 02:36 UTC.


This storm was the right moving supercell after a left split (see Figure 2). Figure 3 shows the time series of PSv2 model output before the split, while Figure 4 shows the time series after the split, including NWS warnings and preliminary storm reports. The storm initially exhibited a strong satellite growth rate and a spike in MRMS MESH, which contributed to the rapid increase in ProbHail and ProbWind.

Fig. 2: ProbSevere, MRMS MergedRef, and NWS severe weather warnings in AWIPS2, depicting the storms affecting the DFW metro area.

You may find the time series of PSv2 model predictors for Figure 2 here and Figure 3 here. 

Fig. 3: Time series of ProbSevere models for a tornadic storm prior to it splitting. NWS warnings and preliminary storm reports are on the lower axis.

Fig. 4: Time series of ProbSevere models for a tornadic right-moving supercell after it split. NWS warnings and preliminary storm reports are on the lower axis (EDIT: tornado report times are the start times of tornadoes from an NWS survey).

An experimental convolutional neural network, which uses ABI channels 02 and 13, as well as flash extent density from the Geostationary Lightning Mapper, was deployed on this scene. The model produces an "Intense Convection Probability" (ICP). The 50% and 90% contours correspond well with robust satellite signatures, such as overshooting tops and enhanced-Vs. While there is also good correspondence with reports, probabilities of < 25% are present for some hail reports early in the event and some wind reports late in the event, showing that all severe weather is difficult to detect with a satellite-only approach. Regardless, such a model may be able to enhance ProbSevere, especially in regions with no radar coverage. See this CIMSS blog post for more information and examples from this model.

ProbTor seemed a bit high

Had a few instances this afternoon where ProbTor seemed a bit high (the extra layer of ProbSevere added on).  NUCAPS soundings showed high LCLs (2-3 km), whereas SPC meso page was on the order of 1.5 km...still pretty high.  Had rotations aloft, and the lowest we saw a rotation was at a point where 0.5 tilt was around 5 kft.  We issued a couple of SVR's with TOR possible but no TOR warnings.

CHARLEY

Wild weather weekend

Severe weather abounded this Memorial Day weekend, punctuated by a strong tornado in Dayton, Ohio. Tornadoes were observed in 13 states over the three days, from Idaho to Pennsylvania and from Texas to Minnesota. The Dayton storm emerged on the south end of a cluster of severe storms entering Ohio from Indiana (Figure 1), with nothing in its way from ingesting air parcels in an excellent environment (~1800 J/kg MLCAPE, ~55 kts eff. bulk shear, ~300 J/kg 0-1km SRH, ~40 kts 1-3km mean wind). This storm rapidly increased in ProbTor and produced its first tornado at 02:35 UTC, right after ProbTor exceeded 85%. The storm exceeded 90% ProbTor for almost an hour (see Figure 2), during which time the Dayton area suffered extensive damage. A tornado emergency was also issued for Dayton by the NWS at around 03:00 UTC. See the archived time series of ProbSevere products and predictors here and here (the storm merged with a storm to its north, prompting a change in ID).

Fig. 1: A cluster of storms with ProbSevere objects, MRMS MergedReflectivity, and NWS severe weather warnings. The Dayton storm is highlighted.

Fig. 2: Time series of the ProbSevere products in relation to severe LSRs and NWS severe weather warnings. 

A handy feature on the ProbSevere webpage is the "ProbSevere Accumulation" tab, which allows users to get a quick-look at the severe weather for a day and check out ProbSevere's performance. Once clicking the tab at the top, users can set the date using the UI on the left. Each day is considered a "convective day", starting at 12Z and going to 12Z the next day. For example, 2019-05-26 12:00 UTC is the start time and includes reports, NWS warnings, and ProbSevere objects from that time until 2019-05-27 11:59 UTC.

You can see the extent of the storms for the convective days 05-25, 05-26, and 05-27 in Figures 3 through 5. The green, blue, and red circles are preliminary LSRs for large hail, severe wind, and tornadoes, respectively. The orange and red polygons are NWS severe thunderstorm and tornado warnings, respectively. The white and pink boxes are centroids for ProbSevere objects at each valid time (every 2 min), colored by the probability of any severe value. In this way, a user or researcher can quickly see which areas ProbSevere 'hit' on, 'missed' on, or had 'false alarms' at the 50% threshold.

Fig. 3: An accumulation of severe LSRs (circles), NWS warnings (orange and red polygons), and ProbSevere centroids, colored by their probability of any severe value (boxes) for 05-25.

Fig. 4: An accumulation of severe LSRs (circles), NWS warnings (orange and red polygons), and ProbSevere centroids, colored by their probability of any severe value (boxes) for 05-26.

Fig. 5: An accumulation of severe LSRs (circles), NWS warnings (orange and red polygons), and ProbSevere centroids, colored by their probability of any severe value (boxes) for 05-27.

April and May have seen above average tornado activity nationwide, with the U.S. inflation adjusted number of tornadoes at 794 through yesterday, at about the 90th percentile for this time of year (Figure 6).

Figure 6: U.S. inflation adjusted cumulative annual distribution of tornadoes.