ProbSevere time series tool

At the 2021 HWT, forecasters will be able to use a new feature of the ProbSevere AWIPS plug-in: a time series tool. Forecasters from previous HWTs have consistently given positive feedback on a web-based meteogram tool, and so we have implemented something similar in AWIPS. 

To use it, you simply double-click on a ProbSevere object, and a window opens up with the time series of ProbHail, ProbWind, ProbTor, and ProbSevere (prob. of any hazard) for the given storm. We hope this will help forecasters better monitor the trends in hazard probabilities.

Figure 1: A severe-hail-producing storm in northeastern North Carolina, and the associated history of its ProbSevere probabilities in a time series window (ProbSevere was equal to ProbHail for this storm).

ProbSevere v3 will also be demonstrated at this year's HWT. PSv3 is driven by a new statistical model (gradient-boosted decision trees) and incorporates new MRMS, ABI, GLM, and SPC mesoanalysis data. This storm was warned at 17:31 UTC, and produced 1-inch hail at 17:38 UTC. At 17:26 UTC, ProbHail v3 jumped to 25%, whereas v2 was only 4%. ProbHail v3 might have been able to highlight this strengthening storm to the forecaster, whereas version 2 did not. A predictor importance analysis of ProbHail v3 for this storm at 17:26 UTC revealed that the highest contributing predictors were:

1. MRMS reflectivity at -20C (52 dBZ)

2. Eff. bulk shear (40 kt)

3. MRMS composite reflectivity (66 dBZ)

4. MRMS MESH (0.55 in)

5. Wet-bulb 0C height (7180 ft)

We expect that ProbSevere v3 will be more accurate and better calibrated than ProbSevere v2, meaning the probabilities more closely match severe report occurrence. 

Windstorm in east Texas

Diffluent flow at 250 mb and a strong theta-E gradient at 850 mb helped spawn a lone severe storm in east Texas last night. This elevated and fast-moving storm caused multiple reports of trees and power lines down in Jasper and Newton counties around 08:00 UTC. 

ProbSevere v3 (PSv3) indicated increasing intensity in this storm about 12-15 minutes before v2. PSv3 uses a different machine-learning model (gradient-boosted decision trees) and utilizes more ABI, GLM, and MRMS data. It also leverages some SPC mesoanalysis fields. UW-CIMSS is running PSv3 experimentally in near-realtime, and it will be evaluated at the 2021 HWT. 

While we've found that PSv3 should be more skillful and better calibrated (i.e., the probabilities better match report occurrence frequency), it may be more difficult to attribute changes in the model probabilities to changes in its predictors. We are working on methods to convey predictor importance to users on a per-storm basis in near-realtime. 

Post-mortem, we evaluated the predictor importance for this storm at 07:34 UTC. We found the most important contributors were:

1. MRMS composite reflectivity (64 dBZ)
2. Eff. bulk shear (58 kt)
3. ENI total lightning density (0.29 flashes/min/km^2)
4. MRMS 0-2 km  azimuthal shear (0.011 s^-1)
5. MRMS VIL (23 g / m^2)
6. MRMS 3-6 km azimuthal shear (0.006 s^-1)
7. ABI + GLM intense convection probability (95%)

The 0-3 km lapse rate (3.7 C/km) was the predictor detracting from the probability the most. 

We hope that PSv3 will give users even more confidence during severe weather warning operations. 

Nocturnal hailstorms in the Upper Midwest and ProbSevere v3

A cold front spawned numerous hail-producing storms in the Upper Midwest during the evening and overnight hours last night. ProbSevere version 3 (PSv3), which uses gradient-boosted decision trees with MRMS, ABI, GLM, Earth Networks, and NWP/SPC mesoanalysis data, was able to improve guidance over version 2 (PSv2) for a number of storms.

One of the more prolific hail-producing storms of the day formed west of Minneapolis, MN. PSv3 improved upon PSv2 with higher probabilities at a critical time before the first NWS warning was issued. 

Figure 1: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for a storm west of Minneapolis, MN.

Figure 2: Time series of PSv3 and PSv2 for a select time period for the highlighted storm in Figure 1.

Looking at 01:50 UTC from the time series in Figure 2, we found that the top five predictors aiding to higher severe probabilities were:

1. ENI max total lightning density (0.28 flashes/min/km^2)
2. Eff. bulk shear (41 kt)
3. MRMS max 3-6 km azshear (0.008 /s)
4. MRMS max composite reflectivity (60.5 dBZ)
5. ABI+GLM intense convection probability (98%)

The intense convection probability (ICP) is an ABI + GLM based deep-learning model used to diagnose intense convection, and is a predictor in PSv3. 

Another storm to the northeast of the storm above also briefly produced severe hail. While the PSv3 probability was relatively low, it was still much higher than PSv2, which is an improvement that may be enough to bring a forecaster's attention to the storm.

Figure 3: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for a storm WNW of Minneapolis, MN.

Figure 4: Time series of PSv3 and PSv2 for a select time period for the highlighted storm in Figure 2.

PSv3 peaked at 37% at 02:08 UTC, 6 minutes before the hail report. Performing a predictor importance analysis at this time, we found that the top five predictors aiding to higher severe probabilities were:

1. MRMS max composite reflectivity (65.5 dBZ)
2. Eff. bulk shear (47 kt)
3. MRMS max VIL (25 g/m^2)
4. MRMS max 3-6 azshear (0.007 /s)
5. MRMS max MESH (0.56 in)

A storm in northwestern Wisconsin fluctuated in probability, but eventually produced 1.25-inch hail.

At the time of the report (03:00 UTC), PSv3 was 47% and PSv3 was 13%. 

Figure 5: ProbSevere contours and MRMS MergedRef for a hail-producing storm in northwest Wisconsin.

At the time of the report, we found the top five predictors in PSv3 were:

1. MRMS max MESH (0.76 in)
2. MRMS max VIL (31 g/m^2)
3. Eff. bulk shear (52 kt)
4. MRMS max composite reflectivity (61.5 dBZ)
5. ENI total lightning density (0.21 flashes/min/km^2)

PSv3 should also help reduce false alarms around the country, relative to PSv2. This warned storm in northern Nebraska serves as an example. The maximum probability for PSv3 was 17%, compared to 49% for PSv2. There were no reports recorded for this storm.

Figure 6: ProbSevere contours, MRMS MergedRef, and NWS severe weather warnings for a storm in northern Nebraska.

One more example, just west of Duluth, MN, where PSv3 was about 20% higher than PSv2 at the time of a 1.25-inch hail report. At the time of the report, we found the top five predictors in PSv3 were:

1. MRMS max MESH (1.19 in)
2. MRMS max composite reflectivity (67.5 dBZ)
3. MRMS max VIL (26 g/m^2)
4. ENI total lightning density (0.23 flashes/min/km^2)
5. MRMS max 3-6 km azshear (0.007 /s)

Figure 7: ProbSevere contours and MRMS MergedRef for a storm west of Duluth, MN.

We hope these examples illustrate the improvement that forecasters will experience in using PSv3 at the Hazardous Weather Testbed. We also hope that they illustrate the benefit of intelligently fusing data together from the different observation systems we have available. We are actively working on ways to help users better interpret model predictions using analyses such as predictor importance ranking.

ProbSevere v3 in Florida

A sagging cold front provided a marginal risk of severe weather in south Florida. The experimental ProbSevere v3 (PSv3) will be evaluated at the Hazardous Weather Testbed this spring and summer. PSv3 models use a different machine-learning method, and incorporate additional MRMS, ABI, GLM, and SPC mesoanalysis fields. We've found that the models should be more skillful and better calibrated, overall.

This storm caused damage to silos and chicken barns just north of Lake Okeechobee yesterday afternoon. PSv3 showed much higher probabilities (≥ 40%), whereas PSv2 maxed out at 9% before the wind report. PSv3 should provide better guidance on severe storms for both busy severe days and marginal severe days.

By inspecting the predictor importance of this storm right before the wind report, it was found that the top-5 contributing predictors were:

1. ENI total lightning density (0.45 fl/km^2/min)
2. ABI satellite growth rate (3.8 %/min)
3. MRMS VIL (29 g/m^2)
4. Eff. shear (42 kt)
5. 0-3 km lapse rate (7.8 C/km)

We are currently working on how best to convey predictor importance to forecasters in AWIPS, which we hope will help users better understand why the model makes the predictions that it does, and ultimately better utilize ProbSevere guidance.

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.

Lone severe storm in Georgia

A single severe thunderstorm formed in an environment with marginal effective shear (~35 kts) and MUCAPE (~1200 J/kg) yesterday afternoon in northern Georgia, traveling southward through the eastern suburbs of Atlanta. The storm produced between quarter-sized and golf-ball-sized hailstones and downed multiple trees and power lines. 

Figure 1: ProbSevere contours (pink is > 70%; gray is < 5%), MRMS MergedReflectivity, and GOES-16 visible reflectance.

In the absence of strong environmental forcing, the ProbHail model is largely influenced by the MRMS MESH and ENI total lightning flash rate. The low wet-bulb 0C height (8500 ft) also contributed positively to ProbHail. Even in marginal environments with few storms, ProbSevere can still highlight potential threats. 

The ProbWind model remained fairly low (≤ 40%), owing mainly to a weak 1-3 km mean wind and low-to-moderate MRMS AzShear values. Work is ongoing to improve ProbWind predictions in both wet and dry microburst environments. 

Figure 2: Time series of ProbSevere models for this storm, with corresponding severe reports and NWS warnings. 

Figure 3: Time series of select predictors and the maximum hazard probability (thick red line), with corresponding severe reports and NWS warnings. 

ICP in an MCS

The Intense Convection Probability (ICP), the product of a convolutional neural network (CNN) trained with ABI 10.3 µm brightness temperature, 0.64 ABI µm reflectance, and GLM flash extent density, highlights intense parts of a MCS that began in Kansas and traveled through the mid-Mississippi Valley. Below, the product was contoured at the 25%, 50%, and 90% levels (blue, cyan, and magenta, respectively), and overlaid 10.3 µm and 0.64 µm sandwich imagery from ABI.

In the movie below, higher ICP is generally found with cold, "bubbly" cloud tops as the MCS propagates, and corresponds well to regions of numerous severe hail and wind reports. Part of the MCS decays in south central Tennessee and northern Alabama, producing many wind reports. The storms in this region had decreasing reflectivity, a dearth of total lightning flash rates, and warming 10.35 µm brightness temperatures, diminishing the ICP. This part of the MCS was becoming decoupled from its source of MUCAPE and forcing, resulting in outflow-dominant storms.

We are hopeful that the ICP will improve ProbSevere by leveraging important satellite information during the mature phase of a storm's lifecycle, as well as quickly identifying developing intense convection emerging from thick ice clouds (e.g., anvil clouds).

Intense Convection Probability product in Kansas and Oklahoma

A shortwave trough is forcing some intense convective storms in Kansas and Oklahoma this morning. With a warm and moist air advection response at 850 mb, and ample deep layer shear (45-55 kts of effective shear), the storms are taking on supercellular characteristics.

UW-CIMSS has developed a convolutional neural network model trained to identify intense convection from geostationary satellite imagery. The model is trained with ABI 10.35 µm brightness temperature, 0.64 µm reflectance, and Geostationary Lightning Mapper (GLM) flash extent density. It was trained to label convection as "intense" as humans would identify intense convection --- with features such as persistent overshooting tops, storm-top thermal couplets or cold-U signatures, above-anvil cirrus plumes, and strong cores of lightning.

The output of this model is the "intense convection probability" (ICP), contoured at 25%, 50%, and 90%  (blue, cyan, and magenta contours, respectively). The NWS severe thunderstorm warnings are also overlaid, showing good correspondence between ICP evolution in storms and human forecaster expectation of severe weather (see Figure 1).

Fig. 1: ICP contours of 25%, 50%, and 90% (blue, cyan, and magenta contours), ABI CH02 + ABI CH13 sandwich product, and NWS severe weather warnings (orange and red box polygons). 

Many hail and wind reports have been recorded in Kansas and northeast Oklahoma, thus far (Figure 2). Work is ongoing to identify other helpful inputs from ABI, enhance the training dataset, and incorporate the ICP product into ProbSevere.

The ICP can be viewed in real-time using this temporary link, powered by SSEC's RealEarth software.

Fig. 2: Severe weather reports, as of 1455Z on 05/04/2020

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.