1. A New Approach to Tornado Warning Guidance Algorithms
A spatial approach to the prediction of tornadoes.
V Lakshmanan, Greg Stumpf, Indra Adrianto
5/1/2019
(NSSL); Gregory Stumpf (MDL)

2. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
Guidance Algorithms
Guidance Algorithms should
Alert a NWS forecaster to storms and environments that are likely to produce tornadoes.
Synthesize information from a variety of sources (radar, model, surface observations, etc.) to provide concise information.
Augment the forecaster’s judgment instead of providing just a single yes/no decision.
Current guidance algorithms fail on all three counts to different degrees.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

3. Current guidance algorithms
TDA
The Tornado-Vortex Detection Algorithm (TDA) attempts to detect tight shear from Doppler radar data.
MDA
The Mesocyclone detection Algorithm attempts to detection all circulation signatures in Doppler radar data.
Surprisingly, the MDA outfitted with a neural network does a better job than the TDA at “detecting” tornadoes.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

4. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
MDA + NSE + NN
The MDA detects circulations and finds a number of characteristics of the storm from the radar data.
Also from near-storm environment (NSE) data.
These characteristics are fed to a NN that is trained to classify circulations as tornadic or non-tornadic.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

5. Problems with current approach
Essentially, the MDA and TDA try to detect which circulations are likely to become tornadic.
Very hard problem because of radar geometry.
Also not exactly what is required in real-life situations.
What matters is which town is going to be affected.
Doesn’t matter which storm produces the tornado.
This also happens to be an easier problem to solve.
We need to start out with the aim of predicting tornadoes 30 minutes in advance and develop statistical techniques to do this.
Right now, the approach is to develop algorithms and hope it works in a forecasting context.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

6. Why will our approach work?
A geographic threat-level is easier to assess.
There are many circulations with a storm.
Only one of them might produce a tornado.
Not important to guess which one will.
Classifying detections is hard.
This is why the current guidance algorithms perform so poorly.
Can make much better use of near storm environment (NSE) information (which is at a more coarse resolution).
5/1/2019
(NSSL); Gregory Stumpf (MDL)

7. Geographic Threat Level
We need to create a grid (of 1km by 1km, maybe coarser) with each point on the grid receiving a threat score that reflects the probability that there will be a tornado at that location in the next 30 minutes.
Current tornado warning lead times are only 11 minutes.
We could double or triple this by solving this geographic threat problem.
It helps that this new formulation is actually more realistic.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

8. What is a geographic threat field?
An interest field that models the threat of a tornado at a particular location within the next 30 minutes.
Could look like the picture on the right.
Perfect guidance either for county-based or polygon-based warnings.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

9. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
What will go into it?
We will develop this threat field using statistically valid principles.
Use fields derived from multiple radars, models and surface observations to come up with a single threat field.
Azimuthal shear
Divergence
Time-continuity of high shear
Radar reflectivity
CAPE level
Reflectivity at the freezing level
Surface temperature, etc.
Advect all these values based on storm movement at that location.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

10. Tornado Density (truth field)
Use a storm database that has been built at NSSL of tornado ground truth to build a “tornado density” plot.
All reported tornadoes in the past 30 minutes advected to location on grid at the current time.
With a radius of influence (probably around 5km).
What’s shown is actually a lightning source density plot created using the same principles.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

11. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
Training inputs
Use information from various WDSS-II algorithms as inputs to this training.
A few potential inputs are shown in the following slides.
Do this for about 30 different storm cases, and we will have training data (a set of inputs, with the tornado density field providing the desired output).
Initially simply devise a linear regression mapping from one to the other.
If promising, we can use more sophisticated methods such as neural networks or support vector machines.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

12. Potential input: low-level shear
Azimuthal shear
Find shear on individual radars.
Combine shear from multiple radars
Gets around radar geometry problems.
Covers entire grid.
Already do-able.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

13. Potential input: reflectivity at isotherm level
Reflectivity at freezing level.
Combine reflectivity from multiple radars.
Ingest model data to find isotherm height.
Find reflectivity at height of isotherm at each point in grid.
Already do-able.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

14. Potential input: time-continuity of rotation
Compute shear from multiple radars as before.
Track regions of high shear across time.
Already do-able – it’s already being used to direct damage surveys.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

15. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
In real-time
After training the system on the archived data base, compute the same inputs in real-time.
Use it to create a tornado threat product.
Can be used as tornado warning guidance.
Long-term goal:
Use after-the-fact damage surveys to continuously train the system to predict tornadoes better.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

16. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
Our approach
Combine radar data from multiple radars into a uniform spatial grid (lat-lon).
Create a candidate field by combining this information.
Use this candidate field, along with features derived from all inputs to predict tornado activity in that area.
Train using 30-minute truth to get a 30-minute lead-time.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

17. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
Tornado Ground Truth
Use ground truth data base that has been corrected in time and space.
Map the tornado reports to the earth’s surface, with a 5km radius.
Put all tornado reports and radar signatures leading to reports (?) between T and T+30
This is the target for the prediction technique.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

18. Inputs to candidate field
Azimuthal shear: a least-squares minimization technique on Velocity.
ReflectivityQC: a neural network on Reflectivity.
The candidate field is based on this idea:
Tornadoes are possible when radar shows tight reflectivity gradients, changes in the directionality of shear and high-reflectivity cores nearby.
OU Master’s students found high correlation between areas having all of high reflectivity, high positive shear and high negative shear and tornado activity (23 cases).
5/1/2019
(NSSL); Gregory Stumpf (MDL)

19. Reflectivity Morphology
Reflectivity field dilated and eroded.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

20. Reflectivity gradient
Left: Sobel operation on reflectivity (local gradient operation).
Right: Large changes in reflectivity (morphological operation)
5/1/2019
(NSSL); Gregory Stumpf (MDL)

21. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
Shear morphology
Morphological operations on the shear field.
Left: Areas close to high negative shear
Right: Areas close to high positive shear
5/1/2019
(NSSL); Gregory Stumpf (MDL)

22. Tornado Possibility Field
Left: Resulting tornado possibility field: a weighted combination of the morphological fields.
Right: Comparison with ground truth field.
Finds all the tornadoes. Relatively high false-alarm rate – can not be used directly for prediction.
5/1/2019
(NSSL); Gregory Stumpf (MDL)

23. Simulation of the Algorithm, May 4, 1999, from KTLX Radar Datacase (1)
Tornado Density Plot and Tornado Possibility Algorithm at 01:11:00 UTC
Reflectivity (0.5 deg) at 01:06:58 UTC
5/1/2019
(NSSL); Gregory Stumpf (MDL)

24. Simulation of the Algorithm, May 4, 1999, from KTLX Radar Datacase (2)
Tornado Density Plot and Tornado Possibility Algorithm at 01:41:00 UTC
Reflectivity (0.5 deg) at 01:36:45 UTC
5/1/2019
(NSSL); Gregory Stumpf (MDL)

25. Simulation of the Algorithm, May 4, 1999, from KTLX Radar Datacase (3)
Tornado Density Plot and Tornado Possibility Algorithm at 02:11:00 UTC
Reflectivity (0.5 deg) at 02:06:29 UTC
5/1/2019
(NSSL); Gregory Stumpf (MDL)

26. Simulation of the Algorithm, May 4, 1999, from KTLX Radar Datacase (4)
Tornado Density Plot and Tornado Possibility Algorithm at 02:45:00 UTC
Reflectivity (0.5 deg) at 02:41:10 UTC
5/1/2019
(NSSL); Gregory Stumpf (MDL)

27. Simulation of the Algorithm, May 4, 1999, from KTLX Radar Datacase (5)
Tornado Density Plot and Tornado Possibility Algorithm at 03:15:00 UTC
Reflectivity (0.5 deg) at 03:10:55 UTC
5/1/2019
(NSSL); Gregory Stumpf (MDL)

28. Valliappa.Lakshmanan@noaa.gov (NSSL); Gregory Stumpf (MDL)
Where do we go from here?
Question: How to get past history?
Project backwards, and use local neighborhood measures.
Question: How to get past/future spatial location?
A linear extrapolation suffices for the 30-minute problem.
Clustering and Kalman filter technique already available.
Research Areas:
Extract candidates field (can be improved).
Devise features to be presented to classification engine.
Training of classification engine.
Can we perform classification spatially?
Question: How to predict?
Predict directly on spatial field (grid-point by grid-point)
Predict cluster-by-cluster.
5/1/2019
(NSSL); Gregory Stumpf (MDL)