1. A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning
V Lakshmanan1,2 and Gregory Stumpf1,3
1CIMMS/University of Oklahoma
2NSSL
3NWS/MDL
12/3/2018

2. Motivation
Short term 0-1hr warning for intense cloud-to-ground lightning is valuable to the National Weather Service
Real-time ground truth available
Real-time learning algorithm that adapts to the changing nature of storms, the near-storm environment, the season, geography, etc?
12/3/2018

3. General Idea Observations Target Computed Functions Inputs Advection
Forecast+30
Target-30
Forecast
t0-30 min
t0+30 min t0
12/3/2018

4. Inputs Inputs are gridded fields
research has shown that the following fields may predict subsequent lightning activity:
Reflectivity at certain constant height and temperature levels
Presence of mixed phase precipitation (graupel) just above melting level
Earlier lightning activity associated with storm
To minimize radar geometry problems, all the inputs are created using 3D multiple-radar grids.
Inputs
Target-30
t0-30 min
12/3/2018

5. Reflectivity at Constant T Levels
Combine data from multiple radars into a 3D multi-radar merged product
Integrate this 3D radar grid with thermodynamic data from the RUC model analysis grids
dBZ at a constant height of T=-10C is shown
3D radar grid from KMLB, KAMX, KTBW, at 1626 UTC 16 July 2004
12/3/2018

6. Echo top input Maximum height of 30dBZ echo is shown
3D radar grid from KMLB, KAMX, KTBW, at 1626 UTC 16 July 2004
12/3/2018

7. Target Target is a lightning density field
Computed from lightning activity in the previous 15 minutes
Advected backward by the prediction interval to account for storm movement.
So that we can do pixel-by-pixel prediction
Inputs
Target-30
t0-30 min
12/3/2018

8. Target Lightning Density Field
Cloud-to-Ground (CG) lightning strikes are instantaneous
Average in space (3km, Gaussian) and time (15 min)
12/3/2018

9. Advecting Target Backwards
We want to predict for each grid pixel
However, storms move
So, need to correct for storm movement
Storm movement estimated using K-means clustering and Kalman filtering
12/3/2018

10. Mapping Function We want a mapping function
Pixel-by-pixel predictor of the vector of inputs to the desired target lightning density
Must be fast enough to compute, and learn, in real-time
Inputs
Target-30
t0-30 min
12/3/2018

11. Linear Radial Basis Functions
Weighted average of multi-dimensional Gaussian functions, so it is a non-linear system
If you keep xn fixed, this is a linear system.
Solve for sigma and weights by inverting a matrix
12/3/2018

12. Mapping Function
For example, one of the inputs is dBZ at a constant height of T = -10C
This is the relationship between the reflectivity values and CG lightning activity 30 minutes later (t min)
12/3/2018

13. Prediction
When predicting, gather the inputs at the current time, then use the same mapping function to make forward prediction
Then advect that forecast field forward by 30 minutes
Inputs
Forecast+30
Forecast
t0+30 min
12/3/2018

14. Example CG ltg Density at t0 dBZ at a constant ht of T=-10C at t0
Forecast
CG ltg Density at t min
Observed
CG ltg Density at t min
12/3/2018

15. Future
Test using a variety of input fields, lightning density functions, and forecast intervals
Results to be reported at a future AMS conference
If successful, may be implemented in AWIPS to serve as guidance for future NWS lightning warning products
12/3/2018

16. Summary Very much a work in progress Thanks for listening! Questions?
12/3/2018