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A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning. V Lakshmanan 1,2 and Gregory Stumpf 1,3 1 CIMMS/University of Oklahoma 2 NSSL 3 NWS/MDL. Motivation. Short term 0-1hr warning for intense cloud-to-ground lightning is valuable to the National Weather Service - PowerPoint PPT Presentation
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04/19/23 [email protected] 1
A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning
V Lakshmanan1,2 and Gregory Stumpf1,3
1CIMMS/University of Oklahoma2NSSL3NWS/MDL
04/19/23 [email protected] 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?
04/19/23 [email protected] 3
General Idea
t0-30 min t0+30 min
t0
Inputs
Target
Inputs
Forecast+30Target-30
Forecast
Observations
Computed
Functions
Advection
04/19/23 [email protected] 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
04/19/23 [email protected] 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=-10C is shown 3D radar grid from KMLB, KAMX, KTBW, at 1626 UTC
16 July 2004
04/19/23 [email protected] 6
Echo top input
Maximum height of 30dBZ echo is shown
3D radar grid from KMLB, KAMX, KTBW, at 1626 UTC 16 July 2004
04/19/23 [email protected] 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
04/19/23 [email protected] 8
Target Lightning Density Field
Cloud-to-Ground (CG) lightning strikes are instantaneous
Average in space (3km, Gaussian) and time (15 min)
04/19/23 [email protected] 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
04/19/23 [email protected] 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
04/19/23 [email protected] 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
04/19/23 [email protected] 12
Mapping Function
For example, one of the inputs is dBZ at a constant height of T = -10C
This is the relationship between the reflectivity values and CG lightning activity 30 minutes later (t0 + 30 min)
04/19/23 [email protected] 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
t0+30 min
Inputs
Forecast+30
Forecast
04/19/23 [email protected] 14
Example
ObservedCG ltg Density at t0 + 30 min
dBZ at a constant ht of T=-10C at t0
ForecastCG ltg Density at t0 + 30 min
CG ltg Density at t0
04/19/23 [email protected] 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