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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

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a real time learning technique to predict cloud to ground lightning

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

lakshman@ou.edu

motivation
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?

lakshman@ou.edu

general idea

Observations

Computed

Functions

Advection

General Idea

Target

Inputs

Inputs

Forecast+30

Target-30

Forecast

t0-30 min

t0+30 min

t0

lakshman@ou.edu

inputs
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

lakshman@ou.edu

reflectivity at constant t levels
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

lakshman@ou.edu

echo top input
Echo top input
  • Maximum height of 30dBZ echo is shown

3D radar grid from KMLB, KAMX, KTBW, at 1626 UTC 16 July 2004

lakshman@ou.edu

target
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

lakshman@ou.edu

target lightning density field
Target Lightning Density Field
  • Cloud-to-Ground (CG) lightning strikes are instantaneous
  • Average in space (3km, Gaussian) and time (15 min)

lakshman@ou.edu

advecting target backwards
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

lakshman@ou.edu

mapping function
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

lakshman@ou.edu

linear radial basis functions
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

lakshman@ou.edu

mapping function1
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 (t0 + 30 min)

lakshman@ou.edu

prediction
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

lakshman@ou.edu

example
Example

CG ltg Density at t0

dBZ at a constant ht of T=-10C at t0

Forecast

CG ltg Density at t0 + 30 min

Observed

CG ltg Density at t0 + 30 min

lakshman@ou.edu

future
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

lakshman@ou.edu

summary
Summary
  • Very much a work in progress
  • Thanks for listening!
  • Questions?

lakshman@ou.edu