04/19/23 lakshman@ou.edu 1

A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning

V Lakshmanan1,2 and Gregory Stumpf1,3

1CIMMS/University of Oklahoma2NSSL3NWS/MDL

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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?

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General Idea

t0-30 min t0+30 min

t0

Inputs

Target

Inputs

Forecast+30Target-30

Forecast

Observations

Computed

Functions

Advection

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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

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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

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Echo top input

Maximum height of 30dBZ echo is shown

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

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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

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Target Lightning Density Field

Cloud-to-Ground (CG) lightning strikes are instantaneous

Average in space (3km, Gaussian) and time (15 min)

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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

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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

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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

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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)

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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

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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

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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

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Summary

Very much a work in progress

Thanks for listening!

Questions?