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A Neural Network for Detecting and Diagnosing Tornadic Circulations. V Lakshmanan, Gregory Stumpf, Arthur Witt University of Oklahoma, National Severe Storms Laboratory, Meteorological Development Laboratory. Motivation. MDA and NSE developed at NSSL MDA identifies storm-scale circulations

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a neural network for detecting and diagnosing tornadic circulations

A Neural Network for Detecting and Diagnosing Tornadic Circulations

V Lakshmanan, Gregory Stumpf, Arthur Witt

University of Oklahoma, National Severe Storms Laboratory, Meteorological Development Laboratory

lakshman@ou.edu

motivation
Motivation
  • MDA and NSE developed at NSSL
    • MDA identifies storm-scale circulations
    • Which may be precursors to tornadoes
  • Marzban (1997) developed a NN based on MDA parameters to classify tornadoes
    • Using 43 cases
    • Found incorporation of NSE promising
  • Radar Operations Center wanted us to examine using a MDA+NSE NN operationally.
    • Extended Marzban’s work to 83 cases
    • With a few modifications

lakshman@ou.edu

mda and nse
MDA and NSE
  • Mesocyclone Detection Algorithm (MDA)
    • designed to detect a wide variety of circulations of varying size and strength by analyzing the radial velocity data from a Doppler weather radar
    • 23 attributes for each circulation
  • Near Storm Environment (NSE)
    • Uses analysis grids from the RUC model to derive 245 different attributes.
  • Full list of attributes used is in the conference pre-prints.

lakshman@ou.edu

scalar measures of performance
Scalar Measures of performance
  • POD = hit / (hit + miss)
  • FAR = fa / (hit + fa)
  • CSI = hit / (hit + miss + fa)
  • HSS = 2*(null * hit - miss * fa) / {(fa+hit)*(fa+null) + (null + miss)*(miss + hit)}
  • We also report Receiver Operating Characteristic (ROC curves)

lakshman@ou.edu

neural network
Neural Network
  • Fully feedforward resilient backpropagation NN
  • Tanh activation function on hidden nodes
  • Logistic (sigmoid) activiation function on output node
  • Error function: weighted sum of cross-entropy and squared sum of all the weights in the network (weight decay)

lakshman@ou.edu

truthing
Truthing
  • Ground truth based on temporal and spatial promixity
    • Done by hand: every circulation was classified.
    • Look for radar signature 20 minutes before a tornado is on the ground to 5 minutes after.

lakshman@ou.edu

nn training method
NN Training Method
  • Extract out truthed MDA detections
  • Normalize the input features
  • Determine apriori probability thresholds
    • 13 attributes known to have univariate tendencies and prune the training set
  • Divide set in the ratio 46:20:34 (train: validate: test)
  • Bootstrap train/validate sets.

lakshman@ou.edu

nn training method contd
NN training method (contd.)
  • Find optimal number of hidden nodes
    • Beyond which validation cross-entropy error increases
  • Choose as warning threshold the threshold at which the output of NN on validation set has maximum HSS.

lakshman@ou.edu

our method vs marzban and stumpf
Our method vs. Marzban and Stumpf
  • Slightly different from Marzban/Stumpf:
    • Error criterion different
      • Weight decay
    • Error minimization method different
      • RProp vs SCG
    • Bootstrapped case-wise instead of pattern-wise
    • Automatic pruning based on apriori prob.

lakshman@ou.edu

43 case comparison
43-case comparison
  • So, we compared against the same 43-cases (with same independent test cases)
  • Most of the difference due to better generalization
    • case-wise bootstrapping

lakshman@ou.edu

mda nn 83 case
MDA NN (83 case)
  • 43 case data set used by Marzban were large/tall/strong
    • Rather easy dataset of tornado detection
    • The next 40 cases more atypical
      • Mini-supercells, squall-line tornadoes, tropical events etc.
  • Manually selected independent 27 cases to have similar distribution of strong and weak tornadoes.
    • Remaining 56 cases used to verify network.
    • Then, use all 83 cases to create “operational” network.

lakshman@ou.edu

83 case mda nn
83 case MDA NN
  • The performance of best network on independent test case of 27 compared with results on 43-case.
  • And performance of best network trained using all 83 cases (no independent test case)

lakshman@ou.edu

83 case mda nn1
83 case MDA NN
  • ROC curves for 27-case independent test

lakshman@ou.edu

mda nse
MDA + NSE
  • Statistics of the dataset change dramatically when we add NSE parameters as inputs
    • 10x as many inputs, so chances of over-fitting much greater.
    • NSE parameters not tied to individual detections
    • NSE parameters highly correlated in space and time.
    • NSE parameters not resolved to radar resolution (20kmx20km vs. 1kmx1km)
    • NSE parameters available hourly; radar data every 5-6 minutes.

lakshman@ou.edu

feature selection
Feature Selection
  • Reduce parameters from 245 to 76 based on meteorological understanding.
  • Remove one attribute of highly correlated pairs (Pearson’s correlation coefficient).
  • Take the top “f” fraction of univariate predictors

lakshman@ou.edu

choose most general network
Choose most general network
  • Variation of the neural network training and validation errors as the number of input features is increased.
  • Choose the number of features where generalization error is minimum (f=0.3)

lakshman@ou.edu

mda nse1
MDA+NSE
  • On independent 27-case set.

lakshman@ou.edu

mda nse 27 case set
MDA+NSE (27-case set)

lakshman@ou.edu

generalization
Generalization
  • Similar HSS scores on training, validation and independent test data sets.
  • In MDA+NSE, we sacrificed higher performance to get better generalization

lakshman@ou.edu

is nse information helpful
Is NSE information helpful?
  • NSE parameters changed the statistics of the data set
  • The MDA+NSE neural network is only marginally better than a MDA NN but:
    • NSE information has the potential to be useful.
    • We used only 4 of the 76 of the 245 features!

lakshman@ou.edu

going further
Going further
  • Where can we go further with this approach?
    • Find better ways to reduce the number of features
    • Use time history of detections
    • Generate many more data cases.
  • All of which will yield very little (we believe).

lakshman@ou.edu

spatio temporal tornado guidance
Spatio-temporal Tornado Guidance
  • Formulate the tornado prediction problem differently.
    • Instead of devising a machine intelligence approach to classify detections
    • Spatio-temporal: of estimating the probability of a tornado event at a particular spatial location within a given time window

lakshman@ou.edu

spatio temporal approach
Spatio-temporal approach
  • Our initial approach:
    • Modify ground truth to create spatial truth field
    • use a least-squares methodology to estimate shear
    • morphological image processing to estimate gradients,
    • fuzzy logic to generate compact measures of tornado possibility
    • a classification neural network to generate the final spatio-temporal probability field.
    • Past and future history, both of observed tornadoes and of the candidate regions, is obtained by tracking clustered radar reflectivity values
    • integrate data from other sensors (e.g: numerical models and lightning).
  • Paper at the IJCNN 2005

lakshman@ou.edu

acknowledgements
Acknowledgements
  • Funding for this research was provided under NOAA-OU Cooperative Agreement NA17RJ1227 and supported by the Radar Operations Center.
  • Caren Marzban and Don Burgess, both of the University of Oklahoma, helped us immensely on the methods and attributes used in this paper

lakshman@ou.edu