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A Neural Network for Detecting and Diagnosing Tornadic Circulations

A Neural Network for Detecting and Diagnosing Tornadic Circulations

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A Neural Network for Detecting and Diagnosing Tornadic Circulations

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 83 case MDA NN • ROC curves for 27-case independent test lakshman@ou.edu

  14. 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

  15. 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

  16. 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

  17. MDA+NSE • On independent 27-case set. lakshman@ou.edu

  18. MDA+NSE (27-case set) lakshman@ou.edu

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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