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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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### 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

- 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

- 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

- 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

- 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

- 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

- 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.)

- 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

- 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

- 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)

- 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

- 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

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

- 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

- 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+NSE (27-case set)

lakshman@ou.edu

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?

- 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

- 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

- 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

- 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

- 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

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