A Neural Network for Damaging Wind Prediction

Caren Marzban National Severe Storms Laboratory, and Cooperative Institute for Mesoscale and Meteorological Studies and Department of Physics, University of Oklahoma, Norman, Oklahoma

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Gregory J. Stumpf National Severe Storms Laboratory andCooperative Institute for Mesoscale and Meteorological Studies, University of Oklahoma, Norman, Oklahoma

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Abstract

A neural network is developed to diagnose which circulations detected by the National Severe Storms Laboratory’s Mesocyclone Detection Algorithm yield damaging wind. In particular, 23 variables characterizing the circulations are selected to be used as the input nodes of a feed-forward, supervised neural network. The outputs of the network represent the existence/nonexistence of damaging wind, based on ground observations. A set of 14 scalar, nonprobabilistic measures and a set of two multidimensional, probabilistic measures are employed to assess the performance of the network. The former set includes measures of accuracy, association, discrimination, skill, and the latter consists of reliability and refinement diagrams. Two classification schemes are also examined.

It is found that a neural network with two hidden nodes outperforms a neural network with no hidden nodes when performance is gauged with any of the 14 scalar measures, except for a measure of discrimination where the results are opposite. The two classification schemes perform comparably to one another. As for the performance of the network in terms of reliability diagrams, it is shown that the process by which the outputs are converted to probabilities allows for the forecasts to be completely reliable. Refinement diagrams complete the representation of the calibration-refinement factorization of the joint distribution of forecasts and observations.

Corresponding author address: Dr. Caren Marzban, National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069.

Email: marzban@gump.nssl.noaa.gov

Abstract

A neural network is developed to diagnose which circulations detected by the National Severe Storms Laboratory’s Mesocyclone Detection Algorithm yield damaging wind. In particular, 23 variables characterizing the circulations are selected to be used as the input nodes of a feed-forward, supervised neural network. The outputs of the network represent the existence/nonexistence of damaging wind, based on ground observations. A set of 14 scalar, nonprobabilistic measures and a set of two multidimensional, probabilistic measures are employed to assess the performance of the network. The former set includes measures of accuracy, association, discrimination, skill, and the latter consists of reliability and refinement diagrams. Two classification schemes are also examined.

It is found that a neural network with two hidden nodes outperforms a neural network with no hidden nodes when performance is gauged with any of the 14 scalar measures, except for a measure of discrimination where the results are opposite. The two classification schemes perform comparably to one another. As for the performance of the network in terms of reliability diagrams, it is shown that the process by which the outputs are converted to probabilities allows for the forecasts to be completely reliable. Refinement diagrams complete the representation of the calibration-refinement factorization of the joint distribution of forecasts and observations.

Corresponding author address: Dr. Caren Marzban, National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069.

Email: marzban@gump.nssl.noaa.gov

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