Extended Abstract (872K)526
Performance of a Probabilistic Cloud-to-Ground Lightning Prediction Algorithm
Valliappa Lakshmanan, CIMMS/Univ. of Oklahoma, NOAA/NSSL, Norman, OK; and J. L. Cintineo and T. M. Smith
A probabilistic cloud-to-ground lightning algorithm was created by
training a neural network on storm characteristics. The input dataset
consisted of all storm cells over the entire coterminous United States
on 12 days in 2008-2009 (one day per month). The input characteristics
include radar and near-storm environmental parameters and the neural
network was set up so that its output is the probability of cloud-to-ground
lightning at a grid location 30 minutes in the future. The probabilistic
output was evaluated on several independent test dates in 2009 and
results of that evaluation are presented.
Poster Session , 20th Conference on Probability and Statistics Poster Session
Wednesday, 20 January 2010, 2:30 PM-4:00 PM, Exhibit Hall B2
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