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 twelve independent test dates in 2008-2009 and results of that evaluation are presented.
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