Evaluation of Correlations between Meteorological Measurements and Observations of Lightning Activity Using Artificial Neural Systems
Abstract
This report shows the feasibility of using artificial neural systems (ANS) for making predictions of cloud to ground lightning strikes. ANS designs offer some potentially useful features. ANS predictors can be incrementally trained for new levels of performance without starting programming from 'scratch' each time the predictor is upgraded. Incremental training could proceed in the field reducing costs and delays of modifications while improving predictor accuracy by tailoring it to site conditions (i.e. topography, etc). Trained ANs provides a ready-made formula for constructing fast parallel, distributed processors. The features built up within the ANS might be analyzed for clues to the physical processes underlying the partially understood phenomenon of lightning. Comparisons are made of the performance of an ANS predictor with the state-of-the-art lightning prediction using a wind convergence based criterion described by Watson et al, 1987. (jes)
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 29, 1989
- Accession Number
- ADA222659
Entities
People
- Donald S. Frankel
- Ilya Schiller
- James S. Draper