Improved ANS Lightning Predictors Using Additional Surface Wind and Electric Field Data
Abstract
Because of the destruction by lightning of Atlas-Centaur 67 and its communication satellite payload on 27 March 1987, new launch commit criteria with respect to lightning were imposed by NASA and the Air Force for missile launches from the national ranges. These criteria are very conservative and restrict the available launch windows, especially during summer months at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in Florida. In an effort to expand the launch windows while maintaining safety, we show that neural networks can be trained to generate spatio-temporal maps of predicted probabilities of lightning over the CCAFS/KSC complex. Input data used for training and testing the neural networks include the five minute averages from all 53 wind sensors, the Total Area Divergence product calculated by Watson, the occurrence of lightning strikes as recorded by magnetic direction finders, and most recently, the electric field mill data. Training the neural network lightning predictor with wind data spanning two days of data and the divergence product increased the PoD to 0.65. The predictor's best performance is at 30 - 60 minutes in the future. We except the electric field data to affect near term prediction more than later times.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 31, 1990
- Accession Number
- ADA236771
Entities
People
- Donald S. Frankel