Date of Award
Master of Science in Operations Research
Department of Operational Sciences
Andrew J. Geyer, PhD
Cape Canaveral Air Force Station (CCAFS), Kennedy Space Center (KSC), and Patrick Air Force Base (PAFB) all reside in the thunderstorm capital of the United States. According to the Florida Climate Center, these installations experience more thunderstorms per year than any other place in the United States. It is the mission of the 45th Weather Squadron to provide timely and accurate warnings of weather conditions such as lightning that pose a risk to assets and personnel CCAFS, KSC and PAFB. To aid 45th Weather Squadron forecasters, a network of 30 Electric Field Mills (EFM) was installed in the area in and around CCAFS, KSC, and PAFB. EFMs record the electrification of the local atmosphere. Several efforts have been made over the years to find an optimal way to utilize the EFM network data to improve lightning prediction. These efforts approached the problem using atmospheric science as well as traditional statistical regression techniques with mixed results. In this paper, hourly statistics were generated from the raw EFMs data set used in Hill . Input variables were generated from surface observations from every station within 50 miles of CCAFS and then combined with the EFM statistics for the same time periods. This combined data set was used to create Long Short-term Memory (LSTM) Neural Networks designed to capture trends within the data for each observation. A variety of different LSTM model structures were created and trained to see which model structure performed best when predicting lightning around CCAFS, KSC, and PAFB. By utilizing design of experiments techniques, optimal parameters for the LSTM model structures are narrowed down providing a solid baseline for future endeavors in predicting lightning.
DTIC Accession Number
Speranza, Dominick V., "Lightning Prediction Using Recurrent Neural Networks" (2019). Theses and Dissertations. 2317.