Date of Award
Doctor of Philosophy (PhD)
Department of Operational Sciences
Raymond R. Hill, PhD
Space launch operations at Kennedy Space Center and Cape Canaveral Space Force Station (KSC/CCSFS) are complicated by unique requirements for near-real time determination of risk from lightning. Lightning forecast weather sensor networks produce data that are noisy, high volume, and high frequency time series for which traditional forecasting methods are often ill-suited. Current approaches result in significant residual uncertainties and consequentially may result in forecasting operational policies that are excessively conservative or inefficient. This work proposes a new methodology of wavelet-enabled semiparametric modeling to develop accurate and timely forecasts robust against chaotic functional data. Wavelets methods are first used to de-noise the weather data, which is then used to estimate a single-index model for forecasting. This semiparametric technique mitigates noise of the chaotic signal while avoiding any possible distributional misspecification. A screening experiment with augmentations is used to demonstrate how to explore the complex factor space of model parameters, guiding decisions regarding model formulation and gaining insight for follow-on research. Imputation methods are applied on the spatially-based EFM time series making use of the inherit autocorrelation in the data, resulting in improved modeling using machine learning and artificial intelligence techniques. Results indicate a promising technique for operationally relevant lightning prediction from chaotic sensor measurements.
DTIC Accession Number
Nystrom, Jared K., "Wavelet Methods for Very-short Term Forecasting of functional Time Series" (2021). Theses and Dissertations. 5085.