Title: Experimental design in complex model formulation for lightning prediction
Authors: Jared Nystrom; Raymond R. Hill; Andrew Geyer; Joseph J. Pignatiello Jr.; Eric Chicken
Addresses: Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA ' Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA ' Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA ' Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA ' Florida State University, Tallahassee, Florida, USA
Abstract: 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. Weather sensor networks for lightning forecasting 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 first proposes a forecasting methodology using wavelet decomposition of chaotic weather sensor time series and semiparametric single-index models to mitigate the chaotic signal and any possible distributional misspecification. Then, 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. Results indicate a promising technique for operationally relevant lightning prediction from chaotic sensor measurements.
Keywords: wavelet analysis; time series analysis; forecasting; design of experiments; DOEs.
International Journal of Experimental Design and Process Optimisation, 2021 Vol.6 No.4, pp.304 - 332
Received: 06 Feb 2021
Accepted: 25 Mar 2021
Published online: 30 May 2022 *