Experimental design in complex model formulation for lightning prediction

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.