Lightning peak current estimation using a system identification approach - Wern T.L.T.; Mukerjee R.N. (2023)
A system identification-based lightning peak current estimation algorithm using upper-air radiosonde observations is developed. The preceding convective and precipitative process leading to thunder cloud formation followed by the cloud electrification and the leader processes together with return stroke and the discharge process, is identified by considering it as a deterministic dynamic system, whose undisturbed and unmeasurable output signal - the lightning peak current, is contaminated with a stochastic disturbance. The model parameters determined thus, are used to predict the likely temporal lightning return stroke peak current magnitudes. Two alternative parametric estimation models namely Autoregressive with Exogeneous Input (ARX) and Autoregressive with Moving-Average Exogeneous Input (ARMAX) are used to estimate model parameters of the pilot study area and predict the likely lightning return stroke peak current in each case. The relative performances of the models are compared to determine the best model for application in 12-hour and 24-hour ahead predictions. For a short-term (12 hour) prediction, ARMAX2921 giving a best fit of 78.8429% turns out to be the most suitable model. For a longer (24 hour) prediction, the ARX291, giving a best fit of 75.0181% emerges to be the suitable model. These preliminary results indicate that lightning peak current may be estimated to a good performance using upper-air radiosonde observations. Springer-Verlag/Wien 2006.