Lightning forecast from chaotic and incomplete time series using wavelet de-noising and spatiotemporal kriging

Abstract

PurposePresent a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.Design/methodology/approachUsing the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.FindingsThe techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.Research limitations/implicationsThe research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.Practical implicationsThese methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.Social implicationsImproved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.Originality/valueBased on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.