Abstract
Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.
Highlights
Lightning is responsible for human injuries and fatalities, the death of livestock, and house and forest fires.[1,2,3] It is a major source of electromagnetic interference and damage to electronic circuits, buildings, and other exposed man-made structures such as transmission lines, wind turbines and photovoltaics
The database consists of the observations of four meteorological parameters with a granularity of 10 min recorded at 12 selected meteorological stations in Switzerland over a time period ranging from 2006 to 2017
The large amount of available data for meteorological parameters and advances in data mining and knowledge discovery, we used Knowledge Discovery in Databases (KDD) techniques to investigate the correlation between lightning and selected meteorological parameters and warn against the risk of long-range lightning activity
Summary
Lightning is responsible for human injuries and fatalities, the death of livestock, and house and forest fires.[1,2,3] It is a major source of electromagnetic interference and damage to electronic circuits, buildings, and other exposed man-made structures such as transmission lines, wind turbines and photovoltaics. In space centers, lightning is a danger to fuel crews, ground operations and rocket launch operations.[5,6] Lightning is a major cause of damage to wind turbines, one of the fastest growing sectors of renewable energy production, causing transient surges and overvoltages in the power grid, inducing interference in control systems and, most importantly, causing significant damage to the blades and other wind turbine components.[7,8] The consequences of these events can be very costly due to energy production losses, extra maintenance costs, or even loss of operating equipment.[9]
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