Lightning intensity value predicting method based on atmospheric electric field data

Abstract

The invention discloses a lightning intensity value predicting method based on atmospheric electric field data, which is applied to the field of lightning early warning. The disclosed lightning intensity value predicting method is used for converting original one-dimensional atmospheric electric field data into a two-dimensional image representation through extracting depth features of the atmospheric electric field data, mining depth feature information of the image, establishing a relationship model of the atmospheric electric field data and a lightning intensity value by combining with a non-linear regression analysis method, and predicting the lightning intensity value. According to the lightning intensity value predicting method, various kinds of detection data is used for performinglightning early warning, i.e., the atmospheric electric field data and lightning data; the one-dimensional atmospheric electric field data is converted into the two-dimensional image representation byadopting a dimension conversion method, so as to enhance the structure representation of the atmospheric electric field data; the depth feature information of the atmospheric electric field data is extracted by using a depth model; and the lightning intensity value is predicted by using the atmospheric electric field data, thereby filling a gap of low utilization of the atmospheric electric fielddata in other predicting methods and a gap of lightning intensity value prediction.

Description

technical field [0001] The invention is applied to the field of lightning early warning and relates to the application of atmospheric electric field data and lightning intensity values. The present invention proposes a method of extracting the depth characteristics of atmospheric electric field data, converting the original one-dimensional atmospheric electric field data into two-dimensional image expression and mining the depth feature information of the image, and combining the nonlinear regression analysis method to establish atmospheric electric field data and lightning intensity The relational model of the value is used to predict the value of lightning intensity. Background technique [0002] Thunderstorms are common convective weather accompanied by lightning, thunder, high winds, hail, and heavy precipitation. The lightning strike process has the characteristics of transient large current, high voltage, and strong electromagnetic radiation. The destructive force pr...

AI Technical Summary

Problems solved by technology

This method only uses atmospheric electric field data to predict the frequency of thunderstorms, but does not predict the intensity of thunderstorms, that is, it lacks the prediction of thunderstorm parameters and cannot obtain the intensity information when thunderstorms occur

[0010] To sum up, most of the current lightning early warning methods only use one kind of observation data, and only predict whether lightning will occur or classify and warn the frequency of thunderstorms. There are also fewer predictive methods for probing data

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