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首页> 外文期刊>Electric power systems research >Can we model the statistical distribution of lightning location system errors better?
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Can we model the statistical distribution of lightning location system errors better?

机译：我们能否更好地模拟雷电定位系统误差的统计分布？

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摘要

Lightning location systems geolocate lightning strokes. Given assumptions made in the geolocation models, errors in the reported locations can occur. Modelling these errors as a bivariate Gaussian distribution of historic stroke detections has found success in the form of confidence ellipses. However, the presence of outliers - strokes with large location errors - indicate that there is a better model for these errors. The Students' t-distribution is a "heavier" tailed distribution. This paper investigates whether the bivariate Students' t-distribution is a better model for such errors. A methodology for modelling and evaluating the distribution of location errors using maximum likelihood estimation, expectation-maximization and a Mahalanobis distance quality-of-fit test is described. This method is applied to stroke reports from the South African Lightning Detection Network and the Austrian Lightning Detection and Information System time-correlated with photographed lightning events to the Brixton Tower, South Africa and current measurements to the Gaisberg Tower, Austria respectively. In both cases, we find outliers in the distribution of location errors - even as the performance of the networks increase. Using the Mahalanobis test, we find the bivariate Students' t-distribution to be a better statistical model for both the South African and the Austrian events.
机译：闪电定位系统可定位雷击。给定地理位置模型中的假设，可能会在报告的位置中发生错误。将这些错误建模为历史笔画检测的二元高斯分布已发现以置信椭圆形式成功。但是，异常值（具有较大位置错误的笔划）的存在表明存在针对这些错误的更好模型。学生的t分布是“较重”的尾分布。本文研究了二元学生t分布是否是针对此类误差的更好模型。描述了使用最大似然估计，期望最大化和马哈拉诺比斯距离拟合质量测试对位置误差的分布进行建模和评估的方法。该方法适用于来自南非闪电检测网络和奥地利闪电检测与信息系统的笔画报告，这些事件与拍摄到的闪电事件到南非的布里克斯顿塔和当前的测量值到奥地利的盖斯伯格塔的时间相关。在这两种情况下，即使网络性能提高，我们也会发现位置误差分布的异常值。使用Mahalanobis检验，我们发现双变量学生的t分布是南非和奥地利事件的更好的统计模型。

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