Peterson Michael, Light Tracy E L, Mach Douglas
ISR-2 Los Alamos National Laboratory Los Alamos NM USA.
Science and Technology Institute Universities Space Research Association Huntsville AL USA.
Earth Space Sci. 2022 Jan;9(1):e2021EA001944. doi: 10.1029/2021EA001944. Epub 2022 Jan 10.
Optical space-based lightning sensors such as the Geostationary Lightning Mapper (GLM) detect and geolocate lightning by recording rapid changes in cloud top illumination. While lightning locations can be determined to within a pixel on the GLM imaging array, these instruments are not individually able to natively report lightning altitude. It has previously been shown that thunderclouds are illuminated differently based on the altitude of the optical source. In this study, we examine how altitude information can be extracted from the spatial distributions of GLM energy recorded from each optical pulse. We match GLM "groups" with Lightning Mapping Array (LMA) source data that accurately report the 3-D positions of coincident Radio-Frequency (RF) emitters. We then use machine learning methods to predict the mean LMA source altitudes matched to GLM groups using metrics from the optical data that describe the amplitude, breadth, and texture of the group spatial energy distribution. The resulting model can predict the LMA mean source altitude from GLM group data with a median absolute error of <1.5 km, which is sufficient to determine the location of the charge layer where the optical energy originated. This model is able to capture changes to the source altitude distribution in response to convective processes in the thunderstorm, and the GLM predictions can reveal the vertical structure of individual flashes - enabling 3-D flash geolocation with GLM for the first time. Future work will account for differences in thunderstorm charge/precipitation structures and viewing angle across the GLM Field of View.
诸如地球静止闪电成像仪(GLM)之类的天基光学闪电传感器通过记录云顶光照的快速变化来检测闪电并进行地理定位。虽然闪电位置可以在GLM成像阵列上精确到一个像素内,但这些仪器本身无法直接报告闪电高度。此前的研究表明,雷云根据光源的高度会有不同的光照情况。在本研究中,我们探讨了如何从每个光脉冲记录的GLM能量的空间分布中提取高度信息。我们将GLM“组”与闪电映射阵列(LMA)源数据进行匹配,后者能准确报告同时发生的射频(RF)发射器的三维位置。然后,我们使用机器学习方法,利用描述组空间能量分布的幅度、宽度和纹理的光学数据指标,预测与GLM组匹配的LMA源平均高度。所得模型能够根据GLM组数据预测LMA平均源高度,中值绝对误差<1.5千米,这足以确定光能量起源的电荷层位置。该模型能够捕捉到源高度分布因雷暴中的对流过程而发生的变化,并且GLM预测能够揭示单个闪电的垂直结构——首次实现了利用GLM进行三维闪电地理定位。未来的工作将考虑雷暴电荷/降水结构以及GLM视场内视角的差异。