The Illumination of Thunderclouds by Lightning: 3. Retrieving Optical Source Altitude. Issue 1 (10th January 2022) - Peterson, Michael; Light, Tracy E. L.; Mach, Douglas
Abstract: 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. Plain Language Summary: Lightning is detected from space by monitoring the Earth for rapid changes in cloud top illumination. We can determine where the lightning occurred from the location of the pixel that was triggered. However, since we are looking down at the Earth from above the cloud tops, there is no simple way to determine the altitude of the lightning flash with this kind of instrument, and this is a significant limitation of sensors like the Geostationary Lightning Mapper (GLM). This study uses machine learning methods to attempt to predict lightning altitude from the spatial distribution of energy across the cloud illuminated by each optical pulse. We find that it is possible to predict source altitude well enough to determine which charge layer an optical pulse originated from, and also identify changes in storm structure over time and the vertical development of individual flashes. While these results are still preliminary and come from a single thunderstorm, they demonstrate that altitude prediction is possible with GLM and additional work could result in a general prediction model for all observations by GLM‐like sensors. Key Points: Machine learning is employed to predict the source altitude for Geostationary Lightning Mapper (GLM) groups from attributes of their spatial optical energy distributions GLM altitude models predict the matched Lightning Mapping Array (LMA) mean source altitudes with a median absolute error of <1.5 km The models capture changes in vertical LMA source distributions from convective invigoration or maturation and resolve vertical flash extent