edu.ucar.opensky::articles:42308 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T18:07:07.047799 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d7p84h6h https://doi.org/10.7289/V5KH0KK6 https Related Dataset #1 NOAA GOES-R Series Geostationary Lightning Mapper (GLM) Level 2 Lightning Detection: Events, Groups, and Flashes https://doi.org/10.48550/arXiv.2106.09757 https Related Preprint #1 CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1 Quality control of geostationary lightning mapper observations for tropical cyclone applications 2024-09-01T00:00:00Z publication https://n2t.net/ark:/85065/d7p84h6h https://doi.org/10.1175/JTECH-D-23-0138.1 Trabing, Benjamin author Hilburn, K. author Stevenson, S. author Musgrave, K. D. author DeMaria, M. author UCAR/NCAR - Library publisher The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, &#8220;bar&#8221; effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC was not applied beforehand. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <style type="text/css"></style><span style="font-family:Arial;font-size:10pt;font-style:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span> None eng geoscientificInformation