Advances in Instrumentation and Algorithms for Atmospheric Electricity Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 22130
Special Issue Editors
Interests: lightning detection; lightning physics; severe storms; machine learning
Interests: remote and in-situ observations of lightning to investigate the initiation, growth, and decay of lightning streamers and leaders; methods and instrumentation to detect, locate, and characterize lightning and thunderstorms; using lightning as a proxy to predict and study severe weather and climate
Interests: lightning protection; global lightning; high voltage
Interests: lightning detection; lightning protection; instrumentation; lightning over the Amazon
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Various instruments invented over the years have greatly helped us to gain more knowledge and a better physical understanding of atmospheric electricity. New spaceborne instruments such as the Geostationary Lightning Mapper (GLM) and the Atmosphere–Space Interactions Monitor (ASIM) provide measurements of lightning and transient luminous events (TLEs) on the continental or global scale. High-resolution measurements from ground-based instruments such as interferometers, three-dimensional lightning mapping systems, and high-speed video cameras now provide critical new information about lightning leader physics and storm electrification. With the growth of modern instruments, many advanced techniques/algorithms have also been developed to extract physical properties of lightning, storms, and TLEs.
This Special Issue focuses on recent developments of instruments and/or algorithms in atmospheric electricity applications. Topics include, but are not limited to: atmospheric electricity instrumentation of all types; data processing methods to locate and image lightning and TLEs; machine-learning algorithms; signal processing techniques; and improved wave propagation models.
Dr. Yanan Zhu
Dr. Michael Stock
Dr. Yakun Liu
Dr. Adonis Ferreira Raiol Leal
Dr. Weitao Lyu
Guest Editors
Manuscript Submission Information
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Keywords
- atmospheric electricity
- lightning physics
- transient luminous events
- instrumentation
- signal processing
- machine learning
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