English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Meteosat Third Generation (MTG) is the next generation of European meteorological geostationary satellites, set to be launched in 2021. Besides ensuring continuity with Meteosat Second Generation imagery mission, the new series will feature new instruments, such as the Lightning Imager (LI), a high-speed optical detector providing near real-time lightning detection capabilities over Europe and Africa. The instrument will register events on pixels, where a lightning pulse generates a transient in the acquired radiance. In parallel, signal variations due to a number of unwanted sources, e.g., acquisition noise or jitter movement, are expected to produce false events. The challenge for on-board and on-ground processing is, thus, to discard as many false events as possible while keeping a majority of the true lightning events. This paper discusses a chain of algorithms that can be used by the LI for the detection of lightning and for the filtering of false events. Some of these algorithms have been developed in the framework of internal research and simulations conducted by the MTG team at the European Space Agency on an in-house LI simulator and therefore will not necessarily reflect the ultimate operational processing chain. The application of the chain on a representative scenario shows that 99.5% of the false events can be eliminated while keeping 83.6% of the true events, before generating the LI higher level data products. Machine learning techniques have also been studied as an alternative for on-ground event processing, and preliminary results indicate promising potential.

Algorithmic Chain for Lightning Detection and False Event Filtering Based on the MTG Lightning Imager