End-to-End Prediction of Lightning Events from Geostationary Satellite Images

Publication type:
Journal article
Metadata:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000839746100001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.3390/rs14153760
eISSN
2072-4292
Externe Identifier
  • Clarivate Analytics Document Solution ID: 3S7AY
Ausgabe der Veröffentlichung
15
Zeitschrift
REMOTE SENSING
Schlüsselwörter
  • neural networks
  • satellite images
  • class imbalance
  • feature attribution
  • lightning prediction
  • nowcasting
  • short-term forecasts
  • machine learning
  • meteorology
Artikelnummer
ARTN 3760
Datum der Veröffentlichung
2022
Status
Published
Titel
End-to-End Prediction of Lightning Events from Geostationary Satellite Images
Sub types
  • Article
Ausgabe der Zeitschrift
14

Data source: Web of Science (Lite)

Other metadata sources:
Abstract
<jats:p>While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180 min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.</jats:p>
Autoren
DOI
10.3390/rs14153760
eISSN
2072-4292
Ausgabe der Veröffentlichung
15
Zeitschrift
Remote Sensing
Sprache
en
Online publication date
2022
Paginierung
3760 - 3760
Status
Published online
Herausgeber
MDPI AG
Herausgeber URL
http://dx.doi.org/10.3390/rs14153760
Datum der Datenerfassung
2022
Titel
End-to-End Prediction of Lightning Events from Geostationary Satellite Images
Ausgabe der Zeitschrift
14

Data source: Crossref

Autoren
Zeitschrift
Remote. Sens.
Artikelnummer
15
Paginierung
3760 - 3760
Datum der Veröffentlichung
2022
Titel
End-to-End Prediction of Lightning Events from Geostationary Satellite Images.
Ausgabe der Zeitschrift
14

Data source: DBLP

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