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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/18416

Title: Field trial of an automated ground-based infrared cloud classification system
Authors: Rumi, Emal
Kerr, David
Sandford, Andrew P.
Coupland, Jeremy M.
Brettle, Mike
Keywords: Cloud classification
Infra-red
Pattern recognition
Image processing
Texture analysis
Convective cloud
Remote sensing
Cumulonimbus cloud
Issue Date: 2015
Publisher: © Wiley
Citation: RUMI, E. ... et al, 2015. Field trial of an automated ground-based infrared cloud classification system. Meteorological Applications, 22(4), pp.779-788.
Abstract: Automated classification of cloud types using a ground-based infrared imager can provide invaluable high resolution and localised information for Air Traffic Controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground-based infrared cloud classification system are presented. The system was designed at Campbell Scientific ltd in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated infrared camera system with a lightning detector in classifying several types of clouds, specifically Cumulonimbus and Towering Cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar and sferics automated cloud reports system. In comparisons with METAR data, a Probability of Detection of up to 82% was achieved, together with a minimum Probability of False Detection of 18%.
Description: This is the peer reviewed version of the following article: RUMI, E. ... et al, 2015. Field trial of an automated ground-based infrared cloud classification system. Meteorological Applications, 22(4), pp.779-788., which has been published in final form at http://dx.doi.org/10.1002/met.1523. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Sponsor: The initial part of this work was accomplished under a Knowledge Transfer Partnership (KTP) program with Loughborough University. We wish to acknowledge the UK Technology Strategy Board (TSB) for funding and support of the project that initiated this work. We would like to thank the Met Office team who provided Campbell Scientific Ltd with the automated METAR data.
Version: Accepted for publication
DOI: 10.1002/met.1523
URI: https://dspace.lboro.ac.uk/2134/18416
Publisher Link: http://dx.doi.org/10.1002/met.1523
ISSN: 1469-8080
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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