Open AccessField trial of an automated ground‐based infrared cloud classification system
Author(s) -
Rumi Emal,
Kerr David,
Sandford Andrew,
Coupland Jeremy,
Brettle Mike
Publication year - 2015
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1523
Subject(s) - cloud computing , remote sensing , radar , computer science , meteorology , environmental science , lightning (connector) , field (mathematics) , detector , infrared , lightning detection , real time computing , geology , telecommunications , thunderstorm , geography , operating system , physics , power (physics) , mathematics , quantum mechanics , pure mathematics , optics
Automated classification of cloud types using a ground‐based infrared ( IR ) imager can provide invaluable high‐resolution and localized 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 IR 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 IR 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%.