Imperial College London
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Professor Helen Brindley

Faculty of Natural Sciences, Department of Physics

Professor in Earth Observation
 
 
 
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Contact

 

+44 (0)20 7594 7673h.brindley

 
 
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Location

 

717Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Magurno:2020:10.3390/rs12132097,
author = {Magurno, D and Cossich, W and Maestri, T and Bantges, R and Brindley, H and Fox, S and Harlow, C and Murray, J and Pickering, J and Warwick, L and Oetjen, H},
doi = {10.3390/rs12132097},
journal = {Remote Sensing},
pages = {1--19},
title = {Cirrus cloud identification from airborne far-infrared and mid-infrared spectra},
url = {http://dx.doi.org/10.3390/rs12132097},
volume = {12},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far- and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm’s performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid- and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA’s 9th Earth Explorer.
AU  - Magurno,D
AU  - Cossich,W
AU  - Maestri,T
AU  - Bantges,R
AU  - Brindley,H
AU  - Fox,S
AU  - Harlow,C
AU  - Murray,J
AU  - Pickering,J
AU  - Warwick,L
AU  - Oetjen,H
DO  - 10.3390/rs12132097
EP  - 19
PY  - 2020///
SN  - 2072-4292
SP  - 1
TI  - Cirrus cloud identification from airborne far-infrared and mid-infrared spectra
T2  - Remote Sensing
UR  - http://dx.doi.org/10.3390/rs12132097
UR  - https://www.mdpi.com/2072-4292/12/13/2097
UR  - http://hdl.handle.net/10044/1/81580
VL  - 12
ER  -
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