Cirrus cloud identification from airborne far-infrared and mid-infrared spectra
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2020-06-30
Date Acceptance
2020-06-25
Citation
Remote Sensing, 2020, 12 (13), pp.1-19
ISSN
2072-4292
Publisher
MDPI AG
Start Page
1
End Page
19
Journal / Book Title
Remote Sensing
Volume
12
Issue
13
Copyright Statement
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
License URL
Sponsor
Natural Environment Research Council (NERC)
Natural Environment Research Council (NERC)
European Space Agency / Estec
Identifier
https://www.mdpi.com/2072-4292/12/13/2097
Grant Number
NE/B50429X/1
NE/K015133/1
4000124917/18/NL/IA
Subjects
0203 Classical Physics
0406 Physical Geography and Environmental Geoscience
0909 Geomatic Engineering
Publication Status
Published
Date Publish Online
2020-06-30