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.
Highlights
The Far-Infrared (FIR), spanning the spectral interval 100–667 cm−1, represents an important fraction of the Earth’s outgoing long wave radiation, which makes a considerable contribution to the planetary energy balance [1]
Three different spectral intervals are considered in this study to evaluate the ability of Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and Airborne Research Interferometer Evaluation System (ARIES) to identify clear and cloudy scenes and to assess the advantages of using FIR and MIR spectral radiances synergistically
A machine learning algorithm (CIC) is applied, for the first time, to a dataset of airborne high spectral resolution infrared measurements in order to evaluate the capability of the algorithm to identify clear and cloudy sky spectra
Summary
The Far-Infrared (FIR), spanning the spectral interval 100–667 cm−1, represents an important fraction of the Earth’s outgoing long wave radiation, which makes a considerable contribution to the planetary energy balance [1]. New focus on the FIR has been developed due to the selection, in September 2019, of the FORUM [5] mission as the ESA’s 9th Earth Explorer. Earth Explorer missions are devoted to innovative measurement techniques to explore and understand different aspects of the Earth system, addressing questions that have a direct bearing on scientific and societal issues, such as the availability of food, water, energy and resources, public health and climate change [5]. FORUM has the objective to evaluate the role of the far-infrared in shaping the current climate and reduce uncertainty in predictions of future climate change by:
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