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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-28
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-28
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Feb 2019

Research article | 12 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Cloud identification and classification from high spectral resolution data in the far and mid infrared

Tiziano Maestri, William Cossich, and Iacopo Sbrolli Tiziano Maestri et al.
  • Alma Mater Studiorum – Università di Bologna, Italy

Abstract. A new Cloud Identification and Classification algorithm, named CIC, is presented. CIC is a machine-learning algorithm, based on Principal Component Analysis, able to perform a cloud detection and scene classification using a univariate distribution and a threshold, which serves as a binary classifier. CIC is tested on a widespread synthetic dataset of high spectral resolution radiances in the far and mid infrared part of the spectrum simulating measures from the ESA Earth Explorer Fast Track 9 competing mission FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) that is currently (2018/19) undergoing the industrial and scientific Phase-A studies. Simulated spectra are representatives of many diverse climatic areas, ranging from the tropical to polar regions. Application of the algorithm to the synthetic dataset provides high scores for clear/cloud identification, especially when optimisation processes are performed. One of the main results consists in pointing out the high information content of spectral radiance in the far-infrared region of the electromagnetic spectrum to identify cloudy scenes specifically thin cirrus clouds.

Tiziano Maestri et al.
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Status: open (until 09 Apr 2019)
Status: open (until 09 Apr 2019)
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Tiziano Maestri et al.
Tiziano Maestri et al.
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Short summary
A new algorithm (CIC) for cloud identification and classification from passive remote sensing data is introduced. CIC is tested on a large synthetic dataset simulating measurements from the ESA Earth Explorer Fast Track 9 competing mission FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring). The high information content within the far infrared part of the spectrum is exploited to improve clouds (especially cirri) detection scores obtained from mid infrared data only.
A new algorithm (CIC) for cloud identification and classification from passive remote sensing...
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