Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy

Optical and microphysical cloud properties are retrieved from measurements acquired in 2013 and 2014 at the Concordia base station in the Antarctic Plateau. Two sensors are used synergistically: a Fourier transform spectroradiometer named REFIR-PAD (Radiation Explorer in Far Infrared-Prototype for A...

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Published in:Remote Sensing
Main Authors: Gianluca Di Natale, Giovanni Bianchini, Massimo Del Guasta, Marco Ridolfi, Tiziano Maestri, William Cossich, Davide Magurno, Luca Palchetti
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
Q
Online Access:https://doi.org/10.3390/rs12213574
https://doaj.org/article/bd4468b506e84f929a0cdcedb7335e22
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spelling ftdoajarticles:oai:doaj.org/article:bd4468b506e84f929a0cdcedb7335e22 2023-05-15T14:01:44+02:00 Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy Gianluca Di Natale Giovanni Bianchini Massimo Del Guasta Marco Ridolfi Tiziano Maestri William Cossich Davide Magurno Luca Palchetti 2020-10-01T00:00:00Z https://doi.org/10.3390/rs12213574 https://doaj.org/article/bd4468b506e84f929a0cdcedb7335e22 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/21/3574 https://doaj.org/toc/2072-4292 doi:10.3390/rs12213574 2072-4292 https://doaj.org/article/bd4468b506e84f929a0cdcedb7335e22 Remote Sensing, Vol 12, Iss 3574, p 3574 (2020) cirrus clouds remote sensing far-infrared Antarctic clouds REFIR-PAD Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12213574 2022-12-31T16:19:08Z Optical and microphysical cloud properties are retrieved from measurements acquired in 2013 and 2014 at the Concordia base station in the Antarctic Plateau. Two sensors are used synergistically: a Fourier transform spectroradiometer named REFIR-PAD (Radiation Explorer in Far Infrared-Prototype for Applications and Developments) and a backscattering-depolarization LiDAR. First, in order to identify the cloudy scenes and assess the cloud thermodynamic phase, the REFIR-PAD spectral radiances are ingested by a machine learning algorithm called Cloud Identification and Classification (CIC). For each of the identified cloudy scenes, the nearest (in time) LiDAR backscattering profile is processed by the Polar Threshold (PT) algorithm that allows derivation of the cloud top and bottom heights. Subsequently, using the CIC and PT results as external constraints, the Simultaneous Atmospheric and Clouds Retrieval (SACR) code is applied to the REFIR-PAD spectral radiances. SACR simultaneously retrieves cloud optical depth and effective dimensions and atmospheric vertical profiles of water vapor and temperature. The analysis determines an average effective diameter of 28 <math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math> m with an optical depth of 0.76 for the ice clouds. Water clouds are only detected during the austral Summer, and the retrieved properties provide an average droplet diameter of 9 <math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math> m and average optical depth equal to four. The estimated retrieval error is about 1% for the ice crystal/droplet size and 2% for the cloud optical depth. The sensitivity of the retrieved parameters to the assumed crystal shape is also assessed. New parametrizations of the optical depth and the longwave downwelling forcing for Antarctic ice and water clouds, as a function of the ice/liquid water path, are presented. The longwave ... Article in Journal/Newspaper Antarc* Antarctic Directory of Open Access Journals: DOAJ Articles Antarctic The Antarctic Austral Remote Sensing 12 21 3574
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic cirrus clouds
remote sensing
far-infrared
Antarctic clouds
REFIR-PAD
Science
Q
spellingShingle cirrus clouds
remote sensing
far-infrared
Antarctic clouds
REFIR-PAD
Science
Q
Gianluca Di Natale
Giovanni Bianchini
Massimo Del Guasta
Marco Ridolfi
Tiziano Maestri
William Cossich
Davide Magurno
Luca Palchetti
Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
topic_facet cirrus clouds
remote sensing
far-infrared
Antarctic clouds
REFIR-PAD
Science
Q
description Optical and microphysical cloud properties are retrieved from measurements acquired in 2013 and 2014 at the Concordia base station in the Antarctic Plateau. Two sensors are used synergistically: a Fourier transform spectroradiometer named REFIR-PAD (Radiation Explorer in Far Infrared-Prototype for Applications and Developments) and a backscattering-depolarization LiDAR. First, in order to identify the cloudy scenes and assess the cloud thermodynamic phase, the REFIR-PAD spectral radiances are ingested by a machine learning algorithm called Cloud Identification and Classification (CIC). For each of the identified cloudy scenes, the nearest (in time) LiDAR backscattering profile is processed by the Polar Threshold (PT) algorithm that allows derivation of the cloud top and bottom heights. Subsequently, using the CIC and PT results as external constraints, the Simultaneous Atmospheric and Clouds Retrieval (SACR) code is applied to the REFIR-PAD spectral radiances. SACR simultaneously retrieves cloud optical depth and effective dimensions and atmospheric vertical profiles of water vapor and temperature. The analysis determines an average effective diameter of 28 <math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math> m with an optical depth of 0.76 for the ice clouds. Water clouds are only detected during the austral Summer, and the retrieved properties provide an average droplet diameter of 9 <math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math> m and average optical depth equal to four. The estimated retrieval error is about 1% for the ice crystal/droplet size and 2% for the cloud optical depth. The sensitivity of the retrieved parameters to the assumed crystal shape is also assessed. New parametrizations of the optical depth and the longwave downwelling forcing for Antarctic ice and water clouds, as a function of the ice/liquid water path, are presented. The longwave ...
format Article in Journal/Newspaper
author Gianluca Di Natale
Giovanni Bianchini
Massimo Del Guasta
Marco Ridolfi
Tiziano Maestri
William Cossich
Davide Magurno
Luca Palchetti
author_facet Gianluca Di Natale
Giovanni Bianchini
Massimo Del Guasta
Marco Ridolfi
Tiziano Maestri
William Cossich
Davide Magurno
Luca Palchetti
author_sort Gianluca Di Natale
title Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
title_short Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
title_full Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
title_fullStr Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
title_full_unstemmed Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
title_sort characterization of the far infrared properties and radiative forcing of antarctic ice and water clouds exploiting the spectrometer-lidar synergy
publisher MDPI AG
publishDate 2020
url https://doi.org/10.3390/rs12213574
https://doaj.org/article/bd4468b506e84f929a0cdcedb7335e22
geographic Antarctic
The Antarctic
Austral
geographic_facet Antarctic
The Antarctic
Austral
genre Antarc*
Antarctic
genre_facet Antarc*
Antarctic
op_source Remote Sensing, Vol 12, Iss 3574, p 3574 (2020)
op_relation https://www.mdpi.com/2072-4292/12/21/3574
https://doaj.org/toc/2072-4292
doi:10.3390/rs12213574
2072-4292
https://doaj.org/article/bd4468b506e84f929a0cdcedb7335e22
op_doi https://doi.org/10.3390/rs12213574
container_title Remote Sensing
container_volume 12
container_issue 21
container_start_page 3574
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