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: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
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Online Access:https://doi.org/10.3390/rs12213574
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spelling ftmdpi:oai:mdpi.com:/2072-4292/12/21/3574/ 2023-08-20T04:02:24+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 agris 2020-10-31 application/pdf https://doi.org/10.3390/rs12213574 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs12213574 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 21; Pages: 3574 cirrus clouds remote sensing far-infrared Antarctic clouds REFIR-PAD Text 2020 ftmdpi https://doi.org/10.3390/rs12213574 2023-08-01T00:23:26Z 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 μm 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 μm 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 downwelling flux, computed from the top of the atmosphere to the surface, ranges between 70 and 220 W/m2. The estimated cloud longwave forcing at the surface is (31 ± 7) W/m2 and (29 ± 6) W/m2 for ice clouds and (64 ± 12) and (62 ± 11) W/m2 for water ... Text Antarc* Antarctic MDPI Open Access Publishing Antarctic The Antarctic Austral Remote Sensing 12 21 3574
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic cirrus clouds
remote sensing
far-infrared
Antarctic clouds
REFIR-PAD
spellingShingle cirrus clouds
remote sensing
far-infrared
Antarctic clouds
REFIR-PAD
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
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 μm 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 μm 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 downwelling flux, computed from the top of the atmosphere to the surface, ranges between 70 and 220 W/m2. The estimated cloud longwave forcing at the surface is (31 ± 7) W/m2 and (29 ± 6) W/m2 for ice clouds and (64 ± 12) and (62 ± 11) W/m2 for water ...
format Text
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 Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/rs12213574
op_coverage agris
geographic Antarctic
The Antarctic
Austral
geographic_facet Antarctic
The Antarctic
Austral
genre Antarc*
Antarctic
genre_facet Antarc*
Antarctic
op_source Remote Sensing; Volume 12; Issue 21; Pages: 3574
op_relation Atmospheric Remote Sensing
https://dx.doi.org/10.3390/rs12213574
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs12213574
container_title Remote Sensing
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container_issue 21
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