Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band

Retrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward teraher...

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Main Authors: Chensi Weng, Lei Liu, Taichang Gao, Shuai Hu, Shulei Li, Fangli Dou, Jian Shang
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Atmosphere
Subjects:
Online Access:https://www.mdpi.com/2073-4433/10/8/437
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author Chensi Weng
Lei Liu
Taichang Gao
Shuai Hu
Shulei Li
Fangli Dou
Jian Shang
author_facet Chensi Weng
Lei Liu
Taichang Gao
Shuai Hu
Shulei Li
Fangli Dou
Jian Shang
author_sort Chensi Weng
collection DOAJ
description Retrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward terahertz radiation in the clear-sky and cloudy-sky are first analyzed using the Atmospheric Radiative Transfer Simulator (ARTS). Nine representative center frequencies with different offsets are selected to study the changes of terahertz radiation caused by microphysical parameters of ice clouds. Then, multiple linear regression method is applied to the inversion of IWP. Combinations of different channels are selected for regression to eliminate the influence of other factors (i.e., particle size and cloud height). The optimal fitting equation are obtained by the stepwise regression method using two oxygen absorption channels (118.75 &#177; 1.1 GHz, 118.75 &#177; 3.0 GHz), two water vapor absorption channels (183.31 &#177; 1.0 GHz, 183.31 &#177; 7.0 GHz), and two window channels (243.20 &#177; 2.5 GHz, 874.4 &#177; 6.0 GHz). Finally, the errors of the proposed inversion method are evaluated. The simulation results show that the absolute errors of this method for the low IWP cases are below 7 g/m<sup>2</sup>, and the relative errors for the high IWP cases are generally ranging from 10 to 30%, indicating that the multi-channel regression inversion method can achieve satisfactory accuracy.
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spelling doaj.art-cd3216ba4eb74464af4c0db79bba45f32022-12-22T02:58:08ZengMDPI AGAtmosphere2073-44332019-07-0110843710.3390/atmos10080437atmos10080437Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz BandChensi Weng0Lei Liu1Taichang Gao2Shuai Hu3Shulei Li4Fangli Dou5Jian Shang6College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaNational Key Laboratory on Electromagnetic Environmental Effects and Electro-Optical Engineering, Army Engineering University, Nanjing 210007, ChinaNational Satellite Meteorological Center, Beijing 100081, ChinaNational Satellite Meteorological Center, Beijing 100081, ChinaRetrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward terahertz radiation in the clear-sky and cloudy-sky are first analyzed using the Atmospheric Radiative Transfer Simulator (ARTS). Nine representative center frequencies with different offsets are selected to study the changes of terahertz radiation caused by microphysical parameters of ice clouds. Then, multiple linear regression method is applied to the inversion of IWP. Combinations of different channels are selected for regression to eliminate the influence of other factors (i.e., particle size and cloud height). The optimal fitting equation are obtained by the stepwise regression method using two oxygen absorption channels (118.75 &#177; 1.1 GHz, 118.75 &#177; 3.0 GHz), two water vapor absorption channels (183.31 &#177; 1.0 GHz, 183.31 &#177; 7.0 GHz), and two window channels (243.20 &#177; 2.5 GHz, 874.4 &#177; 6.0 GHz). Finally, the errors of the proposed inversion method are evaluated. The simulation results show that the absolute errors of this method for the low IWP cases are below 7 g/m<sup>2</sup>, and the relative errors for the high IWP cases are generally ranging from 10 to 30%, indicating that the multi-channel regression inversion method can achieve satisfactory accuracy.https://www.mdpi.com/2073-4433/10/8/437terahertz wavepassive remote sensingice water pathmulti-channel regression
spellingShingle Chensi Weng
Lei Liu
Taichang Gao
Shuai Hu
Shulei Li
Fangli Dou
Jian Shang
Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band
Atmosphere
terahertz wave
passive remote sensing
ice water path
multi-channel regression
title Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band
title_full Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band
title_fullStr Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band
title_full_unstemmed Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band
title_short Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band
title_sort multi channel regression inversion method for passive remote sensing of ice water path in the terahertz band
topic terahertz wave
passive remote sensing
ice water path
multi-channel regression
url https://www.mdpi.com/2073-4433/10/8/437
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