A Machine Learning Algorithm for Retrieving Cloud Top Height with Passive Microwave Radiometry

Jean Francois Rysman; Chantal Claud; Stavros Dafis

doi:10.1109/LGRS.2021.3081920

A Machine Learning Algorithm for Retrieving Cloud Top Height with Passive Microwave Radiometry

Jean Francois Rysman
, Chantal Claud
, Stavros Dafis

Université PSL

Research output: Contribution to journal › Article › peer-review

Abstract

This study aims to retrieve cloud top height (CTH) - excluding cirrus - using passive microwave radiometer observations combined with humidity and temperature profiles. A machine-learning-based approach, combining neural network and gradient boosting methods, is used with Cloud Profiling Radar observations as input. The subsequently derived microwave CTH predictions show a mean average error of 2.1 km and a correlation index of 0.8. The algorithm is used to retrieve the CTH during Hurricane Maria and during a mid-latitude autumn storm. This new algorithm will allow to provide estimates of CTH, at world scale, for a 20-year period.

Original language	English
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	19
DOIs	https://doi.org/10.1109/LGRS.2021.3081920
Publication status	Published - 1 Jan 2022
Externally published	Yes

Keywords

Cloud top height (CTH)
Passive microwave radiometry

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10.1109/LGRS.2021.3081920

Cite this

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title = "A Machine Learning Algorithm for Retrieving Cloud Top Height with Passive Microwave Radiometry",

abstract = "This study aims to retrieve cloud top height (CTH) - excluding cirrus - using passive microwave radiometer observations combined with humidity and temperature profiles. A machine-learning-based approach, combining neural network and gradient boosting methods, is used with Cloud Profiling Radar observations as input. The subsequently derived microwave CTH predictions show a mean average error of 2.1 km and a correlation index of 0.8. The algorithm is used to retrieve the CTH during Hurricane Maria and during a mid-latitude autumn storm. This new algorithm will allow to provide estimates of CTH, at world scale, for a 20-year period.",

keywords = "Cloud top height (CTH), Passive microwave radiometry",

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doi = "10.1109/LGRS.2021.3081920",

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T1 - A Machine Learning Algorithm for Retrieving Cloud Top Height with Passive Microwave Radiometry

AU - Rysman, Jean Francois

AU - Claud, Chantal

AU - Dafis, Stavros

PY - 2022/1/1

Y1 - 2022/1/1

N2 - This study aims to retrieve cloud top height (CTH) - excluding cirrus - using passive microwave radiometer observations combined with humidity and temperature profiles. A machine-learning-based approach, combining neural network and gradient boosting methods, is used with Cloud Profiling Radar observations as input. The subsequently derived microwave CTH predictions show a mean average error of 2.1 km and a correlation index of 0.8. The algorithm is used to retrieve the CTH during Hurricane Maria and during a mid-latitude autumn storm. This new algorithm will allow to provide estimates of CTH, at world scale, for a 20-year period.

AB - This study aims to retrieve cloud top height (CTH) - excluding cirrus - using passive microwave radiometer observations combined with humidity and temperature profiles. A machine-learning-based approach, combining neural network and gradient boosting methods, is used with Cloud Profiling Radar observations as input. The subsequently derived microwave CTH predictions show a mean average error of 2.1 km and a correlation index of 0.8. The algorithm is used to retrieve the CTH during Hurricane Maria and during a mid-latitude autumn storm. This new algorithm will allow to provide estimates of CTH, at world scale, for a 20-year period.

KW - Cloud top height (CTH)

KW - Passive microwave radiometry

U2 - 10.1109/LGRS.2021.3081920

DO - 10.1109/LGRS.2021.3081920

M3 - Article

AN - SCOPUS:85107356293

SN - 1545-598X

VL - 19

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

ER -

A Machine Learning Algorithm for Retrieving Cloud Top Height with Passive Microwave Radiometry

Abstract

Keywords

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