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

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 observati...

Full description

Saved in:
Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Rysman, Jean-Francois, Claud, Chantal, Dafis, Stavros
Format: Article
Language:English
Subjects:
Algorithms
Autumn storms
Cirrus clouds
Cloud top height (CTH)
Clouds
Height
Humidity
Hurricanes
Input variables
Learning algorithms
Machine learning
Microwave imaging
Microwave measurement
Microwave radiometers
Microwave radiometry
Neural networks
passive microwave radiometry
Prediction algorithms
Radar
Radiometers
Radiometry
Storms
Temperature profile
Temperature profiles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3081920