TY - JOUR
T1 - A Machine Learning Algorithm for Retrieving Cloud Top Height with Passive Microwave Radiometry
AU - Rysman, Jean Francois
AU - Claud, Chantal
AU - Dafis, Stavros
N1 - Publisher Copyright:
© 2004-2012 IEEE.
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 -