Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval
Clouds are key regulators of the Earth’s energy budget. Their microphysical and optical properties lead to vastly disparate radiative properties. Retrieving information about clouds is thus crucial to reduce uncerntainties in our estimation of climate change. In this study, we present a common approach to the retrieval of cloud type and cloud base height (CBH), two useful aspects to characterise clouds and their radiative effects.We leverage surface observations of these two cloud characterictics from the network made available by the UK Met Office, linked to satellite retrievals of relevant cloud properties from the MODIS instrument, namely cloud top height, cloud optical thickness and cloud water path. Our approach relies on a convolutional auto-encoder (AE) to project a data cube (dimension of 3 channels, 128 km, 128 km), comprised of the aforementioned cloud properties, to a latent space of lower dimensionality …
%0 Journal Article
%1 lenhardt2024leveraging
%A Lenhardt, Julien
%A Quaas, Johannes
%A Sejdinovic, Dino
%A Klocke, Daniel
%D 2024
%I Copernicus Meetings
%K imported topic_earthenvironment
%N EGU24-18214
%T Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval
%X Clouds are key regulators of the Earth’s energy budget. Their microphysical and optical properties lead to vastly disparate radiative properties. Retrieving information about clouds is thus crucial to reduce uncerntainties in our estimation of climate change. In this study, we present a common approach to the retrieval of cloud type and cloud base height (CBH), two useful aspects to characterise clouds and their radiative effects.We leverage surface observations of these two cloud characterictics from the network made available by the UK Met Office, linked to satellite retrievals of relevant cloud properties from the MODIS instrument, namely cloud top height, cloud optical thickness and cloud water path. Our approach relies on a convolutional auto-encoder (AE) to project a data cube (dimension of 3 channels, 128 km, 128 km), comprised of the aforementioned cloud properties, to a latent space of lower dimensionality …
@article{lenhardt2024leveraging,
abstract = {Clouds are key regulators of the Earth’s energy budget. Their microphysical and optical properties lead to vastly disparate radiative properties. Retrieving information about clouds is thus crucial to reduce uncerntainties in our estimation of climate change. In this study, we present a common approach to the retrieval of cloud type and cloud base height (CBH), two useful aspects to characterise clouds and their radiative effects.We leverage surface observations of these two cloud characterictics from the network made available by the UK Met Office, linked to satellite retrievals of relevant cloud properties from the MODIS instrument, namely cloud top height, cloud optical thickness and cloud water path. Our approach relies on a convolutional auto-encoder (AE) to project a data cube (dimension of 3 channels, 128 km, 128 km), comprised of the aforementioned cloud properties, to a latent space of lower dimensionality …},
added-at = {2024-11-29T12:02:08.000+0100},
author = {Lenhardt, Julien and Quaas, Johannes and Sejdinovic, Dino and Klocke, Daniel},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/241381646baf67dbcc21c3eae5a9f5ac2/joum576e},
citation = {EGU24, 2024},
interhash = {748fc354f1481676872dbc830f177bbf},
intrahash = {41381646baf67dbcc21c3eae5a9f5ac2},
keywords = {imported topic_earthenvironment},
number = {EGU24-18214},
publisher = {Copernicus Meetings},
timestamp = {2024-11-29T12:02:08.000+0100},
title = {Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval},
year = 2024
}
