Preprints
https://doi.org/10.5194/egusphere-2023-1953
https://doi.org/10.5194/egusphere-2023-1953
30 Nov 2023
 | 30 Nov 2023
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

The Chalmers Cloud Ice Climatology: Retrieval implementation and validation

Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson

Abstract. Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds.

The Chalmers Cloud Ice Climatology (CCIC) addresses this challenge by applying novel machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC aims to offer a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically-integrated and vertically-resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification.

A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessment of the retrieval accuracy on a held-out test set demonstrates considerable skill in reproducing the reference IWP and IWC estimates. In addition, CCIC is extensively validated against both in-situ and remote sensing measurements from two flight campaigns and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and, to first order, even IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community.

Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson

Status: open (extended)

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  • RC1: 'Comment on egusphere-2023-1953', Anonymous Referee #1, 20 Dec 2023 reply
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson

Model code and software

The Chalmers Cloud ice Climatology Adrià Amell and Simon Pfreundschuh https://doi.org/10.5281/zenodo.8278127

Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson

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Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limited amount of observations of clouds that are currently available. Our work proposes to address this by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.