Preprints
https://doi.org/10.5194/egusphere-2023-2345
https://doi.org/10.5194/egusphere-2023-2345
15 Feb 2024
 | 15 Feb 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Bayesian Cloud Top Phase Determination for Meteosat Second Generation

Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt

Abstract. A comprehensive understanding of cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly distinguished between ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed phase clouds. To address this need, we introduce a novel method named ProPS, which enables cloud detection and determination of cloud top phase using SEVIRI, the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI), optically thick ice (IC), mixed phase (MP), supercooled liquid (SC), and warm liquid (LQ) clouds. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar-radar cloud product DARDAR. Validation of ProPS using six months of independent DARDAR data shows promising results: The daytime algorithm successfully detects 93 % of clouds and 86 % of clear sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ, providing a significant improvement in accurate cloud top phase discrimination compared to traditional retrieval methods.

Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt

Status: open (until 19 Apr 2024)

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  • RC1: 'Comment on egusphere-2023-2345', Anonymous Referee #1, 07 Mar 2024 reply
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Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt

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Short summary
We introduce ProPS – a new method to detect clouds and their thermodynamic phase using a geostationary satellite. It distinguishes between clear sky, ice, mixed-phase, supercooled and warm liquid clouds. ProPS uses a Bayesian approach with the lidar-radar product DARDAR as reference data. The new method allows studying different cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparisons with climate models.