{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:29:43Z","timestamp":1790724583322,"version":"4.1.0"},"reference-count":78,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)","award":["428312742"],"award-info":[{"award-number":["428312742"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The cloud thermodynamic phase is a crucial parameter to understand the Earth\u2019s radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG\/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at \u22123 \u2218C, followed by that for MP at \u221213 \u2218C. Between 0 and \u221227 \u2218C, the occurrence of SC and MP dominates IC, while below \u221227 \u2218C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites.<\/jats:p>","DOI":"10.3390\/rs15071742","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T02:34:54Z","timestamp":1679625294000},"page":"1742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cloud Top Thermodynamic Phase from Synergistic Lidar-Radar Cloud Products from Polar Orbiting Satellites: Implications for Observations from Geostationary Satellites"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9691-9534","authenticated-orcid":false,"given":"Johanna","family":"Mayer","sequence":"first","affiliation":[{"name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt, Institut f\u00fcr Physik der Atmosph\u00e4re, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5899-0890","authenticated-orcid":false,"given":"Florian","family":"Ewald","sequence":"additional","affiliation":[{"name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt, Institut f\u00fcr Physik der Atmosph\u00e4re, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4793-0101","authenticated-orcid":false,"given":"Luca","family":"Bugliaro","sequence":"additional","affiliation":[{"name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt, Institut f\u00fcr Physik der Atmosph\u00e4re, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8925-7731","authenticated-orcid":false,"given":"Christiane","family":"Voigt","sequence":"additional","affiliation":[{"name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt, Institut f\u00fcr Physik der Atmosph\u00e4re, 82234 We\u00dfling, Germany"},{"name":"Institut f\u00fcr Physik der Atmosph\u00e4re, Johannes Gutenberg-Universit\u00e4t Mainz, 55099 Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","unstructured":"Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M. 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