Cloud glaciation temperature estimation from passive remote sensing data with evolutionary computing. Issue 22 (18th November 2016)

• Record Type: Journal Article
• Authors: Carro‐Calvo, L.; Hoose, C.; Stengel, M.; Salcedo‐Sanz, S.
• Is Part Of: Journal of geophysical research. Volume 121:Issue 22(2016)
• Page Start: 13, 591
• Page End: 13, 608
• Publication Date: 2016-11-18
• Subjects: cloud glaciation -- passive remote sensing -- ice nucleation -- PATMOS‐x -- evolutionary algorithms; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD025552
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The phase partitioning between supercooled liquid water and ice in clouds in the temperature range between 0 and −37°C influences their optical properties and the efficiency of precipitation formation. Passive remote sensing observations provide long‐term records of the cloud top phase at a high spatial resolution. Based on the assumption of a cumulative Gaussian distribution of the ice cloud fraction as a function of temperature, we quantify the cloud glaciation temperature (CGT) as the 50th percentile of the fitted distribution function and its variance for different cloud top pressure intervals, obtained by applying an evolutionary algorithm (EA). EAs are metaheuristics approaches for optimization, used in difficult problems where standard approaches are either not applicable or show poor performance. In this case, the proposed EA is applied to 4 years of Pathfinder Atmospheres‐Extended (PATMOS‐x) data, aggregated into boxes of 1° × 1° and vertical layers of 5.5 hPa. The resulting vertical profile of CGT shows a characteristic sickle shape, indicating low CGTs close to homogeneous freezing in the upper troposphere and significantly higher values in the midtroposphere. In winter, a pronounced land‐sea contrast is found at midlatitudes, with lower CGTs over land. Among this and previous studies, there is disagreement on the sign of the land‐sea difference in CGT, suggesting that it is strongly sensitive to the detected and analyzed cloud types, the time of the day, and the phase retrieval method. Key Points: An evolutionary algorithm has been applied to the estimation of cloud glaciation temperature (CGT) CGT varies with height, exhibiting a minimum in the upper and a maximum in the midtroposphere The vertical, latitudinal, and land‐sea variation of the cloud top CGT need to be further explored