DOI: 10.1002/2016JD025552
论文题名: Cloud glaciation temperature estimation from passive remote sensing data with evolutionary computing
作者: Carro-Calvo L. ; Hoose C. ; Stengel M. ; Salcedo-Sanz S.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2016
卷: 121, 期: 22 起始页码: 591
结束页码: 608
语种: 英语
英文关键词: cloud glaciation
; evolutionary algorithms
; ice nucleation
; passive remote sensing
; PATMOS-x
Scopus关键词: atmospheric modeling
; cloud microphysics
; estimation method
; freezing
; Gaussian method
; glaciation
; heuristics
; nucleation
; optimization
; performance assessment
; remote sensing
; troposphere
; vertical profile
英文摘要: 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. ©2016. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62782
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany; Now at Department of Astrophysics and Atmospheric Sciences, Universidad Complutense de Madrid, Madrid, Spain; Deutscher Wetterdienst, Offenbach, Germany; Department of Signal Processing and Communications, Universidad de Alcalá, Madrid, Spain
Recommended Citation:
Carro-Calvo L.,Hoose C.,Stengel M.,et al. Cloud glaciation temperature estimation from passive remote sensing data with evolutionary computing[J]. Journal of Geophysical Research: Atmospheres,2016-01-01,121(22)