DOI: 10.1002/jgrd.50813
论文题名: Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?
作者: Grützun V. ; Quaas J. ; Morcrette C.J. ; Ament F.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 18 起始页码: 10507
结束页码: 10517
语种: 英语
英文关键词: cloud parameterizations
; evaluation
; global models
; ground-based remote sensing
; perfect model approach
; statistical cloud schemes
Scopus关键词: Atmospheric humidity
; Distribution functions
; Optical radar
; Precipitation (meteorology)
; Probability distributions
; Remote sensing
; Statistics
; Virtual reality
; Cloud parameterizations
; evaluation
; Global models
; Ground-based remote sensing
; PERFECT model
; statistical cloud schemes
; Three dimensional
; accuracy assessment
; cloud microphysics
; ground-based measurement
; image resolution
; mixing
; numerical model
; probability
; remote sensing
; three-dimensional modeling
英文摘要: Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the "perfect model approach." This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme. Key Points Use ground-based remote sensing with extreme care for evaluating cloud schemesPerfect model approach well suited to investigate evaluation methods ©2013. 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/63308
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: Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany; Meteorological Institute, University of Hamburg, Hamburg, Germany; Leipzig Institute for Meteorology, University of Leipzig, Leipzig, Germany; Met Office, Exeter, United Kingdom
Recommended Citation:
Grützun V.,Quaas J.,Morcrette C.J.,et al. Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(18)