DOI: 10.1002/2015JD024456
论文题名: Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method
作者: Minnis P. ; Hong G. ; Sun-Mack S. ; Smith W.L. ; Jr. ; Chen Y. ; Miller S.D.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2016
卷: 121, 期: 9 起始页码: 4907
结束页码: 4932
语种: 英语
英文关键词: CloudSat
; ice cloud
; MODIS
; neural network
; night
; optical depth
Scopus关键词: artificial neural network
; cloud cover
; cloud water
; CloudSat
; ice core
; infrared radiation
; low temperature
; MODIS
; numerical method
; optical depth
英文摘要: Retrieval of ice cloud properties using IR measurements has a distinct advantage over the visible and near-IR techniques by providing consistent monitoring regardless of solar illumination conditions. Historically, the IR bands at 3.7, 6.7, 11.0, and 12.0 μm have been used to infer ice cloud parameters by various methods, but the reliable retrieval of ice cloud optical depth τ is limited to nonopaque cirrus with τ < 8. The Ice Cloud Optical Depth from Infrared using a Neural network (ICODIN) method is developed in this paper by training Moderate Resolution Imaging Spectroradiometer (MODIS) radiances at 3.7, 6.7, 11.0, and 12.0 μm against CloudSat-estimated τ during the nighttime using 2 months of matched global data from 2007. An independent data set comprising observations from the same 2 months of 2008 was used to validate the ICODIN. One 4-channel and three 3-channel versions of the ICODIN were tested. The training and validation results show that IR channels can be used to estimate ice cloud τ up to 150 with correlations above 78% and 69% for all clouds and only opaque ice clouds, respectively. However, τ for the deepest clouds is still underestimated in many instances. The corresponding RMS differences relative to CloudSat are ∼100 and ∼72%. If the opaque clouds are properly identified with the IR methods, the RMS differences in the retrieved optical depths are ∼62%. The 3.7 μm channel appears to be most sensitive to optical depth changes but is constrained by poor precision at low temperatures. A method for estimating total optical depth is explored for estimation of cloud water path in the future. Factors affecting the uncertainties and potential improvements are discussed. With improved techniques for discriminating between opaque and semitransparent ice clouds, the method can ultimately improve cloud property monitoring over the entire diurnal cycle. © 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/62889
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: NASA Langley Research Center, Hampton, VA, United States; Science Systems and Applications Inc., Hampton, VA, United States; Cooperative Institute for Research in the Atmosphere, Ft. Collins, CO, United States
Recommended Citation:
Minnis P.,Hong G.,Sun-Mack S.,et al. Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method[J]. Journal of Geophysical Research: Atmospheres,2016-01-01,121(9)