Open AccessIce‐cloud particle habit classification using principal components
Author(s) -
Lindqvist H.,
Muin K.,
Nousiainen T.,
Um J.,
McFarquhar G. M.,
Haapanala P.,
Makkonen R.,
Hakkarainen H.
Publication year - 2012
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2012jd017573
Subject(s) - principal component analysis , cirrus , ice crystals , radiative transfer , arctic , computation , ice cloud , cloud computing , shortwave , remote sensing , computer science , environmental science , meteorology , artificial intelligence , geology , physics , algorithm , optics , oceanography , operating system
A novel automatic classification method is proposed for identifying the habits of large ice‐cloud particles and deriving the shape distribution of particle ensembles. This IC‐PCA (Ice‐crystal Classification with Principal Component Analysis) tool is based on a principal component analysis of selected physical and statistical features of ice‐crystal perimeters. The method is developed and tested using image data obtained with a Cloud Particle Imager, but can be applied to other silhouette data as well. For three randomly selected test cases of 222, 200, and 201 crystals from tropical, midlatitude, and arctic ice clouds, the combined classification accuracy of the IC‐PCA is 81.1%. Since previous, semiautomatic classification methods are more time‐consuming and include a subjective phase, the automatic and objective IC‐PCA offers a notable improvement in retrieving the shapes of the individual crystals. As the habit distributions of ice‐cloud particles can be applied to computations of radiative impact of cirrus, it is also demonstrated how classification uncertainties propagate into the radiative transfer computations by using the arctic test case as an example. Computations of shortwave radiative fluxes show that the flux differences between clouds of manually and automatically classified crystals can be as large as 10 Wm −2 but also that two manual classifications of the same image data result in even larger differences, implying the need for a systematic and repeatable classification method.