Abstract
Abstract. Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. Improved observations and continuous monitoring of PSCs can help to validate and improve chemistry–climate models that are used to predict the evolution of the polar ozone hole. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared limb sounders. Two datasets were considered in this study. The first dataset is a collection of infrared spectra captured in Northern Hemisphere winter 2006/2007 and Southern Hemisphere winter 2009 by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the European Space Agency's (ESA) Envisat satellite. The second dataset is the cloud scenario database (CSDB) of simulated MIPAS spectra. We first performed an initial analysis to assess the basic characteristics of the CSDB and to decide which features to extract from it. Here, we focused on an approach using brightness temperature differences (BTDs). From both the measured and the simulated infrared spectra, more than 10 000 BTD features were generated. Next, we assessed the use of ML methods for the reduction of the dimensionality of this large feature space using principal component analysis (PCA) and kernel principal component analysis (KPCA) followed by a classification with the support vector machine (SVM). The random forest (RF) technique, which embeds the feature selection step, has also been used as a classifier. All methods were found to be suitable to retrieve information on the composition of PSCs. Of these, RF seems to be the most promising method, being less prone to overfitting and producing results that agree well with established results based on conventional classification methods.
Highlights
Polar stratospheric clouds (PSCs) typically form in the polar winter stratosphere between 15 and 30 km of altitude
We applied principal component analysis (PCA) and kernel principal component analysis (KPCA) for feature extraction from a large set of brightness temperature differences (BTDs). Both PCA and KPCA are reprojecting the original BTD features to a new space, where the eigenvectors are ordered in such a way that they maximize variance contributions of the data
We investigated whether machine learning (ML) methods can be applied for the PSC classification of infrared limb spectra
Summary
Polar stratospheric clouds (PSCs) typically form in the polar winter stratosphere between 15 and 30 km of altitude. PSCs are known to play an important role in ozone depletion caused by heterogeneous reactions under cold conditions, while denitrification of the stratosphere extends the ozone destruction cycles into springtime, as the absence of NOy limits the deactivation process of the reactive ozone-destroying substances (Solomon, 1999; Salawitch et al, 1993). The presence of man-made chlorofluorocarbons (CFCs) in the stratosphere, which have been used for example in industrial compounds present in refrigerants, solvents, blowing agents for plastic foam, affects ozone depletion. CFCs are inert compounds in the troposphere but get transformed under stratospheric conditions to the chlorine reservoir gases HCl and ClONO2. PSC particles are involved in the release of chlorine from the reservoirs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
