Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

Leo Carro-Calvo, Carlos Casanova-Mateo, Julia Sanz-Justo, José Luis Casanova-Roque, Sancho Salcedo-Sanz


This paper proposes a novel prediction method for Total Column Ozone (TCO), based on the combination of Support Vector Regression (SVR) algorithms and different predictive variables coming from satellite data (Suomi National Polar-orbiting Partnership satellite), numerical models (Global Forecasting System model, GFS) and direct measurements. Data from satellite consists of temperature and humidity profiles at different heights, and TCO measurements the days before the prediction. GFS model provides predictions of temperature and humidity for the day of prediction. Alternative data measured in situ, such as aerosol optical depth at different wavelengths, are also considered in the system. The SVR methodology is able to obtain an accurate TCO prediction from these predictive variables, outperforming other regression methodologies such as neural networks. Analysis on the best subset of features in TCO prediction is also carried out in this paper. The experimental part of the paper consists in the application of the SVR to real data collected at the radiometric observatory of Madrid, Spain, where ozone measurements obtained with a Brewer spectrophotometer are available, and allow the system’s training and the evaluation of its performance.


Total column ozone; daily forecasting; satellite data; numerical models; support vector regression

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