Detecting and tracking mesoscale precipitating objects using machine learning algorithms

ABSTRACT

Accurate identification of precipitating clouds is a challenging task. In the present work, Support Vector Machines (SVMs), Decision Trees (DT), and Random Forests (RD) algorithms were applied to extract and track mesoscale convective precipitating clouds from a series of 22 Geostationary Operational Environmental Satellite-13 meteorological image sub-scenes over the continental territory of Colombia. This study’s aims are twofold: (i) to establish whether the use of five meteorological spectral channels, rather than a single infrared (IR) channel, improves rainfall objects detection and (ii) to evaluate the potential of machine learning algorithms to locate precipitation clouds. Results show that while the SVM algorithm provides more accurate classification of rainfall cloud objects than the traditional IR brightness temperature threshold method, such improvement is not statistically significant. Accuracy assessment was performed using STEP (shape (S), theme (T), edge (E), and position (P)) object-based similarity matrix method, taking as reference precipitation satellite images from the Tropical Rainfall Measuring Mission. Best thematic and geometric accuracies were obtained applying the SVM algorithm.

Acknowledgements

The authors are grateful to Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), the Colombian national weather service, for providing the GOES-13 meteorological images Mode-A and the technical resources used in this study. The TRMM product 3B42 images were obtained from the National Aeronautics and Space Administration (NASA), available at http://giovanni.gsfc.nasa.gov/. Special thanks are due to the Editor and three anonymous referees for their comments and suggestions for improving the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.