Classification of Low-Level Atmospheric Structures Based on a Pyramid Representation and a Machine Learning Method
The atmosphere is a highly complex fluid system where multiple intrinsic and extrinsic phenomena superpose at same spatial and temporal dominions and different scales, making its characterization a challenging task. Despite the novel methods for pattern recognition and detection available in the literature, most of climate data analysis and weather forecast rely on the ability of specialized personnel to visually detect atmospheric patterns present in climate data plots. This paper presents a method for classifying low-level wind flow configurations, namely: confluences, difluences, vortices and saddle points. The method combines specialized image features to capture the particular structure of low-level wind flow configurations through a pyramid layout representation and a state-of-the-art machine learning classification method. The method was validated on a set of volumes extracted from climate simulations and manually annotated by experts. The best results into the independent test dataset was 0.81 of average accuracy among the four atmospheric structures.
KeywordsSupport Vector Machine Saddle Point Random Forest Convolutional Neural Network Radial Basis Function Kernel
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- 1.Armenta, G., Pabón, J.: Modeling northern South America and Caribbean climate using PRECIS and WRF for climate variability and change studies. In: Proceedings of the CORDEX-LAC1 Workshop - World Climate Research Programme, Lima, Peru (2013)Google Scholar
- 2.Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1932–1939, June 2009Google Scholar
- 4.Holmén, V.: Methods for vortex identification. Master’s thesis, Lund University (2012)Google Scholar
- 5.Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
- 6.Michalakes, J., Chen, S., Dudhia, J., Hart, L., Klemp, J., Middlecoff, J., Skamarock, W.: Development of a next generation regional weather research and forecast model. In: Developments in Teracomputing: Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology, vol. 1, pp. 269–276. World Scientific (2001)Google Scholar
- 8.Nagi, J., Ducatelle, F., Di Caro, G., Ciresan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., Gambardella, L.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 342–347, November 2011Google Scholar
- 10.Tzeng, F.Y., Ma, K.L.: Intelligent feature extraction and tracking for visualizing large-scale 4d flow simulations. In: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, p. 6. IEEE Computer Society (2005)Google Scholar