Abstract
Cloud motion analysis plays a key role in analyzing the climatic changes. Recent works show that Classic-NL approach outperforms many other conventional motion analysis techniques. This paper presents an efficient approach for assimilation of satellite images using a recursive stochastic filter, Weighted Ensemble Transform Kalman Filter (WETKF), with appropriate dynamical model and image warping-based non-linear measurement model. Here, cloud motion against the occlusions, missing information, and unexpected merging and splitting of clouds has been analyzed. This will pave a way for automatic analysis of motion fields and to draw inferences about their local and global motion over several years. This paper also demonstrates efficacy and robustness of WETKF over Classic-Non-Local-based approach (Bibin Johnson J et al., International conference on computer vision and 11 image processing, 2016) [1].
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References
Bibin Johnson, J. Sheeba Rani and G R K Sai Subrahmanyam: A novel visualization and tracking framework for analyzing inter/intra cloud pattern formulation to study their impact on climate. International conference on computer vision and Image processing. feb(2016)
J Schmetz, K Holmlund, J. Hoffman, B. Strauss, B. Mason, V. Gaertner, A. Koch and L. Van De Berg: Operational cloud-motion winds from Meteosat infrared images. Journal of Applied Materology, 132, 1206–1225 (1993)
S cote and A. R. L. Tatnall: A neural network-based method for tracking features from satellite sensor images. Remote Sensing, 16, 3695–3701(1995)
Harish Doraiswamy, Vivek Natarajan and Ravi S Nanjundiah: An exploration framework to identify and track movement of cloud systems, IEEE Transactions on Visualization and Computer Graphics, 19(12), 2896–2905(2013)
Nicolas Papadakis, Thomas Corpetti and Etienne Memin: Dynamically consistent optical flow estimation. International conference on computer vision (2007)
Thomas C, Corpetti T and Memin E: Data assimilation for convective cells tracking on meteorological image sequences, IEEE Trans. Geosci Remote Sens, 48(8), 3162–3177(2010)
N. Papadakis, E. Memin, A. Cuzol and N. Gengembre: Data assimilation with the weighted ensemble Kalman filter, Tellus A, 62, 673–697(2010)
S. Beyou, A. Cuzol, Gorthi Sai Subrahmanyam and E. Memin: Weighted ensemble transform Kalman filter for image assimilation, Tellus A, 65(2013)
C. H. Bishop, B. J. Etherton and S. J. Majumdar: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Monthly Weather Review, 129, 420–436(2001)
M K Tippett, J L Anderson, C. H. Bishop, T. M. Hamill and J. S. Whitaker: Ensemble Square Root Filters, Monthly Weather Review, 131, 1485–1490(2003)
G. Evensen: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynamics, 53, 343–367(2003)
HRI:Image data in the form of High Rate Transmissions available at 30-minute intervals with coverage over the Indian Ocean (https://www.eumetsat.int)
Deqing Sun, Stefan Roth and Michael J Black: A quantitative analysis of current practices in optical flow estimation and the principles behind them., International Journal of Computer Vision, 106(2), 115–137(2014)
Dhara Patel, Saurabh Upadhyay: “Optical Flow Measurement using Lucas kanade Method”, International Journal of Computer Applications, 61, 6–10(2013)
Nelson, Stephen (Fall 2014): “Tropical Cyclones (Hurricanes)”, Wind Systems: Low Pressure Centers, Tulane University
Acknowledgements
We acknowledge Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), Space Applications Centre, ISRO, European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) for providing satellite images and INRIA for Particle Image Velocimetry (PIV) images.
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Mounika, K., Sheeba Rani, J., Subrahmanyam, G.S. (2018). Stochastic Assimilation Technique for Cloud Motion Analysis. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_9
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DOI: https://doi.org/10.1007/978-981-10-7895-8_9
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