Abstract
Weather nowcasting comprises the detailed description of the current weather along with forecasts obtained by extrapolation for very short-range period of zero to six hours ahead. It is particularly useful when forecasting complicated processes such as rainfall, clouds, and rapidly developing or changing storms. This plays an important role for daily activities like working, traveling, daily planning, flying, etc. Weather forecast can be solved by latest radar, satellite or observational data. However, the main challenges associated with nowcasting are the flawed characterization of transitions between different meteorological structures. In this paper, we propose two novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting. The first method named as PFC-STAR uses a combination of picture fuzzy clustering and spatiotemporal regression. The second one named as PFC-PFR integrates picture fuzzy clustering with picture fuzzy rule. Those methods are equipped with advanced training processes which enhance the accuracy of predicted outputs. The experiments indicate that the proposed methods are better than the relevant ones for weather nowcasting.
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References
Al-amri SS, Kalyankar NV, Khamitkar SD (2010) A comparative study of removal noise from remote sensing image. arXiv preprint arXiv:1002.1148
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96
Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10(2):191–203
Cuong BC (2014) Picture fuzzy sets. Journal of Computer Science and Cybernetics 30(4):409–420
Eberhart RC, Kennedy JA (1995) New optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science 1 (1995, October), pp 39–43
Evans AN (2006) Cloud motion analysis using multichannel correlation-relaxation labeling. IEEE Geosci Remote Sens Lett 3(3):392–396
Germann U, Zawadzki I (2002) Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Mon Weather Rev 130(12):2859–2873
Marzano FS, Rivolta G, Coppola E, Tomassetti B, Verdecchia M (2007) Rainfall nowcasting from multisatellite passive-sensor images using a recurrent neural network. IEEE Trans Geosci Remote Sens 45 (11):3800–3812
Mass C, Mass CF (2011) Nowcasting: The Next Revolution in Weather Prediction. Bulletin of the American Meteorological Society. Available at, http://www.atmos.washington.edu/cliff/BAMSNowcast7.11.pdf
Melgani F (2006) Contextual reconstruction of cloud-contaminated multitemporal multispectral images. IEEE Trans Geosci Remote Sens 44(2):442–455
National Oceanic and Atmospheric Administration (2015) MTSAT West Color Infrared Loop. Available at: http://www.goes.noaa.gov/sohemi/sohemiloops/shirgmscolw.html
Nadig K, Potter W, Hoogenboom G, McClendon R (2013) Comparison of individual and combined ANN models for prediction of air and dew point temperature. Appl Intell 39(2):354–366
Paige CC, Saunders MA (1982) LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Trans Math Softw (TOMS) 8(1):43–71
Park S, Lee SR (2014) Red tides prediction system using fuzzy reasoning and the ensemble method. Appl Intell 40(2):244–255
Pfeifer PE, Deutrch SJ (1980) A Three-Stage Iterative Procedure for Space-Time Modeling Phillip. Technometrics 22(1):35–47
Rivolta G, Marzano FS, Coppola E, Verdecchia M (2006) Artificial neural-network technique for precipitation nowcasting from satellite imagery. Adv Geosci 7(7):97–103
Shukla BP, Pal PK (2012) A source apportionment approach to study the evolution of convective cells: An application to the nowcasting of convective weather systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(1):242–247
Shukla BP, Kishtawal CM, Pal PK (2014) Prediction of Satellite Image Sequence for Weather Nowcasting Using Cluster-Based Spatiotemporal Regression. IEEE Trans Geosci Remote Sens 52(7):4155–4160
Son LH (2015) DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets. Expert Syst Appl 42:51–66
Thong PH, Son LH (2014) A new approach to multi-variables fuzzy forecasting using picture fuzzy clustering and picture fuzzy rules interpolation method. In: Proceeding of 6th International Conference on Knowledge and Systems Engineering, pp 679– 690
Thong PH, Son LH (2015) Picture Fuzzy Clustering: A New Computational Intelligence Method. Soft Comput. doi:10.1007/s00500-015-1712-7
Turner BJ, Zawadzki I, Germann U (2004) Predictability of precipitation from continental radar images—Part III: Operational nowcasting implementation (MAPLE). J Appl Meteorol 43(2):231–248
Ward System Group (1993) Manual of NeuroShell 2. Ward System Group, Frederick
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Acknowledgments
The authors are greatly indebted to the editor-in-chief, Prof. Moonis Ali; anonymous reviewers for their comments and their valuable suggestions that improved the quality and clarity of paper. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2014.01.
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Source codes of this paper are available at the following address:
http://sourceforge.net/p/weathernowcasting/code/ci/source_code/tree/
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Son, L.H., Thong, P.H. Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences. Appl Intell 46, 1–15 (2017). https://doi.org/10.1007/s10489-016-0811-1
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DOI: https://doi.org/10.1007/s10489-016-0811-1