Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences

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• 作者：Le Hoang Son ; Pham Huy Thong
• 关键词：Picture fuzzy clustering ; Picture fuzzy rules ; Satellite images ; Spatiotemporal regression ; Weather nowcasting
• 刊名：Applied Intelligence
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：46
• 期：1
• 页码：1-15
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
• 出版者：Springer US
• ISSN：1573-7497
• 卷排序：46

文摘

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.