A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis

Investigating the evolvement process of Mesoscale Convective Systems (MCSs) over the Tibetan Plateau using satellite remote sensing image sequence is a very important and effective method of forecasting heavy rainfall. This paper presents a spatial data mining approach, by which a possible heavy rainfall forecast can be made, based on MCS tracking using remote sensing satellite images. Firstly, an automatic method for object tracking from the satellite image sequence is proposed, aiming at identification of the qualified MCSs, their characteristics and their moving trajectories. Then, a novel two-phase spatial data mining framework is designed to enable the deduction of the correlations and causalities between MCS activities and possible heavy rainfall occurrences. The proposed approach proves to be capable of lifting the heavy burden of manual rainfall forecasting from the shoulders of meteorologists, by automatically analyzing and interpreting massive, meteorological remote sensing data sets to assist weather forecasting.