Prediction of Satellite Image Sequence for Weather Nowcasting Using Cluster-Based Spatiotemporal Regression

The flawed characterization of transitions between different meteorological structures is often regarded as one of the largest sources of error in weather forecasting. This paper attempts to improve upon the satellite-image-based nowcasting capability of models by coupling a clustering technique into a spatiotemporal autoregression method. Experimental results indicate the superiority of clustering-based regression algorithm in terms of statistically significant skill scores. The tests show an improvement in probability of detection with a decrease in false alarm rate as compared to unclassified predictions. The developed model has also been demonstrated to be useful in nowcasting of convective systems.