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 int...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2014-07, Vol.52 (7), p.4155-4160
Main Authors: Shukla, Bipasha Paul, Kishtawal, C. M., Pal, Pradip K.
Format: Article
Language:English
Subjects:
Algorithms
Applied geophysics
Climatology
Clouds
Clustering algorithms
Clusters
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fuzzy clustering
Image sequences
Internal geophysics
Joining
Meteorology
Nowcasting
Prediction algorithms
Regression
satellite-image-based models
Satellites
Spatiotemporal phenomena
Weather
weather nowcasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2013.2280094