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ABSTRACT
Dust storms cause significant damage to health, property, and the environment worldwide every year. To help mitigate the damage, dust forecast models simulate and predict upcoming dust events, providing valuable information to scientists, decision makers, and the public. These simulation outputs are in four-dimensions (i.e., latitude, longitude, elevation, and time) and represent spatially heterogeneous dust storm features and their evolution over space and time. This research investigates and proposes an automatic multi-threshold, region-growing-based algorithm to identify critical dust storm features from 3D dust storm simulations. A multi-threshold scheme is defined for the identification of dust storm features with different dust concentrations. Based on the multi-thresholds, dust storm features are iteratively identified by developing a region-growing algorithm that splits a clustered dust storm feature into multiple sub-features. The proposed approach is compared with three commonly used methods in image processing and thunderstorm identification. The proposed approach outperforms the other three methods in sensitivity and quantitative/qualitative accuracy. This research approach may also be slightly adjusted to identify critical 3D features from simulation outputs for other severe weather and geographical phenomena.
Acknowledgments
We thank the anonymous reviews for their insightful comments, which significantly improved the paper. Dr. George Taylor edited an earlier version of the paper.
Disclosure statement
No potential conflict of interest was reported by the authors.