Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes that drive intensity change are not fully understood. Becaus...

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Bibliographic Details
Published in:Journal of applied meteorology and climatology 2020-10, Vol.59 (10), p.1671-1689
Main Authors: McNeely, Trey, Lee, Ann B., Wood, Kimberly M., Hammerling, Dorit
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classifiers
Current data
Cyclones
Disasters
Emergency preparedness
Emergency response
Empirical analysis
Empirical orthogonal functions
Generalized linear models
Hurricanes
Imagery
Infrared analysis
Infrared imagery
Learning algorithms
Machine learning
Morphology
Natural disasters
Orthogonal functions
Representations
Satellite imagery
Satellite observation
Spaceborne remote sensing
Statistical models
Storms
Synchronous satellites
Tropical climate
Tropical cyclones
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Summary:Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes that drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine-learning algorithms have limited applicability on this front because of their “black box” structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for overocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed feature suite targets the global organization, radial structure, and bulk morphology (ORB) of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine-learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (vs absence) of such intensity-change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine-learning methods did not perform better than the linear logistic lasso model for current data.
ISSN:1558-8424
1558-8432
DOI:10.1175/JAMC-D-19-0286.1