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. 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.

Using Radar Reflectivity to Evaluate the Vertical Structure of Forecast Convection

Several months of regional convection-permitting forecasts using two microphysical schemes (WSM6 and Thompson) are evaluated to determine the accuracy of the simulated convective structure and convective depth and the impact of microphysical scheme on simulated convective properties and biases. Forecasts are evaluated by using concepts from object-based approaches to compare the three-dimensional simulated reflectivity field with the reflectivity field as observed by radar. Results from analysis of both schemes reveals that forecasts generally perform well near the surface but differ considerably aloft both from observations and from each other. Forecasts are found to contain too many convective cores that are individually larger than in the observations, with at least double the number of observed convective cores reaching the midtroposphere (i.e., 4–8 km). Although the number of cores is overpredicted, WSM6 cores typically contain lower simulated reflectivity values than the observations, and the regions of highest reflectivity values do not extend far enough vertically. Conversely, Thompson cores are found to have significantly higher reflectivity values within cores, with the strongest intensities extending higher than in the observations and having magnitudes higher than any observed cores. Forecast reflectivity distributions within convective cells are found to contain more spread than in the observations. The study also assessed the uncertainty in simulated reflectivity calculations by using a second commonly utilized method to calculate simulated reflectivity. The sensitivity analysis reveals that the primary conclusions with each method are similar but the variability generated by using different simulated reflectivity calculations can be as pronounced as when using different microphysical schemes.