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Trey McNeely
,
Ann B. Lee
,
Kimberly M. Wood
, and
Dorit Hammerling

Abstract

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.

Free access
Stefano Castruccio
,
Ziqing Hu
,
Benjamin Sanderson
,
Alicia Karspeck
, and
Dorit Hammerling

Abstract

While large climate model ensembles are invaluable tools for physically consistent climate prediction, they also present a large burden in terms of computational resources and storage requirements. A complementary approach to large initial-condition ensembles is to train a stochastic generator on fewer runs. While simulations from a statistical model cannot capture the complexity of climate model runs, they can address some specific scientific questions of interest, such as sampling the variability of regional trends. We demonstrate this potential by comparing simulations from a large ensemble and a stochastic generator trained with only four runs, and show that the variability of regional temperature trends is almost indistinguishable. Training stochastic generators on fewer runs might prove especially useful in the context of large climate model intercomparison projects where creating large ensembles for each model is not possible.

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Melissa S. Bukovsky
,
Carlos M. Carrillo
,
David J. Gochis
,
Dorit M. Hammerling
,
Rachel R. McCrary
, and
Linda O. Mearns

Abstract

This study presents climate change results from the North American Regional Climate Change Assessment Program (NARCCAP) suite of dynamically downscaled simulations for the North American monsoon system in the southwestern United States and northwestern Mexico. The focus is on changes in precipitation and the processes driving the projected changes from the regional climate simulations and their driving coupled atmosphere–ocean global climate models. The effect of known biases on the projections is also examined. Overall, there is strong ensemble agreement for a large decrease in precipitation during the monsoon season; however, this agreement and the magnitude of the ensemble-mean change is likely deceiving, as the greatest decreases are produced by the simulations that are the most biased in the baseline/current climate. Furthermore, some of the greatest decreases in precipitation are being driven by changes in processes/phenomena that are less credible (e.g., changes in El Niño–Southern Oscillation, when it is initially not simulated well). In other simulations, the processes driving the precipitation change may be plausible, but other biases (e.g., biases in low-level moisture or precipitation intensity) appear to be affecting the magnitude of the projected changes. The most and least credible simulations are clearly identified, while the other simulations are mixed in their abilities to produce projections of value.

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