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Abstract The rapid intensification (RI) of tropical cyclones (TC), defined here as an intensity increase of ≥ 30 kt in 24 hours, is a difficult but important forecasting problem. Operational RI forecasts have considerably improved since the late 2000s, largely thanks to better statistical models, including machine learning (ML). Most ML applications use scalars from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) development dataset as predictors, describing the TC history, near-TC environment, and satellite presentation of the TC. More recent ML applications use convolutional neural networks (CNN), which can ingest full satellite images (or time series of images) and freely “decide” which spatiotemporal features are important for RI. However, two questions remain unanswered: (1) Does image convolution significantly improve RI skill? (2) What strategies do CNNs use for RI prediction – and can we gain new insights from these strategies? We use an ablation experiment to answer the first question and explainable artificial intelligence (XAI) to answer the second. Convolution leads to only a small performance gain, likely because, as revealed by XAI, the CNN’s main strategy uses image features already well described in scalar predictors used by pre-existing RI models. This work makes three additional contributions to the literature: (1) NNs with SHIPS data outperform pre-existing models in some aspects; (2) NNs provide well calibrated uncertainty quantification (UQ), while pre-existing models have no UQ; (3) the NN without SHIPS data performs surprisingly well and is fairly independent of pre-existing models, suggesting its potential value in an operational ensemble. 

Abstract Minimum central pressure (P_min) is an integrated measure of the tropical cyclone wind field and is known to be a useful indicator of storm damage potential. A simple model that predictsP_minfrom routinely estimated quantities, including storm size, would be of great value. Here, we present a simple linear empirical model for predictingP_minfrom maximum wind speed, a radius of 34-kt (1 kt ≈ 0.51 m s⁻¹) winds (R_34kt), storm center latitude, and the environmental pressure. An empirical model for the pressure deficit is first developed that takes as predictors specific combinations of these quantities that are derived directly from theory based on gradient wind balance and a modified Rankine-type wind profile known to capture storm structure inside ofR_34kt. Model coefficients are estimated using data from the southwestern North Atlantic and eastern North Pacific from 2004 to 2022 using aircraft-based estimates ofP_min, extended best track data, and estimates of environmental pressure from Global Forecast System (GFS) analyses. The model has a near-zero conditional bias even for lowP_min, explaining 94.2% of the variance. Performance is superior to a variety of other model formulations, including a standard wind–pressure model that does not account for storm size or latitude (89.2% variance explained). Model performance is also strong when applied to high-latitude data and data near coastlines. Finally, the model is shown to perform comparably well in an operation-like setting based solely on routinely estimated variables, including the pressure of the outermost closed isobar. Case study applications to five impactful historical storms are discussed. Overall, the model offers a simple, fast, physically based prediction forP_minfor practical use in operations and research. Significance StatementSea level pressure is lowest at the center of a hurricane and is routinely estimated in operational forecasting along with the maximum wind speed. While the latter is currently used to define hurricane intensity, the minimum pressure is also a viable measure of storm intensity that is known to better represent damage risk. A simple empirical model that predicts the minimum pressure from maximum wind speed and size, and based on the physics of the hurricane wind field, does not currently exist. This work develops such a model by using wind field physics to determine the important parameters and then uses a simple statistical model to make the final prediction. This model is quick and easy to use in weather forecasting and risk assessment applications. 

Abstract The radius of maximum wind (R_max) in a tropical cyclone governs the footprint of hazards, including damaging wind, surge, and rainfall. However,R_maxis an inconstant quantity that is difficult to observe directly and is poorly resolved in reanalyses and climate models. In contrast, outer wind radii are much less sensitive to such issues. Here we present a simple empirical model for predictingR_maxfrom the radius of 34-kt (1 kt ≈ 0.51 m s⁻¹) wind (R_17.5 ms). The model only requires as input quantities that are routinely estimated operationally: maximum wind speed,R_17.5 ms, and latitude. The form of the empirical model takes advantage of our physical understanding of tropical cyclone radial structure and is trained on the Extended Best Track database from the North Atlantic 2004–20. Results are similar for the TC-OBS database. The physics reduces the relationship between the two radii to a dependence on two physical parameters, while the observational data enables an optimal estimate of the quantitative dependence on those parameters. The model performs substantially better than existing operational methods for estimatingR_max. The model reproduces the observed statistical increase inR_maxwith latitude and demonstrates that this increase is driven by the increase inR_17.5 mswith latitude. Overall, the model offers a simple and fast first-order prediction ofR_maxthat can be used operationally and in risk models. Significance StatementIf we can better predict the area of strong winds in a tropical cyclone, we can better prepare for its potential impacts. This work develops a simple model to predict the radius where the strongest winds in a tropical cyclone are located. The model is simple and fast and more accurate than existing models, and it also helps us to understand what causes this radius to vary in time, from storm to storm, and at different latitudes. It can be used in both operational forecasting and models of tropical cyclone hazard risk.