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The downshear‐left enhancement of tropical cyclone rainfall has been demonstrated previously, but the radial dependence of this effect was not analyzed in detail. This study quantifies the progressive upwind shift of the wavenumber‐1 maximum rain position with radius relative to the vertical wind shear direction. This shift is visualized as a distinctive upwind spiral of the maximum. It is shown that this spiral pattern is generally observed across various storm intensities, shear strength, and ocean basins. Detailed examination revealed that the maximum downwind deflection angle of the wavenumber‐1 rain maximum relative to the shear direction is smaller for tropical storms than hurricanes, but insensitive to hurricane intensity. It is proposed that the spirality is produced by a continuous decline in angular advection of air parcels with radius. The stability of the deflection angle in hurricanes may be accounted for by a corresponding increase in vertical ascent under strengthening angular flow. Plain Language Summary Tropical cyclones (TCs) can produce torrential rainfall that generates floods, causing significant socio‐economic losses. Understanding the spatial structure of the TC rain field is crucial for improving disaster preparedness. The TC rain field can be thought of as the combination of a symmetric and an asymmetric part. By using a technique called Fourier decomposition, we can break down the asymmetric part into individual wavenumber components. The first component, wavenumber‐1 (WN‐1), is dominant and tends to be larger in the downshear quadrants under vertical wind shear. Using 21 years of global WN‐1 rain fields, we produced composite images aligned with the shear direction. We discovered that the positions at which the WN‐1 maximum occurs progressively shift upwind with increasing distance from the TC center, forming a spiral. We provided the first quantification of the observed spirals and showed that this is a general pattern that exists across different TC intensities, shear strength, and ocean basins. We also identified detailed changes in the pattern with storm intensity and introduced simple models as a first attempt to comprehend these changes. The findings can improve weather forecasts and risk predictions, making us better prepared for hazards associated with TC rainfall. Key Points There exists a general and progressive upwind shift in the wavenumber‐1 maximum with radius in global shear‐relative rainfall composites Maximum downwind deflection of the wavenumber‐1 maxima rises with storm intensity up to Category 1 on the Saffir–Simpson Hurricane Scale Linearity between the angular velocity and the vertical velocity of the storm may stabilize the downwind deflection beyond Category 1

\"As extreme weather becomes more common, the urge to outwit nature can be irresistible. But when our expensive technosolutions backfire, are we worse off than before? How should we adapt to a changing climate? Miller reveals the unintended consequences of bad adaptations or as academics call it, maladaptations--fixes that do more harm than good. From seawalls in coastal Japan, to the reengineered waters in the Ganges River Delta, to the artificial ribbon of water supporting both farms and urban centers in parched Arizona, the author traces the histories of engineering marvels that were once deemed too smart and too big to fail. In each he takes us into the land and culture, seeking out locals and experts to better understand how complicated, grandiose schemes led instead to failure, and to find answers to the technologic holes we've dug ourselves into. Miller urges us to take a hard look at the fortifications we build and how they've fared in the past. He embraces humanity's penchant for problem-solving, but argues that if we are to adapt successfully to climate change, we must recognize that working with nature is not surrender but the only way to assure a secure future.\"--From publisher's description.

Several warm‐core cyclones in the Mediterranean, which were analyzed in the literature, are studied using ERA5 reanalysis, to identify the environment where they develop and distinguish tropical‐like cyclones from non‐tropical warm‐core cyclones. Initially, the cyclone phase space is analyzed to distinguish the cyclones that have a symmetrical deep warm core. Subsequently, the temporal evolution of several parameters is considered, including the distance between the area of maximum tangential wind speed and the cyclone center. Some differences are observed between the cyclones analyzed: one category of cyclones develops in areas of moderate‐low baroclinicity and intense convective processes, as occurs in tropical cyclones. Another group of cyclones develops in a strongly baroclinic environment with weak convective processes and intense vertical wind shear, as occurs in warm seclusions. Two cyclones, showing similarities with polar lows, are also identified. Plain Language Summary Mediterranean tropical‐like cyclones (TLCs) are damaging weather systems, which form over the Mediterranean Sea, resembling tropical cyclones. These cyclones can drive important socio‐economic losses in coastal areas. However, due to their small size and the relatively recent investigation of these cyclones, there is currently no robust categorization of which Mediterranean cyclones can be considered TLC. Therefore, in this work, we propose a method to differentiate cyclones that attain actual tropical‐like characteristics in part of their lifetime, as they develop a warm core through intense convective processes. The main results of this study show that part of the analyzed cyclones have features similar to tropical cyclones. Another group of cyclones has a behavior closer to extratropical cyclones with weak convective processes in an environment with intense vertical wind shear, as occurs in warm seclusions or polar lows. The results of this study propose a key to identify the Mediterranean cyclones that have tropical‐like characteristics. Key Points A new method to detect cyclones with tropical‐like characteristics in the Mediterranean has been developed Part of the cyclones with deep warm core developed in low baroclinicity and with intense convective processes, as tropical cyclones Some cyclones have weak convective processes and intense vertical wind shear environments, such as warm seclusions or polar lows

The radius of maximum wind (RMW) defines the location of the maximum winds in a tropical cyclone and is critical to understanding intensity change as well as hazard impacts. A comparison between the Hurricane Analysis and Forecast System (HAFS) models and two statistical models based off the National Hurricane Center official forecast is conducted relative to a new baseline climatology to better understand whether models have skill in forecasting the RMW of North Atlantic tropical cyclones. On average, the HAFS models are less skillful than the climatology and persistence baseline and two statistically derived RMW estimates. The performance of the HAFS models is dependent on intensity with better skill for stronger tropical cyclones compared to weaker tropical cyclones. To further improve guidance of tropical cyclone hazards, more work needs to be done to improve forecasts of tropical cyclone structure. Plain Language Summary The radius of maximum wind (RMW) is a key structural parameter of tropical cyclones that describes how far the strongest winds are from the storm's center. The RMW is closely tied to significant hazards such as wind, storm surge, and rainfall. However, little forecast guidance is provided for the RMW resulting in forecasters using climatological estimates to help communicate hazard risk. In order to better forecast the RMW, we need to understand the performance of the few guidance techniques available. We compare RMW forecasts from the Hurricane Analysis and Forecast System (HAFS) to two statistical models and a climatological estimate. Forecasts of the RMW from HAFS are not competitive with statistical derivations of the RMW with marginally better to comparable skill for stronger tropical cyclones. The results indicate that there is a strong need for future improvements to better predict tropical cyclone structure in addition to track and intensity. Key Points Forecasting the radius of maximum wind (RMW) is important for forecasting tropical cyclone hazards A RMW climatology and persistence model is created to determine forecast skill Statistical RMW forecasts are skillful and outperform dynamical model guidance

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.

Theoretical understanding of the thermodynamic controls on tropical cyclone (TC) wind intensity, as well as numerical simulations, implies a positive trend in TC intensity in a warming world. The global instrumental record of TC intensity, however, is known to be heterogeneous in both space and time and is generally unsuitable for global trend analysis. To address this, a homogenized data record based on satellite data was previously created for the period 1982–2009. The 28-y homogenized record exhibited increasing global TC intensity trends, but they were not statistically significant at the 95% confidence level. Based on observed trends in the thermodynamic mean state of the tropical environment during this period, however, it was argued that the 28-y period was likely close to, but shorter than, the time required for a statistically significant positive global TC intensity trend to appear. Here the homogenized global TC intensity record is extended to the 39-y period 1979–2017, and statistically significant (at the 95% confidence level) increases are identified. Increases and trends are found in the exceedance probability and proportion of major (Saffir–Simpson categories 3 to 5) TC intensities, which is consistent with expectations based on theoretical understanding and trends identified in numerical simulations in warming scenarios. Major TCs pose, by far, the greatest threat to lives and property. Between the early and latter halves of the time period, the major TC exceedance probability increases by about 8% per decade, with a 95% CI of 2 to 15% per decade.

Global models comprising the sixth-generation Coupled Climate Model Intercomparison Project (CMIP6) are downscaled using a very high-resolution but simplified coupled atmosphere–ocean tropical cyclone model, as a means of estimating the response of global tropical cyclone activity to increasing greenhouse gases. As with a previous downscaling of CMIP5 models, the results show an increase in both the frequency and severity of tropical cyclones, robust across the models downscaled, in response to increasing greenhouse gases. The increase is strongly weighted to the Northern Hemisphere, and especially noteworthy is a large increase in the higher latitudes of the North Atlantic. Changes are insignificant in the South Pacific across metrics. Although the largest increases in track density are far from land, substantial increases in global landfalling power dissipation are indicated. The incidence of rapid intensification increases rapidly with warming, as predicted by existing theory. Measures of robustness across downscaled climate models are presented, and comparisons to tropical cyclones explicitly simulated in climate models are discussed.