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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

Potential intensity theory predicts that the upper-tropospheric temperature acts as an important constraint on tropical cyclone (TC) intensity. The physical mechanisms through which the upper troposphere impacts TC intensity and structure have not been fully explored, however, due in part to limited observations and the complex interactions between clouds, radiation, and TC dynamics. In this study, idealized Weather Research and Forecasting Model ensembles initialized with a combination of three different tropopause temperatures and with no radiation, longwave radiation only, and full diurnal radiation are used to examine the physical mechanisms in the TC–upper-tropospheric temperature relationship on weather time scales. Simulated TC intensity and structure are strongly sensitive to colder tropopause temperatures using only longwave radiation, but are less sensitive using full radiation and no radiation. Colder tropopause temperatures result in deeper convection and increased ice mass aloft in all cases, but are more intense only when radiation was included. Deeper convection leads to increased local longwave cooling rates but reduced top-of-the-atmosphere outgoing longwave radiation, such that the total radiative heat sink is reduced from a Carnot engine perspective in stronger storms. We hypothesize that a balanced response in the secondary circulation described by the Eliassen equation arises from upper-troposphere radiative cooling anomalies that lead to stronger tangential winds. The results of this study further suggest that radiation and cloud–radiative feedbacks have important impacts on weather time scales.

The Propagation of Intraseasonal Tropical Oscillations (PISTON) experiment conducted a field campaign in August–October 2018. The R/V Thomas G. Thompson made two cruises in the western North Pacific region north of Palau and east of the Philippines. Using select field observations and global observational and reanalysis datasets, this study describes the large-scale state and evolution of the atmosphere and ocean during these cruises. Intraseasonal variability was weak during the field program, except for a period of suppressed convection in October. Tropical cyclone activity, on the other hand, was strong. Variability at the ship location was characterized by periods of low-level easterly atmospheric flow with embedded westward propagating synoptic-scale atmospheric disturbances, punctuated by periods of strong low-level westerly winds that were both connected to the Asian monsoon westerlies and associated with tropical cyclones. In the most dramatic case, westerlies persisted for days during and after tropical cyclone Jebi had passed to the north of the ship. In these periods, the sea surface temperaturewas reduced by a couple of degrees by bothwindmixing and net surface heat fluxes that were strongly (∼200 W m−2) out of the ocean, due to both large latent heat flux and cloud shading associated with widespread deep convection. Underway conductivity–temperature transects showed dramatic cooling and deepening of the ocean mixed layer and erosion of the barrier layer after the passage of Typhoon Mangkhut due to entrainment of cooler water from below. Strong zonal currents observed over at least the upper 400m were likely related to the generation and propagation of near-inertial currents.

An increasing body of evidence indicates that publics want more probabilistic information included in their weather forecasts. However, more guidance on incorporating probability information into weather risk communication is needed. The National Hurricane Center (NHC) recently developed prototype forecast graphics that include probabilistic values of intensity at landfall when landfall is possible. The goal of this research was to develop those prototypes into a forecast product that expresses technical uncertainty in an intensity forecast in a manner that is understandable and effective to various publics. In Study 1, an online survey among Florida residents was conducted. Quantitative analysis of the survey data showed few significant differences between the prototypes and the currently operational forecast track graphic, commonly referred to as the cone of uncertainty (COU). Analysis of the responses to open‐ended questions in the survey and feedback from focus group participants consisting of NHC partners working in hurricane‐prone areas guided revisions to improve the prototypes. In Study 2, the modified prototypes produced an improvement in understanding of certain aspects of the intensity forecast. Promisingly, most people surveyed preferred the additional probabilistic information in the prototypes to the status quo COU message. In fact, nearly 90% of respondents indicated that they preferred at least some percentage values in their weather forecasts as opposed to forecasts with words only. This suggests that further development of a probabilistic landfall intensity product might be warranted. Accurately predicting tropical cyclone intensity is a challenge in operational forecasting. Currently, no operational product exists that quantifies the technical uncertainty in tropical cyclone intensity forecasts. This project creates, revises, and tests prototype products designed to fill that gap and provides guidance for improving the National Hurricane Center product suite.

The characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

A growing body of work has documented the existence of diurnal oscillations in the tropical cyclone outflow layer. These diurnal pulses have been examined primarily using satellites or numerical models, and detailed full tropospheric observations or case study analyses of diurnal pulses are lacking. Questions remain on the vertical extent of diurnal pulses and whether diurnal pulses are coupled to convective bands or constrained to the outflow layer. During the Propagation of Intraseasonal Tropical Oscillations (PISTON) field campaign, diurnal oscillations in the upper-level clouds were observed during Typhoon Kong-rey’s (2018) rapid intensification. Over a 3.5 day period where a broad distribution of cold upper-level clouds was overhead, detailed observations of Typhoon Kong-rey’s rainbands show that convection had reduced echo tops but enhanced reflectivity and differential reflectivity aloft compared to other observations during PISTON. Shortwave heating in the upper-levels increased the stability profile in an overall favorable thermodynamic environment for convection during the day, which could help to explain the diurnal differences in convective structure. Under the cirrus canopy, nocturnal convection was deeper and daytime convection shallower in contrast to the rest of the PISTON dataset. Diurnal oscillations in the brightness temperatures were found to be coupled to radially outward propagating convective rainbands that were preceded ~6 hours by outflow jets. The cooling pulses occurred earlier than found in previous studies. The pulses were asymmetric spatially which is likely due to a combination of the vertical wind shear and storm intensity.

To better forecast tropical cyclone (TC) intensity change and understand forecast uncertainty, it is critical to recognize the inherent limitations of forecast models. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. The Intensity Bias and Uncertainty Scheme (IBUS) is developed in an intensity change framework, which estimates the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The analysis uses DSHP, LGEM, and OFCL forecasts from 2010 to 2019 in both the Atlantic and east Pacific basins. Each IBUS contains both a bias correction and forecast uncertainty estimate that is tested on the training dataset and evaluated on the 2020 season. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and basin relative to the original forecasts. The IBUS is also able to communicate forecast uncertainty that explains ∼7%–11% of forecast variance at 48 h for DSHP and LGEM in the Atlantic. Better performance is found in the east Pacific at 96 h where the IBUS explains up to 30% of the errors in DSHP and 14% of the errors for LGEM. The IBUS for OFCL explains 9%–13% of the 48-h forecast uncertainty in the Atlantic and east Pacific with up to 30% variance explained for east Pacific forecasts at 96 h. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts.

Improving estimates of tropical cyclone forecast uncertainty remains an important goal of the Hurricane Forecast Improvement Project (HFIP). Intensity forecast uncertainty near landfall is especially complicated because intensity forecasts depend on track forecasts. Ensembles can be difficult to interpret near land due to differences in both spatial and temporal resolution and differences in landfall timing (if at all) and location. The Monte Carlo Wind Speed Probability (WSP) model is a statistical ensemble based on the error characteristics of forecasts by the National Hurricane Center (NHC) and the spread of several track forecast models. The landfall distribution product (LDP) introduced in this paper was developed to use the statistical ensemble of forecasts from the WSP model to estimate both the track and intensity forecast uncertainty associated with potential landfalls. The LDP includes probabilistic intensity estimates as well as estimates of the most likely and reasonable strongest intensity at landfall. These products could communicate concise intensity uncertainty information to users at risk for tropical cyclone impacts. Demonstration on a retrospective dataset from 2010 to 2018 and evaluation of the LDP on the 2020–21 Atlantic hurricane seasons shows that the probability of landfall and the landfall intensity probabilities generated by the WSP model are reliable and potentially useful for preparedness decision-making. A case study of Hurricane Ida (2021) highlights how the LDP can be implemented to communicate landfall uncertainty to a broad range of users.