Abstract The Geostationary Lightning Mapper (GLM) provides continuous, high-resolution lightning observations that enable a novel investigation of lightning attributes in pretropical cyclogenesis environments. Compositing and tracking methods from the Tracking and Object-Based Analysis of Clouds (tobac) Python package enable the consideration of lightning lifetime alongside GLM-measured area and optical energy. We first apply the object-based framework to lightning observed prior to the formation of Tropical Storm Claudette (2021), a genesis case within range of the Next Generation Weather Radar (NEXRAD) network. Collocated ground-based, dual-polarization radar observations suggest that small-area, low-energy lightning is indicative of stronger convection and updrafts, based on composite vertical radar profiles of tobac lightning features. We then apply the physical interpretations of these lightning attributes to lightning 72 h prior to genesis and within 200 km of the best track invest center for five North Atlantic disturbances. Developing disturbances include Claudette (2021), Ida (2021), Earl (2022), and Beryl (2024), which we compare against nondeveloping AL96 (2024). Small-area, low-energy lightning, previously associated with stronger updrafts and convection, is the dominant lightning mode for the four developing cases with large-area, high-energy lightning also identified at various times. Lightning activity appears to be modulated by deep-layer vertical wind shear, and electrified convection coincides with improvements in convective organization in multiple instances. While more work is needed to evaluate lightning attributes across a larger composite of disturbances, this study offers a novel characterization of lightning in pretropical cyclogenesis environments for the North Atlantic to aid in the understanding and forecasting of tropical cyclone genesis. Significance Statement This study investigates lightning characteristics in five North Atlantic tropical disturbances, four of which became tropical storms, to better understand and forecast tropical storm formation. We apply a novel approach that uses advanced observations from the Geostationary Lightning Mapper and tracking abilities from the Tracking and Object-Based Analysis of Clouds Python package. This framework enables a detailed characterization of lightning that considers lightning area, optical energy, and lifetime, providing more information than previously used flash counts and rates. These expanded lightning attributes may be relevant for understanding and forecasting tropical storm formation. Future work will use this framework to assess lightning across a larger sample of tropical disturbances to evaluate lightning trends across a wider range of cases.

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.

The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, ‘bar’ effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC wasn’t applied beforehand.

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 inten-sity 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 esti-mates 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 uncer-tainty to a broad range of users. SIGNIFICANCE STATEMENT: With the new landfall distribution product (LDP), the National Hurricane Center can provide both track and intensity forecast uncertainty surrounding the landfall of hurricanes. The issuance of a rea-sonable worst case scenario for the strongest winds that could impact a region could amplify messaging to encourage people to take appropriate action prior to a landfall.

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 similar to 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.

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.

The sensitivity of tropical cyclone secondary eyewall formation (SEF) and subsequent eyewall replacement cycles (ERCs) to shortwave radiation is examined in this study by varying the solar constant and diurnal cycle at different times prior to an ERC using idealized simulations from the Weather Research and Forecasting model. The magnitude of shortwave radiation plays an important role in modifying the timing of the SEF with nonlinear interactions amplifying the SEF formation differences at longer lead‐times. Shortwave radiation has a delaying effect on the SEF and ERC primarily through its modifications of the distribution of convective and stratiform heating profiles in the rainbands. Shortwave radiation reduces both the area and diabatic heating of convection in the model domain, while increasing the amount of stratiform precipitation that has weaker low‐level cooling and upper‐level heating rates. The primary mechanism by which shortwave radiation reduces the diabatic heating profile and frequency of convection in the rainbands is through heating of the mid‐upper troposphere which stabilizes the region and reduces convective available potential energy.

The Propagation of Intraseasonal Tropical Oscillations (PISTON) experiment conducted a field campaign inAugust-October 2018. The R/V Thomas G. Thompson made two cruises in thewestern North Pacific region north of Palau and east of the Philippines. Using select field observations and global observational and reanalysis data sets, 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 temperature was reduced by a couple of degrees by both wind mixing and net surface heat fluxes that were strongly (~200Wm−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 400 meters were likely related to the generation and propagation of near-inertial currents.

The repository contains files to reproduce the simulations and analyses described in the manuscript "The Sensitivity of Eyewall Replacement Cycles to Shortwave Radiation" which has been published in the Journal of Geophysical Research: Atmospheres and can be found at https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JD034016. Model configuration can also be found at https://doi.org/10.1175/JAS-D-18-0165.1 Includes input_sounding ----- Initial thermodynamic profile to initialize WRF namelist.input ----- Set of input parameters to run WRF wrfrst_* ----- WRF restart files for the 3 domains for two times (July 7 (96h restart) and July 5 (48h restart)

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.

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.

Potential intensity (PI) theory predicts that the tropopause temperature acts as a powerful constraint on tropical cyclone (TC) intensity and structure. A colder “outflow temperature”, often approximated as the tropopause temperature, produces a stronger TC in both PI theory and numerical simulations of TCs in radiative-convective equilibrium. The physical mechanisms by which the upper tropospheric thermal structure and radiative forcing impact TC intensity and structure have not been fully explored however, due in part to limited observations and the complex interactions between clouds, radiation, and storm dynamics. An idealized version of the Weather Research and Forecasting (WRF) model was employed to provide further insight into these mechanisms and examine how tropopause temperature and radiative tendencies affect tropical cyclone intensity and structure on weather timescales of a few days. Nine simulations were conducted using a combination of three different tropopause temperatures and three radiation configurations (no radiation, longwave only, and full radiation). The simulated TC intensity and structure were sensitive to colder tropopause temperatures using only longwave radiation, but were less sensitive using full-radiation and no-radiation schemes. Preliminary results indicate stronger mean upper tropospheric mass fluxes with decreasing tropopause temperatures, but the results depend strongly on the radiation configuration. The impacts of shortwave and longwave radiation on upper tropospheric stability and mass flux, and their implications for TC intensity and structure change will be presented.