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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

Accurate hurricane intensity prediction remains a critical challenge in numerical weather prediction (NWP). This study implements and evaluates a newly developed Storm‐Following Three‐Dimensional Incremental Analysis Update (3DIAU) methodology for high‐resolution regional hurricane models with storm‐following nest capabilities. Built upon the feature‐relative 4DIAU approach proposed by Lu and Wang (2021), https://doi.org/10.1175/MWR‐D‐21‐0068.1, the method gradually introduces Data Assimilation (DA) increments relative to the storm's position, reducing spin‐up imbalances and improving intensity predictions. Retrospective experiments were conducted over three Atlantic hurricane seasons (2021–2023) using the 2024 operational Hurricane Analysis and Forecast System (HAFS) version 2.0A configuration. Sensitivity experiments suggest that increment weighting should depend on storm strength. The storm‐strength‐dependent configuration yields an average improvement of 3% in intensity prediction skill, with modest gains in long‐term track predictions. A case study further demonstrates that gradual, storm‐relative adjustments mitigate disruptions caused by intermittent DA and enhance forecast performance. The Storm‐Following 3DIAU will be incorporated into the 2025 operational HAFS V2.1 upgrade. Plain Language Summary Hurricanes are powerful storms that can cause widespread destruction, making accurate forecasting critical for preparedness and response. Predicting hurricane intensity, however, is particularly challenging due to the complexity of storm dynamics and the difficulty of incorporating high‐resolution observations into weather models. This study introduces a method called Storm‐Following 3DIAU, which improves how observational data are incorporated into hurricane models. Traditional data assimilation methods can create imbalances in the model, causing errors early in the forecast. By gradually applying corrections relative to the storm's position, the new approach reduces these imbalances and improves the model's ability to predict hurricane intensity. Tests on 69 storms from 2021 to 2023 show that when the method is adjusted based on storm strength, it improves intensity forecasts by about 3% — a meaningful step forward for operational forecasting. Track accuracy forecasts were also slightly better than current methods. The new technique has been successfully tested in real‐time forecasts and is scheduled to become part of operational hurricane forecasting systems by 2025. Key Points A Storm‐Following 3DIAU is developed and implemented into the operational HAFS The Storm‐Following 3DIAU improves short‐term spin‐up in the operational HAFS The Storm‐Following 3DIAU improves intensity prediction of hurricanes over the 3 years of retrospective experiments

The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast System (UFS) framework. The finite-volume cubed sphere (FV3) based convection-allowing HAFS Stand-Alone Regional model (HAFS-SAR) was successfully implemented during Hurricane Forecast Improvement Project (HFIP) real-time experiments for the 2019 Atlantic TC season. HAFS-SAR has a single large 3-km horizontal resolution regional domain covering the North Atlantic basin. A total of 273 cases during the 2019 TC season are systematically evaluated against the best track and compared with three operational forecasting systems: Global Forecast System (GFS), Hurricane Weather Research and Forecasting model (HWRF), and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model (HMON). HAFS-SAR has the best performance in track forecasts among the models presented in this study. The intensity forecasts are improved over GFS, but show less skill compared to HWRF and HMON. The radius of gale force wind is over-predicted in HAFS-SAR, while the hurricane force wind radius has lower error than other models.

This study investigates the impact of assimilating GOES‐16 all‐sky Advanced Baseline Imager (ABI) brightness temperature observations using a newly developed, continuously self‐cycled, dual‐resolution, 3DEnVar data assimilation system within the Hurricane Analysis and Forecast System. Focusing on the pre‐rapid intensification period of Hurricane Laura, the results demonstrated that assimilating ABI observations without proper observation error treatment can be neutral or even detrimental. However, using a symmetric cloud impact approach to adaptively estimate observation errors enhances the Gaussianity of the Observation‐Minus‐Background Probability Distribution Functions, and significantly improves the analysis and predictions of Hurricane Laura. The improvements in the track forecasts can be attributed to better environmental analyses due to more effective use of clear sky observations, while the improved intensity forecasts stem from improved inner‐core dynamic and thermodynamic structures, achieved through the more effective use of cloudy‐sky observations. Plain Language Summary This research focused on improving how we use ABI observations from the GOES‐16 satellite for hurricane forecasting. The main challenge in using these observations is dealing with differences between the model's predictions and actual observations, as well as a limited understanding of the errors in the observations. To address this, a method called “symmetric cloud impact” was used to estimate the observation errors adaptively. A new hurricane data assimilation system, based on the next‐generation hurricane model, was tested with this approach. The results were promising, showing that assimilating these observations can significantly enhance predictions of both the track and intensity of hurricanes. Key Points The GOES‐16 all‐sky Advanced Baseline Imager (ABI) radiance data assimilation (DA) is implemented into the operational Hurricane Analysis and Forecast System system Symmetric cloud impact approach to estimate observation errors adaptively improves the all‐sky ABI brightness temperature assimilation Clear ABI DA improves track forecast via enhanced environment analysis. Cloudy ABI boosts inner‐core analysis, improving intensity forecast

The first version operational Hurricane Analysis and Forecast System (HAFS) implemented the Vortex Initialization (VI) technique to optimize tropical cyclone structure and intensity, which was adopted from the Hurricane Weather Research and Forecasting system (HWRF) and does not initialize cloud hydrometeors and vertical velocity. This limitation in the VI caused the inconsistency issue between hurricane vortex and its cloud in the model initial condition. A new VI, which can relocate or cycle cloud hydrometeors and vertical velocity, has been developed to solve this issue. For the cold start, the VI simply relocates the cloud and vertical velocity fields of Global Forecasting System (GFS) analysis; for the warm start, the cloud and vertical velocity associated with a hurricane in the GFS analysis are replaced by the fields extracted from the 6 h HAFS forecast of a previous cycle. This new VI has been tested for the 2023 HAFS-A real-time experiment configuration, and another sensitivity experiment without relocating or cycling both cloud and vertical velocity is conducted to examine the effect of the new VI. A comparison of the results reveals that the new VI improves the intensity forecast and generates a very realistic initial cloud field in correct position. Validating the model initial conditions with observed radar data reveals that the new VI captures the secondary eyewall of major hurricanes and asymmetric convective structure of weak tropical storms. This improvement of the cloud field in the model initial condition through the new VI expects to provide a better background for further data assimilation. Additional sensitivity experiment that only relocates or cycles cloud hydrometeors without correcting the vertical velocity field results in poorer intensity forecasts, which highlights the importance of vertical velocity in the model initial condition.

When frequency-modulated continuous-wave (FMCW) laser radar (Ladar) is employed for three-dimensional imaging, the echo signal is susceptible to modulation nonlinearity and platform vibration due to modulation and the short wavelength. These effects cause main-lobe widening, side-lobe elevation, and positional shift, which degrades distance detection accuracy. To solve these problems, this paper proposes a compensation method combining multiple synchrosqueezing transform (MSST), equal-phase interval resampling, and high-order ambiguity function (HAF). Firstly, variational mode decomposition (VMD) is applied to the optical prism signal to eliminate low-frequency noise and harmonic peaks. MSST is used to extract the time–frequency curve of the optical prism. The nonlinearity in the transmitted signal is estimated by two-step integration. An internal calibration signal containing nonlinearity is constructed at a higher sampling rate to resample the actual signal at an equal-phase interval. Then, HAF compensates for high-order vibration and residual phase error after resampling. Finally, symmetrical triangle wave modulation is used to remove constant-speed vibration. Verifying by actual data, the proposed method can enhance the main lobe and suppress the side lobe about 1.5 dB for a strong reflection target signal. Natural-target peaks can also be enhanced and the remaining peaks are suppressed, which is helpful to extract an accurate target distance.

Background. The present study investigated the skeletal and dental effect in class II division I growing patients due to mandibular deficiency treated with the hybrid aesthetic functional (HAF) appliance. Methods. A sample of 16 growing patients (5 boys and 11 girls; mean age: 9.50 years, standard deviation: 1.15) with class II division I malocclusion were treated using the HAF appliance for an average period of 10±3 months. For each patient, a cephalometric radiograph was taken before and after treatment, and digital analysis was applied using the WebCeph program. The statistical analysis was performed to evaluate dental and skeletal changes associated with the HAF appliance and determine if there were any statistically significant variations in anatomical measurements between the start and completion of the treatment. Results. The data showed a significant increase in SNB angle (P=0.002), leading to a significant decrease in ANB angle (P=0.001). The mandibular length significantly increased (P=0.008), the lower incisors were flared significantly (P=0.028), and the lower molars were extruded significantly (P≤0.001). Also, this study revealed a significant decrease in Wits appraisal (P≤0.001), overjet (P≤0.001), and overbite (P=0.041). Additionally, a significant increase in lower anterior facial height (P≤0.001), total facial height (P=0.001), and posterior facial height (P=0.037) were observed. Conclusion. The HAF appliance showed that it could be used to correct class II division 1 skeletal discrepancy by mandibular advancement. The HAF appliance increased all facial heights significantly.