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Finger‐like km‐scale features have been observed along the inner‐edge of the eyewall of intense hurricanes. But due to the limited availability of observations, many important aspects of these features remain unknown. In this study, we aim to offer insights on the nature of these phenomena based on a four‐day‐duration O(100 m) grid spacing simulation that covers the inner‐core region of an idealized hurricane. The simulation successfully captured the finger‐like features, which closely resemble observed ones. We propose that these features are formed due to the shear instability associated with vertical distribution of the tangential wind in the inner‐core region. This proposed mechanism offers insights on several key characteristics of the features of interest, including their emergence time, frequency, radial location and vertical extent. Our study also demonstrates the feasibility of using multi‐level nesting for O(100 m) grid spacing hurricane simulations and predictions, aligning with the goals for next generation hurricane models. Plain Language Summary The inner core region of hurricanes harbors complex dynamical features, including small‐scale clouds characterized by finger‐like appearances pointing toward the hurricane eye. These features have been frequently observed in intense hurricanes. However, many basic aspects of these features remain unknown, particularly regarding what controls their occurrence and location. We conduct a numerical simulation with a very fine (about 100 m) horizontal grid spacing to investigate the nature of these features. Our proposed mechanism explains several key characteristics of these features. Key Points We conduct a O(100 m) grid spacing simulation that captures the finger‐like features along the inner edge of the hurricane eyewall We propose a mechanism that links the finger cloud formation and hurricane‐scale dynamics This proposed mechanism explains the emergence time, frequency, radial location and vertical extent of the finger features

As part of the Hurricane Forecast Improvement Project (HFIP), recent boundary layer physics upgrades in the operational Hurricane Weather Research and Forecasting (HWRF) Model have benefited from analyses of in situ aircraft observations in the low-level eyewall region of major hurricanes. This study evaluates the impact of these improvements to the vertical diffusion in the boundary layer on the simulated track, intensity, and structure of four hurricanes using retrospective HWRF forecasts. Structural metrics developed from observational composites are used in the model evaluation process. The results show improvements in track and intensity forecasts in response to the improvement of the vertical diffusion. The results also demonstrate substantial improvements in the simulated storm size, surface inflow angle, near-surface wind profile, and kinematic boundary layer heights in simulations with the improved physics, while only minor improvements are found in the thermodynamic boundary layer height, eyewall slope, and the distributions of vertical velocities in the eyewall. Other structural metrics such as warm core anomaly and warm core height are also explored. Reasons for the structural differences between the two sets of forecasts with different physics are discussed. This work further emphasizes the importance of aircraft observations in model diagnostics and development, endorsing a developmental framework for improving physical parameterizations in hurricane models.

Though tropical cyclone (TC) models have been routinely evaluated against track and intensity observations, little work has been performed to validate modeled TC wind fields over land. In this paper, we present a simple framework for evaluating simulated low‐level inland winds with in‐situ observations and existing TC structure theory. The Automated Surface Observing Systems, Florida Coastal Monitoring Program, and best track data are used to generate a theory‐predicted wind profile that reasonably represents the observed radial distribution of TC wind speeds. We quantitatively and qualitatively evaluated the modeled inland TC wind fields, and described the model performance with a set of simple indicators. The framework was used to examine the performance of a high‐resolution two‐way nested Geophysical Fluid Dynamics Laboratory model on recent U.S. landfalling TCs. Results demonstrate the capacity of using this framework to assess the modeled TC low‐level wind field in the absence of dense inland observations. Plain Language Summary Some of the biggest human impacts of tropical cyclone (TC) winds come after the TC makes landfall. A skillful prediction of the radial distribution of winds is essential for forecasting TC‐induced inland hazards. However, the forecast skill of numerical hurricane models on inland TC wind fields has rarely been evaluated since it is challenging to collect wind observations during landfall, and the network of regular weather observations is too spread out to capture the strongest winds associated with a TC. This inhibits the improvement of forecast models and limits our understanding of the TC's inland evolution. Our work combines available inland in‐situ wind observations over the southeastern U.S. with existing TC structure theory, and presents a new “optimal” estimate of the post‐landfall winds. Our framework is found to be useful for evaluating the post‐landfall TC winds in hurricane forecast models. In addition, the new evaluation technique can intuitively demonstrate how well the model simulates TC intensity and structure. Key Points We introduce a new framework for evaluating modeled inland tropical cyclone (TC) wind fields with observation‐based, theory‐predicted wind profiles The theory‐predicted wind profile well represents the observed radial distribution of inland TC wind speeds We propose simple indicators to summarize the model performance on inland wind field predictions

The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High‐resolution prediction on Earth‐to‐Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skills as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4–5-day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.

NOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.

This study investigates the role of the parameterized boundary layer structure in hurricane intensity change using two retrospective HWRF forecasts of Hurricane Earl (2010) in which the vertical eddy diffusivity Km was modified during physics upgrades. Earl undergoes rapid intensification (RI) in the low-Km forecast as observed in nature, while it weakens briefly before resuming a slow intensification at the RI onset in the high-Km forecast. Angular momentum budget analysis suggests that Km modulates the convergence of angular momentum in the boundary layer, which is a key component of the hurricane spinup dynamics. Reducing Km in the boundary layer causes enhancement of both the inflow and convergence, which in turn leads to stronger and more symmetric deep convection in the low-Km forecast than in the high-Km forecast. The deeper and stronger hurricane vortex with lower static stability in the low-Km forecast is more resilient to shear than that in the high-Km forecast. With a smaller vortex tilt in the low-Km forecast, downdrafts associated with the vortex tilt are reduced, bringing less low-entropy air from the midlevels to the boundary layer, resulting in a less stable boundary layer. Future physics upgrades in operational hurricane models should consider this chain of multiscale interactions to assess their impact on model RI forecasts.

Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.

Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.

This study examines the influence of radiative heating on the prediction of tropical cyclone (TC) intensification in the Hurricane Weather Research and Forecasting (HWRF) model. Previous idealized modeling and observational studies demonstrated that radiative heating can substantially modulate the evolution of TC intensity. However, the relevance of this process under realistic conditions remains unclear. Here, we use observed longwave radiative heating to explore the performance of TC forecasts in HWRF simulations. The performance of TC intensity forecasts is then investigated in the context of radiative heating forecasts. In observations and HWRF forecasts, high clouds near the TC center increase the convergence of radiative fluxes. A sharp spatial gradient (≥60 W/m2) in the flux convergence from the TC center outward toward the environment is associated with subsequent TC intensification. More accurate simulation of the spatial structure of longwave radiative heating is associated with more accurate TC intensity forecasts. Plain Language Summary Satellite measurements observed larger radiation heating near the center of intensifying tropical cyclones. Previous idealized modeling studies suggest that this heating facilitates tropical cyclone development. In this study, we investigate how radiative heating affects the ability of a tropical cyclone forecast model to predict tropical cyclone intensification. Our results demonstrate that the model forecasts of intensity improve when the model better reproduces the observed spatial structure of radiative heating associated with the tropical cyclone. Key Points Tropical cyclones that intensify tend to have greater longwave convergence within the atmospheric column prior to intensification An operational forecast model can capture the signal of TC intensification in longwave radiation The ability to simulate radiation in the forecast model can influence its prediction skills of tropical cyclone intensification

Cold wakes left behind by tropical cyclones (TCs) have been documented since the 1940s. Many questions remain, however, regarding the details of the processes creating these cold wakes and their in-storm feedbacks onto tropical cyclone intensity. This largely reflects a paucity of measurements within the ocean, especially during storms. Moreover, the bulk of TC research efforts have investigated deep ocean processes—where tropical cyclones spend the vast majority of their lifetimes—and very little attention has been paid to coastal ocean processes despite their critical importance to shoreline populations. Using Hurricane Irene (2011) as a case study, the impact of the cooling of a stratified coastal ocean on storm intensity, size, and structure is quantified. Significant ahead-of-eye-center cooling (at least 6°C) of the Mid-Atlantic Bight occurred as a result of coastal baroclinic processes, and operational satellite SST products and existing coupled ocean–atmosphere hurricane models did not capture this cooling. Irene’s sensitivity to the cooling is tested, and its intensity is found to be most sensitive to the cooling over all other tested WRF parameters. Further, including the cooling in atmospheric modeling mitigated the high storm intensity bias in predictions. Finally, it is shown that this cooling—not track, wind shear, or dry air intrusion—was the key missing contribution in modeling Irene’s rapid decay prior to New Jersey landfall. Rapid and significant intensity changes just before landfall can have substantial implications on storm impacts—wind damage, storm surge, and inland flooding—and thus, coastal ocean processes must be resolved in future hurricane models.