Over the last decade, tropical cyclone (TC) track and intensity predictions have improved by nearly 50% in the Atlantic and Northern Indian Ocean, driven by advancements in ocean-coupled numerical models, data assimilation techniques, and an expanding network of observations. However, the prediction of severe weather events driven by convection, particularly those associated with heavy precipitation over land, has not kept pace with these improvements in TC forecasting. While 1-2 km horizontal resolutions are crucial for capturing convection over land and ocean, seamless prediction across scales demands an accurate representation of the coupled evolution of ocean, land, and atmospheric states. To address the complex problem of severe weather across a spectrum of atmospheric motions-including TCs over the ocean and severe convective systems over coastal and inland regions-we have developed the Indian Ocean-Land-Atmosphere (IOLA) Coupled Mesoscale Prediction Framework. This Framework integrates the well-tested nonhydrostatic model (NMM) dynamical core with advanced nesting techniques from the hurricane weather research and forecast (HWRF) system. It further incorporates ocean coupling from HWRF and physics packages adopted from the WRF community model. This represents the first-ever coupled modeling system explicitly designed to tackle extreme weather events across multiple domains and scales. Extensive testing of this novel modeling framework demonstrates that a high-resolution (1-2 km) "all-purpose" severe weather prediction system can effectively address the challenges of forecasting extreme weather over the Indian region. One of the key focuses of this work is the application of 1-km horizontal resolution moving nests over the monsoon region, where synoptic-scale interactions play a critical role in modulating severe weather and heavy precipitation events. With this configuration, the model provides a high equitable threat score (ETS) > 0.18 for heavy to extreme rainfall events for 48 h and above lead times. This framework enables a unified approach to simulating severe weather phenomena accurately and flexibly. Also, it sets a new benchmark for seamless prediction of extreme weather, paving the way for improved resilience against coastal hazards and inland severe weather events.

New satellite constellations to provide high-resolution atmospheric observations from microwave (MW) sounders operating in low-Earth orbit are now coming online and are providing operationally useful data. The first of these missions, the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 7 and 25, 2023 (Eastern Daylight Time, two CubeSats in each of the two launches). TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS is providing rapid-refresh MW measurements (median refresh rate of better than 60 min early in the mission with four functional CubeSats, and now approximately 70-90 min with three functional CubeSats) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of TCs have been captured thus far by the TROPICS mission, revealing the detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing TC analysis at operational centers. Here, we present an overview of the TROPICS mission after two years of successful science operations with a focus on the suite of geophysical (Level 2) products (atmospheric vertical temperature and moisture profiles, instantaneous surface rain rate, and TC intensity) and the science investigations that have been enabled by these new measurements.

The frequency and severity of extreme meteorological and hydrological events, including tropical cyclones (TCs), are being reshaped by global climate change, posing significant challenges to infrastructure resilience and disaster management. This study evaluates the performance of satellite-derived quantitative precipitation estimates (QPEs) for hydrological applications during landfalling TCs, focusing on the interplay between localized rainfall and flooding. Using globally available datasets, we analyze eight TCs, including Hurricane Charley (2004) and Hurricane Michael (2018), to address three critical questions: (i) the reliability of satellite QPEs during TC scenarios, (ii) variability among gridded precipitation products (ground-based, radar, and satellite), and (iii) the implications of these differences for surface hydrology and flood risk.Results indicate that the IMERG, satellite product underpredicts precipitation at higher quantiles but aligns well with ground-based and radar-derived products at lower quantiles. Urban areas exhibit the largest discrepancies in runoff estimates, with errors up to 18 mm, while agricultural and forested regions show more stable performance. Along TC tracks, IMERG reliably estimates hydrological variables in 90% of scenarios, with errors ranging from 0 to 10 mm. These findings underscore the utility of satellite QPEs like IMERG in understanding and forecasting short-term hydrological impacts of TCs, even amidst variations in precipitation intensity and location.This research highlights the critical role of satellite precipitation products in addressing global disparities in real-time flood prediction systems, informing infrastructure planning, and mitigating societal vulnerability to extreme events. It contributes to the broader effort of enhancing early warning systems and proactive disaster risk management in the face of evolving climate extremes.

Development of accurate planetary boundary layer (PBL) parameterizations in high-wind conditions is crucial for improving tropical cyclone (TC) forecasts. Given that eddy-diffusivity mass-flux (EDMF)-type PBL schemes are designed for nonhurricane boundary layers, this study examines the uncertainty of MF parameterizations in hurricane conditions by performing three-dimensional idealized simulations. Results show that the surface-driven MF plays a dominant role in the nonlocal turbulent fluxes and is comparable to the magnitude of downgradient momentum fluxes in the middle portion of TC boundary layers outside the radius of maximum wind (RMW); in contrast, the stratocumulus-top-driven MF is comparably negligible and exerts a marginal impact on TC simulations. To represent the impact of vertical wind shear on damping rising thermal plumes, a new approach of tapering surface-driven MF based on the surface stability parameter is proposed, aiming to retain the surface-driven MF only in unstable boundary layers. Compared to a traditional approach of MF tapering based on 10-m wind speeds, the new approach is physically more appealing as both shear and buoyancy forcings are considered and the width of the effective zone responds to diurnal variations of surface buoyancy forcing. Compared to the experiments retaining the original MF components, using either approach of MF tapering can lead to a stronger and more compact inner core due to enhanced boundary layer inflow outside the RMW; nevertheless, the radius of gale-force wind and inflow layer depth are only notably reduced using the new approach. Comparison to observations and further discussions on MF parameterizations in high-wind conditions are provided.

Understanding how model physics impact tropical cyclone (TC) structure, motion, and evolution is critical for the development of TC forecast models. This study examines the impacts of microphysics and planetary boundary layer (PBL) physics on forecasts using the Hurricane Analysis and Forecast System (HAFS), which is newly operational in 2023. The "HAFS-B" version is specifically evaluated, and three sensitivity tests (for over 400 cases in 15 Atlantic TCs) are compared with retrospective HAFS-B runs. Sensitivity tests are generated by 1) changing the microphysics in HAFS-B from Thompson to GFDL, 2) turning off the TC-specific PBL modifications that have been implemented in operational HAFS-B, and 3) combining the PBL and microphysics modifications. The forecasts are compared through standard verification metrics, and also examination of composite structure. Verification results show that Thompson microphysics slightly degrades the days 3-4 forecast track in HAFS-B, but improves forecasts of long-term intensity. The TC-specific PBL changes lead to a reduction in a negative intensity bias and improvement in RI skill, but cause some degradation in prediction of 34-kt (1 kt ' 0.51 m s21) wind radii. Composites illustrate slightly deeper vortices in runs with the Thompson microphysics, and stronger PBL inflow with the TC-specific PBL modifications. These combined results demonstrate the critical role of model physics in regulating TC structure and intensity, and point to the need to continue to develop improvements to HAFS physics. The study also shows that the combination of both PBL and microphysics modifications (which are both included in one of the two versions of HAFS in the first operational implementation) leads to the best overall results.

The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA's National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%-50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program.

While recent observational studies of intensifying (IN) versus steady-state (SS) hurricanes have noted several differences in their axisymmetric and asymmetric structures, there remain gaps in the characterization of these differences in a fully three-dimensional framework. To address these limitations, this study investigates differences in the shear -relative asymmetric structure between IN and SS hurricanes using airborne Doppler radar data from a dataset covering an extended period of time. Statistics from individual cases show that IN cases are characterized by peak wavenumber-1 ascent concentrated in the upshear-left (USL) quadrant at -12-km height, consistent with previous studies. Moderate updrafts (2-6 m s21) occur more frequently in the downshear eyewall for IN cases than for SS cases, likely leading to a higher frequency of moderate to strong updrafts USL above 9 -km height. Composites of IN cases show that low-level outflow from the eye region associated with maximum wavenumber-1 vorticity inside the radius of maximum wind (RMW) in the downshear -left quadrant converges with low-level inflow outside the RMW, forming a stronger local secondary circulation in the downshear eyewall than SS cases. The vigorous eyewall convection of IN cases produces a net vertical mass flux increasing with height up to -5 km and then is almost constant up to 10 km, whereas the net vertical mass flux of SS cases decreases with height above 4 km. Strong USL upper -level ascent provides greater potential for the vertical development of the hurricane vortex, which is argued to be favorable for continued intensification in shear environments.

Entrainment of dry moat air with low equivalent potential temperature laterally into the eyewall and rainbands is a unique turbulent process in the inner-core region of a tropical cyclone (TC). By analyzing in-situ aircraft measurements collected by the reconnaissance flights that penetrated the eyewalls and rainbands of Hurricanes Rita (2005), Patricia (2015), Harvey (2017), and Michael (2018), as well as numerical simulations of Hurricanes Patricia (2015), and Michael (2018), we show that the moat air entrained into the eyewall and rainbands meets the instability criterion, and therefore, sinks unstably as a convective downdraft. The resultant positive buoyancy fluxes are an important source for the turbulent kinetic energy (TKE) in the eyewall and rainband clouds. This mechanism of TKE generation via lateral entrainment instability should be included in the TKE-type turbulent mixing schemes for a better representation of turbulent transport processes in numerical forecasts of TCs.

Continuous development and evaluation of planetary boundary layer (PBL) parameterizations in hurricane conditions are crucial for improving tropical cyclone (TC) forecasts. A turbulence kinetic energy (TKE)-based eddy -diffusivity mass -flux (EDMF-TKE) PBL scheme, implemented in NOAA's Hurricane Analysis and Forecast System (HAFS), was recently improved in hurricane conditions using large-eddy simulations. This study evaluates the perfor-mance of HAFS TC forecasts with the original (experiment HAFA) and modified EDMF-TKE (experiment HAFY) based on a large sample of cases during the 2021 North Atlantic hurricane season. Results indicate that intensity and struc-ture forecast skill was better overall in HAFY than in HAFA, including during rapid intensification. Composite analyses demonstrate that HAFY produces shallower and stronger boundary layer inflow, especially within 1-3 times the radius of maximum wind (RMW). Stronger inflow and more moisture in the boundary layer contribute to stronger moisture conver-gence near the RMW. These boundary layer characteristics are consistent with stronger, deeper, and more compact TC vortices in HAFY than in HAFA. Nevertheless, track skill in HAFY is slightly reduced, which is in part attributable to the cross-track error from a few early cycles of Hurricane Henri that exhibited ;400 n mi (1 n mi 5 1.852 km) track error at longer lead times. Sensitivity experiments based on HAFY demonstrate that turning off cumulus schemes notably reduces the track errors of Henri while turning off the deep cumulus scheme reduces the intensity errors. This finding hints at the necessity of unifying the mass fluxes in PBL and cumulus schemes in future model physics development.

During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement.

Hurricane Florence made landfall over the Carolinas 14 September 2018, bringing over 30 inches of rainfall. What remains understudied is the possible storm re-intensification by wet and warm antecedent soil moisture (ASM), known as the Brown Ocean Effect (BOE). This study investigates this effect with two approaches: (a) two satellite-based soil moisture (SM) data and (b) model simulation. The averaged Cyclone Global Navigation System and Soil Moisture Active Passive SM enables examination of land-atmosphere interaction at a sub-daily scale. Both observations and simulation results manifest positive feedback between ASM and rainfall intensity, with 3 days prior to landfall being the typical antecedent time scale. Wet (dry) ASM lead to intense (light) and concentrated (widespread) rains. We also found that soil temperature can modulate the BOE. This study aims to advance our understanding of land-atmosphere feedback and calls to acquire accurate antecedent land states to enhance forecast skills. Antecedent wet and warm soil conditions can maintain or re-intensify storms via vertical mixing of water vapor, a phenomenon called the Brown Ocean Effect (BOE). Previous studies investigating the existence of BOE have considered model simulations by perturbing antecedent soil moisture. Given the uncertainties in weather models, it is important to cross-validate the soil-rainfall feedback by both observations and simulations. It is also critical to understand the time scale of such feedback. The growing number of remote sensing soil moisture (SM) and weather radar rainfall products at various resolutions and spatial-temporal sampling offer unprecedented opportunities to examine this effect. Our results show consistent positive soil-rainfall feedback from both observations and simulations. Wet antecedent soils promote intense yet concentrated rains, while dry antecedent soils cause light and widespread rains. Meanwhile, soil temperature was also found to play an important role in mediating the feedback, with colder soils even leading to a negative correlation between wet antecedent soils and extreme rainfall rates. We advocate accurate representation of antecedent land surface states combined with assimilation of remote sensing SM products into models to enhance tropical cyclone forecasts. We hypothesize the wet antecedent soil moisture re-intensified Hurricane FlorenceWe found that Hurricane Florence was re-intensified by wet antecedent soils, leading to intense and concentrated rainfallWe found that cold antecedent soils impair the Brown Ocean effect in Hurricane Florence

Raindrop Size Distributions (RSDs) samples from 15 flight missions though 6 hurricanes collected by Precipitation Imaging Probe (PIP) during National Oceanic and Atmospheric Administration’s hurricane field program in 2020 are used to study gamma fits of the RSDs in hurricanes. The method of moment (MM) is adopted for solving for the three parameters in gamma distribution. The results show that the usage of lower (higher) moments produces large biases for integral rain variables (IRV) of higher (lower) moments. These biases can be alleviated by extracting the best fits from five groups that use increasing higher orders of moments for MM. An intercept (N0)— slope (λ) relation identified from the fitted gamma distributions captures 92% of the variance of the data, where the majority of remaining 8% can be further captured by including the impact of liquid water content (LWC), as shown in the results from a random forest regression model.

Abstract Taking the examples of Hurricane Florence (2018) over the Carolinas and Hurricane Harvey (2017) over the Texas Gulf Coast, the study attempts to understand the performance of slab, single‐layer Urban Canopy Model (UCM), and Building Environment Parameterization (BEP) in simulating hurricane rainfall using the Weather Research and Forecasting (WRF) model. The WRF model simulations showed that for an intense, large‐scale event such as a hurricane, the model quantitative precipitation forecast over the urban domain was sensitive to the model urban physics. The spatial and temporal verification using the modified Kling‐Gupta efficiency and Method for Object based Diagnostic and Evaluation in Time Domain suggests that UCM performance is superior to the BEP scheme. Additionally, using the BEP urban physics scheme over UCM for landfalling hurricane rainfall simulations has helped simulate heavy rainfall hotspots.

This corrigendum corrects the time-averaged plots of the kinetic energy transfer terms in our published article.

Accurately representing boundary layer turbulent processes in numerical models is critical to improve tropical cyclone forecasts. A new turbulence kinetic energy (TKE)-based moist eddy-diffusivity mass-flux (EDMF-TKE) planetary boundary layer scheme has been implemented in NOAA's Hurricane Analysis and Forecast System (HAFS). This study evaluates EDMF-TKE in hurricane conditions based on a recently developed framework using large-eddy simulation (LES). Single-column modeling tests indicate that EDMF-TKE produces much greater TKE values below 500-m height than LES benchmark runs in different high-wind conditions. To improve these results, two parameters in the TKE scheme were modified to ensure a match between the PBL and surface-layer parameterizations. Additional improvements were made by reducing the maximum allowable mixing length to 40 m based on LES and observations, by adopting a different definition of boundary layer height, and by reducing nonlocal mass fluxes in high-wind conditions. With these modifications, the profiles of TKE, eddy viscosity, and winds compare much better with LES results. Three-dimensional idealized simulations and an ensemble of HAFS forecasts of Hurricane Michael (2018) consistently show that the modified EDMF-TKE tends to produce a stronger vortex with a smaller radius of maximum wind than the original EDMF-TKE, while the radius of gale-force wind is unaffected. The modified EDMF-TKE code produces smaller eddy viscosity within the boundary layer compared to the original code, which contributes to stronger inflow, especially within the annulus of 1-3 times the radius of maximum wind. The modified EDMF-TKE shows promise to improve forecast skill of rapid intensification in sheared environments.

Since 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX), led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic and Meteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain and operate the WP-3D and Gulfstream IV-SP (G-IV) Hurricane Hunter aircraft, and NCEP’s National Hurricane Center and Environmental Modeling Center, who task airborne missions to gather data used by forecasters for analysis and forecasting and for ingest into operational numerical weather prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an integrated approach of analyzing observations from aircraft, initializing and evaluating forecast models with those observations, and developing new airborne instrumentation and observing strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts. This summary article not only highlights recent IFEX contributions toward improved TC understanding and prediction, but also reflects more broadly on the accomplishments of the program during the 16 years of its existence. It describes how IFEX addresses high-priority forecast challenges, summarizes recent collaborations, describes advancements in observing systems monitoring structure and intensity, as well as in assimilation of aircraft data into operational models, and emphasizes key advances in understanding of TC processes, particularly those that lead to rapid intensification. The article concludes by laying the foundation for the next generation of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge inundation, and tornadoes, that have gained notoriety during the last few years after several devastating landfalling TCs.

Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration’s hurricane field program in 2020 are used to study reflectivity ( Z ) and rainfall rate (RR) ( R ) relationship (i.e., Z ‐ R relationship). The results show that the Z ‐ R distribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr −1 , after which it decreases rapidly. The range of the estimated RR from a power‐law Z ‐ R relationship can be as large as 50 mm hr −1 at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter ( D m ) along with radar reflectivity improves the estimated RR significantly.

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.