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.

The Hurricane Analysis and Forecast System (HAFS) was implemented operationally in 2023, and work is ongoing to develop and improve future versions of HAFS. During the 2024 hurricane season, an experimental version of HAFS-B (HAFSV2.0.1B or HFXB) was tested in near-real time in the Atlantic and eastern Pacific basins. This experimental version features several upgrades to the model, including data assimilation improvements as well as testing the scale-aware Tiedtke convective scheme in HAFS for the first time. The track bias characteristics were very different from other versions of HAFS run in 2024, with much less across-track bias. This difference is promising for maintaining diversity between HAFS-A and HAFS-B moving forward and also for continuing to improve the overall track forecast skill. HFXB also showed comparable skill to operational models for rapid intensification, though some lingering negative intensity bias is something that will need to be examined further. The detailed analysis of several key storms from the 2024 Atlantic season, including Hurricane Beryl, Hurricane Helene, and Hurricane Milton, highlights some of the strengths of these experimental forecasts, including the ability to accurately predict the fine-scale structure changes associated with rapid intensification, which helps forecasters improve intensity predictions and warnings ahead of the storm. These case studies also highlight areas for improvement in future upgrades to HAFS, motivating further research for both operational and experimental testing.

Improving the prediction of weather events is always an important research area and challenging task to the meteorologists since it poses a major impact on human life, properties and countries economy. The operational and research centers around the globe have been working to better understand the multiscale interactions involved to advance severe weather including Tropical Cyclones and thunderstorm predictions. The present review article focuses on research activities with a specific emphasis on Numerical Weather Prediction (NWP) methods that led to improvements in severe weather prediction over India during the last three decades. This work also highlights the continuous efforts of India Meteorological Department (IMD) in increasing the observational network and severe weather monitoring. The evolution of NWP models and associated advancements in genesis, movement and precipitation forecasts of extreme events by these models are discussed.

Convective cold pools—regions of cooled, dense air formed by evaporating rainfall—play a pivotal role in modulating atmospheric convection, yet their influence on hurricane dynamics remains insufficiently explored, especially in real-world simulations. In this study, we investigate the role of convective cold pools in the evolution of Hurricane Helene (2024) using a modified version of the Hurricane Weather Research and Forecasting model (HWRFxUT). Hurricane Helene formed in the Caribbean and intensified to become one of the deadliest hurricanes in recent history, offering a unique opportunity to study cold pool–hurricane interactions. The model setup includes nested domains at 9 km, 3 km, and 1 km resolution over the contiguous United States and employs a set of sensitivity experiments. Specifically, the rainfall evaporation rate in the Ferrier–Aligo microphysics scheme is altered by 20%, 50%, 150%, and 180% relative to a control run to assess how changes in cold pool characteristics affect the storm.Cold pools are identified using a watershed algorithm, enabling systematic comparisons of their spatial extent and thermodynamic properties across all experiments. Analyses show that modifications to the rainfall evaporation rate significantly influence the development and distribution of cold pools in the vicinity of Hurricane Helene, with consequent impacts on storm rainfall, intensity, and track. The results underscore how changes in cold pool strength can yield marked differences in hurricane structure and evolution. These findings highlight the importance of accurately representing cold pool processes in numerical models to enhance tropical cyclone forecasts and underscore the need for continued research into this critical yet under examined aspect of hurricane physics.

Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.

Accurate weather forecasting is critical for science and society. However, existing methods have not achieved the combination of high accuracy, low uncertainty, and high computational efficiency simultaneously. On one hand, traditional numerical weather prediction (NWP) models are computationally intensive because of their complexity. On the other hand, most machine learningbased weather prediction (MLWP) approaches offer efficiency and accuracy but remain deterministic, lacking the ability to capture forecast uncertainty. To tackle these challenges, we propose a conditional diffusion model, CoDiCast, to generate global weather predictions, integrating accuracy and uncertainty quantification at a modest computational cost. The key idea behind the prediction task is to generate realistic weather scenarios at a future time point, conditioned on observations from the recent past. Due to the probabilistic nature of diffusion models, they can be properly applied to capture the uncertainty of weather predictions. Therefore, we accomplish uncertainty quantifications by repeatedly sampling from stochastic Gaussian noise for each initial weather state and running the denoising process multiple times. Experimental results demonstrate that CoDiCast outperforms several existing MLWP methods in accuracy, and is faster than NWP models in inference speed. Our model can generate 6-day global weather forecasts, at 6-hour steps and 5.625-degree latitudelongitude resolutions, for over 5 variables, in about 12 minutes on a commodity A100 GPU machine with 80GB memory. The source code is available at https://github.com/JimengShi/CoDiCast.

The Integrated Forecasting System (IFS) developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) has been regarded as the best guidance for hurricane track forecasts for years. However, the performance of U.S. models on hurricane forecasts has been catching up. Since 2019, various Finite-Volume Cubed-Sphere Dynamical Core (FV3)-based models, including the National Centers for Environmental Prediction (NCEP) operational Global Forecast System (GFS), newly operational Hurricane Analysis and Forecast System (HAFS), and research-oriented Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD), have consistently demonstrated improved hurricane forecasts in the North Atlantic basin, relative to the previous generation of National Oceanic and Atmospheric Administration (NOAA) operational and research models. This article presents the progress that has been made and identifies areas for improvement for U.S. model development on hurricane forecasts. SIGNIFICANCE STATEMENT: Hurricane predictions in the North Atlantic basin performed by the numerical models developed in the United States have been improved in the past 5 years along with the major upgrade of the models' dynamical core. The gap in hurricane forecast skills between the U.S. models and the world-leading European model has been substantially decreasing. This article presents the 10-yr progression of the state-of-the-art operational and research-oriented U.S. models compared to the European models for North Atlantic tropical cyclone forecasts to demonstrate this achievement.

Accurate weather forecasting is critical for science and society. However, existing methods have not achieved the combination of high accuracy, low uncertainty, and high computational efficiency simultaneously. On one hand, traditional numerical weather prediction (NWP) models are computationally intensive because of their complexity. On the other hand, most machine learning-based weather prediction (MLWP) approaches offer efficiency and accuracy but remain deterministic, lacking the ability to capture forecast uncertainty. To tackle these challenges, we propose a conditional diffusion model, CoDiCast, to generate global weather prediction, integrating accuracy and uncertainty quantification at a modest computational cost. The key idea behind the prediction task is to generate realistic weather scenarios at a future time point, conditioned on observations from the recent past. Due to the probabilistic nature of diffusion models, they can be properly applied to capture the uncertainty of weather predictions. Therefore, we accomplish uncertainty quantifications by repeatedly sampling from stochastic Gaussian noise for each initial weather state and running the denoising process multiple times. Experimental results demonstrate that CoDiCast outperforms several existing MLWP methods in accuracy, and is faster than NWP models in inference speed. Our model can generate 6-day global weather forecasts, at 6-hour steps and 5.625-degree latitude-longitude resolutions, for over 5 variables, in about 12 minutes on a commodity A100 GPU machine with 80GB memory. The source code is available at https://github.com/JimengShi/CoDiCast.

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.

Tropical cyclones models have long used nesting to achieve higher resolution of the inner core than was feasible for entire model domains. These high resolution nests have been shown to better capture storm structures and improve forecast accuracy. The Hurricane Analysis and Forecast System (HAFS) is the new-generation numerical model embedded within NOAA’s Unified Forecast System (UFS). The document highlights the importance of high horizontal resolution (2 km or finer) in accurately simulating the small-scale features of tropical cyclones, such as the eyewall and eye. To meet this need, HAFS was developed by NOAA leveraging a high-resolution, storm-following nest. This nest moves with the cyclone, allowing better representation of small-scale features and more accurate feedback between the cyclone’s inner core and the larger environment. This hurricane following nest capability, implemented in the Finite-Volume Cubed-Sphere (FV3) dynamical core within the UFS framework, can be run both within the regional as well as global forecast systems. A regional version of HAFS with a single moving nest went into operations in 2023. HAFS also includes the first ever moving nest implemented within a global model which is currently being used for research. In this document we provide details of the implementation of moving nests and provide some of the results from both global and regional simulations. For the first time NOAA P3 flight data was used to evaluate the inner core structure from the global run.

In the 2023 hurricane season, the Hurricane Analysis and Forecast System (HAFS) based Ensemble Prediction System (EPS) was being ported to the Amazon Web Service cloud. This relocation aimed to provide real-time hurricane probabilistic forecast guidance for National Hurricane Center (NHC) forecasters. The system comprises Stochastically Perturbed Physics Tendencies (SPPT), Stochastically Kinetic Energy Backscatter (SKEB), and Stochastically Perturbed PBL Humidity (SHUM). Initial and boundary conditions are derived from the National Centers for Environmental Prediction (NCEP) operational Global Ensemble Forecast System (GEFS) 21-member forecast data. The performance of HAFS-EPS for 2023 Atlantic hurricane forecasts was compared with the global GEFS, global ECMWF ensemble, and operational HAFS-A/B forecasts. This comparison highlighted the advantages of higher-resolution regional ensemble forecasts for hurricane track, intensity, Rapid Intensification (RI) probability, and various hazards, including wind, wave, and storm surge probability guidance.

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 Dorian (2019), a category-5 tropical cyclone (TC), was characterized by a large spread in track forecasts as it moved northwest. A set of 80 ensemble forecasts from the Hurricane Analysis and Forecast System (HAFS) was produced to evaluate Dorian's track spread and the factors that contributed to it. Track spread was particularly critical at long lead times (5-7 days after initialization near the Lesser Antilles), because of the uncertainty in the location of landfall and hazards. Four clusters of members were analyzed based on the 7-day track, characterized by Dorian moving: 1) slowly near the northern Bahamas (closest to reality), 2) across the Florida Peninsula, 3) slowly into Florida's east coast, and 4) quickly north of the Bahamas. Ensemble sensitivity techniques were applied to identify areas that were most critical for Dorian's track. Key differences were found in the strength of the subtropical ridge over the western Atlantic Ocean with a weaker ridge and slower easterly steering flow in the offshore groups. Subtle differences in the synoptic pattern over the United States also appeared to affect the timing of Dorian's northward turn, specifically the strength of a shortwave trough moving over the Ohio Valley. Despite some early track differences, the correlation between early and late track errors was not significant. An examination of four members further highlights the differences in steering and the strength of the subtropical ridge. This study demonstrates the utility of ensemble datasets for studying TC forecast uncertainty and the importance of medium-range modeling of synoptic-scale steering features to accurately predict the track of tropical cyclones. Significance StatementHurricane Dorian was a catastrophic hurricane for the Bahamas and got very close to Florida without directly impacting the state. Some early forecasts showed the storm moving directly into or across Florida; others correctly showed the storm stalling over the Bahamas and then turning northward. This track forecast uncertainty made preparations in Florida challenging; therefore, we wanted to better understand why Dorian took the track that it did, to see what this tells us about the factors that affect hurricane tracks, and learn for future storms. We looked at an ensemble of 80 runs of a hurricane model, initiated at the same time. Some runs showed a Florida landfall; others showed Dorian stalling over the Bahamas. The strength of the subtropical ridge over the Atlantic north of Dorian and an upper-level trough of low pressure over the United States were key influences on storm path. These two large-scale features were better forecast in the ensemble members that correctly showed Dorian stalling and turning northward. This study shows how useful ensembles can be for understanding the processes driving hurricane motion and also shows that it is critical to forecast multiple synoptic-scale features correctly to accurately predict a hurricane's track 5-7 days in advance.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v4]Coastal downwelling intensifies landfalling hurricanesAuthorsLewis JamesGrameriDJun AZhangiDGhassan J.AlakaiDAndrewHazeltonSundararamanGopalakrishnanSee all authors Lewis James GrameriDCorresponding Author• Submitting AuthorNOAA AOMLiDhttps://orcid.org/0000-0003-4772-1991view email addressThe email was not providedcopy email addressJun A ZhangiDHurricane Research Division, AOML, NOAAiDhttps://orcid.org/0000-0003-3713-0223view email addressThe email was not providedcopy email addressGhassan J. AlakaiDCooperative Institute for Marine and Atmospheric StudiesiDhttps://orcid.org/0000-0003-3137-8535view email addressThe email was not providedcopy email addressAndrew HazeltonUniversity of Miami CIMASview email addressThe email was not providedcopy email addressSundararaman GopalakrishnanHurricane Research Division, AOML,NOAAview email addressThe email was not providedcopy email address

To forecast tropical cyclone (TC) intensity and structure changes with fidelity, numerical weather prediction models must be “high definition,” i.e., horizontal grid spacing ≤ 3 km, so that they permit clouds and convection and resolve sharp gradients of momentum and moisture in the eyewall and rainbands. Storm-following nests are computationally efficient at fine resolutions, providing a practical approach to improve TC intensity forecasts. Under the Hurricane Forecast Improvement Project, the operational Hurricane Weather Research and Forecasting (HWRF) system was developed to include telescopic, storm-following nests for a single TC per model integration. Subsequently, HWRF evolved into a state-of-the-art tool for TC predictions around the globe, although its single-storm nesting approach does not adequately simulate TC–TC interactions as they are observed. Basin-scale HWRF (HWRF-B) was developed later with a multistorm nesting approach to improve the simulation of TC–TC interactions by producing high-resolution forecasts for multiple TCs simultaneously. In this study, the multistorm nesting approach in HWRF-B was compared with a single-storm nesting approach using an otherwise identical model configuration. The multistorm approach demonstrated TC intensity forecast improvements, including more realistic TC–TC interactions. Storm-following nests developed in HWRF and HWRF-B will be foundational to NOAA’s next-generation hurricane application in the Unified Forecast System.

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.

Better representation of the planetary boundary layer (PBL) in numerical models is one of the keys to improving forecasts of TC structure and intensity, including rapid intensification. To meet this goal, our recent work has used observations to improve the eddy-diffusivity mass flux with prognostic turbulent kinetic energy (EDMF-TKE) PBL scheme in the Hurricane Analysis and Forecast System (HAFS). This study builds on that work by comparing a modified version of EDMF-TKE (MEDMF-TKE) with the hybrid EDMF scheme based on a K-profile method (HEDMF-KP) in the 2020 HAFS-globalnest model. Verification statistics based on 101 cases in the 2020 season demonstrate that MEDMF-TKE improves track forecasts, with a reduction in a large right bias seen in HEDMF-KP forecasts. The comparison of intensity performance is mixed, but the magnitude of low bias at early forecast hours is reduced with the use of the MEDMF-TKE scheme, which produces a wider range of TC intensities. Wind radii forecasts, particularly the radius of maximum wind speed (RMW), are also improved with the MEDMF-TKE scheme. Composites of TC inner-core structure in and above the PBL highlight and explain differences between the two sets of forecasts, with MEDMF-TKE having a stronger and shallower inflow layer, stronger eyewall vertical velocity, and more moisture in the eyewall region. A case study of Hurricane Laura shows that MEDMF-TKE better represented the subtropical ridge and thus the motion of the TC. Finally, analysis of Hurricane Delta through a tangential wind budget highlights how and why MEDMF-TKE leads to faster spinup of the vortex and a better prediction of rapid intensification.