A new method to create a model consensus forecast to improve real-time hurricane track prediction is presented. The method is based on the statistical fitting of historical numerical model track forecasts to the observed storm positions and adjusting for their historical errors and biases. The method is closest to the Hurricane Forecast Improvement Program (HFIP) Corrected Consensus Approach (HCCA) methodology, while using an alternative model formulation. The new method creates a separate consensus model for each forecast hour, making it possible to independently correct the bias of each input model for that specific hour. This approach, which we call the Hurricane Track Fit (HFIT) model, is computationally efficient and scalable to additional numerical models as input. The new method is developed using forecast data from the 2016 to 2022 hurricane seasons in the Atlantic basin with input from some of the top-performing operational track forecast guidance available to the National Hurricane Center. The results of a cross validation for the 2016-22 hurricane track dataset and of forecast tests using an independent dataset of storms from the 2023 to 2024 Atlantic hurricane seasons show that the HFIT consensus model produces statistically significant smaller track forecast errors compared to those from its individual input models. Tests with the independent dataset also yield results consistent with those from other operational consensus models and the official National Hurricane Center (NHC) forecasts. HFIT shows statistically significant improvements at 24 and 36 h over a version of the model that applies equal weights to the HFIT input models. SIGNIFICANCE STATEMENT: This work details the development of a hurricane track forecasting consensus model that is based on multiple linear regression techniques. It is similar to prior work performed on this topic; however, it utilizes a novel technique for formulating the model that can provide forecasters with an additional method of producing consensus hurricane track forecast guidance.

The outer size of a tropical cyclone (TC) is important for predicting potential damage caused by the storm, yet improving the size forecast remains challenging. This study evaluates the TC intensity and outer size forecast performance of five numerical models from the National Oceanic and Atmospheric Administration (NOAA) based on 15 representative North Atlantic hurricanes from 2020 to 2022. We found there is little correlation between TC intensity and outer size forecast accuracy. Higher-resolution models outperform coarser-resolution models in intensity prediction but perform similarly for outer size regardless of resolution. Notably, the initial storm outer size may significantly influence the outer size forecast: initially larger storms may present greater challenges for accurate size prediction. Our results provide new insights into the complex relationship between TC size and intensity forecasting, highlighting the need to understand how environmental factors affect size forecasts and their connection to model resolution and configuration.

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented.

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.

The ECMWF‘s ensemble (ECEPS) predictions are documented for the lifecycles of six tropical cyclones (TCs) that formed during a long-lasting Rossby wave breaking event in the western North Pacific. All six TC tracks started between 20° N and 25° N, and between 136° E and 160° E. All five typhoons recurved north of 30° N, and the three typhoons that did not make landfall had long tracks to 50° N and beyond. The ECEPS weighted mean vector motion track forecasts from pre-formation onward are quite accurate, with track forecast spreads that are primarily related to initial position uncertainties. The ECEPS intensity forecasts have been validated relative to the Joint Typhoon Warning Center (JTWC) Working Best Track (WBT) intensities (when available). The key results for Tokage (11 W) were the ECEPS forecasts of the intensification to a peak intensity of 100 kt, and then a rapid decay as a cold-core cyclone. For Hinnamnor (12 W), the key result was the ECEPS intensity forecasts during the post-extratropical transition period when Hinnamnor was rapidly translating poleward through the Japan Sea. For Muifa (14 W), the key advantage of the ECEPS was that intensity guidance was provided for longer periods than the JTWC 5-day forecast. The most intriguing aspect of the ECEPS forecasts for post-Merbok (15 W) was its prediction of a transition to an intense, warm-core vortex after Merbok had moved beyond 50° N and was headed toward the Aleutian Islands. The most disappointing result was that the ECEPS over-predicted the slow intensification rate of Nanmadol (16 W) until the time-to-typhoon (T2TY), but then failed to predict the large rapid intensification (RI) following the T2TY. The tentative conclusion is that the ECEPS model‘s physics are not capable of predicting the inner-core spin-up rates when a small inner-core vortex is undergoing large RI.

Abstract 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.

We have previously demonstrated that the ECMWF ensemble (ECEPS) provides early forecasts not only of the Time-to-Tropical Storm (T2TS) and of the Time-to-Hurricane (T2HU), but also of the Time-Ending-Hurricane (TEHU) and Time-Ending-Tropical Storm (TETS) times and positions along those 15-day ECEPS track forecasts, which then provides an opportunity for high-wind warnings along the path during the entire lifecycle of these Hurricanes. The focus in this study is the Decision Flowchart that has been developed to assist the forecasters to select the pre-formation disturbance that is most likely to become the next Tropical Storm with the potential to become a Hurricane. The most crucial decision is to detect and eliminate any disturbance that likely originated from a precursor Caribbean false alarm circulation. Summaries of other steps in the Decision Flowchart “To Watch”, or to reject, other storm options in the twice-daily ECEPS forecasts are provided for Hurricanes Enrique and Felicia, and for strong Tropical Storm Guillermo and weak Tropical Storm Jimena. The first detections in the ECEPS forecasts for the Tropical Storms during the 2021 season averaged 6 days, 18 h in advance with a range of only 2 days, 6 h in advance for TS Terry to 9 days, 18 h in advance for TS Sandra.

Building on previous studies of western North Pacific formation and intensity predictions along the ECMWF ensemble medium-range track forecasts, the first objective of this transition to the eastern North Pacific was to provide earlier forecasts of the Time-to-Formation (T2F) and Time-to-Hurricane (T2H) than are available from the National Hurricane Center Advisories. For the first six hurricanes of the 2021 season, the first detections in the ECMWF ensemble were 8 days to 12 days in advance of the T2F times and 9 days to 13 days in advance of the T2H times. The major advance in this study has been to document that the ECMWF ensemble is also capable of predicting Ending-T2H and Ending-T2F timings and positions along those 15-day ECMWF ensemble track forecasts. This study for the first time documents the opportunity for high wind warnings during the entire lifecycle of the 2021 season hurricanes even days in advance of formation. Validations of the pre-hurricane and Ending-hurricane tracks and timings are provided for the lifecycles of seven hurricanes and the “Almost-Hurricane Guillermo”. Because the technique has been accepted for operational testing at the Joint Typhoon Warning Center, a companion article has been submitted that will describe the flowchart methodology for evaluating the twice-daily ECMWF ensemble forecasts to select the most likely pre-hurricane circulation as early as possible while non-selecting the likely false alarm circulations.

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.

We present a new method for creating a model consensus to improve real-time hurricane track prediction. The method is based on the statistical fitting of historic numerical model track forecasts to the observed storm positions and learning from their historical errors and biases. Our method is closest to the HFIP Corrected Consensus Approach (HCCA) methodology while using an alternative model formulation. Our method creates a separate consensus model for each forecast hour making it possible to independently correct the bias of each input model for that specific hour. This approach, which we call the Hurricane Track Fit (HFIT) model, is computationally efficient and scalable to additional numerical models as input, and it produces interpretable coefficients weighing model contributions. The new method is evaluated for the 2014-2021 hurricane seasons in the Atlantic basin using the input from the best-performing operational track forecast guidance at the National Hurricane Center (NHC): the U.S. National Weather Service Global Forecast System deterministic and ensemble mean models, European Centre for Medium-Range Weather Forecasts deterministic model, the NWS Hurricane Weather Research and Forecasting model and the NHC equally weighted numerical model track consensus (TVCA). The results of the cross-validation for the 2014-2021 hurricane track dataset show that the HFIT consensus model consistently reduces the track forecast errors compared to those from the input models and the official NHC forecasts (OFCL). For example, at 24h the HFIT track forecast errors are smaller by 18.5% and 15.6% than those in AVNI and EMXI respectively, and 23% and 15% smaller at 72h. The HFIT forecasts show a reduction of errors compared to OFCL by 8.1% at 24h and 7.5% at 72h. We also discuss the successful real-time operational performance of HFIT during the 2022 hurricane season.

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.

Multiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

When the environmental conditions over the western North Pacific are favorable for tropical cyclone formation, a rapid intensification event will frequently follow formation. In this extension of our combined three-stage 7-day Weighted Analog Intensity Pacific prediction technique, the European Centre for Medium-range Weather Prediction ensemble predictions of the warm core magnitudes of pre-tropical cyclone circulations are utilized to define the Time-to-Formation (35 knots) and to estimate the Likely Storm Category. If that category is a Typhoon, the bifurcation version of our technique is modified to better predict the peak intensity by selecting only Cluster 1 analog storms with the largest peak intensities that are most likely to have under-gone rapid intensification. A second modification to improve the peak intensity magnitude and timing was to fit a cubic spline curve through the weighted-mean peak intensities of the Cluster 1 analogs. The performance of this modified technique has been evaluated for a sequence of western North Pacific tropical cyclones during 2019 in terms of: (i) Detection time in advance of formation; (ii) Accuracy of Time-to-Formation; (iii) Intensification stage prediction; and (iv) Peak intensity magnitude/timing. This modified technique would provide earlier guidance as to the threat of a Typhoon along the 15-day ensemble storm track forecast, which would be a benefit for risk management officials.

Abstract We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.

Typhoon Lekima (2019) with its heavy rains and floods is an excellent example of the need to provide the earliest possible warnings of the formation, intensification, and subsequent track before a typhoon makes landfall along a densely populated coast. To demonstrate an opportunity to provide early (10 days in advance) warnings of the threat of Typhoon Lekima, the ensemble models from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Predictions have been used to provide time-to-formation timing and positions along the weighted-mean vector motion track forecasts. In addition, the seven-day intensity forecasts after the formation using a weighted analog intensity prediction technique are provided. A detailed description of one European Center ensemble forecast is provided to describe the methodology for estimating the formation time and generating the intensity forecasts. Validation summary tables of the formation timing and position errors, and the intensity errors versus the Joint Typhoon Warning Center intensities, are presented. The availability of these ensemble forecasts would have been an opportunity to issue alerts/watches/warnings of Lekima even seven days in advance of when Lekima became a Tropical Storm. These ensemble forecasts also represent an opportunity to extend support on the 5–15 day timescale for the decision-making processes of water resource management and hydrological operations.

Marchok vortex tracker outputs from the European Centre for Medium-Range Weather Forecasts ensemble (ECEPS) and National Centers for Environmental Prediction ensemble (GEFS) are utilized to provide the Time-to-Formation (T2F of 25 kt or 35 kt) timing and positions along the weighted-mean vector motion (WMVM) track forecasts, and our weighted analog intensity Pacific (WAIP) technique provides 7-day intensity forecasts after the T2F. Example T2F(35) forecasts up to 5 days in advance of two typhoons and one non-developer in the western North Pacific are described in detail. An example T2F forecast of pre-Hurricane Kiko in the eastern North Pacific indicated that Hawaii would be under threat by the end of the 15-day ECEPS WMVM track forecast. An example T2F forecast of pre-Hurricane Lorenzo in the eastern Atlantic demonstrates that both the ECEPS and GEFS predict up to 5 days in advance that the precursor African wave will become a Tropical Storm off the west coast and will likely become a hurricane. Validations of the T2F(25) and T2F(35) timing and position errors are provided for all ECEPS and GEFS forecasts of the two typhoons and Hurricanes Kiko and Lorenzo. If the T2F timing errors are small (<1 day), the T2F position errors along the WMVM track forecasts will be small (<300 km). Although the primary focus is on the western North Pacific, the examples from the Atlantic and eastern/central North Pacific indicate the potential for future application in other basins.

The hurricane project at the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) was established in 1970. By the mid-1970s pioneering research had led to the development of a new hurricane model. As the reputation of the model grew, GFDL was approached in 1986 by the director of the National Meteorological Center about establishing a collaboration between the two federal organizations to transition the model into an operational modeling system. After a multiyear effort by GFDL scientists to develop a system that could support rigorous requirements of operations, and multiyear testing had demonstrated its superior performance compared to existing guidance products, operational implementation was made in 1995. Through collaboration between GFDL and the U.S. Navy, the model was also made operational at Fleet Numerical Meteorology and Oceanography Center in 1996. GFDL scientists continued to support and improve the model during the next two decades by collaborating with other scientists at GFDL, the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC),(1) the National Hurricane Center, the U.S. Navy, the University of Rhode Island (URI), Old Dominion University, and the NOAA Hurricane Research Division. Scientists at GFDL, URI, and EMC collaborated to transfer key components of the GFDL model to the NWS new Hurricane Weather Research and Forecasting Model (HWRF) that became operational in 2007. The purpose of the article is to highlight the critical role of these collaborations. It is hoped that the experiences of the authors will serve as an example of how such collaboration can benefit the nation with improved weather guidance products.