Vortex Initialization Research Articles

The Implementation of Cloud and Vertical Velocity Relocation/Cycling System in the Vortex Initialization of the HAFS

The Implementation of Cloud and Vertical Velocity Relocation/Cycling System in the Vortex Initialization of the HAFS

A flexible tropical cyclone vortex initialization technique for GFDL SHiELD

Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.

Exploring the Evolution of Typhoon Lekima (2019) Moving Offshore Northeast of Taiwan with a Multi-Resolution Global Model

Typhoon Lekima occurred in early August 2019 and moved northwestward toward Taiwan. During offshore passage, the typhoon underwent rapid intensification, with a northward deflected track, moving closer to northeastern Taiwan. A global model, MPAS, at a multi-resolution of 60-15-3 km, is utilized with ensemble forecasts to investigate the dynamic processes causing the track deflection and intensity change as well as identify the track uncertainty to initial perturbed conditions under the topographic effects of the Central Mountain Range (CMR). For spinning up the typhoon vortex in ensemble forecasts, dynamic vortex initialization has been enforced with a 3 km resolution targeted at the Taiwan area. As one specific member track is similar to the best track, the track deflection is significantly reduced in the absence of the Taiwan terrain, highlighting the role of the topographic effects of the CMR. For these tracks with similar deflection, the northward deflection is caused by the induced strong flow to the east of the typhoon center in response to the re-circulating flow around southern Taiwan, which produces the wavenumber-one gyre in the asymmetric flow difference to drive the vortex northward. The typhoon translation around the Taiwan terrain is dominated by the changing wavenumber-one horizontal potential vorticity (PV) advection during the track deflection in the ensemble forecasts. The formation of an intense PV tongue along the upper eyewall is a facilitation precondition of RI, while RI can be significantly enhanced in the presence of an intense lower-stratospheric PV core near the upper eye, which is produced by the radial inflow of the developed transverse vortex circulation over the upper-level outflow layer.

Typhoon Forecasts with Dynamic Vortex Initialization Using an Unstructured Mesh Global Model

Abstract A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.

Impacts of Radio Occultation Data on Typhoon Forecasts as Explored by the Global MPAS-GSI System

Global Navigation Satellite System (GNSS) radio occultation (RO) provides plentiful sounding profiles over regions lacking conventional observations. The Gridpoint Statistical Interpolation (GSI) hybrid system for assimilating RO data is integrated in this study with the Model for Prediction Across Scales–Atmosphere (MPAS) to improve tropical cyclone forecasts. After the MPAS-GSI assimilation cycles, dynamical vortex initialization (DVI) that may effectively spin up the initial inner typhoon vortex through cycled model integration is implemented to improve the initial analysis fit to the best track position as well as maximum wind or pressure intensity for Typhoon Nepartak (2016) that moved northwestward toward southern Taiwan. During the cycling assimilation, assimilation with RO data improves the temperature and moisture analysis, and largely reduces the forecast errors compared to those without RO data assimilation. The two RO operators that assimilate local bending angle or refractivity produce similar analyses, but the temperature and moisture increments from bending angle assimilation are slightly larger than those from refractivity assimilation. The MPAS forecasts at 60-15 km resolution show that the typhoon track prediction is improved with RO data, especially using bending angle data. The reduction in track deviations is explained by the wavenumber-one potential vorticity budget for several forecasts associated with the track deflection near southern Taiwan. Assimilation of RO data has fewer impacts on the typhoon intensity forecast compared to the DVI that largely improves the initial and thus forecasted intensity of the typhoon but at the cost of a slightly degraded track. Use of the enhanced 3 km resolution in the typhoon path also further improved the forecasts with and without the DVI. The feasible performance of the MPAS-GSI system with the RO data impact is also illustrated for Typhoon Mitag (2019), that passed around northern Taiwan.

A Numerical Study for Tropical Cyclone Atsani (2020) Past Offshore of Southern Taiwan under Topographic Influences

Tropical Cyclone Atsani occurred in late October 2020 and moved westward offshore south of Taiwan. During its offshore passage, the cyclone deflects northward as it closes to the southern end of Taiwan. A global model MPAS at a multi-resolution of 60-15-3-km is applied to explore the track responses of Atsani and identify the topographic effects of the Central Mountain Range (CMR) on the cyclone circulation and the associated track deflection. With a 3-km resolution targeted at the Taiwan area, the cyclone track deflection can be reasonably simulated, with more sensitivity to physics schemes and dynamic vortex initialization and less sensitivity to initial environmental perturbations. When the Taiwan terrain is removed, the cyclone indeed deflects more northward earlier, in particular for simulations with a stronger cyclone that tends to generate stronger east-west wind asymmetry in the absence of the terrain. Idealized simulations with a regional model WRF at 3-km resolution are also utilized to contrast the track deflection of different departing cyclones, similar to the real case. It was found that northward deflection will be induced near south of the CMR-like terrain for both stronger and weaker westbound cyclones departing at different latitudes south of the terrain. We have explained why a further northward track at earlier stages is induced in the absence of the terrain effects in regard to model initial states. In both real and idealized cases, the track deflection of the cyclone moving around the terrain is dominated by the wavenumber-one horizontal potential vorticity (PV) advection that is somewhat offset by both vertical PV advection and differential diabatic heating.

Effect of Vortex Initialization and Relocation Method in Anticipating Tropical Cyclone Track and Intensity over the Bay of Bengal

Effect of Vortex Initialization and Relocation Method in Anticipating Tropical Cyclone Track and Intensity over the Bay of Bengal

Tropical Cyclone Center Positioning Using Single Channel Microwave Satellite Observations of Brightness Temperature

Satellite observations of brightness temperature from the Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS) humidity sounding channels can provide relatively high horizontal resolution information about cloud and atmospheric moisture in the troposphere, thus revealing the structures of tropical cyclones (TCs). There is usually a high brightness temperature in a TC eye region and low brightness temperature reflecting spiral rain bands. An azimuthal spectral analysis method is used as a center-fixing algorithm to determine the TC center objectively using the brightness temperature observations of the ATMS humidity-sounding channel 18 (183.31 ± 7.0 GHz) and MHS humidity-sounding channel 5 (190.31 GHz). The position in the brightness temperature field encompassing a TC that achieves the largest symmetric component is regarded as the TC center. Two Atlantic hurricanes in 2012, Hurricanes Sandy and Isaac, are first used to analyze the performance of the TC center-fixing technique. Compared with the National Hurricane Center best track, the root-mean-square differences of the center fixing results for Hurricanes Sandy and Isaac are less than 47.3 and 34.3 km, respectively. It is found that the uncertainty of the TC center-fixing algorithm and thus the difference from the best track increases when the brightness temperature distribution within a TC is significantly asymmetric. Then, the TC center-fixing technique is validated for all tropical storms and hurricanes over Northern Atlantic and Western Pacific in 2019. Compared with the best track data, the root-mean-square differences for tropical storms and hurricanes are 33.81 and 26.20 km, respectively. The demonstrated successful performance of the proposed TC center-fixing algorithm to use the single channel of microwave humidity sounders for TC positioning is important for vortex initialization in operational hurricane forecasts.

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

Vortex Initialization in the NCEP Operational Hurricane Models

This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The VI creates an improved background field for the data assimilation and thereby produces an improved analysis for the operational hurricane forecast. The background field after VI can be used as an initial field (as in the HMON model, without data assimilation) or a background field for data assimilation (as in HWRF model).

Assimilation of Himawari-8 Rapid-Scan Atmospheric Motion Vectors on Tropical Cyclone in HWRF System

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). An ensemble-variational hybrid data assimilation system is used as an initialization. The results show that the assimilation of RS-AMVs can improve the track forecast skill, while the weak bias or slow intensification bias increases at the shorter forecast lead time. A vortex initialization in HWRF has a substantial impact on TC structure, but it has neutral impacts on the track and intensity forecasts. A thinning of AMVs mitigates the weak bias caused by RS-AMV assimilation, resulting in the reduction of intensity error. However, it degrades the track forecast skill for a longer lead time. A decomposition of the TC steering flows demonstrated that the change in TC-induced flow was a primary factor for reducing the track forecast error, and the change in environmental flow has less impact on the track forecast. The investigation of the structural change from the assimilation of RS-AMV revealed that the following two factors are likely related to the intensity forecast degradation: (1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and (2) a drying around and outside the RMW.

Numerical Simulation of Rapid Weakening of Hurricane Joaquin with Assimilation of High-Definition Sounding System Dropsondes during the Tropical Cyclone Intensity Experiment: Comparison of Three- and Four-Dimensional Ensemble–Variational Data Assimilation

Abstract Observations from High-Definition Sounding System (HDSS) dropsondes, collected for Hurricane Joaquin during the Office of Naval Research Tropical Cyclone Intensity (TCI) field experiment in 2015, are assimilated into the NCEP Hurricane Weather Research and Forecasting (HWRF) Model. The Gridpoint Statistical Interpolation (GSI)-based hybrid three-dimensional and four-dimensional ensemble–variational (3DEnVar and 4DEnVar) data assimilation configurations are compared. The assimilation of HDSS dropsonde observations can help HWRF initialization by generating consistent analysis between wind and pressure fields and can also compensate for the initial maximum surface wind errors in the absence of initial vortex intensity correction. Compared with GSI–3DEnVar, the assimilation of HDSS dropsonde observations using GSI–4DEnVar generates a more realistic initial vortex intensity and reproduces the rapid weakening (RW) of Hurricane Joaquin, suggesting that the assimilation of high-resolution inner-core observations (e.g., HDSS dropsonde data) based on an advanced data assimilation method (e.g., 4DEnVar) can potentially outperform the vortex initialization scheme currently used in HWRF. Additionally, the assimilation of HDSS dropsonde observations can improve the simulation of vortex structure changes and the accuracy of the vertical motion within the TC inner-core region, which is essential to the successful simulation of the RW of Hurricane Joaquin with HWRF. Additional experiments with GSI–4DEnVar in different configurations also indicate that the performance of GSI–4DEnVar can be further improved with a high-resolution background error covariance and a denser observational bin.

Forecasting tropical cyclones in the Bay of Bengal using quasi-operational WRF and HWRF modeling systems: an assessment study

The Hurricane Weather Research and Forecasting (HWRF) is increasingly becoming known for its better performance in simulating tropical cyclones (TCs) over different basins globally, and the Advanced Research version of the Weather Research and Forecasting (WRF) model is widely used for the same in both research and operational settings. These two models are now operational at India Meteorological Department (IMD) for TC predictions over the North Indian Ocean. The near real-time forecast of HWRF and WRF models is evaluated in a quasi-operational setup based on 62 forecast cases from 10 recent TCs during 2013–2017 over the Bay of Bengal. Multi-satellite estimated winds are used to compare initial and simulated vortex structures, and HWRF has been found to be better. The track prediction is comparable in both the models for shorter forecast lengths (up to 30 h), while the HWRF is skillful (by 27%) for longer forecast, thus leading to a better estimation of landfall position and time. HWRF is significantly better (error < 10 knots) in intensity prediction compared to the WRF model (~ 15 knots). Unlike the WRF model, the HWRF model produced an improved vertical structure of dynamic and thermodynamic processes and could be attributed to the state-of-the-art vortex initialization and relocation method, multiscale interaction and high resolution. The model-predicted vortex structures also supported the credibility of HWRF system. This work highlights the need for having improved initialization and high resolution for TC predictions over the region.

Impact of vortex size and Initialization on prediction of landfalling tropical cyclones over Bay of Bengal

This study focuses on improving the vortex of Tropical Cyclone (TC) using different initialization techniques and its subsequent impact on the performance of Advanced Research Weather Research and Forecasting (ARW) mesoscale model. The Initialization of TC vortex from global analyses (CNTL) is found to be poor due to coarse horizontal and vertical structure, and is improved by assimilating available observations using 3DVAR technique (3DV). In another experiment (VAR_VI), vortex is corrected using vortex initialization and relocation procedures to correspond with India Meteorological Department (IMD) ‘observed’ position and intensity estimates, and then assimilation of additional observations is undertaken.The initial vortex improvement in terms of horizontal and vertical structure is noted in both experiments with the VAR_VI results being better than for the 3DV experiment. Simulations following the VAR_VI experiment showed remarkable improvement in simulating track, intensity and structure of the two TCs it was evaluated for (TC Giri and TC Jal). The large scale dynamical and thermo-dynamical fields such as steering flow, vertical wind shear and warm core structure were also improved. For TC Giri, the VAR_VI could reproduce the observed intensification rate of 10 ms−1 within 12 h, while for TC Jal, the VAR_VI run could simulate the inland rainfall due to sheared convective clouds. Results suggested that the improvements would likely to be more for stronger TCs. This study highlights the continued need and value of improvement of TC vortex using initialization and in-situ and remote sensing observations over the Bay of Bengal region.

Impact of Vortex Initialization in Prediction of Tropical Cyclones over Bay of Bengal with NCUM Model

The present study evaluates the impact of vortex initialization (VI) scheme within the NCMRWF Global Unified model (NCUM-G) for prediction of tropical cyclones (TCs) formed over Bay of Bengal (BoB). For this purpose, two numerical experiments such as control simulation (CNTL) without using VI scheme and VOTX simulation using the VI scheme in the NCUM-G are performed by considering four landfalling TCs formed over BoB basin during the year 2016–17. The results suggest that even though TCs are large synoptic systems, the introduction of VI scheme has a positive impact on the prediction of the location, movement, intensity and development of rain bands associated with the TCs. The initial vortex position and landfall position errors are reduced by ∼64% and ∼39% in VOTX simulations over CNTL, respectively. The mean track errors of all the four TCs are reasonably improved by ∼58% in VOTX over CNTL. The equitable threat score (ETS) and frequency bias are significantly improved in the VOTX for all the TC cases as compared to CNTL. Study results provide a positive proof of concept that the VI scheme within the NCUM-G can help to improve the simulation of track and intensity of TCs over BoB.

A comprehensive 4D-Var vortex initialization using a nonhydrostatic axisymmetric TC model with convection accounted for

A four-dimensional variational (4D-Var) vortex initialization (VI) system is developed for a nonhydrostatic axisymmetric numerical model with convection accounted for (the RE87 model). Derivations of the tangent linear and adjoint models of the RE87 model and the correctness checks are presented. As an initial evaluation of the 4D-Var VI system, a cost function that measures the model fit to satellite microwave retrievals of tropical cyclone (TC), warm-core temperatures and total precipitable water (TPW) from the following four polar-orbiting satellites within a slightly longer than 1-h assimilation window is minimized using the limited-memory quasi-Newton minimization algorithm: the Suomi National Polar-orbiting Partnership, NOAA-20, Fengyun-3D and Global Change Observation Mission—Water Satellite 1. An azimuthal spectral analysis in cylindrical coordinates centred on the TC centres shows that the warm core and TPW fields within TCs are dominated by the axisymmetric component. The 4D-Var VI results assimilating the axisymmetric component of the above satellite retrievals produced a significant reduction in the cost function and the norm of the gradient as the minimization process is completed. The gradient of the cost function is accurately computed by a single integration of the RE87 adjoint model. In the cases of Hurricane Florence and Typhoon Mangkhut, improved forecast of intensifications and more realistic vertical structures of all model state variables (e.g. temperature, water vapour mixing ratio, liquid water content mixing ratio, tangential and radial wind components and vertical velocity) are obtained when compared with a parallel run initialized simply by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis.

Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation

Abstract This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.

A Numerical Study on the Extreme Intensification of Hurricane Patricia (2015)

Abstract In October 2015 Hurricane Patricia stormed through the eastern Pacific, taking its place as the strongest hurricane in recorded history when its intensity reached a record breaking 185 kt (1 kt = 0.51 m s−1). Operational models and the National Hurricane Center’s official forecast failed to predict Patricia’s unprecedented intensification, provoking questions as to whether such an extreme event can actually be forecast. This study reports on the successful simulation of Patricia using a state-of-the-art high-resolution numerical weather prediction model. It was found that high model resolution (Δx ≤ 1 km), vortex initialization, and the parameterization of dissipative heating were key factors in realistically simulating Patricia’s intensity evolution. The simulation was used to investigate Patricia’s environment in terms of sea surface temperature, vertical wind shear, and humidity, under the premise that a simulation able to capture Patricia’s peak intensity would also accurately represent Patricia’s environment. Compared with a climatology derived from the Statistical Hurricane Intensity Prediction Scheme dataset, sea surface temperature ranked in the 99th percentile and environmental vertical wind shear in the 83rd percentile (ordered from high to low). However, humidity ranked more moderately. Ensemble forecasts indicate that Patricia had relatively high predictability in comparison to other well-studied rapid intensification cases such as 2010’s Hurricane Earl. The results from this study imply that high-resolution models are in principle able to predict the intensity of extreme hurricanes like Patricia.

Impact of Vortex Initialization with Satellite Data Assimilation for Predicting Track and Intensity of Tropical Cyclone Chapala

The present study evaluates the impact of vortex initialization (VI) with assimilation of satellite observations in the National Centre for Medium Range Weather Forecasting-UK Met Office Unified (NCUM) global Model for prediction of extremely severe cyclonic storm (ESCS) Chapala formed over the Arabian Sea (AS) during 28 October–03 November 2015. For this purpose, two numerical experiments viz. Vort-Gts (assimilation of only Global Telecommunication System (GTS) observations with VI) and Vort-Rad (assimilation of GTS plus satellite observations with VI) are conducted. The model is run with a total of five different initial conditions (ICs) through 24 h cycles. The standard metrics of cyclone track errors such as Direct Position Errors (DPEs), Cross-Track Errors (CTEs), and Along-Track Errors (ATEs) are computed and found to have improved by 47, 40, and 57%, respectively in Vort-Rad over Vort-Gts. The intensity and land fall (LF) errors are significantly reduced in Vort-Rad in comparison with the Vort-Gts. The statistical skill scores such as equitable threat score (ETS) and bias are better represented in the Vort-Rad.

Impact of Enhanced Atmospheric Motion Vectors on HWRF Hurricane Analyses and Forecasts with Different Data Assimilation Configurations

Abstract The impacts of enhanced satellite-derived atmospheric motion vectors (AMVs) on the numerical prediction of intensity changes during Hurricanes Gonzalo (2014) and Joaquin (2015) are examined. Enhanced AMVs benefit from special data-processing strategies and are examined for impact on model forecasts via assimilation experiments by employing the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting (HWRF) Model using a Gridpoint Statistical Interpolation analysis system (GSI)-based ensemble–variational hybrid system. Two different data assimilation (DA) configurations, one with and one without the use of vortex initialization (VI), are compared. It is found that the assimilation of enhanced AMVs can improve the HWRF track and intensity forecasts of Gonzalo and Joaquin during their intensity change phases. The degree of data impact depends on the DA configuration used. Overall, assimilation of enhanced AMVs in the innermost domain (e.g., storm inner-core region and its immediate vicinity) outperforms other DA configurations, both with and without VI, as it results in better track and intensity forecasts. Compared to the experiment with VI, assimilation of enhanced AMVs without VI reveals more notable data impact on the forecasts of Hurricane Gonzalo, as the VI before DA alters the first guess and reduces the actual number of AMV observations assimilated into the DA system. Even with VI, assimilation of enhanced AMVs in the inner-core region can at least partially mitigate the negative effect of VI on the intensity forecast of Hurricane Gonzalo and alleviate the unrealistic vortex weakening in the simulation by removing unrealistic outflow structure and unfavorable thermodynamic conditions, thus leading to improved intensity forecasts.