# A New Reconstruction Method for Generating a Realistic Tropical Cyclone Wind Field

Nils Herry, Swen Jullien, Léo Vinour, Aline Zribi, Christophe Menkes

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Nils Herry, Swen Jullien, Léo Vinour, Aline Zribi, Christophe Menkes. A New Reconstruction Method for Generating a Realistic Tropical Cyclone Wind Field. 2025. hal-05427687

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A New Reconstruction Method for Generating
a Realistic Tropical Cyclone Wind Field

Nils Herry,ª Swen Jullien,ᵇ Léo Vinour,ᶜ Aline Zribi,ᵇ,d Christophe Menkesª

5

ªENTROPIE (IRD, CNRS, Ifremer, Université de la Réunion, Université de la
Nouvelle-Calédonie), Nouméa, New Caledonia

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ᵇLaboratoire d'Océanographie Physique et Spatiale (LOPS), Ifremer, Université de Brest, CNRS,
IRD, IUEM, Plouzané, France

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ᶜFaculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan

10

d UMR 7266 LIENSs, CNRS, La Rochelle, France

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ABSTRACT: Tropical Cyclones (TCs), with their destructive power, represent a major threat to coastal regions. Existing global reanalyses, that provide long-term atmospheric data, suffer from an underestimation of TC winds due to their relatively coarse resolution. Simplified surface wind parametric models fitted on observations are thus extensively used for risk assessments related to the TC-induced wind and ocean response (e.g. for driving surge or wave models), but their ability to capture the complexity of TC wind fields remains limited. Taking advantage of newly available high-resolution observations of the 2D surface wind field in TCs provided by Synthetic Aperture Radar (SAR) data, state-of-the-art existing reanalysis of synoptic winds, and an advanced parametric formulation able to represent a variety of TC shapes, the present study proposes a methodology to build global atmospheric wind fields including more realistic TCs. Based on SAR observations, we first provide an optimized parameterization of the TC wind profile as a function of best-track variables (maximum wind speed, radius of maximum wind and latitude of the TC center). The TC wind field is then blended with a global reanalysis "background" wind field, allowing to provide a full asymmetric wind field associated to the local synoptic conditions. The newly created TC wind fields are evaluated against observations showing consistent intensity, asymmetry, and storm size, strongly improving the structural and energetic characteristics of the represented TCs compared to the original reanalysis surface wind. This work provides key insights into the representation of TC wind fields in long-term reanalyses for studies requiring realistic estimates of the spatio-temporal distributions of TC surface winds.

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KEYWORDS: Tropical cyclones; Parametric model; Atmospheric reanalysis.

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SIGNIFICANCE STATEMENT: The purpose of this study is to improve the representation of Tropical Cyclone (TC) surface wind fields in long-term atmospheric reanalysis data, crucial for risk assessment applications. A key element of our approach is the ability to reconstruct realistic TC wind fields that accurately capture intensity and spatial structure, in comparison with high-resolution Synthetic Aperture Radar (SAR) observations. This work also provides a dataset of global hourly surface wind fields including realistic TCs on a 0.25° resolution horizontal grid over the period 1980-2024, along with a toolbox to generate any TC case at any chosen time and grid resolution along its track.

# 1. Introduction

Tropical cyclones (TCs) are the most powerful phenomena of the tropical atmosphere. Due to their destructive potential, characterized by strong winds, heavy rainfall, high waves, storm surges, and flooding, they represent a significant threat to coastal regions (Emanuel 2005; Knutson et al. 2010; Peduzzi et al. 2012). By the strong ocean response they generate, they also significantly affect the ocean stratification and contribute to the redistribution of ocean heat at basin scale (e.g. Emanuel 2001; Sriver and Huber 2007; Jullien et al. 2012; Vincent et al. 2013). Estimating the spatio-temporal distributions of TC surface winds is thus a critical concern to better assess their impacts.

Representing a realistic TC wind field is however quite challenging. As TCs are strongly coupled systems, coupled modeling (atmosphere, ocean, and wave) is extensively used for TC case study forecasting and research. High resolution is also an important factor in accurately capturing the internal dynamic of TCs. While such models can produce realistic individual TC case studies, they are computationally expensive for risk assessment applications, and this cost increases with the spatial resolution. They are thus generally limited in the area and time period they can cover with an acceptable computational cost.

Wind conditions in TCs can also be estimated from observations, particularly those provided by Earth observation satellites, but their spatial distribution remains uneven. Climate reanalyses, such as ERA5 produced by the European Centre for Medium-Range Weather Forecasts, combine available observations with numerical simulations using data assimilation techniques, and provide atmospheric conditions at a global scale over the past decades at relatively low spatial resolution
(e.g. 0.25° for ERA5). These datasets are commonly used to force ocean and wave models, and to investigate the short- to long-term climate variability. However, global climate models and reanalyses do not have sufficient spatial resolution to accurately depict the intensity of tropical cyclone wind fields. Their coarse resolution does not allow to capture strong gradients and fine-scale structures of TCs, leading to a significant underestimation of TC winds compared to observations (Jourdain et al. 2014; Hersbach et al. 2020). Despite their underestimation of wind intensities, these reanalyses can reasonably represent outer TC structures (Schenkel et al. 2017) when compared to satellite data.

Alternatively, parametric TC models provide simplified TC surface wind fields generally expressed as an axisymmetric wind radial profile, i.e. wind speed at a radial distance from the TC center, as a function of a few TC parameters such as its maximum intensity and radius. They easily provide synthetic surface wind fields, and are thus widely used for several applications such as storm surge (Houston and Powell 1994; Houston et al. 1999; Peng et al. 2006; Vijayan et al. 2021), wave (e.g. Jullien et al. 2024) or ocean forcing (Vincent et al. 2012; Huang and Oey 2015). They thus represent useful tools to easily generate synthetic surface wind fields and analyze the behavior of tropical storms and cyclones over an area of interest (e.g. Delaporte et al. 2024). The enthusiasm for parametric models began in the early 1950s, along with several TC aircraft field campaigns that allowed to fit the parametric formulations with *in situ* observations. The observed relationship between the tangential wind profile and the radial pressure gradient (Schloemer 1954; Georgiou 1986; Holland 1980) led to the development of so-called gradient wind models expressing the wind profile at the top of the boundary layer as a function of the pressure distribution. With the growing amount of TC observational data (first with aircraft, then with satellite measurements), empirical formulations fitted on existing observations have been proposed (e.g. Willoughby et al. 2006). Over the decades, numerous formulations have been derived to incrementally improve the TC representation by such models (see the reviews of Yan and Zhang 2022; Vinour et al. 2025). The most recent formulations, such as that of Wood et al. (2013), hereafter Wo13, explored more complex mathematical formulations using more adjustable parameters to represent a wider range of profiles (notably double eyewalls). The recent study by Vinour et al. (2025) provides a review of the performance of ten existing parametric formulations fitted to Synthetic Aperture Radar (SAR) satellite data.
Such parametric formulations alone, however, do not provide a complete wind field and notably
do not account for the synoptic flow within which each TC is embedded. To obtain a realistic
wind field that captures both the intensity and the large-scale spatial structure of each storm, a
methodology consists in blending large-scale reanalysis winds with a parametric formulation of TC
winds (e.g. Vincent et al. 2012; Jullien et al. 2024). Building on these previous works, but taking
advantage of the recent more adaptable parametric formulations, state-of-the-art existing reanalysis,
and SAR observations, the present study proposes a methodology to build global atmospheric wind
fields including realistic TCs. The produced wind fields are evaluated and validated for a large
dataset of TCs against newly available SAR observations, being the only observations providing
full 2D surface wind fields with sufficiently high resolution to feature the inner-core TC structure.
Following the assessment of Vinour et al. (2025), the Wood et al. (2013), Wo13, parametric model is
chosen. A new parameterization (hereafter Wo13*) is designed to allow the parameters governing
the shape of the TC wind profile to vary for matching observed TCs with the best performance in the
area where reanalysis data resolution is too coarse to consistently feature the intensity and structure
of the vortex. The present study further emphasizes the importance of determining at which radius
to transition toward reanalysis winds in the outer-core, where reanalyzed winds become relevant,
as well as the added-value of blending to ensure an asymmetry associated to the synoptic flow for
each case.

The methodology is described in Section 2. The evaluation of the newly generated wind
forcing against observations in terms of structure and energetic is presented in Section 3. Fi-
nally, the sensitivity to horizontal resolution and limitations of the approach are discussed in
Section 4. Conclusions and perspectives are given in Section 5. Based on such a thor-
ough assessment, this work provides a dataset of global hourly surface wind fields includ-
ing realistic TCs on a 0.25° resolution horizontal grid over the period 1980-2024 (DOI:
https://doi.org/10.12770/819f5ea3-6897-4e64-b599-cfb98d6c963e), along with a
toolbox to generate any TC case at any chosen time and grid resolution along its track (DOI:
https://doi.org/10.12770/11057ae4-b708-481a-9848-2e4fdef57625, Git repository:
https://gitlab.ifremer.fr/lops-public/toolbox_era5_tc).
## 2. Data and Methods

The methodology developed in the present paper consists in creating a new surface wind forcing, at global scale, including realistic (notably intense enough) tropical cyclones. To do so, we rely on a global reanalysis for the “background” wind field, that we blend, along observed TC tracks, with a parametric TC wind field based on the state-of-the-art observations. The following sub-sections describe the various data, the parametric model used, and the blending methodology.

### a. Reanalysis “background” wind field

The fifth generation atmospheric reanalysis of the global climate (ERA5) produced by the European Centre for Medium-Range Weather Forecasts (Hersbach et al. 2020) is used in the present study as providing state-of-the-art global atmospheric fields over the last decades. ERA5 is produced using the Integrated Forecast System (IFS), which assimilates a wide range of satellite (microwave radiometers, infrared and visible imaging instruments, geostationary satellites, SeaWinds scatterometers) and *in situ* (weather stations, ships, buoys, aircraft, radiosondes) observations. It covers the period from 1940 to present day, and is distributed on a 0.25° horizontal resolution grid with hourly outputs. We use the 10-m wind and surface pressure hourly fields over the 1980-2024 period from ERA5. While being a state-of-the-art reanalysis, ERA5 strongly underestimates maximum winds in TCs (-37% in average over all TC categories) (Jullien et al. 2024). The quality of the reanalysis in representing TC characteristics (intensity, size, characteristic wind radii) is further evaluated in the following.

### b. Synthetic Aperture Radar (SAR) 2D wind fields

The Synthetic Aperture Radar (SAR) is a microwave active sensor available on-board several satellites. Since microwaves pass through clouds, ocean surface wind speeds can be retrieved, including under TC conditions, analyzing the backscatter signal. Using both co- and cross-polarization (Mouche et al. 2017), SAR provides unprecedented high-resolution 2D wind fields in TCs, which have been validated against aircraft measurements (Mouche et al. 2017, 2019), and have proven to provide reliable estimates of TC maximum intensity and characteristic wind radii (Combot et al. 2020). Vinour et al. (2021) further demonstrated that SAR wind fields capture a wide range of symmetric and asymmetric features of the internal TC structure, encompassing the
eyewall, inner-core, and outer-core of the vortex. Here we use SAR-derived 2D wind fields with a 3-km spatial resolution over swaths of 200 to 400 km (depending on the available acquisition mode), retrieved from the Sentinel-1A (S1A) and Sentinel-1B (S1B) European Space Agency (ESA) missions, and the RADARSAT-2 (RS2) Canadian Space Agency (CSA) mission.

Two subsets of the SAR available archive are considered. First, a set of 276 SAR images is used to fit and perform regressions for the TC wind parametric model (see section 2d and Appendix A). This dataset gathers TCs of all categories and basins over the period 2016-2023 with images including a full eyewall and having a maximum wind intensity greater than 34 kt (~17 m.s⁻¹). Then, a second subset of 175 images is used to validate and evaluate the blending of the TC parametric winds with the background ERA5 winds in our new TC+ERA5 blended wind forcing. These observations are chosen as capturing a sufficiently large part of the TCs, including their outer-core, to allow the comparison of wind fields over the whole TC structure. This subset covers the same period (2016 to 2023), and is composed of 38 cases in the North Atlantic (NA) ocean, 30 cases in the South Indian (SI) ocean, 50 cases in the East Pacific (EP) ocean, and 57 cases in the West Pacific (WP) ocean, with all TC categories (from Simpson (1974) Tropical Storm, TS, to category-5 TC) represented in each basin (Fig. 1).

<figure><img src="image_2.png" /><figcaption>FIG. 1: Locations and counts of selected tropical cyclone SAR images by basin and category, according to the Saffir-Simpson threshold values for TC categories: 18, 33, 43, 50, 58, 70 m.s⁻¹.</figcaption></figure>

### c. TC best-track data

The International Best Track Archive for Climate Stewardship (IBTrACS, Knapp et al. 2010; Gahtan et al. 2024) has been developed since 2010 by the National Oceanic and Atmospheric
Administration (NOAA). It collects and delivers the TC parameters from a wide range of existing TC weather forecast agencies worldwide to provide users with a complete and unified dataset of TC parameter estimates. To compute best-track parameters such as maximum wind, TC center position or characteristic radii, agencies consider a wide range of observational data from different sources. These data bring their own uncertainties and limitations: some of them do not cover the entire TC structures (data from aircrafts for instance), some provide information from the top of the atmosphere and are not direct measurements of the surface winds (geostationary satellites), some have a coarse resolution (40-50 km for microwave radiometers), etc (Combot et al. 2020).

We use the track location (longitude and latitude of TC centers), the 1-min maximum sustained wind speed, and if available, the 64-, 50- and 34-kt wind radii defined for each of the four geographical quadrants (North-East, South-East, South-West and North-West) from USA agencies (i.e. National Hurricane Center, Joint Typhoon Warning Center, Central Pacific Hurricane Center) of the 4.01 version of IBTrACS (https://www.ncei.noaa.gov/products/international-best-track-archive). In this dataset, some tracks are provided at 6-hour intervals, while others are available at 3-hour intervals. We interpolate these data at a 1-hour interval for consistency with ERA5 reanalysis data, and at 15-minute intervals for comparisons with Synthetic Aperture Radar (SAR) data. A linear interpolation is used for TC maximum winds and wind radii, and a spline interpolation is used for TC tracks, ensuring consistency with the methods used in IBTrACS.

### d. TC parametric wind model

Parametric wind models represent the wind field of a TC based on a few TC parameters only, generally available from best-track data such as the maximum wind (noted $V_{max}$) and the radius of maximum winds (noted $R_{max}$), as well as one or several adjustable parameters. Generally assuming the wind field as symmetric, they provide an expression of the wind $V(r)$ as a function of the distance from the TC center. Wood and White (2011) and Wood et al. (2013) proposed an analytical expression, modifying the Vatistas (1998)'s model to improve the representation of TC radial wind profiles by better capturing the complexity of the wind structure. Specifically, they introduced a piece-wise model based on three shape parameters:
$$V(r) = V_{\max} \frac{\eta^{\lambda} \left(\frac{r}{R_{\max}}\right)^{\kappa}}{\left(\eta - \kappa + \kappa \left(\frac{r}{R_{\max}}\right)^{\eta/\lambda}\right)^{\lambda}} \quad \begin{cases} \kappa > 0, \\ \lambda > 0, \\ \eta = \kappa - \mu, \quad \mu < 0. \end{cases} \quad (1)$$

with $\kappa$, $\lambda$ and $\eta$ (or $\mu$, as $\eta = \kappa - \mu$) the power-law parameters describing respectively the sharpness, broadness, and decay rate of the wind radial profile (as illustrated in Fig. 1 of Wood et al. 2013). The model considers by default a value of $V_0 = 0$ m.s$^{-1}$ in the center of the TC. In practice, such value is almost never observed, as the eye is partially advected by the surrounding rotating eyewall and, moreover, not totally symmetric and sometimes subject to local convection that generates local winds inside the eye (e.g. Vinour et al. 2021). To obtain a model adapted to observed profiles with non-zero values at the center, $V_0$ is added to the adjustable parameters, therefore considering the following parametric expression:

$$V(r) = V_0 + (V_{\max} - V_0) \frac{\eta^{\lambda} \left(\frac{r}{R_{\max}}\right)^{\kappa}}{\left(\eta - \kappa + \kappa \left(\frac{r}{R_{\max}}\right)^{\eta/\lambda}\right)^{\lambda}} \quad (2)$$

In this expression, the profile tends also toward $V_0$ in the outer-core, but we only use the inner-core section of the profile, and then transition towards ERA5 (see section 2e). The four parameters, $\kappa$, $\lambda$, $\eta$ (or $\mu$, as $\eta = \kappa - \mu$), and $V_0$, can be adjusted by fitting the parametric profile to observations. We therefore use 276 SAR-observed wind profiles to derive an expression for each parameter as a function of TC variables ($R_{\max}$ [m], $V_{\max}$ [m.s$^{-1}$], and latitude $\varphi$ [°]). The methodology, described in details in Appendix A, follows an iterative procedure that allows to optimize the three adjustable parameters in their area of interest. Indeed, Vinour et al. (2025) noticed that the numerous adjustable parameters were not optimally adjusted with a simple least-square optimization due to the interdependence of the parameters, leading to a decreased performance of the model. Our methodology finally provides the following expressions:
$$ \left\{ \begin{aligned} \kappa &= 1.887 - 1.291 \cdot 10^{-5} R_{\text{max}} + 2.216 \cdot 10^{-2} V_{\text{max}} + 7.686 \cdot 10^{-3} |\varphi| \\ \lambda &= 0.677 - 3.261 \cdot 10^{-6} R_{\text{max}} + 2.387 \cdot 10^{-5} V_{\text{max}} - 7.839 \cdot 10^{-4} |\varphi| \\ \mu &= -0.787 - 2.071 \cdot 10^{-6} R_{\text{max}} - 2.623 \cdot 10^{-3} V_{\text{max}} + 1.903 \cdot 10^{-3} |\varphi| \\ V_0 &= 4.805 - 3.186 \cdot 10^{-5} R_{\text{max}} + 7.927 \cdot 10^{-2} V_{\text{max}} + 1.019 \cdot 10^{-1} |\varphi| \end{aligned} \right. \quad (3) $$

It is important to note that $V_{\text{max}}$, and $R_{\text{max}}$ in these expressions represent the symmetric, i.e. azimuthally-averaged, maximum wind intensity and radius. The maximum wind speed from IBTrACS, $V_{\text{max}, BT}$, being the maximum over the 2D wind field (thus including an eventual asymmetry), a linear regression between $V_{\text{max}}$ values from the azimuthally-averaged SAR profiles, and $V_{\text{max}, BT}$ values is used (Fig. 2a), leading to similar results as when using those from Avenas et al. (2023) or Vinour et al. (2025):

$$ V_{\text{max}} = V_{\text{max,BT}}/1.186 \quad (4) $$

Regarding the maximum wind radius provided by the best-track data, uncertainties greater than 30% for tropical storm cases and category-4/5 TCs have been revealed when comparing them with SAR-derived $R_{\text{max}}$ (Combot et al. 2020). In addition, $R_{\text{max}}$ is not provided for all TCs in the best-track database. We therefore consider two alternative $R_{\text{max}}$ evaluations. The first is the parametric expression of $R_{\text{max}}$ as a function of the maximum wind $V_{\text{max}}$ and the latitude $\varphi$, proposed by Willoughby et al. (2006) based on the statistical relationship observed in aircraft data:

$$ R_{\text{max, Wi06}} = 46.4 \cdot e^{(-0.0155 \cdot V_{\text{max}} + 0.0169 \cdot |\varphi|)} \quad (5) $$

This expression has the advantage of being usable with TC $V_{\text{max}}$ and latitude estimates only. The second alternative relies on deriving $R_{\text{max}}$ from outer wind radii, which are generally more reliably observed or modeled, as suggested by Chavas and Knaff (2022). These authors proposed to evaluate $R_{\text{max}}$ using the characteristic 34-kt radius ($R_{34}$), based on a statistical relation derived from observations, between the angular momentum at $R_{\text{max}}$, $M_{\text{max}}$, and the angular momentum at $R_{34}$, $M_{34}$. Avenas et al. (2023) further showed that considering the 50-kt ($R_{50}$) and 64-kt ($R_{64}$)
233 wind radii, when available, improved $R_{max}$ estimation. $R_{max}$ is therefore derived from the angular momentum:
234

$$R_{\text{max, Av23}} = \frac{V_{\text{max}}}{f} \left( \sqrt{1 + \frac{2fM_{\text{max}}}{V_{\text{max}}^2}} - 1 \right) \quad (6)$$

235 with $f$ the Coriolis parameter $f = 2\Omega \sin(\varphi)$, where $\Omega = 7.292 \cdot 10^{-5} s^{-1}$ is Earth's angular velocity and $\varphi$ is the latitude of the TC center, and $M_{max}$ evaluated with the following statistical relations, derived by Avenas et al. (2023) based on radiometer observations:
236
237

$$\left\{
\begin{aligned}
\frac{M_{\text{max}}}{M_{34}} &= 0.531 \cdot e^{-0.00214(V_{\text{max}}-17.5)-0.00314(V_{\text{max}}-17.5)(\frac{1}{2}fR_{34})} \\
\frac{M_{\text{max}}}{M_{50}} &= 0.626 \cdot e^{0.00282(V_{\text{max}}-25.7)-0.00724(V_{\text{max}}-25.7)(\frac{1}{2}fR_{50})} \\
\frac{M_{\text{max}}}{M_{64}} &= 0.612 \cdot e^{0.00946(V_{\text{max}}-32.9)-0.01183(V_{\text{max}}-32.9)(\frac{1}{2}fR_{64})}
\end{aligned}
\right.
\quad (7)$$

238 As for $V_{max}$, to apply these expressions to $R_{34}$, $R_{50}$, $R_{64}$, and $R_{BT}$, which represent the mean wind radii of the four quadrants provided by the best-track data, we compute linear regressions between the wind radii extracted from the azimuthally-averaged SAR profiles and those from IBTrACS (Fig. 2b):
239
240
241

$$\left\{
\begin{aligned}
R_{64} &= R_{64, \text{BT}}/1.044 \\
R_{50} &= R_{50, \text{BT}}/1.238 \\
R_{34} &= R_{34, \text{BT}}/1.342
\end{aligned}
\right.
\quad (8)$$

242 Best-track data are indeed shown to overestimate wind radii compared to SAR observations (Fig. 2b). As noted in Avenas et al. (2023), the main difference results in the fact that the IBTrACS radii correspond to the maximum radial extent of the threshold wind speed (34 kt, 50 kt or 64 kt) in each quadrant. These radii are thus expected to be greater than azimuthally-averaged radii.
243
244
245

246 A parametric vortex can thus be constructed for each TC of the best-track database, using eq. (3). $V_{max}$ and $R_{max}$ TC parameters are respectively derived from the best-track data according to eq. (4), and eq. (5) or (6).
247
248
<figure><img src="image_3.png" /><figcaption>FIG. 2: (a) Maximum wind speeds and (b) 64-, 50- and 34-kt wind radii from IBTrACS against those derived from SAR azimuthally-averaged wind profiles.</figcaption></figure>

e. Creating a blended realistic global wind field

The methodology we followed to create global wind fields including TCs with realistic intensity and inner-core structure is summarized in Fig. 3. The general concept is to replace the ERA5 underestimated winds in TCs by the parametric wind formulation described in section 2d, while keeping ERA5 wind field elsewhere. The procedure builds upon that of Vincent et al. (2012) and Jullien et al. (2024). By contrast with the latter studies, both the parametric wind field and the transition area between the parametric and ERA5 winds are adjusted for each TC, according to its intensity and size. The ERA5 wind field is first filtered within a 200km-radius from the TC track, using an 11-day low-pass filter designed to remove the underestimated ERA5 TC, but keeping the synoptic flow. The parametric wind field is computed from the Wo13-derived optimized formulation **Wo13*** (eq. (2) and (3)), and best-track TC parameters, $V_{max}$ (eq. (4)), $R_{max}$ (eq. (5)) or (6)), and $\phi$. Then the blending is performed as follows:

* A pair of radii, $R_1$ and $R_2$ ($R_1 < R_2$), is defined to delimit the transition zone. Within $R_1$ of the TC track position, the zonal and meridional wind components of the symmetric parametric vortex ($wind_{vortex}$) are added to the asymmetric ERA5 filtered wind field ($wind_{filt}$). In this area,
the wind intensity is thus primarily that of the synthetic vortex, modulated by the asymmetry of the synoptic flow.

* Between $R_1$ and $R_2$, the new enhanced asymmetric wind field is merged with the initial ERA5 wind field ($wind_{ERA5}$) using coefficients:

$$wind = \alpha \cdot (wind_{vortex} + wind_{filt}) + (1 - \alpha) \cdot wind_{ERA5}$$

where $\alpha$ is a linear function ranging from 1 at $R_1$ to 0 at $R_2$, ensuring a smooth transition from the blended wind field to the original ERA5 wind field outside the TC core.

* Beyond $R_2$ from the track, the ERA5 wind field remains unchanged.

The choice of $R_1$ and $R_2$ is adjusted for each profile, while avoiding blending too close to $R_{max}$:

* If a 34-kt wind radius can be found in ERA5 ($R_{34, ERA5}$) and the ratio $R_{max}/R_{34, ERA5}$ (with $R_{max}$ from the parametric vortex) is less than 1/3, then the blending is performed between $R_1 = R_{34, ERA5}/2$ and $R_2 = R_{34, ERA5}$.

* If that ratio is greater than 1/3, then $R_1 = 2R_{max}$ and $R_2 = R_{34, ERA5}$.

* Otherwise, the transition zone is defined between 100 km and 200 km.
<figure><img src="image_4.png" /><figcaption>FIG. 3: Overview of the methodology used to reconstruct new TC wind fields in ERA5, based on best-track estimates as inputs for a parametric model.</figcaption></figure>
This procedure is illustrated in Fig. 4 and 5 for TC Lane on the 23rd of August 2018 at 4h30 UTC. Note that in this methodology, the asymmetry of the wind field is provided by the asymmetry of the ERA5 filtered synoptic wind field (Fig. 5).

Interestingly, our methodology can be applied regardless of the chosen temporal or spatial resolution. In this example, the resolution was set to 1/36° (~3 km) at 15-minute intervals. Such resolution is used for comparison and validation against high-resolution SAR observations in the following. As the aim of the present study is also to provide a global dataset on a 0.25° resolution horizontal grid with hourly outputs, the sensitivity to degradation of the resolution is discussed in section 4b.

<figure><img src="image_5.png" /><figcaption>FIG. 4: Wind profile for TC LANE on 2018-08-23 4h30 UTC. The parametric Wo13* profile is in black, the ERA5 profile is in orange, the filtered ERA5 profile is in pink, the blended final wind profile is in blue. The different sections of the profile are indicated by shading: blue where the profile is the sum of the parametric vortex and ERA5 filtered wind field, green the transition area, yellow the area where ERA5 winds remains unchanged. The IBTrACS $V_{max}$ is marked with a red star. The final blended 2D wind field $V_{max}$ is marked with a blue star. $V_{max}$ and $R_{max}$ of the averaged radial profile, as well as several radii ($R_+$, $R_{64}$, $R_{50}$, $R_{34}$, $R_0$, defined in section 2f) are indicated with dotted lines. Inner-core and outer-core areas as defined in section 2f are shaded with dark and light gray respectively. In that case: $R_{max}$ = 18.0 km and $R_{34}$, ERA5 = 89.9 km, and the Wo13* vortex is blended between $R_1$ = 45 km and $R_2$ = 89.9 km.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>FIG. 5: Wind fields for TC LANE on 2018-08-23 4h30 UTC. (a) ERA5 wind field, (b) filtered ERA5 field, (c) analytical vortex from the optimized Wo13* model, (d) SAR wind field, (e) blended field combining the filtered ERA5 background (b) and the optimized vortex (c), (f) corresponding azimuthally-averaged surface wind profiles from SAR (black), BLEND (blue), and ERA5 (orange).</figcaption></figure>

f. *Metrics for wind field evaluation*

The blended wind field is scrutinized through several key parameters describing the TC wind structure, illustrated for TC Lane in Fig. 4:

• $V_{max}$: the maximum wind of the radial profile.

• $R_{max}$: the radius of maximum wind of the radial profile.

• $R_{64}$, $R_{50}$, $R_{34}$: the radii at 64-kt, 50-kt and 34-kt of the radial profile.

• $R_+$ and $R_0$: two radii characterizing the steady-state integrated kinetic energy (IKE) of the TC, according to Avenas et al. (2024):
o $R_+$: the radius delimiting the area where significant upward motions take place. It is defined as the distance from the center where the vertical component of relative vorticity, $\omega_z$, is 5 times the Coriolis parameter, $f$ (Avenas et al. (2024)):

$$ \left\{ \begin{array}{l} \omega_z = \frac{1}{r} \frac{\partial}{\partial r} (rV) \\ \omega_z (R_+) = 5f \end{array} \right. \qquad (9) $$

o $R_0$: The outer boundary of the steady-state IKE balance integration volume, defined as the radius where the velocity in the upper outflow layer fades away. It is computed as:

$$ R_0 = R_{\max} \cdot \sqrt{2Ro_{\max}} \qquad (10) $$

with $Ro_{\max} = \frac{V_{\max}}{fR_{\max}}$ the Rossby number evaluated at $R_{\max}$.

o IKE: the integrated kinetic energy over the whole profile, or in the inner-core and outer-core sections of the profile:

$$ \begin{array}{l} \text{IKE} = \int_V \frac{1}{2} \rho V(r)^2 \cdot dV = \int_r \frac{1}{2} \rho V(r)^2 \cdot 2\pi r \cdot h \, dr \\ \\ \left\{ \begin{array}{l} \text{IKE}_{\text{inner-core}} = \pi \rho h \int_0^{R^+} V(r)^2 r \, dr \\ \text{IKE}_{\text{outer-core}} = \pi \rho h \int_{R^+}^{R_0} V(r)^2 r \, dr \end{array} \right. \qquad (11) \end{array} $$

with $\rho$ the air density assumed as constant and equal to 1.15 kg.m$^{-3}$, and the integral performed over a unit height $h$ and over two dedicated areas: the inner-core region, that is for radii $r < R_+$, and in the outer-core region, for $R_+ < r < R_0$.

These parameters are evaluated quantitatively against SAR observations using the root-mean-square error (RMSE):

$$ \text{RMSE} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (\hat{x}_i - x_i)^2} \qquad (12) $$

with $\hat{x}_i$ and $x_i$, the parameters derived from the blended and the observed radial SAR wind profiles respectively.
g. Derivation of a blended sea-level pressure field

Here we also propose a corrected pressure field associated with the new enhanced wind field (Fig. 6), based on the wind-pressure relationship used in Knaff and Zehr (2007) and reproduced in eq. (13):

$$ egin{cases} P_c = 23.286 - 0.483V_{\text{max}} - \left(\frac{V_{\text{max}}}{24.254}\right)^2 - 12.587S - 0.483|\varphi| + P_{\text{env}} \\ S = \frac{V_{\text{500km}}}{V_{\text{500c}}} \\ V_{\text{500c}} = V_{\text{max,2D}} \left( \frac{66.785-0.09102V_{\text{max}}+1.0619(|\varphi|-25)}{500} \right)^{0.1147+0.0055V_{\text{max}}-0.001(|\varphi|-25)} \end{cases} \quad (13) $$

where $V_{\text{max}}$ [kts] corresponds to the azimuthally-averaged maximum wind speed obtained after applying the correction from eq. (4) to $V_{\text{max, BT}}$, $\varphi$ represents the latitude of the TC center, and $V_{\text{max, 2D}}$ [kts] is the maximum sustained wind of the new blended wind field. As given in Knaff and Zehr (2007), the wind speed at 500 km, $V_{\text{500km}}$ [kts], is estimated by calculating in ERA5 the azimuthally-averaged wind in the ring located between 400 km and 600 km surrounding the TC center. The environmental pressure, $P_{\text{env}}$ [hPa], is evaluated in ERA5 in the same manner between 800 km and 1000 km to obtain the environmental conditions. Once the central pressure $P_c$ is estimated, a new pressure profile is established based on Holland (1980):

$$ P(r) = P_c + (P_{R1 \to R2} - P_c) \exp \left( - \left( \frac{R_{\text{max}}}{r} \right)^B \right) \quad (14) $$

where $P_{R1 \to R2}$ is the azimuthally-averaged pressure in the ring between $R_1$ and $R_2$ around the TC center, $R_{\text{max}}$ is the radius of maximum wind calculated with eq. (5) or (6), and $B$ is the Holland's parameter that can be estimated with eq. (15) proposed by Willoughby and Rahn (2004) using wind profiles acquired by aircraft:

$$ B = 0.886 + 0.0177V_{\text{max}} - 0.0094|\varphi| \quad (15) $$

Finally, the parametric sea-level pressure field (pressure$_{\text{vortex}}$), derived from eq. (14), is blended with the initial ERA5 pressure field (pressure$_{\text{ERA5}}$) in the same manner as for the wind (section 2e) between $R_1$ and $R_2$, as depicted in Fig. 6:

$$ \text{pressure} = \alpha \cdot \text{pressure}_{\text{vortex}} + (1 - \alpha) \cdot \text{pressure}_{\text{ERA5}} $$
where $\alpha$ is the same linear function ranging from 1 at $R_1$ to 0 at $R_2$.

<figure><img src="image_7.png" /><figcaption>FIG. 6: Illustration for TC Lane of (a) azimuthally-averaged pressure profiles from the mean sea-level pressure fields from (b) ERA5 and from (c) the BLEND. The IBTrACS $P_c$ is marked with a red star.</figcaption></figure>

### 3. Results

The new surface wind forcing, built by blending ERA5 winds with optimized parametric TC wind fields to obtain realistic (e.g. notably intense enough) tropical cyclones, is evaluated against SAR observations for the 175 cases presented in section 2b. First, the TC intensities and sizes are validated, as well as their integrated kinetic energy (IKE). Then, the asymmetry and 2D structure of the winds are described, and the diversity of representable cases is illustrated.

#### a. TC intensity, size and integrated kinetic energy

The ability of the blended wind field to represent the TC maximum intensity among categories is depicted in Fig. 7a and summarized in Table 1. While ERA5 strongly underestimates the maximum wind for all TC categories with increased errors at high intensities, (ranging from $12.9 \text{ m.s}^{-1}$ for the weaker TCs to $36.1 \text{ m.s}^{-1}$ for the most intense), our new blended winds show a much better agreement with observations. Overall, the azimuthally-averaged maximum wind is generally well captured compared to SAR. The blend shows a similar $V_{max}$ distribution and stable errors across all categories (globally of $6.6 \text{ m.s}^{-1}$).

The radius of maximum winds, $R_{max}$, is also shown to be improved with our blended wind field with a mean $R_{max}$ in the blended fields around 30 km compared to approximately 100 km in the
original ERA5 data (Fig. 7b and Table 2). As indicated in section 2d, as not always available and reliable in best-track data, $R_{max}$ is computed from a parametric formulation. Two formulations are proposed, that of Willoughby et al. (2006) and that of Avenas et al. (2023) (see section 2d). Both provide reasonable $R_{max}$ estimations compared to ERA5, with the Avenas formulation being particularly well suited for small $R_{max}$ (< 40 km). When looking at those cases, errors are considerably improved from 72.6 km in ERA5 to 10.3 km with $R_{max, Av23}$. For large radii (> 40km), on the other hand, it can be noted that $R_{max}$ remains quite difficult to predict correctly, even though errors are reduced by half compared to ERA5.

To further describe the ability of our blended wind field in representing realistic TC fields, we analyze metrics characterizing the kinetic energy of the TC: $R_+$, $R_0$, IKE<sub>inner-core</sub> and IKE<sub>outer-core</sub>. These indicators, along with the previously discussed $V_{max}$ and $R_{max}$ are summarized in Table 3, and IKE improvements are depicted in Fig. 7c and 7d. The better representation of the maximum wind area leads to smaller error in $R_+$ (reducing the RMSE from 142% to 33%), in $R_0$ (related to $R_{max}$ and $V_{max}$ in eq. (10)) and in IKE<sub>inner-core</sub> (RMSE decreases from 101% to 53%) for both $R_{max}$ formulations compared to ERA5. In the outer-core region (between $R_+$ and $R_0$), including the transitional zone towards the original ERA5 wind fields, the RMSE of ERA5 and our blended field are consequently closer, but still slightly decreased in our blended forcing (from 51% to 40% with $R_{max, Av23}$). The relatively accurate estimates of $R_0$ in ERA5 can be explained by a compensation between the underestimated $V_{max}$ and the overestimated $R_{max}$ in eq. (10).

<table><caption>TABLE 1: Global means, standard deviations (Std) and category-dependent RMSEs of $V_{max}$. RMSE values (absolute and relative to SAR) are computed between SAR wind profiles and either ERA5 or the blended profiles. Values in bold correspond to the smallest errors.</caption><thead><tr><th>V<sub>max</sub> category [m s<sup>-1</sup>]</th><th>SAR</th><th>ERA5</th><th>Blend</th></tr></thead><tbody><tr><td rowspan="2">Global</td><td>Mean: 41.3</td><td>Mean: 19.5</td><td>Mean: 42.7</td></tr><tr><td>Std: 12.5</td><td>Std: 5.0</td><td>Std: 11.2</td></tr><tr><td>TS – TC cat.1 (37 %)</td><td>–</td><td>RMSE: 12.9 (44%)</td><td>RMSE: <b>6.3</b> (21%)</td></tr><tr><td>TC cat.2-3 (33 %)</td><td>–</td><td>RMSE: 23.4 (55%)</td><td>RMSE: <b>7.2</b> (17%)</td></tr><tr><td>TC cat.4-5 (30 %)</td><td>–</td><td>RMSE: 36.1 (64%)</td><td>RMSE: <b>6.6</b> (12%)</td></tr></tbody></table>
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>FIG. 7: Comparison between SAR-derived estimates (x-axis) and corresponding values from ERA5 (orange) and blended forcings (blue and green) for (a) maximum winds ($V_{max}$), (b) radius of maximum wind ($R_{max}$), and Integrated Kinetic Energy (IKE) in the (c) inner-core and (d) outer-core regions. The blended forcings use $R_{max, Av23}$ (blue) and $R_{max, Wi06}$ (green). Mean and RMSE values (absolute and relative to SAR) are indicated for each dataset. Normalized distributions are shown in the side histograms.</figcaption></figure>
<table><caption>TABLE 2: Global means, standard deviations (Std) and category-dependent RMSEs of R<sub>max</sub>. RMSE values (absolute and relative to SAR) are computed between SAR wind profiles and either ERA5 or the blended profiles. Values in bold correspond to the smallest errors.</caption><thead><tr><th>R<sub>max</sub> category [km]</th><th>SAR</th><th>ERA5</th><th>Blend (R<sub>max,Wi06</sub>)</th><th>Blend (R<sub>max,Av23</sub>)</th></tr></thead><tbody><tr><td rowspan="2">Global</td><td>Mean: 33.0</td><td>Mean: 96.6</td><td>Mean: 32.6</td><td>Mean: 28.2</td></tr><tr><td>Std: 18.2</td><td>Std: 27.9</td><td>Std: 9.5</td><td>Std: 18.4</td></tr><tr><td>R<sub>max</sub> ≤ 40 km (70 %)</td><td>—</td><td>RMSE: 72.6<br>(313 %)</td><td>RMSE: 13.4<br>(58 %)</td><td>RMSE: <b>10.3</b><br>(<b>45%</b>)</td></tr><tr><td>R<sub>max</sub> > 40 km (30 %)</td><td>—</td><td>RMSE: 53.2<br>(95%)</td><td>RMSE: 26.8<br>(48%)</td><td>RMSE: <b>25.1</b><br>(<b>45%</b>)</td></tr></tbody></table>

<table><caption>TABLE 3: Means, standard deviations (Std) and RMSEs of TC parameter distributions used to characterize the TC kinetic energy. RMSE values (absolute and relative to SAR) are computed between SAR wind profiles and either ERA5 or the blended profiles. Values in bold correspond to the smallest errors.</caption><thead><tr><th>DATA</th><th colspan="2">SAR</th><th colspan="3">ERA5</th><th colspan="3">Blend (R<sub>max,Wi06</sub>)</th><th colspan="3">Blend (R<sub>max,Av23</sub>)</th></tr><tr><th>Metric</th><th>Mean</th><th>Std</th><th>Mean</th><th>Std</th><th>RMSE</th><th>Mean</th><th>Std</th><th>RMSE</th><th>Mean</th><th>Std</th><th>RMSE</th></tr></thead><tbody><tr><td>R<sub>+</sub><br>[km]</td><td>41.6</td><td>15.3</td><td>96.1</td><td>25.6</td><td>58.9<br>(142%)</td><td>40.6</td><td>6.5</td><td>15.6<br>(38%)</td><td>37.6</td><td>11.9</td><td><b>13.9</b><br>(<b>33%</b>)</td></tr><tr><td>R<sub>0</sub><br>[km]</td><td>221.8</td><td>56.1</td><td>265.2</td><td>62.5</td><td>69.7<br>(31%)</td><td>231.9</td><td>32.5</td><td>59.8<br>(27%)</td><td>212.4</td><td>51.7</td><td><b>47.8</b><br>(<b>22%</b>)</td></tr><tr><td>IKE<sub>inner-core</sub><br>[TJ]</td><td>3.3</td><td>2.8</td><td>0.7</td><td>1.0</td><td>3.3<br>(101%)</td><td>3.8</td><td>3.7</td><td><b>1.7</b><br>(<b>53%</b>)</td><td>3.9</td><td>3.7</td><td>1.8<br>(54%)</td></tr><tr><td>IKE<sub>outer-core</sub><br>[TJ]</td><td>31.7</td><td>27.5</td><td>21.0</td><td>18.2</td><td>16.1<br>(51%)</td><td>31.5</td><td>17.0</td><td>13.6<br>(43%)</td><td>29.5</td><td>20.7</td><td><b>12.7</b><br>(<b>40%</b>)</td></tr></tbody></table>

b. TC asymmetry

We then evaluate the ability of our blended wind field to feature TC asymmetry. Here, the asymmetry is provided by the background ERA5 field. We compare this asymmetry with the wavenumber-1 mode of the Bessel decomposition of SAR data providing the first order wind asymmetry. To keep the rotation direction consistent, let's note that all angles characterizing the
TC wind asymmetry are given considering the northern hemisphere convention where TCs are
moving to the north (Southern Hemisphere TCs are therefore mirrored). The maximum wind of our
blended wind has a mean angle of 270.5°, which is slightly more to the right than the SAR-observed
mean angle of 292.5°. The angular coverage is, however, included in that of SAR (Fig. 8). This
asymmetry shows that the synoptic background wind flow provides a general right asymmetry. We
also compare this asymmetry with existing parametric formulation of the asymmetry such as that
of Chen (1994), and that of Olfateh et al. (2017) fitted to SAR data (Vinour et al. 2025). Both
formulations consist in adding an asymmetric component to the symmetric wind field. Details
on these parameterizations are given in Appendix B. Chen’s parametric approach provides a
component associated to the TC translation speed. Thus, the resulting field systematically provides
a maximum wind speed on the right side (~270°) where wind and translation are collinear.
Olfateh’s formulation, on the other hand, that has been fitted to SAR, shows a front-right (329.5°)
mean asymmetry. Both Chen and Olfateh parameterizations features less variability than our
blended wind field and SAR data.

<figure><img src="image_12.png" /><figcaption>FIG. 8: Distribution of angles between the maximum wind over the 2D wind field and the direction of TC translation for SAR data (black), the blended wind field (blue), the parametric asymmetry from Chen (1994) (purple), and the parametric asymmetry from Olfateh et al. (2017) fitted to SAR data by Vinour et al. (2025) (red). Solid lines indicate the mean angles while shaded areas represent the lower-upper quartile intervals. The distance to the center does not matter.</figcaption></figure>
The general structure of the 2D wind field is finally illustrated with characteristic wind radii, $R_{34}$, $R_{50}$, $R_{64}$, in the four quadrants relative to the direction of TC translation (Fig. 9 and Table 4). The asymmetry on the right side clearly appears in the blended fields (in blue and green) with spatial extends of $R_{34}$, $R_{50}$ and $R_{64}$ on average larger than on the left side. Although the slight bias in the angle is noticeable with the blended wind field slightly underestimating the front-right $R_{64}$ and $R_{50}$ compared to SAR, the asymmetry associated with the ERA5 synoptic flow and using both $R_{max}$ formulations yields to mean biases below –11 km and –13.7 km for $R_{64}$ and $R_{50}$, respectively. Meanwhile, $R_{50}$ and $R_{64}$ from ERA5 per quadrant are not shown, as they are available for only a few cases for $R_{50}$ (less than 20%) and even fewer cases for $R_{64}$ (less than 2%). Furthermore, given the adjusted transition area, $R_{34}$ radii are correctly estimated in each quadrant, on average being between those from SAR and ERA5 with a mean bias of -6.6 km. Overall, asymmetry trends are quite well represented and blended fields show a consistent wind decay compared to SAR observations.

The asymmetry structure obtained with Olfateh's parametric formulation is also evaluated (Fig. 9 and Table 4). While this parameterization leads to a consistent angle of asymmetry, limitations arise in capturing the correct wind decay beyond the radius of maximum winds. In the front-right quadrant, where the positive asymmetry is prescribed, the parametric asymmetry slightly underestimates on average the $R_{64}$ and $R_{50}$ compared to the blended product. Additionally, larger mean biases in wind radii are apparent elsewhere, especially in the rear-left quadrant relative to TC translation, where the prescribed negative asymmetry underestimates even more the characteristic wind radii.
<figure><img src="image_13.png" /><figcaption>FIG. 9: Average TC size as measured by the median $R_{64}$, $R_{50}$ and $R_{34}$ wind radii [km] for each quadrant relative to the direction of TC translation compared to SAR observations (black and gray shadings). The left panel is for the blended fields using the ERA5-induced asymmetry, with either $R_{max}$, Av23 (blue) or $R_{max}$, Wi06 (green). The right panel corresponds to the blended fields incorporating the Olfateh asymmetry and using $R_{max}$, Av23 (red). On both panels, mean $R_{34}$ values per quadrant from ERA5 are drawn in orange.</figcaption></figure>

<table><caption>TABLE 4: Summary of mean biases for each characteristic wind radii and for each forcing, relative to Fig. 9. Mean $R_{50}$ and $R_{64}$ from ERA5 per quadrant are not displayed, as they are available for only a few cases.</caption><thead><tr><th>Forcing</th><th>BLEND</th><th>BLEND</th><th>Olfateh</th><th>ERA5</th></tr><tr><th>Mean Bias [km]</th><th>R<sub>max</sub>, Wi06</th><th>R<sub>max</sub>, Av23</th><th>R<sub>max</sub>, Av23</th><th></th></tr></thead><tbody><tr><th scope="row">R<sub>64</sub></th><td>-2.0</td><td>-11.0</td><td>-18.4</td><td>-</td></tr><tr><th scope="row">R<sub>50</sub></th><td>-3.2</td><td>-13.7</td><td>-27.6</td><td>-</td></tr><tr><th scope="row">R<sub>34</sub></th><td>-5.0</td><td>-6.6</td><td>-19.8</td><td>-12.4</td></tr></tbody></table>
### c. TC cases

The above analyses have shown that the newly blended wind field is quite efficient in representing the general TC structure in contrast with original ERA5 data, which tends to strongly smooth the inner-core area. We further illustrate its ability to represent various TCs of different intensities and shapes (Fig. 10).

Mangkhut is an intense (category 5) and very large cyclone with high winds up to more than 300 km from its center. Habana (category 4) is a medium-sized cyclone with a symmetric structure, particularly near the region of maximum winds, in contrast with Maria (category 3), a more asymmetric case occurring during a landfall. Lester (category 3), is also asymmetric but depicts a smaller system with a moderate intensity, whereas the case of Fiona (category 2) illustrates a weaker TC. The blended wind field generally manages to represent the eye size, the inner-core wind shape, as well as the broadness of the outer-core, and the surrounding environment, leading to a realistic diversity of shapes. The spreading of high winds in the outer-core is successfully captured by the blended wind field which preserves its wide spatial extent, whether for a very spread-out case in Fig. 10a ($R_{34}$ of 230.8 km), or for a compact case in Fig. 10b ($R_{34}$ of 113.9 km). The symmetric (Fig. 10b) and asymmetric (Fig. 10d and 10e) structures near the eye are well represented, and the broadness of the outer-core is mainly influenced by the surrounding environment (Fig. 10c). A key feature is that by preserving the outermost part of the TCs through the blending criteria, the outer wind conditions represented in ERA5, appear to be largely consistent with SAR observations. The blended field effectively depicts the asymmetric characteristics over sea (Fig. 10d) as well as near the coast (Fig. 10c and 10e), where outer-wind conditions during landfall are preserved from ERA5, including the signature of land with the wind reduction over land. These examples also highlight some limitations such as too intense winds or slightly underestimated wind decays in the reconstructed TCs due to the estimations of TC variables (see discussion section 4c). The same cases obtained with Olfateh's asymmetric parametric formulation are given in Appendix B. They show that the parametric asymmetry can be limited in the reproduction of various TC core spreads (such as Mangkhut and Fiona) or in the representation of almost symmetric eye shapes (such as Habana) and that using the blended product is closer to observations.
<figure><img src="image_14.png" /><figcaption>FIG. 10: Snapshots of TC surface wind fields, each row is dedicated to one TC. (a1)–(e1) SAR observations, (a2)–(e2) BLEND, (a3)–(e3) ERA5, and (a4)–(e4) corresponding azimuthally-averaged wind profiles. All 2D fields are oriented along the direction of translation, and southern hemisphere TCs (e.g., Habana (b)) are mirrored to mimic northern hemisphere orientation.</figcaption></figure>
d. TC central sea-level pressure

The limitations of ERA5 in representing cyclone wind structure also apply to the pressure field and the minimum pressure at the TC center are largely overestimated (Fig 11 and example of Lane Fig 6a and 6b). The wind-pressure relationship, described in section 2g, allows us the reconstruction of a new TC-induced pressure field that is consistent with its associated blended wind field. In contrast to the initial central pressure provided in ERA5, that from the blend is generally well captured compared to the best-track values. Improvements over ERA5 minimum central pressures lead to a reduced RMSE of 6.7 hPa compared to 32.3 hPa initially.

<figure><img src="image_15.png" /><figcaption>FIG. 11: Minimum sea level pressure at the TC center ($P_c$) from ERA5 (orange), and the BLEND using $R_{max, Av23}$ (blue) (y-axis), compared to best-track data (x-axis). Mean and RMSE values (absolute and relative to IBTrACS) are indicated for each dataset. Normalized distributions are displayed in the side histogram.</figcaption></figure>

4. Discussion

a. Added value of extensive high-resolution observations

Up to now, few studies have compared and validated their forcing fields against very high-resolution 2D wind field observations. Previous studies using a similar blending methodology (Vincent et al. 2012; Jullien et al. 2024), applied a transition between the parametric vortex and the
reanalysis wind field over a broader region around the cyclone (between 600-1200 km). In this study, we take advantage of the SAR observations to emphasize the importance of determining at which radius to transition toward reanalysis winds in the outer-core, where reanalyzed winds become relevant. A sensitivity study was therefore conducted to better constrain the TC reconstruction method. The spatial extent of modeled TCs is evaluated with the 34-kt radius (Fig. 12), which has a mean value of 154.5 km in SAR data. This radius is generally correctly represented by ERA5 (in orange) with a mean of 154.6 km and a RMSE of 21.5 km. We therefore chose our transition area between the blended field and the original ERA5 field so that such outer radius is matched. With the final blending method (in blue), results show a fair representation of these outer-core radii with a RMSE of 22.1 km. Unlike a default 100-200 km transition (in pink), this criterion allows the blend to fit to ERA5, and therefore approximately to SAR, before 200 km. When $R_{34}$ does not exist in ERA5, a sensitivity study for the transition area between inner and outer TC core has been conducted: between 100-200 or 150-300, and the 100-200 km transition has been retained. Indeed, the farther the blending area extends, the greater the contribution of the background wind, which leads to an increased overestimation of winds in the outer core, with errors reaching 49.1% with a 150-300-km blending. For comparison, the transition performed in previous studies between 600-1200 km around the TC center (in gray) leads to $R_{34}$ overestimations due to the influence of the parametric vortex, particularly far from the TC center, where it becomes less reliable. This highlights the relevance of using the parametric model only in regions where ERA5 winds are underestimated.

Furthermore, using SAR observations, being the only observations providing full 2D surface wind fields with sufficiently high resolution to feature the inner-core TC structure, allow us to better validate intense cyclones which would not be as accurate with lower-resolution observational data. Recently, Liu et al. (2025) introduced a global reconstruction of TC wind fields from ERA5, incorporating a blending methodology mostly based on Li et al. (2022). Several comparisons were conducted against high-resolution airborne data from the Stepped Frequency Microwave Radiometer (SFMR) and a few ground-based meteorological stations, but correlations are primarily driven by weak and moderate winds within TCs and their outermost regions. Validation was mostly performed against radiometer data from the Soil Moisture Active Passive (SMAP) mission and from the WindSat data. Nevertheless, both datasets have a spatial resolution of 0.25°, which compromises
and underestimates the representation of very high wind speeds in concentrated inner-cores, as observed in stronger TCs. The global assessments thus demonstrated a better alignment with observations for tropical depressions, tropical storms, and lower-category cyclones (category-1/2). Representing smaller but intense systems remains, indeed, one of the main challenges, given the relatively coarse spatial resolution of current reanalysis datasets. Our methodology fills this gap and demonstrates its ability to represent tropical cyclones of all intensities (up to the strongest), with various structures (broadness, asymmetry).

<figure><img src="image_16.png" /><figcaption>FIG. 12: 34-kt wind radii ($R_{34}$) from ERA5 (orange) wind forcing and blended forcing constrained within 100–200 km or with $R_{34}$ criterion (blue) (y-axis), compared to SAR estimates (x-axis); and constrained within 100–200 km only (pink) and 150–300 km (purple) only. A subset of 29 crossover cases, common to all datasets, is used for the comparison. Additionally, 17 cases among this sample are compared to blended forcing from Jullien et al. (2024) where blending was constrained within 600-1200 km only (gray). Solid lines indicate the linear fits. Mean and RMSE values (absolute and relative to SAR) are indicated for each dataset on the right.</figcaption></figure>

b. *Sensitivity to the grid resolution*

Horizontal resolution impacts the representation of cyclonic structures. The proposed addition of a more realistic TC wind field to the ERA5 background, leading to our blended TC wind fields, has been validated in section 3 on high-resolution (3km) co-located SAR observations. We
therefore investigate the influence of lower grid resolutions, i.e. when coarsening the resolution of the inserted parametric TC winds from 1/36° (~3 km) to 1/12° (~9 km), and to 1/4° (~28 km) on the resulting $V_{max}$, $R_{max}$ and IKE (Fig. 14, 13 and Table 5). While a 1/12° resolution still captures the correct intensity along with a defined TC eye, these features may be missed when coarsening the resolution to 1/4° (Fig. 13a). Indeed, such resolution implies inevitable smoothing of the TC eye, that can even sometimes vanish, leading to larger errors in $V_{max}$ (RMSE is increasing from 7.4% to 16.2%). Since $R_{max}$ is often less than or of the order of magnitude of a ~30 km grid cell, such as ERA5 with 1/4°, the eye and the eyewall can fade away, as for TC Lane (Fig. 14a), or be smoothed, as for TC Mangkhut (Fig. 14b). Although the shape of the profiles is not fully represented at low resolution, the wind decay after $R_{max}$ is still well defined in all cases. Energy conservation is noticeable around the TC center within the area influenced by the new blended wind field (Fig. 13b). Using lower resolution has limited impact on the amount of energy that can be transferred to the ocean surface (e.g. surface waves), with an RMSE increasing only from 6.2% to 8.4%. In terms of energy fluxes into an ocean or wave model, at 1/4°, the attenuation of intense winds due to such low resolution is compensated by the coarser representation of the cyclone center and eyewall. Even if some spatial details may be missed at 1/4°, the induced ocean processes are likely to be reasonably represented (Vincent et al. (2012)).

<table><caption>TABLE 5: Mean values and RMSEs of $V_{max}$ and IKE at different grid resolutions. Errors are computed between azimuthally-averaged wind profiles derived from the blended fields at 1/36° and either those at 1/12° or at 1/4°. RMSEs are reported both as absolute values and as percentages relative to the corresponding 1/36° means.</caption><thead><tr><th>Grid Resolution</th><th>1/36°</th><th colspan="2">1/12°</th><th colspan="2">1/4°</th></tr><tr><th>Metric</th><th>Mean</th><th>Mean</th><th>RMSE</th><th>Mean</th><th>RMSE</th></tr></thead><tbody><tr><td>V<sub>max</sub> [m s<sup>-1</sup>]</td><td>42.7</td><td>40.4</td><td>3.2 (7.4%)</td><td>37.3</td><td>6.9 (16.2%)</td></tr><tr><td>IKE [TJ]</td><td>34.6</td><td>33.5</td><td>1.3 (3.6%)</td><td>32.2</td><td>2.7 (7.9%)</td></tr></tbody></table>
<figure><img src="image_17.png" /><figcaption>FIG. 13: $V_{max}$ (a) and IKE (b) from azimuthally-averaged wind profiles derived from the blended fields at 1/36° grid resolution compared to those at 1/12° (blue) and 1/4° (orange) grid resolutions. Integrated Kinetic Energy (IKE) is computed on a 200 km large area around the TC center to include the blending area.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>FIG. 14: Surface wind fields of two TCs, Lane (row a) and Mangkhut (row b), shown at grid resolutions of 1/36° (column 1), 1/12° (column 2), and 1/4° (column 3). The corresponding azimuthally-averaged wind profiles are presented in column 4. The final blended 2D wind field $V_{max}$ are marked with a star.</figcaption></figure>
### c. Limitations

Besides the enhanced but still imperfect representation of TCs through our improved parametric model and its blending with ERA5 wind fields, another source of uncertainty concerns the best-track data. While they are consistent estimates of storm intensity, size and position using all available TC information, uncertainties vary between basins and over time. The approximate uncertainty in position is function of intensity and can vary from 10 km for strong TCs to 40 km for weak ones (Gahtan et al. 2024). However, in a 1/4° grid resolution framework used for the ERA5 blending here, such track uncertainty has limited impact.

Furthermore, the quality of the best-track data has improved considerably over time, starting with sparse observations, an important factor if we want to reconstruct past time series. Before 1980, in all basins, the TC intensity uncertainty was around 10 m.s⁻¹ to 15 m.s⁻¹. Between 1980 and 2000, it reduced to 7 m.s⁻¹, down to 5 m.s⁻¹ and even to 3.5 m.s⁻¹ in the North Atlantic in the latest years (Gahtan et al. 2024). Moreover, the uncertainty linked to the position and intensity may vary from year to year, depending in particular on the frequency of aircraft missions, which provide the most reliable measurements in the TC inner-core (Torn and Snyder 2012). Over time, and depending on the basin, the blended product may become progressively more accurate and reliable.

Also, the temporal and spatial interpolation of ERA5 used for co-location may introduce biases, in particular by assuming the linearity of velocities. However, this approach is considered preferable to a direct comparison at times or positions that are too far apart if only the time steps from the best-tracks are considered (i.e. every 3 or 6 hours). When reconstructing time series, consistency regarding uncertainties in best-track data is, moreover, ensured by using the same interpolation as those used by IBTrACS. Positions are interpolated in time using splines while other parameters (i.e. $V_{max}$, pressure, $R_{64}$, $R_{50}$, $R_{34}$) are interpolated linearly. In this way, the maximum and minimum intensity occurring in the TC lifetime remain unchanged.

Even if the parametric model is adjusted on the basis of observations, an inherent limitation is that we rely on analytical formulations that we apply on the best-track data to estimate the necessary input variables. In particular, this can introduce biases, notably on the estimated $V_{max}$, which is linked to the best-track intensity, but also on the $R_{max}$, which can be expressed as a function of either the intensity and latitude, or alternatively of the angular momentum, characteristic wind radii, and latitude. Nevertheless, best-track data are currently the only and best way to generate
time series at the climate scale, and the resulting TCs still remain intense enough and closer to
observations than original reanalysis surface winds.

Finally, the first-order asymmetry is fairly well represented, using the ERA5 based background
rather than introducing analytical asymmetries (e.g. Olfateh) in the TC parametric model (which
is less accurate on the left-hand side). The first-order asymmetry parameterization, based solely
on translation speed, also remains limited because of the difficulty of taking into account the
vertical wind shear. However, there are still remaining biases: the asymmetry derived from
ERA5 tends to be shifted slightly too far towards the right of the cyclone trajectory. Besides
this general asymmetry, it has to be noted that, fine-scale variations are not reproduced. This
variability observed in the SAR data may be better represented by incorporating a parameterization
of higher-order asymmetries ($WN > 2$), which would allow a more realistic representation of wind
field structures at higher resolution. Parameterizing higher order asymmetries as a function of
best-track estimates, however, represents a considerable challenge as asymmetries above $WN1$
rely mostly on internal transient processes such as Vortex Rossby waves or inner-core mesovortex
(Vinour et al. 2021), rather than on the vortex translation.
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This study aims at improving the representation of cyclonic surface wind fields and pressure in reanalyzed data to be used for a wide range of applications (ocean response, waves, storm surges, global and interranual risk assessment, etc). First, we derived an improved parameterization of the Wo13 tropical cyclone parametric model with parameters adjusted to observed SAR wind profiles. In the proposed parametric model, parameters are expressed as functions of TC variables available in IBTrACS dataset which is available globally and over several decades. To provide full TC wind and pressure fields with realistic synoptic conditions, we blended the parametric models into ERA5 with special care of taking the best advantage of both the parametric representation of the inner-core and the reliable ERA5 winds in the outer region. The blended product showed fair consistency of the blended wind fields with high-resolution SAR observations. We highlighted the performance of our blended product in representing the structural and energetic parameters of the surface wind ($V_{max}$, $R_{max}$, $R_+$, $R_0$, IKE<sub>inner-core</sub>, IKE<sub>outer-core</sub>) in all TC categories, in contrast to ERA5 and previous works proposing blending solutions. We tested various parameterizations of $R_{max}$, as this parameter is not always available in IBTrACS and hold large uncertainties. Both (Av23 and Wi06) formulations revealed an overall good agreement with SAR, Av23 being slightly better for small $R_{max}$. We also illustrated that using ERA5 synoptic flow to constrain the TC asymmetry provided good results, and allowed to represent asymmetric TC features similarly to those observed in terms of intensity and storm size. The blended product was thus shown to be able to represent the wide variety of TCs. We finally showed that although we know that the resolution of TC structure is sensitive to the choice of spatial resolution, our blending of the TC winds on ERA5 "low-resolution" grids (1/4°) kept the overall integrated energy of the TC and offered clear added value compared to the original reanalyzed ERA5 wind fields in TCs.

Based on this study, we provide a dataset of global hourly surface wind fields including realistic TCs on a 0.25° resolution horizontal grid over the period 1980-2024 (DOI: https://doi.org/10.12770/819f5ea3-6897-4e64-b599-cfb98d6c963e), along with a toolbox to generate any TC case at any chosen time and grid resolution along its track (DOI: https://doi.org/10.12770/11057ae4-b708-481a-9848-2e4fdef57625, Git repository: https://gitlab.ifremer.fr/lops-public/toolbox_era5_tc).
Acknowledgments. This work was supported by the SPC-Climate-FLAGSHIP project, as well as the French State under the ANR OROWSHI Project (https://www.france-energies-marines.org/projets/orowshi, grant No. ANR-10-IEED-0006-34), and under the PPR FUTURISKS Project ‘Un Océan de Solutions’ managed by the National Research Agency, under France 2030 (grant No. ANR-22-POCE-0002). It used the HPC resources from GENCI-IDRIS (Grant A0010107661) and the Pôle de Calcul et de Données Marines (PCDM; https://pcdm.ifremer.fr/). The researchers were supported by the French National Institute for Ocean Science (Ifremer) for S.J. and A.Z., the French National Research Institute for Sustainable Development (IRD) for N.H. and C.M., and the JST Moonshot R&D Program, Grant JPMJMS2282 for L.V. The authors would like to thank Arthur Avenas (ESA) and Alexis Mouche (Ifremer) for the valuable discussions and their constructive suggestions that contributed to this work.

Data availability statement. All data used in this study are available online or from the corresponding author upon request. The SAR database obtained from Ifremer/CyclObs, used to evaluate the methodology is available on the CyclObs website: https://cyclobs.ifremer.fr/app/tropical. The 4.01 version of IBTrACS data used in this study is available at https://www.ncei.noaa.gov/products/international-best-track-archive. The provided ERA5 dataset with updated TCs is available at https://doi.org/10.12770/819f5ea3-6897-4e64-b599-cfb98d6c963e. The toolbox allowing to generate TC wind fields is available at https://doi.org/10.12770/11057ae4-b708-481a-9848-2e4fdef57625 (Git repository: https://gitlab.ifremer.fr/lops-public/toolbox_era5_tc). The analyses and figures were performed with Python scripts, available from the corresponding author upon request.
Optimizing the free parameters of the Wo13 parametric wind model

In the study of Vinour et al. (2025), a methodology to optimize the parameters of various parametric wind models using SAR-observed wind profiles was developed. First, parametric models are fitted on each individual SAR-observed surface wind profile. All individual fits provide distributions of parameter values. Then, from these distributions, a multi-linear regression is performed to express each parameter as a function of TC variables: $p = w_0 + w_1 R_{max} + w_2 V_{max} + w_3 |\varphi|$, where $w_0$ is the intercept and $w_1, w_2$ and $w_3$ are coefficients.

As in Wo13, the influence of $\lambda$ is near the peak, i.e. on a smaller area than $\kappa$ in the eyewall and $\mu$ in the outer-core, $\lambda$ did not play a major role in minimizing the error over the whole profile in Vinour et al. (2025). By optimizing the three parameters at the same time, only $\kappa$ and $\mu$ were indeed adjusted and $\lambda$ was left constant at 1 in most cases. This limited the strength of the formulation, which proposes a three-parameters adjustment. As Wo13's adjustable coefficients tend to have effects on different areas of the profile, we develop a methodology to adjust them considering the sections of interest of the profile, leaving the parameter of interest in the associated section free within its boundary values, while the two others are kept constant. The order to adjust each parameter is chosen so that the adjustment of the outer-core (the wider area) does not dominate the minimization of the cost function, to the detriment of the eyewall and of the peak. The idea is to successively constrain the next adjustment by the a priori values obtained by the previous adjustment. Fig. A1 illustrates the iterative and incremental adjustment procedure. First, $\kappa$ is optimized over the eyewall section (defined as radius < 0.9$R_{max}$), then, $\lambda$ is optimized over the peak section (0.9$R_{max}$ < radius < 1.1$R_{max}$), and finally, $\mu$ is optimized over the outer-core section (radius > 1.1$R_{max}$). As a last step, the three parameters are adjusted together on the entire profile taking as first guest their previously adjusted values. These various steps are repeated several times, until convergence is reached by matching a threshold of 0.1 m$^2$.s$^{-2}$ for the mean square error between the fitted and observed radial wind speed values. Three iterations are needed to reach convergence. Using smaller thresholds leads to similar results.

Degrees of freedom are also assigned to the physical parameters ($R_{max}$, $V_{max}$, $V_0$) on which the model depends. A choice is made about the tolerance of $R_{max}$ to compensate for possible fluctuations in velocity near the peak, while remaining close to the observed value of $V_{max}$. In
addition, given the possible velocity increases near the eye due to measurements errors or noise, a large tolerance is left to $V_0$. This allows the model to adopt a better power law over the whole profile and in particular in the outer-core. Moreover, as there is no first guess values given in the literature, the first guesses for $\kappa$ and $\eta$ for the first iterations are determined as the mean value over fitted profiles, respectively 3 and -0.6. Whereas the first guess for $\lambda$ is set at 0.5, given its theoretical boundaries [0;1]. On the other hand, first guesses for the physical parameters ($R_{max}$, $V_{max}$, $V_0$, $\phi$) are taken from the data themselves.

This adjustment method is applied to the 276 SAR-observed wind profiles. Individual fits thus provide optimal parameters for each profile. We then follow a similar procedure as Vinour et al. (2025) by deriving a multi-linear regression for each parameter as a function of TC physical variables ($R_{max}$, $V_{max}$, $\phi$) from the distributions of fitted parameters. The parameterization is finally given by eq. (3) within the main text.

We evaluated the performance of the new parameterization (Wo13*) by computing the RMSE between the observed SAR profiles and the parametric profiles. The distributions of RMSE values are summarized in Table A1. Improvements over the initial Wo13* (with a default adjustment) are characterized by a decrease in the errors (mean RMSE of $3.03 \text{ m.s}^{-1}$ compared to $3.86 \text{ m.s}^{-1}$) and by lower values both of median and quartiles. The RMSEs for the best-performing model reported in Vinour et al. (2025), namely that from Loridan et al. (2015), are shown to illustrate that the new parameterization achieves comparable performances.

<table><caption>TABLE A1: Summary of RMSE between parametric models with regressed parameters and SAR-observed profiles. Values in bold correspond to the new Wo13* parameterization.</caption><thead><tr><th>Model<br>RMSE [m.s<sup>-1</sup>]</th><th>Initial Wo13*</th><th>Wo13*</th><th>Best model from<br>Vinour et al. (2025)</th></tr></thead><tbody><tr><td>1st quartile</td><td>2.06</td><td><strong>1.85</strong></td><td>1.76</td></tr><tr><td>Median</td><td>3.08</td><td><strong>2.55</strong></td><td>2.53</td></tr><tr><td>Mean</td><td>3.86</td><td><strong>3.03</strong></td><td>3.18</td></tr><tr><td>3rd quartile</td><td>4.94</td><td><strong>3.78</strong></td><td>3.89</td></tr></tbody></table>
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /><figcaption>FIG. A1: Above: an example of a SAR mean profile and its Wo13 fitted profile, divided into 3 sections used to compute κ, λ and η (μ). Below: the optimization methodology to fit Wo13 parameters on SAR observed wind profiles.</figcaption></figure>
Parametric asymmetries

In this study, we compare an ERA5 background asymmetry with existing parametric formulation of the asymmetry such as that of Chen (1994), and that of Olfateh et al. (2017) fitted to SAR data (Vinour et al. 2025). Both formulations consist in adding an asymmetric component to the symmetric wind field. Chen's expression is given by:

$$ \left\{ \begin{aligned} V &= V_{\text{sym}} + C \cdot V_T \\ C &= \frac{3 R_{\text{max}}^{3/2} r^{3/2}}{R_{\text{max}}^3 + r^3 + R_{\text{max}}^{3/2} r^{3/2}} \end{aligned} \right. \qquad (B1) $$

where $V_{\text{sym}}$ is the symmetric component and $V_T$ is the TC translation speed.

Olfateh's full parametric asymmetric wind field $V(r, \theta)$ can be expressed as the sum of a symmetric component $V_r(r)$ and a sine function featuring a radial distribution representing the first mode of asymmetry $V_{\text{WN1}}(r)$:

$$ \left\{ \begin{aligned} V(r, \theta) &= V_r(r) + \epsilon V_{\text{WN1}}(r) \sin(\theta + \text{sign}(\varphi)\beta) \\ V_{\text{WN1}}(r) &= \left[ \exp(1) \cdot \frac{R_a^D}{r} \cdot \exp\left(-\frac{R_a^D}{r}\right) \right]^{\frac{1}{2}} \end{aligned} \right. \qquad (B2) $$

where $\beta$ is the azimuth of the asymmetric maximum wind (which is multiplied by the sign of latitude $\varphi$), $\epsilon$ is the factor that determines the degree of asymmetry, $R_a$ is the distance from the center at which the asymmetry is maximum, and $D$ adjusts the shape of the asymmetry radial profile. Regressions for these parameters are given in Vinour et al. (2025) and summarized hereafter:

$$ \left\{ \begin{aligned} \beta(r) &= \min\left(0, 156.1 - 185.6 \frac{r}{R_{\text{max}}}\right) & \text{if } r < 1.2 R_{\text{max}} \\ \beta(r) &= -54.1 - 3.5 \frac{r}{R_{\text{max}}} & \text{if } r \ge 1.2 R_{\text{max}} \\ \epsilon &= 3.26 \times 10^{-1} - 2.89 \times 10^{-3} V_{\text{max}} - 3.30 \times 10^{-2} \ln(R_{\text{max}}) + 8.30 \times 10^{-3} V_T \\ \ln(R_a) &= 5.91 \times 10^{-1} - 1.50 \times 10^{-2} V_{\text{max}} + 9.26 \times 10^{-1} \ln(R_{\text{max}}) + 4.92 \times 10^{-2} V_T \\ D &= 9.30 \times 10^{-1} - 4.16 \times 10^{-3} V_{\text{max}} + 2.55 \times 10^{-1} \ln(R_{\text{max}}) - 2.37 \times 10^{-2} V_T \end{aligned} \right. \qquad (B3) $$
<figure><img src="image_21.png" /><figcaption>FIG. B1: Same as Fig. 10 using $R_{max}$, Av23 but considering wind fields with an asymmetry from Olfateh et al. (2017) (fields a3 to e3).</figcaption></figure>
662 References

663 Avenas, A., A. Mouche, J. Knaff, X. Carton, and B. Chapron, 2024: On the tropical cyclone integrated kinetic energy balance. **51 (16)**, e2024GL108327, https://doi.org/10.1029/2024GL108327, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL108327.

666 Avenas, A., A. Mouche, P. Tandeo, J.-F. Piolle, D. Chavas, R. Fablet, J. Knaff, and B. Chapron, 2023: Reexamining the estimation of tropical cyclone radius of maximum wind from outer size with an extensive synthetic aperture radar dataset. **151 (12)**, 3169-3189, https://doi.org/10.1175/MWR-D-23-0119.1, URL https://journals.ametsoc.org/view/journals/mwre/151/12/MWR-D-23-0119.1.xml.

671 Chavas, D. R., and J. A. Knaff, 2022: A simple model for predicting the tropical cyclone radius of maximum wind from outer size. **37 (5)**, 563-579, https://doi.org/10.1175/WAF-D-21-0103.1, URL https://journals.ametsoc.org/view/journals/wefo/37/5/WAF-D-21-0103.1.xml.

674 Chen, K., 1994: A computation method for typhoon wind field. *Tropic Oceanology*, **13 (2)**, 41-48.

675 Combot, C., A. Mouche, J. Knaff, Y. Zhao, Y. Zhao, L. Vinour, Y. Quilfen, and B. Chapron, 2020: Extensive high-resolution synthetic aperture radar (SAR) data analysis of tropical cyclones: Comparisons with SFMR flights and best track. **148 (11)**, 4545-4563, https://doi.org/10.1175/MWR-D-20-0005.1, URL https://journals.ametsoc.org/view/journals/mwre/148/11/MWR-D-20-0005.1.xml.

680 Delaporte, B., T. Ibanez, G. Keppel, S. Jullien, C. Menkes, and T. Arsouze, 2024: *StormR: Analyzing the Behaviour of Wind Generated by Tropical Storms and Cyclones*. URL https://umr-amap.github.io/StormR/, r package version 0.2.1.

683 Emanuel, K., 2001: Contribution of tropical cyclones to meridional heat transport by the oceans. **106**, 14771-14781, https://doi.org/10.1029/2000JD900641, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2000JD900641.

686 Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. **436 (7051)**, 686-688, https://doi.org/10.1038/nature03906, URL https://www.nature.com/articles/nature03906.
689

Gahtan, J., K. R. Knapp, C. J. Schreck, H. J. Diamond, J. P. Kossin, and M. C. Kruk, 2024: International best track archive for climate stewardship (IBTrACS) project, version 4. NOAA National Centers for Environmental Information, URL https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01552, https://doi.org/10.25921/82TY-9E16.

690

691

692

693

Georgiou, P. N., 1986: Design wind speeds in tropical cyclone-prone regions. **1523**, https://doi.org/https://ir.lib.uwo.ca/digitizedtheses/1523.

694

695

Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. **146 (730)**, 1999–2049, https://doi.org/10.1002/qj.3803, URL https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803.

696

697

698

Holland, G. J., 1980: An analytic model of the wind and pressure profiles in hurricanes. **108 (8)**, 1212–1218, https://doi.org/10.1175/1520-0493(1980)108(1212:AAMOTW)2.0.CO;2, URL http://journals.ametsoc.org/doi/10.1175/1520-0493(1980)108(1212:AAMOTW)2.0.CO;2.

700

701

702

Houston, S. H., and M. D. Powell, 1994: Observed and modeled wind and water-level response from tropical storm marco (1990). **9 (3)**, 427–439, https://doi.org/10.1175/1520-0434(1994)009(0427:OAMWAW)2.0.CO;2, URL http://journals.ametsoc.org/doi/10.1175/1520-0434(1994)009(0427:OAMWAW)2.0.CO;2.

703

704

705

706

Houston, S. H., W. A. Shaffer, M. D. Powell, and J. Chen, 1999: Comparisons of HRD and SLOSH surface wind fields in hurricanes: Implications for storm surge modeling. **14 (5)**, 671–686, https://doi.org/10.1175/1520-0434(1999)014(0671:COHASS)2.0.CO;2, URL http://journals.ametsoc.org/doi/10.1175/1520-0434(1999)014(0671:COHASS)2.0.CO;2.

707

708

709

710

Huang, S., and L. Oey, 2015: Right-side cooling and phytoplankton bloom in the wake of a tropical cyclone. **120 (8)**, 5735–5748, https://doi.org/10.1002/2015JC010896, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015JC010896.

711

712

713

Jourdain, N. C., B. Barnier, N. Ferry, J. Vialard, C. E. Menkes, M. Lengaigne, and L. Parent, 2014: Tropical cyclones in two atmospheric (re)analyses and their response in two oceanic reanalyses. **73**, 108–122, https://doi.org/10.1016/j.ocemod.2013.10.007, URL https://linkinghub.elsevier.com/retrieve/pii/S1463500313001881.

714

715

716
Jullien, S., J. Aucan, E. Kestenare, M. Lengaigne, and C. Menkes, 2024: Unveiling the global influence of tropical cyclones on extreme waves approaching coastal areas. **15 (1)**, 6593, https://doi.org/10.1038/s41467-024-50929-2, URL https://www.nature.com/articles/s41467-024-50929-2.

Jullien, S., and Coauthors, 2012: Impact of tropical cyclones on the heat budget of the south pacific ocean. **42 (11)**, 1882–1906, https://doi.org/10.1175/JPO-D-11-0133.1, URL http://journals.ametsoc.org/doi/10.1175/JPO-D-11-0133.1.

Knaff, J. A., and R. M. Zehr, 2007: Reexamination of tropical cyclone wind-pressure relationships. **22 (1)**, 71–88, https://doi.org/10.1175/WAF965.1, URL https://journals.ametsoc.org/doi/10.1175/WAF965.1.

Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. **91 (3)**, 363–376, https://doi.org/10.1175/2009BAMS2755.1, URL https://journals.ametsoc.org/doi/10.1175/2009BAMS2755.1.

Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. **3 (3)**, 157–163, https://doi.org/10.1038/ngeo779, URL https://www.nature.com/articles/ngeo779.

Li, X., J. Yang, G. Han, L. Ren, G. Zheng, P. Chen, and H. Zhang, 2022: Tropical cyclone wind field reconstruction and validation using measurements from SFMR and SMAP radiometer. **14 (16)**, 3929, https://doi.org/10.3390/rs14163929, URL https://www.mdpi.com/2072-4292/14/16/3929.

Liu, G., S. Jiang, M. Zheng, S. Lin, Y. Kong, and P. Zhan, 2025: A global ERA5-based tropical cyclone wind field dataset enhanced by integrated parametric correction methods. **12 (1)**, 1429, https://doi.org/10.1038/s41597-025-05789-w, URL https://www.nature.com/articles/s41597-025-05789-w.

Loridan, T., S. Khare, E. Scherer, M. Dixon, and E. Bellone, 2015: Parametric modeling of transitioning cyclone wind fields for risk assessment studies in the western north pacific. *Journal of Applied Meteorology and Climatology*, **54 (3)**, 624 – 642,
https://doi.org/10.1175/JAMC-D-14-0095.1, URL https://journals.ametsoc.org/view/journals/apme/54/3/jamc-d-14-0095.1.xml.

Mouche, A., B. Chapron, J. Knaff, Y. Zhao, B. Zhang, and C. Combot, 2019: Copolarized and cross-polarized SAR measurements for high-resolution description of major hurricane wind structures: Application to irma category 5 hurricane. **124 (6)**, 3905–3922, https://doi.org/10.1029/2019JC015056, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019JC015056.

Mouche, A. A., B. Chapron, B. Zhang, and R. Husson, 2017: Combined co- and cross-polarized SAR measurements under extreme wind conditions. **55 (12)**, 6746–6755, https://doi.org/10.1109/TGRS.2017.2732508, URL http://ieeexplore.ieee.org/document/8016397/.

Olfateh, M., D. P. Callaghan, P. Nielsen, and T. E. Baldock, 2017: Tropical cyclone wind field asymmetry—development and evaluation of a new parametric model. **122 (1)**, 458–469, https://doi.org/10.1002/2016JC012237, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2016JC012237.

Peduzzi, P., B. Chatenoux, H. Dao, A. De Bono, C. Herold, J. Kossin, F. Mouton, and O. Nordbeck, 2012: Global trends in tropical cyclone risk. **2 (4)**, 289–294, https://doi.org/10.1038/nclimate1410, URL https://www.nature.com/articles/nclimate1410.

Peng, M., L. Xie, and L. J. Pietrafesa, 2006: Tropical cyclone induced asymmetry of sea level surge and fall and its presentation in a storm surge model with parametric wind fields. **14 (1)**, 81–101, https://doi.org/10.1016/j.ocemod.2006.03.004, URL https://linkinghub.elsevier.com/retrieve/pii/S1463500306000291.

Schenkel, B. A., N. Lin, D. Chavas, M. Oppenheimer, and A. Brammer, 2017: Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT data. **30 (21)**, 8745–8762, https://doi.org/10.1175/JCLI-D-17-0122.1, URL https://journals.ametsoc.org/doi/10.1175/JCLI-D-17-0122.1.

Schloemer, R., 1954: Analysis and synthesis of hurricane wind patterns over lake okeechobee, Florida.
Simpson, R. H., 1974: The hurricane disaster—potential scale. **27 (4)**, 169–186, https://doi.org/10.1080/00431672.1974.9931702, URL http://www.tandfonline.com/doi/abs/10.1080/00431672.1974.9931702.

Sriver, R. L., and M. Huber, 2007: Observational evidence for an ocean heat pump induced by tropical cyclones. **447 (7144)**, 577–580, https://doi.org/10.1038/nature05785, URL https://www.nature.com/articles/nature05785.

Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best-track information. **27 (3)**, 715–729, https://doi.org/10.1175/WAF-D-11-00085.1, URL https://journals.ametsoc.org/doi/10.1175/WAF-D-11-00085.1.

Vatistas, G. H., 1998: New model for intense self-similar vortices. **14 (4)**, 462–469, https://doi.org/10.2514/2.5323, URL https://arc.aiaa.org/doi/10.2514/2.5323.

Vijayan, L., W. Huang, K. Yin, E. Ozguven, S. Burns, and M. Ghorbanzadeh, 2021: Evaluation of parametric wind models for more accurate modeling of storm surge: a case study of hurricane michael. **106 (3)**, 2003–2024, https://doi.org/10.1007/s11069-021-04525-y, URL https://link.springer.com/10.1007/s11069-021-04525-y, publisher: Springer Science and Business Media LLC.

Vincent, E. M., M. Lengaigne, G. Madec, J. Vialard, G. Samson, N. C. Jourdain, C. E. Menkes, and S. Jullien, 2012: Processes setting the characteristics of sea surface cooling induced by tropical cyclones. **117**, 2011JC007396, https://doi.org/10.1029/2011JC007396, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2011JC007396.

Vincent, E. M., G. Madec, M. Lengaigne, J. Vialard, and A. Koch-Larrouy, 2013: Influence of tropical cyclones on sea surface temperature seasonal cycle and ocean heat transport. **41 (7)**, 2019–2038, https://doi.org/10.1007/s00382-012-1556-0, URL http://link.springer.com/10.1007/s00382-012-1556-0.

Vinour, L., S. Jullien, A. Mouche, and A. Avenas, 2025: Review and improvement of tropical cyclone surface wind parametric models using sar imagery. *Journal of Applied Meteorology and Climatology*, e240219, https://doi.org/10.1175/JAMC-D-24-0219.1, URL https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-24-0219.1/JAMC-D-24-0219.1.xml.
798 Vinour, L., S. Jullien, A. Mouche, C. Combot, and M. Mangeas, 2021: Observations of tropical cyclone inner-core fine-scale structure, and its link to intensity variations. https://doi.org/10.1175/JAS-D-20-0245.1, URL https://journals.ametsoc.org/view/journals/atsc/aop/JAS-D-20-0245.1/JAS-D-20-0245.1.xml.

802 Willoughby, H. E., R. W. R. Darling, and M. E. Rahn, 2006: Parametric representation of the primary hurricane vortex. part II: A new family of sectionally continuous profiles. **134 (4)**, 1102–1120, https://doi.org/10.1175/MWR3106.1, URL http://journals.ametsoc.org/doi/10.1175/MWR3106.1.

806 Willoughby, H. E., and M. E. Rahn, 2004: Parametric representation of the primary hurricane vortex. part i: Observations and evaluation of the holland (1980) model. **132 (12)**, 3033–3048, https://doi.org/10.1175/MWR2831.1, URL http://journals.ametsoc.org/doi/10.1175/MWR2831.1.

810 Wood, V. T., and L. W. White, 2011: A new parametric model of vortex tangential-wind profiles: Development, testing, and verification. **68 (5)**, 990–1006, https://doi.org/10.1175/2011JAS3588.1, URL https://journals.ametsoc.org/doi/10.1175/2011JAS3588.1.

813 Wood, V. T., L. W. White, H. E. Willoughby, and D. P. Jorgensen, 2013: A new parametric tropical cyclone tangential wind profile model. **141 (6)**, 1884–1909, https://doi.org/10.1175/MWR-D-12-00115.1, URL https://journals.ametsoc.org/view/journals/mwre/141/6/mwr-d-12-00115.1.xml.

817 Yan, D., and T. Zhang, 2022: Research progress on tropical cyclone parametric wind field models and their application. **51**, 102 207, https://doi.org/10.1016/j.rsma.2022.102207, URL https://linkinghub.elsevier.com/retrieve/pii/S235248552200024X.