Abstract

The first C- and Ku-band dual-frequency scatterometer instrument (WindRAD) on board the world's first early-morning-orbiting meteorological satellite Fengyun-3E (FY-3E) has the capability to measure global ocean surface winds. However, WindRAD cannot determine the center locations of tropical cyclones (TCs) because it is limited by coarse spatial resolution. In this work, a relatively simple model is applied to identify the storm centers, using ocean wind measurements collected by the WindRAD scatterometer. The model—estimated storm centers are determined to minimize the errors between model—simulated winds and WindRAD measurements. Our data set consists of all WindRAD overpasses of TCs during 2022 and 2023 in the West Pacific, East Pacific, North Atlantic and Northern Indian Oceans. The average errors between the WindRAD model estimates and the reported best-track storm center locations are 51, 40, and 25 km, for tropical storms, category 1–2 storms and major storms (>49 m s⁻¹), respectively. Our model is both objective and automatic, thereby avoiding subjectivity and possible errors related to manual analysis.

Key Points

Accurate tropical cyclone (TC) center location can be identified purely from ocean surface winds measured by FY-3E WindRAD scatterometer
Although the scatterometer misses winds in the TC center region, a parametric model can estimate the TC center location close to best-track reports
Systematic analysis of 189 WindRAD TC measurements using this model demonstrates that storm center location errors decrease as TC intensities increase

Plain Language Summary

The C- and Ku-band dual-frequency WindRAD scatterometer on onboard the world's first early-morning-orbit meteorological satellite was launched in July 2021. WindRAD's ability to acquire measurements using a wide variety of viewing geometries provides potentially improved monitoring of tropical cyclones (TCs). However, WindRAD cannot identify the TC eyes because of its coarse spatial resolution. This study proposes a parametric model for axisymmetric storm winds, based on ocean surface winds measured by WindRAD. The model extracts the storm center locations, and allows the reliable reconstruction of the entire TC wind fields, even in areas not measured by WindRAD. A systematic analysis of 189 WindRAD storm images demonstrates that the model can effectively estimate TC center locations. Model-estimated TC centers are verified with best-track data, obtaining average errors of 51, 40, and 25 km for tropical storms, category 1–2 storms and major storms (>49 m s⁻¹), respectively. Errors in storm center locations decrease as storm intensity increases, both in best-track and model results.

1 Introduction

Accurate identification of tropical cyclone (TC) center locations is essential for predictions of intensity estimation (Olander & Velden, 2019), rapid intensification (Rozoff et al., 2015), wind structure analysis (Fang et al., 2024), storm visualization (Wimmers & Velden, 2010), and climatological evolution (Reale et al., 2019). In recent years, sensors onboard satellites have been used in storm studies with increasingly improved imaging rates, spatial coverages, and spatial resolutions. In operational forecasts and analyses of storms, detection of storm centers via satellite observations has been determined by a synergy of observations, using microwave (Hu et al., 2019; Lee et al., 2016; Wang et al., 2024), infrared (Shin et al., 2022; Wang and Li, 2023), and visible (Zheng et al., 2019) measurements. One method is to use ocean surface winds from scatterometers, which can be traced inward to the TC center. However, this is not an optimal approach because scatterometers have directional ambiguity for disorganized, weaker storms (Lin et al., 2013). Therefore, it is still a challenge to independently determine the TC centers and wind structures from satellite ocean winds (Hu et al., 2019; Kumar et al., 2021; Tamizi et al., 2020). Mayers and Ruf (2019) proposed the “MTrack” storm center location method by using the Cyclone Global Navigation Satellite System derived ocean winds. Subsequently, they applied this method to measured ocean winds from the Soil Moisture Active Passive satellite date, and achieved favorable results (Mayers & Ruf, 2021). However, the MTrack method requires the storm center locations as input data, for example, from the best track data. In turn, this leads to reduced independence.

Fengyun-3E (FY-3E), the world's first early-morning-orbiting meteorological satellite for civil applications, was successful launched on 5 July 2021. In conjunction with the FY-3C and the FY-3D satellites, FY-3E fills the gap for the early-morning-orbit satellite measurements and provides global data at 6-hr intervals, which is beneficial for enhancing the global earth observing systems (Chen et al., 2025; Shen et al., 2024; Zhang et al., 2022). Observations are collected via the brand-new wind radar (WindRAD) instrument, which is the world's first C- and Ku-dual-band rotating fan-beam scatterometer (Shang et al., 2024). Compared with traditional scatterometer instruments, the simultaneous dual-band measurements have a potential capability for higher accuracy detection of ocean winds, over a wider measurement range (Liu et al., 2023). More importantly, WindRAD has a continuous swath—width of more than 1,200 km with no gap at nadir. These characteristics constitute unique contributions for TC warning and monitoring.

Although WindRAD has been suggested as a potential new instrument for storm measurements (Huang et al., 2023; Ricciardulli et al., 2023; Shen et al., 2024), the lack of an algorithm to detect the storm center is a limitation. In a previous study, we developed the “Symmetric Hurricane Estimates for Wind” (SHEW) model, to determine storm center locations from high spatial resolution SAR images (Zhang et al., 2014, 2017). However, SHEW model cannot be directly used to estimate the storm center locations from WindRAD measurements because their spatial resolution is too coarse. Therefore, our main focus is to develop a new modified SHEW model to detect TC center positions purely from WindRAD-measured winds. This improved model will be automated and therefore objective, avoiding the subjectivity of manual analysis, and the related errors.

2 Data and Methodology

2.1 WindRAD Ocean Wind Products

In this study, we employ only the WindRAD-derived ocean winds as the input for detection of the TC center positions. Benefiting from its dual-frequency characteristics, the WindRAD scatterometer can simultaneously provide Ku- and C-band data for global ocean wind monitoring, twice a day (Li et al., 2023; Zhang et al., 2024). The quality of WindRAD scatterometer measurements data has been validated by previous studies (He et al., 2023; Liu et al., 2023; Shang et al., 2024). However, rain changes the geometry of wind-driven ocean surface, thereby altering the intrinsic relationship between backscattered signals and ocean wind fields, which affects the wind retrieval ability of the satellite in rainy areas (Ye & Guo, 2024). Compared with C-band ocean backscatter signals, Ku-band measurements suffer more rain effects because of the shorter wavelength (Ren et al., 2016; Zhao et al., 2023). Therefore, we focus only on C-band data for determination of storm center locations. In this paper, A total of 189 storm samples of WindRAD-measured ocean winds were acquired from 2023 to 2024. WindRAD's Level 2 ocean wind products are produced and publicly distributed by the China National Satellite Meteorological Center (NSMC) at the Fengyun Satellite Data Center website. NSMC produces daily wind product files by mapping the WindRAD orbital data to a 0.25 ${}^{\circ}$ longitude by 0.25 ${}^{\circ}$ latitude Earth grid.

2.2 Information on Tropical Cyclones

TC best-track information obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) data sets is recommended as the official archiving site by the World Meteorological Organization. The IBTrACS data sets provide 3-hourly estimates for the TC center locations and intensities for the entire TC lifecycles. In order to validate storm parameters estimated from WindRAD measurements, best-track reports of TC center positions (hereafter BTCs) and intensities are interpolated to WindRAD acquisition times (Fang et al., 2025).

2.3 WindRAD High Wind Correction

Accurate high and extreme ocean wind measurements are essential and constitute the basis for TC storm center locations. However, due to signal saturation in the co-polarized backscatter signal, C-band scatterometer measurements show large underestimates in storm intensities (Li et al., 2022; Liu et al., 2022; Ribal & Young, 2020). To solve this issue, Polverari et al. (2022) proposed a polynomial transformation method in the form of Equation 1 to calibrate wind extremes derived by C-band scatterometers,

{U}_{10}^{\ast }=0.0095\ast {U}_{10}^{2}+1.52\ast {U}_{10}-7.6

(1)

where

{U}_{10}

and

{U}_{10}^{\ast }

indicate the original and calibrated scatterometer ocean winds. Note that when original scatterometer derived ocean winds are less than 12 m/s, the calibrated wind speeds should be ignored. This phenomenon often occurs in the weaker storms. In these cases,

{U}_{10}^{\ast }

values are replaced by the original WindRAD wind speeds.

2.4 Automatic TC Center Determination

In a previous study, we developed a storm structure model with elliptical symmetry (SHEW) to estimate TC center positions from high spatial resolution SAR images (Zhang et al., 2017). However, the spatial resolution of WindRAD measurements is relatively coarse. Therefore, based on measured storm ocean winds from WindRAD, we modified the SHEW model (hereafter, WRDS model), replacing the elliptical structure with an axisymmetric vortex with a circular eyewall. Then, we apply this model to determine the storm center purely from WindRAD ocean surface winds, without any external data input.

A distinct characteristic feature of mature storms is the eye. From the perspective of the ocean surface storm winds, large variations in the wind speed gradients exist in the region between the storm eye and the eyewall (Combot et al., 2020; Ni et al., 2022). Therefore, a roughly—determined storm eye, denoted as a “first assumed center (FAC)”, is easily identified from the WindRAD daily global ocean wind maps. To enhance the efficiency of the WRDS execution, we determined a “search area” from the large-scale global ocean winds, which is centered on the FAC and extends outward in several latitude and longitude directions. In this search area, the operational process of applying the WRDS model to detect storm centers from WindRAD data is as follows. Firstly, we compute the radii for all the WindRAD grids relative to the FAC. Clearly, for the FAC position, a set of radii for all the grid locations within 200 km can be estimated. Secondly, we fit the modified Rankine vortex model (see Equation 2) by using these estimated radii to these WindRAD ocean winds to get the best-fit storm intensity, the decay factor ( $\alpha$ ), and the radius of maximum wind (RMW). Thirdly, we iterate the above process by shifting the latitude and longitude of the FAC to all the next model gird locations within the 200 km. Finally, when WRDS program finishes running, we calculate the coordinates of the storm center latitude and longitude that may be deemed “best”, based on a least-squares fitting method.

In this iteration process, we move the FAC by steps of 0.01° in longitude and latitude, also changing the intensity by steps of 0.1 m/s from 0 to 80 m/s, varying the RMW by steps of 1 km from 0 to 200 km, and also varying the decay factor (

\alpha

), by steps of 0.1 from 0 to 1. For each iteration, we calculate the root-mean-square error (RMSE) between WindRAD measurements and simulated ocean winds using the same pixels. Thus, the WRDS model simultaneously calculates best-fit estimates for the storm center, intensity, RMW and

\alpha

parameters that minimize the differences between WindRAD measurements and model-simulated ocean winds. It is worth emphasizing that for a given estimated storm center, and for all the shifting (using 0.01° intervals) in the regional search area, we need to follow these iterations. In this way, we can uniquely determine the location which gives the storm center position with the smallest RMSE value. Therefore, the FAC initialization will not affect the computational efficiency or the final center accuracy. The modified Rankine vortex function is represented as follows (Gao et al., 2024; Mallen et al., 2005; Rappin et al., 2013; Zhang et al., 2021):

U(r)=\left\{\begin{array}{@{}c@{}}{U}_{\max }\ast \left[\frac{r}{{r}_{m}}\right],r\le {r}_{m}\\ {\quad U}_{\max }\ast {\left[\frac{{r}_{m}}{r}\right]}^{\alpha },{r}_{m}< r\le 200\text{km}\end{array}\right.

(2)

where

r

is the estimated radius for each WindRAD grid to the FAC.

{U}_{\max }

is the storm intensity,

{r}_{m}

is the RMW, and

\alpha

is the decay factor.

3 Results and Discussions

3.1 Extremely High Wind Detection Capability of WindRAD

It is well-known that the C-band co-polarized ocean backscatter signal experiences saturation in extremely high wind speeds (Polverari et al., 2022). Therefore, a polynomial transformation method is used to calibrate WindRAD measurements. A systematic analysis of 189 WindRAD TC measurements shows that original WindRAD measurements underestimate storm intensity with respect to best-track reports, with a very large RMSE of 18.59 m/s and large bias of −15.52 m/s. However, there is good agreement between WindRAD calibrated measurements and best-track reports for storm intensity, with RMSE of 8.12 m/s, mean bias of 3.45 m/s and a high correlation coefficient of 0.83. The overall results show that the proposed technique can significantly enhance the WindRAD monitoring capability during severe storms.

3.2 Examples of Storm Center Locations

Storm Mawar was one of the strongest Northern Hemisphere storms on record in the month of May, and the strongest storm worldwide in 2023. Figure 1 presents the WRDS model results, using WindRAD measurements for Mawar, when it was a well-organized category 5 storm on 25 May 2023 at 07:02 UTC. Based on the wind distribution features, we casually set the FAC (black cross) at (14.53°N, 141.53°E) (Figure 1a). Thus, the wind radii for FAC can be estimated (Figure 1b) and the simulated ocean winds based on FAC can be estimated from Equation 1 (Figure 1c). There is a large RMSE (8.15 m/s) between model-simulated and WindRAD-measured wind speeds. This is because the FAC has an incorrect TC center, which is about 154 km southwest of the BTC. Next, we shift the storm FAC to the next model grid location and perform the iteration process. Through simulation, we obtain the ocean wind field constructed by the WRDS model, as shown in Figure 1d, using the best-fit parameters and model-estimated storm center location. The minimum RMSE (2.48 m/s) is determined from the WRDS model storm center (red dots). This compares better with BTC than with FAC. The distance is 5.7 km between the model-estimated center and BTC. This analysis of Mawar is representative of any such typical storm wind field, including the methodology by which parameters of the WRDS model are adjusted to produce simulated winds that closely match the WindRAD measurements. Note that FAC is not required to be on a WindRAD grid cell, allowing the WRDS model to detect the best-fit storm center.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

The operational process by which the WRDS model can derive storm centers from WindRAD. (a) WindRAD-measured ocean winds of category 5 storm Mawar on 25 May 2023 at 07:02 UTC, showing the first assumed center (FAC) location (the black cross) based on the horizontal wind features. Each WindRAD pixel is a possible location for the tropical cyclone (TC) center. (b) Estimated wind radii relative to the FAC. (c) Simulated TC ocean winds based on FAC. The BTC is denoted by the yellow triangle. (d) Ocean winds that are constructed using the parametric model and best-fit parameter. The minimum RMSE is from the WRDS model—determined storm center (red dots). This compares better with the best-track result than that obtained with FAC.

A second example is TC Mocha. This was a deadly and powerful storm in the North Indian Ocean which affected Myanmar and parts of Bangladesh in May 2023. Figure 2a presents the WindRAD-measured ocean winds on 13 May 2023 at 10:52 UTC when Mocha was category 3, as it stalled over Bay of Bengal. Figure 2b shows the wind radii distribution based on FAC. Due to land-induced interference and insufficient swath coverage, much of the TC's regional eye is not captured by WindRAD. However, the ocean wind distribution characteristics that are simulated by WRDS are very similar to WindRAD observations, which suggests that WRDS is working well, despite the missing ocean wind measurements (Figure 2c). Under these circumstances, WRDS is still able to detect a TC center solution very close to the BTC result, within a distance of 8.16 km (Figure 2d).

A third example is TC Calvin. This was a weak storm that brought heavy rain and strong winds to Hawaii. Figure 3a shows the ocean winds from WindRAD on 14 July 2023 at 14:13 UTC when the storm was category 3. In this case, the time-interpolated BTC is about 42 km outside the WindRAD swath. Note that the WRDS model is independent of the FAC as long as the search pixel is large enough to include the measured regional TC center. Shifting the FAC just shifts the search grid and the model-estimated storm center remains unchanged. While WRDS iterates over the search gird, some assumed centers are off the swath edge and there are no WindRAD data in the immediate regional domain. Under this circumstance, it is still possible to fit the model-estimated measurements and to calculate a RMSE value, as long as there are WindRAD data within a radius of 200 km from the assumed center. The WRDS model results suggest a storm center detected about 36 km east of the WindRAD swath edge, which is very close to the BTC (7.6 km).

A fourth example is TC Idalia. This was a destructive and powerful storm that caused significant damage across parts of North Florida in late August 2023. Figure 4a shows the WindRAD-derived ocean winds on 3 September 2023 at 20:46 UTC. In this case, the WindRAD-derived ocean winds exhibit disorganization and an unusual spiral shape. Therefore, WindRAD cannot measure the typical storm eye characteristic because of the coarse spatial resolution. Because of the spatial smoothing method that is used at each iteration to mimic the spatial resolution of WindRAD, we use WRDS to simulate WindRAD measurements at 25 km spatial resolution (Figures 4a–4d). We show that the WRDS detected storm center is very close to the BTC, within a distance of 13.2 km (Figure 4d). This result is acceptable, considering WindRAD's coarse spatial resolution and the very weak and disorganized storm.

3.3 Statistical Analysis From 189 Storms

In this study, we focused on a collection of storms from WindRAD daily measurements over the global ocean between 2023 and 2024, that attempt to capture TCs. Through our efforts, a total of 189 WindRAD-measured storm scenes were selected that detect TC center locations. These measurements have the following characteristics: (a) cover part, or all of the TC core area, (b) cover storms in different categories, including tropical storms (58), category 1–2 storms (69) and major (category 3–4) storms (62); (c) measurements of storms in different oceanic basins, including the Atlantic (43), the East Pacific (39), the West Pacific (41), the North Indian Ocean (13), and the Southern Hemisphere (53).

For tropical storms (<33 m/s), category 1 and 2 TCs (33–49 m s⁻¹), and major storms (>49 m s⁻¹), the average errors between WRDS and best-track reports for storm center locations are 51, 40, and 25 km (Table 1), respectively. Note that these are upper bounds on WRDS errors because a component of the RMSE is derived from best-track data error. To evaluate the performance of WRDS using WindRAD measurements as input, there must be a “true” TC center for comparison. However, there are no available “truth” measurements for these TC centers—the closest information are the best track data, namely a 3- or 6- hourly reanalysis summary for all storms (Mayers & Ruf, 2021). According to Landsea and Franklin (2013), best-track reports give uncertainties that are approximately 53, 40 and 27 km, for tropical storms, category 1and 2 TCs and major storms, respectively. Therefore, the WRDS errors are within the estimated best-track uncertainties for all TC intensities. In addition, both WRDS and best-track data errors decrease with intensity. This occurs because mature storms are typically better organized with clear, circular and easily identifiable eyes, while weaker TC eyes are less well defined and do not have clearly discernible centers of circulation.

Table 1. The Performance of WRDS Model for Different Storm Category

Storm category	RMSE (km)	Storm cases
Tropical storm	51	58
Category 1–2	40	69
Major storms	25	62

Note. The RMSE values increase with decreasing storm intensity.

4 Conclusions

WindRAD is the first dual-frequency rotating fan-beam scatterometer instrument in the world. Compared with single-band instruments, the dual-band simultaneous measurements have the potential advantages of a wider ocean wind measurement swath, with improved capability for storm center detection and structure analysis. Although WindRAD has been suggested as a new generation instrument for TC studies, the lack of an approach to identify the storm center locations is a limitation. In this study, considering the coarse spatial resolution of WindRAD measurements, we developed a relatively simple model (WRDS) to estimate the storm center locations purely from ocean winds measured by WindRAD. The RMSE value between WRDS and best-track reported storm center locations is 51, 40, and 25 km for tropical storms, category 1–2 storms and major storms (>49 m s⁻¹), respectively. In addition, we note that the WRDS model is capable of operating successfully despite missing measurements of large portions of the storm winds.

WRDS is also automated and therefore, more objective than manually determined winds, made by forecasters. Under such circumstances, WRDS can be an efficient tool to assist marine forecasters to determine TC center locations from WindRAD measurements. One limitation of our model is that the TC-forced area should be extracted in advance, from global ocean wind maps, through a visual interpretation method. In a subsequent study, we will continue to improve the model so that it can be directly applied to the WindRAD-measured global ocean winds. Early warning information for the TC center from multiple meteorological agencies will help forecasters quickly find the FAC and extract the associated regional storm-forced area from the global ocean winds. In addition, for a single storm case, the execution time for WRDS model is usually a few minutes on a personal computer. In the future, near-real-time applications and deployment on large operational computer systems can significantly reduce the computational runtime.

Acknowledgments

This study was supported by the Zhejiang Provincial Natural Science Foundation of China (Grant Numbers LZJMZ25D050008, LQ21D060001), the National Natural Science Foundation of China (Grant Number 42305153), the East China Meteorological Science and Technology Collaborative Innovation Foundation Cooperation Project (Grant Number QYHZ202307), the Zhejiang Meteorological Science and Technology Plan Project (Grant Number 2023YB06), the Major Science and Technology Plan Project of Yazhou Bay Innovation Research Institute of Hainan Tropical Ocean College (2022CXYZD003), the Youth Innovation Team Fund of China Meteorological Administration (Grant Number CMA2023QN12). Support of the Canadian Space Agency and TCA—Transforming Climate Action program based are Dalhousie University Canada are also acknowledged.

Open Research

Data Availability Statement

WindRAD scatterometer ocean wind products used in this study are freely downloaded from the official website of National Satellite Meteorological Center, China Meteorological Administration at http://satellite.nsmc.org.cn/DataPortal/cn/home/index.html.

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Tropical Cyclone Center Estimates Purely From FY-3E WindRAD Measurements in the Dawn-Dusk Orbit