Tropical Cyclone Disaster Research Articles

Pre-Disaster Management of Tropical Cyclone Seroja to Realize Disaster Resilient Area in Rote Ndao Regency, East Nusa Tenggara Province

The author focuses on the problem of tropical cyclone Seroja which is known to have hit Rote Ndao Regency for the first time and how to deal with it if the disaster occurs again, so good management is needed. The purpose of this research is to find out how BPBD manages activities in dealing with the Seroja tropical cyclone disaster if it occurs again. This research uses qualitative methods with data collection techniques used through observation, interviews and documentation. In accordance with the type of research above, the authors used an interactive model from Miles, Huberman and Saldana (2019) to analyze the research data with 3 steps, namely data reduction, data presentation and conclusion drawing/verification.The findings obtained by the author in this study are that the management by BPBD in the Seroja tropical cyclone disaster is quite good, however, it still has constraints on funding and manpower which are still lacking. Disaster management in dealing with the Seroja tropical cyclone in Rote Ndao Regency is classified as good, this is because there is good coordination between BPBD and related regional apparatus in overcoming disasters in pre-disaster activities. in order to improve disaster resilient areas, it is advisable to optimize the use of local communities to be ready and alert in the face of disasters.

Assessing hotspot for tropical cyclone compound rainstorms from a disaster perspective: a case study in Guangdong, China

In Guangdong Province, China, known as a hotspot for tropical cyclone (TC) and rainstorm disasters, investigating the role of hazard factors for both TC and rainstorm disasters is of great scientific and operational importance. Daily observational data from meteorological stations along with rainstorm/TC disaster loss data at the county level in Guangdong during 2001–2020 were analyzed to compare the characteristics of rainstorm and TC disasters. During the study period, although non-TC rainstorm rainfall in Guangdong was generally greater than TC rainstorm rainfall, TC disaster losses were double those of rainstorm disasters. Non-TC rainstorms and their losses predominantly occurred during the first flood season, whereas TC disaster primarily occurred in the second flood season. Although regional-average TC rainstorm rainfall was similar to non-TC rainstorm rainfall during the second flood season in Guangdong, it was greater than non-TC rainstorm rainfall in western Guangdong. Additionally, the coastal areas were frequently affected by TC strong winds, resulting in greater TC disaster losses than rainstorm disaster losses. The maximum daily precipitation was significantly correlated with rainstorm disaster losses. In areas influenced by both TC strong winds and rainstorms, TC disaster losses exhibited a significant positive correlation with the daily maximum wind speed, and the combined effect of strong winds and rainstorms led to greater disaster losses than with TC rainstorms alone. These findings emphasize the effects of TC strong wind and storm surges, besides those of heavy rainfall, in coastal areas of China when assessing the risks related to TC disasters.

Recent northward shift of tropical cyclone economic risk in China

The impact of tropical cyclones (TCs) has intensified with continued global warming and socio-economic development. Quantifying the TC economic exposure is a core element of economic risk assessment for TCs. The centroid of annual economic exposure to TCs in China shifted northward at a rate of 19.71 km per year from 2006 to 2020, where changes in the TC tracks contributed a northward shift of 11.22 km per year and changes in GDP distribution contributed a northward shift of 7.75 km per year. The northward shift of TC economic exposure centroid is more than twice as sensitive to the shift of GDP distribution as to that of TC tracks. The phenomenon of the northward shift in TC economic exposure is particularly evident in the subtropical zone in China. Further northward shift of TC exposure could potentially cause higher socio-economic losses in places underprepared for TC hazards. Our result provides references for TC disaster mitigation and preparedness in China.

A Study on The Heterogeneity of Tropical Cyclone Vulnerability Functions in Different Provinces along The Coastal Areas in China

Coastal regions in China have long been plagued by tropical cyclone (TC) disasters, and TC vulnerability function for disaster-bearing bodies is a widely used quantitative vulnerability assessment method in face of TCs. TC vulnerability functions may vary across different regions due to geographical heterogeneity, hindering this method from universal use. This study systematically collects a 15-year-long dataset from 12 provinces in China. Six alternative TC vulnerability functions are comprehensively investigated by province, and a library of the optimal TC vulnerability functions for three kinds of disaster-bearing bodies, that is, economic, population, and crop in different provinces is developed. The heterogeneity of TC vulnerability in different provinces along the coastal areas in China is then thoroughly analyzed. This paper provides a deeper understanding of the spatial heterogeneity characteristics of disasters and disaster-bearing bodies, and provides good references for local departments to carry out more targeted disaster prevention and reduction work.

Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications.

Applicability Evaluation of the Global Synthetic Tropical Cyclone Hazard Dataset in Coastal China

A tropical cyclone dataset is an important data source for tropical cyclone disaster research, and the evaluation of its applicability is a necessary prerequisite. The Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset is a dataset of global tropical cyclone activity for 10,000 years from 2018, and has become accepted as a major data source for the study of global tropical cyclone hazards. On the basis of the authoritative Tropical Cyclone Best Track (TCBT) dataset proposed by the China Meteorological Administration, this study evaluated the applicability of the GSTCH dataset in relation to two regions: the Northwest Pacific and China’s coastal provinces. For the Northwest Pacific, the results show no significant differences in the means and standard deviations of landfall wind speed, landfall pressure, and annual occurrence number between the two datasets at the 95% confidence level. They also show the cumulative distributions of central minimum pressure and central maximum wind speed along the track passed the Kolmogorov–Smirnov (K-S) test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at sea-area scale. For China’s coastal provinces, the results show that the means or standard deviations of tropical cyclone characteristics between the two datasets were not significantly different in provinces other than Guangdong and Hainan, and further analysis revealed that the cumulative distributions of the tropical cyclone characteristics in Guangdong and Hainan provinces passed the K-S test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at province scale. The applicability evaluation revealed that no significant differences exist between most of the tropical cyclone characteristics in the TCBT and GSTCH datasets, and that the GSTCH dataset is an available and reliable data source for tropical cyclone hazard studies in China’s coastal areas.

Rehabilitasi dan Rekonstruksi Bangunan Gereja Masehi Injili Timor Pasca Bencana Seroja di Kabupaten Sabu Raijua

<em>Sabu Raijua Regency consists of Sabu Island and Raijua Island which are in East Nusa Tenggara. This area was also affected by the Seroja disaster that occurred in April 2021. Many buildings collapsed, including several church buildings which spread across the island due to the strong winds. The method used in this study is descriptive qualitative in an inductive approach by explaining the pattern of damage and repair plans for church buildings due to the Seroja tropical cyclone disaster in the Sabu Raijua district and formulating guidelines for rehabilitation and reconstruction. The purpose of this research is to formulate guidelines for the rehabilitation and reconstruction of church buildings after the Seroja tropical cyclone disaster so that people in carrying out development activities no longer rely on makeshift understanding and technology but need to consider technical requirements. The results of this research are in the form of recommendations set forth in this guideline for the rehabilitation and reconstruction of church buildings on the island of Sabu.</em>

Spatiotemporal Characteristics of Tropical Cyclone Precipitation in Guangdong Province, China, from 1961 to 2020

Tropical cyclones (TCs) are often accompanied by heavy precipitation, which may lead to natural disasters and a serious threat to life and property. However, they also provide indispensable water resources. Studying the temporal and spatial characteristics of TC precipitation is of great importance for TC precipitation forecasting, TC disaster mitigation, and water resource utilization. Guangdong is one of the most frequently and severely TC-affected provinces in China. Due to the different methods used to identify TC precipitation, the conclusions offered by the existing studies are often inconsistent. Moreover, their analyses of the spatiotemporal characteristics of TC precipitation in Guangdong are not sufficiently thorough. In this study, we first selected the historical TCs that affected Guangdong from 1961 to 2020, using an objective separation method for TC wind and rain, based on the observation data from 86 national meteorological stations in Guangdong Province. From these observations covering the past 60 years, the temporal and spatial variations in TC precipitation in Guangdong for four different periods, namely the first rainy season (FRS), the second rainy season (SRS), the non-rainy season (NRS), and over the whole year (WY), were then explored using statistical analysis and multiple cluster methods. The results show that TC frequencies in the four periods all showed a decreasing trend. TC precipitation also showed a decreasing trend in the SRS and NRS, as well as for the WY, but showed a slightly increasing trend in the FRS. Both TC frequency and TC precipitation showed an apparent inter-annual fluctuation and a quasi-periodic pattern. The spatial distribution of TC precipitation in the four periods all showed a decreasing trend from the coastal to the inland stations, but the western coastal areas had higher TC precipitation values than the eastern coastal areas for the SRS, NRS, and WY periods. The spatial variations of TC precipitation in Guangdong in the four periods of the last six decades were quite similar, exhibiting three primary spatial modes and six patterns. Among them, the spatial distribution of TC precipitation being less than normal across the whole province is the most common pattern. The 86 stations can be classified into six groups when using the spatial clustering method and into four groups when using the time-series clustering method. Stations with higher TC precipitation and large inter-annual fluctuations are often distributed in the coastal areas, while stations with less precipitation and small inter-annual fluctuations are distributed in inland areas. However, the primary areas that are affected by TCs may vary in the different periods.

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Estimating the intensity of tropical cyclones (TCs) is usually involved as a critical step in studies on TC disaster warnings and prediction. Satellite cloud images (SCIs) are one of the most effective and preferable data sources for TC research. Despite the great achievements in various SCI-based studies, accurate and efficient estimation of TC intensity still remains a challenge. In recent years, machine learning (ML) techniques have gained fast development and shown significant potential in dealing with big data, particularly with images. This study focuses on the objective estimation of TC intensity based on SCIs via a comprehensive usage of some advanced deep learning (DL) techniques and smoothing methods. Two estimation strategies are proposed and examined which, respectively, involve one and two functional stages. The one-stage strategy uses Vision Transformer (ViT) or Deep Convolutional Neutral Network (DCNN) as the regression model for directly identifying TC intensity, while the second strategy involves a classification stage that aims to stratify SCI samples into a few intensity groups and a subsequent regression stage that specifies the TC intensity. Further efforts are made to improve the estimation accuracy by using smoothing manipulations (via four specific smoothing techniques) in the scenarios of the aforementioned two strategies and their fusion. Results show that DCNN performs better than ViT in the one-stage strategy, while using ViT as the classification model and DCNN as the regression model can result in the best performance in the two-stage strategy. It is interesting that although the strategy of singly using DCNN wins out over any concerned two-stage strategy, the fusion of the two strategies outperforms either the one-stage strategy or the two-stage strategy. Results also suggest that using smoothing techniques are beneficial for the improvement of estimation accuracy. Overall, the best performance is achieved by using a hybrid strategy that consists of the one-stage strategy, the two-stage strategy and smoothing manipulation. The associated RMSE and MAE values are 9.81 kt and 7.51 kt, which prevail over those from most existing studies.

Evaluation of Tropical Cyclone Disaster Loss Using Machine Learning Algorithms with an eXplainable Artificial Intelligence Approach

In the context of global warming, tropical cyclones (TCs) have garnered significant attention as one of the most severe natural disasters in China, particularly in terms of assessing the disaster losses. This study aims to evaluate the TC disaster loss (TCDL) using machine learning (ML) algorithms and identify the impact of specific feature factors on the prediction of model with an eXplainable Artificial Intelligence (XAI) approach, SHapley Additive exPlanations (SHAP). The results show that LightGBM outperforms Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) for estimating the TCDL grades, achieving the highest accuracy value of 0.86. According to the SHAP values, the three most important factors in the LightGBM classifier model are proportion of stations with rainfall exceeding 50 mm (ProRain), maximum wind speed (MaxWind), and maximum daily rainfall (MaxRain). Specifically, in the estimation of high TCDL grade, events characterized with MaxWind exceeding 30 m/s, MaxRain exceeding 200 mm, and ProRain exceeding 30% tend to exhibit a higher susceptibility to TC disaster due to positive SHAP values. This study offers a valuable tool for decision-makers to develop scientific strategies in the risk management of TC disaster.

Impact of uncertainty induced by fatality function on future tropical cyclone risk assessment

Tropical cyclones (TCs) are among the deadliest extreme events occurring under a warming climate. Future TC risk assessment depend on TC projection from climate models and impact function relating TC to its possible consequence. Few studies have explored the uncertainty of impact function in future TC risk assessment compared to uncertainty in future TC characteristics. In this study, we investigate the uncertainty in TC fatality risk assessment induced by geographic and TC category-dependence of fatality function. We focus on all provinces in the mainland of China with historically recorded TC-induced fatalities and examine their TC fatality risks by assessing the difference in the annual average fatalities between current and future climate conditions. Synthetic TCs derived from four climate models and fatality functions parameterized from three grouped historical TC disaster datasets are used to observe the uncertainty induced by climate model and fatality function. Results show that the changes in the TC frequency, wind, TC-induced rainfall intensity, and exposure due to climate change in each province are dependent on the climate models. And the changes in the annual average fatality of each province are dependent on both the climate models and fatality functions. Climate models play a dominant role in determining the spatial pattern of future risk, while the fatality functions can alter the direction and magnitude of the risk change for certain provinces. Our results highlight the role of fatality function in detecting future TC risk under climate change, and inspire further TC impact studies that consider the heterogeneity of both climate conditions and geographical locations.

MGTCF: Multi-Generator Tropical Cyclone Forecasting with Heterogeneous Meteorological Data

Accurate forecasting of tropical cyclone (TC) plays a critical role in the prevention and defense of TC disasters. We must explore a more accurate method for TC prediction. Deep learning methods are increasingly being implemented to make TC prediction more accurate. However, most existing methods lack a generic framework for adapting heterogeneous meteorological data and do not focus on the importance of the environment. Therefore, we propose a Multi-Generator Tropical Cyclone Forecasting model (MGTCF), a generic, extensible, multi-modal TC prediction model with the key modules of Generator Chooser Network (GC-Net) and Environment Net (Env-Net). The proposed method can utilize heterogeneous meteorologic data efficiently and mine environmental factors. In addition, the Multi-generator with Generator Chooser Net is proposed to tackle the drawbacks of single-generator TC prediction methods: the prediction of undesired out-of-distribution samples and the problems stemming from insufficient learning ability. To prove the effectiveness of MGTCF, we conduct extensive experiments on the China Meteorological Administration Tropical Cyclone Best Track Dataset. MGTCF obtains better performance compared with other deep learning methods and outperforms the official prediction method of the China Central Meteorological Observatory in most indexes.

An Earth Observation Framework in Service of the Sendai Framework for Disaster Risk Reduction 2015–2030

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.

Changes in Tropical Cyclone Disasters Over China During 2001–2020

AbstractTropical cyclones (TCs) constitute one of the major types of meteorological disasters in China, causing extensive damage. Based on the latest quality‐controlled provincial‐level TC disaster losses data over China from 2001 to 2020, the spatiotemporal variation in TC disaster and its link with TC activity in the form of prevailing track, precipitation, and intensity were investigated. Results showed that the areas impacted by TCs expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout China, while the direct economic losses resulting from TCs did not increase significantly. Regionally, TC disaster losses increased in Northeast China but decreased in Fujian province. Causal analysis indicated that the increased influential TC (ITC) frequency and TC precipitation together led to the increase in damage for northern China. Also, more westward ITCs resulted in the increase in TC disasters for southwestern China. For southeastern China, the reduction in TC disasters was related to the northward shift in the northwestward ITC track. Although TC disasters increase with exposure, they are heavily influenced by TC track, precipitation, and intensity. Our findings suggest that more attention should be paid to TC prediction and early warning in northern China, especially Northeast China, where TC disasters are growing but there is greater vulnerability and insufficient capacity for TC prevention and mitigation.

Cyclone disaster assessment based on Fermatean hesitant fuzzy information and extended TOPSIS method

Many unknowable elements make it difficult to measure cyclone disasters, traditional methods are insufficient to measure these factors. Fuzzy set theory and its expansions are effective ways to measure these uncertainties for these kinds of uncertainty. An evaluation of the cyclone disaster’s spatial vulnerability is necessary in order to build disaster damage reduction methods. In real life, we may come into a hesitant environment when making decisions. To explore such environments, we introduce hesitant fuzzy set (HFS) into Fermatean fuzzy set (FFS) and extend the existing research effort on FFSs in light of the effective tool of HFSs for expressing the hesitant condition. In this study, we develop a comprehensive tropical cyclone disaster assessment by applying Fermatean hesitant fuzzy (FHF) information. In this paper, various unique aggregation strategies for the analysis of decision-making problems are introduced. As a result, Fermatean hesitant fuzzy average (FHFWA), Fermatean hesitant fuzzy ordered weighted average (FHFOWA), Fermatean hesitant fuzzy weighted geometric (FHFWG), and Fermatean hesitant fuzzy ordered weighted geometric (FHFOWG) operators have been developed. We also go over some of the most important features of these operators. Furthermore, we establish an algorithm for addressing a multiple attribute decision-making issue employing Fermatean hesitant fuzzy data by using these operators. and attribute prioritizing. A real-world problem of cyclone disaster damages in several parts of Pakistan is explored to test the applicability of these operators. In the final section, we expand the TOPSIS approach to a Fermatean hesitant fuzzy environment and compare the outcomes of the extended TOPSIS method with operators established in the FHF-environment.

A new index for assessing the coastal wind disasters based on the HY-2 satellite data

Tropical cyclone disasters have a substantial impact on human activity in coastal zones. The traditional methods used to classify wind disasters include the Beaufort Scale, Saffir-Simpson Scale, and Chicago Mercantile Exchange Hurricane Index (CMEHI). This research established a new index, the Haiyang Cyclone Index (HYCI), which considers spatial distributions and sea level anomalies (SLA) using HY-2 satellite data. The HYCI of three mega coastal cities, Qingdao, Shanghai, and Guangzhou, was calculated. A brief analysis was then performed using Moving Average Convergence Divergence (MACD) and Long Short-Term Memory (LSTM). The proposed HYCI can accurately evaluate the impact of cyclone disasters, thereby confirming its financial application potential.

El Niño Onset Time Affects the Intensity of Landfalling Tropical Cyclones in China

In this work, we studied the influence of spring (SP) and summer (SU) El Niño events on the landfalling tropical cyclones (TCs) in China. The results showed that compared to SU El Niño years, the average latitude of the landfalling TCs in SP El Niño years shifted significantly southward and that the average TC intensity was significantly stronger, especially in the post-landfall period. Additionally, more severe tropical storm-level TCs generated over the South China Sea made landfall in China. Meanwhile, in SP El Niño years, landfalling TCs in southern China had a greater landfall intensity, but landfalling TCs in eastern China were the opposite. These changes in TC intensity during the SP El Niño years could be attributed to more favorable dynamical and thermodynamical conditions, which are beneficial for maintaining TC intensity and duration after landfall. These results could have important implications for an in-depth understanding of TC activities, as well as TC disaster prevention and mitigation.

A study on the location of emergency supplies reserve center in Guangxi for tropical cyclone disasters

On average, Guangxi experiences 2 severe tropical cyclone disasters yearly. A large number of disaster victims need governments at all levels to raise enough emergency supplies to carry out effective relief. However, there is no proper reserve center for tropical cyclone disaster storage and collection of emergency supplies in Guangxi, so it is urgent to choose a suitable location to build the Guangxi tropical cyclone disaster emergency supplies reserve center. The data of 16 severe tropical cyclone disasters and rescues in Guangxi from 2014 to 2021 were collected. An optimal location model of the emergency supplies reserve center for tropical cyclone disasters in Guangxi was established using the improved center of gravity method based on GIS. After four iterations of calculation, the longitude and latitude coordinate S5(108.64, 21.98) were used as a suitable site to construct the Guangxi Tropical Cyclone Disaster Emergency Supplies Reserve Center to ensure the fastest and lowest cost delivery of emergency supplies to the disaster areas in Guangxi. According to the map of Tiandi, the actual place corresponding to this coordinate is near Liuwu Village next to Qinzhou East Railway Station, Qinnan District, Qinzhou, Guangxi. This location coordinate S5 can provide a reference for the Guangxi Zhuang Autonomous Region government to select a suitable address to construct the Guangxi Tropical Cyclone Disaster Emergency Supplies Reserve Center. The improved center of gravity location model based on GIS can also provide a reference for other provinces or cities to carry out emergency supplies reserve center locations.

Slowdown in Landfalling Tropical Cyclone Motion in South China

AbstractChina is one of the countries suffering the most tropical cyclone (TC) disasters, while changes in translation speed of TCs landfalling in China remain unclear. Here we found that the translation speed of landfalling TCs in south China during the boreal summer has significantly decreased by 0.3 m s−1decade−1 during 1979–2019, which is largely driven by a decadal deceleration in steering flow in the late 1990s. The slowdown in steering flow is found to be a balance of the opposite effects of the negative phase of the Pacific Decadal Oscillation and sea surface temperature (SST) warming in the Indian Ocean since the late 1990s. Given the recent Indian Ocean SST warming has been viewed as a footprint of external forcing, the results suggest that the effects of external forcing and natural variability on TC translation speed can offset each other.

Evaluation and Calibration of Remotely Sensed High Winds from the HY-2B/C/D Scatterometer in Tropical Cyclones

Haiyang-2 scatterometers (HY-2A/B/C/D) have limitations in high wind speed retrieval due to the complexity of the remote sensing mechanism and the influence of rainfall on the radar cross section under the conditions of tropical cyclones. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from HY-2B/C/D over the period from July 2019 to December 2021. HY-2B/C/D scatterometer wind products are collocated with SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The results show that the underestimation of high wind speed occurs in the HY-2B/C/D wind speed products. The machine learning algorithms are explored to improve this underestimation issue, including the back propagation neural network (BP-NN), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and Bayesian ridge (BR) regression algorithms. Comparisons show that the BP-NN algorithm shows the best performance with a small RMSE (root-mean-square error) of 3.40 m/s, and high correlation coefficient of 0.88, demonstrating an improvement of 20.4% in RMSE (root-mean-square error) compared with the HY-2B/C/D wind speed products. In addition, the revised high winds are in good agreement with the ground truth measurements from the SFMR (Stepped Frequency Microwave Radiometer), which are useful for tropical cyclone disaster prevention and mitigation and are of vital importance in the numerical simulation of storm surges.