Abstract As global warming intensifies, accurate prediction of increasing extreme rainfall appears to be particularly important for local water management and flood-related policymaking. To investigate the sensitivity of the microphysics parameterization (MP) and cumulus parameterization (CP) in forecasting extreme rainfall, we applied the advanced model of the Weather Research and Forecasting (WRF) Model and predicted two typical extreme rainfall events over the Yangtze River delta (YRD) region based on twelve different scheme settings. The mei-yu (MY) and typhoon (TP) rainfall events were predicted in a convection-allowing resolution of 1 km. Compared with the daily rain gauge and hourly satellite-observed precipitation, the WRF simulation for the TP event generally outperformed that for the MY event at the hourly scale, with a lower comprehensive index of Chen, Chen, Hu, and Zhou (CCHZ)–distance between indices of simulation and observation (DISO) which indicates a lower bias between the WRF simulation and the observation data. As for the MY event, the WRF prediction was more sensitive to the MP scheme than the CP scheme, while it was sensitive to both schemes for the TP event. Among the 12 different scheme settings, the MP of the New Thompson scheme showed the best suitability for the two typical extreme rainfall events. Its combination with the Kain–Fritsch (KF) or Grell–Freitas (GF) scheme showed outstanding performance for both the MY and TP events since the KF/GF scheme takes explicit mass flux calculations of the cloud bottom and advanced convective triggering mechanisms, and the Thompson scheme does better in the ice-phase particle dynamics, hydrometeor distribution, and their transformation mechanisms. Our investigation can offer a scientific and valuable reference for the numerical prediction of extreme rainfall events over similar subtropical coastal regions.

Abstract Peripheral subsidence of Northwest Pacific typhoons can trigger extreme surface ozone episodes, yet the relative contributions of local chemistry and vertical transport remain poorly constrained. Here we measured 30‐min aerodynamic gradient fluxes of NO x , O 3 , and CO at 118 and 168 m on Canton Tower (Pearl River Delta) during 2 September typhoons in 2021 and 2022. Surface O 3 climbed by >50% in both events. In September 2021, moderate subsidence created hot, stagnant conditions that accelerated local photochemical ozone production, yielding upward O 3 flux. In September 2022, stronger, long‐lived subsidence instead injected ozone‐rich air from aloft, causing downward O 3 flux despite favorable chemistry. This flux evidence demonstrates that subsidence intensity toggles the balance between local production and vertical transport of ozone, informing forecasts of coastal extreme‐ozone risk.

Abstract Typhoons and wildfires are major global climate and environmental hazards, yet their potential interactions remain poorly understood, particularly through remote atmospheric forcing. Taking the 2023 Quebec wildfires as an example, we investigate how a tropical cyclone can influence wildfire‐favorable conditions. Observational analyses and Linear Baroclinic Model simulations reveal that western North Pacific Super Typhoon Mawar remotely intensified a North American blocking high by triggering a Rossby wave train, thereby driving dry lightning and anomalous dry air conditions that favored wildfire ignition and spread. Based on Weather Research and Forecasting model sensitivity experiments, we show that Mawar contributed 34 ± 6%, 41 ± 3%, and 55 ± 5% to the blocking's total amplification during the first three days of its rapid development, respectively. This findings highlight that western North Pacific typhoons can remotely modulate blocking highs to drive North American wildfire weather, advancing our understanding of remote typhoon‐wildfire teleconnections.

Poleward migration of Northwest Pacific typhoons brings severe impacts on East Asian high-latitude cities, yet early typhoon climate prediction remains a long-standing scientific challenge. Here we reveal a seemingly-familiar-yet-strange climate oscillation phenomenon, which we name Tropical-leaning Atlantic Oscillation (TAO). Statistical results show that springtime TAO can explain 56% of the variance in a dominant dipole mode of typhoon track variations during July–September of 1979–2023, suggesting that it possesses a robust predictive skill of peak-season typhoon tracks four months in advance. Specifically, springtime TAO is characterized by a sea-level pressure seesaw between the tropical North Atlantic and the Hudson Bay-Davis Strait, relating to the meridional shift of North America-Atlantic subtropical jet stream. It generates cross-seasonal North Atlantic-and-Pacific surface seawater temperature anomalies, thereby triggering Northwest Pacific cyclonic steering flows that tempt (obstruct) typhoons toward East Asian high-latitude (low-latitude) cities during July–September. Climate models project an increasing frequency of positive TAO events. This may potentially contribute to a poleward migration of typhoon activity toward East Asian high-latitude cities as climate warms, yet uncertainty remains due to model biases in simulating tropical surface seawater temperature patterns. Our results highlight an overlooked impact of an emerging internal climatic oscillation on the enhancing typhoon risks toward high-latitudes.

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of Hs = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height (Hs100 = 4.66 m, Tp100 = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, Hs100 = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia.

This study investigates landslide occurrence in Taiwan, a region highly susceptible to landslides due to steep mountains and frequent typhoons (TYPs). The primary objective is to understand how both geomorphological factors and TYP characteristics contribute to landslide occurrence, which is essential for improving hazard prediction and risk management. The research analyzed landslide events that occurred during the TYP seasons of 2019 and 2023. The methodology involved using satellite-derived landslide inventories from SPOT imagery for events larger than 0.1 hectares, tropical cyclone track and intensity data from IBTrACS v4 (classified by Saffir–Simpson Hurricane Scale), and detailed topographic variables (elevation, slope, aspect, Stream Power Index) extracted from a 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM). Land use and land cover classifications were based on Landsat imagery. To establish a timeline, landslides were matched with TYPs within a ±3-day window, and proximity was analyzed using buffer zones ranging from 50 to 500 km around storm centers. Key findings revealed that landslide susceptibility results from a complex interplay of meteorological, topographic, and land cover factors. The critical controls identified include elevations above 2000 m, slope angles between 30 and 45 degrees, southeast- and south-facing aspects, and low Stream Power Index values typical of headwater and upper slope locations. Landslides were most frequent during Category 3 TYPs and were concentrated 300 to 350 km from storm centers, where optimal rainfall conditions for slope failures exist. Interestingly, despite the stronger storms in 2023, the number of landslides was higher in 2019. This emphasizes the importance of interannual variability and terrain preparedness. These findings support sustainable disaster risk reduction and climate-resilient development, aligning with Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action). Furthermore, they provide a foundation for improving hazard assessment and risk mitigation in Taiwan and similar mountainous, TYP-prone regions.

Hydrometeorological forecasting and early warning involve many hazardous elements, with the estimation of intensity and center location of tropical cyclones (TCs) being key. This paper proposes a new multitask deep learning model with attention gate mechanisms to work with satellite images and construct heatmaps for TC's centering and classification. The multi-head keypoint design (MHKD) with the spatial attention mechanism (SAM) is fitted to the decoder layer using multi-resolution inputs from the encoder. In addition, the new loss function is employed with an Euclidean distance to guide centers of heatmaps from lower decoder layers toward higher ones, thereby refining keypoints during the early decoding stage. Experimental results, done on a constructed dataset for the Western North Pacific for 2015-2023 collected from the Japanese Himawari 8/9 geostationary satellite and the best track of the World Meteorological Organization (WMO) Regional Specialized Meteorological Center (RSMC) Tokyo - Typhoon Center, indicate that the proposed model successfully detects most TC existences on combined images from three infrared channels. The model's accuracy can reach over 72% of the Tropical Depression (TD) grade and over 90% for really strong TCs (Severe Tropical Storm (STS) and Typhoon (TY)). Compared to a typical detecting object problem, the main issues come from the complexity of TC cloud patterns, which are nonlinear with actual TC grades or discrimination between TC grades (transition between TD to Tropical Storm (TS), TS to STS, and upgrading and progress of TCs). The proposed MHKD can help reduce the over-estimate rate for the TD grade and under-estimate rates for TS and STS grades, and most notably, the TC center localization yielded an average error of approximately 34 km with a single keypoint or one head attention network (One ATTN) and around 27 km when using three head attention network (Three ATTN).

This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone dynamics. By analyzing historical sea surface temperature data and the typhoon’s trajectory, the three-dimensional response of sea temperature during typhoon “Saola” was explored. The key findings are as follows: 1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days after the typhoon. In deep-sea areas, the highest warming occurs within five days, while the lowest cooling occurs within two days. 2. The nearshore water layers respond quickly to the typhoon, while the deep-sea water layers primarily respond in the middle depths, with a delayed effect. 3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer.

This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.

The Western Pacific region is highly vulnerable to typhoon storm surge disasters, with localized erosion posing a particularly prominent issue for coastal marine structures. The prevalence of extreme typhoon storm surges poses a significant threat to the safety of engineering projects in these areas. In this study, a parameterized wind field model with precise calculation of wind speed was employed to establish a numerical model for typhoon storm tides. Based on the Western Pacific typhoon data from 1949 to 2023, hydraulic simulations were conducted for Hangzhou Bay, Xiangshan Port, and Yueqing Bay, revealing maximum flow velocities of 4.5 m/s, 1.95 m/s, and 2.09 m/s, respectively. These velocities exceeded the maximum possible tidal flow by 0.47–1.17 m/s. Additionally, using Sun’s velocity formula, the initiation flow velocities were calculated to be 1.85 m/s, 1.81 m/s, and 2.06 m/s for the aforementioned locations. Through localized erosion tests conducted around typical bridge piers and the subsequent application of similarity criteria, the maximum depth of localized erosion in the study area was determined to range from 2.16 m to 16.1 m, which corresponds to 1.1–2.3 times the scour caused by the maximum tidal flow scenario. A comparison of the erosion test results with calculations based on several formulas demonstrated that the scour prediction formula proposed by Sun exhibited the highest accuracy. This study supplements the understanding of the impact of typhoon storm surges on bridge pier erosion and provides a scientific basis for the design of bridge foundations.

Madden–Julian Oscillation (MJO) modulates the generation of typhoons (TYs) in the western North Pacific (WNP). Using IBTrACS v04 tropical cyclone best path data, ERA5 reanalysis data, and the MJO index from the Climate Prediction Center (CPC), this paper defines an index to describe the persistent anomalies of the MJO and to examine the statistical characteristics of TYs over 44 years (1978–2021), focusing on the analysis of major differences in environmental conditions after the removal of the ENSO signal over the WNP. The results indicate that the persistent anomalous state of the MJO influences the change in large-scale environmental factors, which, in turn, affects the generation of TYs, as follows: (1) For the I high-value years, the center of the MJO stagnates in the Indian Ocean–South China Sea (SCS), the monsoon trough retreats westward, the warm pool becomes warmer, and the Walker circulation is enhanced. There is stronger upper-level divergence and low-level convergence, larger low-level relative vorticity, higher mid-level relative humidity, and smaller vertical wind shear in the SCS and the seas near the Philippines. Consequently, these conditions foster a conducive environment for TY genesis in the SCS and the seas near the Philippines. (2) For the I low-value years, the center of the MJO stagnates in the WNP–North America region, the monsoon trough extends eastward, the warm pool becomes colder, and the Walker circulation is weakened. Consequently, these conditions are more likely to facilitate TY genesis in the central–eastern WNP. The results show that persistent anomalies in MJO active centers can effectively improve the predictive ability of TY frequency.

Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given the extreme temperature differences and the consequentially marked air–sea flux modulations, ESSTDs are intuitively supposed to play a serious role in modifying TC intensities. Nevertheless, the relationship between ESSTDs and consequential storm intensity changes remains unclear. In this study, satellite-observed microwave SST drops and the International Best Track Archive for Climate Stewardship TC data from 2001 to 2021 were used to elucidate the relationship between ESSTDs and the consequential TC intensity changes in the Western North Pacific typhoon season (July–October). Subsequently, the distributed characteristics of ESSTDs were systematically examined based on statistical analyses. Among them, Typhoon Kilo (2015) triggered an unexpected ESSTD behind its passage, according to existing theories. Numerical experiments based on the Regional Ocean Modeling System were carried out to explore the possible mechanisms that resulted in the ESSTD due to Kilo. The results indicate that heavy rainfall leads to additional SST cooling through the enhanced sensible heat flux leaving the surface layer in addition to the cooling from momentum-driven vertical mixing. This process enhanced the sensible heat flux leaving the sea surface since the temperature of the raindrops could be much colder than the SST in the tropical ocean, specifically under heavy rainfall and relatively less momentum entering the upper ocean during Kilo.

热带中太平洋海温年代际增暖对西太平洋台风的影响

In November 2020, Typhoon (TY) Vamco (locally named Ulysses) made landfall on the main island of Luzon, the Philippines. It brought intense rainfall resulting in widespread flooding making it the 7th costliest TY in the Philippines. Its thermodynamic characteristic from radiosonde observations during its closest passage shows that while the convective available potential energy (CAPE) was not abnormally high, the saturated layer from 850–600 hPa height had lapse rates slightly larger than the moist-adiabat. Also, high precipitable water of up to 70.3 mm and high relative humidity (RH) from the surface to 400 hPa likely explain the heavy rainfall associated with TY Vamco. Global warming has exerted profound effects on weather patterns around the globe. Consequently, the impact of TY Vamco was used as an example of climate change in the local popular media. In this study, we investigated the influence of historical warming on the rainfall characteristics of TY Vamco using the Weather Research and Forecasting model. The pseudo-global warming method was applied using a 40-yr regression of sea surface and air temperature, and RH. We then used the modeled rainfall to simulate the river discharges of two rivers in the northern Philippines that experienced extensive flooding. Results show that SST has a major influence on the intensity of TY Vamco. However, other factors such as orography and changes in mid-tropospheric humidity negate the effects of historical warming, which resulted in comparable rainfall between the past and present simulations.

Abstract. We investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on 25 November 2018 when Typhoon (TY) Usagi directly hit HCMC. During this event, there was more than 300 mm of rainfall over 24 h which led to flooding and considerable material damage. We propose an in-depth study of TY-induced, compound flood drivers at a short timescale by focusing on the days before and after the event. We use a set of data analysis and signal processing tools to characterize and quantify both coastal and inland effects on the hydrosystem. We found that TY Usagi made landfall without forming a significant storm surge. The extreme rainfall does not translate into immediate river discharge but presents a 16 h time lag between peak precipitation and peak residual discharge. Nevertheless, increased river water levels can be seen at both urban and upstream stations with a similar time lag. At the upstream river station, residual discharge represents 1.5 % of available rainwater, and evidence of upstream widespread flooding was found. At the urban river station, we assess the potential surface runoff during the event to be 8.9 % of the upstream residual discharge. However, a time lag in peak river water level and peak rainfall was found and attributed to the combination of high tide and impervious streets which prevented the evacuation of rainwater and resulted in street flooding of up to 0.8 m. Overall, it was found that despite not having a significant storm surge, the coastal tidal forcing is the predominant compound flood driver even during severe, heavy rainfall with tidal fluctuations in river water level and respective discharge much larger than the residuals.

Based on the Final Operational Global Analysis (FNL) data from the National Centers for Environmental Prediction and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts, the all-sky brightness temperatures of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A satellite (FY-4A) are simulated, which are then used to investigate Typhoon In-Fa (2021). The results show that the all-sky simulations based on ERA5 and FNL for FY-4A GIIRS channels 27 (716.25 cm−1), 90 (755.625 cm−1), and 417 (960 cm−1) can reproduce well the structure and intensity of Typhoon In-Fa. But the FNL simulations overestimate the typhoon intensity with more high ice clouds around the typhoon center. Fourier analysis of Typhoon In-Fa at severe tropical storm (STS) and typhoon (TY) stages is conducted. The results reveal that the dominant features of Typhoon In-Fa are primarily large-scale, with a relatively small proportion of observations and simulations dedicated to small-scale features at the STS stage. However, the proportion of large-scale features decreases while the amount of small-scale information increases during the TY stage. The purpose of this study is to assess the performance of FY-4A GIIRS all-sky simulations based on the ERA5 and FNL data, as well as to prepare for future all-sky data assimilations.

The Philippines is ranked first in the World Risk Index 2022 according to the Germany-based Bündnis Entwicklung Hilft and the Institute for International Law of Peace and Armed Conflict (IFHV). The country will likely remain at the top rank of the global risk index over the next few decades, mainly due to the Philippines being in the Pacific Typhoon Belt, where the adverse impacts of climate change are predicted to get worse. To solve this predicament, intensive preparations through development planning are needed, and Probabilistic Risk Assessment (PRA) is required to capture and anticipate future hazards bigger than the historical record. The probabilistic approach is recognized in the Philippine Development Plan 2017-2022 and 2023-2028, the 2019 National Climate Risk Management Framework Policy of the Climate Change Commission (CCC), and the United Nations Disaster Risk Reduction (UNDRR) document entitled, “Why Invest in Probabilistic Risk Assessment?” as necessary for effective climate change adaptation actions. However, the Philippines has yet to realize the nationwide-scale implementation of PRAs in our CCA-DRR efforts, despite it being a strategy recommended by the national government to effectively address worsening natural hazard impacts that severely hamper national development. This paper highlights the importance of PRA in the context of Philippine Climate Change Adaptation and Disaster Risk Reduction (CCA-DRR) efforts to lower the cost of damage from floods, rainfall-triggered landslides, and storm surges and help communities attain their sustainable development goals. We recommend that these policies be systematically implemented without any more delay.

Abstract Since the 1960s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air–sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can “see” through clouds and facilitate observations of the air–sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016 to 2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt (∼17 m s−1) contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in square kilometers. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data are useful for studying tropical cyclones at the air–sea interface and provide results of these behaviors closely to data derived from best track archives.

Typhoon-related changes in moisture pathways and sources for precipitation in Eastern China during the three major rainy seasons

Based on the 181 tropical cyclones data in the western North Pacific Ocean from 2015 to 2020, hourly ozone (O3) concentration data, and meteorological observation data of 18 cities and counties in Hainan Island, this study analyzed the impacts of tropical cyclones on ozone pollution in Hainan Island. We found that 40 (22.1%) tropical cyclones experienced O3 pollution in Hainan Island during the lifetime of tropical cyclones in the past six years. During the years with more tropical cyclones, more O3- polluted days occurred in Hainan Island. Highly polluted days, which were defined as more than or equal to three cities and counties exceeding the standard, were the most serious in 2019 with 39 (54.9%) polluted days. The tropical cyclones related to high pollution (HP) showed an increasing trend, with the trend coefficient and climatic trend rate of 0.725 (exceeding the 95% significance level) and 0.667 a-1, respectively. Tropical cyclone intensity was positively correlated with the maximum 8 h moving average (O3-8h) concentration in Hainan Island. Among them, HP-type tropical cyclones accounted for 35.4% of all samples in the typhoon (TY) intensity level. Cluster analysis of tropical cyclone paths showed that tropical cyclones from the South China Sea (type A) were the most common of the 67 (37%) and were the most likely to cause large-scale and high-concentration O3 pollution events in Hainan Island. The average number of HP tropical cyclones and ρ(O3-8h) of Hainan Island in type A were 7 and 121.90 μg·m-3, respectively. In addition, the tropical cyclone centers were located generally in the middle part of the South China Sea and the western Pacific Ocean near the Bashi Strait during the HP period. The change in meteorological conditions in Hainan Island under the influence of HP tropical cyclones was conducive to the increase in O3 concentration.