Paradigm Shift in Typhoon Forecasting for the Korean Peninsula: A Case Study on the Applicability of the Typhoon-Ready System

Abstract

Climate change has significantly increased multi-hazard disasters caused by typhoons, exposing the limitations of conventional forecasting systems that often neglect regional socio-economic vulnerabilities. This study develops and validates the Typhoon-Ready System (TRS) as an effective disaster-management framework for the Korean Peninsula. The TRS integrates hazard data with socio-economic and environmental vulnerability factors to produce region-specific risk indices. The analysis of four representative typhoons—Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019)—demonstrates TRS’s applicability in identifying high-risk zones and supporting disaster preparedness strategies. The TRS framework incorporates indices, such as the Strong Wind Index (SWI), Heavy Rainfall Index (HRI), Storm Surge Index (SSI), and Air Quality Index (AQI), effectively combining meteorological modeling with vulnerability analysis. Results demonstrate that the TRS outperforms traditional systems by accurately identifying high-risk zones and correlating them with observed damage patterns. For example, the TRS successfully pinpointed high wind risks in Seoul and Incheon during Typhoon Lingling and forecasted severe flooding in Gangneung and Samcheok during Typhoon Rusa. By integrating vulnerability factors, including population density, infrastructure aging, and urbanization levels, the TRS provides a more holistic and accurate risk assessment. This research highlights the necessity of a multi-dimensional forecasting approach for enhancing disaster preparedness and resilience against climate change-induced typhoon impacts.

1. Introduction

Climate change has intensified the frequency and impact of typhoons globally. This has significantly increased the complexity of multi-hazard disasters, including strong winds, extreme rainfall, storm surges, and air quality deterioration [1,2]. The Korean Peninsula, a region highly susceptible to tropical cyclones, has experienced significant socio-economic disruptions due to these escalating impacts. Warming trends in the western Pacific Ocean—with Sea Surface Temperatures (SST) rising faster than the global average—have further heightened the probability of stronger and more destructive typhoons [3,4].

Despite recent advancements, traditional forecasting systems predominantly emphasize meteorological factors, often overlooking critical socio-economic vulnerabilities such as population density, aging infrastructure, and urbanization levels [5].

This research builds upon prior studies and seeks to refine impact-based forecasting approaches, providing a scalable and adaptable model for mitigating the escalating effects of climate change-driven typhoons.

2. Typhoon Development and Configuration

2.1. Overview of the Typhoon-Ready System

The Typhoon-Ready System (TRS) was designed to overcome the limitations of conventional forecasting systems by integrating hazard and vulnerability factors into a unified framework. The TRS incorporates high-resolution meteorological models alongside socio-economic indicators, enabling comprehensive risk assessments tailored for the Korean Peninsula [6,7,8].

2.2. Componets of TRS

2.2.1. Hazard Factors

The TRS framework incorporates multiple hazard factors through advanced numerical modeling techniques to provide comprehensive risk assessments. For a strong wind risk evaluation, the TRS employs 3-s gust data simulated using the Weather Research and Forecasting (WRF) model, enabling the precise capture of extreme wind conditions during typhoon events. The WRF model used in this study underwent sensitivity analysis for the grid and domain, data settings [9,10,11]. We confirmed that the selected domain and grid resolution have a minimal impact on the results, supporting the reliability of the TRS analysis outcomes. The assessment of heavy rainfall risks relies on Probable Maximum Precipitation (PMP) calculations derived from the WRF model, which provides critical data for flood risk estimation. Storm surge analysis is conducted using the Storm Surge Hazard Potential Index (SSHPI), incorporating both tidal levels and atmospheric pressure changes to identify vulnerable coastal areas. Additionally, the system evaluates air pollution risks through the Community Multi-scale Air Quality (CMAQ) model, specifically focusing on PM10 concentration variations that occur during typhoon events. This integration of multiple hazard assessment tools enables the TRS to provide a more nuanced and comprehensive understanding of typhoon-related risks across different dimensions (

Table 1. Table representing the domain configuration of the WRF model.

	Domain 1	Domain 2	Domain 3
Horizontal grid	85 × 85	172 × 172	304 × 304
Horizontal resolution	27 km	9 km	3 km
Vertical layers	27

).

2.2.2. Vulnerability Factors

The performance of the TRS was evaluated through detailed analyses of four major typhoon events that significantly impacted the Korean Peninsula: Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019). These cases were selected based on their diverse characteristics and documented impacts, providing a comprehensive validation framework for the TRS system. The vulnerability factors considered in the TRS are crucial for comprehensive risk assessments. These factors include the following:

• Population density: utilizing the latest census data to identify areas with high human exposure;
• Proportion of old buildings: calculating the regional percentage of structures over 30 years old to assess structural vulnerability;
• Urbanization level: based on impervious surface ratio and land use patterns to evaluate flood risk and urban heat island effects;
• River density: measuring the river length per unit area to assess flood vulnerability.

These factors are integrated into each risk index calculation with appropriate weightings. For example, the Strong Wind Index (SWI) combines 3-s gust data with the distribution of old buildings, while the Heavy Rainfall Index (HRI) incorporates Probable Maximum Precipitation (PMP) [12], river density, and the impervious surface area. By integrating these vulnerability factors with hazard data, the TRS provides a more nuanced and accurate risk assessment than traditional forecasting systems. This approach enables the identification of high-risk zones that may not be apparent when considering meteorological factors alone, thus supporting more effective disaster preparedness and mitigation strategies. The incorporation of these vulnerability factors allows the TRS to capture the complex interactions between natural hazards and socio-economic conditions, providing a more holistic view of typhoon-related risks across different regions of the Korean Peninsula.

2.3. Case Study Intergration

The performance of the TRS was validated based on four major typhoon events—Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019)—each showcasing unique hazard–vulnerability interactions. These case studies emphasize TRS’s ability to provide actionable, region-specific insights for disaster preparedness and mitigation efforts.

3. Selection Criteria and Overview of Typhoon Case Studies

The TRS framework was validated using the methodologies detailed in Na and Jung (2024) through case studies of four major typhoons: Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019). These analyses confirm TRS’s applicability for disaster preparedness and responses in the Korean Peninsula. These studies demonstrate the system’s capability of identifying high-risk regions by integrating meteorological hazards with socio-economic vulnerabilities. The findings underscore TRS’s practical utility for proactive disaster planning and mitigation efforts, particularly in areas where traditional forecasting methods may underestimate potential impacts due to their focus on meteorological factors alone.

3.1. Case Study Selection

To validate the performance of the TRS, four significant typhoon events affecting the Korean Peninsula were analyzed (Figure 1):

• Typhoon Lingling (2019): characterized by severe wind impacts;
• Typhoon Rusa (2002): notable for extreme rainfall events;
• Typhoon Maemi (2003): demonstrated significant storm surge effects;
• Typhoon Mitak (2019): unique for its air quality impacts.

These cases were chosen for their diverse hazard characteristics, providing a robust framework to evaluate the system’s adaptability and effectiveness.

The typhoons analyzed in this study are classified according to the Saffir–Simpson Hurricane Wind Scale as follows:

• Typhoon Lingling (2019): Category 3 with maximum wind speed of 49.3 m/s;
• Typhoon Rusa (2002): Category 2 with maximum wind speed of 41 m/s Typhoon;
• Typhoon Maemi (2003): Category 4 with maximum wind speed of 54 m/s;
• Typhoon Mitag (2019): Category 1 with maximum wind speed of 39 m/s.

3.1.1. Overviews of Typhoon Lingling: Strong Winds

Typhoon Lingling was the 13th typhoon of 2019, known for its severe wind impacts across South Korea. With a central pressure of 965 hPa and maximum wind speeds of 39 m/s, it passed west of Mokpo and made landfall near Haeju, North Korea. Lingling caused extensive wind damage, particularly in metropolitan areas, like Seoul and Incheon, where older buildings and infrastructure were vulnerable to high wind speeds. Notably, maximum wind speeds of 49.3 m/s were recorded in Bukgeoryeolbi-do, demonstrating the destructive potential of Lingling’s strong winds.

3.1.2. Overviews of Typhoon Rusa: Heavy Rainfall

Typhoon Rusa, classified as one of the most destructive typhoons to strike the Korean Peninsula, brought unprecedented rainfall. On 31 August 2002, Gangneung recorded 870.5 mm of rainfall in a single day—62% of its annual average—setting a record in South Korea’s meteorological history. The heavy rainfall led to large-scale river flooding and urban inundation, particularly in the Yeongdong region and areas around Jirisan Mountain. Rusa’s impacts highlighted the critical importance of incorporating regional hydrological vulnerabilities into risk assessments.

3.1.3. Overviews of Typhoon Maemi: Storm Surge

Typhoon Maemi, the 14th typhoon of 2003, demonstrated the devastating effects of storm surges exacerbated by coastal vulnerabilities. With a central pressure of 954 hPa and maximum wind speeds of 48 m/s, Maemi struck the southern coast near Sacheon City and generated record-breaking storm surges. In Masan Port, the sea level rose by over 60 cm due to a combination of a high tide and strong winds, resulting in significant destruction to port facilities and the surrounding infrastructure. This event underscored the need for comprehensive coastal risk assessments.

3.1.4. Overviews of Typhoon Mitak: Air Quality

Typhoon Mitak, the 18th typhoon of 2019, was unique in its impact on air quality. As it moved across South Korea, it disrupted atmospheric circulation patterns, leading to a sharp increase in PM10 concentrations in industrial and urban areas, like Incheon and Ulsan. The high pollution levels persisted even after the typhoon’s passage, emphasizing the secondary environmental risks associated with typhoons and the importance of integrating air quality assessments into disaster-management frameworks.

3.2. Case Study

This study analyzed the performance of the Typhoon-Ready System (TRS) by comparing its risk indices with actual damage data from four significant typhoon cases: Lingling, Rusa, Maemi, and Mitak. The results demonstrate that the TRS provides a more comprehensive and accurate assessment of regional disaster risks compared to traditional hazard-focused forecasting systems.

3.2.1. Results of Typhoon Lingling: Strong Winds

The Strong Wind Index (SWI) of the TRS is calculated by combining the 3-s gust with the distribution of old buildings (Figure 2). High SWI values were observed in Seoul and Incheon areas, which corresponded well with actual damage cases. In contrast to existing forecasts, SWI delivered more accurate risk predictions by accounting for the structural vulnerability of regions. For example, while traditional hazard-based forecasts predicted high wind risks in western coastal and metropolitan areas along Lingling’s path, they lacked a consideration of regional vulnerabilities and could not specifically assess the potential for structural damage. TRS’s SWI results showed high risk indices in Seoul and Incheon where old buildings are concentrated, aligning with actual cases of building damage and casualties in these areas during Typhoon Lingling. The SWI also indicated a high risk in coastal areas, like Pyeongtaek and Gunsan, as shown in Figure 2c. which have concentrations of old buildings, correlating with reported damage to vinyl greenhouses and crops. These results suggest that TRS’s SWI, by combining hazard and vulnerability factors, can enable more reliable, region-specific disaster-prevention strategies, serving as a practical tool to complement existing forecast systems in the era of climate change.

3.2.2. Results of Typhoon Rusa: Heavy Rainfall

The Heavy Rainfall Index (HRI) is calculated considering the Probable Maximum Precipitation (PMP), river density, and impervious surface area (Figure 3). High HRI values were observed in the Gangneung and Samcheok areas of Gangwon-do, which aligned with the actual large-scale river flooding and urban inundation damages. While the record-breaking rainfall in Gangwon-do could be predicted through PMP, HRI provided a more accurate assessment of the flood and inundation risk by considering areas with a high river density and increased impervious surface due to urbanization. For instance, Gangneung and Samcheok not only had very high PMP values but also a high river density and increased impervious surface in some urban areas. TRS’s HRI results showed high risk indices in these areas, corresponding well with the actual large-scale river overflow and urban flooding that occurred during Rusa. The HRI also calculated relatively high-risk indices for some areas where rainfall was not particularly high, reflecting vulnerability factors that were overlooked in existing PMP-based forecasts. This demonstrates HRI’s potential to complement existing rainfall-forecast systems and contribute to tailored disaster-prevention strategies, particularly in urban areas and regions with high river density.

3.2.3. Results of Typhoon Maemi: Storm Surge

The Storm Surge Index (SSI) is calculated by combining the Storm Surge Hazard Potential Index (SSHPI), tidal levels, topography, and urbanization rate (Figure 4). Very high SSI values were observed in Masan Port, which corresponded to the record-breaking storm surge damage. Masan Port recorded a storm surge height of 5.16 m, causing massive destruction to port facilities and the surrounding infrastructure. This historic level of damage highlighted the limitations of existing SSHPI-based forecasts, which focus solely on the physical intensity and fail to account for regional vulnerabilities. The SSI effectively captured the high risk in Masan Port by integrating hazard factors (SSHPIs) with vulnerability factors, such as high tidal levels, the low-lying coastal topography, and the high urbanization rate. This comprehensive approach allowed the SSI to quantitatively predict the potential for damage in areas like Masan Port, where existing SSHPI-based forecasts could not accurately assess the scale of potential impact. The Masan Port case clearly demonstrates SSI’s ability to complement existing physical-intensity-based forecast systems by incorporating vulnerability factors, providing a more nuanced and region-specific risk assessment. This makes SSI a valuable tool for enhancing preparedness and response strategies for storm surge risks, particularly in highly vulnerable coastal urban areas.

3.2.4. Results of Typhoon Mitak: Air Quality

The Air Quality Index (AQI) is calculated considering the PM10 concentration changes, population density, and industrial activity levels (Figure 5). High AQI values were observed in Seoul, the metropolitan area, Incheon, and Ulsan, which aligned with actual cases of air quality deterioration after the typhoon. Typhoon-induced air pollution is influenced not only by physical changes due to strong winds and rainfall but also by regional vulnerability factors, such as the population density and industrial activity levels. TRS’s AQI focuses on quantitatively assessing the potential for air pollution damage during typhoon events by combining these hazard and vulnerability factors. For example, as shown in Figure 5c, PM10 concentrations increased rapidly in Seoul and the metropolitan area, which have high population densities, after the typhoon passed. Additionally, in industrial areas, like Incheon and Ulsan, the re-dispersion of pollutants after the typhoon led to air quality deterioration, which corresponded with the high AQI values calculated using the TRS. This case demonstrates that TRS’s AQI can provide a more realistic assessment of regional air pollution risks during typhoon events by considering both hazard factors and regional vulnerabilities, offering valuable information for air quality management and public-health-protection strategies.

4. Summary and Conclusions

This study builds upon and extends the methodologies presented in previous research [8] The TRS framework integrates high-resolution numerical weather models with socio-economic datasets to calculate risk indices that capture the multi-hazard nature of typhoons. Its modular design allows for the incorporation of various hazard and vulnerability factors, ensuring adaptability to diverse regional contexts.

The system quantifies hazard factors using advanced numerical modeling techniques. Strong wind risks are evaluated using 3-s gust data simulated by the WRF model. Heavy rainfall risks are assessed using Probable PMP calculations derived from the WRF model. Storm surges are analyzed using the SSHPI, incorporating tidal levels and atmospheric pressure changes. Air pollution risks are examined using the CMAQ model, focusing on PM10 concentration variations.

To account for regional vulnerabilities, the TRS integrates critical socio-economic and environmental indicators. Population density is analyzed to evaluate human exposure levels. The proportion of the aging infrastructure and impervious surfaces provides insights into structural vulnerabilities and flood risks. Urbanization levels are considered to capture the amplified risks in highly developed areas prone to severe inundation and compounded impacts.

The TRS framework has been validated through case studies of major typhoons impacting Korea, such as Lingling (2019) and Rusa (2002). These studies demonstrated the system’s ability to identify high-risk regions by integrating meteorological hazards with socio-economic vulnerabilities. The findings highlight the critical importance of the practical utility of TRS for proactive disaster planning and mitigation efforts.

This study confirms that the Typhoon-Ready System (TRS), validated through case studies and leveraging methodologies, serves as an effective disaster-management system for the Korean Peninsula. By integrating physical hazards with socio-economic vulnerabilities, the TRS provides a robust tool for enhancing disaster preparedness and resilience against climate change-induced typhoon impacts. TRS offers a scalable and adaptable model that can be extended to other typhoon-prone regions globally, providing a valuable tool for addressing the escalating impacts of climate-change-driven disasters.