Search Results (9)

This study presents an integrated decision support system (DSS) designed to optimize real-time reservoir operation during typhoons by balancing flood control and water supply. The system combines ensemble quantitative precipitation forecasts (QPF) from WRF/MM5 models, a physically based rainfall–runoff model (KW-GIUH), and a three-stage optimization algorithm for reservoir release decisions. Eighteen ensemble rainfall members are processed to generate 6 h inflow forecasts, which serve as inputs for determining adaptive outflow strategies that consider both storage requirements and downstream flood risks. The DSS was tested using historical typhoon events—Talim, Saola, Trami, and Kong-rey—at the Tseng-Wen Reservoir in Taiwan. Results show that the KW-GIUH model effectively reproduces hydrograph characteristics, with a coefficient of efficiency around 0.80, while the optimization algorithm successfully maintains reservoir levels near target storage, even under imperfect rainfall forecasts. The mean deviation of reservoir water levels from the recorded to the target values is less than 0.18 m. The system enhances operational flexibility by adjusting release rates according to the proposed outflow index and flood-stage classification. During major storms, the DSS effectively allocates storage space for incoming floods while maximizing water retention during recession periods. Overall, the integrated framework demonstrates strong potential to support real-time reservoir management during extreme weather conditions, thereby improving both flood mitigation and water-supply reliability. Full article

Accurate typhoon track forecasting is vital for disaster mitigation in East China, a region frequently impacted by landfalling typhoons. Despite advances in numerical weather prediction, uncertainties remain high, especially within 48 h of landfall, due to complex interactions among tropical cyclones, the subtropical high, and mesoscale systems. This study applies Ensemble-based Sensitivity Analysis (ESA) within a high-resolution regional ensemble prediction system (Shanghai Weather And Risk Model System-Ensemble Prediction System, SWARMS-EN) to investigate forecast uncertainties of three representative typhoons—Gaemi, Bebinca, and Kong-rey—that made landfall in East China in 2024. Our results reveal consistent sensitivity patterns across diverse large-scale environments, particularly around the western flank of the subtropical high and in proximity to nearby low-pressure systems. Track uncertainty was closely tied to fluctuations in the steering flow, notably its zonal component. Moreover, binary typhoon interactions emerged as key drivers of forecast divergence. ESA effectively identified sensitive regions where small initial perturbations exert significant downstream influence on typhoon tracks. This study demonstrates the operational value of ESA for diagnosing forecast error sources and guiding targeted observations. By linking forecast uncertainty to physical mechanisms, this research enhances our understanding of typhoon predictability and supports the development of more adaptive and accurate regional forecasting systems. Full article

When Typhoon Kong-rey hit Taiwan in October 2024, an aircraft attempting to land at Taoyuan International Airport undertook a missed approach and landed successfully on the second attempt. The possible meteorological factors causing this missed approach are studied in this study based on a methodology specifically adopted for Hong Kong International Airport; namely, studying crosswind as derived from aircraft and airport meteorological observations, as well as the low-level turbulence derived from data on the aircraft’s vertical acceleration and high-resolution numerical weather prediction model results. A significant crosswind component and a gusting crosswind are the major reasons for the missed approach. The low-level turbulence appears to have been secondary/minor, as shown by the successful landings of aircraft before and after the event. It is concluded that the methodology supporting airport operations in Hong Kong may be used to explain missed approach cases at other airports under the influence of tropical cyclones. Full article

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article

This study investigates the impact of warm core eddies (WCEs) on the ocean response and intensity of tropical cyclones (TCs) in the Northwest Pacific, focusing on three typhoons in 2018: Jebi, Trami, and Kong-rey. The research uses the Hurricane Weather Research and Forecast (HWRF) model coupled with the MPIPOM-TC ocean model. Idealized WCEs are embedded into the ocean model ahead of each TC. The impacts of WCEs are evaluated by comparing simulations with and without their presence. Uncoupled experiments with the fixed sea surface temperature (SST) serve as a reference for TC maximum potential intensity. To quantitatively assess the impact of WCEs on the SST, enthalpy fluxes, and TC intensity, a Maximum WCE Potential Index (MWPI) is introduced. Our findings indicate that for a WCE with a 200 km radius, the potential to reduce SST cooling ranges from 34 to 37%, while the potential to increase enthalpy fluxes varies between 25 and 39%. The influence of WCEs on TC intensity, as measured by minimum pressure, shows a larger variation from 27% to 48%, depending on the oceanic and atmospheric environmental conditions in each storm. Additional experiments reveal the sensitivity of the MWPI to WCE size, with TC Trami showing less sensitivity due to its slower translational speed. This study underscores the significant role of oceanic thermal conditions, particularly WCEs, in modulating TC intensity. Full article

As one of the most significant disturbance sources in the upper marine environment of the South China Sea, tropical cyclones (typhoons) serve as a typical research subject for investigating the energy transfer process between the ocean and atmosphere. Utilizing satellite remote sensing data and focusing on Typhoon Rey No. 22’s transit event in 2021, this study quantitatively analyzes typhoon-induced energy input through heat pumping and cold suction at both surface and subsurface levels of the ocean. Additionally, it explores the response characteristics and feedback mechanisms of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) in the South China Sea to typhoon events. The research results show that the SST variation along the typhoon track displayed an asymmetric pattern, with a more pronounced warming on the right side and a cold anomaly lasting for 3–5 days on the left side. The subsurface warm anomaly dominated on the right side, showing a maximum temperature difference of 1.54 °C, whereas Ekman suction-induced upwelling led to cooling effects both at the subsurface and surface level on the left side, resulting in a maximum temperature difference of −3.28 °C. During the typhoon event, there was a significant decrease in sea surface heat flux, reaching 323.36 W/m², accompanied by corresponding changes in SST due to processes such as upwelling, seawater mixing, and air–sea heat transfer dynamics where anomalies arising from oceanic dynamic processes and exchange through sea surface heat flux contributed equally. Furthermore, strong suction-induced upwelling during the typhoon influenced chlorophyll concentration within the central and western regions of the South China Sea (13.5° N–16.5° N, 111° E–112.5° E), resulting in significant enhancement and reaching its peak value at approximately 0.65 mg/L. The average chlorophyll concentration increased by approximately 0.31 mg/L. Full article

In this study, 24 h quantitative precipitation forecasts (QPFs) in Taiwan at the ranges of day 1 (0–24 h), day 2 (24–48 h), and day 3 (48–72 h) by a cloud-resolving model are examined using categorical statistics, targeted mainly for the most-rainy 24 h from 10 typhoon cases between 2010 and 2015, following two earlier studies that evaluated the overall performance for all the typhoons that hit Taiwan from 2010 to 2012 and through 2015. In the selected examples with a peak amount of 322 to 1110 mm, the QPFs by the model (with a grid size of 2.5 km) are shown to be of very high quality for two typhoons (Soulik and Soudelor), and fairly good quality for three cases (Fanapi, Megi, and Fung-Wong) up to day 3 and for four others (Saola, Kong-Rey, Nanmadol, and Tembin) within day 2, respectively. The results are more variable for the one remaining case of Matmo, also impressive on day 1 but degraded at longer ranges. Overall, the quality of the QPFs ranges from excellent to satisfactory for all the typhoons studied as the threat score can achieve 0.2 at thresholds fairly close to the observed peak amount in some typhoons, or at least about half of it in most others. Since the majority of the typhoons produced the greatest rainfall amounts over the mountains in Taiwan due to the topographic effect, in agreement with many previous studies, the QPF skills by the model, often extending into high thresholds, as demonstrated, suggest that heavy rainfall from typhoons in Taiwan is generally of high predictability if and when the model has an adequate resolution. Full article

Typhoons frequently occur in the summer in the northwestern Pacific Ocean, and the responses of the upper ocean to typhoons have drawn extensive attention for decades. In the present work, a modified grid-based maximum response (GMR) method was proposed to estimate the sea surface cooling (SSC) caused by typhoons. The current algorithm (CA) is different from the original GMR method mainly in two aspects: (1) it uses a 5 day average rather than a 2 day average of the sea surface temperature (SST) before the typhoon as the reference temperature; (2) it modifies the fixed radius of 400 km to the level-7 Beaufort scale wind-force (~17.1 m/s) radius to determine the area where the SSC should be calculated. Then the MW-IR OISST data derived from satellite observations were used to compare the SSC estimated by different algorithms in four typhoon cases, Megi, LionRock, Trami and KongRey. The results show that, in all cases, maximum response methods have approached similar results, while the others seemed to have underestimated the SSC in degrees. In the slow-moving LionRock case, grid-based methods were found to have better performance, while in the successive typhoon cases, Trami and KongRey, CA showed an improved result in representing the pre-existing sea surface status before the typhoon KongRey by using the pentad mean SST as the reference temperature. In addition, the use of level-7 wind-force coverage made the results much livelier. In a word, the algorithm proposed here is valid in general. It has advantages in estimating the SSC caused by both slow-moving typhoons and successive typhoons, and should be further applied to related research. Full article

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts. Full article