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This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.

Rainfall during typhoons is one of the most important water resources in Taiwan, but heavy typhoon rainfall often leads to serious disasters and consequently results in loss of lives and property. Hence, accurate forecasts of typhoon rainfall are always required as important information for water resources management and rainfall-induced disaster warning system. In this study, a methodology is proposed for providing quantitative forecasts of 24 h cumulative rainfall during typhoons. Firstly, ensemble forecasts of typhoon rainfall are obtained from an ensemble numerical weather prediction (NWP) system. Then, an evolutionary algorithm, i.e., genetic algorithm (GA), is adopted to real-time decide the weights for optimally combining these ensemble forecasts. That is, the novelty of this proposed methodology is the effective integration of the NWP-based ensemble forecasts through an evolutionary algorithm-based strategy. An actual application is conducted to verify the forecasts resulting from the proposed methodology, namely NWP-based ensemble forecasts with a GA-based integration strategy. The results confirm that the forecasts from the proposed methodology are in good agreement with observations. Besides, the results from the GA-based strategy are more accurate as compared to those by simply averaging all ensemble forecasts. On average, the root mean square error decreases about 7%. In conclusion, more accurate typhoon rainfall forecasts are obtained by the proposed methodology, and they are expected to be useful for disaster warning system and water resources management during typhoons.

The dynamic downscaling dataset provides useful insight for future climate change at the local scale. The original downscaled dataset, however, inevitably involves bias and hampers its further applications. This study presents a bias correction method that uses the quantile mapping method to a dynamic downscaled dataset originated from the Meteorological Research Institute (MRI). Daily solar radiation and hourly typhoon rainfall are selected as target variables to be corrected because they are critical variables in hydrological and agricultural practice. The results reveal that the original biases in the two datasets were markedly reduced and the statistical characteristics in the projections were much closer to that in the observations. After correction, the future changes in daily solar radiation and hourly typhoon rainfall are also evaluated. A decrease in solar radiation is suggested up to ~5% in south and west parts of Taiwan in the late 21^(st) century while an increasing amount, about 2%, will occur in the mountain areas. A significant increase in typhoon rainfall amount, up to ~50% is observed in the west and central parts of Taiwan in the late 21^(st) century and ~20% in the early 21^(st) century (2015 - 2039). A decrease in typhoon rainfall amount, up to ~-30%, is clear for the north and east parts of Taiwan.

A record breaking extreme precipitation event produced 3000 mm day-1 of accumulated rainfall over southern Taiwan in August 2009. The interactions between Typhoon Morakot and the prevailing southwesterly (SW) monsoon are the primary mechanism for this heavy precipitation during 5 - 13 August 2009. This extreme precipitation could be produced by the abundant moisture from the SW monsoon associated with the interaction between typhoon and monsoon wind fields, leading to severe property damage. The accurate mapping of extreme precipitation caused from the interaction between a monsoon and typhoon is critical for early warning in Taiwan. This study simulates the heavy rainfall event is based on the Weather Research and Forecast system model (WRF) using the three nested domain configuration. Using data assimilation with a virtual meteorological field using the 3D-Var system, such as wind field to alter the SW monsoon strength in the initial condition, the impacts of intensified convergence and water vapor content on the accumulated rainfall are analyzed to quantize the intensification of typhoon rainfall potential. The results showed a positive correlation between the enhanced precipitation and the intensity of low-level wind speed convergence as well as water vapor content. For the Typhoon Morakot case study the rainfall for could attain approximately 2 × 10^4 mm at 6 hours interval in the southern Taiwan area when 10 × 10^(-6) s^(-1) convergence intensified at 850 hPa level around the southern part of the Taiwan Strait. These results suggest that low-level wind speed, convergence and water vapor content play key roles in the typhoon rainfall potential coupled with the SW monsoon.

Information about the raindrop size distribution (RSD) is vital for comprehending the precipitation microphysics, improving the rainfall estimation algorithms, and appraising the rainfall erosivity. Previous research has revealed that the RSD exhibits diversity with geographical location and weather type, which leads to the assessment of the region and weather-specific RSDs. Based on long-term (2004 to 2016) disdrometer measurements in northern Taiwan, this study attempts to demonstrate the RSD aspects of summer seasons that were bifurcated into two weather conditions, namely typhoon (TY) and non-typhoon (NTY) rainfall. The results show a higher concentration of small drops and a lower concentration of large-sized drops in TY compared to NTY rainfall, and this behavior persisted even after characterizing the RSDs into different rainfall rate classes. RSDs expressed in gamma parameters show higher mass-weighted mean diameter (Dm) and lower normalized intercept parameter (Nw) values in NTY than TY rainfall. Moreover, sorting these two weather conditions (TY and NTY rainfall) into stratiform and convective regimes revealed a larger Dm in NTY than in TY rainfall. The RSD empirical relations used in the valuation of rainfall rate (Z–R, Dm–R, and Nw–R) and rainfall kinetic energy (KE–R and KE–Dm) were enumerated for TY and NTY rainfall, and they exhibited profound diversity between these two weather conditions. Attributions of RSD variability between the TY and NTY rainfall to the thermodynamical and microphysical processes are elucidated with the aid of reanalysis, remote sensing, and ground-based data sets.

Tropical cyclone (TC)‐induced rainfall is a drastic threat to human life and property, and thus rational rainstorm nowcasts within even a lead time of few hours play a key role in disaster mitigation. While recent deep learning‐based algorithms have shown promise, predictions commonly suffer from the troubles of blur, dissipation, and location errors of TC rainbands, particularly for a lead time beyond 1 hr. Here, we develop a new nowcasting model, named TyrainNow, and show a significant improvement for nowcasting rainbands with a lead time up to 2 hr. Concretely, TyrainNow employs a refined multi‐task loss function integrating geographical consistency, temporal coherence and radar image structural similarity. This tailored enhancement is architecture‐agnostic and involves subtle adjustments. Secondly, TyrainNow adopts the quantile mapping technique to correct systematic attenuation biases inherent in the neural network outputs. The new model is verified on the basis of typhoon radar composite reflectivity products in South China, with a focus on the Greater Bay Area. Specifically, the new model achieves a critical success index of 0.099 at the 40 dBZ threshold, marking a substantial improvement from 27% to 330% compared to three other benchmark models, DGMR (0.070), PredRNN‐v2 (0.023), and optical flow model (0.078), averaged over the lead times between 1 and 2 hr. We further verify the model's explainability and generalizability, and recommend it as a scalable and reliable model.

Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfall of 2361 mm in 48 hours. This study hopes to improve a statistical typhoon rainfall forecasting method used over the mountain region of Taiwan via an artificial neural network based southwest monsoon enhancement (ANNSME) model. Rainfall data collected at two mountain weather stations, ALiShan and YuShan, are analyzed to establish the relation to the southwest monsoon moisture flux which is calculated at a designated sea area southwest of Taiwan. The results show that the moisture flux, with southwest monsoon flow, transported water vapor during the landfall periods of Typhoons Mindulle, Bilis, Fungwong, Kalmaegi, Haitaing and Morakot. Based on the moisture flux, a linear regression is used to identify an effective value of moisture flux as the threshold flux which can enhance mountain rainfall in southwestern Taiwan. In particular, a feedforward neural network (FNN) is applied to estimate the residuals from the linear model to the differences between simulated rainfalls by a typhoon rainfall climatology model (TRCM) and observations. Consequently, the ANNSME model integrates the effective moisture flux, linear rainfall model and the FNN for residuals. Even with very limited training cases, our results indicate that the ANNSME model is robust and suitable for improvement of TRCM rainfall prediction. The improved prediction of the total rainfall and of the multiple rainfall peaks is important for emergency operation.

Climate change exerts heterogeneous impacts on regional flood extremes. This study focuses on the XF watershed (dominated by Meiyu) and HJT watershed (dominated by typhoons) in Zhejiang Province from 1964 to 2021. The Annual Maximum Flood (AMF) method and Peaks-Over-Threshold (POT) method were employed to analyze the differences in extreme flood characteristics before and after 1990. The results indicate that the Meiyu-dominated XF watershed shows stability: the 100-year design flood decreased by 2.3% using the AMF method and increased by 3.9% using the POT method. In contrast, the typhoon-dominated HJT watershed exhibits a decoupled response of “decreased frequency - amplified intensity”: the annual exceedance rate decreased from 1.885 to 1.808 events per year (a 4.1% reduction), while the Generalized Pareto Distribution (GPD) scale parameter increased by 42.2%. There is a significant discrepancy in the estimated values between the two methods in the HJT watershed (AMF: +64.2%; POT: +7.0%), highlighting the vulnerability of the AMF method in highly variable systems. This study reveals the differentiated response patterns of floods to climate change under different rainfall mechanisms, providing a scientific basis for regional flood control planning and risk management.

Taiwan, which is in the middle of one of the most active of the western North Pacific Ocean’s tropical cyclone (TC) zones, experienced a dramatic increase in typhoon-related rainfall in the beginning of the twenty-first century. This record-breaking increase has led to suggestions that it is the manifestation of the effects of global warming. With rainfall significantly influenced by its steep terrain, Taiwan offers a natural laboratory to study the role that terrain effects may play in the climate change of TC rainfall. Here, it is shown that most of the recently observed large increases in typhoon-related rainfall are the result of slow-moving TCs and the location of their tracks relative to the meso-α-scale terrain. In addition, stronger interaction between the typhoon circulation and southwest monsoon wind surges after the typhoon center moves into the Taiwan Strait may cause a long-term trend of increasing typhoon rainfall intensity, which is not observed before the typhoon center exits Taiwan. The variation in the location of the track cannot be related to the effects of global warming on western North Pacific TC tracks reported in the literature. The weaker steering flow and the stronger monsoon–TC interaction are consistent with the recently discovered multidecadal trend of intensifying subtropical monsoon and tropical circulations, which is contrary to some theoretical and model projections of global warming. There is also no evidence of a positive feedback between global warming–related water vapor supply and TC intensity, as the number of strong landfalling TCs has decreased significantly since 1960 and the recent heavy rainfall typhoons are all of weak-to-medium intensity.

Typhoon-specific rainfall-induced landslides pose critical hazards in mountainous regions, yet existing warning systems inadequately capture the distinct rainfall dynamics of these extreme events. To address this limitation, we propose an integrated framework combining optimized susceptibility predictions with dynamic rainfall thresholds tailored to typhoon patterns. The approach enhances machine learning accuracy through buffer-based negative sampling and variable weighting. It also introduces a spatiotemporal rainfall analysis to distinguish between short-term intense downpours and cumulative soil saturation. Tested in Zixing City, Hunan Province, China, where over 700 landslides were triggered by Typhoon Gaemi, the framework proved effective. The support vector machine (SVM) model achieved the best performance using frequency ratio (FR) inputs with a 0.5 km buffer (F1-score: 0.859, AUC: 0.914), correctly classifying 86.4 % of landslides as high or very high susceptibility. The rainfall analysis identified 24 h intensity combined with 7 d antecedent rainfall as the optimal trigger, effectively capturing both immediate and cumulative moisture effects. Spatially, rhyolite and granite slopes and areas near roads emerged as hotspots for failure (distance <800 m, FR=1.499 for roads; FR=1.546 for rhyolite). The integrated warning system shows high spatial efficiency, with high-risk areas covering only 34.2 % of the study region yet capturing 71.4 % of historical landslides. Additionally, the framework generated high-risk zone maps that align strongly with historical events. This work highlights the unique nature of typhoon-driven slope instability and provides a transferable framework for disaster risk reduction in cyclone-prone regions.