publications

2026

1. J.Hydrol.:Reg.Stud.

   Applying machine learning for precipitation forecasting in uneven rainfall regions of Taiwan

   Fan-Ming Chiu, Lawrence Jing-Yueh Liu, Uswatun Hasanah, and 1 more author

   Journal of Hydrology: Regional Studies, Feb 2026

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   Study region: Taiwan, with its steep topography, high spatiotemporal rainfall variability, and frequent extreme weather events, which make it difficult to predict the weather accurately. Predictability of rainfall in the short term is essential in flood prevention, hydrological modeling, and water resource management in this area. Study focus: This paper introduces a superior machine learning model in precipitation prediction with surface observation data. Even though the conventional method of Numerical Weather Prediction (NWP) models is still in use, it is computationally demanding and time-consuming. The machine learning models offer a more data-driven option, but heavy rainfall is hard to predict because precipitation data is non-Gaussian and highly skewed. In order to overcome this shortcoming, the Zero-Order Hold (ZOH) method is used to preprocess temporal data, and a regression-based ZD-XGBoost model is suggested. The objective function of the model is meant to fit the uneven distribution of rainfalls and enhance extreme precipitation prediction. New hydrological insights for the region: The suggested ZD-XGBoost model maintains the accuracy of the light rainfall and minimizes the errors of heavy rainfall prediction by about 20% and 40% respectively, on hourly and daily data. These findings indicate that the distribution-conscious machine learning with surface observations can be effectively used to reduce the problem of data imbalance and improve the reliability of rainfall prediction in topographically complex and rainfall-heterogeneous areas like Taiwan.

2024

1. Mon.Wea.Rev.

   Investigating the mechanisms of an intense coastal rainfall event during TAHOPE/PRECIP-IOP3 using a multiscale radar ensemble data assimilation system

   S-C Yang, S-H Chen, L. J-Y Liu, and 4 more authors

   Mon. Wea. Rev., Sep 2024

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   The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.