Sensitivity experiments were conducted on Typhoon Mujigae, which occurred in 2015, wherein the Weather Research and Forecasting Advanced Research (WRF-ARW) model was used to select two local and two nonlocal planetary boundary layer (PBL) parameterization schemes: the quasi-normal scale elimination (QNSE) and Mellor–Yamada–Janjic (MYJ) schemes, and the Yonsei University (YSU) and medium-range forecast (MRF) schemes, respectively. The differences in rainfall response in the typhoon’s inner core and outer region were evaluated by comparing the anomaly rainfall distribution, heat transmission, and mixing processes in the boundary layer among the PBL schemes. The results show that the simulated rainfall in typhoon Mujigae has large uncertainty among the PBL schemes and a significant difference between the inner and outer regions. Compared with the observation, the simulated rainfall was significantly higher in the inner core and slightly lower in the outer region. All PBL schemes accurately identified the rainfall location, although the amounts differed between the schemes. The rainfall levels in the MRF scheme were closest to the observation, followed by those in the YSU and MYJ schemes; the QNSE scheme showed the largest deviation. In general, rainfall simulation using a nonlocal boundary layer scheme such as MRF had the best results for both the inner core and the outer region.

Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality control (QC) methods, Minimum Covariance Determinant (MCD) and Isolation Forest, to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model’s simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated a superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions.

The Beibu Gulf, located over the South China Sea, has frequently experienced tropical cyclones and typhoons over recent decades, significantly impacting marine ranching activities. This study introduces the Typhoon Disaster Prediction Algorithm (TDPA), a high-resolution disaster assessment algorithm specifically designed for marine ranching risk evaluation. By integrating meteorological and disaster data from 59 typhoons (1984-2019), the TDPA identifies key influencing factors through diagnostic analysis and quantifies their contributions using the Analytic Hierarchy Process (AHP). A high-resolution disaster prediction grid is then generated via Projection Pursuit Clustering (PPC) to classify potential typhoon damage levels in real-time. The results demonstrate: (1) Maximum wind speed is the most critical meteorological factor, exhibiting a strong positive correlation with all components of marine ranching disaster losses. (2) Marine aquaculture economic losses account for 96.51 % of total disaster impacts, with a significant correlation to typhoon center distance, amplifying indirect damages.; (3) The TDPA achieves high prediction accuracy, with a Pearson correlation of 0.68, Spearman coefficient of 0.57, and an overall accuracy of 83 %, closely aligning with historical disaster data. By leveraging high-resolution meteorological data, TDPA provides fine-grained spatial and temporal disaster estimates, offering an interpretable and adaptable algorithm for emergency management. This algorithm enhances disaster prevention and mitigation strategies, supporting more effective risk-informed decision-making in the Beibu Gulf region.

In this study, three model perturbation schemes, the stochastically perturbed parameter scheme (SPP), stochastically perturbed physics tendency (SPPT), and multi-physics process parameterization (MP), were used to represent the model errors in the regional ensemble prediction systems (REPS). To study the effects of different model perturbation schemes on heavy rainfall forecasting, three sensitive experiments using three different combinations (EXP1: MP, EXP2: SPPT + SPP, and EXP3: MP + SPPT + SPP) of the model perturbation schemes were set up based on the Weather Research and Forecasting (WRF)-V4.2 model for a heavy rainfall case that occurred in Henan, China during 20–22 July 2021. The results show that the model perturbation schemes can provide forecast uncertainties for this heavy rainfall case. The stochastic physical perturbation method could improve the heavy rainfall forecast skill by approximately 5%, and EXP3 had better performance than EXP1 or EXP2. The spread-to-root mean square error ratios (spread/RMSE) of EXP3 were closer to 1 compared with those of the EXP1 and EXP2; particularly for the meridional wind above 10 m, the spread/RMSE was 0.94 for EXP3 and approximately 0.85 for EXP1 and EXP2. EXP3 exhibited better performance in Brier score verification. EXP3 had a 5% lower Brier score than EXP1 and EXP2, when the rainfall threshold was 25 mm. The growth of the initial ensemble variances of different model perturbation schemes were explored, and the results show that the perturbation energy of EXP3 developed faster, with a magnitude of 27.22 J/kg, whereas those of EXP1 and EXP2 were only 19.18 J/kg and 20.81 J/kg, respectively. The weak initial perturbation associated with the wind shear north of the heavy rainfall location can be easily developed by EXP3.

Statistical characteristics and composite synoptic-scale environmental conditions of explosive cyclones (ECs) over the Japan Sea and Kuroshio/Kuroshio Extension are examined and compared using ERA5 atmospheric reanalysis to give a better understanding of their differences. ECs over the Japan Sea frequently occur in late autumn and early winter and those over the Kuroshio/Kuroshio Extension mainly occur in winter and early spring. The maximum deepening rate, minimum central sea level pressure and explosive-developing lifetime of ECs over the Kuroshio/Kuroshio Extension are generally larger, lower and longer, respectively, than those over the Japan Sea. ECs over the Kuroshio/Kuroshio Extension formed over the East China Sea tend to develop more rapidly, and weak and moderate ECs generally begin to develop explosively over the sea to the east of the Japan Islands, while the strong and super ECs over the sea to the south of Japan Islands have longer explosive-developing tracks. Composite analysis shows that synoptic-scale environmental conditions favoring rapid EC development over these two regions are significantly different. ECs over the Japan Sea have stronger baroclinicity and cyclonic vorticity, but weaker water vapor convergence and upper-level jet stream than those over the Kuroshio/Kuroshio Extension. The key factor contributing to the baroclinicity is the cold air intrusion over the Japan Sea and the strong warm current heating over the Kuroshio/Kuroshio Extension. The potential vorticity shows anomalies in upper and low levels for both EC areas and extends further downwards over the Japan Sea.