Comparison between Multi-Physics and Stochastic Approaches for the 20 July 2021 Henan Heavy Rainfall Case

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

Study of Quality Control Methods Utilizing IRMCD for HY-2B Data Assimilation Application

Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon “Chanba”. The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon’s barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons

Sichuan Rainfall Prediction Using an Analog Ensemble

This study aimed to address the significant bias in 0–44-day precipitation forecasts under numerical weather conditions. To achieve this, we utilized observational data obtained from 156 surface stations in the Sichuan region and reanalysis grid data from the National Centers for Environmental Prediction Climate Forecast System Model version 2. Statistical analysis of the spatiotemporal characteristics of precipitation in Sichuan was conducted, followed by a correction experiment based on the Analog Ensemble algorithm for 0–44-day precipitation forecasts for different seasons in the Sichuan region. The results show that, in terms of spatial distribution, the precipitation amounts and precipitation days in Sichuan Province gradually decreased from east to west. Temporally, the highest number of precipitation days occurred in autumn, while the maximum precipitation amount was observed in summer. The Analog Ensemble algorithm effectively reduced the error in the model forecast results for different seasons in the Sichuan region. However, the correction effectiveness varied seasonally, primarily because of the differing performance of the AnEn method in relation to precipitation events of various magnitudes. Notably, the correction effect was the poorest for heavy-rain forecasts. In addition, the degree of improvement of the Analog Ensemble algorithm varied for different initial forecast times and forecast lead times. As the forecast lead time increased, the correction effect gradually weakened