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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. [ABSTRACT FROM AUTHOR]

The Gridpoint Statistical Interpolation (GSI) assimilation system and Weather Research & Forecasting (WRF) mesoscale regional model were used to investigate the sensitivity of assimilating Global Positioning System radio occultation (GPS-RO) data for a "21·7" heavy rainfall event in Henan Province. The Ensemble Forecast Sensitivity to Observation (EFSO) algorithm was used to assess the influence of the GPS-RO assimilation relative to conventional heavy rainfall observations in Henan. Sensitivity experiments revealed that the 6-h accumulated precipitation simulated by the three experiments surpassed 200 mm, which was similar to the automated station observations, and the rainfall center location and intensity simulated by the assimilated GPS-RO experiment (EXPR2) were also similar to the observations. Following GPS-RO data assimilation, the upper-level west border continental high pressure was improved, which increased the pressure gradient on the east and west sides of the heavy rainfall, resulting in enhanced dynamic lifting and water vapor flow from the lower level to the whole Henan downpour center. The entire convective convergence rising height increased, which had an optimum influence on the dynamic and thermal conditions of heavy rainfall. The EFSO estimates revealed that most observations improved the model predictions, with the conventional surface pressure and U-wind observations making a positive standardized contribution of approximately 0.05 and 0.075, respectively, but T(Temperature) and Q(Specific humidity) making a negative standardized contribution of approximately −0.025. The GNSS GPS-RO greatly improved the prediction results by decreasing the error by 0.125, and the National Satellite Meteorological Center (NSMC) FY-3D contributed the most to the model. • Extraordinarily heavy rainfall in Henan has been forecast using 6 h-by-6 h cyclic assimilation system analysis. • Assimilated the GPS-RO data detected by COMIC and FY3D, and explored its influence on the storm process. • Assessment of the impact of conventional observations as well as GPS-RO data on model forecasts using the Ensemble-based Forecast Sensitivity to Observation. [ABSTRACT FROM AUTHOR]