Research On Stochastic Perturbed Planetary Boundary Layer Scheme Parameters At Convective Scale Based On The WRF Model

The accuracy of the planetary boundary layer(PBL)parameterization scheme directly affects the forecast of near-surface meteorological variables and the simulation of lowertropospheric thermodynamic and kinematic structures,which is very important for the forecast of strong convective weathers such as thunderstorms.However,the inherent uncertainties within the PBL schemes make the deterministic forecast obvious limitations.The stochastic parameter perturbation(SPP)scheme is an ensemble perturbation method that perturbs the uncertain parameters in the physical parameterization scheme,which expresses the forecast uncertainty at its source.In this paper,the stochastic parameter perturbation(SPP)method is applied to the Mellor-Yamada-Nakanishi-Niino(MYNN)PBL parameterization scheme based on the Weather Research and Forecasting(WRF)model.The effect of this Stochastic Perturbed Parameters in the MYNN PBL scheme(SPPM)method on the convection-permitting ensemble prediction system was explored,and the three parameters of the SPPM method(the decorrelation time scale,the decorrelation spatial length scale and the standard deviation in the grid point)were adjusted to explore the optimal patterns to perturb the parameters in the MYNN scheme in the convection scale.The experiment first was conducted on the forecast of a thunderstorm process happened in Beijing.Based on the preliminary conclusions of the case study,the experimental time was extended for a week.Considering the daily variation of the planetary boundary layer,two different experiments initialized at 00 UTC and 12 UTC were set for comparison and analysis.The main conclusions obtained are as follows:(1)In convection-permitting ensemble prediction system,the SPPM method can effectively improve the spread of near-surface variables and low-level variables below 700 h Pa.The root mean square error and continuous ranked probability scores were also reduced,indicating the probability forecasting skills of forecast system were improved.Tests in individual cases show that the method also improves the location and intensity prediction of short-term heavy precipitation.(2)The test of the three parameters of the SPPM method shows that the ensemble spread was significantly improved when the decorrelation time scale was selected as 12 h compared to the setting in the mesoscale(decorrelation time scale 6h,decorrelation space scale 700 km,grid standard deviation 0.15).The skill is also slightly improved when the standard deviation of the grid point is increased to 0.20.The decorrelation spatial length scale is recommended to maintain at a default value of 700 km.The skill is reduced when it is too small(150 km).(3)From the comparison of two different experiments initialized at 00 UTC and 12 UTC,it can be seen that the improvement of forecasting skills is more obvious between 12 UTC and00UTC(at night in Beijing)in both experiments,indicating that the SPPM method mainly improves the estimation of the uncertainty in the process of the boundary layer at night.In conclusion,the SPPM method can effectively express the uncertainty of the PBL parameterization scheme and improve the forecasting skill of the convection-permitting ensemble prediction system.Longer time-dependent perturbations give better estimates of parameter uncertainty.