基于WRF模式的对流尺度边界层方案参数随机扰动方法研究

A study on stochastic perturbed planetary boundary layer scheme parameters at convective scale based on WRF model

中文关键词:  对流尺度集合预报  随机参数扰动  边界层参数化方案  雷暴

英文关键词:Convection-permitting ensemble forecast  Stochastic parameter perturbation  Planetary boundary layer parameterization scheme  Thunderstorms

中文摘要:

      边界层参数化方案的准确性会影响模式对近地面变量和大气低层热动力结构的模拟，对雷暴等强对流天气的预报非常重要，但边界层方案内在的不确定性使得单一预报具有局限性。为了提高对流尺度数值模式中边界层方案的预报效果，基于WRF（The Weather Research and Forecasting Model）模式，应用随机参数扰动（SPP）方法对Mellor-Yamada-Nakanishi-Niino（MYNN）边界层方案中重要的3个不确定参数进行扰动，探究了该方法对北京地区一次雷暴过程模拟的影响。同时考虑了对流尺度集合预报系统的特点，调整随机参数扰动方法的3个参量（去相关时间尺度、空间尺度和格点标准差）探究了对流尺度中对MYNN方案参数进行扰动的最优设置。结果显示：随机扰动MYNN边界层方案参数（SPPM）方法可以有效提高近地面变量和700 hPa以下低层变量的离散度，同时提高了短时强降水位置和强度的预报技巧。3个参量的试验说明，去相关时间尺度增大到12 h集合离散度有明显提高；格点标准差增大到0.20，预报技巧也略有提高；去相关空间尺度维持在默认值700 km较好，尺度过小（150 km）预报技巧明显降低。上述结果表明，在对流尺度中SPPM方法可以有效表达边界层参数化方案的不确定性，提高集合预报系统的预报技巧。

英文摘要:

      The accuracy of the planetary boundary layer (PBL) parameterization scheme directly affects the forecast of near-surface meteorological variables and the simulation of thermodynamic and kinematic structures in the lower-troposphere, both of which are very important for the forecast of strong convective weathers such as thunderstorms. However, the inherent uncertainties in the PBL schemes obviously limit the model capability for deterministic forecast. In order to improve the forecast skill of the PBL scheme in convection-permitting numerical model, the stochastic parameter perturbation (SPP) method is applied to the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL parameterization scheme that is incorporated in the Weather Research and Forecasting (WRF) model. Three important uncertain parameters are perturbed and the influence on the forecast of a thunderstorm process in Beijing is explored. Considering the characteristics of the convection-permitting ensemble prediction system, the decorrelation time scale, the decorrelation spatial length scale and the standard deviation in the grid point of the SPP method are adjusted to explore the optimal patterns to perturb parameters in the MYNN scheme. Results show that the method of stochastic perturbation of parameters in the MYNN PBL scheme (SPPM) effectively improves the spread of near-surface variables and low-level variables below 700 hPa. The position and intensity prediction of the short-term heavy precipitation is also improved. The test of the three parameters shows that the ensemble spread can be significantly improved when the decorrelation time scale is set to 12 h. 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). In conclusion, the SPPM method can effectively address the uncertainty of the PBL parameterization scheme and improve the forecasting skill of the convection-permitting ensemble prediction system.