一次高原地区强降水过程的对流可分辨尺度集合预报评估

Evaluation of Convective-Scale Ensemble Forecast for a Severe Precipitation Event in the Plateau Region

利用FNL(Final Reanalysis Data)、ERA5(ECMWF Reanalysis V5)再分析资料和GPM(Global Precipitation Measurement)全球逐半小时降水数据,选取我国西南高原地区一次强降水过程,研究了对流尺度集合预报中两种初始扰动方法BGM(Breeding Growth Mode)和LBGM法(Local Breeding Growth Mode)对复杂地形强降水的预报能力。基于对象诊断的MODE(Method for Object-Based Diagnostic Evaluation)方法评估了模式对降水对象的位置、结构、强度的模拟能力,并与TS(Threat Score)等评分方法进行对比分析,综合评估模式预报性能,表明:(1)基于BGM和LBGM法生成初始扰动的集合预报系统BGM-EPS和LBGM-EPS,集合平均预报对24 h各个量级降水评分均优于控制预报,且暴雨的TS评分LBGM-EPS优于BGM-EPS;(2)整体上,WRF模式能够较好捕获降水对象,尤其是对于高原山地复杂地形的降水预报效果很好,LBGM-EPS在降水目标的整体相似度表现上优于BGM-EPS,且从扰动总能量随预报时间的演变中能看出LBGM较BGM扰动总能量更大,更能代表预报误差的增长,突出LBGM方法在对流尺度集合预报中表示强对流能力的优势;(3)与传统TS评分等检验方法相比,MODE法更能反映降水预报的空间位置信息,在卷积半径和降水阈值相同情况下,基于LBGM方法的集合平均预报识别降水对象的效果更佳。

Using FNL(Final Reanalysis Data),ERA5(ECMWF Reanalysis V5)reanalysis data,and GPM(Global Precipitation Measurement)global half-hourly precipitation data,a strong precipitation event in the southwestern plateau of China was selected to study the forecasting ability of two initial perturbation methods,Breeding Growth Mode(BGM)and Local Breeding Growth Mode(LBGM),in convective-scale ensemble forecasting of complex terrain rainfall.The MODE(Method for Object-Based Diagnostic Evaluation)method based on object diagnostics was used to evaluate the model's ability to predict the location,structure,and intensity of precipitation objects,and compared with scoring methods such as Threat Score(TS)to comprehensively assess the model's forecasting performance.The results show that:(1)The ensemble forecast systems BGMEPS and LBGM-EPS,generated using BGM and LBGM methods to produce initial perturbations,have better ensemble mean forecast scores for precipitation of all magnitude levels at 24 hours compared to the control forecast,and LBGM-EPS has a higher TS score for heavy rainfall compared to BGM-EPS,this indicates that the LBGM method has a certain improvement effect on ensemble forecasts for heavy precipitation.However,the underlying mechanisms behind the different initial perturbation methods are worthy of further investigation;(2)Overall,the WRF model can capture precipitation objects well,especially for rainfall forecasts in complex terrain of the plateau mountains,with a better overall similarity in precipitation targets for LBGM-EPS compared to BGMEPS,highlighting the advantage of LBGM method in representing convective-scale ensemble forecasting of intense convection.The initial perturbation total energy of BGM and LBGM shows a developing trend with forecast time.In the same forecast time,LBGM has a larger perturbation total energy than BGM,which better represents the growth of forecast error.This can partially explain why the LBGM method outperforms the BGM method in terms of precipitation object matching in the MODE evaluation;(3)Compared with traditional TS scoring and other verification methods,the MODE method can better reflect the spatial position information of precipitation forecasts,and under the same convolution radius and precipitation threshold,the ensemble mean forecast based on LBGM method performs better in identifying precipitation objects.By flexibly setting the convolution radius and determining the precipitation threshold,the WRF model can capture precipitation objects in complex terrain areas during heavy precipitation events.However,the matching degree of precipitation targets in high-altitude areas is lower than that in low-lying areas.The LBGM-EPS method outperforms the BGM-EPS method in terms of the shape of precipitation objects and the matching of precipitation areas,resulting in better identification of precipitation objects.The quality of precipitation object matching using the MODE method is related to parameter settings such as precipitation threshold and convolution radius,rather than the complex terrain background related to terrain gradients.