Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.     

强热带风暴“碧利斯”（0604）极端降水研究回顾

强热带风暴“碧利斯”于2006年7月14日在我国福建省登陆,登陆后一路西行,在福建、浙江、湖南、江西、广东、广西多个省份产生强降水,其陆上维持时间之长、影响范围之广、降水极端性之强,在历史上极为罕见。“碧利斯”引发的降水主要有三个极值中心,分别对应着降水的三个阶段,其中第二阶段降水极端性最为突出,在湖南、江西和广东交界处发生了暴雨增幅,造成严重的洪涝灾害。为了更加全面地认识此次过程,本文对“碧利斯”产生极端降水的机制做了回顾总结,分别从有利的大尺度形势、中尺度系统的影响、地形作用、云微物理过程以及动力因子的诊断分析五个角度进行,最后对“碧利斯”极端降水区别于其他极端降水个例的机理以及进一步可能的研究方向进行了讨论。     

强热带风暴"碧利斯"(0604)极端降水研究回顾

强热带风暴"碧利斯"于2006年7月14日在我国福建省登陆,登陆后一路西行,在福建、浙江、湖南、江西、广东、广西多个省份产生强降水,其陆上维持时间之长、影响范围之广、降水极端性之强,在历史上极为罕见。"碧利斯"引发的降水主要有三个极值中心,分别对应着降水的三个阶段,其中第二阶段降水极端性最为突出,在湖南、江西和广东交界处发生了暴雨增幅,造成严重的洪涝灾害。为了更加全面地认识此次过程,本文对"碧利斯"产生极端降水的机制做了回顾总结,分别从有利的大尺度形势、中尺度系统的影响、地形作用、云微物理过程以及动力因子的诊断分析五个角度进行,最后对"碧利斯"极端降水区别于其他极端降水个例的机理以及进一步可能的研究方向进行了讨论。     

2023年河南麦区连阴雨过程的数值模式精细化检验评估

基于河南省2706个自动气象站降水观测资料及欧洲中期天气预报中心(ECMWF)、中国气象局的中尺度模式预报产品(CMA-MESO)和华东区域气象中心的中尺度模式预报产品(CMA-SH9),从不同量级降水的TS评分和BIAS评分、误差空间分布特征及典型区域预报偏差日变化特征等方面,检验评估了2023年5月25日至6月5日河南省麦收关键期连阴雨过程的数值模式小时降水预报效果。结果表明：ECMWF对R_h(小时降水量)≥0.1 mm/h的预报表现好,CMA-SH9对R_h≥2 mm/h和R_h≥5 mm/h的预报效果较优,CMA-MESO预报性能较差。对于R_h≥2 mm/h, CMA-SH9对上午和夜间的降水预报效果较优,ECMWF对中午到夜里的预报表现好。各模式都表现出在降水量大值区预报偏差大的特点。CMA-SH9对第一阶段的平均有效降水频次预报与实况最为接近,尤其在西部山区;ECMWF则对第二阶段的预报更贴合实况。尽管此次连阴雨过程中各模式小时降水量和小时降水强度的平均值偏差较大,但并没有表现出显著偏强或偏...     

黄淮地区副高边缘型降水数值预报精细化检验北大核心CSCD

以2020-2021年5-10月副高边缘型明显降水过程作为研究对象,针对黄淮地区太行山南麓、伏牛山东麓、东部平原3个典型区域,采用多种检验方法对CMA-MESO,CMA-SH9两模式降水日变化预报性能进行评估。结果表明:在山区,CMA-MESO预报有效降水时次占比与降水强度均偏小,CMA-SH9则相反,两模式分别在伏牛山东麓的04:00-10:00(北京时,下同)和太行山南麓的10:00-16:00预报有效降水时次占比偏小(大)更为显著;在平原,CMA-MESO对03:00-07:00和17:00-20:00有效降水时次占比显著低估,CMA-SH9对于17:00-20:00降水量的高估则主要来源于降水强度预报明显偏大。FSS(fractional skill score)评分结果显示:CMA-MESO对于伏牛山东麓15:00-17:00及21:00-22:00、东部平原02:00-04:00等时段10 mm·h^(-1)以上降水预报能力优于CMA-SH9,在太行山南麓17:00-23:00则相反。基于STFSS(spatial temporal fractional skill score)评分的评估表明:CMA-SH9对于太行山南麓前一日14:00-当日02:00的降水预报较实况显著偏晚,CMA-MESO对于伏牛山东麓02:00-08:00及平原地区08:00-14:00的降水预报均表现出较实况偏早的特征。