基于CMA-BJ的北京地区雷达弱回波区径向风同化及其对暴雨预报的影响OA
Radial Velocity Assimilation Within the Weak Echo Region in Beijing Based on CMA-BJ and Its Impact on Heavy Rainfall Prediction
为发挥雷达弱回波区径向风观测资料的作用,探究其同化对区域模式预报的影响,针对北京西南部区域一个强降水个例开展了基于CMA-BJ 2.0系统的雷达弱回波区径向风观测资料的循环同化及预报试验.资料分析表明,雷达弱回波区径向风多分布在对流系统外围,且高低层均有大量资料分布;循环同化预报试验表明,在同化雷达强回波区径向风的基础上进一步同化雷达弱回波区径向风观测,可以通过循环同化逐步改善分析场的整体动力特征及高低层常规要素的预报效果,进而对模式动力、热力和水汽场有着更为合理的调整,从而有效的提升了3h和6h降水的预报效果.研究表明,雷达弱回波区径向风的合理同化在对流预报中能够发挥重要作用,为其实际业务推广应用提供了参考.
In order to utilize radial velocity observations within the weak echo region and investigate the application of their assimilation on regional model forecasts,a cyclic assimilation and forecasting experiment based on the CMA-BJ 2.0 system was conducted for a heavy precipitation event in southwestern Beijing.The data analysis revealed that the radial velocity in the weak echo region is primarily distributed around the periphery of the convection,with substantial data available at both high and low atmospheric levels.The cyclic assimilation and forecasting experiment demonstrate that assimilating radar radial velocity observations in weak echo region,in addition to those from strong echo region assimilation,can gradually enhance the overall dynamics of the analyzed field.Furthermore,this approach improves the forecast accuracy of conventional elements in both the high and low atmospheric layers.This results in more reasonable adjustments to the dynamics,thermal,and water vapor fields of the model,effectively enhancing the prediction of 3 h and 6 h precipitation.The study highlights the significant role of radial velocity assimilation within the weak echo region in improving convective forecasting,providing valuable insights for operational applications.
刘心智;范水勇;陈耀登;陈敏
南京信息工程大学气候系统预测与变化应对全国重点实验室/气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,江苏 南京 210044北京城市气象研究院,北京 100089南京信息工程大学气候系统预测与变化应对全国重点实验室/气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,江苏 南京 210044北京城市气象研究院,北京 100089
天文与地球科学
雷达资料同化弱回波径向风对流预报
radar data assimilationweak echoesradial velocityconvective forecasts
《热带气象学报》 2026 (2)
250-260,11
国家自然科学基金项目(U2442218、U2442601)气象灾害教育部重点实验室&气象灾害预报预警与评估协同创新中心联合开放课题(KLME202306)共同资助
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