Improving Forecasts During Heavy Precipitation Events: Model Biases and Numerical Experiments

改进强降水事件期间的预测：模型偏差和数值实验

• 批准号: 0079425
• 负责人: Gary Lackmann
• 金额: $ 22.09万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0079425&HistoricalAwards=false
• 关键词: Improving; Forecasts; During; Heavy; Precipitation

Forecasting of heavy precipitation events remains one of the most difficult challenges in numerical weather prediction. The ability of numerical models to represent precipitation is complicated by the necessity to parameterize some precipitation while some is explicitly resolved. Furthermore, errors in precipitation forecasts have the potential to degrade model forecasts through latent heat release and other diabatic processes, especially during heavy precipitation. It is important to improve understanding of how errors in model precipitation forecasts might influence synoptic-scale forecasts. The problem of quantitative precipitation forecasting (QPF), in addition to significant socioeconomic relevance, holds important implications for the problem of atmospheric predictability. Numerical model QPF errors can promote forecast degradation via the influence of diabatic processes such as latent heat release on atmospheric dynamics and thermodynamics. A previous study by the Principal Investigator has documented errors in numerical forecasts during heavy precipitation, and demonstrated that these errors are consistent with model misrepresentation of latent heat release in the vicinity of a convective, cold-frontal rain band. The Principal Investigator proposes to accomplish the following specific objectives:1) Development of a climatology of operational model forecast biases for specific synoptic scenarios in which model representation of convection may limit forecast accuracy;2) Documentation of the physical basis for systematic biases in operational model behavior that relate to the representation of heavy precipitation;3) Examination of the degree to which model representation of lower-tropospheric, diabatically generated potential vorticity (PV) anomalies are sensitive to model representation of precipitation processes (both grid-scale and sub-grid-scale);4) Determination of which convective parameterization schemes yield the most realistic representation of diabatic PV modifications in both the lower and upper troposphere; 5) Exploration of modifications to model representations of precipitation that would improve forecasts of the dynamical feedbacks associated with latent heat release.Successful completion of this research could lead to better utilization of current numerical models by forecasters as well as lead to quantitative improvements in numerical models precipitation forecasts.

强降水预报仍然是数值天气预报中最困难的挑战之一。数值模式表示降水的能力是复杂的，因为需要对一些降水进行参数化，而对一些降水进行显式解析。此外，降水预报中的误差有可能通过潜热释放和其他非绝热过程而降低模式预报，特别是在强降水期间。重要的是要更好地了解模式降水预报中的误差可能如何影响天气尺度预报。定量降水预报问题除了具有重大的社会经济意义外，还对大气可预报性问题具有重要影响。数值模式的QPF误差可以通过潜热释放等非绝热过程对大气动力学和热力学的影响来促进预报退化。首席调查员之前的一项研究记录了强降水期间数值预报的错误，并证明这些错误与对流冷锋雨带附近潜热释放的模型错误描述一致。首席调查员建议完成以下具体目标：1)发展业务模式预报偏差的气候学，其中对流的模式表示可能限制预报精度；2)记录与强降水表示有关的业务模式行为中系统偏差的物理基础；3)检查低对流层、非绝热产生的位涡(PV)异常的模式表示对降水过程(网格尺度和次网格尺度)的模式表示的敏感程度；4)确定哪些对流参数化方案对低对流层和高层的非绝热涡度修改产生最真实的表示；5)探索对降水模式表示的修改，以改进与潜热释放有关的动态反馈预报。这项研究的成功完成将有助于预报员更好地利用现有的数值模式，并导致数值模式降水预报的量化改进。