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

强降水事件的预报一直是数值天气预报中最困难的问题之一。由于有些降水需要参数化，而有些则需要明确解析，因此数值模式表示降水的能力变得复杂。此外，降水预报的误差有可能通过潜热释放和其他非绝热过程降低模式预报的质量，特别是在强降水期间。提高对模式降水预报误差如何影响天气尺度预报的认识是很重要的。定量降水预报（QPF）问题除了具有重要的社会经济意义外，还对大气可预测性问题具有重要意义。数值模式QPF误差会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而导致预报退化。首席研究员之前的一项研究记录了强降水期间数值预报的误差，并证明这些误差与模式对对流冷锋雨带附近潜热释放的错误表示一致。项目负责人建议完成以下具体目标：1)针对对流模式表示可能限制预报精度的特定天气情景，发展业务模式预报偏差的气候学；2)记录与强降水表征相关的操作模式行为系统偏差的物理基础；3)检验对流层下层绝热产生的位涡（PV）异常的模式表示对降水过程（网格尺度和亚网格尺度）的模式表示的敏感程度；4)确定哪种对流参数化方案能最真实地反映对流层下层和上层的非绝热PV变化；5)探索对降水模式表示的修改，以改进与潜热释放相关的动力反馈的预报。这项研究的成功完成可使预报员更好地利用目前的数值模式，并使数值模式降水预报的数量得到改进。

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.