Mesoscale Ensemble Forecasting and Predictability Studies

集合预报是一种使用数值预报模式根据不同的初始条件产生几个预报的技术。研究发现，如果预测是一致的，那么这表明预测比预测不一致时更可靠。虽然天气和气候模式已经开发了这种技术，但将集合预报应用于局地尺度数值模式仍是一个非常新的领域。本研究的基本目标是探讨集合预报方法在中尺度预报中的可行性和有效性。具体地说，首席调查员将调查在高分辨率中尺度模式中集合预报与确定性预报的优势和局限性。为此，将使用一个先进的数值预报模式来解决与短期集合预报有关的一系列问题，包括关于扰动策略、集合配置以及初始误差的结构和演变的问题。中尺度系统集合预报系统的设计提出了许多挑战。也许最重要的问题是合奏的初始化。首席调查员将比较两种不同的方法来构建初始扰动：a)基于观测系统模拟实验的蒙特卡罗方法，以及b)生长模式培育方法。他还将研究在模型物理中扰动不同参数和系数的重要性，以及地形和海洋表面温度等表面场的扰动。作为这项研究的一部分，还将研究摄动横向边界条件的作用。与集合预报有关的其他问题，如集合的最小尺寸、集合性能对分辨率的依赖性以及初始误差的结构和增长，将被讨论。上述总体研究将对总共10个病例进行，平均分为夏季和冬季两种情况。将利用一些验证和评估技术来记录合奏的表现和特点。将特别注意集合概率估计和定量降水预报的可靠性。这项研究的成功完成将提高短期当地天气预报的能力。

An ensemble forecast is a technique in which a numerical forecast model is used to generate several forecasts based on different initial conditions. It has been found that if the forecasts are consistent, then this indicates a more reliable forecast than if the forecasts diverge. While such techniques have been developed for synoptic and climate models, application of ensemble forecasts to local-scale numerical models is still a very new field.The fundamental goal of this research is to investigate the feasibility and usefulness of an ensemble approach in mesoscale prediction. Specifically, the Principal Investigator will investigate the advantages and limitations of ensemble versus deterministic forecasting in mesoscale models at high resolution. To this end, an advanced numerical forecast model will be used to address a range of issues pertaining to short-range ensemble forecasting, including those concerning perturbation strategies, ensemble configuration, and the structure and evolution of initial errors. The design of an ensemble forecasting system for mesoscale systems poses many challenges. Perhaps the most important concerns the initialization of an ensemble. The Principal Investigator will compare two different approaches for constructing the initial perturbations: a) the Monte-Carlo method based on observing systems simulation experiments, and b) the breeding of growing modes method. He will also investigate the importance of perturbing different parameters and coefficients in model physics, as well as perturbation of surface fields such as topography and sea surface temperature. The role of perturbing lateral boundary conditions will also be examined as part of this study. Other issues related to ensemble forecasting, such as minimum size of an ensemble, dependency of ensemble performance on resolution, and structure and growth of initial errors will be addressed. The aforementioned ensemble studies will be carried out on a total of ten cases, split evenly between summer and winter situations. A number of validation and evaluation techniques will be utilized to document the performance and characteristics of the ensembles. Particular attention will be paid to the reliability of ensemble-derived probability estimates and quantitative precipitation forecasts. Successful completion of this research will advance capabilities in short-term local weather forecasting.