Mesoscale Ensemble Forecasting and Predictability Studies

集合预报是一种利用数值预报模型在不同的初始条件下产生多个预报的技术。已经发现，如果预测是一致的，那么这表明一个更可靠的预测比如果预测分歧。虽然这些技术已用于天气和气候模式，但将集合预报应用于局地尺度数值模式仍然是一个非常新的领域。本研究的基本目标是探讨集合方法在中尺度预报中的可行性和实用性。具体来说，首席研究员将研究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，将使用先进的数值预报模型来解决一系列与短期集合预报有关的问题，包括有关摄动策略，集合配置以及初始误差的结构和演变的问题。中尺度系统的集成预报系统的设计面临许多挑战。也许最重要的是集成的初始化。首席研究员将比较两种不同的初始微扰构造方法：a)基于观测系统模拟实验的蒙特卡罗方法，b)生长模式育种方法。他还将研究扰动不同参数和系数在模型物理中的重要性，以及地形和海面温度等表面场的扰动。扰动横向边界条件的作用也将作为本研究的一部分进行研究。与集成预测相关的其他问题，例如集成的最小尺寸，集成性能对分辨率的依赖性，以及初始误差的结构和增长将被解决。上述整体研究将在总共10个案例中进行，在夏季和冬季情况中平均分配。一些验证和评估技术将被用于记录集成的性能和特征。将特别注意集合推导的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本地天气预报的能力。

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.