Mesoscale Ensemble Forecasting and Predictability Studies

中尺度集合预报和可预测性研究

• 批准号: 9730985
• 负责人: Mohan Ramamurthy
• 金额: $ 32.37万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-11-15 至 2002-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9730985&HistoricalAwards=false
• 关键词: Mesoscale; Ensemble; Forecasting; Predictability; Studies

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.

集合预报是一种使用数值预报模式根据不同的初始条件生成多个预报的技术。 已经发现，如果预测是一致的，那么这表明预测比预测发散更可靠。 虽然这些技术已经发展到天气和气候模式，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调查的集合方法在中尺度预报的可行性和有用性。 具体而言，首席研究员将调查集合与高分辨率中尺度模式中的确定性预报的优点和局限性。 为此，将使用一种先进的数值预报模式来处理与短期集合预报有关的一系列问题，包括与扰动战略、集合配置以及初始误差的结构和演变有关的问题。 中尺度系统集合预报系统的设计提出了许多挑战。 也许最重要的是关于集合的初始化。 主要研究者将比较用于构建初始扰动的两种不同方法：a）基于观测系统模拟实验的蒙特-卡罗方法，以及B）生长模式繁殖方法。他还将研究扰动模型物理学中不同参数和系数的重要性，以及地形和海面温度等表面场的扰动。 扰动侧边界条件的作用也将作为本研究的一部分进行研究。 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得到解决。 上述总体研究将在总共10种情况下进行，平均分配在夏季和冬季情况下。 将利用一些验证和评价技术来记录这些系统的性能和特点。 将特别注意集合得出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当地天气预报的能力。