Optimal Design of Multi-scale Ensemble Systems for Convective-Scale Probabilistic Forecasting

对流尺度概率预报多尺度集合系统的优化设计

• 批准号: 1046081
• 负责人: Xuguang Wang
• 金额: $ 39.58万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-15 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1046081&HistoricalAwards=false
• 关键词: Optimal; Design; Multi; scale; Ensemble

The prediction of convective-scale hazardous weather is very important from both meteorological and public service/societal impact perspectives. The unique challenge in convective scale forecast is that the accuracy of the forecasts depends not only on processes at the convective scale but also on the mesoscale and synoptic-scale environment supporting them. Therefore, reliable and sharp probabilistic forecasts for convective scales require proper sampling of errors from multiple scales. The main goal of this research is to determine the optimal design of ensemble forecast systems under such multi-scale scenarios, for the purpose of convective-scale probabilistic forecasting. This issue has not been addressed previously and has become a pressing issue as convective-scale ensemble forecasting is not only desirable but also entirely possible with the advancement of computational technologies.The research will build on the foundation and initial capabilities established at the Center for Analysis and Prediction of Storms (CAPS), which has run a 4-km convection-allowing resolution ensemble forecasting system in real-time with 20 members, plus one 1-km forecast that can be considered an additional member during the springs since 2007 over the Continental U.S. Seven interlinked questions will be investigated in order to achieve the research goal. 1) What are the optimal initial condition perturbations for convective-scale ensemble in the multi-scale scenario? 2) If a nested-grid approach is used to capture multiple-scales, how do the outer and inner domain perturbations interact through the lateral boundary condition (LBC) and fundamentally how do the various scale perturbations interact within and across the domain? 3) What is the optimal perturbation strategy for the coupled land surface model? 4) What is the effectiveness of the multi-model/physics and stochastic physics methods in sampling model error for convective scales? 5) What is the relative importance and contributions of sampling uncertainties in the initial conditions, lateral boundary conditions, atmospheric models including model physics and dynamics, and the land surface models, and what is their optimal combination? 6) What is the best tradeoff between ensemble size and model resolution? 7) What are the effective probabilistic forecast verification and evaluation metrics for convective-scale ensemble?Intellectual merit: The project will answer many of the fundamental scientific questions concerning optimal design of ensemble system for convective scale probabilistic forecasting under the multi-scale scenario. New knowledge on how the large-scale and convective-scale ensemble perturbations interact with each other and how the interaction impacts the ensemble design for convective scales, new knowledge on the effective methods to account for model error in convective scale ensemble forecasting, new knowledge on the interaction of land surface and atmospheric ensemble perturbations, new knowledge on the relative importance and impact of different sources of errors on convective-scale probabilistic forecasting; and new knowledge on the most appropriate objective verification method for convective scale probabilistic forecasting will be learned from this study. Broader impact: The scientific findings of this project will provide guidance for the design of, and accelerate the national efforts in developing and implementing the next-generation operational mesoscale ensemble forecast systems. It will directly address two key national priorities in weather: Warn on Forecast for High Impact Weather and the Next-Generation Forecast System, as key component of the Next-Generation Air Transportation System (NextGen, http://www.faa.gov/about/initiatives/nextgen/). It will also address one of the most important goals of weather research - to improve our ability to accurately predict intense hazardous weather that negatively impacts the American economy and the lives of its citizens, causing large monetary loss and the loss of many lives each year. This project will provide much needed education and training for graduate students in the important areas of convective-scale probabilistic forecasting. The research findings will also have a direct path to operations through the group's significant role in the NOAA Hazardous Weather Testbed (HWT) Spring Forecast Experiments and its interaction and collaboration with the Developmental Testbed Center (DTC) in National Center for Atmospheric Research in Boulder, Colorado.

从气象和公共服务/社会影响的角度来看，对流规模危险天气的预测都非常重要。 对流尺度预报的独特挑战是，预报的准确性不仅取决于对流尺度的过程，还取决于支持它们的中尺度和天气尺度环境。 因此，对流尺度的可靠且敏锐的概率预测需要对多个尺度的误差进行适当的采样。 本研究的主要目标是确定多尺度场景下集合预报系统的优化设计，以实现对流尺度概率预报。 这个问题以前没有得到解决，现在已经成为一个紧迫的问题，因为对流规模的集合预报不仅是可取的，而且随着计算技术的进步完全可能实现。这项研究将建立在分析中心建立的基础和初步能力的基础上风暴预测 (CAPS)，该系统已拥有 20 名成员，实时运行 4 公里允许对流分辨率的集合预报系统，加上自春季以来可被视为额外成员的 1 公里预报系统2007年将对美国大陆上空的七个相互关联的问题进行调查，以实现研究目标。 1）多尺度场景下对流尺度系综的最佳初始条件扰动是什么？ 2）如果使用嵌套网格方法来捕获多尺度，那么外部和内部域扰动如何通过横向边界条件（LBC）相互作用，以及从根本上讲，各种尺度扰动如何在域内和跨域相互作用？ 3）耦合地表模型的最优摄动策略是什么？ 4）多模型/物理和随机物理方法在对流尺度模型误差采样方面的有效性如何？ 5）初始条件、横向边界条件、大气模型（包括模型物理和动力学）以及地表模型中采样不确定性的相对重要性和贡献是什么？它们的最佳组合是什么？ 6) 集合大小和模型分辨率之间的最佳权衡是什么？ 7）对流尺度集合的有效概率预报验证和评估指标有哪些？ 智力价值：该项目将回答多尺度场景下对流尺度概率预报集合系统优化设计的许多基本科学问题。 关于大尺度和对流尺度集合摄动如何相互作用以及相互作用如何影响对流尺度集合设计的新知识，关于在对流尺度集合预报中解释模型误差的有效方法的新知识，关于地表和大气集合扰动的相互作用，关于不同误差源对对流尺度概率预报的相对重要性和影响的新知识；将从这项研究中学到关于对流尺度概率预报最合适的客观验证方法的新知识。更广泛的影响：该项目的科学发现将为设计提供指导，并加速国家开发和实施下一代业务中尺度集合预报系统的努力。 它将直接解决两个关键的国家天气优先事项：高影响天气预报警告和下一代预报系统，作为下一代航空运输系统的关键组成部分（NextGen，http://www.faa.gov/关于/倡议/nextgen/）。 它还将解决天气研究最重要的目标之一 - 提高我们准确预测强烈危险天气的能力，这种天气对美国经济和公民的生活产生负面影响，每年造成巨大的金钱损失和许多人的生命损失。 该项目将为研究生提供对流尺度概率预测重要领域急需的教育和培训。 通过该小组在 NOAA 危险天气试验台 (HWT) 春季预报实验中的重要作用，以及与博尔德国家大气研究中心的发展试验台中心 (DTC) 的互动和合作，研究结果还将有直接的应用途径，科罗拉多州。