Collaborative Research: Ensemble-Based Predictability, Sensitivity and Data Assimilation in PREDICT

协作研究：PREDICT 中基于集成的可预测性、敏感性和数据同化

• 批准号: 0848409
• 负责人: Ryan Torn
• 金额: $ 27.86万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0848409&HistoricalAwards=false
• 关键词: Collaborative; Research; Ensemble; Based; Predictability

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This study is part of the Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) project. The central scientific hypothesis of this study is that properly simulating the initial formation of synoptic-scale, pre-tropical-depression wave trough in numerical forecasting models is most essential for the accurate modeling of tropical cyclone genesis. It is within this disturbance that the critical layer, which acts to shield an incipient rotating disturbance from wind shear and dry air intrusion, is hypothesized to exist. To investigate this and other hypotheses, an ensemble-based framework based on the THORPEX Interactive Grand Global Ensemble (TIGGE), the Weather Research and Forecasting (WRF) model and an ensemble Kalman filter will be prepared in Year 1 for real-time prediction during the PREDICT field experiment (mid-Year 2). During the field experiment observations will be performed of tropical cyclones developing and travelling through the eastern Atlantic and Caribbean regions. Ensemble-based techniques will be used to diagnose the important physical processes, and the associated scales and parameters, that need to be sampled to optimally limit prediction errors. The remainder of Year 2 and all of Year 3 will be devoted to analysis of observations using models as diagnostic tools, including production of a convective-scale reanalysis that will include all data collected during the field campaign and will be used by other PREDICT PIs for their research. The intellectual merit of the proposed research lies in the potential to advance understanding of the processes and predictability associated with tropical cyclone genesis, via multi-scale, multi-faceted numerical model ensembles. The broader impacts of the proposed activity include improved probabilistic predictions of tropical cyclone genesis, and subsequent intensity change. Better forecasts of wind and rainfall structure at landfall will be possible. Two graduate students will be trained to run models that capture inner-core processes within tropical cyclones and to address unsolved issues related to their predictability. Furthermore, data and results will be shared between the research and operational communities.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这项研究是热带地区云系萧条前调查（PREDICT）项目的一部分。本研究的中心科学假设是，在数值预报模式中适当地模拟天气尺度的热带低压前波槽的初始形成对于热带气旋生成的准确模拟是最重要的。正是在这种扰动中，临界层，它的作用是屏蔽风切变和干空气入侵的初期旋转扰动，被假设存在。为了调查这一假设和其他假设，将在第一年编制一个基于集合的框架，该框架以THORPEX交互式全球大环境、天气研究和预报模式和集合卡尔曼滤波器为基础，用于在预测实地试验期间（第二年年中）进行实时预测。在实地试验期间，将对热带气旋的形成和穿越东大西洋和加勒比区域进行观测。基于集成的技术将被用来诊断重要的物理过程，以及相关的尺度和参数，需要进行采样，以最佳地限制预测误差。第二年的剩余时间和第三年的全部时间将用于使用模型作为诊断工具分析观测结果，包括制作对流尺度再分析，其中将包括实地活动期间收集的所有数据，并将由其他PREDICT PI用于其研究。拟议研究的智力价值在于通过多尺度、多方面的数值模式集合，有可能增进对与热带气旋成因相关的过程和可预测性的理解。拟议活动的更广泛影响包括改进对热带气旋生成和随后强度变化的概率预测。更好地预报登陆时的风和降雨结构将是可能的。两名研究生将接受培训，以运行捕捉热带气旋内核过程的模型，并解决与其可预测性有关的未决问题。此外，数据和成果将在研究界和业务界之间共享。