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

这个奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。这项研究是大萧条前热带云系调查(FOREST)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模拟天气尺度的前热带低压波槽的初始形成，对于准确模拟热带气旋的成因是至关重要的。正是在这种扰动中，假设存在一个临界层，它起到保护初始旋转扰动免受风切变和干燥空气入侵的作用。为了研究这一假设和其他假设，将在第一年准备一个基于集合的框架，该框架基于THORPEX互动大全球集合(TIGGE)、天气研究和预报(WRF)模式和集合卡尔曼滤波，用于在预报现场试验期间(第二年年中)进行实时预测。在实地试验期间，将对热带气旋在东大西洋和加勒比地区的发展和移动进行观测。基于集合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，这些过程需要进行采样，以最佳地限制预测误差。第2年的剩余时间和第3年的全部时间将专门用于利用模型作为诊断工具对观测数据进行分析，包括制作对流尺度的再分析，其中将包括实地活动期间收集的所有数据，并将由其他预测指标用于其研究。拟议研究的学术价值在于有可能通过多尺度、多方面的数值模式集合，促进对热带气旋形成过程和可预测性的了解。拟议活动的更广泛影响包括改进对热带气旋发生的概率预测，以及随后的强度变化。对登陆时的风和降雨结构进行更好的预报将成为可能。两名研究生将接受培训，以运行捕捉热带气旋内部核心过程的模型，并解决与其可预测性相关的未解决问题。此外，数据和成果将在研究界和业务界之间共享。

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.