Ensemble-Based Hurricane State Estimation, Intensity Prediction, and Targeting

基于集合的飓风状态估计、强度预测和目标确定

• 批准号: 0842384
• 负责人: Gregory Hakim
• 金额: $ 39.5万
• 依托单位: University of Washington
• 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: 2009-06-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0842384&HistoricalAwards=false
• 关键词: Ensemble; Based; Hurricane; State; Estimation

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).While much has been learned about hurricane structure and intensity change from observational analyses and mesoscale model simulations, much less is known about the predictability of these features. Arguably the most important contribution to the well-known lack of improvement in dynamical forecasts of intensity change is the need for dynamically consistent initial conditions. The challenge in creating initial conditions is the assimilation of observations in a hurricane environment, which has large spatial variability. To address this issue, the Principal Investigator (PI) will apply an ensemble approach to hurricane state estimation, predictability, and targeting, with an emphasis on structure and intensity change. The research builds upon recent work by the PI and collaborators using ensemble techniques for data assimilation, predictability, and targeting. Research activities include assimilating observations using an ensemble Kalman filter in a systematic manner, starting with updates of only the axisymmetric structure, and proceeding thereafter by including updates to increasing azimuthal wavenumbers. The predictability time scale of each component will be evaluated using standard error metrics and more recently developed methods based on information theory, including predictable component analysis. Storms to be investigated include: Rita, Katrina, and Ophelia from the Rainband and Intensity Experiment (RAINEX); Emily from the Tropical Cloud Systems and Processes (TCSP) experiment; and Bonnie (1998). Storms will be analyzed using two experiment types: (1) actual observations, and (2) simulated observations drawn from truth simulations constrained to approximately follow the track and intensity of the actual storms. Ensemble-based sensitivity analysis will be used to assess the impact of observations, including targeting, through observation denial in the real-data cases. Intellectual merit: The research plan builds a bridge between observational studies, which have established properties of hurricane structure and intensity change but lack dynamical continuity, and theoretical and modeling studies that have the spatial and temporal continuity to assess the dynamics of these features, but lack a close link to the observations of real storms. The assimilated datasets that result from the research offer the spatial and temporal resolution of numerical simulations, but should also remain faithful to available observations. These data will be used to systematically assess the predictability of storm structure and intensity, which is a crucial aspect of hurricane dynamics, for which little is known. Broader impact: The broader impact to society of improved forecasts of hurricane intensity is well known. Given a successful execution of the research, a transition to operations seems feasible. Moreover, demonstrated ability to target observations to routinely improve predictions of hurricane intensity would have a transformational impact on operational hurricane forecasting and thus also on emergency planning for these storms.

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。虽然从观测分析和中尺度模式模拟中已经了解了飓风的结构和强度变化，但对这些特征的可预测性知之甚少。众所周知，强度变化的动态预测缺乏改进，可以说最重要的原因是需要动态一致的初始条件。创建初始条件的挑战在于同化飓风环境中的观测数据，因为飓风环境具有很大的空间变异性。为了解决这个问题，首席研究员（PI）将采用一种集成方法来估计飓风状态，可预测性和目标，重点是结构和强度变化。该研究建立在PI和合作者最近的工作基础上，使用集成技术进行数据同化、可预测性和目标定位。研究活动包括以系统的方式使用集合卡尔曼滤波器吸收观测，从仅更新轴对称结构开始，然后通过包括增加方面波数的更新来进行。每个组件的可预测性时间尺度将使用标准误差度量和基于信息论（包括可预测组件分析）的最新开发方法进行评估。将被调查的风暴包括：来自雨带和强度实验（RAINEX）的丽塔、卡特里娜和奥菲莉亚；来自热带云系统和过程（TCSP）实验的Emily；和邦妮（1998）。风暴将使用两种实验类型进行分析：(1)实际观测，(2)模拟观测，这些模拟观测是根据实际风暴的轨迹和强度进行的。基于集合的灵敏度分析将用于评估观测结果的影响，包括在真实数据案例中通过拒绝观测结果来确定目标。知识价值：该研究计划在观测研究和理论和建模研究之间建立了一座桥梁，前者已经建立了飓风结构和强度变化的特性，但缺乏动力连续性，后者具有评估这些特征的动力的时空连续性，但缺乏与实际风暴观测的密切联系。从研究中得到的同化数据集提供了数值模拟的空间和时间分辨率，但也应保持对现有观测的忠实。这些数据将用于系统地评估风暴结构和强度的可预测性，这是飓风动力学的一个关键方面，对此我们知之甚少。更广泛的影响：改进飓风强度预报对社会的更广泛影响是众所周知的。考虑到研究的成功执行，向运营的过渡似乎是可行的。此外，以观测为目标定期改进飓风强度预测的能力将对飓风业务预报产生变革性影响，从而也对这些风暴的应急规划产生变革性影响。